&EPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington, DC 20460
EPA/600<8-90/066A
Auoust1990
Interim Methods for Review
Development of Draft
Inhalation Reference (DoNot
Concentrations
Cite or Quote)
Notice
This document is a preliminary draft. It has not been formally
released by EPA and should not at this stage be construed to
represent Agency policy. It is being circulated for comment on its
technical accuracy and policy implications.
-------
(Do Not EPA600/8-90/066A
Cite or Quote) August 1990
Review Draft
Interim Methods for Development of
Inhalation Reference Concentrations
NOTICE
This document is a preliminary draft. It has not been formally released by
EPA and should not at this stage be construed to represent Agency policy.
It is being circulated for comment on its technical accuracy and policy
implications.
Environmental Criteria and Assessment Office
Office of Health and Environmental Assessment
Office of Research and Development
U.S. Enviromenta! Protection Agency
Research Triangle Park, NC 27711
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DISCLAIMER
This report is an external draft for review purposes only and does not constitute Agency
policy. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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CONTENTS
Page
TABLES vi
FIGURES viii
ABBREVIATIONS x
AUTHORS, CONTRIBUTORS, AND REVIEWERS xii
GLOSSARY , xv
1. INTRODUCTION 1-1
1.1 DEVELOPING BENCHMARK VALUES IN THE U.S.
ENVIRONMENTAL PROTECTION AGENCY 1-1
1.2 GENERAL PRINCIPLES OF NONCANCER TOXICITY
RISK ASSESSMENT 1-3
1.3 STATE-OF-THE-ART APPLICATIONS TO THE
DEVELOPMENT OF THE INHALATION RfC
METHODOLOGY 1-6
2. CONCEPTUAL BASIS FOR INHALATION RISK
ASSESSMENT METHODOLOGY 2-1
2.1 FACTORS CONTROLLING COMPARATIVE
INHALED DOSE 2-1
2.1.1 Respiratory Anatomy and Physiology . 2-3
2.1.1.1 Respiratory Regions and Branching Patterns 2-3
2.1.1.2 Clearance Mechanisms and Cell Types 2-12
2.1.1.3 Summary 2-15
2.1.2 Physicochemical Characteristics of the Inhaled
Agent 2-17
2.1.2.1 Particles 2-19
2.1.2.2 Gases and Vapors 2-19
2.1.3 Impact of Experimental Protocol 2-23
2.1.3.1 Pharmacologic Effects of Agents 2-23
2.1.3.2 Measurement Techniques 2-23
2.1.3.3 Definitions/Underlying Assumptions 2-25
2.1.3.4 Exposure Technology 2-26
2.1.4 Summary 2-30
2.2 PORTAL-OF-ENTRY CONSIDERATIONS: ASPECTS OF
COMPARATIVE PULMONARY TOXICITY 2-31
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CONTENTS (cont'd)
3. QUALITATIVE EVALUATION OF THE DATA BASE 3-1
3.1 GUIDELINES FOR SELECTIONS OF KEY STUDIES 3-1
3.1.1 Human Data 3-1
3.1.1.1 Epidemiologic Data 3-2
3.1.1.2 Nonepidemiologic Data 3-6
3.1.1.3 Intraspeties Variability and
Identifying Sensitive Subgroups 3-7
3.1.2 Animal Data . . 3-11
3.1,2.1 Appropriateness of Species as a Model
for Humans 3-12
3.1.2.2 Study Design 3-13
3.1.2.3 Study Validity and Relevance to
Extrapolation 3-14
3.1.3 Summarizing the Evidence 3-15
3.2 TOXICOLOGICAL ISSUES IN DATA EVALUATION 3-19
3.2.1 Qualitative Evaluation of Dose Response and
Dose Effect Data . 3-19
3.2.1.1 Relationship to the Uncertainty Factor
Approach 3-19
3.2.2 Selecting Effect Levels: Inhalation-Specific Issues 3-22
3.3 DEFICIENT DATA BASES AND ALTERNATIVE
SOLUTIONS 3-26
3.3.1 Guidance on Evaluating a Data Base for
Completeness 3-26
3.3.2 Historical Use and Limitations of Occupational
Exposure Limit Values 3-27
4. QUANTITATIVE METHODOLOGICAL PROCEDURES 4-1
4.1 PROCEDURES ADDRESSING LIFETIME EXPOSURE 4-1
4.1.1 Approach for RfC Estimation 4-1
4.1.1.1 Minimum Criteria 4-9
4.1.1.2 Calculation of Human Equivalent
Concentrations 4-10
4.1.1.3 Route-to-Route Extrapolation 4-31
4,1.1.4 Issues for Further Investigation 4-33
4.1.2 Approach for RfC Estimation Using Human Data 4-34
4.1.2.1 Introduction 4-34
4,1.2.2 Selecting the Threshold Estimate 4-35
4.1.2.3 Defining the Exposure Level 4-35
4.1.2.4 Uncertainty Factors for Human Data 4-36
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CONTENTS (cont'd)
Page
4,2 PROCEDURES FOR ESTIMATING PARTIAL
LIFETIME EXPOSURES 4-37
4.2.1 Acute 4-37
4.2.2 Approach for Subchronic Inhalation RfC
Estimation (RfCs) 4-37
4.2.3 Issues Requiring Further Investigation 4-40
4.3 CRITERIA FOR SPECIFYING LEVEL OF
CONFIDENCE 4-40
5. REFERENCES 5-1
APPENDIX A: NOVEL APPROACHES TO THE ESTIMATION
OF REFERENCE DOSE (RfC) A-l
APPENDIX B: USE OF PHARMACOKINETIC DATA IN RISK
ASSESSMENT, SELECTED EXAMPLES B-l
APPENDIX C: ADVERSE HUMAN RESPIRATORY HEALTH
EFFECTS C-l
APPENDIX D: CRITERIA FOR ASSESSING THE QUALITY OF
INDIVIDUAL EPIDEMIOLOGICAL STUDIES D-l
APPENDIX E: CRITERIA FOR ASSESSING THE QUALITY OF
INDIVIDUAL ANIMAL TOXICITY STUDIES E-l
APPENDIX F: CRITERIA FOR CAUSAL SIGNIFICANCE F-l
APPENDIX G: CHOICE OF TOXICITY DATA G-l
APPENDIX H: CALCULATION OF RDDR AND AN EXAMPLE
APPLICATION OF DOSIMETRIC ADJUSTMENT
FOR PARTICLE EXPOSURES H-l
APPENDIX I: DERIVATION OF AN APPROACH TO
DETERMINE HUMAN EQUIVALENT
CONCENTRATIONS FOR
EXTRARESPIRATORY EFFECTS OF
GAS EXPOSURES BASED ON A PB-PK
MODEL USING SELECTED PARAMETER
VALUES 1-1
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TABLES
Number Page
2-1 Comparative Airway Anatomy as Revealed on Casts 2-5
2-2 Normal Surface Airway Epithelium: Cell Types 2-14
2-3 Some Specific Lung Cell Types and Their Function . 2-16
2-4 Main Species Differences in Epithelial Cells and Glands 2-18
3-1 Prevalence of Subgroups Hypersusceptible to Effects of
Common Pollutants 3-9
3-2 Proposed Approach for Summarizing the Evidence from
Diverse Data 3-16
3-3 Human Data for Use in Health Risk Assessment . 3-18
4-1 Four Types of Response Levels (Ranked in Order of Increasing
Severity of Toxic Effect) Considered in Deriving RfCs
for Systemic Toxicants . 4-2
4-2 Response Levels Considered in Deriving Inhalation RfCs in
Relationship to Empirical Severity Rating Values. (Ranks
are from Lowest to Highest Severity.) 4-3
4-3 Guidelines for the Use of Uncertainty Factors in Deriving
Reference Dose (RfC) 4-5
4-4 Minimum Data Base for Both High and Low Confidence
in the RfC 4-42
A-l Various Effect Levels and Their Definitions Used in
Figure A-2 A-4
B-l Absorption of 1,3-Butadiene by Inhalation Following a 6-hour
Exposure Period B-l
B-2 Recovery of 14C-Tetrachloroethylene Radioactivity After
Inhalation Exposure for 6 hours to Sprague-Dawley Rats B-4
G-l Comparison of the Highest Individual Species Human Equivalent
NOAEL and its LOAEL (or LEL) G-4
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TABLES (cont'd)
Number Page
H-l RDDR Values by Mass Median Diameter and Standard
Deviation for Rats H-5
H-2 Summary of Systemic Toxicity NOAELs for Ep(a)oxide
Observed in Fischer 344 Rats H-17
1-1 Definition of Symbols 1-4
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FIGURES
Figure
2-1 Schematic representation of selected parameters
influencing regional deposition of particles in
the respiratory tract 2-7
2-2 Regional deposition of monodisperse particles by
indicated particle diameter for mouth breathing
(alveolar and tracheobronchial) and nose
breathing (alveolar) 2-8
2-3 Schematic representation of selected parameters
influencing regional deposition of gases in the
respiratory tract 2-9
2-4 An example of the log-normal distribution function
of an aerosol 2-20
2-5 Plot of same aerosol as in Figure 2-4 on log-
probability paper 2-20
4-1 Flowchart for calculation of Human Equivalent
Concentrations 4-11
4-2 Time course of periodicity for F344 rat exposed
6 hours/day, 5 days/week to the theoretical gas
with partition coefficients as shown 4-27
4-3 Relationship of patition coefficients to periodicity
in F344 rat arterial blood for subchronic (90-days)
and chronic exposure regimens of 6 hours/day,
5 days/week 4-28
A-l Effect-dose-duration plot of all relevant human and
animal oral toxicity data for methoxychlor . A-3
A-2 Hypothetical dose-response data for slight body
weight decrease or liver necrosis in rats and
dogs, respectively A-8
H-l Schematic of the integration of aerosol distribution
(A) and deposition efficiency (B) curves for
calculation of (ROD) H-2
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FIGURES (cont'd)
Figure Page
H-2 RDDR of the rat to the human by particle diameter
(MMAD) for the TB region ......................... H-12
H-3 RDDR of the rat to the human by particle diameter
(MMAD) for the PU region ...................... ... H-13
H-4 RDDR of the rat to the human by particle diameter
(MMAD) for the TOT system in (A) normal augmenters
and (B) mouth breathers .... ................ ....... H-15
H-5 RDDR of three species to the human by particle
diameter (MMAD) for the TH region .............. ..... H-19
H-6 The relationship between particle removal half-time
(t1/2p) and dissolution half-time (t1/2s) upon the
bioavailability of a single deposited dose of inhaled
paniculate matter over a 730-day period ........ ......... H-22
1-1 Schematic of the physiologically based pharmacokinetic
model assumed to describe the uptake and distribution
of inhaled compounds ............................. 1-3
1-2 Plot of NOAEL^HEC] vs. NOAELrAj for the rat for four
possible methods (proposed, established, similar
and optimal) of determining NOAELpjECj estimates
as defined in the text ............................. 1-13
1-3 Plot of NOAELpjEC] vs. NOAELrA] for the mouse for four
possible methods (proposed, established, similar
and optimal) of determining NOAELpjg^ estimates
as defined in the text .......... ......... .......... 1-13
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LIST OF ABBREVIATIONS
ADI
bw
CNS
ae
D
Dar
DNA
Dp
DWEL
PEL
FVC
GI
HA
i.v.
kg
LEL
LOAEL
LOEL
MF
mg
MMAD
NOAEL
NOEL
PEL
ppm
ROD
Acceptable daily intake
Body weight
Central nervous system
Aerodynamic equivalent diameter
Aerodynamic resistance diameter
Deoxyribonucleic acid
Particle diameter
Drinking water equivalent level
Frank-effect level
Forced expiratory volume at one second
Forced vital capacity
Gastrointestinal
Health advisory
Intravenous
Kilogram
Lowest-effect level
Lowest-observed-adverse-effect level
Lowest-observed-effect level
Modifying factor
Milligram
Microgram
Micrometer
Mass median aerodynamic diameter
No-observed-adverse-effect level
No-observed-effect level
Permissible exposure level
Parts per million
Regional deposited dose
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RDDR Regional deposited dose ratio
RfC Chronic inhalation reference dose
RfCL Subchronic inhalation reference dose
a
RNA Ribonucleic acid
o Geometric standard deviation
TLV Threshold limit value
UF Uncertainty factor
URT Upper respiratory tract
VT Tidal volume
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
This document was prepared under the direction of the Environmental Criteria and
Assessment Offices in Cincinnati, Ohio and in Research Triangle Park, North Carolina.
The principal authors, listed in alphabetical order, are:
Karen Blackburn
U.S. EPA, ORD, OHEA, ECAO
Cincinnati, Ohio 45268
Michael Dourson, Ph.D.
U.S. EPA, ORD, OHEA, ECAO
Cincinnati, Ohio 45268
Linda Erdreich, Ph.D.*
Environmental Research Information, Inc.
New York, New York 10018-3011
* Formerly with ECAO, Cincinnati
The contributing authors, listed in alphabetical order, are:
Annie M. Jarabek
U.S. EPA, ORD, OHEA, ECAO
Research Triangle Park, NC 27711
John Overton, Jr., Ph.D.
U.S. EPA, ORD, OHR, HERL
Research Triangle Park, NC 27711
Christopher DeRosa, Ph.D.
U.S. EPA, ORD, OHEA, ECAO
Cincinnati, Ohio 45268
Judith A. Graham, Ph.D.
U.S. EPA, ORD, OHEA, ECAO
Research Triangle Park, NC 27711
Mark Greenberg
U.S. EPA, ORD, OHEA, ECAO
Research Triangle Park, NC 27711
Elaine C. Grose, Ph.D.
U.S. EPA, ORD, OHR, HERL
Research Triangle Park, NC 27711
Richard Hertzberg, Ph.D.
U.S. EPA, ORD, OHEA, ECAO
Cincinnati, Ohio 45268
Bruce Peirano
U.S. EPA, ORD, OHEA, ECAO
Cincinnati, Ohio 45268
William Pepelko, Ph.D.
U.S. EPA, ORD, OHEA, HHAG
Washington, DC 20460
Greg Theiss
U.S. EPA, ORD, OPPE
Washington, DC 20450
xn
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The following individuals participated as peer reviewers at the workshop/ public meeting
held at the U.S. EPA Environmental Research Center in Research Triangle Park on
October 5-6, 1987, and provided valuable comments and written contributions on both the
workshop and revised drafts:
Dr. Charles Hobbs
Assistant Director, Inhalation Toxicology Research Institute
Lovelace Biomedical and Environmental Research Institute, Inc.
P.O. Box 5890
Albuquerque, New Mexico 87185
Dr. Michael D. Lebowitz
Division of Respiratory Sciences
University of Arizona
Tucson, Arizona 85724
Dr. Daniel B. Menzel
Director, Laboratory of Environmental Pharmacology and Toxicology
P.O. Box 3813
Duke University Medical Center
Durham, North Carolina 27710
Dr. Richard Schlesinger
Director, Laboratory for Pulmonary Biology and Toxicology
Institute of Environmental Medicine
New York University Medical Center
Long Meadow Road
Tuxedo, New York 10987
Dr. Vera Thomas
Department of Anesthesiology
University of Miami School of Medicine
P.O. Box 016370
Miami, Florida 33101
Dr. Theodore Torkelson
Toxicology Consultant
315 Birch Street
Roscommon, Michigan 48653
Dr. Curtis Travis
Office of Risk Analysis
P.O. Box X
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831
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The authors wish to acknowledge the scientific guidance and support provided by
Dr. Fred Miller (ORD, OHR, HERL), Ms. Margaret Menache (NSI-Technology Services
Corporation), Dr. Daniel Guth (OAR, OAQPS, PAB) and Dr. Judith Bellin (Risk Assessment
Forum).
The authors thank Bette Zwayer and Carol Haynes (ECAO-Cin) for diligently and
graciously preparing the first drafts of the manuscript and Judith Olsen for excellent editorial
support. The authors thank Ivra Bunn, Lynette Davis, Patricia Felix, Miriam Gattis, and
J-X>rrie Godley of NSI Technology Services Corporation (RTF) for preparing the workshop,
revised and final drafts, and the final document.
The authors would also Like to express a special tribute to the late Director of
ECAO-Cin, Dr. Jerry F. Stara. Without his vision and guidance, this effort would not have
been possible.
Address changes for April 1989 Document:
Dr. Judith S. Bellin
383 O Street, SW
Washington, DC 20024
Dr. Daniel B. Menzel
University of California at Irvine
Southern Occupational Health Center
Irvine, CA 92717
Dr. Frederick J. Miller
Department of Medicine
Duke University Medical Center
Durham, NC 27710
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GLOSSARY
Activity Median Diameter (AMD)
Refers to the median of the distribution of radioactivity, lexicological, or biological
activity with respect to particle size.
Acute Exposure
A one-time or short-term exposure with a duration of less than or equal to 24 hours.
Aerodynamic Diameter
Term used to describe particles with common inertial properties to avoid the
complications associated with the effects of particle size, shape, and physical density.
Aerodynamic equivalent diameter (Dae)
"Aerodynamic diameter" generally used. The diameter of a unit density sphere (pp =
1 g/em ) having the same settling velocity (due to gravity) as the particle of interest of
whatever shape and density. Refer to Raabe (1976) for equation.
Aerodynamic (viscous) resistance diameter (D^)
The "Lovelace" definition for aerodynamic diameter. Characteristic expression based on
terms describing a particle in the Stokes' regime. Refer to Raabe (1976) for equation.
Aerosol
All-inclusive term. A suspension of liquid or solid particles in air.
Critical Effect
The first adverse effect, or its known precursor, that occurs as the dose rate increases.
Chronic Exposure
Multiple exposures occurring over an extended period of time, or a significant fraction
of the animal's or the individual's lifetime.
Diffusion Diameter
Diameter of a sphere having the same diffusion mobility as the particle in question. D
<0.5/im.
Forced expiratory volume (FEVj) at one second
The volume of air which can be forcibly exhaled during the first second of expiration
following a maximal inspiration.
Forced vital capacity (FVC)
The maximal volume of air which can be exhaled as forcibly and rapidly as possible
after a maximal inspiration.
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Generation
Refers to the branching pattern of the airways. Each division into a major daughter
(larger in diameter) and minor daughter airway is termed a generation. Numbering
begins with the trachea.
Henry's Law Constant
The law can be expressed in several equivalent forms, a convenient form being: C =
RCl where Cg and Cl are the gas-(g) and liquid-(l) phase concentrations. The constant
(H) is the ratio at equilibrium of the gas phase concentration to the liquid-phase
concentration of the gas (i.e., moles per liter in air/moles per liter in solution).
Lowest-Effect Level (LEL)
Same as Lowest-Observed-Adverse-Effect Level.
Lowest-Observed-Adverse-Effect Level (LOAEL)
The lowest exposure level at which there are statistically or biologically significant
increases in frequency or seventy of adverse effects between the exposed population and
its appropriate control group.
Mass Median Aerodynamic Diameter (MMAD)
Mass median of the distribution of mass with respect to aerodynamic diameter. Graphs
for these distributions are constructed by plotting frequency against aerodynamic
diameters.
Modifying Factor (MF)
An uncertainty factor that is greater than zero and less than or equal to 10; its magnitude
reflects professional judgment regarding scientific uncertainties of the data base or study
design not explicitly treated by the uncertainty factors (e.g., the number of animals
tested). The default value for the MF is 1.
No-Observed-Adverse-Effect Level (NOAEL)
An exposure level at which there are no statistically or biologically significant increases
in the frequency or severity of adverse effects between the exposed population and its
appropriate control. Some effects may be produced at this level, but they are not
considered as adverse, nor precursors to specific adverse effects. In an experiment with
several NOAELs, the regulatory focus is primarily on the highest one, leading to the
common usage of the term NOAEL as the highest exposure without adverse effect.
Portal-of-Entry Effect
A local effect produced at the tissue or organ of first contact between the biological
system and the toxicant.
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Reference Concentration (RfC)
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
reference dose is for continuous inhalation exposures and is appropriately expressed in
units of mg/m3. It may be expressed as mg/kg/day, in order to compare with oral RfD
units, utilizing specified conversion assumptions.
Regional Deposited Dose (RDD)
The deposited dose (mg/cm2 of lung region surface area per minute) calculated for the
region of interest as related to the observed effect (i.e., calculated for the
tracheobronchial region for an effect concerning the conducting airways).
Regional Deposited Dose Ratio (RDDR)
The ratio of the regional deposited dose in the animal species of interest (RDDA) to that
of humans (RDDH). This ratio is used to adjust the exposure effect level for
interspecies dosimetric differences.
Reserve Volume
Volume of air remaining in the lungs after a maximal expiration.
Respiratory Bronchiole
Noncartilagenous airway with lumen open along one side to alveoli; when walls are
completely alveolarized it is usually referred to as an alveolar duct. Essentially absent
in rats.
Stokes' Law
The total drag force or resistance of the medium due to fluid motion relative to the
particle is the sum of form and friction drag. When particle motion is described by this
equation, it is said to be in the Stokes regime.
Subchronic Exposure
Multiple or continous exposures occuring over about 10% of an experimental species
lifetime, usually over 3 months.
Terminal Bronchiole
Noncartilagenous airway that conducts airstream to respiratory bronchiole.
Threshold
The dose or exposure below which a significant adverse effect is not expected.
Carcinogenicity is thought to be a nonthreshold endpoint, thus, no exposure can be
presumed to be without some risk of adverse effect. Noncarcinogenicity is presumed to
be a threshold endpoint, thus, some exposures are presumed to be without risk of
adverse effects.
Tidal Volume (VT)
Volume of air inhaled/exhaled during normal breathing
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Uncertainty Factor (UF)
One of several, generally 10-fold factors, used in operationally deriving the Reference
Concentration (RfC) from experimental data. UFs are intended to account for (1) the
variation in sensitivity among the members of the human population; (2) the uncertainty
in extrapolating animal data to the case of humans; (3) the uncertainty in extrapolating
from data obtained in a study that is of less-than-lifetime exposure; (4) the uncertainty in
using LOAEL data rather than NOAEL data; and (5) the inability of any single study to
adequately address all possible adverse outcomes in humans.
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1. INTRODUCTION
1.1 DEVELOPING BENCHMARK VALUES IN THE U.S.
ENVIRONMENTAL PROTECTION AGENCY
38--"
TMS document focuses on lexicological issues central to the development of an approach
for the quantitative assessment of risks of health effects other than cancer and gene mutations
for inhaled agents and to the development of an interim methodology for the estimation of
inhalation reference concentrations (RfCs) (earlier terminology was "inhalation reference
dose" or "RfDj"). An inhalation reference concentration (RfC) is an estimate (with
uncertainty spanning perhaps an order of magnitude) of continuous exposure to the human
population (including sensitive subgroups) that is likely to be without appreciable risk of
deleterious effects during a lifetime. The RfC is appropriately expressed in units of mg/m3.
The documentation discusses criteria and information to be considered hi selecting key studies
for inhalation RfC derivation, provides an overview of the respiratory system and its intra-
and interspecies variables, and discusses areas of uncertainty and data gaps in relation to the
proposed interim methodology.
The U.S. Environmental Protection Agency (U.S. EPA) has a history of advocating the
evaluation of scientific data and calculation of Acceptable Daily Intake (ADI) values for
noncarcinogens as benchmark values for deriving regulatory levels to protect exposed
populations from adverse effects. The Office of Pesticide Programs used the concept of ADI
for tolerance estimates of pesticides in foodstuffs. The Office of Health and Environmental
Assessment used ADI values for characterizing levels of pollutants in ambient waters (Federal
Register, 1980). The National Research Council (1977, 1980) recommended the ADI
approach to characterize levels of pollutants in drinking water with respect to human health;
the U.S. EPA Office of Drinking Water has adopted the National Academy of Sciences
(NAS) approach.
The U.S. Environmental Protection Agency (1987a) has developed guidelines for the
evaluation of available data pertaining to xenobiotics for purposes of developing oral reference
doses (RfDs) analogous in intent to the ADI approach for oral exposures. While similar to
ADIs in intent, RfDs were based upon a more rigorously defined methodology (Barnes and
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Dourson, 1988), In addition, guidelines for developing risk assessments have been
promulgated for mutagerticity, carcinogenicity, mixtures, teratogenicity and reproduction, and
for estimation of exposure (Federal Register, 1986a through e). Draft guidelines also are
available for female and male reproductive toxicity (Federal Register 1988a, b).
The U.S. EPA's effort to develop oral RfDs involved several parallel efforts:
(1) development of guidelines for establishing levels of confidence in RfDs; (2) verification of
existing RfDs; and (3) identification and analysis of toxicologic data pertinent to the
development of RfDs.
In order to adapt this approach to derive inhalation reference concentration as
benchmark values analogous to those existing for the oral reference dose (RfD), it was
necessary to develop the scientific basis for estimating inhalation values, develop guidelines,
and encourage broad scientific review. The same general principals were used, but the
methodology was expanded to account for the dynamics of the respiratory system a s the
portal of entry. The major difference is that the inhalation RfC methodology includes
dosimetric adjustments to account for the species-specific relationships of inhaled
concentrations and deposited/delivered doses. Particles and gases are treated separately and
the site of the observed toxic effect (respiratory or extrarespiratory) is considered in applying
the dosimetric adjustments.
The EPA recognizes that regional, state, and local health protection departments need
uniform and scientifically sound risk assessment procedures for the estimation of benchmark
inhalation values. The proliferation of diverse risk assessment values for inhalation exposure
and the resulting confusion this has caused attests to the importance of a consensus approach
to uniform guidelines. It is the intention of the EPA that the interim RfC approach described
will be useful to many in their risk management programs as one piece of the risk assessment
process. The approach outlined is not intended to discourage novel or more sophisticated risk
assessment procedures when sufficient data are available. The recognized deficiencies in this
RfC approach and other novel approaches under development are described in Appendix A,
and examples of the use of pharmacokinetic data in risk assessment are provided in
Appendix B. Current research and ongoing projects to refine inhalation concentration
estimates are outlined in Appendices H and I. The interim RfC methodology proposed is
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consistent with previous EPA approaches, however, and is considered suitable for
implementation.
The issue paper on Occupational Exposure Limit (OEL) values, developed by the
Inhalation Technical Panel of EPA's Risk Assessment Forum, discusses the history, use, and
limitations of OELs as surrogates for ambient exposure RfC values (U.S. Environmental
Protection Agency, 1990).
1.2 GENERAL PRINCIPLES OF NONCANCER TOXICITY RISK
ASSESSMENT*
Toxic endpoints other than cancer and gene mutations are often referred to as
"noncancer toxicity" because of effects on the function of various organ systems. Most
chemicals that produce noncancer toxicity do not cause a similar degree of toxicity in all
organs, but usually demonstrate major toxicity to one or two organs. These are referred to as
the target organs of toxicity for that chemical (Doull et aL, 1980). Generally, based on
understanding of homeostatic and adaptive mechanisms, noncancer toxicity is treated as if
there is an identifiable threshold (both for the individual and for the population); however, the
EPA is aware of the difficulties in the identification of population thresholds (Gaylor, 1985)
below which effects are not observable. This threshold approach distinguishes noncancer
endpoints from carcinogenic and mutagenic endpoints, which are often treated operationally as
nonthreshold processes.
The individual threshold hypothesis holds that a range of exposures from zero to some
finite value can be tolerated by the organism without adverse effects. For example, there
could be a large number of cells performing the same or similar function whose population
must be significantly depleted before an adverse effect is seen. Further, it is often prudent to
focus on the most sensitive members of the population and, therefore, regulatory efforts are
made to keep exposures below levels at which the more sensitive individuals in the population
would be expected to respond.
*This text is excerpted and adapted from U.S. Environmental Protection Agency (1987a) and Barnes and Dourson
(1988).
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Empirical observation generally reveals that as the dosage of a toxicant is increased, the
toxic response (in terms of severity and/or incidence of effect) also increases. This dose-
response relationship is well-founded in the theory and practice of toxicology and
pharmacology. Such behavior is exemplified by three types of data: (1) quantal responses, in
which the number of responding individuals in a population increases; (2) dose-graded
responses, in which the severity of the toxic response within an individual increases with
dose; and (3) continuous responses, in which changes in a biological parameter (e.g., body or
organ weight) vary with dose.
The majority of previous risk assessment efforts for noncancer health effects have been
directed at oral exposures. Human data appropriate for quantifying risk assessments for oral
exposure are limited; therefore, the majority of these assessments have relied on animal data.
These animal studies typically reflect situations in which exposure to the toxicant has been
carefully controlled, and the problems of heterogeneity of the exposed population and
concurrent exposures to other toxicants have been minimized. In evaluating animal data, a
series of professional judgments are made involving, among other things, consideration of the
scientific quality of the studies. Presented with data from several animal studies, the risk
assessor first seeks to identify the animal model that is most relevant to humans, based on
compatibility of biological effects using the most defensible biological rationale; for instance,
by using comparative metabolic, pharmacokinetic, and pharmacodynamic data. In the
absence of a clearly most relevant species, however, the most sensitive species is used as a
matter of science policy at the EPA. For inhalation RfCs, the most sensitive species is the
species that shows an adverse effect at an exposure level which when dosimetrically adjusted,
results in the lowest human equivalent concentration. Guidance for full utilization of human
data has not been extensively explored because of the limited availability of relevant human
oral data. However, for the inhalation route, a substantially greater quantity of human data
useful to risk assessment is anticipated. Subsequent sections of this document will explore the
issues associated with human data that are particularly relevant to the inhalation route of
exposure.
In the simplest terms, an experimental exposure level is selected from a given study of a
species representing the highest level tested at which no adverse effect was demonstrated.
The inhalation RfC methodology requires conversion of these "No-Observed-Adverse-Effect
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Levels" (NOAELs) observed in animals to human equivalent concentrations (HECs) before
the data array and effect levels can be evaluated and compared. A chemical may elicit more
than one toxic effect (endpoint) in tests of the same or different duration (acute, subchronic,
and chronic exposure studies), even in one test species. In general, NOAELrHECjS for these
effects will differ. The critical toxic effect used in the dose-response assessment is the one
generally characterized by the lowest NOAEL|-HEC-|. The NOAELrHEC] is the key datum
gleaned from the study of the dose-response relationship and, traditionally, is the first basis
for the scientific evaluation of the benchmark level in the RfC approach. This approach is
based, in part, on the assumption that if the critical toxic effect is prevented, then all toxic
effects are prevented.
The RfC is a benchmark dose operationally derived from the NOAELpEC-| of the
critical effect by consistent application of generally order of magnitude uncertainty factors
(UFs) that represent the second basis for the scientific evaluation of the RfC. The uncertainty
factors reflect potential extrapolation uncertainty between the characteristics of the study
situation and the projection to daily exposure of humans. The RfCs and the composite
uncertainty factors vary in magnitude depending upon the particular study; for example, a
valid NOAEL for chronically exposed healthy humans is normally divided by a UF of 10-fold
to extrapolate to a more susceptible population. In addition, a modifying factor (MF), which
is based on a professional judgment of the entire data base of the chemical, may be included.
That is:
RfC[HEC] = NOAEL[HEC] / (UF x MF)
Inhalation RfC pertain to continuous exposures for a lifetime. If exposure assumptions are
changed and appropriate toxicologic data utilized, benchmark values may be calculated for
exposure durations of less than a lifetime (see Section 4.2). An evaluation of the adequacy of
presently used uncertainty factors in extrapolating from subchronic to chronic inhalation
exposure is an outstanding issue to be addressed.
The EPA is attempting to standardize its approach in determining RfCs. This
standardization will include statements on the confidence that the evaluators have in the RfC.
High confidence is an indication that the RfC is unlikely to change as more data become
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available because there is consistency among the toxic responses observed in different sexes,
species, study designs or in dose-response relationships. It is recognized, however, that
increasingly sophisticated tests may change the perspective of evaluation. Often, high
confidence is associated with RfCs that are based on human data for the exposure route of
concern. Low confidence indicates that the RfC may be especially vulnerable to change if
additional chronic toxicity data become available.
1.3 STATE-OF-THE-ART APPLICATIONS TO THE DEVELOPMENT
OF THE INHALATION RfC METHODOLOGY
All risk assessments involve some degree of reliance upon assumptions, which substitute
for unavailable quantitative information and by that impart varying degrees of uncertainty in
the risk assessment methodology. However, as state-of-the-art research and health risk
science progresses, the precision of risk assessments will be improved, insofar as these
advancements are incorporated into the assessments. Risk assessments ultimately serve as the
basis for personal or governmental risk management decisions on safeguarding health and
have consequential economic impacts. This makes it imperative that scientific advancements
in risk assessment be made and that they be appropriately incorporated into risk assessment
processes, including the derivation of inhalation RfCs. Based on this, the current inhalation
RfC methodology is termed "interim," in view of planned future updating as advancements in
risk assessment are made.
The Office of Research and Development (ORD) is conducting a rigorous research
program to improve the scientific basis of risk assessments. When key information becomes
available from this program, as well as relevant research from other institutions, it will be
incorporated into the inhalation RfC methodology. This must be balanced against the
necessity of a certain degree of consistency in risk assessment procedures, to improve the
feasibility of broad regulatory application of the assessments. Therefore, the Office of Health
and Environmental Assessment, ORD, will regularly evaluate scientific advancements in the
field and make recommendations for significant improvements in the inhalation RfC
methodology. Every two years, these recommendations are expected to be presented to an
expert panel of EPA and extramural scientists for peer review. Modifications in the
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methodology will be made as appropriate. If research advancements having a striking impact
on the methodology were to occur prior to this two-year recurring review, then the timing of
the process would be altered appropriately.
As generic issues arise during the verification sessions of the inhalation RfD workgroup,
they will be sent to a Risk Assessment Forum made up of an appointed technical panel of
experts for review and resolution. The technical panel of the Risk Assessment Forum then
will provide recommendations and guidance on such issues. This mechanism has provided
useful input to the oral RfD methodology to date and is anticipated to provide refinements to
the inhalation RfC methodology as well.
This interim methodology will be buttressed by a technical support document providing
tabulated Regional Deposited Dose Ratios (RDDRs) for various species that will be produced
in the near future. These ratios are used to adjust animal experimental exposure
concentrations to human equivalent concentrations as discussed hi Chapter 4 and Appendix H.
The technical support document will provide a detailed description of their derivation and
limitations of their application. Research also is already underway to provide a second
technical support document of Regional Retained Dose Ratios (RRDRs). These ratios will
integrate clearance functions into the deposited values for estimates more appropriate to
assessing chronic exposure conditions.
At the time of the two-year review, it is expected that research advancements on uptake
modeling of gases (discussed in Chapter 4 and Appendix I) will provide guidance on
dosimetric adjustments for different categories of gases. Continued work on hygroscopic
particle modeling may provide chemical-specific adjustment factors or a revised default
condition for this category of aerosols.
Other ORD research projects anticipated to have significant impact on the methodology
include: (1) guidance on the limitations and application of physiologically-based
pharmacokinetic model parameters to route-to-route extrapolation, and (2) approaches for
less-than-lifetime assessment. An appropriate characterization of activity patterns of human
ventilatory levels also is expected to be developed so that the aerosol deposition and gas
uptake models can be utilized to provide more realistic estimates of probable human exposure.
In summary, one objective of the Interim Inhalation RfC methodology is that it always
be scientifically based, and thus, the methodology should be considered dynamic. Pertinent
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issues and their solutions will be incorporated as identified on a continuing basis. Periodic
peer review will provide quality assurance. These actions will make the methodology suffi-
ciently reliable to serve as one of the key bases for decisions on protecting the public health.
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2. CONCEPTUAL BASIS FOR INHALATION RISK
ASSESSMENT METHODOLOGY
As discussed in the introduction, there are some fundamental differences to be
considered in performing risk assessments of inhalation exposures to chemicals and of oral
exposures. The primary differences are the degree that the complex relationship between
exposure dose and dose delivered to the target site can be addressed and the more common
occurrence of portal-of-entry effects. Both of these are described below to serve as a basis
for criteria that must be added to the oral RfD methodology to facilitate development of
inhalation RfCs.
2.1 FACTORS CONTROLLING COMPARATIVE INHALED DOSE
It is anticipated that the derivation of inhalation RfCs will not be as straightforward as
that of oral RfDs, given the dynamics of the respiratory system and its diversity across
species. The various species used in inhalation toxicology studies do not receive identical
doses in comparable respiratory tract regions when exposed to the same particle or gas
concentration (Brain and Mensah, 1983). The biologic endpoint or health effect may be more
directly related to the quantitative pattern of mass deposited within the respiratory tract than
to the exposure concentration. Regional deposition pattern determines not only the initial
lung tissue dose but also the specific pathways and rates by which the inhaled agents are
cleared and redistributed (Schlesinger, 1985).
This section presents the issues associated with the major factors controlling the
deposition pattern, which are: (1) respiratory anatomy and physiology (Section 2.1.1); and
(2) the physicochemical characteristics of the inhaled agent (Section 2.1.2). Section 2.1.3
presents restrictions imposed by experimental procedures and technology, and working
assumptions that affect the two major controlling factors.
The factors that control inhaled dose are discussed relative to the significant mechanisms
by which particles and gases may initially be deposited or taken up in the lung. For particles
this includes inertia! impaction, sedimentation (gravitational), diffusion, interception, and
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electrostatic precipitation, while mechanisms important for gases include convection,
diffusion, chemical reaction, and solubility. Detailed consideration of these mechanisms is
beyond the scope of this discussion. The reader is referred elsewhere for more extensive
discussions of particle deposition (U.S. Environmental Protection Agency, 1982; U.S.
Environmental Protection Agency, 1986b; Hatch and Gross, 1964; Raabe, 1979; Hinds,
1982; Lippmann and Schlesinger, 1984) and gas absorption (U.S. Environmental Protection
Agency, 1986c; Fiserova-Bergerova, 1983; Overton, 1984; Overton and Miller, 1988).
It must be emphasized that dissection of the factors that control inhaled dose into
discrete discussions is deceptive and masks the dynamic nature of the intact respiratory
system. For example, although deposition in a particular respiratory region will be discussed
separately from the clearance mechanisms for that region, retention (the actual amount of
inhaled agent found in the lungs at any time) is determined by the relative rates of deposition
and clearance. Retention and the toxicologic properties of the inhaled agent are presumably
related to the magnitude of the pharmacologic, physiologic, or pathologic response. Thus,
although the deposition, clearance mechanisms, and physiochemical properties of the agent
are described in distinct sections, assessment of the overall toxicity requires integration of the
various factors into a dynamic picture.
Future improvements in this process will be accomplished in the area of extrapolation
modeling (Miller et al., 1983a; Fiserova-Bergerova, 1983). This involves determining the
effective dose delivered to the target organ of various species and the sensitivity of the target
organ to that dose. Once such dosimetry has been established, and species sensitivity
accounted for, the effective pollutant concentration in animals can be quantitatively related to
concentration responses in humans. Extrapolation models should incorporate parameters such
as species anatomical and ventilatory differences, metabolic processes, and the
physicochemical properties of the pollutant and should be physiologically based upon the
factors that govern transport and removal of the pollutant.
In the interim, a qualitative knowledge and application of how regional deposition and
disposition patterns, and metabolism of an inhaled dose may differ between humans and
experimental animals commonly used in inhalation toxicology investigations will provide
more accurate cross-species dosimetric extrapolations.
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2.1.1 Respiratory Anatomy and Physiology
The respiratory systems of humans and various experimental animals differ in anatomy
and physiology in many quantitative and qualitative ways. These variations affect air flow
patterns in the respiratory system, and in turn, the deposition of an inhaled agent, as well as
the retention of that agent in the system. The variations in anatomy and physiology will be
discussed according to respiratory regions and branching patterns, clearance mechanisms, and
cell types. Clearance mechanisms as used here include processes such as the mucociliary
escalator, solubilization in various compartments, uptake, and metabolism.
2.1.1.1 Respiratory Regions and Branching Patterns
The respiratory system in both humans and experimental animals can be divided into
three regions on the basis of structure, size, and function: nasopharyngeal, tracheobronchial,
and pulmonary (alveolar). The retained dose of an inhaled agent in each of these regions is
governed by the individual species anatomy (e.g., airway size and branching pattern) and
physiology (e.g., breathing rate and clearance mechanisms).
Airway size and branching pattern affect the aerodynamics of the respiratory system in
the following ways:
• The airway diameter affects the aerodynamics of the flow and the distance from the
agent molecule or particle to the airway surface.
• The cross-sectional area of the airway determines the airflow velocity for a given
volumetric flow.
• Diameter and branching pattern variations affect the mixing between tidal and reserve
air.
Differences in airway sizes and branching between species thus result in significantly different
patterns of gas transport and particle deposition.
Effect on Aerosol Deposition Mechanisms
Air flow in the extrathoracic region is characterized by high velocity and abrupt
directional changes. Thus, the predominant deposition mechanism in the extrathoracic region
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is inertial impaction. Changes in airstream direction or magnitude of air velocity streamlines
or eddy components do not affect airborne particles due to their inertia. Large particles
(> 5 pirn) are more efficiently removed from the airstream in this region,
Impaction remains a significant deposition mechanism for particles larger than 2.5 /*m
aerodynamic equivalent diameter (Dae) in the larger airways of the tracheobronchial region
and competes with sedimentation, with each mechanism being influenced by mean flow rate
and residence time, respectively. As the airways successively bifurcate, the total cross-
sectional area increases. This increases airway volume in the region and the air velocity is
decreased. With decreases in velocity and more gradual changes in air flow direction as the
branching continues, there is more time for gravitational forces (sedimentation) to deposit the
particle. For particles =4 /*m Dae, a transition zone between the two mechanisms, from
impaction to predominantly sedimentation, has been observed (U.S. Environmental Protection
Agency, 1982). This transition shifts toward smaller particles for nose breathing.
Differences in airway size and branching pattern are a major source of inter species
variability in inhaled dose for the tracheobronchial region. Larger airway diameter results in
greater turbulence for the same relative flow velocity (e.g., between a particle and air).
Therefore, flow may be turbulent in the large airways of humans, while for an identical flow
velocity, it would be laminar in the smaller experimental animal. Relative to humans,
experimental animals also tend to have tracheas that are much longer in relation to their
diameter. This could result in increased deposition in humans because of the increased
likelihood of laryngeal jet flow extending into the bronchi. Humans are characterized by a
more symmetrical dichotomous branching than that found in most laboratory mammals, which
have highly asymmetrical branching (monopodial). The more symmetrical dichotomous
pattern in humans is susceptible to deposition at the carina because of its exposure to high air
flow velocities toward the center of the air flow profile. These comparative airway anatomy
differences are summarized in Table 2-1.
Sedimentation becomes insignificant relative to diffusion as the particles become
smaller. Deposition by diffusion results from the random (Brownian) motion of very small
particles caused by the collision of gas molecules in air. The terminal settling velocity of a
particle approaches 0.001 cm/s for a unit density sphere with a physical diameter of 0.5 ^m,
so that gravitational forces become negligible. The main deposition mechanism is diffusion
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TABLE 2-1. COMPARATIVE AIRWAY ANATOMY AS REVEALED ON CASTS
6,
I
Gross Structure
Mammal/
Body Mass
Human/70 kg
Rhesus
monkey/2 kg
Beagle dog/
10kg
Ferret/
0.61 kg
Guinea pig/
lk«
Rabbit/
4.5kg
Rat/0.3 kg
Golden
hamster/
0.14kg
Left Lung
Lobes
upper and
lower
superior,
middle, and
inferior
apical.
intermediate,
and basal
NRb
superior
and
inferior
superior
and
inferior
one lobe
superior
and
inferior
Right Lung
Lobes
upper, middle
and lower
superior,
middle, and
inferior.
azygous
apical,
intermediate,
and basal
NR
superior.
middle and
inferior
cranial,
middle, caudal
and postcaval
cranial,
middle, caudal,
and postcaval
cranial, middle
caudal, and
postcaval
Airway
Branching
relatively
symmetric
monopodial
strongly
monopodial
strongly
monopodial
monopodial
strongly
monopodial
strongly
monopodial
strongly
monopodial
Typical Structure
(Generation 6)
Branch Angles Typical Number
Trachea Major Airway (Major Daughter/ of Branches
L/D* Airway L/D Minor Daughter) to Terminal
(cm) Bifurcations (ratio) (degrees) Bronchiole
12/2 Sharp for about 2.2 11/33 14-17
the first 10
generations,
relatively
blunt thereafter
3/0.3 Mixed blunt 2.6 20/62 10-18
and sharp
17/1.6 Blunt trachea! 1.3 8/62 15-22
bifurcation,
others sharp
10/0.5 Sharp 2.0 16/57 12-20
5.7/0.4 Very sharp 1.7 7/76 12-20
and high
6/0.5 Sharp 1.9 15/75 12-20
2.3/0.26 Very sharp and 1.5 13/60 12-20
very high
throughout lung
2.4/0.26 Very sharp 1.2 15/63 10-18
Respiratory
Bronchioles
About 3-5 orders
About 4 orders
About 3-5 orders
About 3-4 orders
About 1 order
About 1-2 orders
Rudimentary
About 1 order
*L/D = Length/diameter ratio
bNR = Not reported
Source: Phalen and Oldham, 1983; Patra, 1986; Crapo, 1987.
-------
for a particle whose physical (geometric) size is <0.5 jtm. Impaction and sedimentation are
the main deposition mechanisms for a particle whose size is greater than 0.5 jim. Hence, D^
= 0.5 ftm is convenient for use as the boundary. Although this convention may lead to
confusion in the case of very dense particles, most environmental aerosols have densities
below 3 g/cm3 (U.S. Environmental Protection Agency, 1982). Diffusional deposition is
important in the small airways and in the pulmonary region where distances between the
particles and airway epithelium are small.
These mechanisms for particle deposition in the respiratory tract are schematically
represented in Figure 2-1. Experimental deposition data and extrapolated estimates on
humans that illustrate these same concepts are shown by the curves for pulmonary (alveolar)
and tracheobronchial deposition in Figure 2-2. Deposition fraction is shown plotted against
particle diameter. It is important to note that over half of the total mass of a typical ambient
mass distribution would be deposited in the extrathoracic region during normal nasal
breathing, with most of this being coarse particles (U.S. Environmental Protection Agency,
1986b). With mouth-only breathing, the regional deposition pattern changes dramatically,
with extrathoracic deposition being reduced and both tracheobronchial and pulmonary
deposition enhanced. Oronasal breathing (partly via the mouth and partly nasally), however,
typically occurs in healthy adults while undergoing moderate to heavy exercise. Thus, the
appropriate activity pattern of subjects for risk assessment estimation remains an important
issue. Miller et al. (1988) recently examined extrathoracic and thoracic deposition as a
function of particle size for ventilation rates ranging from normal respiration to heavy
exercise. A family of deposition estimate curves were generated as a function of breathing
pattern. Anatomic and functional differences between adults and children are likely to yield
complex interactions with the major mechanisms affecting respiratory tract deposition, again
with implications for risk assessment. Age-dependent dosimetric adjustments may be
possible, pending data availability for children.
Effect on Gas Deposition and Uptake
The major processes affecting gas transport involve convection, diffusion, absorption,
solubility, and chemical reactions. These mechanisms are schematically represented in
Figure 2-3. The bulk movement of inspired gas in the respiratory tract is induced by a
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DIRECTIONAL
CHANGE
AIR
VELOCITY
VERY
ABRUPT
IMPACT10N
1
IMPACTJON L
SEDIMENTATION
LESS
ABRUPT
MILD
ELECTROSTATIC
PRECIPITATION
S* WINTEHCEPTION
Figure 2-1. Schematic representation of selected parameters influencing regional
deposition of particles in the respiratory tract.
Source: Adapted from Casarett, 1975; Raabe, 1979; Lippmarm and Schlesinger, 1984.
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o
H
O
<
cc
LL
z
o
w
o
a.
u
Q
I 1 ill)
RANGE OF ALVEOLAR DEPOSITION,
MOUTH BREATHING
ESTIMATE OF ALVEOLAR DEPOSITION, NOSE BREATHING
RANGE OF TRACHEOBRONCHIAL DEPOSITION
MOUTH BREATHING
EXTRAPOLATION OF ABOVE TO POINT ( Q ) PREDICTED
BY MILLER it *l, U979I
-Q • EMMETT «t at, (1982). 337 W3 s , 6s BREATHING CYCLE
^•i ^ II^««P^^V* i«»«rtfl. TC n —.^ J ." 1 d_ ftO C A TLJ I fcl/* ^\/^t C
O • HEYOER (1986): 750 cm- s" • 4t BREATHING CYCLE
A A HEYDER (1986): 250 cm3 *-1. 4i BREATHING CYCLE
0.5 f-O^ SVAHTENGREN (19861
OPEN SYMBOLS: TRACHEOBRONCHIAL DEPOSITION
SOLID SYMBOLS; ALVEOLAR DEPOSITION
2.0 3,0 4.0 5.0
AERODYNAMIC DIAMETER,
PHYSICAL DIAMETER,
Figure 2-2. Regional deposition of monodisperse particles by indicated particle diameter
for mouth breathing (alveolar and tracheobronchial) and nose breathing (alveolar).
Deposition is expressed as fraction of particles entering the mouth or nose. The alveolar
band indicates the range of results found by different investigators using different
subjects and flow parameters for pulmonary (alveolar) deposition following mouth
breathing. The tracheobronchial (TB) band indicates intersubject variability in
deposition over the size range measured by Chan and Lippmann (1980). The
extrapolation of the upper bound of the TB curve in the larger particle size range also is
shown and appears to be substantiated by data listed in the legend.
Source: U.S. Environmental Protection Agency, 1986b.
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INSPIRATION
DIFFUSION LAYER)
Figure 2-3. Schematic representation of selected parameters influencing regional
deposition of gases in the respiratory tract.
Source: Overton, 1984.
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pressure gradient and is termed convection (U.S. Environmental Protection Agency, 1982).
Convection can be broken down into components of advection (horizontal movement of a
mass of air relative to the airway wall) and eddy dispersion (air mixing by turbulence so that
individual fluid elements transport the gas and generate flux). Molecular diffusion is
superimposed at all times on convection (bulk flow) due to local concentration gradients.
Absorption removes gases from the lumen and affects concentration gradients.
The average concentration of a gas in a tube (i.e., an "idealized" airway) can be
described by one-dimensional convection and dispersion. A pulse of substance moves down a
tube with an average air velocity equal to the medium's (air's) average velocity, and its
spread in the axial direction is governed by an effective dispersion coefficient that can be
described by Pick's law of diffusion (Overton, 1984). This effective dispersion coefficient is
larger than the molecular diffusion coefficient except in the pulmonary region. As illustrated
in Figure 2-3, perpendicular transport in this region can carry a gas molecule into the alveoli,
but because of the alveolar walls, there is no net axial transport as is present in the central
channel. The average axial transport is slowed because only a fraction of the molecules in the
cross-sectional average can move axially, resulting in a dispersion process with a dispersion
coefficient less than the molecule coefficient. The coefficient is a function of the molecular
diffusion coefficient, the total air volume, and the generation's alveolar airspace volume
(Overton, 1984).
Molecules are transferred from the flowing gas into the liquid layer lining the airway
wall by molecular diffusion. A simple description for this process postulates a thin, stagnant
layer based on the assumption that the air velocity becomes very small as the air-liquid
interface is approached. Transfer through this layer depends on the gas-phase diffusion
coefficient, layer thickness, and the gas concentrations at the boundaries of the layer. If the
molecules are absorbed, then the concentration of the gas in the diffusion layer is decreased at
the liquid boundary. As the ability of the liquid to remove the gas increases, the relative
concentration at the gas-liquid boundary decreases, and the mass transfer from the gas phase
to the liquid phase increases. For poorly soluble, hydrophobic, and nonreactive gases, little
gas is removed by the airways. The transport and chemistry into the adjacent liquid and
tissue layers will be described in Section 2.1.2.2, which describes the physicochemical
characteristics of gases and vapors. These next layers can serve as a "sink" to help "drive"
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the delivery of gas across this layer. Capillary blood flow (i.e., perfusion) is important to the
gas uptake in that it removes the gas or its chemical reaction products on the other side of
these liquid and tissue layers. Thus, addressing species differences in alveolar ventilation and
cardiac output is critical to estimate initial absorbed dose. The importance of regional
differences (e.g., the distance from the air to the capillaries in the tracheobronchial region is
7-20 times that in the pulmonary region [Overton and Miller, 1988]) and interspecies
differences in the anatomic relationship of the airspace to capillary blood should be
considered. Transfer also is enhanced by a reduction in diffusion layer thickness that is
dependent on the nearby rate of airflow; the higher the flow velocity, the thinner the layer,
again emphasizing the significance of airway morphology.
To attempt to model the effects that the intricate morphological structure of the
respiratory tract has on the nature of gas mixing and flows, representations of the mechanical
mixing imparted by tube bifurcations, turbulence, and secondary flows due to molecular
diffusion must be formulated. Location identity, diameter, and length are considered to be
the relevant measurements for gas transport (Overton, 1984). Because of the morphology of
the respiratory tract and air flow patterns, the relative contribution of these gas transport
processes is a function of location and point in the breathing cycle (i.e., depth and rate) (U.S.
Environmental Protection Agency, 1982; Overton, 1984). The interspecies differences in the
nature and structure of the respiratory tract, as summarized in Table 2-1, critically influence
the differences in transport and deposition of gases across species. The airways also show a
considerable degree of intraspecific size variability and are most likely the primary factor
responsible for the deposition variability seen within single species (Schlesinger, 1985).
Additionally, gender influences airway anatomy, and age has dramatic influences on
respiratory dynamics.
The differences in airway anatomy summarized in this Section (2,1.1) form the
structural basis for the species differences in gas and aerosol deposition. Extensive
investigations that resulted in the quantitation of the effects that these differences have on the
deposition of insoluble particles have resulted in the dosimetry adjustments for inhaled dose
that are outlined in Section 4.1.1.3. Current research on interspecies differences for gas
distribution and deposition should result in similar adjustments for gaseous inhaled agents. In
addition to the structure of the lung, the regional thickness and composition of the airway
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epithelium (a function of cell types and distributions) is an important factor in gas absorption,
and contributes to the solubility and extent of reaction of the gas. Other anatomic and
physiologic factors that influence gas uptake include: (1) ventilation, which affects the tidal
volume and ventilation to perfusion ratios; (2) body build, which affects the volume of
distribution (including cardiac output and tissue volume); and (3) metabolic capacities. These
are all factors to evaluate when estimating inhaled dose, interpreting injury response, and
extrapolating effects between species.
2.1.1.2 Clearance Mechanisms and Cell Types
Inhaled material is removed from the respiratory tract by clearance mechanisms, which
vary depending on the site of deposition and the properties of the inhaled agent. For gases,
the sequence in which anatomic sites are affected appears to be more dependent on
concentration than on exposure duration. However, at a given local anatomic site and at a
specific concentration, the stages in the pathogenesis of the lesion relate to the duration of
exposure (U.S. Environmental Protection Agency, 1986c). The speed and efficiency by
which the agents are cleared can be critical determinants of their toxic potential. Rapid
removal lessens the time available to cause critical damage to the pulmonary tissue and to
permit systemic absorption of agents that have target organs other than the lung (Menzel and
Amdur, 1986). The mechanisms involved include (1) exhalation of volatiles; (2) mucociliary
transport; (3) macrophage phagocytosis; (4) chemical reactions; (5) metabolism by various
cell types; and (6) dissolution and absorption into the blood, lymphatic, or lung fluids.
The transport and chemical uptake mechanisms for gases described in Section 2.1.2.2
are a function of respiratory tract region. Conceptually, a gas can move from the airway
lumen, through the liquid lining layer, through the tissue layer, through the capillary
endothelium, to reach the blood. This passage is influenced by the physiochemical properties
of the gas as well as the biochemistry and thickness of the layers between the lumen and
blood. For example, a very highly reactive gas may not reach the blood if it reacts
biochemically with mucus and the mucus has sufficient volume (thickness) to serve as a sink.
This same gas may not react with the saturated lipid of surfactant, and if deposited
significantly in the pulmonary region, could reach alveolar tissue. The thickness and
efficiency of the epithelial barrier also influences absorption. Both of these main factors
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(liquid lining and epithelial barrier) are present in all species but have species-specific
differences, only a few of which have been quantified. Mucus is a complex secretion with
contributions from various epithelial cells. The numbers and distribution of these cells may
affect the composition and properties of the mucus, which in turn interacts with the
physicochemical properties of the agent. The species differences in the thickness of the
alveolar epithelial cells could account for variations observed in the diffusion of gases into the
bloodstream (Crapo et al., 1983). The lung also is a very efficient excretory organ for
volatile organic chemicals after the exposure ceases or is lowered. The efficacy of pulmonary
excretion correlates indirectly with the saturated vapor pressure of the chemical.
Clearance of particles involves different mechanisms. Particles deposited on the anterior
nares are cleared by mechanical processes such as nose wiping, blowing (humans), or
sneezing (animals/humans). Panicles in this area can have long biological half-lives. Those
deposited in the nasopharynx or oropharynx, however, are swallowed within minutes and
passed through the esophagus down to the gastrointestinal tract.
Particles deposited in the tracheobronchial region are transported out of the respiratory
tract by the mucociliary system, an interaction between the mucous secretions and the cilia
that provide the mechanisms of movement. Such transport occurs along the area from the
larynx to the terminal bronchioles. Insoluble particles are transported up to the esophagus
where they are swallowed. The rate of this transport also affects the gas transport
mechanisms in the diffusion layer. The rate varies with the depth of the airways (greater
velocities in the proximal airways) and across species. Generally, the biological half-lives of
particles deposited in the tracheobronchial region are on the order of hours.
Clearance from the pulmonary region of the lung takes the longest, usually a rapid phase
of hours, and slower phases with biological half-lives of days, months, or years, depending
on particle size and solubility. Processes contributing to the removal of deposited materials in
this area include phagocytosis by macrophages and removal by the blood or lymph, and
dissolution into the blood, lymph, or lung fluids (Johanson and Gould, 1977).
The numerous cell types found in different species also contribute to the varying
clearance patterns from the respiratory regions and differences in the nature of the response.
Table 2-2 presents the distributions of various cell types across species commonly used in
inhalation toxicologic investigations. Recent investigation have also shown species differences
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TABLE 2-2. NORMAL SURFACE AIRWAY EPITHELIUM: CELL TYPES
Humans
Monkey
Dog
Ferret
Guinea
Pig
Rabbit
Rat
Hamster
Mouse
Epithelial
Ciliated
Mucous
Serous
Clara
Endocrine
Type I
Type II
Transitional
Special type
Brush
Intermediate
Basal
to
Migratory
Lymphocyte
Globule leukocyte
Mast cell
inX
?
o
o
I
o
d
o
»
n
Macrophage
Neural
Neuroepithelial body
Nerve terminals
+ = reported present;
( + ) = not specifically reported in sources cited;
- = unidentified; a = fetal tissue;
b = in specific pathogen-free rats;
c = only young animals
d = ciliomucous, mucoserous, endocrine-mucous;
Source: Jeffery, 1983; Crapoetal., 1983.
e = seromucous;
f = ciliomucous, seromucous;
g = ciliomucous;
h = not in "normal" biopsy material;
i = "migratory cell";
j = bronchiolus only
-------
in cellular organization at the terminal respiratory bronchioles/alveolar duct junctions and in
the ultrastructure of the same cell type across species (St. George et al., 1988). The possible
functions of these cell types are provided in Table 2-3, while the differences seen in the cell
types across species are summarized in Table 2-4. Such species differences are important to
consider when determining if the animal is an appropriate model for the chemical's
mechanism of action. For example, the rat may be an inappropriate species for the evalua-
tion of hypersensitivity because of its lack of mast cells.
Due to the major influence of respiratory tract structure on the dosimetry of inhaled
agents, extrapolation from animal models to humans requires analysis of lexicological studies
complicated by the complexity and diversity of the respiratory tract across species. Because
of this, it is imperative that both similarities and differences across species in respiratory tract
structure be incorporated into modeling efforts. More recent data on cellular morphometrics
and interspecies differences in cell populations (Mercer and Crapo, 1987; St. George et al.,
1988) will aid in dosimetry adjustments for clearance, metabolism, and uptake. As an
example, modeling for the metabolic capacity of the human lung instead of considering it only
as a physical barrier can result in disparate estimates of extrapulmonary dose. Epithelial
secretions in response to injury may recruit scavenger cells such as polymorphonuclear
leukocytes, which can biotransform inhaled agents. Different species have different amounts,
distribution, and levels of cytochrome P-450 of their Clara cells, which could account for
differences in metabolism of some agents.
Interspecies differences in clearance rates have the potential to alter the estimated dose
to a given species and thus could significantly alter the derived RfC. Differences in clearance
rates now are being calculated into the interspecies ratios used for dosimetric adjustment of
the exposure concentrations used in RfC derivation for estimation of a retained dose (see
Chapter 4 and Appendices H and I). Similar adjustments for differences in gas uptake due to
differences in ventilation, perfusion, metabolism, and excretion are also warranted.
2.1.1.3 Summary
This comparative overview of the complexity and diversity of the respiratory system in
different species of mammals that are used in risk assessment, although difficult to use in a
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TABLE 2-3. SOME SPECIFIC LUNG CELL TYPES AND THEIR FUNCTION
Cell Types
Location and Function
Epithelium
Clara cells
Ciliated cells
Type n alveolar
cells
Type I alveolar
Mucous
Serous
Brush cells
Globule leukocyte
Endocrine
Submucosal
Goblet (mucus)
ceUs
Serous cells
Endocrine cells
Lymphocytes
Myoepithelial
high metabolic activity; secretory; nonciliated; function not
well-defined; may serve as precursor of goblet and ciliated cells
most common epithelial cells in airways; may secrete
mucous-like substances; controls perciliary fluid
covers 3 percent of alveolar surface; secrete surfactant; replace
injured Type 1 cells; high metabolic activity
large and covers considerable surface area per cell; covers
>95 percent of alveolar surface; forms the alveolar epithelium
and facilitates gas exchange; low metabolic activity; incapable of
self-reproduction
mucus-secreting
mucus-secreting; perciliary fluid; stem cell
chemoreceptor cells; preciliated
immunoglobulin transportation; releases inflammatory mediators
secreto- and vase-regulatory
epithelial linings; common in trachea and bronchioles; contribute
to mucus production
mucus-secreting; perciliary fluid; stem cell/proliferative
secretes amines and neuropeptides
immunoresponsive
expulsion of mucus
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TABLE 2-3 (cont'd). SOME SPECIFIC LUNG CELL TYPES AND THEIR FUNCTION
Cell Types
Location and Function
Bronchoalveolai mast
cells
Macrophage
Endothelial cells
Fibroblasts
(interstitial)
migratory cells located throughout respiratory tract; release
mediators of bronchoconstriction when antigens bind to IgE
antibodies on surface
phagocytic; secrete mediators of inflammatory reactions;
modulate lymphocytes and otherwise participate in immune
response
40 percent of lung parenchyma cells; metabolize blood-borne
substances; proliferative
predominant in alveolar wall and constitutes the basement
membrane; become activated during disease states and produce
elastin and collagen; proliferation leads to fibrosis, modulation
of growth, bronchial tone, and mucosal secretion
Source: Jeffery, 1983; Bowden, 1983; Marin, 1986; Nadel et al., 1985; Plopper et al., 1983; Bum, 1985;
Brain, 1986.
quantitative manner at this point, strongly suggests the potential for wide variation in
deposited dose, cellular function, metabolism, and response to injury. Until the comparative
morphometric and physiologic studies quantitate the functional implications of these
differences, the risk assessor who is extrapolating across different species must choose results
judiciously, based on a qualitative knowledge of comparative airway structure and function,
2.1.2 Physicochemical Characteristics of the Inhaled Agent
The physicochemical characteristics of the inhaled agent will influence the deposition
and retention within the respiratory tract, translocation within the respiratory system,
distribution to other tissues, and ultimately, the toxic effect. It is therefore important to
consider characteristics of the inhaled agent as well when attempting to evaluate and
extrapolate the effects of a particular exposure.
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TABLE 2-4. MAIN SPECIES DIFFERENCES IN EPITHELIAL CELLS
AND GLANDS
Epithelial Morphology
Thickness and pseudostratification
Thickness and structure of "basement membrane"
Mucus-secreting cells
number
histochemistry
predominant ultrastructure type
Clara cells
morphology (smooth endoplasmic reticulum)
distribution
Endocrine cell frequency
Cilia
extent of coverage
structure of rootlet
lamellar bodies
glycogen stores
Presence of brush cell
Basal cells
number
shape
tonofilaments
Presence of Globule Leukocyte
Innervation
extent
distribution
type
Gland Morphology
Amount
Distribution
Main histochemical cell type
Presence of collecting duct
Innervation
Source: Jeffery, 1983.
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2.1.2.1 Particles
For a given particle exposure, the two most important parameters determining deposition
are the mean diameter and the distribution of the particle diameters. The size and shape of
the particles influence their aerodynamic behavior and, thus, their deposition (Raabe, 1979;
U.S. Environmental Protection Agency, 1982, 1986b). The definition of diameter for a
spherical particle is unambiguous, but for irregular particles, a variety of definitions exist.
Nonspherical particle size often is described by its aerodynamic properties. Fibrous material
may be described by actual length, actual diameter, coil length, coil diameter, aspect ratio, or
coil to aspect ratio.
Information about particle size distribution aids in the evaluation of the effective inhaled
dose (Hofmarm, 1982). Recommendations defining the particle size ranges for inspirability to
the various regions have been published by an ad hoc working group of the International
Standards Organization (1981). Particle size distribution should be provided to the risk
assessor in addition to the particle diameter to characterize more completely the aerosol. For
studies where total mass of inhaled particles is used in assessing health effects, it is
appropriate to evaluate the particle size distribution in terms of mass, such as the mass median
diameter. Figures 2-4 and 2-5 illustrate the distribution of various parameters used to
characterize aerosol size.
It is useful to consider the particle's physical parameters that are responsible for the
health effect of concern. The activity diameter of a particle may be the most appropriate
expression of size for this purpose. This expression takes into account the "activity" of the
physical property of the particle. For example, if the toxin is distributed only on the surface,
then the activity median diameter is equal to the surface median diameter; calculations based
on total mass would be inappropriate in such situations. If the toxicant is soluble, the surface
area of the particle will influence the rate of dissolution since solubilization occurs at the
surface. Such a situation needs to be understood better, especially for complex particles.
2.1.2.2 Gases and Vapors
The deposition site and rate of uptake of a volatile chemical are determined by its
reactivity and solubility characteristics. Thus, the pharmacoldnetics of gases and vapors are
governed by:
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Count mode (0,619 ym) d
Count madian (1.0 Mm) d,
Count mean (1.272 urn) d
Diameter of average
area (1,614 Mm) df
Diameter of average
mass (2.056 umj d
Area median (2.614 ym) da
-Area mean (3.324 jum) d,
p Mass median
(4.226 yim '
Mass mean
S5.374
2 4
PARTtCLE DIAMETER,
Figure 2-4. An example of the log-normal distribution function of an aerosol.
Source: Orr and Keng, 1976.
QJ
10 10
PARTICLE 01AM£TER,0,um
30
Figure 2-5. Plot of same aerosol as in Figure 2-4 on log-probability paper. The curves
illustrate the various size parameters that can be computed using the Hatch-Choate
equations.
Source: Marple and Rubow. 1980.
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Rate of transfer from the environment to the tissue,
Capacity of the body to retain the material, and
Elimination of the parent compound and metabolites by chemical reaction,
metabolism, exhalation or excretion.
As mentioned in Section 2.1.1.1, the transport processes in the liquid and tissue layers
adjacent to the airway lumen influence the relationship of the gas with the air-liquid
boundary, Physicochemical characteristics of the gas that contribute to the relative
importance of these processes include its chemical reactivity and solubility,
The chemical reactions of the gas with both the liquid and tissue layers may be
important. For example, reactions with the liquid layer could result in an increased flux from
the airway but reduce (relative to no reactions) the delivery of the gas to the tissue. If the gas
is the only toxic molecule, then this reaction would protect the tissue. Conversely, if the
reaction products are toxic, then reactions with the tissue layer would increase the delivery of
toxic molecules to the tissue (Overton, 1984). Chemical reactivity with the biological
constituents of the tissue is similarly important to the gas' toxic potential to the lung tissue
and to the amount of gas and reaction products that enter the blood for potential
extrapulmonary toxicity. Theoretically, knowledge of all the chemical species involved and
the reaction rates of the reactants and products is necessary to characterize a system for
dosimetry. Sometimes the complexities may be reduced into relative classifications (e.g.,
slow, fast, instantaneous) using approximation techniques for time and spatial dependence
(Overton and Miller, 1988). Gases that are not soluble or reactive are relatively inert to the
airways and penetrate to the alveoli. Examples are nitrogen and volatile hydrophobic
chemicals. The major factor driving the uptake of these gases is the removal of the gas from
alveolar air by capillary blood. The concentration in alveolar air and capillary blood is
generally considered to reach equilibrium. Thus, uptake of alveolar gases depends on air to
blood partitioning, ventilation/perfusion, and air and blood concentrations.
For gases that are soluble, uptake is linearly related to solubility (Overton and Miller,
1988). There are many different expressions for the solubility of gases, differing in terms of
units as well as in terms of what chemical form of the gaseous species in the liquid phase is
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related to the gas-phase quantities. As long as the concentration of dissolved gas is small, and
the pressure and temperature is not close to the critical temperature and pressure, then
Henry's Law is obeyed (Overton and Miller, 1988). It should be noted that the Henry's Law
constant is independent of chemical reactions so that it relates the molecular form of the gas
in water and air, and not the total quantity absorbed in water to air quantities. Considering
the importance of chemical reactions as described above, solubilities as indicated by Henry's
Law constants may not be appropriate to fully describe uptake. Further, extrapolation of
Henry's Law constants from water data to biological fluids and tissues is not always
appropriate, particularly for organic compounds.
Because uptake and disposition of inhaled vapors and gases are driven by the
equilibration of their partial pressures in tissues with their partial pressures in ambient air,
solubility may be aptly described by Ostwald solubility coefficients at body temperature.
Ostwald solubility coefficients and partition coefficients (concentration ratios of the volatile
chemical in two phases with equilibrated partial pressures) have the same values (Fiserova-
Bergovera et al,, 1984). The tissue-gas partition coefficient of a chemical has been shown to
correlate with its fat-gas and blood-gas partition coefficients so that linear correlation
equations may provide a useful means of estimating tissue-gas and blood-gas partition
coefficients (Fiserova-Bergovera and Diaz, 1986).
Thus, a thorough consideration of both reactivity and solubility is needed when
evaluating a gas for its absorption potential. Absorption generalizations based on molecular
weight are not recommended. As an example, the difference in solubility between methanol
and ethane, which have similar molecular weights, is a result of the presence of the hydroxyl
group on methanol. Interspecies comparisons necessitate consideration of the effects of the
differences in anatomy and physiology described previously, but it can generally be stated that
the more soluble and less reactive the gas, the more similar the deposition will be between
humans and animals. Interspecies differences in body fat induce interspecies differences in
uptake and distribution of lipophilic chemicals.
The physicochemical gas characteristics of reactivity and solubility will interact with
physiologic parameters such as pulmonary ventilation, cardiac output (perfusion), metabolic
pathways, tissue volumes, and excretory capacities. The relative contribution or interaction of
these is, in turn, affected by the exposure conditions (concentration and duration), so that as
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emphasized previously, integration of these various factors is necessary to estimate the
deposited (on airway surfaces) and absorbed doses in order to assess toxicity.
2.1.3 Impact of Experimental Protocol
The techniques and measurements used in inhalation toxicology investigations may affect
the exposure conditions or the interpretation of toxic effects, thereby altering the results used
for risk assessment. Areas that introduce uncertainty into interspecies extrapolations of
inhaled dose include measurement techniques, the definitions and underlying assumptions used
in the procedures, and the exposure technology. Careful consideration should be given to
each when estimating the effective inhaled dose.
2.1.3.1 Phannacologic Effects of Agents
The test agents may affect lung ventilation. Administration of a chemical with narcotic
properties will lower physical activity, while an irritant might increase movement. The test
agent could also alter clearance mechanisms. All these states would affect deposition, uptake
and retention of the dose. In addition, the agent could disrupt the immune system and render
the animal more susceptible to disease during long-term testing, thereby altering the study
results.
There are several examples of irritating or potentially anesthetic chemicals that can
depress ventilation. Chang et al. (1983) reported a 40 percent decrease in minute volume in
mice exposed to 15 ppm formaldehyde. This inhibition was maintained during the entire
course of the daily exposure period. Ventilation was decreased to as little as 1/15 of resting
values during exposure of mice to 10 ppm ozone, and to as little as 1/3 of resting values
during exposure of mice to acrylate esters (Bruce et al., 1979).
2.1.3.2 Measurement Techniques
Since measurements of ventilation and breathing mechanics often are used to evaluate
respiratory functional alterations or to estimate inhaled/retained dose, performance parameters
of such measurements are critical to their interpretation. The patterns of respiration
(breathing route, depth, and rate) affect the air flow characteristics which, in turn, influence
the relationship between competing particle deposition mechanisms and the relative
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contribution of gas transport processes. The penetration depth of the exposure air is
determined by the tidal volume (VT), the airway caliber, and the ratio of functional residual
capacity to total lung capacity (FRC/TLC). As the FRC/TLC increases, deposition would be
expected to increase (Schlesinger, 1985). For example, rapid breathing often is associated
with increased deposition of larger particles in the upper respiratory tract, as compared to
slow, deep breathing. Thus, performance parameters include both the factors that influence
the test species (including human) respiration characteristics and the performance limitations
of the techniques.
Anesthesia
Anesthesia greatly influences the respiration characteristics of the test animal. This is a
consideration when evaluating pulmonary function parameters for adverse effects. Prolonged
anesthesia can compromise the respiratory system, altering normal function and response.
Anesthesia also can alter the metabolism of the study compound. Anesthesia has been
reported to interfere with autonomic control, produce atelectasis, decrease lung compliance,
block reflex responses, and introduce an undesirable risk to animals committed to long-term
toxicology studies (Dorato et al., 1983). These alterations in ventilation and breathing
mechanics produced by anesthesia could have severe effects on the results of respiratory
function measurements. This possibility provided the impetus to the development of
procedures for measuring respiration in unsedated laboratory animals (Amdur and Mead,
1958; Mauderly et al., 1979). Data now are available on respiratory characteristics in sedated
and unsedated animals; consideration of anesthesia should be included in data analysis to
ensure appropriate comparisons.
Breathing Pattern
Consideration should be given to the possible alteration of breathing pattern due to the
exposure concentration which would, in turn, alter the delivered dose. Exposure of certain
agents such as irritants may lead to concentration-dependent changes in pulmonary mechanics
measurements (Costa and Tepper, 1988; Alarie, 1981). Correct quantification of inhaled dose
may therefore require measurement of respiratory rate and tidal volume during the course of
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the exposure. Such differences in delivered "dose" correlated with the species-dependent
differences have been reported for formaldehyde toxicity (Chang et al., 1983).
Although clinical exposures and respiratory measurements (at least the noninvasive ones
for functional mechanics) will be done on nonsedated humans, the breathing pattern remains
an important consideration. Experimental protocol often dictates the breathing pattern (i.e.,
nonspontaneous breathing) where a subject patterns his or her breathing to a metronome or is
instructed to take a deep breath on every fifth inhalation. Since the efficiency of time-
dependent deposition mechanisms is greater during inspiration than expiration, an ideal
"academic" breathing pattern would keep the inspiration time/expiration time ratio (t^tg)
constant (Heyder et al., 1975). Relevance of this academic pattern to risk assessment,
however, remains equivocal and most investigations do not attempt to maintain a constant
ratio. Documentation of breathing patterns should be included in the experimental protocol
and considered in the extrapolation of dose.
Equipment Specifications
The equipment used will impart restrictions on any interpretation (i.e., limitations of
sensitivity for exposure analysis) of investigative results. Any underlying assumption or
limitation of the equipment used should be considered when evaluating test results. The
reader is referred to Costa and Tepper (1988) for a discussion of pulmonary function testing
principles, methods, and equipment limitations.
2.1.3.3 Definitions/Underlying Assumptions
Additional variability and uncertainty in evaluating available inhalation studies occur
because investigators have used different definitions of various respiratory regions and have
employed different methods to estimate total or regional deposition. For example, total
deposition often is estimated by calculating the difference between the amount of compound in
the inhaled air and that in the exhaled air. By making assumptions about mixing and dead
space, estimates of regional deposition may be obtained using measurements of the compound
concentration in different volume fractions of the expired air. As another example, the
definition of upper respiratory tract in various studies has included any or all of the following
anatomic regions: nasopharynx, oropharynx, larynx or upper trachea. In other studies,
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deposition values based on chemical or radiologic assays of tissues after exposure assume no
particle translocation before or during dissection. Some investigators include measurement of
material in the gastrointestinal tract (GI) in their reported value for upper respiratory tract
deposition, while others ignore this translocation. The underlying assumptions and working
definitions for different experimental conditions can contribute a large degree of variability in
reported results. Conversion to some common basis will be necessary in order to calculate
and accurately compare inhaled doses.
2.1.3.4 Exposure Technology
Generation of the compound under study and subsequent exposure also will affect the
derived inhaled dose. Exact determination of the dose achieved in inhalation studies is a
complex process. Proper generation, appropriate characterization, and accurate delivery of
the test atmosphere are integral to this determination. Varieties and limitations of the
available technology must be considered when evaluating the selection of methods and
interpreting experimental results.
Inhalation Modes
The various exposure techniques can be divided according to the extent to which the test
species are exposed. The techniques range from whole body exposure at the one extreme to
exposures limited only to the lower respiratory tract (Lippmann, 1980). These techniques
include whole body, head-only, nose-only, nasal, oral, and tracheal cannula exposures, and
trachea! instillations. Practical considerations such as economic feasibility, special precautions
for safe and efficient performance, amount of material, test compound stability, exposure
duration, and the measurements desired dictate the selection of an exposure technique for a
given study design. For example, whole body exposure of laboratory animals in cages is the
most common method to conduct chronic inhalation exposures for more than 1-2 hours per
day, while nose-only exposures are most often used for short durations. A systematic
investigation of the effects of these different delivery techniques on the regional deposition in
various species is needed.
Wolff et al. (1982) studied the deposition and retention of 0.1 fim 67Ga^O3 aggregate
aerosols in Fischer 344 rats following whole body and nose-only exposures. In this
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investigation, lung deposition for whole body exposures was similar to that for nose-only
exposures (~ 15 percent of the inhaled particles). Due to preening, passage of material into
the GI tract, however, was 1.6-fold greater for whole body exposures than with nose-only
exposures. This could be important in cases where there is either a specific GI response (i.e.,
stomach lesions) or substantial GI absorption which may result in a systemic effect. Rotation
of animals in whole body chambers is recommended and should be included in the
experimental design (Griffis et al., 1981) to minimize dosimetric differences that would result
if the aerosol was not uniformly distributed in the chamber. The effects of factors such as
thermal and/or other stress upon animals in confinement tubes used for nose- or head-only
exposures need to be considered, particularly since these factors may be species-dependent.
For example, rats in confinement tubes for short exposures have been shown to have
respiratory values and body temperatures that remain constant, while Syrian golden hamsters
exhibit increasing ventilation and temperature (Raabe et al., 1973). Adaptation to exposure or
measurements may be a function of behavior, such as ability to be trained (Mauderly and
Kritchevsky, 1979), but in general, animals in confinement tubes or animals forced to breathe
through mouthpieces will experience abnormal stress (Raabe et al., 1973). This should be
accounted for in the experimental protocol. The tubes can be modified into plethysmographs
to monitor respiratory function changes, or cooled to a constant temperature. The inhalation
mode affects human exposures as well. Since the nasal passages are more efficient at
removing particles (particularly for large particles) than the oral cavity, increased lung
deposition of larger particles could occur through mouth breathing. This would affect both
the amount and the size distribution of an inhaled aerosol. Even the specific configuration of
the mouthpieces used in oral exposures can affect the extent of deposition (Schlesinger,
1985). Miller et al. (1988) showed that regional respiratory tract deposition of insoluble
particles in humans is a complex function of breathing route, ventilatory level, and the
paniculate physicochemical and aerodynamic properties.
Generation and Characterization
Just as the working definitions and underlying assumptions alter the interpretation of
measurement techniques, the operative exposure level (e.g., for use in risk assessment,
prediction models, etc.) of a test agent is a function of how its particulate composition (mean
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particle diameter and distribution) and gas concentration are expressed. Other specific
characteristics (e.g., adequate test substance mixing in chamber, hygroscopicity, charge
density) should be accounted for as part of this description. The soundness and interpretation
of the animal data are dependent on the methods employed to generate and analyze the test
atmosphere data since the methods influence deposition calculations.
The two most common ways in which particle size is expressed are the count median
diameter (CMD) and mass median diameter (MMD). The toxicity of a material is most
consistently related to its mass distribution. Measurement of mass has the further advantage
of a minor quantitative error at the small end of the size spectrum. To assess risk, however,
the activity diameter may be a more appropriate expression of particle size as discussed in
Section 2.1.2.1. Methods of particle measurement include settling, filtration, wet and dry
impingement, multiple impaction, electrical precipitation, thermal precipitation,
centrifugation, and observation of optical effects. Each of these has its own principle of
operation and limits of sensitivity which, in turn, affect the expression or characterization of
the test aerosol. Fiber exposures are further complicated by the need to describe the aspect
criteria and distributions. As discussed in the section on anatomy and physiology, certain
mechanisms contribute to the deposition fraction in each respiratory region. Failure to
account for characteristics such as hygroscopicity or charge density when generating an
aerosol could change its deposition in certain regions. This variability in the aerosol
characterization would be expressed as uncertainty in the risk assessment.
Gaseous contaminant atmospheres are usually somewhat easier to characterize.
Delivered concentrations must be consistent across exposure location and duration and may be
less than the generated concentration. If the gas is extremely reactive, loss due to reactions
with the walls of the transport system (e.g., tubing) will occur. Losses due to decomposition
or alteration of the test substance during some generation procedures also may be a factor.
Gas flow rate (delivery) must be known, steady, and calibrated for the given gas since it is
density-dependent. Analysis of the air is limited by the detection device specifications. If
online analysis is not feasible, consideration should be given to the frequency of samples
taken. The period between samples for intermittent analysis should be less than one-tenth of
the total exposure time for any given day (McKeima, 1982),
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For all generation and characterization of pollutants, periodic calibration of all
measurement systems is a critical quality control/quality assurance step. This also needs to be
considered when evaluating the study,
Exposure Regimen
Extrapolation from one exposure regimen to another has uncertainties, most of which
are not quantified. For most chemicals, either particles or gases, the quantitative relationship
between concentration and duration of exposure is not studied. Some studies have indicated
that the relationship is dependent on many factors, including (1) number of exposure hours
per day; (2) the exposure scenario, that is, continuous vs. interrupted (e.g., 1 week of
exposure, 1 week of air, 1 week of exposure, etc.), vs. intermittent (X hours per day, Y days
per week) regimens; (3) time of endpoint assessment (i.e., acute vs. subchronic vs. chronic
studies); (4) endpoint(s); and (5) the mechanisms of toxicity. Examples for particles and
gases follow which illustrate some of the complexities involved in extrapolating across
exposure scenarios.
The actual amount of particles or gas found in the lungs at any time is determined by
the relative rates of deposition and clearance. The efficiencies of the deposition mechanisms
are different in each region of the lung. The defense mechanisms and clearance rates for each
of these regions also are different. Thus, it is expected that the kinetics of the toxic effect of
an exposure will be influenced by the duration of exposure. There is experimental evidence
for such a differential dependence of effect on exposure duration. Albert et al. (1971)
showed that low single doses or early effects of repeated exposure to cigarette smoke were
associated with acceleration of clearance rates in the tracheobronchial trees of both donkeys
and humans. Heavier doses and long-term repeated exposures were associated with sporadic
clearance, stasis intervals, and some retrograde movement. Unfortunately, there has not been
a systematic comparison and quantification of differential clearance rates across species. This
will be necessary before the effects of duration can be assessed in the same models or default
values can be developed.
Ozone can be used as an illustration for gases since it has a large health effects data
base. Kenoyer et al. (1981) showed that rats exposed to ozone for 4 hours showed delays in
the early clearance and an acceleration in the late clearance rate of tracer particles. These
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investigators postulated that the delays in early clearance could be caused by effects that
decrease mucous transport (e.g., decreased ciliary beat rate or change in mucous properties),
while acceleration of the late clearance rate was most likely due to an increase in numbers or
activities of alveolar macrophages. Rats exposed intermittently (7-8 hours/day to 03 for
approximately one week) had similar changes in lung antioxidant enzymes to animals exposed
continuously (24 hours/day), even though the dose, as expressed as the product of concen-
tration (C) and time (T) of exposure, was different (Mustafa and Lee, 1976). Monkeys
exposed to 03 for 18 months continuously, or for 9 months bimonthly for 18 months had
some similar alterations in lung morphology; additional alterations were observed in the
intermittent exposure group having a lower (C x T) (Tyler et al., 1985). Huang et al. (1988)
has shown, using morphometric measurements of the proximal alveolar region of lungs of rats
receiving prolonged low level exposures of 03, that the increase in the relative volume of
Type I epithelial cells was related to the (C x T), whereas other morphometric indices were
more dependent on concentration than on time.
For NO2, the data base is equally complex on the exposure scenario issue. Using the
mouse infectivity model (an index of antibacterial lung defenses), concentration was found to
be more important than duration of exposure in causing the effect (Gardner et al., 1979).
When a typical urban pattern of NO2 was used (i.e., a baseline of continuous exposure to a
low level of NO2 on which were superimposed two 1-hour peaks of NO2 each weekday), the
study indicated that on a (C x T) basis, this regimen was not more toxic than a continuous
exposure to the baseline level after a short period of exposure (Graham et al., 1987). After a
chronic exposure, the spikes to the baseline increased the effects relative to the baseline
exposure only (Miller et al., 1987a).
The topic of extrapolating across different exposure scenarios is beyond the scope of this
document. However, the few examples provided illustrate the complexity of the issue. Risk
assessors will have to consider the effects of exposure on a case-by-case basis and utilize
default assumptions until the needed research data are available.
2.1.4 Summary
This Section (2.1) has provided an overview of critical anatomic and physiologic
interspecies differences, significant physicochemical characteristics of an agent that should be
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considered when evaluating an exposure, and the experimental procedures which may
influence exposure conditions and interpretations of toxic responses. It was intended to
emphasize areas that should be given careful consideration and integration into an overall risk
estimate when analyzing the data base used for the derivation of an inhalation reference dose.
The next Section (2.2) discusses the significance of the lung as the portal-of-entry for
inhalation exposure.
2.2 PORTAL-OF-ENTRY CONSIDERATIONS: ASPECTS OF
COMPARATIVE PULMONARY TOXICITY
Inhalation represents a route of exposure in which a variety of interrelated factors
influence not only the nature of the effects (portal versus systemic) but also the manner by
which they occur. The influence of target cell populations in the respiratory tract on the
nature of the response is a factor unique to the inhalation route of exposure. Unlike the liver,
a first-pass organ in oral exposures that has a more homogenous population of limited types of
cells, the respiratory tract has more than 40 cell types (Sorokin, 1970). Xenobiotics which
exert their action by direct effects of the parent compound or by metabolites can manifest
profound differences in the nature and degree of response, depending on the route of
exposure.
The likelihood of adverse effects in the respiratory tract can be affected by
(1) production, distribution, and reactivity of metabolites by and among specific cell types;
(2) the degree to which detoxification systems are overwhelmed (e.g., glutathione depletion);
(3) efficiency and sensitivity of repair processes (e.g., type II cell proliferation);
(4) efficiency of clearance processes; (5) airway mechanics; and (6) mechanism of action
(e.g., pharmacologic or immunologic) (Bond, 1989; Boyd, 1980; Calabrese, 1983; Gram
et al., 1986; Thrush et al., 1982; Nadel et al., 1985; Marin, 1986).
There are numerous pulmonary defense systems that protect the respiratory tract. While
some pulmonary defense systems are truly protective, it must be kept in mind that many
"activate" inhaled agents and may be responsible for adverse effects. Pulmonary defense
systems can be physical in nature (e.g., filtration of particles by nasal hair), mechanical (e.g.,
expiration), enzymatic, or cellular (e.g. phagocytosis).
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Nasal hair can be envisioned as a first line of defense. However, trapping of agents in
the nose can serve as a source of irritation and/or more serious adverse effects. Some agents
(e.g., formaldehyde, acrolein) have been shown to cause severe lesions in nasal epithelial
cells (Morgan et al., 1986). The mouth also can be envisioned as another first-line defense
system. Mouth-breathing in humans can result in solubilization of vapors in saliva and
deposition of particles. Swallowing can reduce pulmonary exposure but increase presentation
of the agent systemically via gastrointestinal tract absorption.
Once an agent penetrates to the tracheobronchial region, agent deposition and/or
solubilization occurs in the mucous blanket covering the surface epithelium. Clearance is
discussed in Section 2.1.1.2.
Metabolism of potentially toxic inhaled compounds is achieved by a variety of enzyme
reactions involving oxidation, reduction, hydrolysis, and conjugation. The enzymes may
work individually, concurrently, or consecutively to detoxify or, in some cases, toxify inhaled
foreign compounds (Ohmiya and Mehendale, 1984; Minchin and Boyd, 1983; Dahl et al.,
1987). These enzymes may vary in activity across species and organs (Ohmiya and
Mehendale, 1984; Ziegler, 1980; Tynes and Hodgson, 1985; Plopper et al., 1983; Litterst
etal., 1975).
The oxidation, reduction, and hydrolysis reactions are catalyzed primarily by the
cytochrome P-450 and FAD containing monooxygenases. The cytochrome P-450 isoenzymes
are ubiquitous hemoproteins located in the endoplasmic reticulum of a variety of cells and are
responsible for the oxidation of foreign compounds. Recent studies have elucidated
isoenzyme specificity, inducibility, catalytic activity, and localization in the rabbit and rat
lung (Domin and Philpot, 1986; Vanderslice et al., 1987). Until recently, it was thought that
the cytochrome P-450 isoenzymes were the only primary monooxygenases in the lung.
However, recent studies have shown that the FAD-containing monooxygenases play an
important role in detoxification of foreign compounds.
The Clara cells lining the respiratory and terminal bronchioles are thought to be the
primary site of cytochrome P-450 because of the presence of endoplasmic reticulum.
However, the ultrastructure of the Clara cell varies across species (Plopper et al., 1980). In
the ox, cat, and dog, more than 60% of the cytoplasmic volume is glycogen with a relatively
small proportion of the cell volume containing endoplasmic reticulum or mitochondria. Thus,
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species differences in Clara cell ultrastructure can be reflected in significant differences in
xenobiotic metabolism potential (Plopper et al., 1983; St. George et al., 1988). Differences
in localization of cytochrome P-450 activity have been suggested as a likely basis for some
differences in respiratory tract toxicity (O'Brien et al., 1985).
Individually or in concert with the cytochrome P-450 isoenzymes, conjugation reactions
are catalyzed by the GSH-S-transferases which transform potentially toxic parent compounds
or activated metabolites into nontoxic water soluble compounds. The cofactor required for
these reactions is the tripeptide, glutathione (GSH). GSH is synthesized in the lung, as well
as in other major organs, and also is reduced from the oxidized state (GSSG) to the reduced
state (GSH) by GSH reductase. Under extreme conditions of GSH depletion in the lung, it
has been hypothesized that extrapulmonary GSH is mobilized and transported to the lung from
the liver (Berggren et al., 1984). GSH has been identified in isolated Type II epithelial cells,
Clara cells, and ciliated cells of the lung, but it is not known if it is present in all pulmonary
cells. GSH also is the cofactor utilized by the enzyme GSH peroxidase. GSH peroxidase
catalyzes the metabolism of hydrogen peroxide and organic peroxides formed by the
ozonization of unsaturated fatty acids. Other key antioxidant components in the lung include
ascorbic acid, alpha-tocopherol, superoxide dismutase, and catalase (Massaro et al., 1988).
A variety of other cellular defense mechanisms can be marshaled which can diminish or
enhance toxic insult. Certain cell types can be stimulated to release mediators, such as mast
cell release of histamine. Histamine can cause bronchoconstriction, which can be protective,
by limiting the amount of pollutant inhaled, or can be toxic, in terms of limiting oxygen
uptake. Synthesis or metabolism of prostaglandins also can affect airway and vascular
caliber. The chemotactic factors released can recruit phagocytic cells involved in clearance.
It should be recognized that the respiratory tract contains a variety of different cell types that
possess different metabolizing potential and are distributed in a manner which varies among
species. A list of common cell types and their function is provided in Table 2-3 in
Section 2.1.2.1. Macrophages, for example, constitute a cellular protection system and not
only protect inner surfaces of the respiratory tract from damage caused by particles and
microorganisms, but also have the potential to cause damage themselves (Rossi, 1986).
Macrophages contain a variety of proteases and mediators that are useful in destroying
xenobiotics but are destructive to healthy tissue (Brain, 1986). Although recruitment of
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macrophages to the lung is related to the dose, the adaptive increase in macrophages can be
exceeded (Bowden, 1986). This threshold may vary among species. The alteration of
macrophage functioning has the potential to shift the balance between protective and adverse
effects.
Concurrent with the action of inhaled agents upon critical cell types in the respiratory
tract, a portion of the dose in the pulmonary region is likely to be transported across the
alveolar epithelium and enter systemic circulation. Changes in permeability can result from
the action of some of the mediators and proteases mentioned. The greater the amount
reaching systemic circulation, the greater the likelihood for adverse effects in other systems
(e.g., liver, kidney, central nervous system). The rapidity and extent to which systemic
absorption occurs and the time-to-steady-state blood levels are influenced by (1) ventilation
rates and airway mechanics, (2) blood transit time in capillary beds (i.e., perfusion limited),
(3) metabolic conversion in the respiratory tract and other organs, (4) alveolar surface area,
(5) thickness of the air-blood barrier, and (6) the blood:air and bloodrtissue partition
coefficients. Many of these factors vary among species and, thus, should be considered in
key study identification.
After the inhaled agent enters systemic circulation, the liver may produce additional
metabolites that, if the half-life is sufficiently long, may re-enter the lungs and exacerbate the
portal-of-entry effects or produce additional adverse effects (Boyd and Statham, 1983). Other
agents, that do not require bioactivation, have been shown to damage the lung when applied
systemicaUy (Kehrer and Kacew, 1985).
Exhalation of volatile agents (including from administration routes other than inhalation)
is an important excretory pathway that is dependent on tissue levels and exposure regimen.
For inhalation exposures, the exposure duration influences the amount of chemical entering
the systemic circulation, the amount metabolized, and the concentration of the chemical in
tissues. Using a simulation model, Fiserova-Bergovera et al. (1984) demonstrated that for
chemicals that are not metabolized, tissue concentrations of "poorly soluble" (A0ji/gas < 10)
chemicals change very minimally after two hours of exposure. The pulmonary uptake rate
approaches zero at the end of a 2-hour exposure and apparent equilibrium is established.
"Easily soluble" chemicals (10 > ^Oii/gas < 10,000) require more than one day of exposure to
reach apparent equilibrium and "highly soluble" chemicals (Aoil/gas > 10,000) require more
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than 1 year of exposure. If the chemical is metabolized, pulmonary uptake and the amount
metabolized increase with exposure duration, but the effect of metabolism may be more
complex if exposure concentrations are so high that metabolic pathways approach saturation
kinetics and cause metabolism to deviate from first order kinetics.
Conversely, pulmonary clearance decreases with increasing biosolubility (refers to
solubility of gases and vapors in biologic materials) and thereby affects the cumulation of
chemicals during intermittent exposure regimens. Simulation of an 8 hour/day, 5 days/week
schedule for a three-week exposure duration to a 70 kg man showed that poorly soluble
chemicals (as defined previously) have no tendency to accumulate in the body, while easily
and highly soluble chemicals do have a tendency to accumulate because the intermissions
between exposures are not long enough to allow the chemical to be removed from adipose
tissue (Fiserova-Bergovera et al., 1984). Excursions in exposure concentrations had a great
effect on tissue concentrations of poorly soluble chemicals, but had little effect on tissue
concentrations of highly soluble chemicals. Concentrations in well-perfused tissues were
more affected by excursions in exposure concentrations than concentrations in muscle or
adipose tissues.
The results of these simulation efforts emphasize the uncertainty that the dual function
(uptake and exhalation) of the respiratory system adds to any attempt to estimate either
respiratory tract or extrarespiratory "dose" of volatile agents. These simulations also
emphasize the need for careful consideration of the uptake, metabolism, and excretion
parameters for these agents when attempting the exposure duration and concentration
conversions discussed in Chapter 4, and when ruling out the possibility of a pulmonary
endpoint when using oral data as part of the data base.
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3. QUALITATIVE EVALUATION OF THE DATA BASE
The aim of the inhalation RfC methodology is to establish a relationship between a
particular agent in the air and a specific health effect. Evidence must be collected from
diverse sources and synthesized into an overall judgment of health hazard (Hackney and Linn,
1979). Qualitative evaluation of a diverse data base necessitates a systematic approach for
obtaining agreement on the validity and selection of studies to be used in the quantitative
methodological procedures of the risk assessment.
3.1 GUIDELINES FOR SELECTIONS OF KEY STUDIES
Key studies are those that contribute most significantly to the weight of evidence as to
whether or not a particular chemical is potentially hazardous in humans (U.S. Environmental
Protection Agency, 1987a). The studies also may be used in the quantitative dose-response
analysis of risk assessment. These studies are of two types: (1) epidemiologic, clinical or
case reports on humans; and (2) experimental studies on animals. Each has unique
considerations that will be addressed separately. Once the key studies demonstrating the
critical toxic effect have been identified, the selection of effect level and the inhalation RfC
derivation arises from an objective scientific evaluation of the data available on the chemical.
The limitations and the uncertainty factors involved in this derivation are a function of the
quality of the key study and will be addressed in Section 3.2. Data base deficiencies and
alternative approaches for risk assessment will be presented in Section 3.3.
3.1.1 Human Data
Utilization of human data avoids the necessity of extrapolating from animals to humans,
thereby decreasing uncertainty in the risk assessment. Such data have often been useful to the
oral RfD work group in qualitatively establishing the presence of an adverse effect in exposed
human populations (U.S. Environmental Protection Agency, 1987a). There are significantly
more human data on inhalation than on ingestion exposures, however, so that criteria for
evaluating studies and their results need to be stated explicitly. Since 1977, when the Clean
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Air Act identified goals related to air quality and health, the task of clarifying how population
studies can be used for determining scientifically reasonable standards and how to define an
adverse respiratory health effect has been rigorously debated (Lebowitz, 1983; American
Thoracic Society, 1985; National Research Council, 1985). Many of the results from these
efforts can be applied as guidelines for the inhalation RfC methodology.
Three types of human studies are most often utilized to obtain data pertinent to
understanding the risk of chemicals to humans: (1) epidemiologic studies, (2) clinical studies
or controlled exposure experiments, and (3) case reports (Erdreich and Burnett, 1985). Each
of these three study types can provide important information needed to protect public health.
When using these studies for risk assessment, several factors are important in evaluating their
quality and in determining the level of certainty associated with their use. The factors that
are considered when evaluating an epidemiologic study are relevant in evaluating the other
types of human studies, but the discussion on epidemiologic studies is the most extensive.
3.1.1.1 Epidemiologic Data
There are essentially three areas of concern in assessing the quality of an epidemiologic
study. These involve the design and methodological approaches used for: (1) exposure
measures, (2) effect measures, and (3) the control of covariables and confounding variables
(Lebowitz, 1983).
The study population and study design must adequately address the health effect in
question in order to support a risk assessment (Lebowitz, 1983). In order to accomplish this
goal, the exposure measures must be appropriate and of sufficient quality; the statistical
analysis methods must be suitable to the study design and goals; the health effect measures
must be reliable and valid; and the covariables and confounding variables need to be
controlled or eliminated.
Assessment of Exposure Measures
The problem of the accuracy and relevance of exposure measurements is not unique to
epidemiologic investigations, but it can be exacerbated due to the long-term nature of these
studies. For example, the nature of aerometric data changes over time because of different
industrial hygiene practices and because individuals change jobs and residences, and thus their
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exposures change over time. Accurate documentation of air toxicant levels is, therefore,
critical in determining the usefulness of an investigation as well as documentation that the
analysis of the air toxicant is appropriate and of sufficient sensitivity. It also is advisable to
have the concentrations of other pollutants reported to help rule out confounding or interactive
effects. The number, location, and timing of monitors must be suitable to allow an
appropriate determination of exposure of the subjects to the pollutant being studied and to the
pollutants that could confound the results. When appropriate, the exposure measure/estimate
should take into account indoor/outdoor exposures and activity and subject location data. The
exposure measure/estimate needs to represent the actual exposure in a sufficiently satisfactory
way so as to represent the "true" exposure.
Assessment of Effect Measures
Effect measures refer to the methods used to ascertain disease indices. For
epidemiologic studies these include incidence, standardized mortality ratios, and relative risk
ratios.
Criteria for assessment require the proper selection and characterization of both the
exposed and control groups. For example, criteria for inclusion in the control category of a
case-control study must ensure that this group has no exposure to the agent of concern.
Another selection issue is that of needing reference populations or control groups for studies
without internal control groups, particularly when evaluating spirometric data (Ferris, 1978;
American Thoracic Society, 1979; Crapo et al., 1981; Knudson et al., 1976). Each
population used to predict "normal" pulmonary function tests has its own characteristics,
which should be considered when used for comparisons. Other considerations include the
adequacy of study duration and quality of the follow-up. A disease with a long latency before
clinical presentation requires a longer study duration than one with an acute onset. Valid
ascertainment (such as verification according to the International Classification of Diseases
IX) of the causes of morbidity and death also is necessary.
Evaluation of epidemiologic studies may require interpretation of a variety of subjective
health effects data. Questionnaire responses may be biased by the way in which questions are
worded, the training of an interviewer, or the setting. A committee of the American Thoracic
Society (ATS) charged with defining an adverse respiratory health effect, however, has come
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to a consensus that "in general, increased prevalence of chronic respiratory symptoms as
determined from questionnaire surveys should be considered to be an adverse health effect"
(American Thoracic Society, 1985). Questionnaires should be validated as part of the
investigation protocol unless a standard questionnaire that has previously been validated is
used (Medical Research Council, 1960; Ferris, 1978; National Institute for Occupational
Safety and Health, 1986).
In order to assess quantitative results, it is very important to consider differences
between statistical significance and medical or biological significance. Both the variability of
an outcome measure and the magnitude of an exposure's effect determine the level of
statistical significance. For example, data from a large study population analyzed with
sophisticated techniques may yield statistically significant effects of small magnitude that
cannot readily be interpreted biologically. Conversely, large effects of clinical importance
may not be statistically significant if the study population is too small; that is, if the studies
presented negative or no-effect results due to the lack of power or the small number of
subjects in the study. Judgments concerning medical or biological significance should be
based on the magnitude of effect. For example, cough and/or phlegm production can be
considered less important than effects resulting in hospital admissions. Underlying
assumptions and nuances of the statistical procedures applied to the data also need to be
considered. This will probably best be accomplished on a case-by-case basis, as has been
done by the oral RfD work group.
Because the inhalation RfC considers both portal-of-entry and systemic effects, it would
be helpful to define an "adverse respiratory health effect." An ATS committee published
guidelines that defined such an effect as medically significant physiologic or pathologic
changes generally evidenced by one or more of the following (American Thoracic Society,
1985):
• Interference with the normal activity of the affected person or persons
• Episodic respiratory illness
• Incapacitating illness
• Permanent respiratory injury or
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• Progressive respiratory dysfunction
Appendix C provides detailed descriptions of adverse respiratory effects in humans.
Assessing the Control of Confounding and Covariables
Epidemiologic investigations have to relate an exposure to a given health effect, but tills
includes accounting for the "background" health effect (pathologic condition) that exists in
individuals due to predisposing factors and pre-existing health conditions, or from other
variables, such as occupational exposures.
Various host factors contribute as risk factors for disease and can influence the health
indices assessed. For example, asthmatics may be particularly susceptible to effects from
exposure to irritant gases. Epidemiologic evaluation of these factors often not only accounts
for such interactions but also can help to characterize susceptible or sensitive groups.
Covariables can be as important as the major aerometric variables themselves in affecting
human health. Other exposures, such as concomitant occupational exposures and smoking, in
particular, can affect the disease outcome. Meteorologic variables such as air velocity,
temperature, and humidity also are very important factors when considering respiratory health
effects. These covariables should be controlled by both the study design and analysis as
appropriate.
Assessment of individual epidemiologic studies should bear in mind that the final step in
the inferential process from an epidemiologic investigation requires the extension of its results
to persons, populations, or settings not specifically included in the study. The confidence
with which this is done for positive results is usually based implicitly on how successful the
investigators have been in identifying and handling the potential risk factors and covariables
that produce or influence the pollution-effect association they have observed. Uncertainties
also arise because the general population includes some people, such as children, who may be
more susceptible than people in the sample from which the epidemiologic data were derived.
Factors such as the "healthy worker" effect and the bias of a predominantly male worker
sample must be considered when using occupational studies (National Research Council,
1985). Intraindividual variability concerns are addressed in Section 3.1.1.3.
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Swmuny
Specific recommendations for the evaluation of epidemiologic investigations have been
adapted from Lebowitz (1983), American Thoracic Society (1985), and Interagency
Regulatory Liaison Group (1981). Appendix D provides guidelines for evaluating individual
epidemiologic studies and the considerations involved in evaluating the statistical analyses.
3.1.1.2 Nonepidemiologic Data
Human data also include clinical studies and case reports. The case reports and acute
exposures provide support for the weight-of-evidence decision, but are often of limited utility
in establishing a quantitative relationship between environmental exposures and anticipated
effects (Barnes and Dourson, 1988; U.S. Environmental Protection Agency, 1987a). They
are often valuable in determining the nature of the effect in humans.
Clinical Studies
Clinical studies may contain exposure-response information that can be used in
estimating effects. Most clinical studies combine the strong point of animal toxicology,
rigorous control of the experimental exposure and subject, with the strong point of
epidemiology, the unquestioned relevance to human health (Hackney and Linn, 1983). In
addition, clinical studies can be independently replicated somewhat more easily (requiring a
reasonably short time and resource commitment) than the other types. There are limitations,
however, that include short exposure duration, "noninvasive11 techniques that might not
ascertain the full array of effects, and small groups of test subjects. The test atmospheres are
usually within that expected to produce only mild and temporary health effects. Certainly,
clinical studies should be recognized and given credence to the extent that they are
scientifically rigorous, relevant to human health concerns, and can be independently
replicated. They may be particularly useful for less-than-Ufetime risk assessment. The
prediction of long-term effects from short-term observations remains questionable, but
confidence in clinical findings can be bolstered by supporting evidence from epidemiology
and animal toxicology, and vice versa.
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Case Reports
Individual case reports of adverse effects due to a specific agent also can provide some
help in evaluating the potential risk from exposure to a toxic air pollutant. These reports are
especially valuable qualitatively for indicating that the quantitative effect observed in animals
occurs in exposed humans. These reports must be examined carefully and used with
discretion since they represent a very small sample and are usually related to heavy exposures
(Goldstein, 1983). Nevertheless, these observations should not be overlooked, especially
when a large number of case histories exist with the same endpoint. Research needs to
address the interrelationships of findings from short-term observations, epidemiology, and
animal toxicology, and to establish appropriate links among them in order to support
regulatory decisions.
3.1.1.3 Intraspecies Variability and Identifying Sensitive Subgroups
In order to control factors other than the chemical being tested, animals used in toxicity
studies (e.g., rodents) are often bred for homogeneity. In contrast, the human population is
heterogeneous. The broad genetic variation of the human population in metabolism and in
tissue response to chemicals causes individual differences in susceptibility to toxic chemicals.
A sensitive or hypersusceptible individual is one who will experience an adverse health effect
to one or more pollutants significantly earlier in the course of exposure, or at lower doses
than the average individual, because of host factors that predispose the individual to the
harmful effects. Sensitive individuals may be those whose genetic makeup puts them at the
extreme end of a continuous distribution of a biological function, such as the amount of
enzyme production, or those who possess a unique genetic difference, such as an altered
enzyme, that makes them markedly different from the general population.
In addition to genetic factors, personal characteristics such as age, sex, health status, or
habits make some people more susceptible (Calabrese, 1978). The activity pattern of people
is a major host factor influencing the dose-response by its effect on delivered dose.
Generally, exercise increases the delivered dose and alters the regional deposition of the dose.
The principles involved have not been quantified sufficiently to date, but should be considered
qualitatively when comparing studies or population subgroups.
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Environmental risk assessment should consider host factors that both increase
susceptibility and that occur relatively frequently in the population, Erdreich and Sonich
Mullin (1984) estimated the prevalence of population subgroups of individuals who are
potentially hypersusceptible to some common pollutants. Table 3-1 shows five categories of
individuals who, based on empirical observations or compromised physiological functions, are
assumed hypersusceptible to the listed chemicals.
As a result of epidemiologic investigations, it is well recognized that a population of
adult workers experiences less morbidity and mortality than the general population (Fox and
Collier, 1976; Wenetal., 1983; Monson, 1986). However, sufficient qualitative and
quantitative information on interindividual variability and hypersusceptibility for specific
chemicals rarely exists.
If the decisions on the RfC are to be made on data derived from subgroups of the
general population such as workers who are generally a selected group of healthy adults, the
extrapolation procedure must contain appropriate adjustments to account for the anticipated
broader variability in the general population. Worker populations are nonrepiesentative in
terms of age distribution and general health status. Hypersusceptible people may not be
represented because they may not seek or sustain employment, particularly in situations such
as those represented in workplace exposure studies. Occasionally, data are available on more
sensitive subgroups such as children or asthmatics. In these cases, risk assessments can be
made for the general population with greater confidence. In the absence of data on the more
susceptible individuals in the population or lack of identification of such individuals,
uncertainty factors are used to protect unidentified individuals at greater risk.
There are two steps necessary to obtain information addressing the problem of sensitive
individuals: (1) examine chemical-specific data for empirical evidence of sensitivity and
hypersusceptibility, and (2) ascertain whether the mechanism of toxicity for a given chemical
suggests that any population group would be extremely sensitive.
In addition to this chemical-specific evaluation, guidance should be developed
concerning the prevalence of sensitive subgroups and the range of sensitivities in the general
population exposed to inhaled toxicants. The U.S. Environmental Protection Agency (1986a)
has initiated research to assess the magnitude of interindividual variability in pharmacokinetic
parameters related to the delivery of the biologically effective dose, in order to develop
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TABLE 3-1. PREVALENCE OF SUBGROUPS HYPERSUSCEFITBLE TO EFFECTS
OF COMMON POLLUTANTS
Hyper-
susceptible
Prevalence3
Chemicals'3
Reference0
Embryo, fetus, pregnant women:
neonate 21/1000C
carcinogens, solvents,
CO, mercury, lead,
PCBs, pesticides
Rice, 1981; Kurzel
andCetrulo, 1981;
Saxenaetal.,
1981
Young children
ages 1-4:
70/1000
hepatotoxins, PCBs,
metals
Calabrese, 1981;
Friberg etal,,
1979
Lung disease
emphysema,
asthma:
37/1000d
ozone, Cd, partic-
ulates, SO2! NO2
Holland et al.,
1979; Redmond,
1981
Coronary heart
disease
coronary heart
disease:
16-27/1000d
chlorinated solvents,
fluorocarbons, CO
McCauley and Bull,
1980; Aviado, 1978
U.S. Environmental
Protection Agency,
1984a
Liver disease
liver abnor-
malities:
20/1000e
carbon tetrachloride,
PCBs, insecticides,
carcinogens
Calabrese, 1978
*A11 estimates based on 1970 census.
Representative samples of chemicals to which these individuals may he hypersusceptible. Some evidence from
animal studies only.
cAuthors' estimate from 1970 census statistics data.
dHealth Interview Survey (National Center for Health Statistics, 1970).
Health Interview Survey (National Center for Health Statistics, 1975).
Source: Adapted from Erdreich and Sonich Mullin, 1984.
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guidance for appropriate uncertainty factors. Differences among normal healthy adults may
be as much as 10-fold (U.S. Environmental Protection Agency, 1986a). Therefore, the
potential that exists for broad differences when children, the elderly, the ill, and those
previously exposed are included must be considered.
The issues discussed in this section are summarized as follows:
Evaluation of the Epidemiologic Data Base
Examine epidemiologic and clinical data for dose-response information in potential or
previously identified sensitive groups (e.g., studies in asthmatics, children).
Examine animal data for studies in models of sensitive individuals.
Evaluate epidemiologic studies to ascertain genetic and personal factors that increase
the risk of adverse response. Evaluate implications of these risk factors for
identifying sensitive groups.
Examine data for reports of ranges of responses or response variables, and for data
containing individual responses. This is particularly important in evaluating human
data for assessing the range of variability in response because epidemiologic studies
may not include exposure levels associated with a NOEL, but with an effect.
Evaluate available biological monitoring data and clinical and experimental data for
indications of characteristics of increased susceptibility. For example, respiratory
irritants may induce responses earlier in individuals with a-1-antitrypsin deficiency.
Evaluate data on mechanisms of toxicity, pharmacokinetics, and critical target organs
to identify characteristics that may imply broad interindividual variability or
hypersusceptible individuals. For example, the elderly may be more sensitive to
certain chemicals in relation to age-related changes in oxidative metabolism potential.
Evaluation of Individual Studies
Assess the makeup of the study population and control groups to identify presence or
absence of sensitive individuals. Data on healthy workers, for example, are not
representative of the general population and will require reduction of NOAELS or
LELs by uncertainty factors.
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Consider the activity pattern of the subjects. Whether the subjects received exposure
while at rest or at level(s) of exercise will influence the inhaled dose as well as the
pattern of deposition.
In longitudinal (cohort) studies, evaluate information in relation to the natural history
of the disease, i.e., the progression of lesions. Normal changes over time, such as
increased FEVj as children get older, and decline of FEVL with aging in older adults,
should not be adversely affected. Cross-sectional studies may suggest such
associations but will not support causality as strongly as will cohort studies.
For parameters that have known variability with age, such as FEV^ evaluate results
within age groups and ascertain whether appropriate reference populations were used.
Areas for further investigation and development of specific guidance include;
• To what extent can we develop guidance on which conditions and diseases predispose
individuals to hypersusceptibility? It is important to emphasize conditions that are
more common in the population (3-5%). Susceptibility factors can be linked with
characteristics of chemicals or to specific chemical classes to facilitate generic risk
assessment procedures.
• How do known differences in components of respiratory function, such as age-
related differences in FEVj, affect susceptibility to systemic toxicity from airborne
chemicals?
3.1.2 Animal Data
When the data base lacks appropriate information on effects in humans, as is frequently
the case, the principal studies are drawn from experiments conducted on nonhuman mammals.
Animals most often used include the rat, mouse, guinea pig, hamster, rabbit, monkey, and
dog. Such animal studies have often been conducted with controlled exposure conditions on
relatively homogenous populations, but nevertheless, present the risk assessor with concerns
about evaluating dose and exposure regimen. Unlike the human, the laboratory rodent
strains, because of inbreeding, have homogeneous constitutions. Genetic background
differences and numerous other interspecies differences are confounding factors during key
study selection.
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Evaluation of the quality of individual animal toxicity studies requires consideration of
factors associated with the study's hypothesis, design, execution, analysis, and interpretation
(U.S. Environmental Protection Agency, 1987a). Guidelines for assessing individual animal
studies are provided in Appendix E and are adopted from a number of recommendations
(National Research Council, 1984; Society of Toxicology, 1982; James, 1985; Muller et al.,
1984; Lu, 1985a). The reader is referred to this appendix for a more detailed description of
those issues discussed here.
3.1.2.1 Appropriateness of Species as a Model for Humans
Identification of the most appropriate animal species is the end result of an interpretative
process that examines all facets of a data base from study design to data relevance to the
extrapolation methodology.
The most sensitive species is selected from evaluation of key studies. While this
approach (i.e., NOAEL identification) may have the advantage of affording a greater degree
of protection, the species most sensitive to an agent may not be as lexicologically relevant as
other species for extrapolation to man because of a variety of interspecies variables.
Selection of an appropriate animal model and key study depends on the depth of
understanding of the human disease syndrome, adverse effect, or indicator of toxicity selected
as the criterion for evaluation. While a particular animal species may share a number of
similarities with humans in respiratory tract physiology, it may be dissimilar in crucial
parameters and thus, make it a less than adequate source as a model. This subject area has
been reviewed recently (Hakkinen and Witschi, 1985) and various mammalian species (rat,
hamster, rabbit) were identified as appropriate species for extrapolation from several
perspectives. Other reviews that discuss the current limitations and need for the development
of animal models as surrogates for humans include those of Reid (1980), Slauson and Harm
(1980), and Calabrese (1983).
For agents whose lexicological outcome is dependent on the degree to which it is
metabolized, the most appropriate animal species is contingent upon proper evaluation of the
numerous interspecies differences with respect to metabolism (see also Section 2.2). The
studies of Plopper et al. (1983) suggest that animal species differ widely in metabolizing
potential of the respiratory tract. Hamsters and rabbits have much greater metabolizing
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potentials than do monkeys and rats. Interspecies differences in the metabolic pathway, as
shown for xylene (National Toxicology Program, 1986), may serve as a basis for selecting
one study for RfC derivation and rejecting another.
Appropriate animal model selection may be contingent upon pathological identification
of early changes consistent with the human syndrome; for example, a clear choice of an
appropriate animal species has not been established for emphysema (Snider et aL, 1986). The
hamster may be considered as most similar to man, with respect to emphysema, as measured
by serum a-antitrypsin levels. Hamsters have the lowest antiprotease levels of 10 species
tested (Snider et al., 1986). Individuals with deficient blood levels because of a genetic
defect are characterized as a high-risk subgroup for emphysema. However, primates have
comparable antitrypsin profiles (Ihrig et al., 1971).
Species-dependent variables in mucous production and secretion are factors in selecting
an appropriate animal model (see also Section 2.2). Ozone exposure, for example, increases
mucous secretion in rats but not in monkeys (Gardner, 1984).
3.1.2.2 Study Design
An ideal study addresses a clearly defined hypothesis, follows a carefully prescribed
protocol, is conducted in adherence to good laboratory practice, and includes appropriate and
sufficient subsequent analysis to support its conclusions. The U.S. EPA Good Laboratory
Practice Standards (Code of Federal Regulations, 1983a,b) are designed to ensure the quality
and integrity of data used in hazard evaluation. These regulations contain detailed guidance
on provisions for personnel, facilities for animal care, animal supply, handling of test and
control substances, equipment, operation of testing facilities, characterization of test and
control chemicals, protocol and conduct of a laboratory study, report records, record storage,
and record retrieval. Studies that do not precisely follow these guidelines may still be judged
adequate if the committee to develop inhalation RfCs determines that, in the context of
results, the deviations are not important. The type of deviation (variation) and its magnitude,
as well as the potential for its interaction among all the variables, must be assessed by the
committee (National Research Council, 1984). For example, a study may still be judged
adequate, despite an insufficient number of test animals specified by the appropriate reference
protocol guidelines, if the results are so definitive that the addition of more test animals would
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almost certainly not have affected the conclusion. Risk assessments that use studies with
deficiencies may include a modifying factor to account for the added uncertainty in its use
(see Section 4.1).
The appropriate application of statistics in both the design and interpretation of studies is
an area in animal toxicity testing that is often neglected or distorted (Muller et al., 1984).
Consideration of statistical applications restricted to confirmatory analysis (i.e., outcome is
dependent on the mathematically randomized test condition and is independent of other
observations) vs. exploratory analysis (i.e, many tests on a variable) should be emphasized.
3.1.2.3 Study Validity and Relevance to Extrapolation
The validity of the study and its relevance to human extrapolation is another major area
to consider when assessing individual animal studies. It involves the evaluation of a number
of factors, including all elements of exposure definition (dose, duration, administration route,
and physicochemical characterization of the chemical used), reliability of and limits to the
procedures used for both exposure and effects measurements, relevance of the dose level
tested to the anticipated human exposure level, nature of the effect (consistency with the area
of toxicology assessed and the suspected mechanism of action), and the similarities and
differences between the test species and humans (e.g., in absorption and metabolism).
Animal studies are conducted using a variety of exposure scenarios in which the
magnitude, frequency, and duration of exposure may vary considerably. Studies may use
different durations (acute, subchronic, and chronic) as well as schedules (single, intermittent,
and continuous). All of these studies contribute to the hazard identification of the risk
assessment. Special consideration should be addressed to those studies of appropriate duration
for the reference level to be determined (i.e., chronic investigations for the RfC).
These exposure concerns (dose and duration) are compounded when the risk assessor is
presented with data from several animal studies. An attempt to identify the animal model
most relevant to humans should be made on the most defensible biological rationale (e.g.,
comparable metabolism and pharmacokinetic profiles). In the absence of such a model, the
most sensitive species (i.e., the species showing a toxic effect at the lowest administered dose)
is adopted for use as a matter of science policy at the U.S. Environmental Protection Agency
(1987a; Barnes and Dourson, 1988). This selection process is more difficult if the animal
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data are for various exposure routes, especially if the routes are different from that in the
human situation of concern.
Because the data base may be deficient for the route of exposure of interest, it is the
Agency's view that the toxicity potential manifested by one route is relevant to any other
exposure route unless convincing contrary evidence exists (U.S. Environmental Protection
Agency, 1987a; Barnes and Dourson, 1988). Consideration must be given to the differences
in the pharmacokinetics for the chemical resulting from the different exposure routes.
Bioavailability of the chemical administered is another important factor for
consideration/uncertainty in the evaluation of dose. Detailed consideration is given to this
topic in Section 4.1.1.2,
3.1.3 Summarizing the Evidence
The culmination of the hazard identification phase of any risk assessment involves
integrating a diverse data collection into a cohesive, biologically plausible toxicity "picture";
that is, to develop the weight-of-evidence that the chemical poses a hazard to humans. The
salient points from each of the animal and human studies in the entire data base should be
summarized as should the analysis devoted to examining the variation or consistency among
factors (usually related to the mechanism of action), in order to establish the likely outcome
for exposure to this chemical. From this analysis, an appropriate animal model or additional
factors pertinent to human extrapolation may be identified.
The utility of a given study is often related to the nature and quality of the other
available data (Erdreich and Burnett, 1985). For example, clinical descriptions can provide
insight on pharmacokinetics and may validate that the target organ or disease in animals is
likely to be the same effect observed in the exposed human population. However, if a cohort
study describing the nature of the dose-response relationship were available, the clinical
description would rarely give additional information. An apparent conflict may arise in the
analysis when an association is observed in toxicologic but not epidemiologic data, or vice
versa. The analysis then should focus on reasons for the apparent difference in order to
resolve the assessment. For example, the epidemiologic data may have contained other
exposures not accounted for, or the animal species tested may have been inappropriate for the
mechanism of action. A framework for approaching data summary is provided in Table 3-2.
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TABLE 3-2. PROPOSED APPROACH FOR SUMMARIZING THE
EVIDENCE FROM DIVERSE DATA
CONCEPT 1: STRENGTH OF THE ASSOCIATION
The stronger the association, the greater the confidence that the agent causes the effect.
• Presence of low LD5a, low NOEL, high potency index
• Dose-response gradient evident
• High incidence rate, large excess risk
• High level of statistical significance in relevant studies
CONCEPT 2: CONSISTENCY
The association is observed in various circumstances.
• Observed in a number of experimental species
« Various routes
• Different dose regimens
« Descriptive epidemiologic data
• Analytical epidemiologic studies
CONCEPT 3: BIOLOGICAL PLAUSIBILITY
The association is plausible in terms of other scientific information related to the causal
mechanism.
• A gradient of responses observed
• Short-term or in vitro tests
• Pharmacokinetics
« Molecular action and pathology
* Structure-activity relationship
• Preclinical indicators
* Biological monitoring of exposure
Source: Erdreich, 1988.
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Table 3-3 provides the specific uses of various types of epidemiologic data in such an
approach. These guidelines have evolved from criteria used to establish causal significance,
such as those developed by the American Thoracic Society (1985) to assess the causal
significance of an air toxicant and a health effect. The criteria for establishing causal
significance can be found in Appendix F. In general, the following factors enhance the
weight-of-evidence on a chemical (U.S. Environmental Protection Agency, 1987a):
• Clear evidence of a dose-response relationship
• Similar effects across sex, strain, species, exposure routes, or in multiple experiments
• Biologically plausible relationship between metabolism data, the postulated
mechanism of action, and the effect of concern
• Similar toxicity exhibited by structurally related compounds,
• Some correlation between the observed chemical toxicity and human evidence
Developing improved weight-of-evidence schemes for various noncancer health effect
categories is the focus of ongoing efforts by the Agency to improve health risk assessment
methodologies (Perlin and McCormack, 1988).
The greater the weight of evidence, the greater the confidence in the conclusion derived.
Another difficulty encountered in this process is when certain studies produce clearly positive
or negative results, yet may have to be considered as flawed. The flaws may have arisen
from inappropriate design or execution in performance (i.e., lack of statistical power or
adjustment of dosage during the course of the study to avoid undesirable toxic effects). The
treatment of flawed results is critical; although there is something to be learned from every
study, the extent mat a study should be used is dependent on the nature of the flaw (Society
of Toxicology, 1982). A seriously flawed negative study could only provide a false sense of
security, whereas a flawed positive study may be entitled to some weight. Although there is
no substitute for good science, grey areas such as this are ultimately a matter of scientific
judgment. The risk assessor will have to decide what is and is not useful within the
framework outlined earlier.
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TABLE 3-3. HUMAN DATA FOR USE IN HEALTH RISK ASSESSMENT
Study (Alternative Terms)
Comment on Potential Use
Cohort (longitudinal,
prospective, incidence)
Case-control (retrospective,
dose or case-referent)
Cross-sectional (prevalence)
Geographic correlation15
Clinical trials
Experimental studies
EPIDEMIOLOGIC DATA
Rates as percent response useful in risk
characterization. Measure of excess risk can be
obtained. If dose or exposure data are available, dose-
response curves can be constructed. Studies with
ordinal exposure data support strength of evidence and
hazard identification.
No direct measure of disease rates. If exposure data
are available, a NOEL may be identified.3 Studies with
ordinal or nominal exposure data may support strength
of evidence and hazard identification.
Similar to case-control for short-term effects.
Prevalence data less reliable for effects from chronic
exposures.
An inexpensive screening procedure. Crude indicator of
potential hazard. Rates are usually only indirectly
related to exposure. Generates hypotheses for analytical
studies.
Generally not applicable to environmental issues,
because exposures are treatments or preventive
measures. Intervention trials in which an exposure is
removed or changed (e.g., medication, smoking, diet)
are useful in strength of the evidence for evaluating
causality.
NONEPIDEMIOLOGIC DATA
The only human data with controlled exposure levels.
Usually interval level exposure data but low dose,
limited exposure time. Use for hazard identification,
dose-response, risk characterization.
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TABLE 3-3 (cont'd). HUMAN DATA FOR USE IN HEALTH RISK ASSESSMENT
Study (Alternative Terms)
Comment on Potential Use
"Exposed-control" comparisons
(noncohort; see text for
discussion)
Case series0
Case reports
Rates may be biased because of self-selection or
incomplete ascertainment of exposed population.
Cannot be used to support absence of hazard. Clinical
descriptions useful for hazard identification.
Can be used to demonstrate hazard if syndrome is
unusual. Usually high level, short-term exposure. May
yield data point for adverse-effect levels. Cannot be
used to show absence of hazard.
Suggests nature of acute endpoints in humans. Cannot
be used to support absence of hazard.
aExposure history is difficult to reconstruct, particularly outside of the occupational setting.
May be available pertinent to air pollution exposure.
cSeveral cases seen by or reported by a single investigator. Cases may be attributed to unique exposure
incident, but total exposed population is not defined.
Source: Adapted from Erdreich and Burnett, 1985.
Studies meeting the criteria detailed in Sections 1.1 and 1.2 (epidemiologic,
nonepidemiologic and/or experimental studies on animals that "fit" into this framework) are
used in the risk assessment phase.
3.2 TOXICOLOGICAL ISSUES IN DATA EVALUATION
3.2.1 Qualitative Evaluation of Dose Response and Dose Effect Data
3.2.1.1 Relationship to the Uncertainty Factor Approach
Evaluation of dose-consequence relationships involves two distinct steps. The first
relates to the evaluation of an individual study with emphasis on the following:
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Identifying the critical effect. The critical effect has been defined as the effect that
occurs first on the increasing dose scale. The critical effect is either an adverse effect
or a known precursor to an adverse effect (U.S. Environmental Protection Agency,
1987a). The American Thoracic Society has proposed a classification scheme for
severity of respiratory effects in humans which is presented in Appendix C.
Evaluating the dose-response curve for the critical effect with the goal of identifying
doses that bracket the experimental threshold region.
These issues are selected based on the assumption that the study has already been
evaluated for adequacy in terms of design and conduct. Issues pertaining to the evaluation of
inhalation studies are discussed in Chapters 2 and 4.
The second step involves comparison of dose-response and dose-effect curves across
studies (within and across species). The first comparison is a qualitative evaluation of effects.
When disparity in dose-effect patterns is apparent, studies need to be evaluated to ascertain, if
possible, whether the differences are due to differences in the monitored endpoints or
procedure across studies, or whether they suggest that species differences exist in dose-effect
curves (see Section 4.1).
If species differences are apparent, the question arises as to which species is the most
appropriate model for humans. Differences in dose-effect curves could be due to inherent
differences in target receptor sensitivity (pharmacodynamics) or to differences in
concentration of the compound or metabolite reaching the receptor (pharmacokinetics). This
distinction is important when trying to identify the most appropriate species for modeling the
human response.
The dose delivered to the target tissue is important when evaluating dose-effect and
dose-response curves across species. The target tissue dose is determined by absorption,
distribution, metabolism, and excretion. For the inhalation route, the absorption component
is particularly problematic. Although absorbed doses per se have not been estimated as part
of the oral RfD process, the assumption has been made implicitly that absorption is either
equivalent across species, or that the divergences are minimal and can be subsumed within the
interspecies uncertainty factor along with other pharmacokinetic and pharmacodynamic
considerations. For inhalation, not only is there a question of absorption estimates, but there
also is uncertainty in estimating the amount of material inhaled and/or deposited and, thus,
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available for absorption, as well as potential differences in uptake of material from the
pulmonary tract due to the wide differences across species in airway anatomy and physiology
and body fat compartments (see Section 2.1). These differences suggest that until more
sophisticated methods of estimating "equivalent" inhalation doses across species are
developed, estimation of equivalent dose, as one subpart of the interspecies extrapolation
question, may be more uncertain than for oral exposures. Procedures applicable to relatively
insoluble particles for adjusting doses based upon described differences in deposition across
species are discussed in Chapter 4. Where appropriate, adjustments in doses based upon
known interspecies differences in pulmonary deposition must be applied before arraying the
dose effect data to compare species sensitivity. Approaches for estimating interspecies dose
differences for gases and vapors of organic solvents which are metabolized have been
developed (Fiserova-Bergerova, 1983) using physiologically-based pharmacokinetic models.
This type of model has been applied by EPA for quantitative cancer risk assessment for
perchloroethylene and methylene chloride (U.S. Environmental Protection Agency, 1986e,
1987b), but general applicability is not yet possible due to the need for chemical- and
species-specific information on metabolism which is not available for most chemicals.
Further validation of these models and development of the necessary data base should result in
a routinely applicable approach to interspecies dose adjustments. Equivalent approaches for
dose adjustment for soluble gases and hygroscopic particles are not yet as fully developed.
Error in estimation of equivalent dose also may complicate selection of the most appropriate
animal model for extrapolation. In particular, difficulties may be encountered when human
studies with inadequate exposure information suggest effects that differ from the animal
models, or when human data are absent and the critical effect in animals has no known human
counterpart.
The final stage in the data evaluation process is the accurate estimation of a subthreshold
exposure level for the heterogeneous human population. Although it would be easy to project
"safe" doses for many compounds which are orders of magnitude below actual threshold
doses with a great deal of confidence, achieving these minimal exposure levels could be very
costly and/or technologically infeasible. Therefore, the goal is to accurately project a
subthreshold dose that is close to the threshold. If we could precisely characterize the human
dose-response curve for the known human critical effect while completely characterizing
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human variability, then there would be little uncertainty in these RfC estimates. The current
RfC process is geared to develop subthreshold estimates in the presence of uncertainty. For
example, if a range of species sensitivities is apparent (following dose correction as described
in Chapter 4) and human data are unavailable, it is assumed that the most sensitive species
should be used to develop an RfC. When chronic data are unavailable, subchronic data are
adjusted by an empirical factor when, in some instances, there may not be a progressive dose-
time interaction. As a result, with the elimination of uncertainty many of the determined
subthreshold doses could potentially be higher or lower than those presently proposed.
The uncertainty factor approach addresses major areas of uncertainty relating to the
inability to know the collective human dose-response curves for the critical effect. These
factors are empirically based. Their initial proposal and implementation have been restricted
to oral exposures. Validation of these factors based upon experimental data has been
attempted, but is difficult primarily due to deficiencies in the available data base. If this
empirical factor approach is applied to the inhalation RfC process, a critical question becomes
whether or not any component(s) of the extrapolation process leading to the RfC estimate
appears to be inherently more uncertain or variable for the inhalation route. Particular aspects
of this question will be discussed in later sections of this document. Specific information
relevant to uncertainty factors per se is presented in Chapter 4.
3.2.2 Selecting Effect Levels: Inhalation-Specific Issues
Traditionally, ADI levels have been calculated by dividing the appropriate effect or no-
effect level of the critical toxic effect from human or animal toxicity studies by one or more
uncertainty factors. The critical effect is defined as either the adverse effect* that first
appears in the dose scale as dose is increased, or as the known precursor to the first adverse
effect. It is assumed that if the critical effect is prevented, then all subsequent adverse effects
are prevented. The derivation of the RfC follows these same principles.
*Here adverse effects are considered to be functional impairments or pathological lesions that may affect the
performance of the whole organism, or that reduce an organism's ability to respond to an additional challenge
(Federal Register, 1980). One of the major problems encountered with this concept is the reporting of "observed
effect levels" as contrasted to "observed adverse effect levels." The terms "adverse" and "not adverse" are at times
satisfactorily defined, but more subtle responses are being identified because of increasingly sophisticated testing
protocols, resulting in a need for judgment regarding the exact definition of adversity.
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As is often the case, NOELS, NOAELs, and LOAELs exist in a given data base for
several animal species. When comparing effect levels across species, it is assumed that the
doses will be adjusted to reflect currently characterized interspecies differences in pulmonary
deposition (see Chapter 4). What is the appropriate choice of no-effect or effect level given
this diversity? In the course of verification discussions on various RfDs and RfCs during the
last year, the RfD/RfC workgroup has provided some common ground on this issue. The
workgroup suggested the following conditions in choosing the appropriate animal effect or no-
effect level as a basis of an RfC:
When all scientific issues and effect or no-effect levels are generally equal, choose
the most appropriate effect level of a species that is known to resemble the human in
response to this particular chemical, for example, by similar toxicokinetics.
When the previous condition is not met, choose the most sensitive species as judged
by an mterspecies comparison of the highest individual species NOAEL (or NOAEL)
and its LOAEL (or LEL).
If scientific issues or effect or no-effect levels are judged to be generally equal,
choose the effect or no-effect level that yields the RfC with greatest confidence
reflecting quality of the study and data base.
An expanded discussion and an example exercise of choosing the effect level is provided
in Appendix G.
In order to implement the guidance as described, adverse and nonadverse effects must be
distinguished. Historically, the distinction between adverse effects and nonadverse effects has
been problematic. Although numerous groups have addressed this issue, most often
conclusions contain an element of scientific judgment in addition to objective criteria.
Considerable experience and precedent for such decisions have accrued over the last several
years in the process of developing oral RfDs and other health-related guidelines. Although
inhalation data have in some instances been utilized for the development of oral estimates, the
information content of the studies in terms of respiratory system effects has not been
rigorously evaluated, because it was appropriately not considered relevant to the oral guideline
efforts. As a result, the question of adversity for pulmonary endpoints has not been
extensively explored in the context of oral RfD development. However, other groups have
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addressed this and consensus guidelines have been developed. The American Thoracic
Society committee report has been discussed previously and is reproduced in Appendix C.
There still appears to be considerable uncertainty concerning how to differentiate in the
early stages of respiratory disease between acute reversible effects, which are the immediate
consequence of an exposure episode, and potential progression to chronic, nonreversible
pulmonary pathology. This is an important issue both in terms of evaluation of pulmonary
effects per se, as well as for decisions concerning the critical effect in inhalation studies.
For inhalation studies in particular, there is a dichotomy in terms of the types of
endpoints monitored in human versus animal studies. Human data concerning the
consequences of inhalation exposure generally consist of information on subjective symptoms
along with clinical data concerning pulmonary function. The relationship between the clinical
picture and lung pathology is poorly defined. On the other hand, animal standard
lexicological protocols generally incorporate pulmonary tissue evaluation as part of the routine
necropsy, but do not evaluate pulmonary function. Of course, once the lung has been
identified as a target tissue, more detailed studies of it as a target organ may be conducted.
When these more detailed data are available, two additional questions are raised: (1) how do
we evaluate the significance of alterations in test species' pulmonary performance in terms of
potential human effects and, (2) if tests showing differences in pulmonary biochemistry are
available, what is the utility of the biochemical changes as predictors of disease? Correlations
between functional decrements and immunologic, biochemical, and pathologic changes need
to be quantitated. Work in progress on animal models (see Section 3.1.2.1), biological
exposure indices (Lowry, 1986), and in vitro alterations of lung biochemistry as predictive of
lung disease (Last, 1983) will contribute to this end.
For present purposes, each inhalation study should be evaluated for possible indications
that the respiratory system is the critical target organ. Animal data that provide only cursory
evaluation of pulmonary endpoints make careful evaluation of human studies essential.
Human data should be carefully evaluated with special emphasis on the significance of
respiratory system endpoints. In instances where extrarespiratory effects are the critical
effect, effect levels would be evaluated in a manner consistent with decisions made in the oral
RfD process. This approach was initially described in Federal Register (1980). Existing,
verified RfD/RfC summary sheets provide insight into current judgments concerning adversity
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of particular endpoints. Extrapolation from oral to inhalation exposures may be utilized only
after careful consideration of factors presented in Sections 4,1.1.3 and 4.3.
For compounds that appear to produce their critical effect within the respiratory system
itself, decisions concerning adversity need to be made on a case-by-case basis. Appendix C
provides specific information concerning evaluation of the severity of pulmonary endpoints in
humans. Costa and Tepper (1988) provide an excellent summary of lung function assessment
in animals.
Although most pollutants would be expected to elicit a dose-response upon exposure,
some pollutants cause tolerance/adaptation and some are atypical, such as those that act by
allergic or asthmatic mechanisms. These allergic sensitizers may be considered a subgroup
under agents that produce their critical effect in the respiratory system. Toluene diisocyanate
is a well-known example of a sensitizing agent that affects immunological and
pharmacological mechanisms and induces asthma. Sensitizing responses appear to be
triggered by high initial doses. Subsequently, any level of exposure may be sufficient to
induce the asthmatic syndrome in sensitized individuals. There is evidence that IgE antibody
levels and inflammatory pulmonary reactions play a role in such syndromes. If these are
indeed nonthreshold phenomena upon challenge exposure, then methods other than the
traditional uncertainty factor approach will be required to address this subclass of compounds
for quantitative risk assessment.
Areas for further investigation and development of specific guidance include the
following:
• Specific guidance for evaluation of pulmonary endpoints in terms of
adversity/severity for both human data and animal investigations.
• Specific guidance for interpreting effects when both human and animal data are
available.
• Specific guidance for interpreting the impact of short-term exposures to human
subjects and subsequent pulmonary effects to chronic exposure situations, if any.
• Specific guidance concerning the comparability of effect levels following intermittent
exposures to continuous exposure scenarios.
• Specific guidance on how to deal with sensitizing agents in the RfC process.
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3.3 DEFICIENT DATA BASES AND ALTERNATIVE SOLUTIONS
The assessment of the total lexicological data base available for the chemical at that time
must be evaluated to derive an RfC (Clegg, 1979). In addition to the uncertainties discussed
in Section 3.2, determination of an RfC also involves a judgment about the study used in the
RfC calculation. These judgments relate to quality and completeness of the entire data base,
including uncertainty in the dose-response information and the estimated NOEL. Although
there is no readily definable way to measure the magnitude of uncertainty in any given RfC
(Environ Corporation, 1985) at present, research to address this issue is underway. The
minimum data needs for establishing an RfC predicated on addressing this uncertainty are
discussed in Section 4.1.1.1. Section 3.3.2 discusses the role of occupational exposure limit
values in RfC development.
3.3.1 Guidance on Evaluating a Data Base for Completeness
Current toxicity testing strategies are hierarchical sequences of tests designed to develop
a profile of a chemical's toxicity (Environ Corporation, 1985). Initial testing tiers consist of
relatively rapid, inexpensive tests designed to identify acute toxicity. This information is not
directly useful in predicting chronic adverse effects in humans, but can be used to guide
decisions as to type and extent of continued testing, such as subchronic, chronic or
reproductive bioassays.
The toxicity "profiles" or information required as a minimum data base also are
somewhat structured according to this hierarchy. The magnitude of insufficiency varies on a
case-by-case basis and is reflected in the rating of uncertainty in the data base. This also
would be tempered by the existing data base. Section 4.3. discusses the data base from the
perspective of confidence in the RfC
The information available in an incomplete data base also may indicate that the RfC
should be provisional pending further investigations. For example, the U.S. Food and Drug
Administration (1982) suggests that if a chemical tested in a subchronic study is found to
cause focal hyperplasia, metaplasia, proliferative lesions or necrosis, then a carcinogenicity
study in two rodent species is warranted. Likewise, if reproductive effects are found, then
teratology testing also should be conducted.
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3.3.2 Historical Use and Limitations of Occupational Exposure Limit
Values
OEL values, particularly the Threshold Limit Value (TLV) recommended by the
American Conference of Governmental and Industrial Hygienists (ACGIH), have had
widespread use in risk assessment/management programs because of a lack of uniform
benchmark values relevant to ambient air exposures. The use and limitations of OELs have
been discussed in an issue paper, prepared by the Inhalation Technical Panel of the Risk
Assessment Forum, that is supplementary to this document (U.S. Environmental Protection
Agency, 1990).
OELs have historically been considered as surrogates for benchmark values for ambient
exposures because they comprise the largest documented summary of toxicological,
epidemiological, and clinical information pertaining to human exposure to airborne
contaminants. They include the Occupational Safety and Health Administration Permissible
Exposure Limits (PELs) or full text standards, the National Institute of Occupational Safety
and Health Recommended Standards, and the ACGIH TLVs. OELs differ among themselves
in regard to the philosophy of the sponsoring organization, legal mandate, objectives,
assumptions, and evaluation of scientific data. They share the common elements of inhalation
exposure and goal of protection of human health.
Although OELs represent a large body of readily available information (e.g., there are
>600 OELs), there are several factors which limit their usefulness in the derivation of RfCs.
First, OELs may not be established based on chronic effects and may differ from RfCs in
severity of effect. Second, OELs assume intermittent exposure periods, whereas RfCs are set
to protect against continuous exposure. Third, OELs may not incorporate the most current
toxicological information because toxicological review is not on a regular basis. Fourth, the
unavailability of unpublished corporate documentation precludes scientific scrutiny of the
primary basis for a number of TLVs (Castleman and Ziem, 1988). Fifth, the evaluation of
toxiciry data by agencies deriving OELs may differ from that of EPA with respect to weight-
of-evidence classification, application of uncertainty factors, and other issues. Finally, the
use of OELs is established to protect the average healthy worker (ages 18 to 65 years) against
the adverse effects of inhaled pollutants; inhalation RfCs, on the other hand, are relevant to
those of any age and/or health status.
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The Agency does not endorse the general use of OELs in deriving RfCs. The OEL data
base should be evaluated on a case-by-case basis according to the methodology for inhalation
RfC derivation. The biological endpoint, quality and nature of the underlying data sets, the
exposure scenarios, and applicability to highly-sensitive subpopulations are among those
factors that must be considered for relevance to nonoccupational exposures.
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exchange with blood that enters the alveoli. Alveolar ventilation rates are approximately
67 percent of minute volumes for mice, rats, and humans (U.S. Environmental Protection
Agency, 1988c).
Assumptions and Default Values. As with aerosols, after evaluation of the adequacy of
the generation system, the initial step in the calculation of HECs is characterization of the
exposure.
Gas exposures are characterized by concentration (mg/m3), temperature, and pressure.
If the concentration is expressed in ppm, the actual temperature and pressure should be used
to convert the units to (mg/m3). When the actual temperature and pressure values are not
provided in a study, it should be suspect for deficient quality. Some studies, however,
express values already corrected for these parameters, usually corrected at 25 °C and 760 mm
Hg. These values are the recommended default values for temperature and pressure,
respectively.
Other assumptions and default values for gas and vapor extrapolations have been
discussed in Section 4.1.1.2 and details are provided in Appendix I.
4.1.1.3 Route-to-Route Extrapolation
Estimating equivalencies of dose-response relationships from one route of exposure to
another introduces an additional uncertainty in the derivation of an inhalation RfC.
Consequently, whenever possible, the inhalation RfC should be based on data involving
inhalation exposures. If inhalation data are insufficient, data from other routes of exposure
may be useful in the inhalation RfC derivation process, provided that portal-of-entry effects in
the lung can be ruled out (see Section 4.3).
Oral data are the most common alternatives to inhalation data. Dose-response data from
other routes of exposure, such as intravenous, intraperitoneal, subcutaneous, dermal, and
intramuscular routes also may be available. Intravenous data provide reliable information on
blood levels. The other routes generally have a much more limited usefulness in route-to-
route extrapolation because the pharmacokinetics are, in general, poorly characterized.
When portal-of-entry effects have been ruled out, estimates of equivalent doses can be
based upon the following:
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Available pharmacokinetic data for the routes of interest
Measurements of absorption efficiency by each route of interest
Comparative excretion data when the associated metabolic path-ways are
equivalent by each route of interest
Comparative systemic toxicity data when such data indicate equivalent effects by
each route of interest.
If sufficient pharmacokinetic data are available, physiologically based pharmacokinetic
(PB-PK) models are particularly useful tools for predicting disposition differences due to
exposure route differences. Their use is predicated on the assumption that an effective
(target-tissue) dose achieved by one route in a particular species is expected to be equally
effective when achieved by another exposure route or in some other species. For example,
the proper measure of target-tissue dose for a chemical with pharmacologic activity would be
the tissue concentration divided by some measure of the receptor-binding constant for that
chemical. Such models account for fundamental physiologic and biochemical parameters such
as blood flows, ventilatory parameters, metabolic capacities, and renal clearance, tailored by
the physicochemical and biochemical properties of the agent in question. The behavior of a
substance administered by a different exposure route can be determined by adding equations
that describe the nature of the new input function. Similarly, since known physiologic
parameters are used, different species (e.g., humans vs. test species) can be modeled by
replacing the appropriate constants. It should be emphasized that PB-PK models must be used
in conjunction with toxicity and mechanistic studies in order to relate the effective dose
associated with a certain level of risk for the test species and conditions to other scenarios.
This concept can break down when considering chemicals that exhibit first-pass effects
(a pharmacologic phenomenon) and/or portal-of-entry effects (a toxic response). It is
imperative to rule out pulmonary portal-of-entry endpoints before attempting route-to-route
extrapolation from other data. Where a chemical is known or suspected of having a first-
pass effect by the tested route, or where a portal-of-entry effect is known or suspected, then
route-to-route extrapolation for derivation of an RfC is not appropriate. Agents for which this
approach must be used with particular caution include metals, irritants, and sensitizers.
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Before route-to-route extrapolations are attempted, it is strongly suggested that articles by
PepeLko and Withey (1985), the National Research Council (1986), and the publication on
Pharmacokinetics in Risk Assessment (National Research Council, 1987) be reviewed for a
better understanding of the complexities and limitations of some of the available extrapolation
methods. Limitations also are outlined in Section 4.3.
Outstanding issues in route-to-route extrapolation include the following.
• When are the available data too sparse for estimating the different route
absorption parameters?
• What default positions, if any, will be used when one or both of the route-
specific absorptions cannot be estimated?
• How should the different exposure regimens by the different routes (e.g.,
continuous vs. intermittent exposures) be dealt with?
• How should vehicle effects on the pharmacokinetics of the oral studies (e.g.,
ppm in diet vs. ppm in water) be dealt with?
4.1.1.4 Issues for Further Investigation
Consistent application of the procedures in this chapter will require consensus on the
most appropriate data sets (e.g., species deposition data) and reconciliation of data values for
use in the dosimetry calculations. Default values used among the U.S. EPA offices should be
reviewed, including a discerning reevaluation of the data source, selection rationale, and
application limitations. Recent documents on recommended values for use in risk assessment
(U.S. Environmental Protection Agency, 1988c) and for use in physiologically based models
(U.S. Environmental Protection Agency, 1988b) are useful sources of default values for
parameters such as ventilation rates and body weights for use in these equations when these
values are not supplied in individual investigations. Available allometric equations (Adolph, >
1949; Weibel, 1972; U.S. Environmental Protection Agency, 1988b,c), relating body size to
the parameters of interest such as ventilatory rates and lung surface areas also may be
appropriate. Currently, an inhalation task group of the Agency's RfD verification workgroup
is addressing the issue of the use of default parameters. It must be emphasized at this time
that the use of default or derived values must be consistent with the dosimetric modeling
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parameters and approaches used in adjusting concentrations to human equivalent values, such
as the parameters used to derive the regional RDDR (see discussions in Sections 4.1.1.2 and
and Appendices H and I).
4.1.2 Approach for RfC Estimation Using Human Data
4.1.2.1 Introduction
Whenever possible, a human study is selected as the critical study for derivation of an
RfC to avoid the myriad problems of extrapolating from animals to humans.
When using epidemiologic data to assess risk in the context of a method designed for
data on experimental animals, the dependence of epidemiologic studies on existing exposure
conditions and the necessity of using noninvasive diagnostic methods present two complicating
factors. One is that existing exposure levels may not include a NOAEL. Toxicologic studies
are generally designed to identify the NOAEL. For ethical reasons, many clinical studies in
humans often focus on exposure scenarios that are associated with minimal effects and short
exposure durations, although they also may identify a NOEL. In contrast, epidemiologic
studies cannot be so designed because exposure levels are dependent on existing exposures.
In both controlled human and animal studies, the estimates are biased by the dose or exposure
level selected or available for study. These estimates are subject to random error, the
magnitude of which depends on various design aspects, such as the size of the study
population or test groups, and the underlying variability of the test animals or study subjects.
The second factor to consider for epidemiological studies is that the entire spectrum of
potential adverse effects cannot be evaluated, thus, it is difficult to determine the critical
effect. Prospective epidemiologic studies that assess biological markers or preclinical
endpoints are better sources of NOAELs to estimate the threshold region. Clinical studies
may be based on low exposure levels selected by the investigator and investigate sensitive
endpoints, but these studies are generally of short duration and are more useful for estimating
short-term effects (see Section 4.2). The following discussion describes approaches to address
these obstacles.
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4. QUANTITATIVE METHODOLOGICAL
PROCEDURES
4.1 PROCEDURES ADDRESSING LIFETIME EXPOSURE*
An inhalation RfC has a numerical value, and hence, a quantitative nature. As will be
discussed, numerous theories, assumptions, and empirical data provide the quantitative
framework for the RfC calculations. At present, the methodology is more advanced for
addressing lifetime exposure (Section 4.1), but approaches for estimating partial lifetime
exposures (Section 4.2) are under development. To account for inherent uncertainties in the
chemical-specific data base and essential qualitative judgments, levels of confidence
(Section 4.3) are assigned, enhancing the interpretation of a numerical RfC.
4.1.1 Approach for RfC Estimation
RfCs are typically calculated using a single exposure level and uncertainty factors that
account for specific deficiencies in the toxicity data base. Both the exposure level and the
uncertainty factors are selected and evaluated in the context of all available chemical-specific
literature. After all lexicological, epidemiologic, and supporting data have been reviewed and
evaluated, a key study is selected that reflects optimal data on the critical effect. Dose-
response data points for all reported effects are examined as a component of this review.
Issues of particular significance in this endeavor include:
• A delineation of all toxic effects and associated exposure levels.
• Determination, to the extent possible, of effect-specific experimental threshold
regions (i.e.,the NOAEL-LOAEL interface or bracket) (see Tables 4-1 and
4-2).
• Determination of the critical effect. Of the multiple toxic endpoints potentially
observed, the critical effect selected is defined as the one associated with the
lowest NOAEL-LOAEL bracket.
*Parts of this text are excerpted from U.S. Environmental Protection Agency (1987a; Barnes and Dourson, 1988).
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TABLE 4-1. FOUR TYPES OF RESPONSE LEVELS (RANKED IN ORDER OF
INCREASING SEVERITY OF TOXIC EFFECT) CONSIDERED
IN DERIVING RfCs FOR SYSTEMIC TOXICANTS
NOEL: No-Observed-Effect-Level. That exposure level at which there are no
statistically or biologically significant increases in frequency or severity of
effects between the exposed population and its appropriate control.
NOAEL: No-Observed-Adverse-Effect-Level. That exposure level at which there are
no statistically or biologically significant increases in frequency or severity of
adverse effects3 between the exposed population and its appropriate control.
Effects are produced at this level, but they are not considered to be adverse.
LOAEL:
PEL:
Lowest-Observed-Adverse-Effeet-Level. The lowest exposure level in a
study or group of studies that produces statistically or biologically
significant increases in frequency or severity of adverse effects between the
exposed population and its appropriate control.
Frank Effect Level . That exposure level which produces frankly apparent
and unmistakable adverse effects, such as irreversible functional impairment
or mortality, at a statistically or biologically significant increase in frequency
or severity between an exposed population and its appropriate control.
aAdverse effects are defined as any effects resulting in functional impairment and/or pathological lesions that
may affect the performance of the whole organism, or that reduce an organism's ability to respond to an
additional challenge.
Frank effects are defined as overt or gross adverse effects (e.g., severe convulsions, lethality, etc.).
Special consideration of species, portal-of-entry effects, and/or route-specific
differences in pharmacokmetic parameters and the slope of the dose-response
curve.
The threshold concept is the basis for the derivation of the RfC. Essentially, an
experimental exposure level is selected from the available studies which represents the highest
level tested in which the critical effect was not demonstrated. Conversion of experimental
exposure levels to human equivalent concentration (NOAEL^HECj) estimates, by adjustment
for dosimetric differences between the experimental species and humans, should be made
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TABLE 4-2. RESPONSE LEVELS CONSIDERED IN DERIVING INHALATION
RfCs IN RELATIONSHIP TO EMPIRICAL SEVERITY RATING
VALUES. (RANKS ARE FROM LOWEST TO HIGHEST SEVERITY.)*
Effect or
No-Effect Level
Rank
General Effect
NOEL 0
NOAEL 1
NOAEL 2
NOAEL 3
NOAEL/LOAEL 4
LOAEL 5
(LO)AEL** 6
(LO)AEL/FEL 7
PEL 8
PEL 9
PEL 10
No observed effects.
Enzyme induction or other biochemical change,
consistent with possible mechanism of action, with no
pathologic changes and no change in organ weights.
Enzyme induction and subcellular proliferation or other
changes in organelles, consistent with possible
mechanism of action, but no other apparent effects.
Hyperplasia, hypertrophy or atrophy, but no change in
organ weights.
Hyperplasia, hypertrophy or atrophy, with changes in
organ weights.
Reversible cellular changes including cloudy swelling,
hydropic change, or fatty changes.
Degenerative or necrotic tissue changes with no
apparent decrement in organ function.
Reversible slight changes in organ function.
Pathological changes with definite organ dysfunction
that are unlikely to be fully reversible.
Pronounced pathologic changes with severe organ
dysfunction with long-term sequelae.
Death or pronounced life shortening.
* Adapted from DeRosa et al. (1985) and Hartung (1986).
series
**The parentheses around the "LO" in the acronym "LOAEL" refer to the fact that any study may have a serif
of doses that evoke toxic effects of rank 5 through 7. All such doses are referred to as adverse effect levels
(AELS). The lowest AEL is the (LO)AEL.
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before these choices are performed (see Section 4.1.1.2 and Appendices G, H, I). This
chosen human equivalent concentration (NOAELrjjgqi) represents the first quantitative basis
for the scientific evaluation of the risk posed to humans by noncancer toxicants. The
inhalation RfC is operationally derived from this NOAELjHEq by consistent application of
generally order of magnitude uncertainty factors (UFs) that reflect the second quantitative
basis of this scientific evaluation of risk. Uncertainty factors are associated with various
specific recognized uncertainties in extrapolating from the type of study serving as the basis
for the RfC to the scenario of interest for the risk assessment. An additional modifying factor
(MF) reflects professional judgment of the entire data available on the specific agent (see
Table 4-3).
The RfC is derived from the NOAEL as:
RfC = NOAEL^Ecj/OJF x MF) (4-1)
where:
= NOAEL, adjusted for dosimetric differences between animal species and
humans, expressed as human equivalent concentration,
UF = an uncertainty factor suited to the characteristics of the data (Table 4-3),
and
MF = a modifying factor based on professional judgment of the entire data base
(e.g., sample size of chosen study).
In general, the choice of these factors reflects the uncertainty associated with estimation
of an RfC from different human or animal toxicity data bases. For example, if sufficient data
from chronic duration exposure studies are available on the threshold region of a chemical's
critical toxic effect in a known sensitive human population, then the UF used to estimate the
RfC may be 1. That is, these data are judged to be sufficiently predictive of a population
subthreshold dose, so that additional UFs are not needed.
A UF of 10 is generally used to estimated RfCs with appropriate chronic human data,
and reflects intraspecies human variability to the adverse effects of a chemical (i.e., H in
Table 4-3). A UF of 100 is generally used to estimate RfCs with chronic animal data,
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TABLE 4-3. GUIDELINES FOR THE USE OF UNCERTAINTY FACTORS IN DERIVING REFERENCE DOSE (RfD)*
o
o
8
n
Standard Uncertainty Factors (UFs)
11 Human to sensitive human
A Animal to human
S Subchronic to chronic
LOAEL to NOAEL (refer also
to Table 4-1)
**D
Incomplete to complete
data base
Modifying Factor (MF)
Use a 10-fold factor when extrapolating from valid experimental results from
studies using prolonged exposure to average healthy humans. This factor is
intended to account for the variation in sensitivity among the members of the
human population.
Use an additional 10-fold factor when extrapolating from valid results of long-
term studies on experimental animals when results of studies of human exposure
are not available or are inadequate. This factor is intended to account for the
uncertainty in extrapolating animal data to the case of average healthy humans.
Use up to an additional 10-fold factor when extrapolating from less than chronic
results on experimental animals or humans when there are no useful long-term
human data. This factor is intended to account for the uncertainty in extrapolating
from less than chronic NOAELs to chronic NOAELs.
Use up to an additional 10-fold factor when deriving an RfD from a LOAEL,
instead of a NOAEL. This factor is intended to account for the uncertainty in
extrapolating from LOAELS to NOAELs.
Use up to a 10-fold factor when extrapolating from valid results in
experimental animals when the data are "incomplete." This factor is intended to
account for the inability of any single animal study to adequately address all
possible adverse outcomes in humans.
Use professional judgment to determine another uncertainty factor (MF) that is
<10. The magnitude of the MF depends upon the professional assessment of
scientific uncertainties of the study and data base not explicitly treated above;
e.g., the number of animals tested. The default value for the MP is 1.
""Adapted from: U.S. Environmental Protection Agency, 1987a.
**Use of this UF is now undergoing discussion in Risk Assessment Forum (see also discussion in Section 4-3).
-------
thereby accounting for both interhuman and interspecies variability (i.e., H x A). It is
generally acknowledged that these estimates are uncertain. If specific information exists to
indicate a different but more exact interhuman or interspecies extrapolation procedure for that
chemical, it should be used and the rationale underlying its use clearly explained.
An RfC based on a NOAEL with satisfactory subchronic animal data would require a
factor to address the uncertainty in extrapolating data from subchronic to chronic exposures
(i.e., S), as well as the two former uncertainty factors (i.e., H x A).
A UF of 10 generally is applied to estimated RfCs using LOAELs if NOAELs are
unavailable (i.e., L). This UF is employed to define an exposure level below the LOAEL
expected to be in the range of a NOAEL.
Under some circumstances, the U.S. Environmental Protection Agency applies a UF up
to 10 when the data base is deficient in some major aspect; for example, if it lacks a
two-generation reproductive study (i.e., D). The U.S. Food and Drug Administration has
addressed this issue with the use of a twofold safety factor. Thus, in situations where a
subchronic animal bioassay was available, but information in a second experimental species
was lacking, a 2,000-fold safety factor (i.e., 2D x lOjj x 10A x 10s) was used to estimate an
acceptable daily intake (Shibko, 1981).
It is important to note that when sufficient human data are available on a chemical's
critical effect and pharmacokinetics, the UFs may be smaller than those described in
Table 4-3, or unnecessary. Likewise, in cases where data do not completely fulfill the
conditions for a category or UF, or appear to be intermediate between two categories, an
intermediate UF is suggested to estimate the RfC (Federal Register, 1980).* When a single
subchronic study that does not define a NOAEL is the only available information, the U.S.
EPA recognizes that all five areas of uncertainty are present. In this case, the overall UF
used is generally 10,000. This coalescing of several areas of uncertainty is based on the
*Other authors have discussed these areas of uncertainty or UFs in general. The interested reader is referred to
Zielhuis and van der Kreek (1979) for a discussion of these factors in setting health-based permissible levels for
occupational exposure, and Dourson and Stara (1983) for a summary of these factors regarding oral exposures.
Other publications include Gaylor (1983), who discusses the use of safety factors for controlling risk; Crump
(1984), who discusses problems with me current methods that includes UFs; Krewski et al. (1984), who contrast
safety factors and mathematical models as methods for determining "safe" levels of exposure; Calabrese (1985),
who discusses UFs and interindividual variation; and Lu (1983, 1985b), who discusses safety factors from the
perspective of the World Health Organization.
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knowledge that each individual factor is generally conservative from the standpoint of the
behavior of the average chemical (Dourson and Stara, 1983), and that the multiplication of
four or five values of 10 is likely to yield unrealistically conservative RfCs.
The areas of scientific uncertainty discussed in the preceding section do not represent all
the uncertainties in a dose-response assessment; for example, the number of animals that
determines the NOAEL is not normally considered in the previous factors. The fewer the
number of animals used at a dose, the more likely the dose is to be a NOAEL (other factors
being equivalent). The effect of small sample size has long been recognized in toxicology
(Bliss, 1938) and recent research has focused on adjusting for this by taking the power of
individual studies into account (Brown and Erdreich, 1989). Although never explicitly stated,
when faced with such an uncertainty scientists have modified the usual 10-fold factors either
up or down. For example, a 100-fold UP may be raised to 125 if the number of animals in a
chronic study was fewer than thought reasonable by the risk assessor. While this evaluation is
scientifically in the correct direction, it introduces two difficulties in the resulting assessment.
The first is that the adjustment of the standard 10-fold values is perceived as arbitrary, and
the second is that the precision of some of the resulting UFs is not at all appropriate in
relationship to the underlying biology (in this example a UF of 125 has a precision of three
digits).
The U.S. EPA's use of the MF is an attempt to separate the "traditional" areas of
scientific uncertainty that have been quantified to some extent, from these latter areas of
scientific uncertainty that have not been quantified. The intent is to arrive at the best choice
of an RfC, which in many cases will include an analysis of the same overall uncertainties as
addressed historically, while avoiding the perception of arbitrariness and, moreover, be
consistent with the overall precision of the value.
There are certain circumstances specific to inhalation that may require changes in UFs.
For example, the UF used when extrapolating from a subchronic to a chronic study is
assumed to be adequate for oral studies in the great majority of cases, A UF of extrapolation
of subchronic to chronic exposures for inhalation studies also should be adequate with certain
exceptions. Possible exceptions include the following:
Exposure to chemicals that are considered likely to induce hypersensitivity
(e.g., beryllium)
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Exposure to chemicals that are considered likely to induce very slowly
developing ("smoldering") effects
Exposure to inhaled relatively insoluble particulate matter where the clearance
rate may slow or stop when a threshold for clearance is reached. Thus, after
long-term exposure lung loads can reach much higher levels than could
reasonably be expected from lower level, chronic exposure conditions
The appropriate UF for these situations should be decided on a case-by-case basis until
more definitive guidelines are available.
If multiple NOAELs are available in one animal species, the highest NOAEL for that
individual species is used in comparison to other species NOAELs. If multiple NOAELs for
the critical effect are available in different species, the lowest of these NOAELs generally is
selected as the exposure level that most closely defines the threshold for adverse effects of the
dose-response curve. It is consistent with U.S. EPA policy to use data on the most sensitive
animal species as a surrogate to humans unless data exists to the contrary. In the inhalation
RfC methodology, this evaluation is based on NOAELrHECjS. Often an appropriate NOAEL
will not be available. In that event, other estimates of effect-specific thresholds may be used.
Based on the dose-response classification system presented in Table 4-1, the following
guidelines may be employed (adapted from Federal Register, EPA, 1980):
An PEL from a study with no other dose-response levels is unsuitable for the
derivation of an RfC.
A NOEL from a study with no other dose-response levels is unsuitable for the
derivation of an RfC. If multiple NOELs are available without additional data,
NOAELs, or LOAELs, the highest NOEL should be used.
A NOAEL or LOAEL may be suitable for RfC derivation. A well-defined
NOAEL from at least a 90-day study may be used directly, applying the
appropriate UF, In the case of a LOAEL, an additional UF (10^ is applied.
Note: caution must be exercised not to substitute FELs for LOAELs.
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If, for reasonably closely spaced doses, only a NOEL and a LOAEL of equal
quality are available, then the appropriate uncertainty factor is applied to the
NOEL.
Please refer to Section 3.2 and Appendix G for a complete discussion of these issues.
4.1.1.1 Minimum Criteria
Data bases vary considerably in their completeness. With a more complete data base,
the magnitude of the required UF is reduced. Well-defined and conducted subchronic toxicity
studies are generally considered to be reliable predictors of many forms of toxicity with the
notable exceptions of carcinogenic, teratogenic, or reproductive effects. Consequently, the
minimum data base acceptable for development of an RfC is a subchronic toxicity study that
clearly identifies the "threshold region" of the dose-response curve. Section 4.3 also
discusses this minimum data base from the viewpoint of distinguishing between high and low
confidence in the RfC.
It should be recognized, however, that for some substances, results of other studies may
suggest the possibility of effects not detected in the subchronic studies that constitute this
minimum data base. When such findings are reported, it is desirable to consider the results of
the risk assessment as tentative, indicate that the confidence in the estimate is low, and pursue
additional toxicity testing. For example, if a compound tested in a subchronic study is found
to cause focal hyperplasia, metaplasia, proliferative lesions, or necrosis, then a cancer
bioassay is clearly indicated. Alternatively, if a subchronic study demonstrates reproductive
organ toxicity or neurotoxic effects, reproductive/teratologic or neuropathology studies may
be appropriate.
Extrapolation from subchronic to chronic exposure conditions (S in Table 4-3)
necessitates the utilization of an additional UF of 10 in most cases. Empirical evidence
supports the proposition that subchronic toxicity data can be used in this way for risk
assessment purposes. McNamara (1976) has demonstrated that a 10-fold factor applied to a
subchronic NOEL would predict a chronic NOEL for 95 percent of the 122 compounds for
which both chronic and subchronic data for the oral route of exposure were available. To the
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degree that route-specific and duration-specific data are not available, increased reliance on
additional extrapolation assumptions and larger UF is necessary.
In summary, with more extensive data the threshold region of the dose-response curve is
more reliably approximated and the magnitude of the associated uncertainty in the risk
assessment is reduced. For this reason it is desirable to state qualitatively the confidence level
attached to the RfC, and the study from which the NOAEL was selected, and to rate the
overall data base as high, medium, or low, as described in Section 4.3.
4.1.1.2 Calculation of Human Equivalent Concentrations
Extrapolation of animal inhalation data to humans requires estimation of the "dose"
(i.e., agent mass deposited per unit tissue volume considered along with physiological and
biological factors) delivered to specific target sites in the respiratory tract or made available to
uptake and metabolic processes for systemic distribution (Martonen and Miller, 1986). To
this end, physiologically based pharmacokinetic (PB-PK) and mathematical dosimetry models
have evolved into particularly useful tools for predicting disposition differences for risk
assessment (Miller et al., 1987b). Their use is predicated on the assumption that an effective
(target-tissue) dose in a particular species is expected to be equally effective when achieved in
some other species. However, it is likely that species differences in sensitivity occur due to
such species-specific factors as host defense, repair processes, and genetics, so that the use of
a ten-fold UF to account for interspecies variability, despite application of dosimetric
adjustments, requires additional research. This section outlines the methods for calculating
HECs estimates by using adjustment factors that have resulted from similar modeling efforts
of species dosimetric differences. The factors are used to adjust the observed exposure effect
levels (i.e., NOAELs, LOAELs, etc.) in animals to estimate a concentration mat would be an
equivalent exposure to humans. These human equivalent concentrations then can be the basis
for comparison and choice of the critical effect and study as discussed in Appendix G.
Figure 4-1 is a flowchart for the calculation of HECs and provides an outline for the
contents of this section. Conversion of units from ppm to mg/m3 is required before
dosimetric adjustments can be applied and this calculation is discussed in Section 4.1.1.2.
The next step in calculating a HEC is to convert the exposure regimen of the experiment in
question to that of the human exposure scenario; that is, a continuous (24-hour) lifetime
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CONVERT
ppm TO mg/m3
(EON. 4-2a, b)
ADJUST FOR
EXPOSURE REGIMEN
(EQN. 4-3)
1. EVALUATE ^PARTTCLE ^£ GAS
GENERATION-. ^|£P
SYSTEM 0tJNI
2. CHARACTERIZE
BY MMAD, CTg. OR
3. DEFAULT VALUES
1. EVALUATE
•*• GENERATION
SYSTEM
2. CHARACTERIZE BY
CONCENTRATION,
TEMPERATURE,
PRESSURE, OR
3. DEFAULT VALUES
IDENTIFY
THE TARGET
EFFECT(S)
IDENTIFY
THE TARGET
EFFECT(S)
RESPIRATORY
RESPIRATORY \ 1 EXTRARESPIRATORY
(EQN. 4-4, 4-5) ^ J^^
EXTRARESFfRATORY MORL
(EQN. 4_6, 4-7) VS. SOLUBLE?" ATTAJNED?
s \
, 4-9) f
YES I YES
(EQN. 4-8, 4-9) f (EQN. 4-10) NO
NO (EQN. 4-11)
(EQN 4-10)
Figure 4-1. Flowchart for calculation of Human Equivalent Concentrations.
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(70-year) exposure, as described in Section 4. 1. 1.2. The third phase of the approach is to
apply the dosimetric adjustments appropriate for the type of agent to be assessed (particle or
gas/vapor), and the effect to be assessed (respiratory tract or extrarespiratory toxicity beyond
the respiratory tract [systemic] resulting from an inhalation exposure). The dosimetric
adjustments to derive HECs for respiratory tract effects and extrarespiratory effects of
particles are provided in Section 4.1.1.2. A discussion of the dosimetric adjustments to
derive HECs for respiratory tract effects of gases and for extrarespiratory effects of gases are
in Section 4. 1.1. 2.
Although the presentation in this section divides the dosimetry calculations into those
applied to extrapolate respiratory tract effects vs. extrarespiratory effects, it should be
recognized that there is no strict compartmentalization of effects of a given chemical. A
given inhaled chemical could cause both respiratory tract effects and extrarespiratory effects,
Thus, the decision on which of the equations to use in this chapter is governed by the
endpoint of interest in concert with the properties of the chemical to be assessed.
Dose Conversion: Units
In the rare event that investigations using particulate exposures would report the
concentration in ppm, a mass-density relationship would be used to convert the exposure
concentration to mg/m3. Inhalation toxicity studies on gases typically employ exposure levels
expressed as mg/m3 and/or ppm. Exposure levels for gases, including the NOAEL selected
for RfC derivation, should be expressed in standard units of mg/m3. For exposure levels
expressed as ppm, the Ideal Gas Law can be used to derive the corresponding mg/m3 level:
= ppm x x MW X232ZX - P - xW_ixlmg (4-2a)
nr 22.4 1 g-mole T 760 mm Hg m3 g
where:
ppm = concentration expressed on a volumetric basis t
106
MW = molecular weight in grams,
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22.4 i = the volume occupied by 1 g-mol of any compound in the gaseous state at 0°C
and 760 mm Hg,
T = actual temperature in degrees Kelvin, and
P = actual pressure in mm Hg.
At 25°C and 760 mm Hg, 1 g-mole of a perfect gas or vapor occupies 24A5L
Therefore, under these conditions, the conversion becomes:
mg/m3 = ppmxMW (4-2b)
24.45
Dose Adjustments for Discontinuous Exposure Protocols
Many inhalation toxicity studies entail exposure regimens that are discontinuous. Often
exposures are for 6-8 hours/day and 5 days/week. RfCs are constructed to reflect a
benchmark level for continuous exposure. By extension, the RfC also is assumed to be
protective for discontinuous exposures at the same air concentration. A normalization to
some given exposure (e.g., 24 hours/day for a lifetime of 70 years) is needed to adjust for the
wide variety of experimental exposures to permit comparisons between studies. As discussed
earlier, the RfC proposed herein is to reflect lifetime continuous exposure, and this scenario is
the objective of normalization. Attention should be paid to the degree the applied situation
deviates from the experimental, and to the physicochemical (solubility and reactivity)
parameters of the inhaled agent and species-dependent factors (e.g., distribution volumes and
metabolic pathways) that might temper this conversion. To calculate duration-adjusted
exposure levels in mg/m3 for experimental animals, the appropriate equation is:
NOAELrADJ1(mg/m3) = E(mg/m3) x D(hours/day/24 hours) x W(days/7days)
(4-3)
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where:
E = experimental exposure level,
D = number of (hours exposed/day)/24 hours, and
W = number of (days of exposure/week)/? days.
Use of extreme caution is emphasized with this conversion equation, especially as the
effect in question increases in its severity. The toxicity of an exposure is fundamentally
dependent upon the character of the "concentration-time" (C x T) curve, which is a hyperbola
whose arms converge asymptotically toward the axes of the coordinates (Bliss, 1940). Bliss
and James (1966) have shown that such curves can be extrapolated with minimal error when
the time points in the experiment are located on the segment of the curve asymptotically
approaching the axes of the coordinates. The exposure duration should ideally embrace the
time span in which the rate of onset of specific toxic effects sharply change, reflecting the
degree of arc in the curve of the (C x T) relationship. Fiserova-Bergovera et al. (1980),
using a compartmentalized model based on first-order kinetics, demonstrated that duration of
exposure to a gas can have profound effects on the fractions of uptake exhaled or
metabolized. Concentrations in tissues reflected the concentration variations in exposure, but
the variation in tissues was greater during exposure to low solubility gases than to lipid
soluble vapors (blood to air partition coefficients of 0.5 and 10.0, respectively), due to the
faster equilibration of partial pressures of the low solubility gases. Variations between tissue
and exposure concentrations were diminished if the substances were metabolized. Since the
toxic effect is related to tissue concentration, consideration should be given to these duration
and solubility effects. Extrapolation should be attempted only if a steady-state was attained.
Likewise, linear extrapolation from one concentration exposure to another is possible only if
all processes involved in the uptake and elimination of the inhaled agent are first order.
Differences are caused primarily by concentration-dependent metabolic clearance (Fiserova-
Bergerova et al., 1987). Limitations of this type of conversion also are discussed in
Section 2.2.
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Dosimetry: Particles
Inhalation lexicologists have advanced their ability to measure the deposition values for
particles in the various regions of the lungs across species. Initially the data were primarily
total deposition values for polydisperse and sometimes unstable aerosols, but data now exist
for insoluble monodisperse aerosols of different sizes under different breathing conditions
(U.S. Environmental Protection Agency, 1982). Data are available across most experimental
species of interest on the regional deposition of applicable particle size ranges and on the
necessary physiologic parameters (e.g., tidal volumes and regional surface areas) incorporated
in dose adjustments (Overton et al., 1987; Miller et al., 1987b; Miller et al., 1988;
Raabe et al., 1988; Patra et al., 1986; Patra, 1986). Deposition data are usually presented or
modeled as the deposition fraction for each respiratory tract region of the species of interest.
Deposition fraction is the ratio of the number or mass of particles deposited in the respiratory
tract to the number or mass of particles inhaled, as illustrated in Figures 2-2 and H-l [B].
Deposition data also may be normalized for the percent entering a region, particularly for the
tracheobronchial region. Although not presented in the approach outlined below, iterative
calculations are available to make normalized data amenable to the deposition fraction
application (Miller et al., 1988). Refer to Appendix H for an explanation of these
calculations.
A vast amount of knowledge also has been gained in the technology and methods for
generating and characterizing aerosols. State-of-the-art inhalation toxicology studies will have
characterized the paniculate exposure by a given particle diameter (e.g., Dae, D^, MMAD)
and the geometric standard deviation (a ). The distribution of particle sizes for the aerosol
then can be conveniently described (and/or graphically plotted as in Figures 2-5 and H-l [A])
as a probability density function.
Because of these advances in quantitation of species-specific regional respiratory tract
deposition and physiologic parameters, the following describes how interspecies dosimetric
comparisons can be made using data typical for particles. This application is an adaptation
(Miller et al., 1983b; Graham et al., 1985) and is limited at this time to relatively insoluble
and nonhygroscopic particles. The calculations to derive HECs lung effects and
extrarespiratory effects of particles will then be discussed in Sections 4.1.1.2.
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The product of deposition efficiency and particle distribution curves can be integrated to
compute the deposited dose of exposure particles in a given region of the respiratory tract for
the experimental species in question. That is, for each particle size range, the product of the
particle distribution and deposition fraction in that range can be computed for a given
respiratory tract region. Summation of these products across all the particle size ranges yields
an estimate of the fractional deposition in the region. These estimates then can be applied to
the exposure concentration and adjusted for ventilation parameters and lung surface area to
*5
calculate the regional deposited dose (RDD) in mg/min-cnr of respiratory tract. Determining
the RDD in this manner for each species allows regional deposited dose ratios (RDDR) to be
calculated in order to adjust the exposure effect level for dosimetric differences between the
experimental species and humans.
Notationally, for the 1th size range of an exposure aerosol with a given particle diameter
and crg, let
Pi = the particulate mass fraction in that size range, and
Ej = the deposition efficiency for the species and respiratory tract region (i.e.,
extrathoratic, tracheobronchial and/or pulmonary, or total) of interest;
then the RDD expressed as mg/min-cm2 of respiratory tract region can be computed as:
RDD = 10"6 YVTL S P; Ei (4-4)*
where:
n = number of size ranges,
Y = exposure level (mg/m3),
*This is an adaptation (Miller et al., 1983b and Graham et al., 1985) limited to insoluble and nonhygroscopic
particles only.
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VT = tidal volume (mf),
f = breathing frequency (breaths/minute), and
n
S = regional surface area (cm ) of toxic effect observed.
This RDD can be calculated for each region of interest; that is the extrathoracic
(RDDET), the tracheobronchial (RDDjg), the pulmonary (RDDPU) region the thoracic
(RDDTH) or the total respiratory tract (RDDTOT). It should be calculated according to the
effect of interest. For example, the RDD summed across the TB and PU regions, the
thoracic RDD (RDDTH), would be used to compute the RDD for assessment of a "lung
effect" (RDDTH = RDDjB + RDDpu); whereas the RDDET alone would be calculated for
an effect concerning the nasal turbinates.
The RDD in each species then can be used to adjust the exposure effect level for
dosimetric differences between species by calculating the RDDR, defined as the ratio of RDD
in the animal species of interest (subscript A) to that of humans (subscript H) as:
NOAELpjjjc] (mg/m3) = NOAEL^jj (mg/m3) x RDDR (4-5)
where:
NOAELrjjECn = the NOAEL human equivalent concentration,
= the NOAEL adjusted for duration according to Equation 4-3, and
RDDR = (RDD)A/(RDD)H, the ratio of regional deposited in animal species to
that of humans for region of interest for the toxic effect.
Appendix H describes the derivation of the RDD values for humans and discusses the
surface area values used for both animals and humans. The surface area values used are the
best available estimates for the various species at this time. Research as described in
Appendix H under Research and Development may provide estimates of greater accuracy as
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the methodology develops. Appendix H also provides a table for the calculation of RDDR
for rats and an example application of its use in dosimetric adjustment.
Respiratory Tract Effects. The general dosimetric approach for insoluble aerosols
outlined above provides the basis for calculations for estimating HECs when the toxic effect
of interest is in the respiratory tract. The equivalent dose across species is assumed to be the
aerosol mass (mg) per minute (min) per surface area (cm2) of the respiratory tract region of
concern.
The initial step of the calculation is to characterize the particulate exposure by its
MMAD and a . This information will be used in conjunction with deposition efficiency to
calculate a regional deposited dose. The respiratory tract region of the observed toxic effect
dictates the RDD calculated. For example, if the toxic effect of interest was an effect on the
nasal epithelium, Equation 4-4 would be modified to calculate the RDD for that region only
as:
-6 n
RDDET = 10 YVTL s p R (4-4)
where:
P: = the particulate mass fraction in the exposure size distribution (MMAD, a ),
&
Ej = the deposition efficiency of that size distribution (MMAD, a ) in the
extrathoracic region for the species of interest,
n - number of size ranges,
Y = exposure level (mg/m3),
VT = tidal volume (m£),
f = breathing frequency (breaths/minute), and
t\
SET — surface area of the extrathoracic region (cm ).
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The RDD in the species that exhibited the ET effect then is related to the human RDD,
also calculated for the ET region and the same MMAD and o as a ratio. This ratio then is
used as in Equation 4-5, to calculate a human equivalent concentration for the exposure
NOAEL as follows:
NOAEL[HEC] (mg/m3) = NOAEL[ADJ] (mg/m3) x RDDR(ET) (4-5)
where:
= the NOAEL human equivalent concentration,
= the NOAEL adjusted for duration according to Equation 4-3,
and
RDDR = (RDDEj^/ORDDE-jOjj, the ratio of regional deposited dose in
the extrathoracic region in the animal species to that of humans.
Extrarespiratory Effects. When the toxic effect of interest for RfC evaluation is
observed outside the respiratory tract, the following equation is used to calculate the RDD
expressed as mg/min-kg:
2 PiEi (4-6)
where:
p. = the paniculate mass fraction in the exposure size distribution (MMAD, a ),
Ei = the deposition efficiency of that size distribution (MMAD, a ) in the entire
respiratory tract for the species of interest,
n = number of size ranges,
Y = exposure level (mg/m3),
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VT = tidal volume (mf),
f = breathing frequency (breaths/min), and
BW = body weight (kg).
In the case of extrarespiratory effects of particles, the equivalent dose across species is
assumed to be the mass of particles (mg) deposited per body weight (kg). Until clearance and
distribution parameters can be incorporated, it is assumed that 100 percent of the deposited
dose to the entire respiratory system is available for uptake to the systemic circulation. This
assumption may result in slightly less conservative HEC estimates than using retained dose
and accounting for differential uptake from various respiratory regions, but is more accurate
than using the exposure concentration.
The ratio of the extrarespiratory RDDs calculated for the experimental species and the
human then is used to calculate the HEC for a systemic effect as follows:
(mg/m3) = NOAEL[ADJ] (mg/m3) x RDDRER (4-7)
where:
NOAEL[HECj= the NOAEL human equivalent concentration,
NOAEL[ADJj= the NOAEL adjusted for duration according to Equation 4-3,
and
RDDRER= (RDDER)A/(RDDER)H, the ratio of the dose available for uptake
from the entire respiratory system of the experimental animal
species to that of humans.
Assumptions and Default Values. The initial step in the calculation of HECs, after
evaluation of the generation system for its adequacy, involves characterization of the aerosol
exposure by its MMAD and a Studies that do not provide this information should be
suspect for deficient quality. Some of the older toxicology literature may not provide this
information, however, and a default value may need to be invoked. The first approach in this
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situation is to attempt an estimate of particle size and distribution based on the generation
apparatus used. In conjunction with this information, the knowledge that prior to the late
1970s, the generation technology was not sufficiently sophisticated to deliver consistent
exposures of particle sizes above 3 /xm (MMAD) can be used to construct a default approach.
The recommended default approach is to use the particle diameter (MMAD) and distribution
(er ) characteristic for the given generation system that is < 3 /xm and that yields the smallest
(i.e., most conservative) RDDR values for the lung region of interest. The Hatch-Choate
equations can be used to convert lognormal distributions of one type of diameter (e.g., count
median diameter) to another (e.g., MMAD) (Hinds, 1982).
The MMAD for liquid and hygroscopic particles may vary with location in the
respiratory tract since its size, shape, and density may change due to water uptake in the
humid respiratory tract. Consequently, the deposited dose may be different from that of
nonhygroscopic particles of like size distribution upon inhalation (Martonen et al., 1985).
Theoretical models have been developed to analyze the influences of hygroscopic growth on
inhaled aerosol behavior (Martonen et al., 1985; Martonen, 1982; Martonen and Patel, 1981),
but application in risk assessment awaits definition of the primary factors influencing
hygroscopic growth on species- and agent-specific bases. The factors include initial particle
geometry and density, material hygroscopic growth characteristics, respiratory parameters,
and temperature and relative humidity profiles. Observations on the data from modeling
efforts to date indicate that hygroscopic particles in the diffusion-dominated regime have
reduced deposition relative to nonhygroscopic particles of identical preinspired size, whereas
those hygroscopic particles affected by inertial and gravitational forces have an increase in
deposition relative to nonhygroscopic particles (Martonen et al., 1985). These observations
may be explained by changes in the relative effectiveness of the particle deposition efficiency
mechanisms. Thus, dosimetric adjustment of an inhaled dose by the deposition efficiency for
nonhygroscopic particles would underestimate (i.e., be more conservative than) the deposited
dose for the larger (affected by inertial and gravitational forces) hygroscopic particles, and
overestimate the deposited dose for the smaller diffusion-dependent hygroscopic particles.
The total deposited dose of inhaled nonhygroscopic particles, however, is always less than the
initial total dose (exposure dose). Also, the relative changes in deposition will be in a similar
direction in experimental animal species and humans. Dosimetric adjustment by the insoluble
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(nonhygroscopic) deposition efficiencies is recommended as a conservative default for the
hydroscopic particles, pending modification by the elucidation of the hygroscopic models.
It is recognized that this approach is based on deposition efficiency data obtained or
derived under a particular set of ventilatory parameters; that is, the experimental parameters
for the animal and a derived human breathing pattern (13.8 £/min or 20 m3/day). The
assumption in this application is that it is valid to linearly extrapolate from these values to
other sets of breathing parameters. The parameters of this assumption, such as the effect of
activity pattern and allometric relationships between lung weight, lung surface area and body
weight (Adolph, 1949; Weibel, 1972; U.S. Environmental Protection Agency, 1988c) will be
investigated as part of this methodology development. A discussion of the impact that
breathing pattern has on the human deposition estimates can be found in Appendix H. Also,
the human ambient exposure scenario, when known, may be characterized by a different
MMAD and o than that used to derive the health risk assessment. Comparisons between
ratios calculated with a MMAD and a the same as the animal exposure and calculated with
the human estimate using the anticipated ambient MMAD and o may provide some insight
on the uncertainty of this extrapolation.
In addition to inspired air concentration, minute volume respiration rate, surface area,
and deposition efficiency, the effective dose of inhaled particulate matter will vary with
bioavailability. The fraction of particulate matter dissolved and assumed to be bioavailable
can be expected to increase with greater particle solubility, as well as with longer residence
time in the lungs. Until clearance and distribution parameters can be systematically
incorporated, 100% of the deposited dose to the entire respiratory system (TOTAL) is
assumed to be available for uptake to the systemic circulation. This assumption may result in
less conservative HEC estimates than using retained dose and accounting for differtial uptake
from various respiratory tract regions, but is more accurate than using the exposure
concentration. Models have recently been used to simulate clearance and estimate retention
in various species (Snipes, 1989). The EPA has recognized the importance of incorporating
clearance components to its dosimetric adjustments in order to calculate Regional Retained
Dose Ratios (RRDRs) for estimates of long-term lung burdens. These RRDR adjustments
would be more appropriate to apply to chronic inhalation bioassays. In addition,
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consideration will also be given to the issues concerning bioavailability as discussed in
Appendix H.
Dosimetry: Gases and vapors
The approach outlined in the insoluble particle application illustrates the feasibility of
interspecies dosimetry calculations for extrapolating the lexicological results of inhaled agents
to human exposure conditions for risk evaluation. Dosimetry data facilitates evaluation of
concentration-response data with respect to dose-response relationships. Dosimetry models
also should be developed to account for the physical, biological, and chemical factors that
affect gas uptake and the clearance mechanisms for various inhaled agents. Predictive
physiologically based modeling for reactive gases has been demonstrated (Overton and Miller,
1988). Predictive physiologically based modeling has also been demonstrated for gases and
vapors of organic solvents that may be metabolically activated (Fiserova-Bergerova, 1983;
Andersen et ah, 1987; Overton, 1989). For these agents, the uptake and distribution of the
parent compound depends on the physicochemical properties of the agent (i.e., solubility in
blood and tissue) and physiological properties (i.e., ventilation, perfusion, tissue mass). The
lexicological effects can be a function of the parent compound or are a function of
metabolism of the parent compound to a toxic metabolite, which depends on the rate of
toxification and detoxification reactions. Consideration should be given to the discussion by
the National Research Council (1986) on interspecies extrapolation based on mechanism of
action. Three classes were distinguished based on whether the parent compound, stable
metabolite, or reactive metabolite produces the toxic effect and suggests measures of dose for
each of these classes. These factors are often species-specific and dose-dependent, as well as
being chemical-specific and, therefore, require a substantial data base (beyond that which
exists in most circumstances) in order to model comparative species dosimetry of gases based
on mechanism of action. A project is underway by ECAO-RTP and HERL to identify the
key determinants of uptake and tissue dose for a variety of gases with different properties (see
"Research and Development", Appendix I). Identification of the limiting anatomic and
physiologic parameters, physicochemical characteristics, and exposure concentration and
duration conditions will facilitate the application of these models routinely to interspecies dose
adjustments.
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Respiratory Tract Effects. For gases and vapors that are very reactive and that have
their toxic effect in the lung, an analogous approach to that of the insoluble particles approach
for respiratory tract effects is used. The equivalent dose across species again is assumed to be
n
the mass (mg) of toxic agent per minute (min) per surface area (cm ) of the lung region of
concern. Ventilatory parameters and regional lung surface areas are used to dosimetrically
adjust for the species differences, as in Equations 4-4, but the particle distribution and
deposition efficiency integration term is dropped. Thus, the regional gas dose, (RGD), is
calculated as:
RGD = -IttfjOtiL (4-8)
where:
Y = exposure level (mg/m3),
Vt = tidal volume (mf),
f = breathing frequency (breaths/minute), and
S = regional surface area (cm2) of toxic effect observed.
It should be noted that this approach assumes that the entire inspired concentration goes
to the region of concern, whereas not all inspired gas is necessarily deposited. For example,
an alveolar ventilation rate would be appropriate to use with a strictly pulmonary effect. As
in the case of the RDD for aerosols, the toxic effect observed will dictate the RGD to
calculate. That is, the appropriate surface area (i.e., ET, TB, PU, TH, or TOT) must be
used in Equation 4-8 to correspond with the region of observed toxicity. The ratio of the
appropriate RGDs, calculated for the experimental species and humans, is then derived. This
regional gas dose ratio (RGDR) then is used to dosimetrically adjust the experimental NOAEL
to a human equivalent concentration:
NOAEL (mg/m3)[HEC] = NOAEL^jj (mg/m3) x RGDR (4-9)
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where:
= theNOAELHEC
= the NOAEL adjusted for duration according to Equation 4-3, and
RGDR = (RGD)A/(RGD)H, the ratio of regional gas dose in animal species to
that of humans for region of interest for the toxic effect.
For gases with respiratory tract effects that have significant solubility in the blood
relative to their reactivity with lung tissue (e.g., methyl bromide), the approach outlined
below for gases which reach periodic concentrations and cause extrarespiratory effects is
recommended (Equation 4-10). This default is used to account for uptake into the systemic
circulation which may have decreased the amount of gas causing a direct effect in the lung
and to account for the concentration available to the lung via blood circulation.
Extrarespiratory Effects. For gases and vapors that exhibit their toxic effects outside of
the respiratory tract, an approach for the scenario when the arterial concentration (leaving the
lung) of the gas in the animal was periodic (or could be expected to be) with respect to time
(Equation 4-10) is recommended, A default approach for the case when such periodicity is
suspected not to have occurred also is provided (Equation 4-11),
Derivation of the procedure and Equation 4-10 for estimating NOAELjHECjS for gases
with extrarespiratory effects was based on a PB-PK model described in Appendix I. The
procedure will give equivalent or more conservative values for the NOAELrHECnS than those
obtained by using the PB-PK model, and can be used with compounds for which modeling
would be applicable, but for which some or all values of the important parameters (A, V,^,
KJJ,) are not available. The approach assumes that physiologic and kinetic processes can be
described by a PB-PK model, assumes allometric scaling of physiologic and kinetic
parameters, and assumes that all concentrations of the inhaled compound within the animal
are periodic with respect to time. Based on the PB-PK model of Ramsey and Andersen
(1984), algebraic equations that relate organ and tissue compartment concentrations to
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exposure concentrations under equilibrium conditions were derived. Since toxic effects
observed in chronic bioassays are the basis for the determination of NOAELs from which RfC
values for human exposures are derived, the procedure assumes that chronic animal exposure
scenarios are equivalent to human lifetime exposures. The procedure also assumes that the
toxic effects observed are related to the arterial blood concentration (concentration leaving
lung compartment in the model) of the inhaled compound and that NOAEL[-HEC]S should be
such that the human time-integrated arterial blood concentration is less than or equal to that of
the exposed laboratory animal. This latter assumption is equivalent to assuming that time-
average concentrations are equal to the equilibrium concentration adjusted for exposure
duration (i.e., Equation 4-3). A mathematical derivation was used to obtain the proposed
method of simple algebraic equations to compute NOAEL[HEC^s, A more detailed description
of the development of the procedure is given in Appendix I.
Another assumption is that the concentration of the inhaled compound within the animal
achieved periodicity with respect to time. That is the internal concentration of the inhaled
agent achieved a consistent pattern over the weeks of exposure. An illustration of periodicity
is provided (Figure 4-2). Periodicity of the arterial concentration of the agent was not
achieved until the fifth week for the plotted theoretical exposure simulation. Practically, the
conditions of periodicity should be met during "most" of exposure duration. For example, if
this condition is met for nine tenths of the time (e.g., periodic during the last 90 weeks of a
100 week experiment), then estimates of average concentrations will be in error by less than
10%.
Assuming the animal alveolar blood concentrations were periodic with respect to time
for the majority of the experiment duration, the NOAEL^q for extrarespiratory effects of
gases or vapors is calculated as:
(mg/m3) = NOAHL (mg/m3) x -A (4-10)
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-^
-4
Tl
H
i
O
o
O
cj
O)
75
60
c
o
CO 45
c
o
u
c
o
o
."eg
0)
30
15
Blood:Air Partition Coefficient = 1,000
Fat:Blood Partition Coefficient = 100
0.0
0.5 1.0 1.5
Time (hoursx10~3)
2.0
2.5
W Figure 4-2. Time course of periodicity for F344 rat exposed 6 hours/day, 5 days/week to theoretical gas with partition coefficients
G as shown. (Jarabek et al., 1990).
-------
Y = FATrBLOQD PARTITION COEFnCIENT
10,000
= CHRONIC
= CHRONIC -r SUBCHRONIC
Figure 4-3. Relationship of partition coefficients to periodicity in F344 rat arterial blood
for subchronic (90-days) and chronic exposure regimens of 6 hours/day, 5
days/week.
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where:
NOAELrjjECi = the NOAEL human equivalent concentration,
= the NOAEL adjusted for duration according to Equation 4-3, and
AA/AH = the ratio of the blood to air partition coefficient of the chemical for
the animal species to the human value, used only if AA < AH.
For the cases where \A > AH, model results have shown that the
generalized Equation 4-10 may not provide conservative estimates.
The detailed derivation of boundary limits on \ is given in
Appendix I. For the situation in which AA > AH and in the case
where A values are unknown, the default value of AA/AH = 1 is
recommended. An analysis of the available data on rats for blood to
air partition coefficients shows that the AA is greater than AH in most
cases.
Figure 4-3 provides guidance on the relationship of the blood to air and fat to blood
partition coefficients with respect to achieving periodicity of an inhaled agent in the arterial
blood of a 380-gram F344 rat. (It should be noted that often tissue to air partition
coefficients are reported, e.g., fat to air. The fat to blood partition coefficient can be
calculated by multiplying the fat to air partition coefficient by the blood to air partition
coefficient.) The PB-PK model as described in Appendix I was run to simulate a
6 hours/day, 5 days/week exposure regimen of 10 ppm. Physiologic parameters, such as
ventilation rate, were scaled as described in Appendix I. No metabolic parameters were
incorporated in the model for the simulations, since the arterial blood concentration takes
longer to reach periodicity without metabolism. This figure thus represents the most
conservative values for the partition coefficients for that exposure regimen. The blood to air
and fat to blood partition coefficients were chosen based on sensitivity analyses that indicated
these two parameters were important to describing the time course of the concentration of an
agent in the arterial blood, and upon data availability.
The importance of the relationship between the partition coefficients and the attainment
of periodicity is particularly significant when extrapolating from studies of different durations.
For example, for an agent with a blood to air partition coefficient of 1 ,000 and a fat to blood
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partition coefficient of 100, it would be inappropriate to extrapolate from a subchronic
exposure regimen since the criterion of attaining periodicity for 90% of the exposure duration
is not met. Periodicity is attained with these same parameters when the study is carried out
for a longer duration, however, so that the approach based on the ratio of animal to human
partition coefficients can be used on a chronic study without violation of critical assumptions.
Similar matrices to Figure 4-2 can be developed for the relationship between partition
coefficients and the attainment of periodicity of the agent in the arterial blood of each
experimental species of interest. Use of physiologic parameters for other species and/or
different exposure regimens at various concentrations will influence this relationship and
should be considered when determining the extrapolation approach to use for derivation of a
human equivalent concentration.
The default calculation for the situation in which periodicity during 10% of exposure
duration is suspected not to have been achieved is given by:
NOAEL[HEC](mg/m3) = NOAEL[ADJ](mg/m3) x (VA/BW)A (4-11)
$A/BW)H
where:
NOAELrHECj = the NOAEL Human equivalent concentration,
NOAEL^jj = the NOAEL adjusted for duration according to Equation 4-3,
and
(CrA/BW)A = the ratio of the alveolar ventilation rate (m£/min) divided by
-T-—— BW (kg) of the animal species to the same parameters for
^ A JH humans.
Since this default approach engenders more uncertainty and is less conservative with
respect to the above approach, use of a modifying factor should be considered.
Use of the alveolar ventilation rate is recommended to account for the volume of the
respiratory tract in which no gas exchange occurs; often termed the "physiologic dead space".
The alveolar ventilation rate is the volume of inspired air per minute available for gas
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4.1.2.2 Selecting the Threshold Estimate
In some epidemiologic studies only severe effects such as mortality are examined. In
such studies a NOAEL has inherent limitations. A study in which sensitive endpoints are
evaluated may contain a LOAEL but no NOAEL. If the effect is sensitive (i.e., it occurs
early in the natural history of the disease), a LOAEL may be judged suitable for use in
calculating an RfC in lieu of a NOAEL, because the uncertainty of extrapolating human data
for a well-defined critical effect from a LOAEL to a NOAEL is judged to be less than the
uncertainty involved in extrapolating from animal data to humans. The circumstances
governing this selection include deficiency in toxicologic and physiologic data bases, small
sample size in the experimental studies, or physiologic or pharmacokinetic data suggesting
that animal data are unlikely to be good predictors for humans. The use of the UF for
extrapolating from a LOAEL to a NOAEL has been explained previously in Section 4.1.1.
The data base supporting an occupational exposure level (OEL) may be examined for
data to be incorporated in the data array for analysis supporting RfC derivation. Caution is
recommended: While the OELs are based on the concept of a biological threshold, there are
no standardized criteria for the data base and safety factors used. Furthermore, the OELs are
designed to protect "nearly all workers" and not the entire population. These and other
limitations are discussed in the issue paper (U.S. Environmental Protection Agency, 1989).
4.1.2.3 Defining the Exposure Level
Epidemiologists cannot control the exposure levels for a study in a systematic fashion,
but instead attempt to measure the levels to which the study population is exposed insofar as
the measurement technology permits. In actual exposure situations, the levels vary in time
and location. Epidemiologic studies can utilize a variety of parameters to characterize
exposure, although in retrospective studies they are usually quite limited by the available data.
The ideal exposure measure for humans who move about in their environment is
individual data, such as might be obtained with the use of a personal monitor. However, in
addition to the expense and practical difficulties, this technology is available for measuring
only a few chemicals. Individual exposure can be constructed by mapping the individual's
time in various exposure zones, rooms, or areas. If information on levels in the environment
is not available, duration of employment often is used as a surrogate for exposure.
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Parameters commonly used to measure environmental levels are cumulative exposure,
peak exposure level, time-weighted average, and ratio of average to peak exposure.
Currently it is unclear which of these is best related to disease and under what circumstances
or chemical characteristics of the agent is one parameter better than another. For example,
cumulative exposure is more appropriate as half-life of a substance is increased, therefore, to
derive RfCs that identify levels of environmental exposures that are free of adverse effects,
cumulative exposure or time-weighted averages are appropriate for substances with long half-
lives. The circumstances can be evaluated on a case-by-case basis and different exposure
parameters may be used if the rationale is presented. For conversion of units, the approach is
the same as that for animal data (Equations 4-2a and 4-2b). Conversions are the same for
exposure duration (Equation 4-3), again, with the same precautions as discussed.
Considerations for route extrapolation would be the same as for animal data; however, it is
highly unlikely that human ingestion data would be available in a form useful for quantitative
risk assessment.
4.1.2.4 Uncertainty Factors for Human Data
The best data to use for calculating an RfC would be a population study of humans that
includes sensitive individuals exposed for lifetime or chronic duration, and evaluates the
critical endpoint or an appropriate early marker for the disease. A NOAEL derived from a
well-done epidemiologic study of this description may require no UF. A similar study in
humans that contains only a LOAEL would require the use of a factor of up to ten-fold to
reduce the exposure to the range of a NOAEL (see Table 4-3, 10L). Chronic studies on
populations that do not include sensitive individuals may require a 10-fold UF. For example,
studies of workers are considered to contain only relatively healthy adults. A NOAEL from a
study that entails subchronic exposure would require a reduction by a 10-fold UF (see
Table 4-3, 10S). However, the amount of exposure in a human study that constitutes
subchronic is not defined, and could depend on the nature of the effect and the likelihood of
increased severity or greater percent response with duration. In the absence of data on the
relationship of animal to human lifespan for predicting health effects, a linear correlation of
percent lifespan is assumed. Therefore, if a chronic study in animals is 12% of lifespan, then
9 years of human exposure must be studied. Information on the natural history and
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progression for the diseases should be considered and explained; information on follow-up
after exposure, often available in epidemiologic studies, is important.
In some cases, short-term studies of effects in humans can give important information
on irritation, sensory effects, or sensitivity and reversibility, yet give no information on the
effect of chronic exposure. If the data base suggests that the effective level of a short-term
human study is below that which would cause chronic health effects, this can be used to
derive the RfC, designated as a subchronic inhalation RfC (RfCs). This is described further
in Section 4.2.2.
4.2 PROCEDURES FOR ESTIMATING PARTIAL LIFETIME
EXPOSURES
4.2.1 Acute
Application of the RfC approach to acute exposures is contingent upon determination of
relevant exposure durations for humans. Documentation on this area of interest is under
development in the U.S. EPA.
4.2.2 Approach for Subchronic Inhalation RfC Estimation (RfC.)
j
The RfCg strictly parallels the inhalation RfC in concept. The distinction is one of
exposure duration. While the RfC is specifically developed to be protective for daily
exposure to a compound over the course of a lifetime, the RfCs applies to specified durations
that are less than lifetime. Multiple duration-specific RfCs may be developed for a compound
depending upon the medium and possible exposure scenarios, as well as the needs of a
particular program office. For example, the Office of Drinking Water develops oral drinking
water health advisories for 1-day and for 10-day exposures.
Once the duration of a particular exposure is defined, all of the laboratory and
epidemiologic data need to be evaluated in this exposure-time context. When adequate data
on humans or on laboratory animals are available for the required exposure-time interval,
RfCs development could proceed in the same manner as described for the RfC (see
Section 4.1). Data on humans may be available for short-term exposures even when the
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chronic value (RfC) has been based on animal data. It is important therefore to examine the
available human data to ascertain whether less-than-lifetime exposures are included.
Determining exposure-time equivalencies among species is an issue requiring further
investigation. Research on the boundary limits of the blood to air and blood to fat partition
coefficients for establishing periodicity of arterial concentrations during intermittent exposures
as described hi Section 4.1.1.2, may provide some insight. These limits will be different for
90-day versus chronic bioassays. Previous discussions have utilized the concept of percent of
the lifespan. For example, chronic studies often are defined as having a duration of
> 90 days. Whether short-term exposures should also be evaluated in terms of percent of the
lifespan, physiological time, or by some other method, requires further investigation.
Essentially, an index of the damage process relative to the repair process for a number of
different lesion types is necessary. In addition to exposure duration, postexposure observation
time is also an important issue. For example, brief exposure to certain pulmonary irritants
may result in no immediately observable adverse effects, but may be linked with pulmonary
pathology at a later evaluation time. No guidance is currently available concerning adequate
periods of postexposure observation for acute, short-term and subchronic exposure regimens.
The duration of an adequate postexposure time period may be compound-specific.
When experimental data are available only for shorter "equivalent" exposure durations
than the desired duration-specific RfCs, or when postexposure observation is deemed
inadequate, application of a UF may be appropriate. This is similar to the application of a
UF for duration when estimating an RfC from subchronic animal data. Criteria are needed to
determine the degree of divergence between the experimental exposure duration and time to
elicit effects, which would necessitate application of an additional UF. In addition, it needs
to be determined if a standard factor, such as 10, would be applied whenever the criteria for
duration are not met, or whether UFs of graded magnitude might be employed, depending
upon the degree of divergence between the experimental exposure duration and the duration
interval modeled by the RfCg.
It is important to evaluate any proposed RfCs in the context of all available toxicity
data. Although free-standing NOELs/NOAELs* are not recommended for either RfC or RfCs
*"Free-standing" NOELs or NOAELs are those without corresponding LOAELs. In such cases the experimental
threshold region has not been determined.
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estimation, on occasion they represent the only data available. Use of a dose level well below
an actual threshold value can result in an anomalous RfCs, when compared to longer
exposure-duration RfCs or RfCs that are based on a more complete data set. In other words,
it would be inappropriate to estimate an RfCs that is of smaller magnitude than an RfC for the
same compound.
The RfCs may be calculated for any required exposure interval when adequate
toxicological data are available, utilizing the approaches described in Section 4.1 as shown
below:
RfCs = NOAEL[HEC]/(UF x MF) (4-12)
The UFs are the same as described in Section 4.1.1. except that the NOAEL from
Table 4-3 would be more generally interpreted to reflect discrepancies between the available
duration-specific data and the duration of the proposed RfCs. This may necessitate correction
for added uncertainty.
For human data, the exposure concentration associated with a human NOAEL may be
utilized directly to develop a subchronic RfCs in units of air concentration. This
concentration needs first to be adjusted for exposure duration (i.e., converted to represent an
equivalent continuous exposure level) as shown in Equation 4-3, with the noted caution
pertaining to this type of extrapolation. Following this adjustment, the RfCs may be
calculated as:
RfCs (mg/m3) = NOAEL[ADJ] (mg/m3)/(UF x MF) (4-13)
Some agents may not be suitable for either chronic or subchronic RfC estimation
because they act in a manner distinct from those agents whose action is concentration and/or
time-dependent. An example of such compounds are those that cause occupational asthma
(Chan-Yeung and Lam, 1986) or induce hypersensitivity reactions. Others include agents in
which adverse effects continue to progress over a period of years.
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4.2.3 Issues Requiring Further Investigation
• Development of guidance on how to compare exposure duration for subchronic
animal exposures with duration for subchronic human exposures for the purpose of
determining whether the criterion of "equivalent duration" is met by a particular data
set
• Development of specific guidance concerning application of duration-related UFs for
partial lifetime exposure development
4.3 CRITERIA FOR SPECIFYING LEVEL OF CONFIDENCE
The selection of a NOABL or other appropriate measure of threshold response involves
a process that incorporates scientific subjective judgment and statistical measures of
significance. The qualitative and quantitative nature of this process results in estimated
benchmark values such as the RfC associated with varying degrees of confidence that can be
described as high, medium, and low. The confidence ascribed to the result is a function of
both the quality of the individual study and the completeness of the supporting data base. For
example, the RfD/RfC verification work group assigns confidence levels to the individual
study, the data base, and the RfC. Thus, if the individual study is of excellent quality, it
most likely will receive a high confidence rating, even though it may be subchronic in
duration. Duration of the chosen study, as well as supporting studies and the spectrum of
investigated endpoints (e.g., reproductive effects), are considered in the rating of confidence
in the data base. Low confidence in the data base might be given to an excellent chosen
subchronic study with few supporting studies and few endpoints examined. The confidence in
the RfC then would reflect these two ratings by a rating of medium to low, indicating
uncertainty (lack of confidence) and suggesting that further investigations may refine this
number.
The degree of confidence in a particular laboratory animal study involves a number of
parameters. These parameters include, but are not limited to, the following.
• Adequacy of study design
- Is the route of exposure relevant to humans?
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- Were an appropriate number of animals and/or sexes used for determination of
statistical significance?
- Was the duration of exposure sufficient to allow results to be extrapolated to humans
under different exposure conditions?
- Were appropriate statistical techniques applied?
- Were the analytical techniques sufficient to adequately measure the level of the test
substance in the exposure protocol, including biological media?
- Is the animal species and strain appropriate as a surrogate for humans?
- Are the techniques for measurement of the biological endpoints scientifically sound
and of sufficient sensitivity?
- To what degree are the biological endpoints qualitatively and/or quantitatively
extrapolatable to humans?
Demonstration of dose-response relationships
Were sufficient exposure levels used to demonstrate the highest NOAEL for the
endpoint of concern?
Is the shape of the dose-response curve consistent with the known pharmacokmetics of
the test substance?
Has the dose-response curve been replicated by or is it consistent with data from other
laboratories and other laboratory animal species?
Species differences
- Are the metabolism and pharmacokinetics hi the animal species sira'lar to those for
humans?
- Is the species response consistent with that in other species?
- Is the species from which the threshold value derived the most sensitive species?
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TABLE 4-4. MINIMUM DATA BASE FOR BOTH HIGH AND LOW
CONFIDENCE IN THE ORAL RfD
Mammalian Data Basea
Confidence
Comments
1. A. Two toxicity studies
in different species
B. One reproductive study
C. Two developmental
toxicity studies in
different species
2. 1A and IB, as above
3. Two of three studies,
as above in 1A and IB;
one or two developmental
toxicity studies
4. Two of three studies,
as above in 1A and IB
5. One of three studies,
as above in 1A and IB;
one or two developmental
toxicity studies
6. One of three studies,
as above in 1A and IB
High
Medium to high
Medium to high
Medium
Medium to low
Low
Minimum data base for
high confidence
Minimum data base for
estimation of an RfD
aComposed of core minimum Office of Pesticide Programs-rated studies, or studies published in refereed
journals. It is understood that adequate toxicity data in humans can form the basis of an RfD and yield high
confidence in the RfD without this data base.
Other factors
The number of biological endpoints evaluated and associated with dose-
response relationships
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Sufficient description of exposure protocol, statistical tests, and results to
make an evaluation
Condition of animals used in the study
The degree of confidence in a particular data base also involves a number of parameters.
These parameters include, but are not limited to, the following.
• Minimum data base for high confidence in an inhalation RfC:
- Respiratory, two well-performed chronic inhalation studies.
- Nonrespiratory, same as oral RfD (Table 4-4) (oral studies may be appropriate for
addressing questions of potential developmental and reproductive toxicity); chronic
inhalation studies may substitute for chronic oral bioassays if they are comprehensive
(i.e., examined all critical endpoints)
• Minimum data base for low confidence in RfC:
One inhalation subchronic bioassay (mat examined respiratory parameters in addition
to others)
A subchronic oral study can be used, if information on inhalation is not available,
with sound professional judgment.
Oral data should not be used in the following instances:
(1) When groups of chemicals that are expected to have different toxicity by the two
routes; for example, metals, irritants and sensitizers;
(2) when a first-pass effect is expected by the liver, or when the pulmonary system was
not adequately studied in the oral studies;
(3) when a pulmonary effect is established but dosimetry comparison cannot be clearly
established between the two routes; and
(4) when short-term inhalation studies or in vitro studies indicate potential portal-of-
entry effects at the lung, but studies themselves are not adequate for an RfC
development.
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- Other considerations are encouraged.
The interested reader is also referred to Pepelko and Withey (1985) and National Research
Council, 1986, 1987).
The level of confidence in a particular threshold value will be higher if it is derived from
human data and supported by animal data. The parameters and factors involved in the
evaluation of human data are described in Section 3.1.1.
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APPENDIX A
NOVEL APPROACHES TO THE ESTIMATION
OF INHALATION REFERENCE CONCENTRATION (RfC)
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I. INTRODUCTION*
Current methods for estimating human health risks from exposure to threshold-acting
toxicants in water or food, such as those established by the U.S. Environmental Protection
Agency (Federal Register, 1980; U.S. Environmental Protection Agency 1987a; Stara et al.,
1981), the Food and Drug Administration (Kokoski, 1976), the National Research Council
(1977, 1980) or the World Health Organization, and the Food and Agricultural Organization
(Bigwood, 1973; Vettorazzi, 1977, 1980; Lu, 1983), consider only chronic or lifetime
exposure to individual chemicals. These methods generally estimate a single, constant daily
intake rate which is low enough to be considered safe or acceptable, referred to as an
acceptable daily intake (ADI).
Two general scientific problems with this approach have been long recognized (Krewski
et al., 1984), in addition to its limited usefulness (i.e., lifetime health risk assessment only).
The first problem is that this method does not readily account for the number of animals used
to determine the appropriate NOEL. For example, if a chemical has a NOEL based on
10 animals and a similar NOEL based on 100 animals, the risk assessor often will choose the
NOEL based on the larger study because it yields greater confidence in the resulting ADI**.
However, if these NOELs were for different chemicals, similar RfCs might be derived even
though one would be associated with much less confidence. It might be useful if the number
of animals used to determine the appropriate NOEL would in some way affect the value of
the resulting RfC, in addition to the level of confidence. The second problem with the
current approach is that the slope of the dose-response curve of the critical toxic effect is
generally ignored in the estimation of the RfC. Many scientists have argued that this slope
should in some way directly affect the resulting RfC, with steep curves presumably yielding
higher values because threshold is more quickly obtained.
Furthermore, the current approach to noncancer risk assessment yields an RfC that is
presented as a single number. As such, it reflects neither the statistical variability in the
*Note: Although material presented in this appendix is based upon oral data, the approaches may be applicable to
the inhalation RfC methodology as well. Applications would necessarily give consideration as well to the
inhalation- specific issues (e.g., dose adjustment) discussed in this document.
**Now referred to by the U.S. EPA as an Oral Reference Dose (RfD) (U.S. Environmental Protection Agency,
1987a) or as an inhalation RfC.
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NOAEL resulting from design factors of critical studies nor the known variability in
uncertainty factors used to account for deficiencies in the data base. The results of this
variability is the unknown range of uncertainty in the estimate. Risk management decisions
for regulation or enforcement need more quantitative information on the inherent and
recognized uncertainties in this assessment.
The purpose of this text is to illustrate several revised approaches to estimate RfCs that
include methods for partial lifetime assessment, methods for RfC estimation with quantal or
continuous toxicity data, and methods for estimating the statistical variability of NOELs and
uncertainty factors. These methods address to a degree the known scientific problems with
the current approach. The development of these methods can be found in Stara and Erdreich
(1984a,b); these methods also are described in Stara et al. (1985) and Stara et al. (1987), and
more fully in Crump (1984), Dourson (1986), and Dourson et al. (1985, 1986, and 1987).
H. AN APPROACH TO USE ALL TOXICITY DATA AND SUPPORT PARTIAL
LIFETIME RISK ASSESSMENTS
a. Proposed Approach. Health risk assessments generally require evaluation of
several types of toxicity data derived from several different species, different doses, different
exposure durations, varied endpoints, and varied quality. This variety often makes the health
risk assessment extremely difficult. Therefore, it is valuable to have all such toxicity data
displayed simultaneously, if possible.
A graphic method is presented for this purpose (see Figure A-l). After thorough
evaluation of the literature, toxicity data on a particular chemical might be summarized by
several variables: (1) exposure concentration (mg/kg/day), (2) exposure duration, and
(3) ranking of effects. The basis of the proposed method is empirical observation. The
toxicity data from all studies (including human) are assigned to categories of severity based on
observed effects in the case of graded data, or on the statistical or biological significance in
the case of quantal or continuous data. Each of the effect severity levels described above is
represented by a unique symbol (Table A-l).
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ADI (KID)
1 '
LOT OJ
' ?
7.H 70
figure A-l. Effect-dose-duration plot of ali relevant human and animal oral toxicity
data for methoxychlor. Effect levels indicated by symbols are defined in Table A-l.
Animal doses have been converted by a body surface area factor to approximate the
equivalent human dose. Dose durations are divided by the appropriate species lifespan
to yield a fraction, which, when multiplied by 70 years (the assumed average human
lifespan), gives the corresponding position on the x-axis. Study usefulness is denoted by
symbol size. Target organs are LV (liver), RF (reproductive organ), GR (growth
reduction), and SP (spleen). The dose axis is divided into areas expected to cause either
(A) gross toxicity and death, (B) adverse effects, (C) nonadverse effects, or (D) no
effects.
Source: Dourson (1986).
After graphic representation of all available toxicity data, a boundary line is estimated
(in Figure A-l the line has been fitted by eye) that represents for any given time the highest
NOAEL for which no lower AEL is observed. Recent work by the U.S. EPA discusses
statistical approaches to this boundary estimation (Hertzberg, 1989). Interpolation along this
NOAEL curve can be performed to estimate the NOAEL for any desired partial-lifetime
A-3
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TABLE A-l. VARIOUS EFFECT LEVELS AND THEIR
DEFINITIONS USED IN FIGURE A-2
Effect
Level8
Symbol
Definitionb
PEL
o
NOAEL
NOEL
Frank-Effect Level. That exposure level which produces
unmistakable adverse effects, such as irreversible
functional impairment or mortality, at a statistically
or biologically significant increase in frequency or
severity between an exposed population and its appropriate
control.
Adverse-Effect Level. That exposure level at which
there are statistically or biologically significant
increases in frequency or severity of adverse effects
between the exposed population and its
appropriate control.
No-Observed-Adverse-Effect Level. That exposure level
at which there are no statistically or biologically
significant increases in frequency or severity of
adverse effects between the exposed population and
its appropriate control. Effects are produced at this
level, but they are not considered to be adverse.
No-Observed-Effect Level. That exposure level at
which there are no statistically or biologically
significant increases in frequency or severity of
effects between exposed population and its
appropriate control.
a listed in order of decreasing severity.
Adverse effects are considered as functional impairment or pathological lesions which may affect the
performance of the whole organism, or which reduce an organism's ability to respond to an additional
challenge (Federal Register, 1980).
exposure. In order to obtain a corresponding acceptable intake, the estimated NOAEL could
be divided by an uncertainty factor. In Figure A-l an uncertainty factor of 100 is used and
accounts for the expected intrahuman and interspecies variability to the toxicity of a chemical
(in lieu of chemical-specific data). Both the choice of the highest NOAEL line (without lower
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AELs) and the suggested uncertainty factor of 100 are consistent with and a logical extension
of previously established scientific principles of the U.S. EPA (Federal Register, 1980;
U.S. Environmental Protection Agency, 1987a), the Food and Drug Administration (KokosM,
1976), and the National Research Council (1977, 1980) in the use of effect levels and
uncertainty factors in order to estimate ADIs or RfDs.
b. Assumptions and Limitations. The primary advantage of the graphic method is
that it provides a mechanism for viewing all of the data simultaneously, resulting in an
integrated profile of a compound's toxicity. In addition, exposure duration-response trends, if
present, are clearly delineated, providing a possible strategy for estimating acceptable intakes
for partial-lifetime exposures.
The graphical method relies on a simple severity ranking system for data presentation
(for example, NOEL, NOAEL, AEL, and PEL). Obviously with such a simple system,
effects within a given category (that is, all AELs) may not be identical, nor is it assumed that
they are. Indeed, the critical effect is often a function of exposure duration. In these cases
the effects within a given category will not be the same across time. However, the change in
critical effect over duration (and, therefore, the change in effects within a category) is perhaps
only of secondary regulatory importance. Since the NOAEL line is based on NOAELs of
critical effects from all durations, the approach is consistent with the regulatory objective of
guarding against any adverse effect. Moreover, while assumptions are needed in the process
of extrapolation of dose and duration from animal studies to their human equivalent
counterparts, this graphical method should enable regulatory scientists, at a glance, to judge
the overall strength of evidence of toxicity and to determine data gaps wherever they appear.
One limitation of this proposed procedure is that the development of the dose rate scale
does not make provisions for incorporating interspecies differences in the metabolic patterns
of dealing with different chemicals; that is, the method does not take into account differences
in activation and detoxification, and such. It also is assumed that the log-log plot does not
overly compress the data. The problems are particularly great for very short durations of
exposure. In general, the dose rate to duration ratio plots that the U.S. EPA has done so far
on other chemicals have been characterized by a paucity of data for short-term exposures.
Another limitation is that the time interval to develop pathologic signs after acute toxic insult
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may be more related to body size and pharmacokinetic parameters than a given measure of
exposure duration such as days. In addition, most chemicals have scant data, and, thus, plots
of these data may not yield useful generalizations.
The experiments used to develop the data base which was used to derive acceptable
limiting concentrations for short durations were rarely, if ever, designed with that purpose in
mind. Short-term experiments have been done in animals of many ages representing most
phases of the total life span. Long-term experiments (of necessity) start with young animals
and follow them through their life span. If there are age-dependent differences in the sensi-
tivity of the experimental species, these would confound the data sets we are using.
c. Status. In summary, this novel method for estimating RfCs utilizes more of the
available toxicity data than the current methodologies, and offers a consistent approach for
possibly estimating health risks for less-than-lifetime toxicant exposure. A computer program
facilitates use of this approach and produces the graphical display (Hertzberg, 1989).
Moreover, statistical methods are being developed in order to estimate boundaries.
m. APPROACH* WITH QUANTAL OR CONTINUOUS TOXICITY DATA
a. Proposed Approach. Traditionally, NOAELs have been defined for quantal
endpoints mat have nonzero background incidences by choosing an experimental dose level
which does not contribute to a statistically significant increase in incidence of adverse effects
when compared to a control group. In parallel, NOAELs have been defined for continuous
data by choosing an experimental dose level which does not constitute a significantly different
mean value for a parameter, indicating an adverse effect when compared to a mean value for
a control group.
As previously discussed in Section n, two limitations are inherent in this approach. The
first problem is related to the insensitivity of the current method to NOELs that use different
numbers of animals, 0/10 vs. 0/1,000. The second limitation is related to the general lack of
use of the slope of the dose-response curve in the current approach.
"This method is described in more detail by Crump (1984).
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The approach suggested here is not as subject to these limitations because it uses more
of the dose-response or dose-effect curve. For example, an RfC might be calculated from a
dose-response curve by defining an adverse effect as a risk level of more than a certain
percentage above background, such as 10%. In this presentation, 10% is chosen because
many of the mathematical models in current use agree well at estimated risks in this range and
because the better studies have sufficient numbers of doses and animals per dose to measure
this level directly. The lower 95% confidence limit (CL) on the dose associated with this risk
then is calculated. In order to obtain an RfC, the dose associated with this lower 95% CL
might be reduced by a chemical-specific, species adjustment factor, a tenfold uncertainty
factor (this reflects the common practice), remove hyphen or as in the case of Figure A-2, the
cube root of the animal body weight to human body weight ratio. Uncertainty factors might
then be used to divide this adjusted value to yield the RfC.
In this presentation, uncertainty factors range between 10 and 100. The first uncertainty
factor of 10 is interpreted as accounting for the expected variability in the general human
population to the toxicity of the chemical. The second uncertainty factor, between 1 and
10, is thought to be necessary because the adjusted 95% CL corresponding to 10% response is
considered to represent a LOAEL rather than a NOAEL. In this example, the choice for the
value of this variable factor depends on both the severity of the adverse effect (i.e., more
severe effects yield a larger factor) and the slope of the dose-response, or dose-effect curve
(i.e., shallower slopes also yield a larger factor). For example, a choice for this variable
uncertainty factor of 1 should be associated with both a minimal adverse effect and a steep
dose-response or dose-effect curve.
An example of this procedure is given in Figure A-2, which is a hypothetical plot of the
percentage of rats responding with a slight body weight decrease of 5% vs. oral dose rate
(mg/kg BW/day) or the percentage of dogs with liver necrosis vs. oral dose rate.
Hypothetical responses are indicated by solid lines; lower 95% CLs on the dose rate are
shown as dashed lines. The lower 95% CLs of the oral dose rates at a 10% response are
adjusted by division by the cube root of the ratio of body weight between humans and rats or
dogs. For rats of 400 g weight, this value is 5.6; for dogs of 10 kg weight, it is 1.9; both
calculations assume a 70-kg body weight. In order to estimate RfC from the rat data (shown
in Figure A-2 as ADIR) the adjusted lower 95% CL is divided by a tenfold uncertainty factor
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Ul
V)
70
60
50
ra 40
oc
Ul
a
O
cc
IU
O.
20
• SILCHT BODY WEIGHT DECREASE
A LIVER NECROSIS
TO-
ADIr
DOSE ADJUSTMENT FACTOH-5,6 /
f
ADIr
10 kg DOGS
I !
DOSE ADJUSTMENT FACTOR-1.9
"0.1 1.0 10 100 1000
DOSE RATE, mg/kg bw/day
Figure A-2. Hypothetical dose-response data for slight body weight decrease (o) or liver
necrosis (A) in rats and dogs, respectively. Solid lines indicate hypothetical data; dashed
lines represent lower 95% confidence limits (CLs). See text for additional explanation.
Source: Doureon (1986).
to account for the expected variability in the general human population to the toxicity of a
chemical in lieu of specific data, and an additional 1.0-fold factor because the effect is both
minimally severe and has a steep dose-response slope. Thus, the total uncertainty factor is
10. In order to estimate an RfC from the dog data (shown in Figure A-2 as ADID) the
adjusted lower 95% CL is divided by a 10-fold uncertainty factor to account for the expected
human variability, as before, and an additional 10-fold uncertainty factor because the effect is
more severe than a slight body weight decrease and the slope of the dose-response is
shallower. Thus, the total uncertainty factor is 100.
b. Assumptions and Limitations. The proposed methods for estimating the
10% dose-effect or dose-response levels for continuous and quanta! data, respectively, offer
several advantages when compared with traditional methodologies (Crump, 1984). These
advantages, as well as difficulties with this approach, have been discussed (Dourson et al.,
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1985; Crump, 1986). For example, with this new approach, both the slope of the dose-
response curve and the number of animals used in an experiment can affect to some degree
the estimation of the RfD when quanta! or continuous toxicity data are available. Difficulties
include finding appropriate data sets to model, choosing among equally good data sets that
may yield different RfDs, and, for cost-benefit analysis, assuming that a certain percentage
response in an animal study is equivalent to a similar percentage response in humans.
c. Status. This novel method utilizes more of the available toxicity data than the
current methodology, and perhaps offers a consistent approach for possibly estimating health
risks above the RfCs. It also addresses to some degree several of the criticisms of the current
approach, such as use of dose-response slopes and the number of animals tested in defining
NOELs. This method will be tested on a large set of toxicity data.
IV, RESEARCH ON REFINEMENTS TO THE INHALATION RfC APPROACH
1. Improved Estimates of Uncertainty Factors
a. Proposed Approach. The objective of this research is to improve quantitative
estimates of uncertainty factors and modifying factors used in the U.S. EPA's current
approach. By evaluating the effect of deviations from the ideal desirable data base,
uncertainty factors can be expressed as a range rather than as a single number. Models are
being developed of the likely distribution of probability in the standard uncertainty factors.
The first step in this approach is to assemble an appropriate data base for the issue in
question (i.e., which uncertainty factor is being addressed, such as the use of 10 to
extrapolate subchronic to chronic data). To evaluate the standard uncertainty factors (UFs)
for the RfC and to develop better estimates, it is necessary to have a relatively complete data
base for a group of chemicals; for example, one that contains subchronic and chronic data and
NOELs and LOAELs. Since UFs have been designed to reduce, for example, the LOAEL to
a NOAEL or to reduce a subchronic NOAEL to a chronic NOAEL, the variable of interest is
a ratio. This approach is to plot a frequency histogram of the ratio of the surrogate NOAEL,
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the LOAEL, or the subchronic NOAEL, to the best data point and fit a probability
distribution to the data.
Sufficient toxicity data on sensitive populations are generally not available to test the UF
for interindividual variability. However, the U.S. EPA has identified components of
variability that contribute to sensitivity, and has evaluated the distribution of these
pharmacokinetic parameters which determine variation in delivered dose, such as areas under
the curve of blood concentration over time. Pharmacokinetic variables that affect target organ
dose fit a log normal distribution. The analysis shows that values vary as much as 10-fold
among normal healthy individuals (U.S. Environmental Protection Agency, 1986a); (Hattis
etal., 1987).
The next step would be to model the likely overall variability in a risk estimated by
means of a Monte Carlo simulation using these distributions for input. Currently, not all
results fit a known probability distribution.
b. Assumptions and Limitations. This approach is designed to obtain better
quantitative estimates for some assumptions currently used, such as the 10-fold UF for
adjusting subchronic data to chronic. It assumes data similar to that currently used to derive
RfCs.
c. Status. More data are needed to model these UFs. The data base for
interindividual variability could be expanded from a pilot study. When the probability
distributions for each component of uncertainty in an RfC can be approximated, it will be
possible to perform a Monte Carlo simulation to indicate the overall variability in the data and
to estimate the probability for the RfC given the standard UFs. Further analyses of data on
the sources of variability are needed before distribution assumptions can be made.
The estimate of the range of uncertainty for the UFs is not chemical specific. This
approach will convey the scientific uncertainty to risk managers more completely than does
the current approach. Uncertainty/sensitivity analysis presents data in a different form from
that which risk managers are accustomed to and, therefore, will require explanation of these
modifications.
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2. A Statistical Procedure for Improved Estimates of the NOAEL
a. Proposed Approach. A statistical procedure has been developed that is applicable
to dichotomous data (i.e., presence/absence of a response of interest), for which comparison
of unadjusted response rates is valid. (Unadjusted for differences in intercurrent mortality, or
other factors that could be confounded with a treatment effect.) In samples at a control, low,
and high dose, the responses are assumed to be independently distributed from binomial
distributions with parameters Po, Plt P2, respectively. It is further assumed that
PO < PI 51*2> an<^ ti1^ a treatment effect, if present, increases the response rate. An
important aspect of the statistical method employed here is that observed response rates are
replaced by the maximum likelihood estimates of Po, Pls and P2.
The procedure estimates the maximum likelihood for all doses and estimates the standard
deviation of the NOAEL estimate. It also estimates, for each experimental dose, the
probability of getting the observed result under the hypothesis of "no treatment effect." Thus,
the NOAEL can be expressed as a range. The power of the test is a function of background
rate, with lower backgrounds yielding higher power. The test characteristics are discussed in
U.S. Environmental Protection Agency (1988a).
The following example demonstrates the type of results obtained from this procedure.
In a study using a control and doses of 30 and 100 mg/m3, the procedure rejects the
hypothesis of no treatment effect at the high dose (p <0.04). The expected value of the
NOAEL is 47 mg/m3, and the bounds at one standard deviation are 17 and 77 mg/m3. The
probability of obtaining the observed response under the null hypothesis is 76% at 30 mg/m3
and 24% at 100 ppm. In comparison, under the existing risk assessment procedure, the study
would provide only a NOAEL of 30 mg/m3.
The response probabilities express the level of certainty of confidence in the data. The
range of one standard deviation could easily be expressed in the RfC simply by applying UFs
to upper and lower limits of the estimate.
b. Assumptions and Limitations. This procedure is designed for dichotomous
(incidence) data and is a sequential test appropriate for three dose groups. While initially
designed for three doses and sample sizes up to 20, it has the capacity to be extended for
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more dose groups and larger sample sizes. It assumed mat a treatment effect, if present,
increases the response rate, and that responses are to be independently distributed from
binomial distributions.
c. Status. The document describing the method developed (U.S. Environmental
Protection Agency 1988a) has been reviewed by U.S. EPA statisticians and revised according
to these comments. The procedure has been presented at two scientific meetings. A
computer program is available for easy implementation of the procedure on PCs.
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APPENDIX B
USE OF PHARMACOKINETIC DATA IN
RISK ASSESSMENT, SELECTED EXAMPLES
-------
While the U.S. Environmental Protection Agency has had little experience in the
development of inhalation reference concentrations (RfCs), potency estimates for inhalation
exposure to carcinogens have been developed for quite some time. Examples of the way that
the Agency has utilized pharmacokinetic data to adjust dose estimates for carcinogens
illustrate both the necessity for utilizing aU available pharmacokinetic data, as well as the kind
of empirical adjustments which can be made to dose estimates, even in situations where
complex physiologically based pharmacokinetic modeling is not feasible.
Example 1: Nonlinear absorption with increasing air concentration.
This example is taken from a U.S. Environmental Protection Agency publication (1985)
which discusses the carcinogenicity of butadiene. The retained dose vs. exposure
concentration data that were developed separately from the carcinogenicity evaluation are
shown in Table B-l.
TABLE B-l. ABSORPTION OF 1,3-BUTADIENE BY INHALATION FOLLOWING
A 6-HOUR EXPOSURE PERIOD
Species
Rats
Mice
Exposure
(ppm)
70
930
7,100
7
80
1,040
(MB/*)
125
1,700
12,800
13
145
1,900
1,3-
Butadiene
inhaled
(jjimoi)
235
3,100
17,000
1.7
34.7
435.0
1,3-
Butadiene
retained
(prnol)
16.3
64.7
243.0
0.9
3.2
19.1
(^mol/kg)
40
160
660
33
120
660
Percent
Retained
7.1
3.1
1.5
54.0
9.6
4.7
The actual exposure concentrations in the cancer bioassays were 625 ppm and
1,250 ppm for mice, and 1,000 ppm and 8,000 ppm for rats. By graphing log ppm exposure
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vs. log-retained dose from the pharmacokinetic study, the U.S. Environmental Protection
Agency (1985) estimated the retained doses for each of the experimental exposure
concentrations used in the cancer bioassay; that is 25.7 and 38.9 mg/kg retained dose for
mice, and 10.5 and 37.1 mg/kg for rats. After developing a unit risk estimate based on the
relationship between retained dose and tumor incidence, the unit risk was converted back into
units of air concentration by making an assumption concerning percent retention by humans at
low exposure concentrations. If a model that assumed that retained dose was proportional to
exposure concentration were assumed, the data would have suggested a greater than 100-fold
difference in retained dose from low dose to high dose in the rat study, when in fact only a
5-fold difference was apparent, based upon retained dose estimates. Similarly, a dose
proportional to concentration assumption for mice would have suggested a 150-fold difference
between low and high dose while the retained dose fraction suggests only an 11-fold
difference.
The significance of this for inhalation RfC estimation is considerable, especially in
situations where an RfC might be derived based upon a LOAEL. For example, if we
theoretically had a single exposure concentration inhalation study of butadiene which provided
data indicating that 1,040 ppm was a LOAEL, the following situation could be envisioned. If
a dose proportional to concentration model is assumed, either based upon computing dose
utilizing ventilatory volume or using a metabolic rate estimate, the following scenario could
be envisioned:
1,040 ppm = 1,900 mg/m3
1,900 mg/m3 x 0.01 m3/day (mouse ventilatory volume for 6 hours)
•s- 0.03 kg (mouse body weight) -s- UF of 1,000 (10 LOAEL to
NOAEL, 10 for inlerspecies, 10 for sensitive subgroups) =
6,3 mg/kg/day x 70 kg •*• 20 m3 = 2.22 mg/m3 as the reference air
concentration for 24-hour human exposure.
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In contrast, using the retention data, the mouse exposure concentration corresponding to
o
a 10-fold lower retained dose (estimated from data in Table B-l) is 45.9 mg/nr. This would
be equivalent to estimating a NOAEL exposure level for the mouse based upon retained dose:
45.9 mg/m3 x 0.01 m3/6 hours -s- 0.03 kg -=- 100 UF (10 for
interspecies, 10 for intraspecies) = .11 mg/kg/day x 70 kg -r 0.5
(estimate of human retained dose at low concentrations) -7- 20 m3 =
»j
1.07 mg/nr as the reference air concentration for 24-hour human
exposure.
This represents a twofold difference which would be essentially equivalent to reducing
the UF for extrapolating from a LOAEL from 10 to 5. This example assumes that a steady
state is reached within the 6-hour exposure period. If this is not the case, linear extrapolation
to a 24-hour exposure period would be inappropriate.
Example 2: Metabolic Saturation at High Exposure Concentrations
Since animal bioassays are traditionally conducted at high exposure concentrations and
the results extrapolated to lower exposure concentrations, the issue of saturable metabolic
capacity is relevant. This consideration is equally appropriate to both the oral and inhalation
exposure routes. While the impact of capacity-limited metabolism may be of greater concern
for carcinogen exposures where a linear, nonthreshold dose-response curve is assumed and
risks resulting from human exposures to very small quantities of the chemical of concern are
extrapolated from high dose or concentration animal exposures, a potential for impact in the
assessment of noncancer endpoints still exists. Typically, an RfC is developed by applying a
composite uncertainty factor of from 100 to up to 10,000, to an exposure level or dose which
has been experimentally evaluated in an animal test system. If the critical effect is the result
of the interaction of a metabolite with the target tissue, and if nonlinearity in the metabolized
fraction of the dose exists within the range of doses or exposure concentrations encompassed
by the difference between the experimentally evaluated dose and the projected RfC exposure
level, the actual difference between the experimental and extrapolated dose will be less than
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that predicted, based upon a linear relationship between exposure and effective dose to the
target tissue. The result of this could be interpreted as an effective erosion of the magnitude
of the composite uncertainty factor. On the other hand, if good pharmacokinetic data are
available for both the experimental animal system and the human, it may be feasible to reduce
the magnitude of the uncertainty factor.
An impediment to the use of pharmacokinetic data for the adjustment of animal dose
response data in evaluations of noncancer endpoints is that the chemical species resulting in
the critical effect is less frequently identified than for carcinogenic responses. However, it is
still appropriate to evaluate all of the available pharmacokinetic data for potential relevance to
the RfC derivation exercise. This will become increasingly important as the Agency moves
from single medium, single route assessments towards methods for effectively partitioning
RfCs across media/routes.
The following example is taken from U.S. EPA (1986e). In this assessment, unit risk
estimates were developed for human exposure to low levels of tetrachloroethylene by first
developing animal dose-response relationships based upon the extrapolated animal metabolized
dose at each inhalation exposure concentration.
Table B-2 illustrates that while the total radioactivity recovered in the 72 hours
following exposure of rats for a 6-hour time interval to two concentrations of
14C-tetrachloroethylene showed linearity between total recovered radioactivity and exposure
concentration, there was nonlinearity in the fraction of the radioactivity attributed to
metabolism.
TABLE B-2. RECOVERY OF 14C-TETRACHLOROETHYLENE RADIOACTIVITY
AFTER INHALATION EXPOSURE FOR 6 HOURS TO SPRAGUE-DAWLEY RATS
IQppm 600 ppm
mg-equivalent tetrachloroethylene
Expired Unchanged 1.008(68%) 68.39(88%)
Metabolized 0.467(32%) 9.11(12%)
Total 1.475 77.5
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APPENDIX C
ADVERSE HUMAN RESPIRATORY HEALTH EFFECTS
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ADVERSE HUMAN RESPIRATORY HEALTH EFFECTS*
These criteria were developed to assist in the interpretations of the epidemiologic
literature on what constitutes an adverse respiratory health effect of air pollution. Adverse
human health effects caused by air pollution are listed in hierarchical order, with the most
severe at the top and the least severe at the bottom. The reader is referred to the American
Thoracic Society (1985) guidelines for more detailed discussion.
1. Increased mortality. (Increased as used here and subsequently means significantly
(p <0.05) increased above that recorded in some standard, comparable population. In
selected situations, p < 0.1 may be appropriate.)
2. Increased incidence of cancer.
3. Increased frequency of symptomatic asthmatic attacks.
4. Increased incidence of lower respiratory tract infections.
5. Increased exacerbations of disease in humans with chronic cardiopulmonary or other
disease which could be reflected in a variety of ways, including the following:
• Less able to cope with daily activities (i.e., shortness of breath or increased
anginal episodes).
• Increased hospitalizations, both frequency and duration.
• Increased emergency ward or physician visits.
• Increased pulmonary medication.
• Decreased pulmonary function.
6. Reduction in forced expiratory volume at one second (FEVj) or forced vital capacity
(FVC) or other tests of pulmonary function such as the following:
• Chronic reduction in FEVj or FVC associated with clinical symptoms.
*Source: American Thoracic Society, 1985.
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• A significant increase in number of persons with FEVj below normal limits;
chronically reduced FEVj is a predictor of increased risk of mortality.
Transient or reversible reductions that are not associated with an asthmatic
attack appear to be less important. It should be emphasized that a small but
statistically significant reduction in a population mean FEVj or FEVg 75 is
probably medically significant to them, but when diluted with the rest of the
population, the change appears to be small.
• An increased rate of decline in pulmonary function (FEV^, relative to
predicted value in adults with increasing age or failure of children to
maintain their predicted FEVj growth-curve. Such data must be
standardized for sex, race, height, and other demographic and
anthropometric factors.
7. Increased prevalence of wheezing in the chest, apart from colds, or of wheezing most
days or nights. (The significance of wheezing with colds needs more study and
evaluation.)
8. Increased prevalence or incidence of chest tightness.
9. Increased prevalence or incidence of cough/phlegm production requiring medical
attention.
10. Increased incidence of acute upper respiratory tract infections that interfere with normal
activity.
11. Acute upper respiratory tract infections that do not interfere with normal activity.
12. Eye, nose, and throat irritation that may interfere with normal activity (i.e., driving a
car) if severe.
13. Detection of odors.
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APPENDIX D
CRITERIA FOR ASSESSING THE QUALITY
OF INDIVIDUAL EPIDEMIOLOGICAL STUDIES
-------
CRITERIA FOR ASSESSING THE QUALITY OF
INDIVIDUAL EPIDEMIOLOGICAL STUDIES*
A minimally acceptable study should meet the following criteria, which fundamentally
represent good scientific practice. The study should have been reported or should be in press
in the peer-reviewed literature.
1. The pertinent scientific background, such as reviews and supporting rationale upon
which the study was based, should be given. Sponsorship and funding sources should
be acknowledged.
2. The objectives of the study should be clearly stated and the study design described in
relation to them. Underlying assumptions and limitations of the design also should be
given.
3. The study population and comparison group description should include the specific
population from which they were drawn and the method of selection. The rationale and
criteria for inclusion/exclusion in the study should be given, particularly for exposure
classifications. The appropriateness and limitations of the comparison group should be
discussed. The extent to which the choice of subjects depended on existing or specially
developed record systems, and implications of this upon the analysis, should be
considered. The steps taken to ensure confidentiality of the subjects should be
accounted for.
4. Methods of data collection should be described in detail, since these procedures will
influence the derived interpretation and inferences. The validity (accuracy) and
reliability (reproducibility) of the methods used to determine exposure should be stated.
Response rates, including reasons for implications of differing rates, should be given.
The direction and possible magnitude of any bias introduced into the study as a result of
these rates should be described. The procedures used for following the study, methods
to ensure completeness, and length of follow-up for each group or subgroup must be
included. Other validity checks (e.g., avoiding bias by the independent ascertainment
* Adapted from: Interagency Regulatory Liason Group, 1981; Lebowitz, 1983; American Thoracic Society, 1985,
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and classification of study variables, such as blind reading of histologic slides or clerical
processing of data) also should be included.
5. Major demographic and anthropometric confounding factors should have been accounted
for, such as age, sex, ethnic group, socioeconomic status, smoking status, and
occupational exposure. Temperature, season, and day of the week are particularly
important for acute studies of respiratory effects and also should be accounted for.
6. The procedures and statistical methods used to describe the data, estimate parameters, or
test specific hypotheses should be presented. References and/or specific formulae also
should be given for the statistical tests and for any programming procedures or packages
that were applied.
The underlying assumptions and potential bias of the statistical methods should be
stated. Explicit description of any method used to account for confounding factors (e.g.,
adjustment or matching) should be described explicitly. This includes methods to account for
missing data, such as from nonresponse, attrition, or loss-to-follow-up. When reporting
hypothesis tests, the measure of effect, statistical significance, power, and other criteria (e.g.,
one- vs. two-tailed test rationale) should be given. The point estimates and their standard
errors and/or confidence intervals should be given when using estimation.
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APPENDIX E
CRITERIA FOR ASSESSING THE QUALITY
OF INDIVIDUAL ANIMAL TOXICITY STUDIES
-------
CRITERIA FOR ASSESSING THE QUALITY OF INDIVIDUAL
ANIMAL TOXICITY STUDIES*
A minimally acceptable study should meet the following criteria, which fundamentally
represent good scientific practice.
1. All elements of exposure should be clearly defined.
• The exposure amount, administration route, exposure schedule, and
exposure duration must be described. Consideration should also be given to
the concentration and time of exposure used vs. the expected level of human
exposure.
• If animal body weights, ages, or gender are not provided, consideration
should be given to the uncertainty in appropriate default values.
• Exposure information should include physicochemical characteristics of the
substance used, such as purity, stability, pH, partition coefficient, particle
size distribution, and vehicle. These properties can influence the local
effects and the rate and extent of absorption, which can subsequently modify
the toxic manifestations.
• Exposure information should include description of generation and
characterization technology used. The number of air changes, temperature,
and relative humidity are exposure chamber characteristics which should be
monitored. Cage (or other animal holder) rotation schedule should be
described.
• Animal care and holding procedures should be described.
2. Controls should be comparable with test animals in all respects except the treatment
variable ("negative").
• Concurrent controls must minimally include an "air-only" exposure group;
if a vehicle is used, it is desirable that there be a "vehicle-only" group.
• Historical control data can be useful in the evaluation of results, particularly
where the differences between control and treated animals are small and are
within anticipated incidences based on examination of historical control
data.
* Adapted from: Society of Toxicology, 1982; MuUer et al., 1984; National Research Council, 1984; James,
1985:and Lu, 1985a.
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3. Endpoints should answer the specific hypothesis addressed in the study, and the
observed effects should be sufficient in number or degree (severity) to establish a dose-
response relationship that can be used in estimating the hazard to the target species.
• The outcome of the reported experiment should be dependent on the test
conditions and not influenced by competing toxicities.
4. The test performed must be valid and relevant to human extrapolation. The validity of
using the test to mimic human responses must clv/ays be assessed. Issues to consider
include the following:
• Does the test measure a toxicity directly or does it measure a response
purported to indicate an eventual change (i.e., severity of the lesion)?
• Does the test indicate causality or merely suggest a chance correlation?
• Was an unproven or unvalidated procedure used?
• Is the test considered more or less reliable than other tests for that endpoint?
• Is the species a relevant or reliable human surrogate? If this test conflicts
with data in other species, can a reason for the discrepancy be discerned?
• How reliable is high exposure (animal) data for extrapolation to low
exposure (human scenario)?
5. Conclusions from the experiment should be justified by the data included in the report
and consistent with the current scientific understanding of the test, the area of toxicology
being tested, and the suspected mechanism of toxic action.
6. Due consideration hi both the design and the interpretation of studies must be given for
appropriate statistical analysis of the data.
Statistical tests for significance can be performed only on those experimental
units that have been randomized (some exceptions include weight-matching)
among the dosed and concurrent control groups.
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• Some frequent violations of statistical assumptions in toxicity testing
include:
Lack of independence of observations.
Assuming a higher level of measurement than available (e.g., interval
rattier than ordinal).
Inappropriate type of distribution assumed.
Faulty specification of model (i.e. linear rather than nonlinear).
Heterogeneity of variance or covariance.
Large Type n error.
7. Subjective elements in scoring should be minimized. Quantitative grading of an effect
should be used whenever possible.
8. Evidence of adherence to good laboratory practices is required unless exceptions have
been negotiated (current testing) or considered (data obtained from studies carried out
many years ago). See also Section 3.1.2.3,
9. Peer review of scientific papers and of reports is extremely desirable and increases
confidence in the adequacy of the work.
10. Reported results have increased credibility if they are reproduced by other researchers
and supported by findings in other investigations.
11. Similarity of results to those of tests conducted on structurally related compounds should
be considered.
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APPENDIX F
CRITERIA FOR CAUSAL SIGNIFICANCE
-------
CRITERIA FOR CAUSAL SIGNIFICANCE
Statistical methods cannot establish proof of a causal relationship but can define an
association with a certain probability. The causal significance of an association is a matter of
judgment that goes beyond any statement of statistical probability. To assess the causal
significance of an air toxicant and a health effect, a number of criteria must be used, no one
of which is pathognomonic by itself. These criteria include the following:
Consistency (reproducibility) of the association. Causal inferences are
strengthened when a variety of investigators have reproduced the findings
under a variety of circumstances.
Strength of the association. The larger the calculated relative risk, the
greater the likelihood that the observed association is causal.
Specificity of the association. Causality is more likely if a particular
exposure is associated with only one illness and vice versa. This guideline
rarely applies to air pollution research, in which all the diseases of major
concern are multifactorial.
Temporal relationship of the association.
Coherence of the association. An epidemiologic inference of causality is
greatly strengthed when it conforms to knowledge concerning the biologic
behavior of a toxin and its mechanism of action. This evidence may be
obtained from clinical research or toxicologic studies.
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APPENDIX G
CHOICE OF TOXICITY DATA
-------
CHOICE OF TOXICITY DATA*
Empirical observation generally reveals that as the dosage of a toxicant is increased, the
toxic response (in terms of severity and/or incidence of effect) also increases. This dose-
response relationship is well-founded in the theory and practice of toxicology and
pharmacology. Such behavior is observed hi: (1) quanta! responses, in which the proportion
of responding individuals in a population increases with dose; (2) graded responses, in which
the severity of the toxic response within an individual increases with dose; and (3) continuous
responses, in which changes in a biological parameter (e.g., body or organ weight) vary with
dose.
However, in evaluating a dose-response relationship, certain difficulties arise. For
example, one must decide on the critical endpoint to measure as the response. One also must
decide on the correct measure of dose. In addition to the interspecies extrapolation aspects
of the question of the appropriate units for dose, the more fundamental question of
administered dose vs. absorbed dose vs. target organ dose should be considered. These
questions are the subject of much current research.
1. Critical Study and Species. Often animal data are selected as the governing information
for quantitative risk assessments, since human data are generally either unavailable or
insufficient for this purpose. These animal studies typically reflect situations in which
exposure to the toxicant has been carefully controlled, and the problems of heterogeneity
of the exposed population and concurrent exposures to other toxicants have been
minimized. In evaluating animal data, a series of professional judgments are made that
involve, among others, consideration of the scientific quality of the studies. Presented
with data from several animal studies, the risk assessor first seeks to identify the animal
model that is most relevant to humans, based on the most defensible biological rationale;
for instance, using comparative pharmacokinetic data. In the absence of a clearly most
relevant species, however, the most sensitive species (i.e, the species showing a toxic
effect at the lowest administered dose) is adopted as a matter of science policy at EPA,
*Adapted from U.S. Environmental Protection Agency, 1987a.
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since no assurance exists that humans are not innately more sensitive than any species
tested. This selection process is made more difficult if animal tests have been
conducted using different routes of exposure, particularly if the routes are different from
those involved in the human situation under investigation.
In any event, the use of data from carefully controlled studies of genetically
homogeneous animals inescapably confronts the risk assessor with the problems of
extrapolating between species, and the need to account for human heterogeneity and
concurrent human exposures to other chemicals, which may modify the human risk.
While there has generally been a lack of well-controlled cohort studies that investigate
noncancer endpoints and human exposure to chemicals of interest by the oral exposure route
(a useful exception being the cases of cholinesterase inhibition), it is anticipated that there
will be considerably more human data which may be selected as the critical data for
inhalation exposure assessments. Risk assessments based on human data have the advantage
of avoiding the problems inherent in interspecies extrapolation. In many instances, as is the
case with the animal investigations, use of such studies involves extrapolation from relatively
high doses and relatively healthy populations (such as those found in occupational settings) to
the low doses found in the environmental situations to which the general population is more
likely to be exposed. In some cases, a well-designed and well-conducted epidemiologic
study that shows no association between known exposures and toxicity can be used to directly
project an RfD, as has been done in the case of oral exposure to fluoride (U.S. Environmental
Protection Agency, 1986d).
2. Critical Data. In the simplest terms, an experimental exposure level is selected from the
critical study that represents the highest level tested in which the critical toxic effect was
not demonstrated. Where appropriate, adjustments in doses based upon known
interspecies differences in respiratory tract deposition must be applied before arraying
the dose-effect data to compare species sensitivity. This NOAEL is the key datum
gleaned from the study of the dose-response relationship and, traditionally, is the
primary basis for the scientific evaluation of the risk posed to humans by systemic
G-2 DRAFT - DO NOT QUOTE OR CITE
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toxicants. This approach is based on the assumption that if the critical toxic effect is
prevented, then all toxic effects are prevented.
3. Dosimetric Adjustments. Exposure effect levels observed in animal studies of any given
data array on a chemical must be converted to human equivalent concentrations before
comparisons of species sensitivity and the choice of the appropriate animal effect and
critical study can be made. Conversions to human equivalent concentrations are made
by applying adjustment factors to account for dosimetric differences of agents (particles
or gases) between individual animal species and humans, as discussed in Chapter 4 and
Appendices H and I.
4. Examples of "Appropriate" Choice. In the course of many risk assessment discussions
during the last several years, the Agency has decided on the following conditions in
choosing the appropriate animal effect or no-effect level as a basis of an RfC. If an
appropriate human study with a well-defined NOAEL is available as to a chemical's
critical effect, it is used in preference to animal toxicity data in estimating RfCs. When
such human data are not available, the following sequence is used to choose the
appropriate study, species and NOAEL as a basis of RfC estimation.
It should be noted that this choice should be based on human equivalent
concentrations, that is, concentrations adjusted for dosimetric differences between
animals and humans as described in Chapter 4.
The Agency chooses the most appropriate NOAEL of the critical effect from
a well-conducted study on a species that is known to resemble the human in
response to this particular chemical (e.g., by comparative
pharmacokinetics).
When the above condition is not met, the Agency generally chooses the
most sensitive study, species, and NOAEL, as judged by an interspecies
comparison of the NOAEL and LOAEL. Table G-l outlines examples of
this condition.
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TABLE G-l. COMPARISON OF THE HIGHEST INDIVIDUAL SPECIES HUMAN
EQUIVALENT* NOAEL AND ITS LOAEL (OR LEL)
Effect Level
(mg/m3)
Dog
Species
Rat
Mouse
Comments
(Given The Same Critical Effect)
Example 1:
LOAEL (LEL)
NOAEL
Example 2:
LOAEL (LEL)
NOAEL
Example 3:
LOAEL (LEL)
NOAEL
100 120 - The proper choice is generally the
highest dog NOAEL of 50 mg/m3,
50 60 80 since the potential experimental
threshold in dogs (i.e., the
potential LOAEL) may be below the
highest NOAELs in both rats and
mice.
120 100 90 The proper choice is generally the
mouse LOAEL (or LEL) of 90 mg/m3,
since the potential experimental
90 75 - threshold in mice may be less than
the highest NOAELs for both dogs
and rats. Judgment is needed in
this example to ensure that the
adverse effects seen in all three
species are truly minimal. For
example, if any of the LOAELs
(or LELs) in the species represented
an increase in mortality, no firm
basis for the development of an
RfC exists. This is based on the
general observation that mortality
data are far removed quantitatively
from chronic LOAELs and NOAELs,
and thus, the data base has failed
to establish the likely experimental
threshold for the most sensitive
endpoiirt.
75 80 90 The proper choice is generally the
dog LOAEL of 75 mg/m3, since by
definition this represents the
most sensitive species (see,
however, the caution in Example 2).
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TABLE G-l. (cont'd) COMPARISON OF THE HIGHEST INDIVIDUAL SPECIES
HUMAN EQUIVALENT* NOAEL AND ITS LOAEL (OR LEL)
Effect Level
(mg/m3)
Dog
Species
Rat
Comments
Mouse (Given The Same Critical Effect)
Example 4:
LOAEL (LEL)
NOAEL
The proper choice is generally the
highest rat NOAEL of 90 mg/m3,
100 90 120 since no assurance exists that
the experimental threshold in rats
is not below the highest NOAELs of
both dogs and mice. This situation is
unusual and should be judged carefully;
since a LOAEL (or LEL) has not been
determined, the RfC may be unduly
conservative. Strict interpretation of
this example might lead to strikingly
lower RfCs if other species are
tested at much lower doses. Such
RfCs may not be appropriate.
* Human equivalent NOAEL or LOAEL refers to concentrations adjusted for dosimetric differences between
animals and humans.
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APPENDIX H
CALCULATION OF RDDR AND AN EXAMPLE
APPLICATION OF DOSIMETRIC
ADJUSTMENT FOR PARTICLE EXPOSURES
-------
INTRODUCTION
The purpose of this appendix is to illustrate how the Regional Deposited Dose Ratio
(RDDR) is calculated for use in the adjustment of exposure effect levels for dosimetric
differences between species as in Section 4.1.1.2. Further refinement of this adjustment, as
recommended by the external workshop review committee, is described in the research and
development section at the end of this Appendix. The adjustment of exposure effect levels in
rats for the theoretical compound ep(a)oxide will be used to illustrate this application. The
health effects data shown for the compound ep(a)oxide are motivated by actual data on the
lexicological effects of various aerosols.
METHODS
The initial regional respiratory tract deposition of a given aerosol exposure to an
experimental species can be calculated using typical aerosol distribution data (i.e., an aerosol
characterized by a given mass median aerodynamic diameter (MMAD) and a geometric
standard deviation [a ]). The fractional deposition of an aerosol initially deposited for a
given species is generated by integrating the product of an aerosol distribution and the
deposition efficiency curves in regions (extrathoracic, tracheobronchial, and pulmonary) of
the lung. A schematic of this integration is shown in Figure H-l for the rat. The area under
the particle distribution curve of each particle size diameter interval, for example, the interval
of 2-3 jum (grey shading), is integrated with the deposition efficiency curve of a particular
lung region for that same interval. Summation of these products across all the particle size
ranges yields the total fractional deposition to that region. The fractional deposition estimate
for a specific region is then applied to the exposure level (expressed as mg/m3) and adjusted
for ventilatory and respiratory surface areas to calculate the Regional Deposited Dose (RDD)
(Jarabek et al., 1989a). This calculation is shown notationally in Equation 4-4. The RDD is
calculated for each region of the lung; mat is the extrathoracic (ET), region the tracheo-
bronchial (TB) region, the pulmonary (PU), region the thoracic (TH), region and the total
respiratory (TOT) system. These estimates are then adjusted for ventilatory parameters and
lung surface areas.
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A. Aerosol Distribution
O
c
0)
c
0)
0.0 1.0 2.0 3.0 4.0
Particle Diameter (Da. jam)
c
o
*5
m
it
c
3,
"m
o
0)
1 Q
1 • w
0.9
0.8
0.7
0.6
0.5
04
0.3
0.2
0.1
0.0
1 ' ' '- 'o ' 4- ' ' ' '
/ **
i i "•••*•
/ /.,
j !$f O-o Pulmonary
/ A'** D-D Tracheobronchiai
£r-£ Extrathoracic
''; ;^ O-O Total
" ^^ mm
- ^^=^3L^ ''••••
*^ -I lie''"" '"w ' .-,««- ^
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11 ^Pf lit!!!?
'
-
-
-
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_
-
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_
-
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0123456789 10 11
Particle Diameter (MMAD, urn)
Figure H-l. Schematic of the integration of aerosol distribution (A) and deposition
efficiency (B) ciures for calculation of (ROD).
Source: Jarabek et al. (1989s)
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The rat data used in this presentation for RDD and RDDR calculations (Jarabek et al.,
1989a) are those of Raabe et al. (1988). The ET deposition deposition was calculated as the
sum of the laryngeal, nasopharyngeal, and gastrointestinal fractions reported. These data
were reported as means so that it was not possible to fit nonlinear regression models as was
done for the humans. RDDs were estimated by linear interpolation instead.
The human fractional deposition estimates were calculated similarly to calculations for
n
the rat. Extrathoracic deposition was estimated as a function of (pd Q) where p is particle
mass density (g/m3), d is the geometric particle diameter (/*m), and Q is the airflow rate
(cm3/sec). Equations were estimated separately for experiments in which nasal breathing or
oral breathing was used (Miller et al., 1988). Extrathoracic deposition then was calculated
for normal augmenters (people who habitually breathe through the nose except in exercise
conditions) and for mouth breathers using a proportionality factor for the split in airflow
between nose and mouth as given in Niinimaa et al. (1981). Logistic regression models were
used to estimate the human TB region deposition as a function of aerodynamic diameter. The
models used were those developed by Miller et al. (1988), based on percentage of particles
entering the trachea and were fit to TB deposition from several laboratories. The PU region
deposition estimates for humans were calculated based on a theoretical model presented in
Martonen and Miller (1986). These estimates can then be applied to the same exposure
(MMAD, 0g) and concentration (mg/m3) as that to which the experimental animals were
exposed and adjusted for ventilatory parameters and respiratory tract surface areas to calculate
the human RDD.
The surface area value of the ET region for the rat was calculated from the length and
perimeter data in Schreider and Raabe (1981). For humans, the ET region surface area value
was estimated by representing the region as sequential cylinders, using empirical data for
volume and length values from solid silicone casts (Patra et al. 1986). The "whole" lung
model of Yeh et al. (1979) was used to estimate the surface-area values for the TB and PU
regions of the rat. The human data of Weibel (1963) on the number of dimensions of airways
(represented as cylinders) in each generation were modified in a manner similar to that of
Paiva (1973) to estimate the human surface-area values for the TB and PU regions (Miller
et al. 1985). The procedure used to adjust the airway dimensions of the TB and PU from
total lung capacity to function residual capacity (FRC = 50% TLC) is described in Overton
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et al. (1987). The minute volume reported by Raabe et al, (1988) was used for the rat. The
default value used by the U.S. EPA, 20 m3/day (13.8 f/min), was used for the human value.
It is recognized that this approach is based on deposition efficiency data obtained or
derived under a particular set of ventilatory parameters; that is, the experimental parameters
for the animal and a derived human breathing pattern (13.8 £/min or 20 m3/day). The
assumption in this application is that it is valid to linearly extrapolate from these values to
other sets of breathing parameters. The parameters of this assumption, such as the effect of
activity pattern and allometric relationships between lung weight, lung surface area, minute
volume, and body weight (Adolph, 1949; Weibel, 1972; U.S. Environmental Protection
Agency, 1988c) remain to be investigated as part of this methodology development.
The RDD for the species in question then can be divided by the corresponding RDD for
humans to calculate the relative ratio of deposition in that species to the deposition in humans.
That is, the Regional Deposited Dose Ratio (RDDR) then is calculated by:
RDDR = (RDD)A/(RDD)H
where: (RDD)A = regional deposited dose in species of interest, adjusted for surface
area and ventilatory volumes, and
(RDD)H = regional deposited dose in humans, adjusted for surface area and
ventilatory volumes.
The appropriate RDDR to calculate is dictated by the observed toxicologic effect. For
example, the RDDR for extrarespiratory (ER) effects (RDDRER) would be computed
(Equation 4-6, 4-7) to determine the dose to the entire respiratory system in order to assess an
ER toxic effect (i.e., the assumed default until clearance, uptake, metabolism, and distribution
functions are incorporated). However, the RDDR for the TB region alone (RDDTB) would
be calculated for an effect involving conducting airways, and the RDDRPU for an effect
involving the PU region. An effect involving the entire respiratory system would be correct
by RDDRTOT.
It should be noted that for "lung" (TH) effects, the appropriate RDDR to use for
adjustment is the RDDR for the TB and PU regions together. The RDDR values for the TB
and PU regions cannot be added together as they appear in Table H-l, however, due to the
H-4 DRAFT - DO NOT CITE OR QUOTE
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TABLE H-l. RDDR VALUES BY MASS MEDIAN DIAMETER AND
STANDARD DEVIATION FOR RATS*
Sigma g
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
MMAD
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
ET
1.5195
0.4432
0.2263
0.1437
0.1023
0.0782
0.0663
0.0634
0.0704
0.0829
0.1383
0.1643
0.1796
0.1835
0.1794
0.1747
0.1728
0.1731
0.1740
0.1738
0.1730
0.1713
0.1691
0.1670
0.1650
0.1632
0.1615
0.1604
1.3296
0.3940
0.2046
0.1313
0.0988
0.0837
0.0807
0.0842
0.0912
0.1008
0.1407
0.1630
TB
H
61.8242
20.6081
12.3648
8.8320
6.3086
4.5963
3.8552
3.3463
3.0191
1.5052
0.9147
0.6871
0.7164
0.8607
0.9277
0.8472
0.6849
0.5029
0.3458
0.2408
0.1802
0.1460
0.1305
0.1262
0.1322
0.1388
0.1461
H
61.8242
20.6081
12.6172
7.8857
5.4674
4.3372
3.4847
2.9602
2.5641
1.4140
0.9813
PU
0.6385
1.1253
1.5359
1.7485
1.4387
1.1253
1.0277
1.0760
1.2105
1.3301
1.2869
1.0862
0.9317
0.8296
0.7494
0.6628
0.5933
0.4945
0.4720
0.3544
0.3102
0.2690
0.2846
0.2914
0.3618
0.3643
0.4601
0.5464
0.6245
1.0824
1.4387
1.4760
1.3577
1.2386
1.1990
1.1972
1.2316
1.2554
1.2310
1.0812
TH
1.1165
1.9483
2.5809
2.8390
2.3108
1.8061
1.6071
1.6400
1.7682
1.8755
1.5325
1.1512
0.9376
0.9024
0.9648
0.9856
0.9131
0.7586
0.5915
0.4337
0.3252
0.2546
0.2232
0.2064
0.2132
0.2162
0.2344
0.2513
1.0919
1.8748
2.4236
2.4448
2.1744
1.9370
1.8297
1.7697
1.7556
1.7315
1.4575
1.1841
TOT
1.7661
2.5931
2.6349
2.2689
1.7196
1.3298
1.1469
1.1125
1.1877
1.3024
1.6286
1.7450
1.8156
1.8413
1.8293
1.8051
1.7844
1.7680
1.7608
1.7502
1.7484
1.7466
1.7449
1.7431
1.7466
1.7466
1.7484
1.7518
1.7196
2.4566
2.4383
2.0151
1.6362
1.4026
1.3164
1.3022
1.3369
1.3936
1.6255
1.7403
ER
0.0096
0.0141
0.0143
0.0123
0.0093
0.0072
0.0062
0.0060
0.0064
0.0071
0.0088
0.0095
0.0098
0.0100
0.0099
0.0098
0.0097
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0094
0.0095
0.0095
0.0095
0.0095
0.0093
0.0133
0.0132
0.0109
0.0089
0.0076
0.0071
0.0071
0.0072
0.0076
0.0088
0.0094
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TABLE H-l. (cont'd) RDDR VALUES BY MASS MEDIAN DIAMETER AND
STANDARD DEVIATION FOR RATS*
Sigma g
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
MMAD
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
ET
0.1734
0.1766
0.1770
0.1760
0.1746
0.1735
0.1723
0.1713
0.1700
0.1686
0.1671
0.1659
0.1644
0.1630
0.1618
0.1605
1.0637
0.3431
0.1859
0.1262
0.1040
0.0964
0.0973
0.1011
0.1081
0.1149
0.1441
0.1607
0.1690
0.1726
0.1740
0.1736
0.1729
0.1720
0.1709
0.1697
0.1684
0.1671
0.1660
0.1649
TB
0.8327
0.7969
0.7796
0.7433
0.6787
0.6022
0.5297
0.4534
0.3836
0.3337
0.2804
0.2456
0.2220
0.2062
0.2019
0.1971
H
30.9121
15.7715
9.0123
6.5609
4.8666
3.7187
3.0492
2.6105
2.1990
1.3458
1.0281
0.8755
0.7987
0.7333
0.6778
0.6207
0.5698
0.5232
0.4797
0.4347
0.4024
0.3685
0.3441
PU
0.9466
0.8474
0.7615
0.6842
0.6162
0.5766
0.5204
0.4726
0.4371
0.4371
0.4007
0.4163
0.4371
0.4512
0.4708
0.5322
0.6144
1.0332
1.2915
1.3577
1.3211
1.2932
1.2562
1.2466
1.2333
1.2468
1.1704
1.0519
0.9500
0.8529
0.7838
0.7286
0.6712
0.6411
0.5971
0.5749
0.5564
0.5335
0.5380
0.5208
TH
1.0325
0.9645
0.9175
0.8645
0.7944
0.7251
0.6487
0.5696
0.4981
0.4530
0.3909
0.3599
0.3390
0.3228
0.3182
0.3223
1.0742
1.7744
2.1791
2.1980
2.0919
1.9810
1.8576
1.7829
1.7185
1.6619
1.3927
1.1928
1.0614
0.9693
0.8978
0.8374
0.7739
0.7250
0.6718
0.6290
0.5842
0.5479
0.5177
0.4886
TOT
1.7912
1.8075
1.8083
1.7989
1.7850
1.7773
1.7681
1.7626
1.7572
1.7554
1.7501
1.7501
1.7501
1.7483
1.7501
1.7483
1.6755
2.2848
2.2014
1.8621
1.6288
1.4980
1.4390
1.4272
1.4504
1.4806
1.6340
1.7234
1.7662
1.7826
1.7885
1.7850
1.7785
1.7761
1.7702
1.7682
1.7627
1.7590
1.7590
1.7554
ER
0.0097
0.0098
0.0098
0.0098
0.0097
0.0096
0.0096
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0091
0.0124
0.0119
0.0101
0.0088
0.0081
0.0078
0.0077
0.0079
0.0080
0.0089
0.0093
0.0096
0.0097
0.0097
0.0097
0.0096
0.0096
0.0096
0.0096
0.0096
0.0095
0.0095
0.0095
H-6 DRAFT - DO NOT CITE OR QUOTE
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TABLE H-l. (cont'd) RDDR VALUES BY MASS MEDIAN DIAMETER AND
STANDARD DEVIATION FOR RATS*
Sigma g
1.600
1,600
1.600
1.600
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
2.000
2.000
2.000
2.000
2.000
2.000
2.000
MMAD
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
ET
0.1635
0.1623
0.1615
0.1605
0.9670
0.2995
0.1699
0.1265
0.1118
0.1089
0.1099
0.1145
0.1201
0.1249
0.1468
0.1594
0.1660
0.1694
0.1707
0.1709
0.1707
0.1700
0.1692
0.1682
0. 1670
0.1660
0.1652
0.1640
0.1632
0.1624
0.1615
0.1607
0.7598
0.2664
0.1632
0.1307
0.1201
0.1198
0.1209
TB
0.3184
0.2912
0.2748
0.2704
61.8242
31.5429
12.6172
8.0434
5.5726
4.0820
3.2116
2.7104
2.3263
1.9922
1.3024
1.0281
0.8766
0.7886
0.7161
0.6641
0.6200
0.5715
0.5330
0.4937
0.4648
0.4393
0.4129
0.3891
0.3725
0.3552
0.3373
0.3186
61.8242
21.0286
10.7246
6.5609
4.4581
3.4697
2.8262
PU
0.4996
0.5198
0.5299
0.5246
0.6014
0.9714
1.2148
1.2924
1.3021
1.2846
1.2562
1.2420
1.2399
1.2224
1.1323
1.0435
0.9538
0.8893
0.8107
0.7714
0.7178
0.7034
0.6557
0.6467
0.6263
0.6020
0.5933
0.5828
0.5952
0.5693
0.5828
0.5828
0.5920
0.9240
1.1486
1.2258
1.2668
1.2764
1.2442
TH
0.4563
0.4327
0.4163
0.4086
1.0460
1.6847
2.0222
2.0745
2.0228
1.9252
1.8130
1.7433
1.6819
1.6047
1.3548
1.1905
1.0653
0.9826
0.8992
0.8474
0.7931
0.7520
0.7026
0.6676
0.6357
0.6046
0.5776
0.5517
0.5390
0.5134
0.4981
0.4778
1.0297
1.5911
1.9047
1.9551
1.9130
1.8595
1.7568
TOT
1.7501
1.7466
1.7466
1.7448
1.6255
2.1303
2.0311
1.7987
1.6442
1.5605
1.5108
1.5125
1.5297
1.5401
1.6454
1.7134
1.7468
1.7670
1.7696
1.7744
1.7736
1.7730
1.7689
1.7668
1.7611
1.7592
1.7573
1.7537
1.7537
1.7501
1.7484
1.7449
1.5784
1.9877
1.9289
1.7619
1.6507
1.6105
1.5707
ER
0.0095
0.0095
0.0095
0.0095
0.0088
0.0115
0.0110
0.0098
0.0089
0.0085
0.0082
0.0082
0.0083
0.0083
0.0089
0.0093
0.0095
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0086
0.0108
0.0105
0.0096
0.0089
0.0087
0.0085
H-7 DRAFT - DO NOT CITE OR QUOTE
-------
TABLE H-l. (cont'd) RDDR VALUES BY MASS MEDIAN DIAMETER AND
STANDARD DEVIATION FOR RATS*
Sigma g
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
MMAD
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
ET
0.1250
0.1286
0.1324
0.1489
0.1590
0.1639
0.1668
0.1681
0.1686
0.1685
0.1681
0. 1675
0.1668
0.1660
0.1652
0.1644
0.1637
0.1630
0.1623
0.1616
0.1611
0.6648
0.2416
0.1593
0.1354
0.1281
0.1279
0.1289
0.1319
0.1351
0.1382
0.1510
0.1582
0.1625
0.1649
0.1661
0.1666
0.1667
0.1664
0.1660
0.1655
TB
2.4393
2.1269
1.8926
1.2821
1.0222
0.8874
0.7823
0.7210
0.6659
0.6194
0.5797
0.5456
0.5162
0.4817
0.4673
0.4363
0.4166
0.4004
0.3920
0.3748
0.3659
61.8242
15.7715
9.1925
5.4674
4.0078
3.1543
2.6636
2.2869
2.0119
1.8025
1.2417
1.0029
0.8746
0.7886
0.7121
0.6673
0.6249
0.5951
0.5542
0.5306
PU
1.2256
1.2240
1.2021
1.1167
1.0286
0.9647
0.8979
0.8493
0.8029
0.7686
0.7406
0.7083
0.6946
0.6943
0.6661
0.6520
0.6358
0.6514
0.6325
0.6358
0.6245
0.5859
0.8946
1.0748
1.1714
1.2171
1.2223
1.2157
1.2093
1.2031
1.1814
1.1004
1.0339
0.9767
0.9238
0.8656
0.8283
0.8055
0.7702
0.7622
0.7325
TH
1.6924
1.6351
1.5712
1.3387
1.1798
1.0780
0.9832
0.9214
0.8632
0.8153
0.7738
0.7340
0.7049
0.6758
0.6524
0.6196
0.5955
0.5848
0.5695
0.5530
0.5402
1.0191
1.5204
1.7733
1.8342
1.8312
1.7711
1.7168
1.6555
1.6000
1.5382
1.3147
1.1742
1.0775
1.0011
0.9234
0.8760
0.8357
0.7982
0.7633
0.7320
TOT
1.5741
1.5807
1.5890
1.6596
1.7105
1.7379
1.7506
1.7601
1.7653
1.7666
1.7660
1.7638
1.7635
1.7596
1.7595
1.7540
1.7521
1.7520
1.7502
1.7484
1.7466
1.5485
1.8827
1.8340
1.7305
1.6684
1.6331
1.6155
1.6141
1.6202
1.6251
1.6710
1.7052
1.7310
1.7444
1.7482
1.7538
1.7593
1.7589
1.7585
1.7564
ER
0.0085
0.0086
0.0086
0.0090
0.0093
0.0094
0.0095
0.0095
0.0096
0.0096
0.0096
0.0096
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0084
0.0102
0.0099
0.0094
0.0090
0.0089
0.0088
0.0087
0.0088
0.0088
0.0091
0.0092
0.0094
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
H-8
DRAFT - DO NOT CITE OR QUOTE
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TABLE H-l. (cont'd) RDDR VALUES BY MASS MEDIAN DIAMETER AND
STANDARD DEVIATION FOR RATS*
Sigma g
2.200
2.200
2.200
2.200
2,200
2.200
2.200
2.200
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
MMAD
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
4.500
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
ET
0.1649
0.1644
0.1637
0.1633
0.1627
0.1622
0.1615
0.1610
0.5674
0.2278
0.1595
0.1404
0.1350
0.1344
0.1357
0.1377
0.1398
0.1424
0.1523
0.1579
0.1614
0.1637
0.1645
0.1651
0.1651
0.1651
0.1648
0.1645
0.1641
0.1636
0.1633
0.1629
0.1625
0.1619
0.1616
0.1611
TB
0.5118
0.4927
0.4686
0.4489
0.4287
0.4206
0.4122
0.3911
63.0859
15.7715
7.1497
4.7765
3.5859
2.9440
2.4799
2.1523
1.8764
1.6918
1.2217
1.0127
0.8803
0.7954
0.7284
0.6687
0.6309
0.6002
0.5757
0.5512
0.5267
0.5022
0.4900
0.4701
0.4622
0.4497
0.4290
0.4206
PU
0.7349
0.7263
0.7166
0.6934
0.6800
0.6857
0.6780
0.6842
0.5769
0.8548
1.0339
1.1276
1.1823
1.1990
1.2042
1.1819
1.1710
1.1604
1.0832
1.0239
0.9714
0.9337
0.8921
0.8649
0.8345
0.8216
0.8070
0.7798
0.7744
0.7568
0.7494
0.7286
0.7362
0.7312
0.7061
0.6994
TH
0.7172
0.6973
0.6721
0.6457
0.6218
0.6153
0.6043
0.5851
1.0125
1.4548
1.6730
1.7577
1.7573
1.7297
1.6787
1.6082
1.5430
1.4942
1.2975
1.1746
1.0783
1.0102
0.9476
0.8942
0.8533
0.8248
0.7994
0.7682
0.7457
0.7178
0.7036
0.6779
0.6730
0.6593
0.6308
0.6201
TOT
1.7581
1.7579
1.7541
1.7522
1.7504
1.7503
1.7467
1.7450
1.5217
1.8103
1.7783
1.7420
1.6838
1.6637
1.6524
1.6430
1.6385
1.6451
1.6795
1.7074
1.7240
1.7401
1.7442
1.7480
1.7497
1.7534
1.7550
1.7548
1.7546
1.7508
1.7525
1.7506
1.7524
1.7487
1.7468
1.7450
ER
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0082
0.0098
0.0096
0.0093
0.0091
0.0090
0.0090
0.0089
0.0089
0.0089
0.0091
0.0093
0.0093
0.0094
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
*H = Humans receive some deposition, but rats do not.
R = Rats receive some deposition, but humans do not.
Source: Adapted from Jarabek et al., 1989a,
H-9 DRAFT - DO NOT CITE OR QUOTE
-------
surface area and ventilatory parameter corrections to the respective deposited dose of each.
Therefore, a TH column has been provided which includes the appropriate calculations.
The RDDR then can be used to scale the exposure concentration associated with the
observed effect to an equivalent concentration which reflects dosimetric differences between
humans and the experimental species in question. That is, the RDDR provides a factor for
adjusting the no-observed-adverse-effects-level (NOAEL), according to Equation 4-5 for
respiratory tract effects:
NOAEL[HEC] (mg/m3) = NOAEL[ADJ] (mg/m3) x RDDR(ETj TB pu TH or TOT)
where:
= the NOAEL adjusted for duration according to Equation 4-3, and
RDDR = (RDD)A/(RDD)H, the ratio of regional dose in animal species to that
of humans across regions of interest for the toxicologic effect.
This is the NOAEL level that then would be arrayed with other NOAELS to determine the
most sensitive species and the key study as described in Appendix D. RDDR values for the
rat to the human deposition are provided in Table H-l.
As mentioned, the RDDR^ER^ is computed to adjust for ER effects. Equation 4-6 is used to
calculate the RDD expressed as mg/min-kg:
RDDER = IP"6 YVTL- S PJ EJ
BW
where:
PA = the paniculate mass fraction in the exposure size distribution (MMAD, a ),
Ej = the deposition efficiency of that size distribution (MMAD, a ) in the entire
respiratory tract for the species of interest,
n = number of size ranges,
Y = exposure level (mg/m3),
Vt = tidal volume (ml),
f = breathing frequency (breaths/min), and
H-10 DRAFT - DO NOT CITE OR QUOTE
-------
BW = body weight (kg).
The ratio is the extrarespiratory RDDs calculated for the experimental species and human then
is used to calculate the HEC Equation 4-7:
NOAELpjgqCmg/m3) = NOAEL[ADJj(mg/m3) x RDDRER
where:
= the NOAEL human equivalent concentration,
= the NOAEL adjusted for duration according to Equation 4-3, and
RDDRER = (RDDER)A/(RDDER)H, the ratio of the dose available for the entire
respiratory system of the experimental animal species to that of
humans.
It should be noted that body weight and not surface area is in the denominator of the
calculation for RDD for ER effects. THE RDDR VALUES IN TABLE H-l FOR ER
EFFECTS DO NOT HAVE BODY WEIGHT FACTORED IN AT THIS TIME, PENDING
RESOLUTION ON RECOMMENDED VALUES FOR BODY WEIGHTS, (SEE
SECTION 4. 1. 1.4). THUS, THESE RATIOS WILL NEED TO BE MULTIPLIED BY
(BW)H/(BW)A WHEN USED. THOSE VALUES FOR WHICH AN "H" APPEARS
INDICATE NUMBERS FOR WHICH HUMANS RECEIVE SOME DEPOSITION BUT
RATS DO NOT. THE "R"s INDICATE VALUES FOR WHICH RATS RECEIVE SOME
DEPOSITION AND HUMANS DO NOT. IN THESE CASES, RDD VALUES MAY
PROVIDE SOME INSIGHT ON THE ASSESSMENT, BUT SHOULD BE DISCUSSED
WITH AN EPA SCIENTIST FIRST.
A plot of the RDDR for rats vs. humans for the TB region is shown in Figure H-2 and
for the PU region in Figure H-3. The plots show two different standard deviations of aerosol
distributions, a a of 1.4 and 2.4 (essentially monodisperse and polydisperse distributions), to
o
illustrate the sensitivity of the burden ratios to that parameter. The line is drawn across the
plot from the RDDR value of 1.0 as a demarcation. Values of RDDR greater than 1.0
indicate where the rat receives more of an inhaled dose relative to humans, and thus
H-l 1 DRAFT - DO NOT CITE OR QUOTE
-------
•too
.210
CO
CC
ffi
0)
.1
Minute Volume, mi
Surface Area, cm2
Rat
130.0
37.S
Human
13800.0
5036.0
= 2.4
0123456789 10
Particle Diameter (MMAD, jjm)
Figure H-2. RDDR of the rat to the human by particle diameter (MMAD) for the TB
region.
Source; Jarabek et al., 1989a.
H-12 DRAFT - DO NOT CITE OR QUOTE
-------
CO
cc
0)
(ft
o
o
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Minute Volume, ml
Surface Area, cm2
Rat
130.0
3424.0
Human
13800.0
635545.0
012345678
Particle Diameter (MMAD,
9 10
Figure H-3. RDDR of the rat to the human by particle diameter (MMAD) for the PU
region.
Source: Jarabek et al. 1989a.
H-13 DRAFT - DO NOT CITE OR QUOTE
-------
adjustment by the RDDR would result in a larger NOAELHEC than the animal NOAELADJ
estimate. Below the demarcation line, the animals receive less of that characteristic dose
relative to humans, and adjustment by the RDDR would result in a decreased NOAELHEC
relative to the animal NOAEL^j estimate. Note that the rat receives a much higher burden
in the TB region (Figure H-2) relative to humans for particles less than 2 ^.m, while humans
receive higher relative doses in the TB region for particles greater than 2 ^m. With the
exception of the particle size range of 0.2 to 2 ^m, where the rat receives more, humans
receive a greater dose relative to rats across the entire particle size range in the PU region
(Figure H-3), and the equivalent exposure concentrations would be scaled downward. These
plots help to illustrate the effect of dosimetric adjustment on the apparent (observed) effect
concentration.
The influence of breathing route (i.e., nose-breathing with normal augmentation through
the mouth vs. mouth breathing alone) on DDRs is significant as illustrated in Figure H-4,
plots A vs. B. The total RDDR for mouth breathers (B) is higher for the entire particle size
range in comparison to normal augmenters (A). This difference emphasizes the need for an
activity pattern scenario for humans (e.g., x hours rest, y hours light activity, z hours heavy
exercise) to account for changes in deposition pattern due to breathing patterns, rather than
calculating RDDRs for humans using an assumed default ventilatory parameter (i.e.,
n
20 m /day or 13.8 f/min). A range in minute ventilation from 12 to 132 £/min has been
associated with representative types of exercise from light to severe (U.S. Environmental
Protection Agency, 1986c). Humans normally augment respiratory airflow with oronasal
breathing when minute ventilation exceeds approximately 35 f/min (U.S. Environmental
Protection Agency, 1986c), and this breathing mode significantly alters the regional
deposition of inhaled particles (Miller et al., 1988). This alteration in regional deposition
then significantly alters the RDDR used to adjust the experimental exposure concentration to a
human equivalent concentration, and thus, significantly alters the derived RfC. Computation
of a representative activity pattern for humans as proposed will make better use of models that
estimate deposition burdens as a function of the complex interaction between breathing route,
ventilation level, and particle aerodynamic properties. This will provide a more realistic
estimate of probable human exposure.
H-14 DRAFT - DO NOT CITE OR QUOTE
-------
(A)
(B)
3.5
3.0
5 2.5
CO
= 2.0
*'W
1.0
0.5
Minute Volume, ml
Surface Area, cm2
Rat
130.0
3473.2
Human
138000)
640758.0
0123456789 10
Particle Diameter (MMAD. [jm)
3.3
3.0
52.5
CO
« 2.0
(0
,§1.5
1.0
n c
i i I f 1 T
Rat Human
Minute Volume, ml 130.0 13800.0
Surface Area, cm2 3473.2 640758.0
'ft
\......,^=- "-
'•\y — "a=i.4
.. O _ o 4
g-4.4
0123456789 10
Particle Diameter (MMAD, pm)
Figure H-4. RDDR of the rat to the human by particle diameter (MMAD) for the TOT
system in (A) normal augmenters and (B) mouth breathers. A
proportionality factor for the split in air flow between nose and mouth
(Niinimaa et al., 1981) was used in human deposition calculation for plot
(A).
Source; Jarabek et al. 1989a.
H-15 DRAFT - DO NOT CITE OR QUOTE
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EXAMPLE CALCULATIONS
Ep(a)oxide is a hypothetical noxious agent found as a insoluble particulate emission
from municipal waste combustion sources, and there is a need to calculate a proposed RfC.
Associated health effects of ep(a)oxide include both central nervous system (CNS) and
respiratory functional and structural abnormalities. Recently, two well-conducted, chronic
inhalation toxicology investigations have been performed by two different laboratories that
evaluate these effects in rats. The NOAELS of the critical effect data evaluated in these
investigations are summarized in Table H-2, but since dosimetric adjustments have not been
made for the exposure conditions or the observed toxic effects, comparison is not possible.
The following outlines the steps which would need to be executed to perform this adjustment.
It should be noted that in this example both investigations were performed on the rat, while
other studies may require that an RDDR be tabulated for other species in question.
Equation 4-3 would first be applied to the results in order to adjust for the discontinuous
exposure protocol.
NOAEL[ADJ-| (mg/m3) = E (mg/m3) x D (hours/day/24/hours) x W (days/7 days)
where:
E = experimental exposure level,
D = number of hours exposed/day 724 hours, and
W = number of days of exposure/7 days.
The calculation for duration adjustment of the Laboratory 1 exposure is:
3) = 120.0 x 8/24 x 5/7
= 29 mg/m3.
The calculation for ep(a)oxide results from Laboratory 2 is given by:
NOAELrADJ] (mg/m3) = 12 x 8/24 x 5/7
= 2.9 mg/m3.
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TABLE H-2. SUMMARY OF SYSTEMIC TOXICITY NOAELS* FOR EP(a)OXIDE
OBSERVED IN FISCHER 344 RATS
Exposure
120 mg/m3
MMAD = 2.0 /im
ag = 1.6
12 mg/m3
MMAD = 0.2 urn
ag = 1.8
System
Duration Examined
8 h/day CNS
5 days/week
for 9 months
8 h/day Respiratory
5 days/week
for 12 months
Effects
No exposure-related
effects on EMG or
limb tremor
No exposure-related
decrease in
mucociliary clear-
ance or alterations
in epithelial
architecture/goblet
cell hypertrophy
Reference
Lab 1
Lab 2
*It should be noted that only the NOAEL data (adverse effects occurred at higher exposure concentrations in
each investigation) is provided for this ep(a)oxide and not a full data array. Choice of toxicity data is
discussed in Appendix G and entails an analysis of all data, NOAEL/LOAEL interfaces, and such. This table
is provided only to illustrate the dosimetric adjustments.
The RDDR for each exposure condition and toxicologic effect then is calculated by
using Table H-2.
The effect of interest is an ER effect for the exposure conditions (a = 1.6, MMAD =
££
2.0 ^m) investigated by Laboratory 1 so that an RDDR corresponding to a oe of 1.6 and
&
MMAD of 2.0 should be read from the ER column (see page H-7). The resulting RDDR is
0.0093. However, as previously discussed, these values in Table H-l for RDDRER do not
have the ratio of body weights factored in, so this value will need to be adjusted by
(BW)H/(BW)A. The default value for body weight for male Fischer 344 rats is .38 kg
(U.S. Environmental Protection Agency, 1988c), and the default body weight for humans is
70 kg.thus, .0093 multiplied by 70/.38 results in a RDDRER of 1.7. This ratio then is used
in Equation 4-7 to calculate the NOAELHEC for ER effects as:
x RDDRER.
29x1.7
49.3 mg/m3
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For the results of Laboratory 2, an RDDR is calculated for only the TB region since
measurements of mucociliary clearance and histopathology were used lo assess effects in the
tracheobronchial region. Therefore, dose adjustment by the TB region RDDR value is
appropriate. The RDDR for the TB region corresponding to an exposure condition of
ffg = 1.8 and an MMAD = 0.2 pm is 31.54 (see page H-7).
Equation 4-5 then is used to adjust the exposure effect levels for dosimetric differences
as follows:
(mg/m3) = NOAEL[ADJ] (mg/m3) x RDDRpu.
The NOAEL observed in the investigations of Laboratory 2 adjusted for dosimetric
differences is:
NOAELrHEC1 (mg/m3) = 2.9 ing/nr^n x 31.54.
= 91.5
Thus, after dosimetric adjustment, the NOAELrHECj for ER effects (CNS) of
49.3 mg/m3 from the investigations of Laboratory 1 is lower than that observed for the TB
effects (91.5 mg/m3) observed by Laboratory 2.
This emphasizes the need for dosimetric adjustments prior to data array analysis and key
study selection, since, as in this example, an observed NOAEL in the same animal species
that appears to be 10-fold greater than another NOAEL may actually result in a smaller
NOAEL^jgq once such adjustments are made. Dosimetric adjustments also will affect
comparisons across species. As illustrated in Figure H-5, exposure to rats, mice, and guinea
pigs to the same exposure concentration with an MMAD of 2.0 /wm and a a of 1.4 would
result in different NOAEL^jg^ estimates (1.1, 1.7 and .74 times the exposure concentration,
respectively). Again, this illustration emphasizes the need to correct exposure concentrations
to human equivalents before choosing the critical effect and key study.
RESEARCH AND DEVELOPMENT
The EPA recognizes that the establishment of RfCs critically depends on the quantitative
extrapolation of regional respiratory tract doses from animals to humans. The RDDR as
described in this Appendix must address both the deposition and fate of deposited particles to
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8.0
7.0
6.0
S.D
2.0
1.0
0.0 -
M
II
11
I I
I I
I I
I
I
I
Minute Volume, ml
Surface Area, cm2
Rat
130.0
3461.6
Mouse
27.0
294.8
Guinea
«g1
175.0
9112.8
Guinea
Pig 2
175.0
9656.0
Human
13800.0
640581.0
Rat
—-—- Mouse
—~ Guinea Pig 1
Guinea Pig 2
012345678.9 10
Particle Diameter (MMAD,
Figure H-5. RDDR of three species to the human by particle diameter (MMAD) for the
TH region. Guinea pig 1 and 2 refer to calculations using different lung
surface area data.
Source: Jarabek et ol. I989b.
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adequately accomplish this. That is, factors which account for the continuous redistribution
and clearance of inhaled particles within the lungs of the species of interest to risk assessment
(including humans) during chronic exposures must be incorporated into the RDDR derivation
in order to calculate Regional Retained Dose Ratios (RRDRs). These RRDR adjustments
would be more appropriate to apply to chronic inhalation bioassays.
A work group has been formed with members of ECAO-RTP and HERL to expand the
current RDDR methodology, using empirical data and existing theoretical models to
incorporate clearance and to derive a similar dose adjustment factor for gas and vapor
exposures. The mission of this group is to incorporate into the methodology for particles as
many of the following factors as is feasible.
• Regional Deposition
particle size
particle distribution (a )
particle volatility or hygroscopicity
. detailed regional respiratory tract morphology for multiple species
extrathoracic and intrathoracic deposition
alternative modes of breathing (nasal, oronasal, and oral) and activity
patterns
• Fate of Inhaled Particles
mucociliary transport and clearance
alveolar clearance
phagocytosis and translation by macrophages
dissolution
free particle translocation
particle solubility
chemical activity: local vs. systemic
Pepelko (1987) investigated the feasibility of dose adjustments based on reported
pulmonary clearance rates. The bioavailabilities of single inhaled doses of particulate matter
having dissolution half-times ranging from one day to over five years were estimated by
calculating the amount dissolved each day and summing over a two-year period. Two years
was selected because it approximated the remaining lifetime of an exposed rat.
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The equation used to carry out this calculation is;
Total bioavailable percentage = 100ks- l-e'(kp + ks)t
where:
k^ = the rate constant for elimination via physical transport of particles from the
lungs
kg = the rate constant for particle dissolution
and
t = time in days.
Values of 60 and 240 days were selected as representative of physical clearance rates in
rats and humans, respectively. It should be cautioned that these values were selected only as
examples, since actual clearance rates are somewhat uncertain and vary with conditions.
The results are shown in Figure H-6. As can be seen, for very short dissolution half-
time (t1/2s) values, physical clearance rates had little effect upon total bioavailability. In fact,
for a tlf2s of one day, the calculated bioavailable percentages were 98.4 and 99.6 for particle
removal half-time (t1(r2p) values of 60 and 240 days, respectively. On the other hand, when
t1/2s is increased to 120 days, the estimated bioavailability equals only 32% for a t1/2 of
60 days, compared to 67% when the t1(,2 is equal to 240 days. For particles with very long
dissolution half-times, the total bioavailability is predicted to be small in both cases, although
the relative amount will continue to be up to three times as great when the t^^, equals
240 days.
Other uncertainties in the estimation of bioavailability result from regional and
interspecies differences in physiology. Particles deposited in alveolar regions, for example,
are almost invariably taken up by phagocytic cells, which have been shown to alter the rate of
dissolution (Andre et al., 1987). Considerable quantities of particles are transported to and
stored in the lung-associated lymph nodes of dogs (Snipes et al., 1983). Since this material is
still in the body and subject to dissolution and absorption, use of reported clearance half-
times will result in an underestimate of bioavailability, unless the rates of translocation to the
lymph nodes are known, allowing an appropriate adjustment to be made. Certain metals,
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100
180 320 480 640 800 960 11201280 144O 16O017BO 1920
Solublllzatlon t,/2 (dayi)
Figure H-6. The relationship between particle removal half-time (t1/2p) and dissolution
half-time (t1/2s) upon the bioavailability of a single deposited dose of
inhaled particulate matter over a 730-day period.
Source: Pepeiko (1987).
such as beryllium, cadmium, lead, and arsenic have very long-term clearance components
(Rhoads and Sanders, 1985; Reeves et al., 1967). While the slow clearance may be partially
ascribed to toxicity, at least a portion was considered by the authors to be due to uptake by
lung cells and formation of a stable complex with metallothionein-like proteins. Although
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there also is some evidence that alveolar clearance is better described by two exponential rate
constants than one, in both small animals (Snipes et al., 1983) and in humans (Bohning et al.,
1982), only a single value has been reported in most studied.
The use of reported clearance rates also may result in an underestimate of bioavailability
in animals when extrapolating from a chronic toxicology study, because continuous exposure
at high concentrations may result in lung overloading with concomitant decreases, or even
cessation of clearance (Chan et al., 1984; Griffis et al., 1983). Further, there are few
comparisons across species using the same type of particles. This investigation helps to
illustrate the interaction of clearance with bioavailability for chronic dose adjustments and
serves to emphasize that these and other considerations must be addressed in the model
development.
The initial output of the research effort to expand the scope of the methodology will be
an analytic model from which RRDRs for particles are derived. The most difficult task of the
research work group will be the development of a model that satisfies all of the criteria listed
on page H-19. The achievement of this goal will involve compromises between scientific
accuracy and general applicability in risk assessment procedures. The project has already
identified some data gaps that has initiated an investigation to obtain regional surface area and
clearance rates using consistent methodologies across species in order to ensure compatible
and precise estimates for model input. The output is anticipated to be a support document of
RRDR tables to be used in the RfC risk assessment methodology for dose adjustment and
reduction of uncertainty in interspecies extrapolation for aerosol exposures. Specifications on
how to apply these ratios as scaling factors and limitations (e.g., duration of exposures) will
be explicitly stated. Compilation of regional surface area data, using consistent inflation,
fixation, and morphometry techniques across species, will facilitate investigation of the
limitations on linear extrapolation of minute volumes and surface areas as well as the
allometric relationships between lung weight, lung surface area and body weight. Further, it
is expected that the characterization of anatomic and physiologic parameters across species,
involved in the development of the aerosol model, will provide the basis for mass transport
estimates needed to expand and refine existing gas deposition and uptake models (e.g., ozone
and volatile organics). A gas and vapor model which accounts simultaneously for
characteristics such as solubility, reactivity, and metabolic transformation then may be
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developed (see Appendix I). A similar support document of adjustment factors for these
agents is envisioned.
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APPENDIX I
DERIVATION OF AN APPROACH TO
DETERMINE HUMAN EQUIVALENT
CONCENTRATIONS FOR EXTRARESPIRATORY
EFFECTS OF GAS EXPOSURES BASED ON A PB-PK
MODEL USING SELECTED PARAMETER VALUES
-------
INTRODUCTION
This appendix describes in detail the derivation of the procedure used in Chapter 4 to
estimate No-Observed-Adverse-Effect level human equivalent concentrations (NOAELrHECjS)
for extrarespiratory effects of gases or vapors. The derivation is mathematical in nature in
that the equations of state that describe the disposition of inhaled compounds in a generalized
physiologically based pharmacokinetic (PB-PK) model are manipulated so as to obtain a
conservative estimate of NOAELrj^qS as a function of the average animal exposure
concentrations (NOAELrADJj). A PB-PK model is used because of the success of this type of
model. For example, PB-PK models that describe the body as five compartments (gas
exchange and the fat, poorly-perfused, richly-perfused, and liver/metabolizing tissue groups)
have been applied successfully to estimating the internal concentrations of chemicals (e.g.,
styrene, methanol, and ethylene dichloride) for the purpose of risk assessment. Although,
PB-PK modeling is the choice procedure in risk assessment for dose extrapolation, this
approach is not possible without the values of physiological and biochemical parameters,
which are used in the modeling process, and without a better understanding of the agent's
mechanism of action. These data generally are not available for most compounds.
The proposed method is based on a PB-PK model in which all of any number of
compartments are in parallel and in which for any compartment there can be any number of
paths of removal by linear and saturable processes. Selected relevant parameter values are
replaced by qualitative assumptions about species similarity and the response of internal
concentrations to exposure scenarios. In order to obtain a NOAELp^q, the assumption is
made that the effective dose for dose-response purposes is the arterial blood concentration of
the gas or its concentration multiplied by time (C x T). (These assumptions are specified in
detail in the METHODS section.) This latter assumption is consistent with our current
understanding of systemic toxicity for a majority of chemicals, since the toxicity of most
environmental chemicals is related to the concentration of the parent compound at the target
site over a period of time.
In addition to deriving conservative NOAELpjgq estimates based on arterial blood
concentrations, the method also predicts that the blood concentration of an inhaled compound
in any human tissue compartment does not exceed the blood concentration in the
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corresponding animal compartment. Although the present approach does not directly address
the issue of metabolites being the toxic agent, the procedure predicts (based on the similarity
assumption) that the rate of metabolite production per cardiac output rate or per target tissue
perfusion rate in humans does not exceed that in animals.
METHODS
Assumption imposed by the RfC methodology:
Assumption I. Noncancer toxic effects observed in chronic animal bioassays are the basis for
the determination of NOAELs and the operational derivation of RfCs for human exposures, as
described in Chapter 4. The animal exposure scenario is experiment-dependent and usually
intermittent (e.g., 6 h/day, 5 days/week for many weeks). Human exposure concentration is
continuous and constant for 70 years. The "lifetime" chronic animal exposure scenario is
equivalent to the human chronic exposure scenario for the purpose of extrapolating the
NOAEL.
Additional assumptions for the proposed method:
Assumption II. Relatively soon after the beginning of the experiment, and for most of the
experiment, all the concentrations of the inhaled gas within the animal's body are periodic
with respect to time. Figure 4-2 illustrates periodicity. Practically, these conditions should
be met during "most" of the experiment duration. For example, if the condition is met for
nine-tenths of the time (e.g., periodic during the last 90 weeks of a 100-week experiment),
then estimates of average concentrations will be in error by less than 10%. During most of
the time humans are exposed, given Assumption I of continuous exposure, their internal
concentrations are constant and in dynamic equilibrium with their exposure concentration.
Assumption III. A PB-PK model describes the uptake and disposition of inhaled compounds
in animals and humans. The model is diagramed in Figure 1-1 and the equations of state are
given by Equations (1-1) to (1-6). Table 1-1 defines the variables and constants in the
equations.
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CE
OP
CV
QC
Cp
f
CA
N
4
rN
_C£.
QP
QC
CA
GAS-EXCHAMGE
COMPARTMENT
ANY NUMBER OF
METABOLIZING AND
NON-METABOLIZING
COMPARTMENTS
Figure 1-1. Schematic of the physiologically based pharmacokinetic model assumed to
describe the uptake and distribution of inhaled compounds.
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TABLE 1-1. DEFINITION OF SYMBOLS
General
V
N
X
T
Subscripts
i
P
j
A
H
HEC
Compartment volume
The number of non-gas exchange compartments
Mass of inhaled compound in gas exchange compartment
Mass in compartment other than gas exchange
Multiplication symbol
Overbar indicates average
Blood to air partition coefficient
Period of exposure time
i-th path of loss of primary compound
Gas exchange compartment
j-th non-gas exchange compartment
Animal
Human
Human equivalent concentration
Flow Rates (ml/h)
QP Alveolar ventilation
QC Cardiac output
Q Into and out of non-gas exchange compartment
Concentrations (mg/1)
C In venous blood within and leaving a non-gas exchange compartment
CE Exposure
C In air of pulmonary region
CA In arterial blood
CV In venous blood entering gas exchange region
Biochemical
r Removal rate due to metabolism, reactions, excretion, etc. (mg/h)
VMAX Maximum velocity of saturable path (mg/h)
KM Michaelis constant (mg/1)
KF First-order rate constant (1/h)
VKF Equals to V x KF (1/h)
1-4
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= QP*(CE - Cp) + QC*(CV - CA) - rp(CA) (1-1)
dMj/dt = Qj*(CA - Cj) - rj(Cj); j = 1, 2, 3, ... N (1-2)
rp(CA) = 2VKFpi*CA + SVMAXpi*CA/(KMpi + CA) (
i i
ij(Cj) = SVKFj^Cj + ZVMAXji'Cj/OCMji + Cj); j = 1 to N (I-3b)
1 1
QC*CV = 2Qj*Cj (1-4)
j
QC = ZQj (1-5)
j
CA = X*Cp (1-6)
Equations (1-1), (1-2), (1-4), and (1-5) are the dynamical equations of state or mass
balance equations for the model. Equations (I-3a,b) define the possible loss rates in each
compartment in terms of linear rates (e.g., VKF:j*Cj) and rates of the Michaelis-Menton type
(e.g., VMAX-^CA/ITCMpj + CA]). In each compartment, the model allows for more than
one path of elimination or metabolism or for no losses (i.e., set all of a compartment's kinetic
parameters, VKF and VMAX, to zero). Equation (1-6) gives the assumed relationship
between the arterial blood concentration and the concentration in the air of the pulmonary
region.
According to Assumption I, the exposure concentration is periodic with period of
exposure time (T) for animals and constant for humans; in both cases, concentration of
exposure (CE) can be written as:
CE = f(t)*CE (1-7)
where:
= the average exposure concentration, and
f = a periodic function of time (t) such that:
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f(t)*dt = 1. a-8)
Assumption IV. Because the biologically effective toxic dose to a given target tissue depends
on the animal species and chemical compound, its specification is typically not available so
that definition of a surrogate dose must be somewhat arbitrary. However, the toxic effects of
some compounds are expected to be directly related to the inhaled parent compound in the
blood. Furthermore, the choice of the average blood concentration is conservative and is an
internal dose "closer" to the target than a dose based on exposure concentration. Basing the
effective dose extrapolation on another surrogate (e.g., metabolite) would require knowledge
of the mechanisms of action and additional information about human and animal physiological
parameters. Thus, for animal to human exposure extrapolation, the human equivalent
exposure concentration (CErHECj) is defined in terms of the time-integrated arterial blood
concentration (CA x T) of the inhaled parent compound by requiring that
(CA x T)H < (CA x T)A. This assumption (combined with Assumption I) is equivalent to
requiring that the human equilibrium concentration of arterial blood (leaving the lung) be less
than or equal to the time-averaged arterial blood concentration of the animal; that is,
CAH < CAA. The equality condition defines the upper limit on an acceptable human arterial
blood concentration; thus, for mathematical simplicity this assumption is formulated as:
CAH =CAA (1-9)
Because of this requirement, CAH is a function of CEA since CAA depends on CEA.
Assumption V. Similarity of species is assumed in that KM and the ratios Q/QP, VKF/QP,
and VMAX/QP are defined as species independent for each removal process (see
Table 1-1 for definitions). The invariance of the first ratio is based on the assumption that the
percent of blood flow to any compartment is independent of species and that cardiac output
(QC = sum of all Q) scales, with respect to body weight, in the same way as the ventilation
rate (QP); i.e., the ratio of QC to QP is species-independent. The metabolic constants
VMAX and VKF are assumed to scale in the same way as QP. Justification for this
assumption about rates is based on the observation that for many species, rates scale in the
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same way with respect to body weight; e.g., in proportion to basal metabolism, body surface
area, or body weight to some power (Travis and White, 1988). The invariance of the ratios
VKF/QP and VMAX/QP follows.
Subject to the Assumptions, Equations (1-1) to (1-9) must be manipulated to determine
C-EHEC as a function of me average animal exposure concentration, CEA. Because the
concentrations and masses of a parent compound within a compartment are assumed to be
periodic, the integral of the left-hand side (LHS) of Equations (1-1) and (1-2) over a time
length of the period is zero; for example:
(dM/dt')*df = M(t + T) - M(t) = 0. (1-10)
Also note that for equilibrium or steady state, as in the human case, the LHS of each of these
equations is zero by definition. Performing the period average of both sides of
Equations (1-1) to (1-6), the following are obtained.
O = QP*(CE - Cp + QC*(CV - CA) *p (Ml)
O = Qj*(CA - C]) -7p; j - 1, 2, 3, ... N (1-12)
rp = SVKFpi*CA + SVMAXpj* [CA^KM^ + CA)] (I-13a)
i i
'C +
ji + C,-)]; j = 1 to N (M3b)
QC*CV = ZQjiCj (I- 14)
j
QC = ZQj (1-15)
j
CA = A*Cp~ (1-16)
The steady state equations for humans are obtained from Equations (1-1) and (1-2) by setting
the LHS of these equations to zero (the equilibrium or steady-state condition). The complete
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set of equations of state for humans can be obtained from Equations (1-11) through (1-16) by
redefining the average concentrations or terms as equilibrium values (i.e., remove the
overbars).
The above equations are simplified by combining Equations (1-11) and (1-16) to give:
(QP/A + QC)*CA = QP*CE + QC*CV - rp, (1-17)
and Equation (1-12) is expressed as:
Qj*CA = Qj'-'Cj + rj; j = 1 to N. (1-18)
Both sides of Equations (1-17) and (1-18) are divided by QP and Qj, respectively, to
give:
u*CA = CE + w * CV - rp/QP, and (I-19a)
CA = Cj + rj/Qj; j = 1 to N (I-19b)
where:
w = QC/QP, and (I-19c)
u = (X'1 + QC/QP). a-19d)
Generally, the constants w and u are species-dependent, and will be identified as such with
subscripts A and H for laboratory animal and human, respectively. However, for simplicity
and unless otherwise noted, averaged concentrations (indicated by overbar) will be those of
animals and nonaveraged concentrations will be those of humans.
Applied to humans, Equations (I-19a) and (I-19b) are written as:
UH* CA = CE + WH * CV - rpH(CA)/QPH, and (I~20a)
CA = Cj + rjH(C|)/QjH; j = 1 to N. (I-20b)
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For animals, Equations (I-19a) and (I-19b) are written as:
UA* CA = CE + WA * CV - rpA/QPA, and (I-20c)
CA = Cj + rjA/QjA; j = 1 to N. (I-20d)
The loss terms in Equations (1-3), r (CA) and the n(Q)'s, are concave functions with
ir J J
the property that their second derivatives with respect to CA and G, respectively, are less
than or equal to zero. As a consequence, the average of each of these functions is less than
or equal to the function evaluated at the average concentration. Suppressing the subscripts,
this property is expressed as:
r < r(C). (1-21)
Considering Equations (1-21), (I-2Qc), and (I-20d), the following is noted:
UA* CA ;> CE + WA* CV - rpA(CA)/QPA, and (I-22a)
CA ^ Cj + rjA (Cj)/QjA; j = 1 to N. (I-22b)
Using Equation (1-9), Assumption IV (that is, CAH = CA, Equations (I-20a) and
(I-20b) for human are written in terms of the animal arterial blood concentration by replacing
CA with CA as follows:
UH* CA = CE + WH * CV - rpH(CA)/QPH; (T-23a)
CA = Cj + rjH(Cj)/QjH; j = 1 to N. (I-23b)
Subtract the LHS and the right hand side (RHS) of Equation (I-23a) from the LHS and
RHS of Equation (I-22a), respectively, to obtain:
(UA - UH)* CA > CE - CE +
-------
Because of Assumption V, for any concentration value, C:
rpA(C)/QPA = rpH(C)/QPH, and (I-25a)
also,
WA = WH, and (I-25c)
UA - UH = A'1 - A'1 . (I-25d)
A H A H
Thus, Equation (1-24) can be written as:
(A'1 - A'1) * CA > CE - CE + w*(CV - CV), or (I-26a)
AH
CE > CE + w*(CV - CV) + (A4 - A'1) * CA. (I-26b)
Comparing Equations (I-22b) and (I-23b), one sees that the blood concentration of the
inhaled compound in any human compartment is less than or equal to the average blood
concentration in the corresponding animal compartment; that is:
Cj 0 can be dropped from Equation (I-26b)
without affecting the inequality as follows:
CE > CE f (A'1 - A'1 ) * CA. (1-29)
AH
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Note that CE is the constant inhaled human concentration that would give rise to a human
constant blood level that is no greater than CA. If we choose the actual human exposure
concentration to be less than or equal to the CE, defined by CA = CA, then the actual CA
will be less than or equal to CA.
The following two cases are now considered with respect to the partition coefficient.
Case I: AA > AH.
The second term on the RHS of Equation (1-29) is greater than or equal to zero; thus,
the term can be dropped from the RHS without affecting the inequality. Obviously, a
conservative human exposure concentration is CE. Therefore, in terms of the variables in
Chapter 4, a conservative NOAELpjEQ] is given by:
NOAEL[HEC] = CE = NOAEL[ADJ] (1-30)
where:
= the observed NOAEL concentration adjusted for exposure duration
(Equation 4-3).
Case II: AA < AH.
The second term on the RHS of Equation (1-29) is negative in this instance. The
quantity of chemical inhaled must be greater than or equal to the quantity exhaled; this
requires that CE £ Cp or CA < XA* CE. In Equation (1-29), CA can be replaced by the
larger value, AA* CE, and still preserve the inequality, hence:
CE > CE + (A'1 - A"1 ) * XA * CE, or (1-3 la)
A H.
CE > CE *(AAAH). (1-3Ib)
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In this case, a conservative NOAELrHECj is given by:
NOAEL[HEC} = (VAH) * CE = (AAAH) * NOAE^^ (1-32)
where:
NOAFJLny^n = the observed NOAEL concentration adjusted for exposure duration
(Equation 4-3).
RESULTS
A perspective on the proposed method can be attained by examination of Figures 1-2 and
1-3, plots of NOAELrHECi vs. NOAELrAj for the rat and mouse, respectively. These plots
were created by choosing the equivalent exposure concentration that resulted in the human
arterial blood concentration being equal to the average arterial blood concentration of the
animal, using several methods, for the representative volatile organic compound
dicholoromethane (DCM).
The "established" method refers to using ratio of the ventilation rate divided by body
weight in the animal to the ventilation rate divided by body weight in the human ratio for
calculating NOAEL[HECj estimates (Federal Register, 1980), with the modification that
alveolar ventilation rates are used (U.S. Environmental Protection Agency, 1988b). The
NOAELr^jjj of the animal (Equation 4-3) is multiplied by the ratio to calculate the
NOAEL[HECj estimate using this method. The "optimal" method refers to the use of the
model with an extensive set of experimentally determined physiological parameters for the
three species (Andersen et al., 1987). The same model and human parameters were used for
the "similar" method, but the animal parameters were determined by scaling from the human
values, as defined in Assumption V.
In keeping with the results of the derivation that is the subject of this Appendix, the
"proposed" NOAELj^Q estimates are less than the "similar" method estimates. With
respect to the relationship of the proposed predictions to the other methods of calculation, the
following observations are noted.
The "proposed" method lines are parallel to the "established" lines and result in 3.4 and
6.9 times smaller, or more conservative, NOAELrHECj estimates for the rat and mouse,
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1,000
co
g
z
UJ
I
10
OJCHLDflOUETHANE
RAT
10
OPTIMAL
Sn/ULAR
PROPOSED
ESTABLISHED
100 1,000
NOAELA (mg/m3)
10,000
tlgure 1-2. Hot of NOAEI^c, vs. NOAEL[A] for the rat for four possible methods
^proposed, established, similar and optimal) of detennining NOAE!^^] estimates as
defined in the text. The inhaled compound is dichloromethane.
Source: Oveiton and Jarabek, 1989a,b.
1,000
CO
"a
LU
_1
LJJ
O
iacH
DICHLOflOMETHANE
MOUSE
10
OPTIMAL
SIMILAR
PHOPOSEO
ESTABUSHED
10
10,000
100 1,000
NOAELA (mg/m3)
Figure 1-3. Plot of NOAELp^j vs. NOAELA for the mouse for four possible methods
(proposed, established, similar and optimal) of determining
defined in the text. The inhaled compound is dichloromethane.
Source: Overton and Jarabek, 1989a,b.
estimates as
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respectively. The "proposed" rat NOAEL^gq estimates also fall below (i.e., are more
conservative than) those of the "optimal" method by a range of 1.4 to 2.4. Except at high
ty
exposure concentrations (above approximately 1,600 mg/m ), where the estimates are smaller
by about 1.3, the "proposed" mouse NOAELpjEC] estimates are up to 1.5 times greater than
the "optimal" NOAELrHECi estimates. This supports current evidence that the mouse is not
"similar" to humans in some cases (Reitz et al., 1988). The "proposed" method estimates,
however, more closely approximate the "optimal" method estimates than do the "established"
estimates. It also should be noted that the "optimal", "similar", and "proposed" methods
result in more conservative estimates for the mouse vs. rat, whereas the established
methodology results in the opposite relationship of estimates between the two species.
DISCUSSION
Considering the "optimal" method estimates to represent the best possible dose
extrapolation based on internal blood concentrations, then the "proposed" method is more
realistic than the "established" method. Since the blood to air partition coefficients are more
readily available than are complete physiological parameter data, the proposed method
represents a simple default approach when extensive PB-PK modeling is not feasible.
RESEARCH AND DEVELOPMENT
The approach presented in this Appendix has resulted from modeling research focused
on determining the key parameters of gas uptake, distribution and target tissue accumulation.
The future effort will incorporate the anatomic and some aspects of the clearance data being
compiled for research to support the particle modeling as described in Appendix H. Model
evaluation plans include comparing the efficiency of various dose surrogates and an approach
to address the apparent non-similarity of the mouse. Application of the model to address
mixtures of gases and of dose partitioning between gas and particles is also envisioned.
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•fr U.S. GOVERNMENT PRINTING OFFICE: 1990 - 748-159/2M65
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TECHNICAL REPORT DATA
(Pleat rod Instructions an the rtvmt be fan completing}
1. REPORT NO.
EPA 600/8-90/066A
2,
3, RECIPIENT'S ACCESSION NO.
PB90-238890
4, TITLE AND SUBTITLE
Interim Methods for Development of Inhalation
Reference Concentrations
S, REPORT DATE
August1990
«. PERFORMING ORGANIZATION CODE
600/23
?. AUTMORIS)
See list of authors
«. PERFORMING ORGANIZATION REPORT NO
ECAO-R-0204
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Environmental Criteria and Assessment Office (MD-52)
Office of Health and Environmental Assessment, ORD
U. S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO,
12. SPONSORING AGENCY NAME AND ADDRESS
Office of Health and Environmental Assessment (RD-689)
Office of Research and Development
D. S. Environmental Protection Agency
Washington, D. C. 20460
13, TYPE OF REPORT AND PERIOD COVERED
SAB Review Draft
1*. SPONSORING AGENCY CODE
600/21
IS. SUPPLEMENTARY NOTES
ISiSABSTHACT
An Inhalation reference concentration (RfC) Is an estimate of continuous
Inhalation exposure over a human lifetime that 1s unlikely to pose significant
risk of adverse noncancer health effects and serves as a benchmark value for
assisting In risk managmement decisions. Derivation of an RfC involves dose-
response assessment of animal data to determine the exposure levels at which no
significant Increase 1n the frequency or severity of adverse effects between the
exposed population and Us appropriate control exists. This assessment requires
an interspecies dose extrapolaton from a no-observed-adverse-efect level (NOAEL)
exposure concentration of an animal to a human equivalent NOAEL (NOAEL^c). The
RfC Is derived from the NOAELne by the application of generally order-of-
magnltude uncertainty factors. Intermittent exposure scenarios In animals are
extrapolated to chronic continuous human exposures. Relationships between
external exposures and Internal doses depend upon complex simultaneous and
consecutive processes of absorpton, distributor metabolism, storage,
detoxification, and elimination. To estimate NOAEL^-s when chemical-spedfie
physiologically-based pharmacoklnetlc models are not available, a dosimetric
extrapolation procedure based on anatomical and physiological parameters of the
exposed human and animal and the physical parameters of the toxic chemical has
been developed which gives equivalent or more conservative exposure
concentrations values than those that would be obtained with a PB-PK model.
7.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
C. COSATi Field/Group
B. DISTRIBUTION STATEMENT
Release to Public
19, SECURITY CLASS (This Report}
Unclassified
21, IKO..JPF PAGES
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22. PRICE
CPA F*n» 2220-1 (R«», 4-77) PREVIOUS EDITION i» OMOLETI
1
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