EUR 15606 EN
EPA / 600/R-94 7042
European Commision
Science, Research, and Development
U.S. Environmental Protection Agency
Health Effects Research Laboratory
"Human genetic risks from exposure to
chemicals, focusing on the feasibility of a
parallelogram approach"
Proceedings of the
EC/US Workshop on Risk Assessment
October 11 -14,1993
Durham, North Carolina, U.S.A.
Mouse Somatic Cells
Measured Mutations
and Adducts
Mouse Germ Cells
Measured Mutations
and Adducts
Human Somatic Cells
Measured Mutations
and Adducts
Human Germ Cells
Estimated Mutations
Comparisons
Estimates
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DISCLAIMER
EUR 15606 EN
This report EUR 15606 EN of the ENVIRONMENT Research Programme of the
European Commission (Directorate-General XII for Science, Research, and
Development). The texts in this volume reflect the views of their authors and do not
in any way engage the Commission.
EPA/600/R-94/042
The following is a record of presentations and discussions at the European
Commission (EC)/United States (U.S.) Workshop on Genetic Risk Assessment:
"Human Genetic Risks from Exposure to Chemicals, Focusing on the Feasibility of a
Parallelogram Approach," which was held at the Omni Durham Hotel and Convention
Center in Durham, North Carolina, on October 11-14,1993. Each speaker was given
the opportunity to edit the verbatim transcript of his/her presentation. This material
was prepared by Research and Evaluation Associates, Inc., under contract to the U.S.
Environmental Protection Agency. The views or opinions expressed in the attached
do not necessarily represent the views or policies of the U.S. Environmental Protection
Agency.
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TABLE OF CONTENTS
SECTION PAGE
List of Figures iv
List of Tables ix
Overview 1
Introduction 3
Workshop Organization 4
Synopses 10
Plenary Sessions 10
Compound Reviews 18
Rapporteur's Reports 18
Working Group Summary 27
Crosscutting Papers 28
Concluding Remarks 29
Plenary Sessions 33
Introductory Remarks 35
Dr. Lawrence Reiter 35
Dr. Heinrich Ott 37
Parallelogram Concept (Dr. Alan Wright) 41
Key Scientific Issues in Assessing Genetic Risks 41
Merits of Parallelogram Approach 55
Limits of Parallelogram Approach 57
Discussion 66
ICPEMC Efforts (Dr. David Brusick) 71
Regulatory Perspective (Dr. Kerry Dearfield) 87
What Are the Reasons for Mutagenictty Testing? 88
How to Determine if There is a Genotoxidty Concern? 91
How to Determine Real Risks to Humans? 97
Discussion 109
Research Background for EO (Dr. Lars Ehrenberg) 113
The Use of Hemoglobin Adducts 114
Ethylene Oxide Data 131
EO Mutagenicity Risk Assessment (Dr. Vicki Dellarco) 139
Discussion 159
A Reconsideration of EO Risk Assessment (Dr. Julian Preston) 161
Translocation Mechanisms and the Stages of
Spermatogenesis 163
Translocation Mechanisms and Oogenesis 168
Implications for Risk Assessment 170
Discussion 177
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TABLE OF CONTENTS (continued)
SECTION PAGE
Data Summaries of Compounds
Ethylene Oxide (Dr. AT. Natarajan) ............................ 183
Discussion ......................................... 195
Acrylamide (Dr. Kerry Dearfield) .............................. 199
Discussion ......................................... 221
1,3-Butadiene (Dr. Ilse-Dore Adler) ............................ 225
Discussion ......................................... 242
Cyclophosphamide (Dr. Diana Anderson) ....................... 247
Adducts In Vitro ..................................... 251
Adducts In Vivo ..................................... 253
Adducts in Humans .................................. 253
Somatic Mammalian Cell Genotoxicity In Vitro and
In Vivo ........................................... 254
Discussion ......................................... 270
Rapporteur's Reports ............................................ 275
Ethylene Oxide (Dr. Julian Preston) ........................... 277
Initial Report ....................................... 277
Final Report ........... ............................. 287
Discussion ......................................... 303
Acrylamide (Dr. George Douglas) ............................. 309
Initial Report ....................................... 309
Discussion ......................................... 310
Final Report ................... ...................... 311
Discussion ......................................... 318
1,3-Butadiene (Dr. Marja Sorsa) .............................. 321
Initial Report ....................................... 321
Final Report ........................................ 333
Discussion ......................................... 339
Cyclophosphamide (Dr. Paul Selby) ........................... 341
Initial Report ....................................... 341
Final Report ........................................ 345
Crosscutting Remarks ........................................... 353
Differential Repair of Chemically-Induced Damage:
Somatic Vs. Germ Cells (Dr. Ekkehart Vogel) .................... 357
The Male Germ-cell Cycle ................................
DMA Damage and Repair in Drosophila ......................
DMA Damage and Repair in the Mouse ......................
Comparison of Mouse and Drosophila .......................
Conclusions and Perspectives .............................
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TABLE OF CONTENTS (continued)
SECTION PAGE
Chemically-Induced Mutation: Comparative
Outcome in Somatic Vs. Germ Cells (Dr. Susan Lewis) 372
Special Attributes of Germ Cells
Female Germ Cells
Cell Environment
Sex Specificity of Mutagens
Induction of Mutagens Resulting in Inborn
Errors of Metabolism
Chemically-Induced Mutation: Comparative
Outcome in Somatic Vs. Germ Cells (Dr. James Allen) 376
Qualitative and Quantitative Results from
Short-Term Tests (Dr. Michael Waters) 383
Chemicals for Future Study of Heritable
Genetic Damage in Human Populations
(Dr. Michael Shelby) 403
Discussion
Wrap-Up and Concluding Remarks 413
Wrap-Up (Dr. David Brusick) 415
Concluding Remarks (Dr. Canice Nolan) 425
References 427
References 429
Appendix A - List of Participants A-1
Appendix B - Agenda B-1
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UST OF FIGURES
Figure E§Qe
1-1. The Sobels' Parallelogram 5
2-1. A Current Perception of the Principal Stages
& Systemic Determinants of Chemical Mutagenesis 42
2-2. Induction of Mutation by Genotoxic Chemicals 44
2-3. Management of Genotoxic Risks 44
2-4. Quantitative Genotoxic Risk Assessment 47
2-5. Factors Contributing to Mutation Load 47
2-6. Experimental Determination of Mutation 49
2-7. Extrapolative Stages in Assessing Heritable Risks 49
2-8. Systemic Determinants of Mutation 51
2-9 53
2-10. The Parallelogram 56
2-11. Induced Mutations in V79 Chinese Hamster Cells and the
Mouse Specific-locus Assay 56
2-12. In Vivo-In Vivo Extrapolative Model 58
2-13. In Vitro - In Vivo Extrapolative Model 62
2-14. Cytogenetic Damage in Human and Mouse Peripheral
Blood Lymphocytes Exposed to Radiation In Vitro and In Vivo 64
2-15. Conclusions 65
2-16. Mutation Frequency or DMA Dose 69
2-17. Mutation Frequency 69
2-18. Operational Definition 72
2-19 72
2-20. Risk Extrapolation Road-Map 74
2-21. Primary Project Objectives 75
2-22. Data Availability for Risk Extrapolation 76
2-23. Proposed Scaling Factors (REF) for Mouse to Human
Risk Extrapolation 77
2-24. Calculations Used in Risk Assessment 79
2-25. Limitations of the Methods 80
2-26. Summary of the Risk Extrapolation Approach 82
2-27. Ethylene Oxide , 83
2-28. Acrylamide 84
2-29. What Do These Numbers Mean? 85
2-30. Purpose of Mutagenicity Testing From Subdivision F 89
2-31. Initial Battery 92
2-32. USEPA OPPTS Mutagenicity Test Scheme 92
2-33 95
2-34. Examples of Human Diseases and Conditions Caused by
Mutations in Germ Cells 98
2-35. Difficulty of Identifying Human Germ-Cell Mutagens 99
2-36 101
vi
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LIST OF FIGURES (continued)
Figure Page
2-37. Quantitative Estimation of the Genetic Risk Associated with the
Induction of Heritable Translocations at Low-Dose Exposure:
Ethylene Oxide as an Example 101
2-38. Ethylene Oxide 102
2-39. Germ Cell Risk Extrapolation from Mice to Humans 105
2-40. Ethylene Oxide (EO) 113
2-41 115
2-42 115
2-43. Relationships between Alkylation of Hb and DNA by
Ethylene Oxide 117
2-44 120
2-45 122
2-46 124
2-47. Hb Adduct Levels in EO-Treated Rats 124
2-48. Persistence of 7-(2-hydroxyethyl)guanine in Exposure of Rats (a) to
300 PPM Ethylene Oxide and Ethylene Oxide 125
2-49. Concentration-Response Curves for Induction of Dominant Lethal
Mutations in Male Germ Cells 127
2-50. Deviations from Linearity 128
2-51. Non-linearity; Saturation of Detoxification at High Doses 129
2-52. Comparison of the Dose Response of N-(2-hydroxyethyl)valine in
Hemoglobin and 7-(2-hydroxyethyl) guanine in DNA of
Rats and Mice 130
2-53 132
2-54 132
2-55 134
2-56 135
2-57 138
2-58 142
2-59 145
2-60. ETO Dose-Rate Studies 148
2-61 148
2-62 150
2-63 150
2-64 152
2-65. Biological Consequences of Rearranged Chromosomes 153
2-66. Two Chromosome "Breaks" in Close Proximity 153
2-67. Weibull (Extreme Value Distribution) 154
2-68. Genetic Risk to Human by Extrapolations 162
2-69. Dose Response Curve for X-Ray-lnduced Reciprocal Translocations in
Mouse Spermatogonial Stem Cells 162
2-70. Spermatogenesis 165
2-71. Cell Cycle 165
2-72. Oogenesis 169
vii
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LJST OF FIGURES (continued)
2-73 171
2-74. Calculation of Total Unbalanced Genotypes that are Present in
Live-Born Individuals 173
2-75. The Induction of Transiocation in Mouse Post-Miotic Germ
Cells by EO 176
3-1. Acrylamide, Glycidamide 202
3-2 205
3-3. AA-Protein Adducts, GLY-DNA Adducts 207
3-4 212
3-5. Gene Mutations, Clastogenicity 214
3-6. Doubling Dose Method 219
3-7. REFs for Acrylamide 220
3-8 226
3-9 226
3-10. Bone Marrow Micronudeus Test After 1,3-Butadiene Exposure for
(6h/d on 2 d, Nose Only) 228
3-11. Bone Marrow SCE Test After 1,3-Butadiene Exposure for
(6h/d on 2 d, Nose Only) 229
3-12 231
3-13 231
3-14. Detection of Germ Cell Specific Mutagens (1991) 232
3-15. Cooperations 232
3-16. Micronucleus-Test Results 18-24 h after 1,3-Butadiene Inhalation
Exposure 234
3-17. Micronuclei in Early Spermatids of Mice 236
3-18. Dominant Lethal Test after Inhalation Exposure to 1,3-Butadiene 238
3-19. Cyclophosphamide 249
3-20. The Metabolites of Cyclophosphamide Metabolism 249
3-21. Cytotoxic Metabolites of Cyclophosphamide Metabolism 250
3-22. The Structures of Guanine Adducts Formed by the Reaction with
Phosphoramide Mustard 252
3-23. Cyclophosphamide: Effect of the Dose Fractionation on the
Frequency of Dominant Lethals Induced in Mice 265
4-1. Reciprocal Translocations Induced in Mouse
Germ Cells by EO 278
4-2. Dominant visible Mutation Data for the Following
Ethylene Oxide Exposure 282
4-3. Calculation of Dominant Mutation Rate in Humans
Following Ethylene Oxide Exposure 283
vw
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LIST OF FIGURES (continued)
4-4. HPRT Mutants in Mouse Splenocytes, HPRT Mutants in Human
Splenocytes 285
4-5. Dominant Visible Mutation Data for the Following
Ethylene Oxide Exposure 288
4-6. Calculation of Dominant Mutation Rate in Humans
Following Ethylene Oxide Exposure 289
4-7. HPRT Mutants in Mouse Splenocytes, HPRT Mutants in Human
Lymphocytes 290
4-8. A Parallelogram for Predicting Mutation induction in
Human Gene Cells Following Ethylene Oxide Exposure 292
4-9. The "Perfect" Data Set for Genetic Risk Assessment by the
Parallelogram Approach 302
4-10. Specific Differences in the Metabolism of Butadiene In Vivo 322
4-11. Cartinogenicity of BD 324
4-12. Interspedes Differences 325
4-13. HPRT Mouse Splenocytes 326
4-14. Human Monitoring of BD 327
4-15. HPRT Humans 328
4-16. Genetic Risk Estimates for cGy of Paternal Exposure to X or
Gamma Radiation Made by the Direct Method 352
5-1 358
5-2 360
5-3. ..'. 360
5-4. Sertoli Cell Barrier 377
5-5 379
5-6. 384
5-7. OPP Mutagenicity Test Guideline 384
5-8. OTS Mutagenicity Test Scheme 386
5-9. How Was the Database Assembled? 387
5-10. Germ Cell Mutagen Tests 387
5-11. Short-term Test Results for Agents Positive in More than One
Germ Cell Assay 388
5-12. Short-term Test Results for Agents Confirmed Positive in One
Germ Cell Assay 390
5-13. Positive Germ Cell Vs. Somatic Cell Tests 391
5-14. Short-term Results for Agents Negative in More that One Germ
Cell Assay 392
5-15. Short-term Results for Agents Negative in One Germ
Cell Assay 394
5-16. Negative Germ Cell Vs. Somatic Cell Tests 394
5-17. Performance of Somatic Vs. Germ Cell Tests for Agents
Confirmed in Germ Cell Studies 396
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LIST OF FIGURES (continued)
Figure Page
5-18. LEDs of Germ Cell Mutagens Tested in CBA Vs MVM 398
5-19. LEDs of Germ Cell Mutagens Tested in CBA Vs MVM 398
5-20. LEDs of Germ Cell Mutagens Tested in DLM Vs MHT 399
5-21. LEDs of Germ Cell Mutagens Tested in DLM Vs MVM 399
5-22. LEDs of Germ Cell Mutagens Tested in MHT Vs SLT 401
5-23. Agents Positive in Mammalian Germ Cell Tests 402
6-1. Catch 22 Situation 416
6-2. Consequences 416
6-3. Summary 419
6-4. Application of Parallelogram to Risk Assessment 422
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LIST OF TABLES
1-1. Questions Posed to Working Groups and Answers 6
2-1. Adduct Levels in Rats Exposed to EO 118
2-2. Human Data, Hb Adduct Levels from Ethylene Oxide 119
2-3. Best Values for Parameters and for Increments of Adduct Level or
Dose per Unit of Exposure Dose or Absorbed Dose 121
2-4. Annual Dose of EO in the 8.5 Million Population of Sweden 136
2-5. Excess Risk per 10,000 Live Births 155
2-6. Death of Conceptuses and Defects among Living Fetuses 157
3-1. Cytogenetic Studies and Point Mutation Studies in
Cells of Humans Exposed to Ethylene Oxide 187
3-2. Micronucleus Studies in Peripheral Blood Lymphocytes or
Other Types of Cell* 188
3-3. Sister Chromatid Exchange (SCE) Studies in
Peripheral Blood Lymphocytes 191
3-4. Acrylamide Exposure 216
3-5. Genotoxicity of 1,3-Butadiene In Vitro 227
3-6. Testing of 1,3-Butadiene in the Spot Test with (TxHT)F1
Mouse Embryos 235
3-7 240
3-8. Species Comparison of Dose of Expoxybutene in Blood,
Calculated from Hemoglobin Adduct Levels, and
Predicted Dose in the Body of Epoxybutene Following
Exposure to Butadiene 241
3-9. EC/US Workshop on Risk Assessment Parallelogram/
Chemical Matrix - Somatic Genotoxic In Vitro
Effects of Cydophosphamide on Mammals
Including Humans 255
3-10. EC/US Workshop on Risk Assessment Parallelogram/
Chemical Matrix - Somatic Genotoxic In Vivo
Effects of Cydophosphamide on Mammals and
Human Somatic Cells 256
3-11. Examples of Somatic In Vivo Effects of CP in Mammals
Induding Man 258
3-12. Activity of Cydophosphamide Detected in Individual
Assay Systems 259
3-13 264
3-14. Doubling Dose of Cydophosphamide in mg/kg For Different
Genetic Endpoints in Mice 266
4-1. Proposed Scaling Factors (REF) for Mouse to
Human Risk Extrapolation 312
xl
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LIST OF TABLES (continued)
Table Paoe
4-2. Estimate of Comparative Doubling Dose of HPRT Mutations by
Exposure to Butadiene 336
5-1. Specific-locus Test Results for Chemicals that have given
Conclusive Results in Both Stem-Cell Spermtogonia and
Post-Cell Stages' 362
5-2. Genetic Activity Profiles of Three Categories of Alkylating
Agents in Visible Specific-locus Assays of Drosophila
(vermillion locus) and the Mouse (7 loci) in Comparison
with Tumorigenic Potency in Rodents 365
5-3. Factors to be Considered in Assessing Gene Mutations in
Germ Cells : 374
XM
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Overview
This document contains the Proceedings of the European Commission/ United States
(EC/US) Workshop on Genetic Risk Assessment held October 11 -14,1993 in Durham,
North Carolina, U.SA The title of the workshop was "Human Genetic Risks from
Exposure to Chemicals, Focusing on the Feasibility of a Parallelogram Approach".
The parallelogram approach to genetic risk assessment and the workshop
itself were proposed by the late Emeritus Professor Frits Sobels of the University of
Leiden, The Netherlands. Professor Sobels was deeply involved in the planning of the
meeting until his death on the 6th of July, 1993. It is appropriate, therefore, that the
Workshop and these Proceedings are dedicated to his memory.
i
The objective of the workshop was to identify the methodology, data
requirements, and mechanistic research to understand the human impact of germ cell
mutagens. The European Commission and the U.S. Environmental Protection Agency
as joint sponsors of the meeting anticipate that the discussions and conclusions from
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the Workshop (see Synopses and Table 1-1) will be of substantial value in planning
future research to address the information requirements of genetic risk assessment.
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Introduction
This workshop was the concept of Emeritus Professor Frits Sobels of the University
of Leiden, The Netherlands. The underlying idea of the Sobels' parallelogram
approach is that an estimate (corrected by DMA adduct dosimetry) of the genetic
damage in human germ cells can be obtained by measuring a common endpoint in
human and mouse somatic cells. The main objective of the workshop was to identify
the methodology, data requirements, and mechanistic research needed to understand
the human health impact of germ cell mutagens.
Four chemicals were selected for review at the meeting: ethylene oxide, 1,3-
butadiene, acrylamide, and cyclophosphamide. The first three are important industrial
chemicals with substantial use worldwide and, therefore, considerable potential human
exposure. The fourth, cyclophosphamide, is a commonly used cancer
chemotherapeutic agent.
This first EC/US workshop on risk assessment was highly focused on the
feasibility of the parallelogram concept to estimate potential germ cell effects in
humans. It represented an evaluation of current knowledge and the identification of
future research needs for a more precise assessment of human genetic risks from
exposure to mutagenic chemicals.
Following introductory remarks by Dr. Lawrence Reiter of the US Environmental
Protection Agency (EPA), the workshop was dedicated by Dr. Heinrich Ott of the
European Commission (EC) to his and our friend, the late Professor Frits Sobels.
Professor Sobels was the initiator of the workshop, and he was deeply involved in its
planning, until his death on the 6th of July 1993.
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Some years ago, Sobels (1977,1982) began to formulate strategies applying
DNA adduct dosimetry to improve the assessment of risks for genotoxic chemicals,
particularly heritable risks. The underlying idea of the Sobels' parallelogram
approach (Figure 1 -1) is that an estimate of the genetic damage in human germ cells
can be obtained by measuring a common endpoint in human and mouse somatic
cells, (such as gene mutation in lymphocytes), and in germ cells of mice, the desired
target tissue inaccessible in humans.
Workshop Organization
The workshop was organized into plenary and working group sessions. The plenary
sessions began with a general introduction to the parallelogram concept and genetic
risk assessment, and continued with an examination of mutagenicfty data on ethylene
oxide (EO) as an example. Papers were presented on the research background for
EO, a draft risk assessment and a re-evaluation of the EO risk assessment. Following
overview presentations by each of the four chemical working group chairpersons, the
working groups were convened to carefully review the existing data for each
compound. Working group progress reports were presented in plenary by each of
the four rapporteurs. After two rounds of review and analysis, the rapporteurs
concluded their reports with answers to a series of questions (Table 1-1) that had
been posed to each working group. Finally, the workshop participants heard four
crosscutting papers representing current research on germ cell mutagens and future
research directions in genetic risk assessment.
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The Sobel's Parallelogram
Mouse Somatic Cells
Mouse Germ Cells
Measured Mutations
and Adducts
Measured Mutations ^
and Adducts
Human Somatic Cells
Measured Mutations
and Adducts
Human Germ Cells
Estimated Mutations
Comparisons < ^
Estimates >•
Figure 1-1
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Table 1-1. Questions Posed to Working Groups and Answers.
1) Which are the most important studies available to support genetic risk
assessment activities?
ACRYLAMIDE (AA)
EPA grout worker
study.
OSHA occupational
exposure study.
Ingestion through
drinking water.
BUTADIENE (BD)
Germ cell studies.
Human exposure data
CYLOPHOSPHAMIDE (CP)
Mouse heritable
translocatlon, specific locus
and
malformation data.
Human exposure data
ETHYLENE OXIDE (EO)
Mouse germ cell data
(Lewis, et al., 1986).
Somatic cell data
(Walker and Skopek,
1993).
Human data (Fates et
al., 1991).
1 a) What endpoints (including other than genetic ones) are considered "adverse
effects" from a risk assessment perspective? Congenital malformation and
infertility data, spontaneous abortions, tumor incidence.
2) Identify inadequacies and gaps in the data base for genetic risk assessment.
ACRYLAMIDE (AA)
Human somatic cell
response needs more
definition with better
largei ussue oooiinoiiy
data
BUTADIENE (BD)
Need metabolism and
pharmacokinetics data
before proceeding with
genetic studies.
CYLOPHOSPHAMIDE (CP)
Need adduct data in mouse
and human somatic cells
and in mouse germ cells.
Human dose-response data
are inadequate.
ETHYLENE OXIDE (EO)
Need better human
exposure data,
additional somatic
mutational
endpoints.
Need doslmetry and
additional endpoints in
all germ cell stages.
6
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Table 1-1. Questions Posed to Working Groups and Answers (Cont).
3) Is the necessary dosimetry data available? If not, what else is needed?
ACRYLAMIDE (AA)
No. Need the same
biological endpoints and
dosimetry In humans
and In animals.
Need better exposure
assessment in humans.
BUTADIENE (BD)
No. Target dose and
pharmacokinetlcs
data needed.
CYLOPHOSPHAMIDE (CP)
No. Good target dose data
needed.
Need gene mutation data in
human cells and aneuploidy
data In mouse germ cells.
ETHYLENE OXIDE (EO)
No. See 2. (above).
4) What are the major assumptions upon which the genetic risk assessment is
based?
ACRYLAMIDE (AA)
BUTADIENE (BD)
CYLOPHOSPHAMIDE (CP)
ETHYLENE OXIDE (EO)
Mouse equals man; all
other assumptions are
inherent In this one
major assumption.
Rodent DMA response
Is equivalent to human
DMA response.
Equivalent background
and Induced mutation
rates In animal model
and humans.
Equivalent germ cell
specificity in mouse
and human at all
Assume linearity of dose
response; similar sensitivity
of mice and humans;
similar dose responses and
levels of cytotoxictty;
relative sensitivity somatic
cell types; relative sensitivity
of males vs. females; high
to low dose; similar dose
rate effects; background or
"spontaneous" mutations;
similar synerglstlc effects.
Similar sensitivity of
mice and humans,
somatic and germ cells.
No dose rate effects.
Equivalent sensitivity of
males and females.
Assume linear high to
low dose extrapolation.
Assume that HPRT
mutations assayed are
representative of the
genome as a whole.
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Table 1-1. Questions Posed to Working Groups and Answers (Coot).
5) What are the major uncertainties in the risk assessment?
ACRYLAMIDE (AA)
Similar extrapolation
factors; target
dosimetry;
Induction kinetics for
different endpoints.
BUTADIENE (BD)
Extrapolations; for
doses, dose rates
and species.
CYLOPHOSPHAMIDE (CP)
All of the assumptions.
ETHYLENE OXIDE (EO)
All of the assumptions.
6) What research is required to address the major assumptions and
uncertainties?
ACRYLAMIDE (AA)
Demonstrate a
human germ cell
specific mutagen.
Elucidate risk
extrapolation factors.
Improve
pharrnacokinette
modelling for animal
models and for
humans.
BUTADIENE (BD)
Confirm occupational
exposure study (HPRT
variant frequency)
using another method;
Research needed
on human, mouse and
rat metabolism, DMA
relationships, and
mutation spectra
Induced by BD and
metabolites.
CYLOPHOSPHAMIDE (CP)
Adducts, human data;
female data required.
ETHYLENE OXIDE (EO)
In addition to 2. (above);
research Is needed to
validate assumptions,
need work on
retrospective
dosimetry (particularly
for reciprocal
translocatlons using
hybridization
techniques).
Are you able to establish a priority for the needed research? No.
8
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Table 1-1. Questions Posed to Working Groups and Answers (Con?).
8) Is the parallelogram approach applicable to the compound under
investigation? If not, why not? What alternative approaches appear more
suitable? Ust pros and cons of each.
ACRYLAMIDE (AA)
BUTADIENE (BO)
CYLOPHOSPHAMIDE (CP)
ETHYLENE OXIDE (EO)
Yes/no. Conceptually,
It is a very useful tool to
focus needs for
extrapolation to
humans; many
components are
already In use In other
risk assessment
methods, do not need
all four comers to make
progress.
Not yet. There Is too
much missing data
and It Is not yet clear
which Is the
appropriate animal
model, If It is not the
mouse, then
reconsideration is
required on the
appropriate endpoints
to study.
Yes, It is possible If some
major assumptions are
made out too many parts of
the parallelogram are
missing; there are not
enough data
Yes, It is possible to
construct the
parallelogram but the
group was
uncomfortable in doing
so because of missing
data
9) How would one address the problem of "no human germ cell mutagens*?
ACRYLAMIDE (AA)
BUTADIENE (BD)
CYLOPHOSPHAMIDE (CP)
ETHYLENE OXIDE (EO)
Perhaps an
epidemtologlcal study
showing a positive link
between exposure to a
mutagen and a birth
defect
Need more btomarker
studies in an
epidemiologies)
approach in humans;
but for BD It Is too
early to attempt an
epidemtotogical
approach; the
problem is not'no
germ cell mutagens', ft
Is 'no germ cell
mutagens
demonstrated'.
Perhaps radiation and
cytostatlcs are human germ
cell mutagens; need to
show Inherited damage, but
It Is satisfactory to
extrapolate from animal
models with good
comparative mechanistic,
metabolism and
pharmacoklnetlcs data
The problem Is not 'no
germ cell mutagens', It
Is, 'no germ cell
mutagens
demonstrated'; the best
way forward is to look at
cancer Incidence in
progeny; there are
indicative
epidemtologlcal data
worth pursuing.
10) What would you consider an "ideal data set"?
assumptions validated.
All four comers filled and
9
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Synopses
The following sub-section provides a synopsis for each of major sections of the
conference: Plenary sessions, Data Summaries of Compounds, Rapporteur's Reports,
Crosscutting Remarks, and Concluding Remarks.
Plenary Sessions
Alan Wright introduced the parallelogram concept, prefacing his remarks with an
overview on genetic risk assessment: "An appreciation of the mechanisms of
genotoxic action has permitted simplifications in correcting for the concerted action
of factors (related to dose and effect) by allowing correction to be applied in two
stages. The first stage compensates for differences in determination of the critical
dose which, in turn, determines the rate of formation of the promutagenic lesions in
DMA. The second stage involves compensating for differences in the operation of
factors to determine the progression of the key promutagenic lesions into mutations."
In its original form, the concept known as the parallelogram approach was
developed by Sobeis (1977) to estimate mutations in rodent germ cells, by
determining mutagenic potency in rodent somatic cells in vitro and applying
measurements of the amounts of specific DMA adducts, formed in the in vitro test
system and in mouse germ cells in vivo. [Note that Brewen and Preston (1974,1975)
presented a mouse-human, somatic-germ cell proportionality approach for radiation-
induced chromosome aberrations and were able to confirm the human germ cell data
using cells from irradiated human testes].
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Evidence to support the contention that measurements of specific DMA
adducts may provide a basis for extrapolating mutagenicrty data from one cell type
to another, within an individual or species or even between species, was reported by
van Zeeland (1985). Very similar mutation frequencies were observed when
measurements of 06-ethylguanine in DNA were used as a basis for calculating the
mutagenic potencies of ethyl nitrosourea in inducing HPRT mutations in V79 cells and
specific locus mutations in mouse spermatogonia.
In attempting to estimate mutation frequencies in human germ cells, according
to the extended parallelogram model (Sobels, 1982, 1989), a common genetic
endpoint is measured in the somatic cells of human and an experimental animal.
Then, the subsequent determination of the potency of the agent in the germ cells in
the experimental model permits the ratio of somatic to germ cell mutation to be
calculated in the experimental model. Provided this ratio holds for humans, one can
then calculate the risk to man.
The key assumption in the extended parallelogram model, of course, is that
the ratio between somatic and germ cell mutation is the same in the experimental
model and in man. As Wright pointed out, given the marked variations in gene
expression between tissues and species, there is no theoretical justification for this
assumption [Note, however, the previous work of Brewen and Preston (1974,1975)].
Thus, the validation of the extended Sobels' parallelogram model depends on
producing convincing evidence that the relative mutagenic effectiveness of a given
genotoxic agent towards target somatic cells, (for example, peripheral blood
lymphocytes,) and germ cells in the test species parallels that in humans over a broad
dose range. This may not be an easy task since, in practice, when environmental
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exposures are reduced to very low levels, the small incremental effect caused by a
specific chemical may no longer be detected within the relatively high and variable
background of mutation.
Furthermore, according to Wright, one must consider that, for any given dose
of a genotoxic chemical, the principal determinants of interspecies extrapolation
constants are the rates and fidelity of DMA repair and replication. Intuitively, one
assumes proportionality or parallelism between the repair of damage induced by
different agents, in the same organism. However, high dose exposures typically
employed in experimental models may lead to rapid saturation of repair systems and,
therefore, to results which may not adequately reflect the role of repair in determining
mutation, especially at the low dose exposure levels operative in the human
environment.
Wright pointed out that a number of approaches have been proposed and
developed to compensate for species differences in DMA repair and replication; one
notable approach is the radiation equivalence concept which is also designed to
assess cancer risks. However, all of these approaches are based on the
determination of relative mutagenic potencies, and they all necessitate proportionality
or parallelism between the repair of damage induced by different agents, in the same
test system or, alternatively, the same agent in different tissues of an individual or
strain. Also, accurate assessment of the dose of the ultimate mutagen delivered to
the target DMA is fundamental to all of these approaches.
Wright concluded, "I think in view of the potential limitations of the
(parallelogram) approach, it would be prudent to explore alternative strategies to
correct for species differences and (differences in) the progression phase of mutation."
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David Brusick summarized the recent efforts of the International Commission
for Protection against Environmental Mutagens and Carcinogens (ICPEMC) in genetic
risk assessment. He noted that Frits Sobels was one of the founding fathers of
ICPEMC and was the organization's first chairman.
ICPEMC was asked by Health and Welfare Canada and by the EPA to examine
whether risk data in mice can be extrapolated to human populations so as to describe
the impact of exposure to germ cell mutagens in terms of the number of probable new
mutations in the F, population. Brusick described the process used by ICPEMC (for
EO and acrylamide) for determining from the animal data the appropriate scaling
factors, the assembly of human population exposure data and the exposure
assessment, the calculation of the risk using the direct or the doubling dose method,
and finally, estimation of (based on the exposure) the induced genetic disease in the
F, population. The assumptions employed by ICPEMC in the reports (ICPEMC
1993a,b) described by Brusick played an important role in the discussions that
followed during the meeting.
Kerry DearfiekJ described the risk assessment guidelines of the EPA Office of
Prevention, Pesticides, and Toxic Substances (OPPTS) (USEPA I986a,b) and
particularly its Mutagenicrty Testing Guidelines (Dearfield, 1991). He discussed the
assay methods used by the Agency to evaluate the capacity of the chemicals to alter
genetic material and the way in which these findings are incorporated into an
assessment of risk of heritable genetic alterations in humans. While not explicitly
employing the parallelogram method, he said, "We have utilized many of the concepts
of the parallelogram in our regulatory battery."
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Reflecting on the EPA's draft risk assessment for EO, he observed that
"OSHA's permissible exposure level (PEL) is one ppm. At one-half of that, which is
a very real occupational exposure, there are going to be an additional three persons
in 10,000 having. . .heritable translocations. How do you communicate that
risk? . . .You can also say that 3 x 1CT4 may be included in the variability of the
background. The background is 19 x 10"*. So, how do you detect 3 over 19 in the
human population? More importantly, how does this number '3' get translated into
a phenotypically-expressed adverse health consequence?" Finally, Dearfield made
a strong point of the fact that EO shows reproductive and developmental effects as
well as neurotoxicity, all of which may be related to the electrophilicity and/or the
genetic activity of the chemical.
Lars Ehrenberg presented a research perspective on the EO exposure
assessment and target tissue dosimetry (via hemoglobin and DMA adducts). He
pointed out that measuring the adduct level in hemoglobin, as a surrogate dose
monitor, has the advantage that the molecules are not repaired: They are more long-
lived, and from the steady state level of adducts from long-term exposed persons, we
can estimate the contribution for the adduct level per unit time, and then evaluate the
dose as for DMA There is a tendency to overestimate exposure doses with the
effect that the calculated in vivo dose per unit of exposure dose becomes too low.
The tests where we get the higher figures are from those studies where we have the
most careful measurements of exposure dose." In describing dominant lethal
mutations as a function of EO exposure from Waldy Generoso's work (Generoso, et
al., 1990), Ehrenberg pointed to a strong increase in lethality at higher doses. "What
could be the causes of this? There could be saturation of detoxification and
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saturation of DMA repair to make the curve turn upwards. There could be induction
of detoxification that will turn it the other way. Induction of repair could affect the curve
at very low doses You see that the curve drops at very high doses because of
the damage to sensitive cells." Thus, Ehrenberg provided a specific example of
concepts discussed by previous speakers.
Vicki Dellarco described the EPA Draft EO risk assessment (Rhomberg et at.,
1990) based on heritable translocations in mice (Generoso et al., 1990). The project
was intended to develop a framework for quantitative genetic risk assessment, to
highlight the issues and uncertainties, and to promote discussion on data
requirements and future research directions.
In discussing the assumptions used by the EPA, she said, "Ethylene oxide
dominant lethals (in mice) are induced in mid to late spermatids to early spermatozoa.
The window (in the mouse) is 10 days. So, one major assumption we are making is
that there is not a stem cell effect. That is a critical assumption, because if there was
a stem cell effect, even after exposure had ceased, the individual would be at an
increased risk for his entire reproductive life; whereas with a post-stem cell effect,
increased risk is only a function of recent exposures. Risk would recede with
termination of exposure."
She pointed out that EO concentrations in tissue are in balance with a
constant external air concentration. "Assuming proportionality at low to moderate
doses isn't such a bad assumption Where our approach fails is looking at risk
from episodic high exposures, given the known dose rate effects of EO So, for
the ppm-hour dose-metric, we have to assume that response is a function of exposure
times time. We have to determine the species equivalence of dose in mouse and
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man. The assumption is that the probability of inducing a heritable translocation is
a function of adduct load irrespective of the species; also, the ratio of ppm-hour of
external exposure to the concentration times time product, in the testis, is the same
for mice and man."
In discussing the dose-response modelling for EO, she said, "We are assuming
(that the target is) DMA. If it is protamine, then one may chose to select different
mathematical models to fit the observed data on translocation frequency." Another
point emphasized by Dellarco was that it was necessary to assume that males and
females are at similar risk. She said, "This is a weak assumption given that the male
is unlikely to be a reliable predictor of female risk. More studies are needed in
females to assess population risk. Finally, the zygote should be included in an
assessment of genetic risk.*
Julian Preston introduced a number of mechanistic considerations related to
the EPA risk assessment on EO and to genetic risk assessment in general. He
pointed out that the probabilities in differentiating spermatogonial cells for chemical
induction of a mutation or a translocation will be related to the total cell cycle and the
proportion of that cell cycle that is in DMA synthesis phase. "Although you have a
higher probability of producing a mutation or a translocation in a rapidly cycling cell,
you have a higher probability of killing that same cell, because of a secondary
event In the case of the stem cell, the probability of getting a mutant cell either
with a translocation or a mutation is rather high relative to cell killing.11
Preston said that he believes that one should strongly consider the stem cells,
especially under chronic exposure conditions. He went on to say that for post-meiotic
germ cells, the next S phase occurs following fertilization. Thus, there is a long period
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of time to accumulate damage (several days), and there is no repair taking place in
these cells during this time. The repair takes place after fertilization occurs. Preston
said, "There is a race between repair of damage induced in this cell stage and
replication at the S phase. You have to divide up the spermatogenic cell cycle into
the different components; and then relate those to exposure, or to effective dose to
those particular cell stages, based upon duration within the spermatogenic cell cycle."
He also asserted that there are likely to be different probabilities of mutation across
species, as related to the spermatogenic cell cycle.
Discussing similar considerations for oogenesis in the female, he said: "For
translocations and for mutations, there would be a rather low probability of induction
for the female. The higher probability, one would anticipate, would be for aneuploidy
events."
Regarding translocation carriers, Preston argued, "I consider only the ones
that don't result in spontaneous abortions as a component of genetic risk." Preston
calculated that only 9% of the unbalanced genotypes result in a viable, but
unbalanced offspring. With regard to the modelling of the dose response curves,
Preston said, "I would argue that at low exposures, it would really be most applicable,
for translocations, to use something which is very much a dose squared component
curve. That might not be the same as the rest of the curve, at high exposures; but
at low exposures, you need two lesions to produce a translocation, and you can well
argue that those are produced as independent events, because you need replication
errors on two chromosomes."
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Compound Reviews
Nat Natarajan provided a general overview of the published data base on EO, as did
Kerry Dearfiekf for Acrylamide (AA), Use-Dore Adter for 1,3-Butadiene (BD) and Diana
Anderson for Cyclophosphamide (CP). Space does not permit reporting of these
presentations. However, it is anticipated that the Work Group reports on the risk
evaluations for these compounds will be published in the near future in Mutation
Research Reviews in Genetic Toxicology. The overall conclusions of the Working
Groups regarding risk evaluations are captured under the Rapporteur's Reports that
follow below for each of the four compounds.
Rapporteur's Reports
Ethytene Oxide (EO). The EO Working Group consisted of Nat Natarajan, as
Chairman, Vicki Dellarco, Lars Ehrenberg, Waldy Generoso, Susan Lewis, Julian
Preston, and Ad Tates.
Julian Preston, as rapporteur, discussed the heritable translocation risk
estimate for EO with regard to the applicability of the Generoso et al. (1990) data set
and the necessary additional data for purposes of genetic risk estimation.
The working group decided there were two factors that were of importance, that led
to a specific conclusion on the utility of the heritable translocation assay. One factor
was Lars Ehrenberg's calculation of dose, and the second one was the independence
of spontaneous and EO-induced lesions. Ehrenberg calculated the target dose to
DNA, which flattened the curve at the low exposure levels and reduced the effect. For
reciprocal translocations, as a function of dose to the DNA, this further reduced the
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risk estimate from the linear extrapolation on the ppm curve discussed by Vicki
Dellarco.
The group also discussed the second point in some detail, whether to use an
additive assessment of slope. That is, do spontaneous lesions interact with the EO-
induced ones, or are these independent events? They concluded that because of the
very low probability of having a spontaneous event, they would consider those as
additive and not interactive. Again, these decisions reduced a linear response to a
curve that is convex downward, and, again, reduced the estimated effect at low
exposure levels.
The conclusion from these two points was that genetic risk from reciprocal
translocations at low exposures and for post-meiotic cells (for which the data set was
collected), would be extremely low.
There was no new data on gene mutations in germ cells, so the group
examined some data that was published by Susan Lewis and her colleagues on
electrophoretic variants and dominant visible mutations in the mouse (Lewis, et al.,
1986). The exposures covered the entire spermatogenic cycle and yielded four
dominant mutations in 1891 offspring. Two were heritable translocations.
Using an average exposure time for the four mutants (in fact, the first one
showed up at around 60,000 ppm hours), 20 phenotypes (50 loci) scored and an
assumed 2000 similar dominant loci in the human, the estimate was 160 dominant
visible mutations in humans, assuming that all sensitivities are equal between the
species. That would be 160 mutations times 10"5 per ppm hour, or one mutation per
1600 individuals per year of exposure, based upon a one ppm exposure.
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Preston: "We took the one ppm exposure and converted that into dominant
visible mutations We progressed from that germ cell calculation, on the only data
that we could do any manipulative calculation with, and moved into somatic cell
studies, to try and link mouse with man by another approach."
The group assumed that from the somatic cell data, for HPRT mutations in
mouse and man, they could use a sensitivity factor of one. Again, they assumed
2,000 dominant loci, and assumed that each was giving rise to an independent
phenotype. From the mouse data, the group had derived the value 4.0 x 10'10 per
locus, per ppm-hour. The human value would be equivalent, for per locus frequency
for the total number of dominant mutations. One would expect 8.0 x 10*7 dominant
mutations per ppm-hour.
Preston concluded, That is not to be taken as an extremely accurate number,
but I think it gives the types of data sets that are available, and what you can do with
those. We felt that was a reasonable approach to take. Lars Ehrenberg did a quick
comparison with radiation numbers, and it would indicate that the ethylene oxide
number here for dominant mutations might be of the order of ten to 100 times higher
than a radiation equivalent mutation frequency, on a per rad basis. The approach that
we took was based upon the data we had available. We had four dominant mutations
in the mouse study that we utilized, and the HPRT mutation in somatic cells. We
finished UP making a decision that the parallelogram approach could be utilized on
those data. And, of course, we are certainly open to discussion on that approach."
The group also discussed the feasibility and appropriateness of using the
relationship between tumor incidence as a measure of relative sensitivity on the
assumption that the initiation of a tumor, at least, is modeling mutation induction in
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somatic cells. They specifically discussed the use of leukemias, on the assumption
that the human myelogenous leukemias are the same tumor type as in a mouse.
They also considered the appropriateness of spontaneous abortion for risk
assessment; the group agreed that it is clearly an important component of genetic
risk, but could not distinguish genetic, zygotic, or cell killing responses. Preston said:
"We still thought, it is worth considering more.. .and if it is not a genetic component,
then you still need the model. . .for the zygotic data, as a separate assessment of
risk."
Acrylamide (AA). The AA Working Group consisted of Kerry Dearfield, as Chairman,
David Brusick, George Douglas, Udo Ehling, Martha Moore, and Gary Sega.
George Douglas served as rapporteur for the AA group. With regard to the
parallelogram approach, the group concluded that there were no human data, other
than one hemoglobin adduct study, that would permit entry into the parallelogram.
The parallelogram model was, therefore, used instead to delineate what additional
tests were required to understand the risk involved from AA. There was discussion
(without a satisfactory conclusion) about the possible partitioning of effects involving
metabolites; for example, there was a question whether glycidimide was the
metabolite causing gene mutation, versus AA causing heritable translocations.
The ICPEMC Report, described earlier by David Brusick, served as a basis for
the working group's risk evaluation. In general, the same default risk extrapolation
factors were applied, although different numbers were used in some cases,
particularly for the exposure assessment. The Ehling and Neuhauser-Klaus paper
(1992) was used for the gene mutation risk assessment; therefore, the gene mutation
numbers were based on a single dose point (a serious deficiency in terms of the
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exercise). For heritable translocations, Shelby, et al. (1987) and Adler (1990)
contributed the necessary data. There were appropriate dosage data, for the
heritable translocations in the ICPEMC reports. Risk numbers were calculated
separately for gene mutations and chromosomal aberrations.
Using the ICPEMC approach, all exposure numbers were converted to
milligrams/kilogram/day. The group used a doubling dose approach, and elected to
derive different doubling dose estimates for separate data points when the dose
response was curvilinear, as in the Adler study (1990). In addition, they used a
modification of the direct method, in that recessive mutation data for the mouse was
used to estimate dominant mutations in humans. At a 100 mg/kg dose, the gene
mutation doubling dose was 53. The direct estimate did not resemble the doubling
dose estimate.
For the chromosomal aberrations, the direct and doubling dose estimates were
much closer. Three different sets of estimates were made. Based on the Shelby, et
ai. (1987) study, the doubling dose was 0.1 mg/kg. For the Adler (1990) data, a
single dose estimate of 50 mg/kg yielded a doubling dose of 3.3 mg/kg. By modeling
the dosage from the curve representing her three points, additional numbers were
calculated. The net result was that 531 x 10"6 new genetic diseases were predicted,
based on a one-day exposure-obviously a very high number.
Douglas: "I should point out that these numbers do not include are any
factoring of tissue accessibility, which is something that was a consideration in the
ethylene oxide analysis that has been published by EPA. Female exposure was not
considered at all. The numbers are based on daily exposure. They are not based on
time-weighted averaging, or reproductive life span of the people exposed I would
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say that I am comfortable with the.. .default assumptions. But I would be a lot more
comfortable if we had empirical data to tell us, in the case of AA, what these
extrapolation factors are."
Butadiene (BD). The BD Working Group was chaired by Ilse-Dore Adler, and the
members of th group were Judi Cochrane, Siv Osterman-Golkar, Thomas Skopek,
Marja Sorsa and Ekkehart Vogel.
Marja Sorsa, rapporteur for the group, reported that a special problem with BD
is its metabolism and the specific metabolism in the animal germ cell stages as
compared to metabolism in humans. The mutagenic activity of diepoxybutane, in
various studies in mice, seems to be from 10 to 100 times higher than for the
monoepoxybutane. And there are additional putative reactive intermediates:
dihydroxyepoxybutane, crotonaldehyde, and hydroxymethytvinylketone. Siv
Osterman-Golkar informed the group that the presence of the metabolite M-1 [or 1,2-
dihydroxy-4-(N-acetylcysteinyl)butane] in urine from humans exposed to BD suggests
that hydroxymethyMnylketone is one of the reactive metabolites in humans. As for
the parallelogram approach at the endpoint level, there were three cytogenetic
studies, one published and two unpublished, which seemed to confirm that, at the
exposure level encountered in the manufacturing industry (generally below the three
ppm level), there was no response.
In mice, however, the lowest effective doses are surprising low, such that both
for micronuclei, as well as SCE, only a week's exposure at 6.25 ppm was needed for
a positive response. In rats, the only data that suggested a positive response was
for SCE, with a positive response at 500 ppm. There was no response in rats in the
case of chromosome aberrations or micronuclei at doses as high as 1300 ppm five
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days per week. For germ cells, there were dominant lethal results in mice at two dose
levels (and two different laboratories), and a heritable translocation experiment is
presently ongoing at the same exposure levels in Adler's laboratory. Based on the
available cvtoaenetic data, it was not possible to continue the parallelogram
calculations for cytogenetic effects.
The group confirmed that two studies on gene mutations in rodents in vivo are
about to be published. Fortunately, Cochrane and Skopek's data (1993a, 19935)
were available to the group (the study is positive for HPRT mutations in mice). In the
Cochrane and Skopek data, the lowest dose was 625 ppm.
In humans, there was only one study identified by the group, from BD
manufacturing (Legator et al., 1993). The number of workers in this study was very
small, but the exposure assessment provided a data base on ambient concentrations
in the production facility that suggests that time-weighted-average values do not
increase above three ppm, and that the general ambient levels are between one and
three ppm. Additionally, there was a measurement of the specific M-1 metabolite that
allowed individual correlation of the amount of the specific BD metabolite in the urine
samples of these workers, with the HPRT variant frequencies in their lymphocytes.
The correlation was excellent. There were control persons both from outside the
facility, and persons working in the BD factory, but not in production. Furthermore,
all of the individuals in the study were nonsmokers.
Unfortunately, DMA adduct studies were not yet available for humans. Siv
Osterman-Golkar's preliminary results (Osterman-Golkar, 1993), showthat hemoglobin
adducts of the monoexpoxide can be measured in humans. These adducts were
measurable in workers from the Texas manufacturing plant mentioned above. The
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monoexpoxide adducts have also been determined in rodents. For equivalent
exposures, the adduct levels are three to five times higher in mice than in rats.
Marja Sorsa indicated that much of the material available to the group is now
in press in the Journal of Occupational Medicine. [This is a meeting report on the
International Symposium on Health Hazards of Butadiene and Styrene, held in Espoo,
Finland, 18-21 April 1993]. Sorsa reported that the meeting was concerned primarily
with BD as a carcinogen: The lowest effective dose for cancer in mice is only six
ppm. Rats are far less sensitive, and the main carcinogenic response is in the
mammary gland. Human cohort studies have shown a slight effect on hemolymphatic
cancers. The most heavily discussed human studies showed a large excess of
leukemia. (ARC, in 1992, classified BD as a probable human carcinogen in Group 2A.
Sorsa: There were several presentations at the Espoo meeting concerning the
metabolic rate of elimination as the key issue in the difference between mice and rats.
And I think you can safely state, that BD monoepoxide is higher in mice than in rats.
... Very low levels of the monoepoxide were reported to be effective in the mouse
bone marrow. The role of other metabolites is still uncertain."
Cyctophosphamkte (CP). The CP Working Group consisted of Diana Anderson, Jack
Bishop, Colin Garner, Patricia Ostrosky, and Paul Selby.
Paul Selby served as the rapporteur of the group and began his presentation:
"It seems clear that with cyclophosphamide, metabolism needs to be better
understood; there are some peculiar findings that have been found after certain
treatment regimes. Possibly some of that might result from differences in metabolism
over time and that needs to be pursued— The information on adduct formation for
this chemical is extremely complex; it is not clear what adducts should be measured
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in man. Apparently, there are no data in man or at least nothing more than sketchy
data. Adducts have been studied in mammals, but, curiously, not in reproductive
cells Clearly, pharmacokinetic information is vital to any risk estimate that might
be made and cell stage specificity has to be taken into consideration. There are
problems with high dose/low dose extrapolation. In this situation, most of the people
who are exposed, are exposed in many fractionated exposures so low level exposure
would not be very common in the population."
Approximately 200 studies have been carried out using CP on somatic cells,
although most of these have focused on clastogenic effects. There is relatively little
information on induction of gene mutations in vivo in somatic cells; there is one study
in humans and one in experimental animals that reported positive results. Selby
pointed out that more information was needed on point mutations in in vivo studies
using methods that measure, for example, hemoglobin adducts, HPRT mutations, and
glycophorin variants.
Many studies have shown that dominant lethals and heritable translocations,
as well as specific locus mutations, are induced by CP. For heritable translocations
and specific locus mutations, the effects are seen primarily in the second and third
weeks following treatment. For specific locus mutations there is no suggestion of any
effect in spermatogonial stem cells.
The specific locus test is one of the most important sources of information and
represents a precise method of measuring mutations in all germ cells. From
molecular genetics, knowledge is rapidly expanding about events at the DMA level
near the seven genes monitored in the test. But a major problem is how to relate
these recessive mutations to clinical effects in first generation offspring? With
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clastogenic effects, the heritable translocation test also has much historical data. But
it is also difficult to relate translocations to damage in first generation offspring.
Selby: "A figure of 9% (congenital malformations) was given the 1986
UNSCEAR Report (UN, 1986) But the data, many of which were from humans,
are incredibly soft One quite accurate figure is that approximately 8% of live born
humans will have a serious abnormality by the time they are 25 years old; not all of
those are genetic, but many of them do have genetic components."
In the data of Jenkinson and Anderson (1990), with a treatment that was
approximately 4-5 milligrams CP/kilogram/day, dominant lethality was found, and a
very similar pattern was found for congenital malformations. According to Selby, "If
you subtract control from experimental, then you get to a plateau where about 9% of
the offspring are exhibiting quite striking effects.... One of the difficulties is that it
is dealing with a rather small array of different abnormalities Of the 9% induced,
about 60% of them are runts, growth retardations About a third of the animals
with these abnormalities were demonstrated to have gross chromosomal
abnormalities.... But there is very little understanding of whether this is really of
clinical relevance in many of the cases."
Working Group Summary
In summary, and as reported in Table 1-1, each of the working groups was able to
make significant progress in applying a parallelogram approach to the available
experimental data for mutagenicity. Additional endpoints considered "adverse effects"
from a risk assessment perspective included infertility, spontaneous abortions, tumor
incidence, and, especially., congenital malformations. The importance of addressing
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all relevant risk factors was emphasized repeatedly. The need for better human
exposure and target dosimetry data, especially in mouse germ cells, was consistently
mentioned. The crucial assumption for genetic risk assessment, that "mouse equals
man," also remains the major uncertainty. The working groups were unanimous in
concluding that The problem is not 'no germ cell mutagens,' it is, 'no germ cell
mutagens demonstrated'; the best way forward is to carefully examine genetic and all
other related 'adverse effects' in progeny of humans (and in experimental animals)
exposed to mutagenic agents." Clearly, there are indicative epidemiological and
experimental data worth pursuing.
Crosscutting Papers
Four crosscutting papers were presented on the final afternoon of the meeting. These
papers were intended to provide perspective on the topic of genetic risk assessment
in the context of the present research knowledge base and future research directions.
Since it is anticipated that these papers will be submitted for publication separately,
they will not be discussed in detail here.
The first paper was entitled "DMA Damage and Repair in Somatic Cells and in
Germ Cells in vivo" by E.W. Vogel and AT. Natarajan. In this presentation, Ekkehart
Vogel compared the activity of germ cell mutagens in Drosophila vs. the mouse vs.
cancer in rodents, with regard to cell (stage) specific molecular adducts, and
mechanisms of damage and repair. Following was a report on "Chemically-induced
Mutation: Comparative Outcome in Somatic vs. Germ Cells" by J. Allen, S. Lewis, M.
Moore, and U. Ehling. The paper was jointly presented by James Allen and Susan
Lewis and focused on the unique responses of germ cells to chemical exposures,
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particularly as related to the synaptonemal complex and the events taking place
during meiosis vs. mitosis in somatic cells. The third presentation was by Michael
Waters, on The Performance of Short-Term Tests in the Detection of Germ Cell
Mutagens: Qualitative and Quantitative Results," by M.D. Waters, H.F. Stack, MA
Jackson, B.A. Bridges, and I-D. Adler. This paper analyzed the performance of short-
term microbial, cultured mammalian cell and rodent bone marrow tests in the
discriminating confirmed germ cell mutagens and nonmutagens. Both qualitative
responses and effective doses were analyzed. The final crosscutting presentation
was given by Michael Shelby, based on a paper by M.D. Shelby and M.D. Waters on
"Chemicals for Future Study of Heritable Genetic Damage in Human Populations."
Shelby described what is known about chemicals that induce germ cell damage in
animals, and discussed how this information may be beneficial in designing future
genetic epidemiology studies in humans.
Concluding Remarks
David Brusick introduced the wrap-up presentation for the workshop with the
comment, "It appears to me, that we certainly do not have an absence of information
to do risk assessment I think, in fact, we know too much.. .and we end up with
two problems: One is.. .paralysis by analysis-analyzing so much that you fail to act.
. . . The second problem is that we have intimidated potential users of this
information.. .with very fine details which may or may not have any relevance to the
overall number "
Brusick continued, The regulator says, "We.. .do not have enough information
on the dosimetry, dose-effect rates, dose, route of exposure, pharmacokinetic
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information, et cetera' The regulated community says, "We are not going to give
you this information until you can show us how you are going to use it'.... As a
consequence, data are generally developed out of our R&D programs. And most of
those, as we know, are not [well] funded. . . . They do not have. . .the data
development power that industry has in developing products, if that information were
made available
We have to. . .increase the visibility, and demonstrate the importance of
genetic risk assessment.. .broaden it to include congenital anomalies in the zygotes,
male transmitted congenital anomalies.. .cancer in the F1 and F2, and.. .fertility, and
other associated reproductive and developmental problems.
The third (problem), is how to communicate the risk numbers that we develop.
We need to simplify the description without oversimplifying it. . .explain it as a
relatively simple process. We must express risk in terms of adverse outcomes, and
not genetic endpoints We need to reach a consensus on the basic rules for the
process.. .on the assumptions that we are going to make..., then examine how to
express it. Is it going to be a population risk? Are we going to talk about individual
risks? .. .Are we going to include reproductive behavior? Are we going to talk about
risk per mating opportunity, risk per conception, risk per Ft? All of these things may
become important....
We have risks for chromosome damage. We have risks for gene mutation.
Is it possible, once we have calculated these, to then come up with the single number
or will we always be faced with a matrix of risk numbers? . . .I think we should
discuss cumulative exposures, versus acute. . .for spermatogonial mutagens, you
accumulate risk on a daily basis
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Finally, application of the parallelogram to risk assessment It is a method
that is intuitive.. .many of the extrapolation factors are built in. We do not have to
worry about extrapolating from the somatic cell to the germ cell.. .in the same animal.
. . . Those factors are taken into account, but the limitations probably are more
important than the advantages: If.. .the shape of the [dose response] curve is linear
that is one thing; but if it is not linear, then you do not know that the relationship
between somatic and germ cells in the animal is [valid] over a range of doses.. .and
it is difficult to compare across compounds. We should standardize the somatic cell
determinations in humans, to one or two endpoints. . .hemoglobin adducts,. . .
dosimetry, or SCEs.. .then begin to look at one chemical versus another.... Now
we do not know the relative sensitivities of these comparisons across different
compounds... ."
Brusick concluded, "From my perspective, it [the parallelogram] is certainly
a model that we can use, but.. .1 think we have enough power in using the direct, or
modified direct analysis.. .for risk assessment."
Cantee Nolan closed the meeting with the following remarks: "Returning to the
objectives of the meeting, to identify the methodology, data requirements, and
mechanistic research needed to understand the health impact of germ cell mutagens,
I think we have gone a long way towards doing that. When we chose the four
chemicals, we knew that we would have problems, that there were data missing. I
think problems have been identified in applying the parallelograms to each of those
compounds. ... We have identified needs for data. I think that the parallelogram
does have advantages over other assessment methods and I think It should be given
a chance with a full set of data
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We are quite pleased with the meeting. We think we have what we want, we
had some good overviews, and some crosscutting papers. I would hope, that this will
not be a one of a kind meeting, that we will have follow-up meetings to this one. I
would like to thank you all for participating, but I would like to thank particularly our
hosts. . .and I would like to thank Frits, because Frits really did most of the
preparatory work on the European side for this.
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Plenary Sessions
This section contains the introductory remarks and the following six plenary presentations of the
conference.
Introductory Remarks
Parallelogram Concept
ICPEMC Efforts
Regulatory Perspective
Research Background
EO Mutagenicity Risk Assessment
A Reconsideration of EO Risk Assessment
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Introductory Remarks
The introductory remarks were given by Dr. Lawrence Reiter. Dr. Reiter is from the
Health Effects Research Laboratory at the US Environmental Protection Agency in
Research Triangle, North Carolina, U.S.A. The conference was dedicated to the
memory of Emeritus Professor Frits Sobels by Dr. Heinrich Ott of the ENVIRONMENT
Research Programme of DG XII of the European Commission.
DR. REFTER: It is a pleasure for me to welcome you to the Research Triangle
Park area and to this first EC/US Workshop on Risk Assessment. We are especially
happy to welcome our European guests for what we hope will become a continuing
interchange through an ongoing scientific collaboration between the Health Effects
Research Laboratory at EPA and the Directorate General for Research and
Development of the EC.
As you know, the title of this workshop is "Human Genetic Risks from
Exposure to Chemicals, Focusing on the Feasibility of a Parallelogram Approach."
The title and the workshop were the concept of Professor Frits Sobels who passed
away on July 6,1963. I am sure that all of you were greatly saddened at his passing.
Those who knew him personally will be able to recount better than I his many
scientific and administrative achievements. Among them were: Professor and
Chairman of the Department of Radiation Genetics and Chemical Mutagenesis at
Leiden University, and Member of the Royal Academy of Sciences of The Netherlands.
His energy and enthusiasm for science and his strong personal friendships with many
of you provided much of the motivation for what we are to accomplish here over the
next three days. His presence is greatly missed.
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As stated in your invitation letter, the main objective of this workshop is to
identify the methodology, data requirements and mechanistic research needed to
understand the health impact of germ cell mutagens. The rationale for choosing this
topic is that a coordinated effort to quantify the genetic risks resulting from exposure
to mutagenic chemicals is greatly needed. Chemicals appropriate for such an
approach are those for which there is considerable population exposure and which
produce genetic effects in human somatic cells.
Professor Sobels believed that extrapolation from data on population exposure
and genetic effect to what can be expected in germ cells of the exposed individuals
should be possible by using good dosimetry and a parallelogram approach. The
underlying idea of the parallelogram approach is that for most chemicals an estimate
of the genetic damage in human germ cells can be obtained by measuring a common
endpoint in humans and mice, like genetic damage in lymphocytes, and a comparable
genetic endpoint in germ cells of mice, the desired target issue inaccessible in
humans.
The chemicals selected for review at this meeting are: Ethylene Oxide, 1,3-
Butadiene, Acrylamide, and Cyclophosphamide. The first three of these are important
industrial chemicals with substantial use worldwide, and therefore, considerable
human exposure. The fourth, Cyclophosphamide, is one of the most widely used
cancer chemotherapeutic agents. We are very interested, therefore, in what you will
decide about the applicability of the parallelogram approach, or alternative
extrapolation methods to these agents. But more importantly, we are interested in
your views about future research directions, especially as they will contribute to our
fundamental understanding of the impact of environmental chemicals on human
36
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genetic risk which, of course, includes a significant component of cancer risk and
some component of developmental and reproductive risk.
We realize, of course, that this is the first EC/US conference on risk
assessment and that it is highly focused on the parallelogram concept and germ cell
effects in humans, tt is anticipated that an important outcome from this workshop will
be an evaluation of current knowledge and future research directions for the more
precise assessment of human genetic risks. We also want to emphasize, however,
that our concerns for human health risk from exposure to environmental agents must
be broader than this.
Therefore, while we look to a successful outcome of this workshop, we also
look forward to future workshops encompassing cancer, developmental, and
reproductive risk assessment and other health effects as new knowledge and new
methodology becomes available
DR. WATERS: Thank you, Larry. I would like to call now on Heinz Ott from
the EC for some introductory remarks.
DR. OTT: Good morning, ladies and gentlemen. It is a great pleasure for me
to welcome you on behalf of the European Commission and, in particular, on behalf
of Professor Paolo Fasella (Director General for Science and Research) to this first
EC/US workshop on risk assessment which is dedicated to the late Frits Sobels.
I do hope that this will be the start for a more intense and more formal
cooperation in this area which is to be based on an agreement between the EC and
the United States which is, at the present, being negotiated. We hope that this will
facilitate organizing such things in the future. I also hope that we can organize the
next workshop in a not too remote future in Europe.
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If you say EC/US, I have to make a small parenthesis, because we have here
friends from European countries which are, not members of the EC, in particular, from
Sweden and Finland. They are, however, part of the European scientific community
and they have been participating in the Community research programs since some
time. We are now finalizing what is called the European Economic Area. It means a
partial extension of the EC to the so-called EFTA States. These are the non-EC
countries in Western Europe, in particular, the Nordic countries, Iceland, and Austria,
and we look forward to that. Maybe we will have to rename the next workshop in that
sense.
As I mentioned at the beginning, we have decided to dedicate this workshop
to the late Frits Sobeis. He was actually the man who proposed to have this
workshop, and he was deeply involved in Its preparation. We know that he was very
enthusiastic about It in order to make a point on the utility of the parallelogram
approach for risk assessment which he considered, and I think he was right, his baby
and his invention.
I regret personally very much that he cannot be here. I should add here a
personal remark. When I came to Brussels in 1972 to build up the first environment
research program for the European Community, we had also in mind to make a
program specifically to develop tests for the genetic effects of chemicals. I was
advised to consult Frits Sobeis at that time on what to do. He was, from the very
beginning, enthusiastic about the idea, and I may say that almost all what has been
done in this area of the program in the last 20 years had been inspired and triggered
in one or another way by Frits Sobeis.
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The program developed from a small program with a few laboratories
developing genetic effects tests for regulatory purposes to a rather big program which
is encompassing now also population monitoring and other technologies which go
far beyond the testing of chemicals for regulatory purposes.
I think I should underline here the merits of Frits Sobels for the European
research in this area and I think also for the research around the globe.
I may kindly ask you perhaps to remain a minute in silence for the memory of
our friend, Frits.
All that remains to me is to thank very much the EPA and Research Triangle
Park laboratories, in particular, Dr. Larry Reiter and Dr. Mike Waters, for taking on the
trouble to organize this and for the hospitality. I wish the symposium to be a success.
Thank you very much.
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Parallelogram Concept
The first plenary presentation was on the Parallelogram Concept and was given by
Dr. Alan. S. Wright. Dr. Wright is from the Tunstall Laboratory of Shell Research
Limited, Sittingbourne Research Centre, Kent, United Kingdom.
DR. WRIGHT: Ladies and gentlemen, I want to talk about the assessment of
genetic risks, particularly heritable risks and the possible role of the parallelogram
approach in making such assessments.
First of all, I want to dwell briefly on why it is important to develop rational
approaches to assess mutagenic risks. Then, I will touch on the key scientific issues
that are fundamental to assessing genetic risks before going on to discuss the merits
and limitations of the parallelogram approach as a model approach to assess genetic
risks.
I will leave any judgment about the performance of the parallelogram to this
workshop. This assessment is a major task of the workshop.
Key Scientific Issues in Assessing Genetic Risks
Concerns about mutagenic chemicals stem not only from what they do but also from
how they do it.
The majority of mutagenic chemicals operate by a genotoxic mechanism in
which they undergo direct covalent reactions with tissue DMA (Figure 2-1). However,
most of the compounds we are exposed to require metabolic activation into reactive
forms (electrophiles) to permit reaction with DMA. The products of these reactions,
e.g. DMA adducts, are generally promutagenic.
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A Current Perception of the Principal Stages & Systemic
Determinants of Chemical Mutagenesis
S31997.7
Precursor Mutagen
I Metabolic Metabolic *• Detoxification
1 activation detoxification *- Products
Ultimate Mutagen
I Penetration to
1 molecular target
Tissue DNA
DNA^repair ! 1Key (PrimarY & critical) interaction
Chemically modified DNA (key lesions)
I
Miscoding during cell
replication or error-prone repair
Mutation in individual cells (heritable lesions)
Figure 2-1
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Although the rates of formation of these key chemical lesions in DMA can
deviate quite markedly from linearity in terms of exposure dose, there is no theoretical
reason, nor is there any experimental evidence for a threshold in the induction of such
primary DNA damage. Similarly, there is no evidence that the repair of this damage
is so rapid and effective as to prevent the progression of even low levels of primary
DNA damage into mutation. Consequently, there is no absolutely safe exposure level.
Exposures to genotoxic chemicals pose an increased risk of mutation and cancer and
congenital abnormalities.
Furthermore, the mutagenic actions of genotoxic chemicals are additive,
cumulative and, essentially, irreversible (Figure 2-2). For these reasons, even very low
exposures are viewed with concern. Indeed, concerns about genotoxic chemicals are
invariably focused on low-level risks posed by low exposures encountered in the
general, domestic or occupational environments. High exposures to these agents
cannot be countenanced except in chemotherapy. Therefore, our concerns should
be focused on the development of methods to assess low-level risks posed by these
agents. Quantitative risk assessment is essential for the proper management of these
hazards.
This overhead defines the four key elements necessary for the management
of genotoxic risks (Figure 2-3). The emphasis in this presentation is on the fourth
element - the quantitation of risks.
Certain exposures, for example, to indigenous genotoxic agents and some
natural food components are unavoidable. The elimination or reduction of exposures
to potentially avoidable hazards, for example, combustion products or certain base
chemicals, is often very difficult and costly.
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Induction of Mutation by
Genotoxic Chemicals
B3I9B7.1
1. No threshold
2. Additive
3. Cumulative
4. Irreversible
Figure 2-2
Management of
Genotoxic Risks
931M7.2
1. Hazard detection
2. Hazard identification
3. Human exposure monitoring
4. Quantitative assessment of human risks
(Human dose-response relationships)
Figure 2-3
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Quantitative risk assessment is needed to prioritize these hazards and, most
importantly, to determine safety margins. Certainly, a failure to determine potency
would lead to some uncertainty about the adequacy of safety margins and, possibly,
to unnecessary measures to reduce exposures. Thus, despite their additive and
cumulative effects, even genotoxic chemicals can pose a negligible risk to human
health.
The definition as to what constitutes a negligible or acceptable risk is, of
course, a socio-political judgment; nevertheless, this judgment has to be realistic in
the case of unavoidable hazards, (for example, indigenous hazards), and it has to be
achievable in the case of avoidable hazards.
I think the advent of modem adduct technology which, for the first time
permits the detection of hazards at exposure levels which may be consistent with
negligible or acceptable risks, emphasizes the fact that quantitative risk assessment
is absolutely necessary; that is, quantitation is needed to discriminate between
acceptable and unacceptable risks.
Thus, it is becoming clear that, as we progress towards increasingly sensitive
methods, quantitative risk assessment becomes an indispensable facet of the
procedures for the qualitative detection of hazards. This is an inevitable outcome of
improvements in sensitivity and resolving power.
The emergence of adduct technology and its applications in molecular
dosimetry to detect and monitor critical exposures provides the key to quantitative
assessment of cancer risks and heritable risks posed by exposures to genotoxic
chemicals. Frits Sobels was well aware of this, and some years ago, he began to
45
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formulate strategies based on DNA dosimetry to improve the assessment of genotoxic
risks, particularly heritable risks.
As I see them, the aims of genotoxic risk assessment are to prioritize human
genotoxic risk factors and to determine safety margins (Figure 2-4). The needs are
for a strategy and methods to identify and quantify low-level risks.
Direct epidemiological approaches to assess heritable risks posed by low
exposures to genotoxic chemicals are fraught with problems. Probably the most
serious problem is the fact that the biological methods employed to determine
heritable effects in human populations lack intrinsic resolving power. In particular, the
limitations in resolving power make it increasingly difficult to attribute any observed
effect to any specific genotoxic chemical as the exposure to the chemical is reduced
to low levels.
The next figure is a schematic representation of the factors contributing to the
mutation load (Figure 2-5). The spontaneous component is shown in inverted
commas because there is perhaps some doubt about the fundamental basis of these
so-called spontaneous mutations.
The subdivisions in the increment caused by exposures to genotoxic
chemicals signify increments due to particular (specific) chemicals. When exposure
is reduced to the very low levels encountered in the environment, the small
incremental effect caused by a specific chemical may no longer be detectable within
the relatively high and variable background of mutation due to all other causes.
Increasing the sensitivity of the detection systems may not improve this
situation. The overriding need is for methods possessing the high resolving power
required to discriminate between effects induced by individual genotoxic chemicals.
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Quantitative Genotoxic
Risk Assessment
Aims
1. Prioritize human risk factors
2. Determine safety margins
Needs
1. Strategy and methods to determine
low level risks
831997.3
Figure 2-4
Factors Contributing to
Mutation Load
KU907.1S
Chemicals
(Indigenous and Exogenous)
Radiation
"Spontaneous"
Figure 2-5
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Indeed, it is unlikely that even the most sensitive of the emerging mutation
assays (for example, the assays based on mismatch technology which, incidently, can
probably be applied to human sperm) will prove effective in this respect, at least at
low exposure levels.
The next figure illustrates the experimental determination of mutation (Figure
2-6). The relatively high and variable background necessitates high doses of the test
chemical. It may even be possible to observe compound-specific mutation spectra
at such high doses. However, it is extremely unlikely that such spectra can be
observed at realistic low doses.
Currently, only adduct technology has the potential to actually discriminate
between the effects, that is, induction of promutagenic lesions, of different genotoxic
chemicals or classes of chemicals at very low exposures.
The limitations in current epidemiological approaches to assess human
heritable risks necessitate alternative indirect approaches based on experimental
models. In order to generate measurable effects, the experimental studies normally
use higher doses or concentrations than those encountered in the environment.
As a consequence, the estimation of the risk to humans requires two essential
extrapolative stages (Figure 2-7). The first step involves extrapolation from the high
dose range to the low dose range of interest. A linear projection is normally used and
this may give rise to a gross overestimation or underestimation of the true risks,
dependent upon the doses or concentrations required to saturate, induce, or inhibit
metabolic activation or detoxification systems and DMA repair systems.
The emergence of increasingly sensitive methods to determine mutagenesis
may reduce the scale of extrapolation by permitting the use of lower doses, but the
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Experimental Determination
of Mutation
Test Chemical
Chemicals
(Indigenous and Exogenous)
Radiation
"Spontaneous"
Figure 2-6
Extrapolative Stages in
Assessing Heritable Risks
1. High-dose—How-dose extrapolations
in experimental model
2. Translation of low-dose extrapolation
to humans
931997.4
Figure 2-7
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practicability of such reductions is dependent upon the variability of background
mutation in the model.
The second extrapolative step involves translation of the experimental, (that is,
the low-dose risk estimate), to humans. Of course, this process is very complex and
is fraught with uncertainty. The aim is to reduce extrapolative errors by correcting or
compensating for differences between prospective risk models and humans in the
operation of systemic factors that determine the quantitative relationships between
exposure dose and the biological effect or the response.
I have summarized these factors in the next figure (Figure 2-8). (This figure is
a literal statement of the first figure).
Clearly, the estimation of the individual contributions of each of these factors
is a complex and daunting task which probably cannot be effected at the necessary
level of sensitivity. However, an appreciation of the mechanisms of genotoxic action
has permitted simplifications allowing correction to be applied in two stages. The first
stage is designed to compensate for differences in determination of the critical dose
which, in turn, determines the rate of formation of the promutagenic lesions in DMA.
The second stage seeks to compensate for differences in the operation of
factors determining the progression of the key (promutagenic) lesions into mutations.
First consider the determination of DNA dose. The key advance in this respect
was the introduction of the target dose concept by Ehrenberg in the early 1970s. This
new dose concept was developed to provide a measure of the doses of the ultimate
genotoxic agents reaching the DNA of cells in tissues following exposure to a
particular genotoxic chemical or a precursor genotoxic agent.
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Systemic Determinants of Mutation
831987.5
1. Factors determining the qualitative nature
and magnitude of primary DNA damage:
• Metabolic activation
• Metabolic detoxification
• Absorptive and transportation processes
• Cellular and tissue compartment transfer processes
2. Factors determining the progression of primary
chemical damage into mutations:
• Rates of repair of promutagenic lesions.
• Fidelity of repair of promutagenic lesions.
• Cell replication rates
Figure 2-8
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Target or DMA dose can be determined on the basis of the reaction between
the genotoxic chemicals and DMA, but may also be based on the use of surrogate
monitors. For example, the hemoglobin in circulating red cells. The latter approach
is particularly valuable in monitoring human exposure and estimating DMA doses in
human tissue.
Probably the most effective of the hemoglobin methods is based on the
determination of adducts formed by reaction with the amino groups of the n-terminal
valine residues of the alpha and beta chains. This approach has been developed by
Margareta Tdrnquist in Stockholm.
The important point about target dose is that the determinations are designed
to automatically compensate for all individual and species differences in metabolism
and related toxicokinetic factors that control the quantitative relationships between the
exposure dose and the dose of the specific ultimate toxicant delivered to the target.
The relationship between the exposure dose and target dose may be very
complex. For example, exposure to a specific genotoxic chemical or a precursor
genotoxic agent, (like 1,3-butadiene) may lead to the generation of more than one
ultimate reactive species. Actually, the activation profile of butadiene is probably a
lot more complicated than shown in this figure (Figure 2-9). However, the mono- and
di-epoxides are probably the two most important genotoxic metabolites of butadiene.
Of course, the problem is that these metabolites display disparate mutagenic
potencies. Complications of this sort have to be considered when addressing the
problem of quantitative risk assessment or extrapolation.
if we now consider attempts to correct for species differences in the systemic
determinants of the progression phase of mutation, the principal determinants are:
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831W7.6
CH2=CH-CH = CH2
[O]
* A
CH2=CH-CH-CH2
[O]
0
CH,
-CH-CH-CH
Figure 2-9
53
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firstly, the rates and fidelity of repair and, secondly, the rates of DNA replication
(Figure 2-8).
Clearly, any model approach employed to compensate for species differences
in the operation of these factors should avoid high exposures which may exert
differential inhibitory effects on DNA repair and replication which, of course, could
confound extrapolation to low doses and interspecies comparisons. A number of
approaches have been proposed and developed to compensate for species
differences in DNA repair and replication; one notable approach is the radiation
equivalence concept which is, in my opinion, the best validated and which is also
designed to assess cancer risks.
All of these approaches are based on the determination of relative mutagenic
potencies, and they necessitate proportionality or parallelism between the repair of
damage induced by different agents (for example, radiation and alkylating agents in
the same test system or, alternatively, the same agent in different tissues of an
individual or strain/species). Accurate assessment of the dose of the ultimate
mutagen delivered to the target (DNA) Is fundamental to all of these approaches.
Advances in molecular dosimetry in the 70s and early '80s led Frits Sobels to
suggest that the determination of specific DNA adducts such as the strongly
promutagenic adducts formed by the reaction of alkylating agents with the 06 atom
of guanine residues would provide a basis for estimating heritable risks that were
otherwise difficult or impossible to determine directly.
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Merits of Parallelogram Approach
In its original form, the concept, known as the parallelogram approach, was
developed to estimate mutations in rodent germ cells by determining mutagenic
potency in rodent somatic cells in vitro and applying determinations of the amounts
of specific DNA adducts formed in the in vitro test system and in mouse germ cells
in vivo to estimate mutations in the germ cells.
The original parallelogram model is reproduced (Figure 2-10) exactly as in
Frits' first publication on this topic. In retrospect he would probably have signified the
specific use of mouse somatic cells for the in vitro determinations rather than the
more generalized approach signified by the term •mammalian cells". The critical
assumptions implicit in this approach are: (1) the mutagenic effect of a genotoxic
chemical in different cells or tissues of a given individual is a direct function of the
amounts of specific DNA adducts, and (2) the proportionality between the amounts
of DNA adducts and mutagenic action of a given genotoxic agent is constant for
different cell types within a given individual or strain. Those are the fundamental
presumptions of this model.
Evidence to support the contention that measurements of specific DNA
adducts may provide a basis for extrapolating mutagenictty data from one cell type
to another within an individual or species or even between species has been reported
by Bert van Zeeland's group (Figure 2-11). They observed very similar mutation
frequencies when measurements of the levels of 06-ethylguanine in DNA were used
as a basis for calculating the mutagenic potencies of ENU in inducing HPRT
mutations in VTO cells and specific locus mutations in mouse spermatogonia. Clearly,
the correlation is excellent.
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The Parallelogram
Mammalian
cells in vitro
Mouse
germ cells
Measured specific
DNA adducts
Measured mutations
Measured specific
DNA adducts
Estimated mutations
Figure 2-10
Induced mutations in V79 Chinese hamster cells (T)
and the mouse specific-locus assay (n)
250
^ 200
b
150
0)
CT
100
a
"5
50
93IW7.17
(from van Zealand era/., 1985)
5 10 15 20
Ethylations per 106 nucleotides
Figure 2-11
56
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Part of the rationale for such an approach is that the direct determination of
DNA adducts may, in fact, reflect DMA repair as well as adduct formation. However,
on a cautionary note, care should be taken, insofar as is possible in such
experiments, to avoid very high exposures which may lead to rapid saturation of repair
systems and, therefore, to results which may not adequately reflect the important role
of repair in determining mutation. Low doses should therefore be used wherever
possible.
Turning to the problem of interspecies extrapolation, the parallelogram concept
has been extended in an attempt to estimate mutation frequencies in human germ
cells (Figure 2-12). In principle, a common genetic endpoint is measured in somatic
cells of humans and an experimental species in vivo. For example, one might
measure mutation or genetic damage in peripheral blood lymphocytes in a rodent
species and in humans exposed to the same genotoxic agent. The subsequent
determination of the potency of the agent in the germ cells in the experimental model
would permit the ratio of germinal to somatic damage to be calculated in the
experimental species. Provided this ratio holds for human, this numerical value can
be applied to calculate the risk to human germ cells.
Limits of Parallelogram Approach
You will note that some of the arrows and boxes employed in Figure 2-12 are broken.
The broken lines signify a major problem inherent in this approach, i.e. the difficulty
in obtaining adequate human data.
There are several assumptions in this type of model. The key assumption is
that the ratio of germinal to somatic damage has the same numerical value in the
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In vivo •*-*"//) vivo Extrapolative Model
Mouse
931997.10
Human
Germ cells in vivo
Measured potency
Somatic cells in vivo
Measured potency
Germ cells in vivo
Estimated potency
________
\ Somatic cells in vivo\
Measured potency !
Figure 2-12
58
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experimental model and in man. Given the marked variations in gene expression
between tissues and species, there is obviously no theoretical justification for this
supposition. Validation is necessary.
Validation hinges on producing convincing evidence that the relative
mutagenic effectiveness of a given genotoxic chemical towards a selected class of
target somatic cells, (for example, peripheral blood lymphocytes), and germ cells in
the test species, (for example, mouse), parallels that in humans over a broad dose
range, including very low doses.
I have already touched on the emergence of a new generation of mutation
assays based on mismatch technology. The new methods may provide a basis for
direct validation of the model by permitting direct determinations of mutations in
human sperm caused by high accidental or clinical exposures to genotoxic chemicals;
but, as already noted, it is unlikely that even the most sensitive of the emerging
mutation assays will actually permit the determination of very small increments in
mutation caused by low environmental exposures. In my view, such direct validation
of the parallelogram concept is not yet possible, and alternative indirect strategies
need to be developed.
One possible approach would be to attempt to demonstrate that the
proportionality or extrapolation "constants* relating somatic and germ cell damage are
independent of species by performing comparative assessments in a number of
different species with a broad range of chemicals tested at a range of doses,
particularly low doses.
As pointed out by ICPEMC Committee 4 (Lyon et al., 1983), the problem in
developing an effective parallelogram approach to predict germ cell damage is in
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finding a suitable genetic endpoint that can be measured in the somatic cells of both
experimental species and humans and which has extrapolative value for the prediction
of germ cell damage.
The determination of chromosome aberrations in peripheral blood lymphocytes
provides an effective method for the qualitative prediction of genetic damage to germ
cells, but the results of comparative studies reported by Van Buul suggests major
differences in the ratio of somatic and germ cell damage between mice and Rhesus
monkeys treated with X-rays. These results have cast some doubt on the value of
chromosome aberrations for quantitative applications in the parallelogram mode.
However, it should be stressed that the data generated in Van Buul's studies are
probably too scanty to warrant firm conclusions, and I think it is quite clear that much
more work is needed to test the general feasibility of the parallelogram approach. Of
course, this is a major aim of the current workshop.
The use of radiation in such validation studies make it relatively easy to
determine the critical dose and so facilitates valid interspecies comparisons. Accurate
target dosimetry with genotoxic chemicals is more difficult, but it is, nevertheless,
entirely feasible. Such target dosimetry provides a basis for making valid
comparisons of mutagenic potency between species and between tissues within an
individual.
However, even when target doses are identical, there is no guarantee that
major species differences will not occur in the ratio between somatic and germ cell
mutation due, for example, to species differences in the ratio of the rates of DMA
repair in germ and somatic cells. The occurrence of such differences would, of
course, militate against a simple parallelogram approach and would necessitate
60
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additional corrections such as those employed in the rad-equivalence model to be
applied.
As already noted, one of the problems in developing and validating a
parallelogram approach to predict human germ cell mutations is in obtaining reliable
mutagenicity data in human somatic cells exposed in vivo, particularly at low dose
levels. Consequently, the ability to extrapolate in vitro mutagenicity data to the same
type of somatic cells in vivo is, therefore, potentially very valuable.
If such in vitro/in vivo extrapolation proves to be valid then we can envisage
an in vitro-in vivo parallelogram illustrated in the next figure (2-13). (Note the absence
of broken lines). Alternatively we may envisage a combination of the in vitro/in vivo
(Figure 2-13) - in vivo/in vivo (Figure 2-12) extrapolative models in which the potencies
in somatic cells in vivo are estimated on the basis of the measured potencies in vitro.
Information about the extrapolative value of in vitro data may be obtained by
conducting appropriate in vitro-in vivo comparisons In the same type(s) of somatic
cells in a range of experimental species. Consistent results would promote
confidence in the extrapolative value of the corresponding human in vitro data to the
human situation in vivo.
This type of parallelogram approach is, in fact, employed as a standard
procedure for validating the metabolizing systems of preparations of human tissues
employed to detect and identify human genotoxic carcinogens in vitro. In
Pharmacokinetics, such dosimetry approaches are described as matrix approaches
and have a very high resolving power. As already mentioned, biological mutation
assays lack such intrinsic resolving power. Nevertheless, studies in humans exposed
61
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In vitro •*-*- In vivo Extrapolative Model
Mouse
931997.9
Human
Germ cells in vivo
Measured potency
Somatic cells in vitro
Measured potency
Germ cells in vivo
Estimated potency
Somatic cells in vitro
Measured potency
Figure 2-13
62
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to gamma radiation have provided valuable quantitative data relating the induction of
genetic damage in human somatic cells to radiation dose in vivo.
These in vivo data, taken from one of Mike Waters' papers (Waters et al., 1986)
(Figure 2-14), are compared with the results of laboratory studies to determine the
cytogenetic effects induced by gamma radiation in human peripheral blood
lymphocytes exposed in vitro and in mouse lymphocytes exposed in vivo and in vitro.
In the original paper, these data were used to construct a series of parallelograms
corresponding to doses of 25,50,100, and 200 rads. However, a graphical construct
is perhaps more revealing. In this particular case, it is clear that the mouse in vitro
data is highly predictive of the in vivo response. Application of the mouse in vitro/in
vivo proportionality constants to the human in vitro data is reasonably predictive of
the damage in human cells in vivo, that is, within a factor of 2.
I have summarized the main conclusions in this final figure (Figure 2-15). The
data, reported by Mike Waters et al. (1986), using the radiation model are
encouraging, but fall short of the requirements for validating models to estimate the
risks of heritable mutations in humans posed by low exposures to genotoxic
chemicals. Comprehensive studies using a range of genotoxic agents and a range
of species are needed to promote confidence in the use of experimentally determined
values of the relative risks of germinal and somatic cell mutations to predict the risk
to human germ cells.
Direct validation is probably not possible, certainly at low exposures. Indirect
validation via human somatic cells in vivo is difficult at low exposures where it is
difficult to obtain reliable human data. Indirect validation via human somatic cells in
63
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§ «o -
.i
Rads
Cytogenetic damage in human and mouse peripheral blood lymphocytes
exposed to -y-radiation in vitro and in vivo. (Data from Waters etal., 1986).
Figure 2-14
64
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CONCLUSIONS
1. DIRECT VALIDATION NOT POSSIBLE AT LOW EXPOSURES.
2. INDIRECT VALIDATION VIA HUMAN SOMATIC CELLS IN VIVO-
DIFFICULT AT LOW EXPOSURES.
3. INDIRECT VALIDATION VIA HUMAN SOMATIC CELLS IN VITRO
POSSIBLE.
4. TARGET DOSIMETRY ESSENTIAL
5. EXPLORE ALTERNATIVE STRATEGIES TO CORRECT FOR
SPECIES DIFFERENCES IN "PROGRESSION".
Figure 2-15
vitro is obviously possible, but we still have the problem of high dose-low dose
extrapolation. Perhaps some of the more sensitive new techniques may assist with
this type of extrapolation. However, the practicability of these new approaches in
actually reducing the scale of the extrapolation is largely dependent upon the
background variability in the model.
Target dosimetry is an essential requirement of all of these approaches.
Precise determination of the critical dose is needed, particularly, when extrapolating
from the in vitro to the in vivo situation. Determination of the critical dose of the
ultimate genotoxic agent is probably most readily and accurately determined, at least
in humans, by using a surrogate dose monitor such as the haemoglobin of circulating
erythrocytes.
65
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In view of the potential limitations of the parallelogram approach it would be
prudent to explore alternative strategies to correct for species differences in the
progression phase of mutation.
Thank you very much.
Discussion
DR. ANDERSON: When you have a lot of data, your last point, I mean, if you
come to that conclusion in general terms, for compounds where you have a lot of
data, surely we should be able to apply it. Are you making this last point for
everything?
DR WRIGHT: For everything?
DR. ANDERSON: Yes, you are saying use alternative strategies. Because of
the problems with the approach, we should have alternative strategies lined up.
DR. WRIGHT: Yes, I believe that, yes.
DR. ANDERSON: But where you have got a lot of data for all chemicals and
even human data, do you not think it is applicable then? Are you making a broad
statement at this point?
DR WRIGHT: My statement is addressed to the broad issue of assessing low-
level risks. I do not believe we have any reliable human data relating genetic effects
to low-level exposures to genotoxic agents. The point I want to make is that, in
human populations, it is extremely difficult to attribute any observed biological effect
to exposure to a specific chemical when the exposure is very low and is in a mixed,
(for example, environmental), exposure situation. It is relatively easy to attribute such
an effect to general environmental factors but it is much more difficult to identify the
66
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specific causative agent(s). Such identification can only be achieved using adduct
technology.
DR ANDERSON: Okay, thank you.
DR. GARNER: Alan, you mentioned something which is a little perhaps
controversial. You said that there was no such thing as a spontaneous mutation or
there may not be such a thing as a spontaneous mutation.
DR WRIGHT: I said may not.
Da GARNER: Will you define what you implied there?
Da WRIGHT: I am suggesting that background exposures to radiation and
genotoxic chemicals may contribute to the frequency of "spontaneous" mutation. We
do not know to what extent these background exposures may contribute to the
"spontaneous" mutation rate. This remains to be resolved. We are probably exposed
to low levels of thousands of genotoxic chemicals both indigenous and exogenous.
The exposures may, in large measure, be responsible for the spontaneous mutation
» f
rate.
Da SORSA: Maybe at this point it is a bit useless to throw these problems,
because we will need to discuss these further. I agree very much to all the hesitations
which you showed in the conclusions and especially to the problems of the species
specific differences. Of course, concerning humans, we are a polymorphic species.
Da WRIGHT: Yes.
Da SORSA: And we never know which mammalian or rodent is
representative, we represent.
One further problem which you did not specify and I think which we have to
discuss during these days here is the endpoint specificity, because there are
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differences in endpoints when we measure the damage in somatic cells either in
humans or in experimental species.
DR WRIGHT: Yes, this is one of the reasons why I very much favor the
development of the techniques based on mismatch technology. This new technology
provides the basis of a universal approach which may be applied to any tissue or
sample of DMA. The approach does not rely on a phenotypic endpoint of any type.
DR WATERS: Fred De Serres?
DR. DE SERRES: You raised the issue in your talk about the shape of the
dose-response curves and, you know, the assumption that most of them are going
to be linear.
I think this is a point that needs to be discussed very thoroughly. Most dose
response curves are not linear. You can get very different induction kinetics for
different classes, and, you know, different types of genetic damage will have very
different consequences in the human population.
So, I just think this is a point that should not be glossed over but dealt with
very thoroughly.
DR. WRIGHT: Yes, I would accept that view and totally agree with you, Fred.
I think the target dose approach helps to reduce some of these problems (Figure
2-16).
Certainly, if we examine data relating, let's say, the generation of DMA adducts
to exposure dose, these relationships are often not linear (Figure 2-17). However, if
we then examine the data relating DMA adducts to mutation, then these relationships
become much more linear.
68
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031987.11
Mutation
frequency
or
DNA dose
Exposure dose
Mutation
frequency
Rgure2-16
931997.12
DNA dose
Figure 2-17
69
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Da DE SERRES: Right, but, you know, the problem is trying to extrapolate
to very low exposures.
DR. WRIGHT: One of the problems is that, it is extremely difficult to make
accurate measurements in the very low dose range. I think we have to assume that
the dose response curve, is linear in the very low dose range. It is not possible to
measure biological effects at such vanishingly small exposures.
Da DE SERRES: I think this is a really extremely difficult area and one we
have to address very thoroughly.
Da WRIGHT: Yes, I would agree with that.
Da DE SERRES: Because I think it is just as dangerous to extrapolate, you
know, to what we expect at low levels of exposure and say that we are going to get
an order of magnitude higher effect than you would actually get as it is to
underestimate. So, you have to have caution in both directions.
Da WRIGHT: Yes. I did in fact state that such extrapolative problems could
lead to gross overestimation or underestimation of the true risks.
Da DE SERRES: Right. Well, it is particularly important where you are
dealing with exposures in the workplace, and you are trying to determine the effects
of that exposure on workers or on people who are exposed to agents where the
exposure cannot be avoided.
Da WRIGHT: Yes. However, I believe that it is important that when we reach
a point where the exposures are too low or the populations are too small to permit
the detection or measurement of genetic effects, then we must employ conservative
models to estimate the risks.
Da DE SERRES: Right, thank you.
70
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ICPEMC Efforts
Dr. David Brusick summarized the risk assessment efforts of the International
Commission for protection against Environmental Mutagens and Carcinogens
(ICPEMC). Dr. Brusick is from the Hazleton Labs America, Inc., located in Vienna,
Virginia, U.S.A.
DR. BRUSICK: Frits Sobels was one of the founding parents of ICPEMC and
was the first chairman of ICPEMC; so even though what I am going to talk about is
not the parallelogram approach, I think it is still appropriate to review it.
We were contacted by Health Canada and also the EPA to look at the
following questions: if you have risk data in mammals, in mice, can we extrapolate the
risk data from that model to human populations and, in some way, describe the
impact relative to the number of new mutations in the F1 so that this information can
be useful to decision-makers?
The major problem in this field has been that regulators or decision makers,
whether they be in the regulatory agencies or in industry or whatever, are faced with
a large amount of data, but are not given very good guidance as to how that data can
be used in making decisions that affect risks to human populations.
I was the project manager for ICPEMC. George Douglas was the Health
Canada project manager, and Kerry Dearfield was the EPA project manager. The
members of the team included Heinrich Mailing who was the chairman, Jack Favor
who is not here, Gary Sega who is here. David Layton did a lot of exposure analysis,
environmental exposure information. John Wassom was very instrumental in his
71
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group in providing data, and we also had input from Jim Burkhart at NIEHS and also
John Mulvihill, and Andrew Ceizel also was involved (ICPEMC, I993a,b).
As I said, this is a very, very practical approach. So, the operational definition
(Figure 2-18) of what we were trying to do in a genetic risk assessment was a
determination of the number of additional dominant genetic diseases, not mutations
but diseases, that would be added to the existing genetic burdens of the F1 progeny
derived from human individuals exposed to a mutagenic compound (Figure 2-19).
OPERATIONAL DEFINITION
Genetic Risk Assessment: A Determination of the Number of
Additional Dominant Genetic Diseases Added to the Existing
Genetic Burden of the F1 Progeny, Derived from Individuals
Exposed to a Mutagenic Compound.
Figure 2-18
While it may be possible to wait until all of the needed information gaps
are filled, there are disadvantages in waiting until all of the uncertainties
have been eliminated. The genetic disease burden is a serious health
issue for most societies. In addition to the emotional burden, the medical
costs associated with each induced phenotype result in a significant
contribution to the total health care cost. Further, genetic toxicology has
an obligation to explain the human hazard significance of positive results
produced by to vitro tests and mammalian somatic cell assays.
Figure 2-19
72
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To start with, we had to define pretty much a road map of where we were
going, and that is described here. This is the risk extrapolation road map that we
used, and it starts with basic information (Figure 2-20).
First of all, look at the induced mutation frequency per unit dose in
experimental animals. Most of the data that we have comes from models. It can be
heritable translocations, dominant-specific locus mutations, or recessive-specific
locus mutations (Figure 2-21).
We then have to factor in the kinds of exposures that humans are experiencing
and, as a result, should be able to get the per locus induced mutation frequency
following mutagenic exposure at the level that humans are exposed to (Figure 2-22).
We would then have some sort of an estimate of the expected per locus
mutation frequency which could then be factored by the number of disease-causing
loci in the human genome. You need to know how many of the genes in the genome
are responsible for genetic disease.
You can then take the mutation frequency given per unit dose multiplied by
that number of genes. You could then calculate the expected frequency of disease-
causing mutations in an F1 population following a given exposure of the mutagen.
Then, that would be taken times whatever F, population is derived from the
exposed population. You would then have an estimate of the number of new
diseases, dominant diseases, in that F1 population.
Now, the only other thing that we wanted to add into this is some way to
account for differences between the rodents and humans and also to account for the
differences in the kinetics of induction, (that is, dose rate differences, route of
exposure differences).
73
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RISK EXTRAPOLATION ROAD-MAP
(1) Induced mutation frequency
per target locus per unit dose,
determined in animal
experiments
Exposure dose In
humans
Expected per locus
induced mutation
frequency following
mutagen exposure
(9
(m,)
(2) Expected per locus mutation
frequency due to mutagen
exposure
Number of disease
causing loci In
human genome
Expected frequency of
disease-causing
mutations in F1
population following
mutagen exposure
(m,)
(m,)
(3) Expected frequency of
disease causing mutations in
F1 population following
mutagen exposure
F, population size
Expected number of new
mutations causing
disease in the F1
individuals resulting from
exposure of the parental
population
(m,)
("J
THE BASTC FORMULA WILL BE MODF1O) BY SCALING FACTORS
nm s REF x m1 x n0
where REF Is a product of several uncertainty and scaling factors
added at each step of the equation
Figure 2-20
74
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PRIMARY PROJECT OBJECTIVES
DEVELOP A RISK ASSESSMENT MODEL FOR USE AS A "STRAW MAN"
• THE MODEL SHOULD BE SIMILAR TO MODELS
TRADITIONALLY USED FOR OTHER TYPES OF TOXIC
RISK ASSESSMENT
Animal mutation induction studies and human
exposure information are the basic data
requirements.
• DATA REQUIREMENTS SHOULD BE FLEXIBLE: THE
MODEL COULD USE DOMINANT OR RECESSIVE/GENE
OR CHROMOSOME MUTATION DATA
The model should be able to use whatever data
are available.
• THE MODEL SHOULD ALLOW FOR THE
INCORPORATION OF EXTRAPOLATION SCALING
FACTORS
The model must be able to extrapolate
responses from the mouse to humans through
the use of scaling factors based on experimental
evidence.
• RISK EXPRESSION SHOULD BE A QUANTITATIVE
ESTIMATE OF THE NUMBER OF NEW DISEASES
EXPRESSED IN THE F1 POPULATION
Risk must be expressed as the number of new
genetic diseases in the progeny produced from
exposed populations. This limits the risk to
dominant disease phenotypes.
Figure 2-21
75
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Data Availability for Risk Extrapolation
Presumed Human
Mutation Process
Mouse
Model
Mouse to Human
Extrapolation
Uncertainties
Exposure
Compound
Administration
Exposure Route
Dose Route
Pharmacokinetics
Pharmacokinetics
Metabolic Variability
Sex Specificity
DNA Reactivity
DNA Reactivity
DNA Repair Capacity
Germ Cell Stage
Specificity
Induced Germ
Cell Muation
Induced Germ
Cell Muation
Estimate of Induced
Human Disease
Burden
Locus Specificity
Mutant Expression
Germ cell risk extrapolation from mouse to humans requires data from
several sources including (a) human exposure, (b) mouse mutation
inductions and (c) extrapolation scaling and uncertainty assumptions.
This figure attempts to demonstrate how these three types of
information are related to each other and the overall process.
Information appearing in the boxes in columns 1 and 2 can be
experimentally obtained. The remaining information is derived from
estimates. Column 3 identifies the factors considered essential in
making extrapolations from mouse to human.
Figure 2-22
76
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So, again, reaching into the toxicological sciences, there is a whole science
of looking at scaling factors or safety factors or whatever you wish to call them, and
coming up with some estimate of the risk.
So, we added in what we call risk extrapolation factors; and risk extrapolation
factors are just nothing more than a combination of all of these numbers that we
might be able to estimate that have to then be factored into this equation to come up
with the estimate of the number of new mutations causing dominant diseases in the
F1 population (Figure 2-23).
Proposed Scaling Factors (REF) for Mouse to Human Risk Extrapolation
Parameters Experimentally Determined* Default Factor
Locus Specificity Compound specific 2 to 5 (assumes human
sensitivity)
DNA Repair Variability Compound specific 0.1 (assumes mouse
sensitivity)
Metabolic Variability Compound specific 1 (70*)
Dose Rate Variability Compound specific 0.1
Exposure Route Compound specific 0.5
Germ Cell Stage Specificity Compound specific 1 (assumes equivalence)
Dose Response Kinetics Compound specific 1 (assumes linearity)
Each factor that is experimentally determined for the compound under assessment will
be used. If compound specific Information Is not known, the appropriate default factor
will be used.
If metabolism and detoxification are dependant upon glutathtone transferase
Figure 2-23
Once you have that number, it is then possible to look at some estimate of the
cost to treat or to manage the health care for each of those genetic diseases. So,
you will have an incremental cost that can be then compared to the economic
77
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advantages of the material, of the process, or whatever it might be, and do your risk
and risk/benefit calculations.
So, when we actually came up with the final list - locus specificity, DMA repair
variability, metabolic variability, dose rate variability, exposure route, germ cell stage
specificity, and dose response kinetics - again, very little in the way of dose response
data) we looked for the chemical-specific information and some sort of a default
factor.
The calculations that we used essentially included the standard calculations
using a direct method for mutation analysis or the doubling dose method (Figure
2-24). Possibly, the doubling dose data is one that we can apply a little bit better,
because there is at least some reasonable amount of data on doubling dose per
mutation in human populations to specific types of endpoints.
Risk is calculated for dominant traits. We are not calculating the risk for
recessive mutations in the Fr There will be recessive mutations induced. The
problem is that estimating what the impact of recessive mutation will be is difficult.
First of all, the contribution of recessive mutations is probably going to be
relatively small (Figure 2-25). It may accumulate in the population and, in 100 years
or so, might be very important. We hope that in 100 years from now we can probably
do a lot more with respect to controlling and diagnosing and treating genetic
diseases. So, the F1 dominant diseases are probably the most important to look at,
at least initially.
Some risks are not even going to be dealt with at all, for example, aneuploidy.
We don't have good data that we can put in and do a risk assessment for aneuploidy,
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CALCULATIONS USED IN RISK ASSESSMENT
Direct Method
RISK
Where
MMo».xLHuni-nxDxN I
^••••^^^^^^^^^^^^••••BM
LHu
D
N
m*n
' induced per locus mutation rate per unit
dose exposure estimated in the mouse
number of loci in humans at which dominant
disease mutation may arise
- exposure dose
- number of offspring descendant from
exposed parents
Doubling Dose Method
Where
Spon
D
DD
H(JRWnt
N
Risk Extrapolation
overall spontaneous mutation rate to
dominant disease alleles in humans
exposure dose
doubling dose estimated in the mouse (that
dose which induces a mutation rate equal
to the spontaneous mutation rate). The DD
is calculated as the mouse spontaneous
mutation rate per unit dose
number of offspring descendent from
exposed parents
Number of New
Diseases in the = REF x SponHinMm x D/DD x N
Offspring
OR
Number of New
Diseases in the = REF x
Offspring
xlOOOxDxN
Figure 2-24
79
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UMITAT1ONS OF THE METHODS
EACH RISK MODEL HAS DATA REQUIREMENTS WHICH CANNOT BE FILLED WITH
EXISTING INFORMATION SOURCES (I.e. Number of Loci Producing Dominant Disease
Traits In Humans, Spontaneous Mutation Rates for Humans).
ANIMAL DATA MAY ONLY BE AVAILABLE FOR ONE TYPE OF ALTERATION (I.e. Gene
Mutation) AND NOT OTHERS. THUS, A COMPLETE RISK ASSESSMENT CANNOT BE
PERFORMED.
ANIMAL DATA MAY ONLY BE AVAILABLE FOR DOMINANT MUTATIONS ESTIMATES
WILL REQUIRE FACTORING DOMINANT.-RECESSIVE INDUCTION RATIOS.
RISK IS CALCULATED ONLY FOR DOMINANT TRAITS. RECESSIVE MUTATIONS WILL
BE INDUCED BUT WILL HAVE MINIMAL IMPACT ON THE F1.
SOME RISKS WILL NOT BE ADDRESS IN THE ASSESSMENTS (i.e. Data for
Aneuploidy are Seldom Available).
SOME CRITICAL DATA GAPS MUST BE FILLED WITH ASSUMPTIONS (l.e. Male and
Female Risks are Equivalent; Mouse and Human Mutations nave Equivalent
Penetrance; There Is no Age Influence)
Figure 225
and, obviously, we have got some critical data gaps in male to female risk. We are
looking at risk that is not sex specific, although most of the data is coming from
males. Age effects, penetrance of mutations and so on, again, are limitations that
have to eventually be addressed, but I think we can go with what we have got.
We were asked by Health Canada to look at acrylamide (ICPEMC, 19935), and
by EPA to review ethylene oxide (ICPEMC, 1993a). First of all, acquire the animal
data. Determine from that animal data using information specifically directed to those
chemicals, either ethylene oxide or acrylamide, come up with some sort of scaling
factors that have to be added to the equation, then assemble human population
exposure data, get the exposure data assessment if it is available, calculate the risk
80
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using the direct or the doubling dose method, and then estimate the exposure
induced genetic burden in the Ft population.
So, the next two figures essentially summarize the exposure data and the
numbers that were generated for ethylene oxide and acrylamide using the approach
we have (Figures 2-26 and 2-27). Human exposure numbers that we used in the
calculations for ethylene oxide were 36 ppm for a six-hour day, five days of the week.
This is an occupational exposure, relatively high exposure. Using the direct method
and scaling factors that we used, single gene dominant mutations per 10,000 births
calculated out at 12, and for chromosome diseases per 10,000 births, 123.
Using the doubling dose method, though, it was considerably higher, and that
was based upon an estimate of 1000 loci producing dominant F1 diseases. Doubling
dose was 26 single gene diseases per 10,000 and 207 chromosome diseases, and
we had a scaling factor of 0.2 that was used.
Acrylamide used fairly low exposure (Figure 2-28). These are the permissible
exposure levels for occupational and environmental exposure to acrylamide: 0.07
mg/kg per day for occupational and 0.03 mg/kg per day for regular general
environmental exposure.
Again, now looking at single gene diseases, F1 dominant per million births for
occupational exposure was estimated at 24 and environmental 10, and for
chromosome, 169 per million births and 73 for environmental exposures.
The doubling dose method was considerably lower in this case, about 0.4 and
0.2 for single gene diseases and 1 and 0.5 for chromosome diseases. Again, based
upon the fact that we didn't have a lot of information, we ended up with a scaling
factor of about 0.2, which ended up being a lot of default scaling factors.
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Summary of the Risk Extrapolation Approach
Produce or acquire the
Appropriate Animal Germ Cell
Mutation Data
Determine the REFs
for the Extrapolation
Assemble human population
and exposure data
Assemble exposure
assessment data,
if appropriate
Calculate Risk
Option A
Direct Method x REF
OR
OptionB
Doublind Dose Method x REF
Estimate exposure-induced
genetic disease burden in
F, offspring
Use of Options A or B depends upon the availability of critical data
regarding the number of disease associated loci in humans (needed for
the Direct Method) or the spontaneous background mutation rates
(needed for the Doubling Dose Method).
Figure 2-26
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HUMAN EXPOSURE:
RISK ESTIMATES:
METHOD
Direct
Doubling Dose
ETHYLENE OXIDE
Occupational exposure at 36 ppm for an six
hour day, five days per week.
SGD/104 BIRTHS
12
26
CD/104 BIRTHS
123
207
REFS EMPLOYED:
Parameter REF
Locus Specificity 2
DMA Repair Variability 0.1
Metabolic Variability 1
Exposure Route 1
Germ Cell Stage Specificity 1
Dose Response Kinetics 1
1
REF = 0.2
Figure 2-27
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HUMAN EXPOSURE:
ACRYLAMIDE
Occupational exposure at 0.07 mg/kgper day.
days per week. Environmental exposures at 0.03
mg/kgper day.
RISK ESTIMATES:
METHOD
SGD/106 BIRTHS
CD/106 BIRTHS
Direct
Occupational
Environmental
24
10
169
73
Doubling Dose
Occupational
(0.4)
(0.2)
1
(0.5)
REFS EMPLOYED:
Parameter
Locus Specificity
DMA Repair Variability
Metabolic Variability
Exposure Route
Germ Cell Stage Specificity
Dose Response Kinetics
REF =
REF
2
0.1
1
1
1
1
1
0.2
Figure 2-28
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So, the numbers that we have are essentially estimates of the frequency of
dominant genetic disease burden in the offspring of persons exposed to those given
doses of the environmental mutagens (Figure 2-29). These numbers can be used to
assess the health burden following exposure with background, or they can be used
to compare one chemical to another for comparative risk.
WHAT DO THESE NUMBERS MEAN?
The risk numbers generated above are estimates of the frequency of dominant
genetic disease burden in the offspring of persons exposed to a given dose of
an environmental mutagen. These numbers can be used to assess the health
burden following exposure with the background or they can be used to
compare one chemical to another for comparative risk. Estimates of treatment
cost per genetic disease are also available for use in economic considerations
of risk:benefit decisions.
Figure 2-29
The only other elements that we addressed In the reports were, for Health
Canada, we were asked to look at the possibility of taking somatic cell mutation data
and using a way of coming up with some sort of risk reduction factors to determine
the upper level permissible exposure to a mutagen that would protect the germ cells.
Again, this was an interesting problem to tackle, but we did produce
essentially a proposal that would allow one to look at mutations in somatic cells and,
using a series of safety factors based upon comparing germ cell and somatic cell
data, estimate an upper exposure limit for protecting germ cells.
EPA also asked this group to answer two other questions. One was, would
it be possible, in the absence of any germ cell data, to utilize risk assessment derived
from somatic cell or, "cancer risk analysis" to act as a surrogate for genetic risk? In
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other words, would we then have a conservative estimate of mutational risk that we
could apply directly to germ cells? So, if you are protecting for cancer, would you,
in a sense, also protect for the germ cell effects?
The other question was, like cancer, is it possible to develop a level of induced
mutation which would not be of a significant health concern and would not, therefore,
require the generation of risk management and risk benefit type of processes being
initiated? In other words, is there an acceptable level of new mutation?
DR. SELBY: In fact, referring to McCusick's catalog, I think most clinicians
would agree that even the number in there would be a very great underestimate of
dominance, because so little of human genetic disease Is really even understood at
the locus level yet
However, this is perhaps not really much of a problem, because the direct
method, at least in the way it has been applied for radiation risk estimation which is,
of course, much more developed than that for chemicals, does not use strictly the
number of genes anyway. It uses a multiplication factor that can be arrived at in
various ways, one of which would depend on a guess as to the number of genes, but
there are other ways.
So, in this case, you just gather information on a body system and use a
multiplication factor to go from that to total damage. So, it may not be that critical to
know what the number of genes is.
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REGULATORY PERSPECTIVE
Dr. Kerry Dearfield discussed the regulatory perspective of genotoxic risk assessment.
Dr. Dearfield is from the Health Effects Division, Office of Pesticide Programs at the
EPA in Washington, D.C., U.S.A.
DR. DEARFIELD: Thank you. I am really glad this meeting is being held
because I think it is a very good extension of the meeting we had in Melbourne,
Australia earlier this year with the International Conference on Environmental
Mutagens. When we were there, we highlighted the real need for genetic risk
assessments and why they are important in assessing chemical exposures.
I think, in this workshop, we are going to have a very good working
assessment of the different chemicals that have been identified. We will try to
determine what some of the real numbers might be from the exposures from these
particular chemicals. Also, I think this will provide us with real examples in trying to
determine what is a real genetic risk.
I think, as responsible genetic lexicologists, we have become very good at
identifying genotoxic chemicals. Just look at the proliferation of genetic tests that we
have and how we have spent an almost inordinate amount of time trying to develop
these tests. Just how many different ways can you identify a genotoxic agent?
We have also been trying to characterize some of the genetic activity of these
chemicals. It is very important to consider such things as mechanisms and how
chemicals interact with the genome. These aspects are crucial when trying to
determine how the chemical causes a genetic effect.
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A major point I would like to make is once we have identified and
characterized the genetic effect, how do we communicate risk to the people who may
be exposed to these chemicals? This is probably the major point we have overlooked
in these many years in trying to do genetic risk assessment.
We say we can do genetic risk assessment. We can determine how many
induced mutations from heritable translocation tests, for example; but how do we then
translate that to a phenotypic expression of that particular event in a human that has
been exposed to that chemical?
We haven't really done that very well, and I believe that is one of the reasons
why this workshop is very important. We can start making examples with the
chemicals selected here that have real human exposure and then extrapolate them
to what it might mean to adverse outcomes in humans exposed to these chemicals.
What Are the Reasons for Mutagenicity Testing?
So, why do we do mutagenicity testing? From the regulatory perspective which is
what I have been asked to present today, and being from the Office of Pesticide
Programs of the U.S. Environmental Protection Agency, we have devised a scheme
of how we would try to, first of all, identify genotoxic chemicals. Then, hopefully,
characterize the activity as best we can and enter into a genetic risk assessment. Our
risk managers then decide whether we should take some type of regulatory action on
these particular chemicals. And that is where we need help from you all, the experts
in genetic risk assessments. Major questions are: How do we set levels of concern?
How do we determine if there is a real genetic risk to the exposed human population?
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These are questions for later discussion, so let's go back to the beginning
now. Why do we do this? (Figure 2-30)
PURPOSE OF MUTAGENICrTY
TESTING FROM SUBDIVISION F
For each test substance, required tests are necessary to assess the
potential of a test chemical to affect genetic material. Results from such assays
may be used as part of a qualitative and/or quantitative risk assessment. The
objective underlying the use of the results from mutagenicity assessment are:
(1) To detect, with appropriate assay methods, capacity of a chemical to
alter genetic material in cells;
(2) To incorporate these findings in
(A) The assessment of heritable genetic alterations of concern to
humans;
(B) The weight-of-evidence approach for a carcinogenicity
classification of a chemical when results from carcinogenicity
studies are being considered. Furthermore, mutagenicity testing
information may be helpful in the selection of an appropriate high
to low dose risk extrapolation model if the chemical is a
demonstrable carcinogen.
Figure 2-30
From our introductory materials to our mutagenicity risk assessment guidelines
from the Office of Pesticide Programs, we use these assays to assess the potential
of a test chemical to affect genetic material. This helps us to identify whether the
chemical poses a genotoxic concern or not.
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We do this with appropriate assay methods to check the capacity of the
chemical to alter genetic material. Once we have done that, we move forward and
incorporate these findings into an assessment of heritable genetic alterations of
concerns to humans. This is our genetic risk assessment approach. This is a very
simple statement, but there is a lot of time, thinking, and resources that go into this
apparently simple consideration. But it is not simple, as we know.
Part B (Figure 2-30) presents somewhat of the conundrum that we have gotten
ourselves into over the years as genetic toxicologists. For many, many years, we
have used genotoxic evidence as part of the weight of evidence approach for
carcinogenicity classification, in particular, how best to do extrapolations from high
to low doses, for example. This is where we have gotten into the areas of genotoxic
versus non-genotoxic carcinogenic chemicals. This Is a whole different issue that isn't
part of this workshop.
But what I want to mention is that we have gotten ourselves into a frame of
mind that genotoxicity testing is sort of a subset of carcinogenicity testing. That is
something we have to rethink; while it is still very important for carcinogenicity, it is
also important for all the other toxicities. I will have a slide later to show you that it
is also important for not only cancer but for other things such as developmental,
neurological, and other effects.
We have to think of genetic toxicology as a major field that can provide insight
not only for cancer but for all sorts of other toxicities. I think that is one major
message we need to get out; and when we communicate genetic risk, it is not just
for cancer anymore.
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So, this Is the background of why we do mutagenictty testing in the Agency.
Now, to get into the actual details, our Office has come up with an initial battery of
tests that we like to use to identify whether there is a genotoxicity concern or not
(Figure 2-31).
How to Determine if there is a Genotoxicity Concern?
We have a minimum three-test battery. It can be a little more extensive if you like.
Generally, it is anchored by the Salmonella test. We have a second test of in vitro
gene mutation assay. We generally look to the mouse lymphoma assay for this, but
there are other choices that can be used. One can use other cell lines such as the
AS52 cell line. Also, you can use the CHO cell line in concert with an in vitro
cytogenetic assay.
I am detailing this battery because I am going to mention these assays as
examples in a parallelogram I will show in a few minutes. The third test we use, an
in vivo cytogenetics test, is satisfied by what most people have been likely using, a
mouse micronucleus assay.
This is an initial battery to give us an idea of whether there is genotoxic
potential for chemicals that are being tested for our purposes of pesticide registration.
Now, if these tests are all negative, then, most likely, there will not be much of a
mutagenicrty concern and there probably will be no more testing required of that
chemical.
Now let me put this initial battery into the larger scheme (Figure 2-32). In the
top row is the initial battery we just discussed. We have a tiered approach here and
here are the second and third tiers. Once you have identified a concern out of the
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*ln Vitro Gene Mutation Assay (choice):
(A) Mouse lymphoma L5178Y cells, tk locus,
small and large colonies
(B) Chinese hamster ovary cells strain AS52
(C) Chinese hamster ovary (CHO) or
Chinese hamster lung fibroblasts (V79)
cells + appropriate in vitro test for
clastogenicity
* In Vivo Cytogenetics (usually in rodent bone
marrow)(choice):
(A) Metaphase analysis (aberrations)
Figure 2-31
Salmonella
+ In Vitro + In Vivo Bone
Gene Mutation Marrow Cytogenetics
•Aberrations
or
• Micronucleus
Interactions with Gonadal DNA
*
Specific Locus
•Visible
or
• Biochemical
Dominant Lethal
*
Heritable Translocation
Figure 2-32
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initial battery, then we begin going into the characterization of that particular genotoxic
effect. In the second tier, we are looking for whether it interacts with germ cells and
has potential for heritable effects.
This does not erase the idea that these data still can support other toxicity
concerns such as cancer, reproductive effects, and other toxiclties; but we are only
discussing heritable effects now. That is something one needs to remember when
coming into the Agency. Once you begin moving up the mutagenicrty scheme for
heritable risk, you are moving out of the cancer realm.
As one moves into the second tier, we want to start asking does the chemical
interact with the germ cells. This isn't asking if it is heritable or not yet, but is there
an interaction with germ cell DMA. The arrows on the slide indicate likely paths of
direction we may take. Say you would get a positive micronucleus test. More likely
than not, we might ask you to follow up with a dominant lethal assay. Other assay
types include aberration studies in spermatogonia or spermatocytes, or SCE or UDS
in germ cells.
If these tests are negative, we have to make a decision. We would most likely
say based on the information we have, there is no evidence for interaction with the
germ cells. Therefore, our concern for heritable effects would drop at this point. We
may have to make the decision even though there might be limited data. That is what
is required of a regulatory agency. Others may want to follow up if they like, if they
have other ideas.
However, if these tests are positive, then we move to the third tier where we
could be looking at either specific locus tests and/or the heritable translocation test
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(Figure 2-32, bottom). These are quantitative tests because now we are going to try
to identify and characterize a possible heritable concern.
If these tests are negative, then again the concern for heritable effects drops.
But if they are positive, we take the data and model it into a genetic risk assessment;
that is, we do a quantitative risk analysis. This is what our guidelines say we will do.
While I have put the specific locus and heritable translocation tests at the third
tier, this does not mean these are the only two tests you can use. Obviously, there
are other tests that are possible for quantitation; but for now, the field of genetic
toxicology has demonstrated to us, the Agency, that these are the most appropriate
tests to use for quantitative risk analysis right now. However, if a case can be made
for dominant skeletal effects or some other test, that is a possibility to use in the third
tier. Our scheme does not absolutely rule it out.
How does this relate to the parallelogram approach? I have constructed a
couple of parallelograms here, not exactly the one that Dr. Sobels had put together,
but things that we think about in terms of assessing the risks the chemicals have
(Figure 2-33).
We have a lot of in vitro animal data. A lot of times, we have in vivo animal
data as well. Sometimes we have in vitro human data such as aberrations in cultured
lymphocytes. We don't have too many in vivo human data, but we are starting to get
that more and more now with such things as the HPRT assay in human lymphocytes.
In terms of the regulatory area, this top parallelogram (Figure 2-33) gives us
a good qualitative feel that yes, there is a genotoxic concern and that we need to
continue our efforts to see if we can do a human germ cell risk assessment.
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r T j Qualitative
Regulatory battery looks at this
Genetic
Events
Exposure
Regulatory battery looks at this
Figure 2-33
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Now let's look at somatic versus germ cells (Figure 2-33, bottom). This is
conceptually part of the parallelogram that Dr. Sobels constructed. Many times we
get in vivo somatic animal data. We also have in vivo germ cell data which is
generally from the heritable translocation test or specific locus test. Sometimes, we
have in vivo somatic human data. Most always, we do not have in vivo germ cell data
for humans, so we are trying to make extrapolations to this particular point.
We talk about specific genetic events such as clastogenic mutagenic events
versus point mutations. Also, this type of parallelogram can be used to talk about
exposures (Figure 2-33). I will show you more of those in a little while or more
probably this afternoon when I talk about acrylamide.
I am showing this figure (Figure 2-33) because, as far as the regulatory
aspects go, I think that we use the parallelogram approach without actually really
thinking about it, without actually consciously constructing the battery with the
parallelogram. I think that, conceptually, we have utilized many of the concepts of the
parallelogram in our regulatory battery.
You can see that the testing battery that I showed you before captures all of
these elements found within the dashed lines. It gives you a good idea if there might
be a risk for humans. So, once you have gotten the in vivo somatic data as well as
the in vivo germ cell data as shown at the bottom of our battery, you start making
some estimations about the in vivo germ cell risk to humans. So, even though we
didn't construct our battery with the parallelogram specifically in mind, I think,
conceptually, those of us in the field have been using these concepts over and over
and over again in trying to make extrapolations from mouse to man, from high dose
to low dose.
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How to Determine Real fl/sfcs to Humans?
So, why do we do genetic toxicology research and want to regulate chemicals that
present a genotoxic concern? This figure is from the UNEP/ICPEMC (1992) document
Dave Brusick talked about (Figure 2-34). These are examples of human diseases,
conditions caused by mutation in germ cells.
As you can see from this whole list here of genetic diseases and the number
of cases just found in the USA alone, that the genetic burden causes a lot of
problems. This list runs from dyslexia all the way down to various cancers, cancer in
general and specifically retinoblastoma. But you can see it is just not cancer; for
example, there are a lot of neurological effects such as the fragile-X syndrome and
manic depression.
It is very important that we do genetic risk assessment, because it feeds into
all of the other toxicities, and I think this slide highlights that very well. There are
many genetic diseases that are not all related to cancer alone. That is why, in the
regulatory program, we want to identify the mutagenic process as one of the major
points we look at.
However, having said all that, it has been very difficult to identify human germ
cell mutagens. We are very good at detecting germ cell mutagens in animal systems,
but we haven't really identified a bona fide human germ cell mutagen. I have to admit
that is one of the major questions our Agency managers ask us.
We say we have concern about human mutagens. When asked for an
example, it is difficult to respond because we cant identify a chemical that has
caused a particular mutation that has caused a particular disease or an adverse
outcome through the germ line, even though we believe it happens.
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EXAMPLES OF HUMAN DISEASES AND
CONDITIONS CAUSED BY MUTATIONS
IN GERM CELLS
Genetic Disease or Condition
Dyslexia
Hardening of arteries
Cancer
Manic depression
Schizophrenia
Juvenile diabetes
Adult polycystic kidney disease
Familial Alzheimer's disease
Multiple sclerosis
AAT deficiency
Myotonic muscular dystrophy
Fragile X chromosome syndrome
Sickle-cell anaemia
Duchenne's muscular dystrophy
Cystic fibrosis
Huntington's disease
Hemophilia
Phenylketonuria
Retinoblastoma (childhood eye
cancer)
From: UNEP and ICPEMC (1992)
Estimated No. of Cases in USA
15,000,000
6,700,000
5,000,000
2,000,000
1,500,000
1,000,000
500,000
250,000
250,000
120,000
100,000
100,000
65,000
32,000
30,000
25,000
20,000
16,000
10,000
Figure 2-34
Here are a couple of reasons why it is difficult to identify them (Figure 2-35).
There is a rarity of situations which permit enough detectable mutations, thus
reducing the effect of an epidemiology study.
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There is the rarity of the individual genetic diseases with marker genes to
identify associated genetic disease. Also, there is difficulty in identifying and studying
a suitable population exposed to substantial levels of mutagens. That has been a real
problem, but I think with some of the examples we have at this workshop, we are
beginning to identify some of those populations.
DIFFICULTY OF IDENTIFYING
HUMAN GERM-CELL MUTAGENS
1)
2)
3)
Rarity of situations in which enough mutations are induced to be
detected in an epidemiological study.
Rarity of individual genetic diseases and of 'marker' genes identified as
associated with a genetic disease.
Difficulty in identifying and studying suitable populations exposed to
substantial levels of mutagens.
Therefore, it has not been possible to link any human genetic disease with
specific mutations induced by radiations or chemicals.
From: UNEP and ICPEMC (1992)
Figure 2-35
For example, with acrylamide, we have some very highly exposed small
populations we can use in our exposure assessment. So, I think we are beginning
to address point 3 (Figure 2-35). The problem is that the individual genetic disease
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linked to a specific genetic marker that has not been found yet that has been induced
by a particular chemical.
Just to drive that point home a little bit, I would like to show this little paradigm
(Figure 2-36) because this demonstrates why It is tough to sell our approach to risk
managers. From animal studies, we know a particular chemical can cause a particular
genetic alteration, a heritable translocation, for example, in the ethylene oxide case.
We also know in the human situation that a particular adverse health effect has
associated with it a specific genetic alteration, for example, the liver cancer induced
by aflatoxin B1, which has a very specific point mutation in the ras locus in the liver.
But we cannot make this final connection between a particular chemical which causes
a particular genetic alteration that is associated with a particular adverse outcome.
This is where we are missing the human mutagen. This is what we have to deal with.
Intuitively, we know ft occurs, but we haven't got the examples yet.
Having said all that, what we want to do is start trying to provide examples to
show how we would do a genetic risk assessment. So, a group of us from the
Agency got together and performed a genetic risk assessment with ethylene oxide as
an example (Figure 2-37).
Notice in the title it says ethylene oxide as an example. This is not the
definitive risk assessment of ethylene oxide, but it was something we wanted to have
the genetic toxicology community start thinking about, start focusing on. For
example, what is needed to do a genetic risk assessment? What are the appropriate
studies that need to be done to obtain appropriate information to do genetic risk
assessment? More importantly, finally, what do these numbers mean?
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Chemical —^ Genetic Alteration(s)
9
Specific Genetic ^ Adverse Health
Alterations Effect(s)
Figure 2-36
Environmtnlal and Molecular Mulageneits 16:104-125 (1990)
Quantitative Estimation of the Genetic
Risk Associated With the Induction of
Heritable Translocations at
Low-Dose Exposure: Ethylene Oxide
as an Example
Lorenz Rhomberg, Vicki L Dellarco, Cheryl Siegel-ScoH,
Kerry L Dearfield, and David Jacobson-Kram
Office of Health and Environmental Assessment (L.K., V.L.D.. DJ.-K.), Office of
Toxic Substances (CS.-S.), and Office of Pesticide Programs (K.L.D.), US
Environmental Protection Agency, Washington, D.C.
This paper explores how quantitative risk as- alkyloTmg ogenl, expected equivalency of doses
sessmert methods' might be extended to analysis across species, germ-cell sensitivity, and «x-
of risks to the human germ line. High inhalation tropolation of dose-response relationship to low
exposures to ethylene oxide ore reported to exposure levels. Various dose-response models
cause heritable translocations in male mice with ore discussed in terms of their applicability to
a steep and nonlinear dose-response curve. We genetic end points and their ability to reflect the
explore quantitative estimation of risk to humans underlying basis of induced heritable Iranslcca-
from law exposures based on these animal data, tions.
addressing questions of tissue dosimetry for this
Kty words: genitic riik aueiiimnt, gum-cell mutogwiieily, genetic toxicology
Figure 2-37
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I like to put this example up (Figure 2-38). You all will probably be seeing this
continually for the next day and a half, but this example crystallizes, I think, the whole
thinking about how you would approach a genetic risk assessment.
ETHYLENE OXIDE
Upper bound estimate of the risk from continuously exposing
a male parent to 0.5 ppm EO for eight hours a day, five days
a week for three weeks is:
2.8 x1(T*
or, 2.8 offspring with heritable translocations per 10,000 live
births from exposed fathers. This estimate represents the
additional risk of heritable transiocations which are induced by
exposure to EO excluding spontaneous, or background
genetic damage.
Calculated from assessment is:
"A quantitative estimate of the genetic risk associated with the
induction of heritable translocations at low-dose exposure:
Ethylene Oxide as an example".
Published in Environmental & Molecular Mutagenesis 16: 104-
125, 1990.
Figure 2-38
Just assume everything is correct in our assumptions here. We know we have
made a lot of assumptions, like Dave Brusick said earlier. We also made a lot of
naive simplifications. We said there were no dose rate effects for example, but we do
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know there are dose rate effects with ethylene oxide; but just assume that everything
here is right.
If you have a male who is continuously exposed to 0.5 ppm for eight hours a
day, five days a week for three weeks, you obtain a 2.8 x 1O"4 risk or 2.8 offspring with
heritable translocations per 10,000 live births from exposed fathers. I will just say 3.
It is easier to conceptualize 3 births than 2.8. This represents an additional risk over
background.
So, now we know a number that is correlated with a particular human
exposure. But really, what does this mean in terms of the person who has been
exposed to 0.5 ppm and subsequent risk?
Remember the OSHA's PEL, permissible exposure level, is 1 ppm. So, you are
telling exposed persons that at one-half of that which is a very real occupational
exposure, they are going to have heritable translocations in an additional 3 births in
10,000. How do you communicate that risk?
As you saw from David Brusick's slide earlier, you can translate this number
into diseases. In our example, we considered what happens at a higher exposure.
When you get exposure at about 100 ppm, you double the background rate.
Now, the background rate was calculated to be 19 x 10**. Is a doubling of the
background where you really express concern and tell the exposed person there
might be problems? These are questions that are real tough, especially in many
instances a regulatory mandate behind it says if you feel that is a real level of
concern, then we have got to intervene and reduce the exposure to these people.
So, I think this is a very good example in trying to highlight where is the level of
concern that you want to intervene at.
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From another perspective, you can also say that 3 x 10"* is within the variability
of the background. The background, as I mentioned, is 19 x 10"*. So, do you or how
do you protect 3 over 19 in the human population? That is a very tough question to
address.
More importantly, how does this number 3 get translated into a phenotypically
expressed adverse health consequence? I think that is a real difficult thing to do right
now, and that is why we are trying to have ICPEMC give us some guidance, some
process that we all can use and get an idea of what we want to do with the whole
process.
So, I think with this workshop we are going to build on this particular example
with other examples and try to get everyone thinking about the important pieces that
you need to do a genetic risk assessment and how you would extrapolate that to
human beings. In addition, there is a lot of research involved with this.
DR. NOLAN: Kerry, is that 19 in 10,000 live births?
DR. DEARFIELD: Yes, 19x10"*. That came out of the Mary Lyon et al. paper
from ICPEMC (Lyon et al., 1983). I had an earlier draft of ICPEMC's report and it had
this figure here (Figure 2-39). Dave Brusick redrew this graph, but it is the same
picture he showed earlier.
Like I said, there is a lot of research that can be done. I think that is one of
the things this workshop will need to identify. What are some of the things we can
do?
Well, I think these risk extrapolation factors (Figure 2-39) comprise one area
that we can address with research. We can use the mouse model and examine such
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Human
REFs
Mouse Model
Route
Matrix
Dose/Rate
Bioavailability
Metabolic
Specificity
DNA Repair Capacity
Dose-response Kinetics
Target Size
Sex/Strain Specificity
Germ Cell Stage Specificity
DNA Lesion Homology
Locus Specificity „ - -
.-
Disease Homology
Mutation Expression
Private Genes
Pharmacokinetics
Germ Cell Risk Extrapolation From Mice To Humans
The presumed process of genetic disease induction in
humans modeled from experimental data derived from
Figure 2-39
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things as pharmacokinetics. We need to determine if the same set of
pharmacokinetics that you see in the human is modeled by the animal systems.
Concerning DMA reactivity, is that similar in mouse and in man? We can go down
that whole list ICPEMC assembled here. These are all the different things that are
being looked at. I now, for example, for ethylene oxide, the Ethylene Oxide Task
Force from industry is very interested in providing us with some of this information.
They are looking at some of the pharmacokinetics, at the metabolic specificity, and
other aspects.
So, these are research avenues that can be taken to help us refine what
measured germ cell mutations mean for new diseases. Actually, this is another whole
extrapolation in going from the induced amount of heritable translocation, for example,
in the human being to what that means in terms of new disease.
These are all the different types of information we need to be thinking about;
and for me, from the regulatory point of view, these are the real tough things to talk
about when I brief our risk managers. It is very easy to say yes, it is Salmonella
positive. We have followed-up for example with a dominant lethal test. Now a
heritable translocation test has been done, and now we have this ethylene oxide risk
assessment, for example.
Let me point out the difficulty with communicating this example from a briefing
we gave to our risk managers on the EO quantitative risk assessment. Ethylene oxide
is in what we call special review in the Office of Pesticide Programs. It is a fumigant
for spices. It is actually not a real heavy-duty use in terms of pesticide use. However,
we are interested, obviously, in hospital workers and others; but right now, because
the courts have mandated it, OSHA takes care of hospital workers and not us. So,
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we are only looking at spices, but we are trying to work this genetic risk assessment
up anyway.
We have an EO carcinogenicrty risk, and this is 2.6 x 10"2. K is fairly high, for
all of you who are familiar with carcinogenicrty risk assessments. This might actually
drive the whole risk assessment, and that is an issue that Dave Brusick mentioned at
the very end of his talk.
As an aside, in carcinogenicrty risk assessments, and additional question that
is thought about is if you regulate on the basis of cancer, does it actually protect you
against genetic risk? That is a real question we need to ask, because there are a lot
of resources to do both assessments.
Right now, industry is approaching both assessments (that is, cancer and
mutagenicity) and they are part of our regulations. However, routine heritable
translocation or specific locus testing is not fully accepted yet. There is real
resistance to the top tier. Again, this is fall-out from the problem where we haven't
really communicated that these are the tests that we need to use to quantitate
heritable risk, and it is not just for cancer.
Right now, I cant tell you which way that debate is going, but if something
else is going to be used as a surrogate for genetic risk assessment, then I don't know
what genetic lexicologists are in business for. That sounds pretty dire, I admit, but
I think we have some of the answers.
Back to the EO example, let me get to the numbers. This is our number, 2.8
x 10~* and these are our assumptions. We made a conservative assumption that each
heritable translocation will lead to an unacceptable health condition. That is a very
conservative assumption!
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When you think about it, this is only heritable translocations. We didn't factor
in specific locus mutations, for example, or other events we know ethylene oxide
causes. So, this estimate may actually be an underestimation; but maybe because
it is so conservative a number, it may actually be an overestimation. We just dont
know. So, this is why we need efforts like this workshop to get a handle on this
number.
The major response from the risk managers was that 2.8 x 10"4 is pretty high
risk. But they are thinking in terms of 10* for cancer, but you are mixing apples and
oranges when comparing to genetic risk. But cancer risk is historically where these
risk managers have experience. We haven't really educated them on genetic
risk—mainly because we haven't considered it much ourselves and do not have many
examples.
We have to figure out what the numbers we generate really mean in terms of
adverse outcomes. This is a real problem when you try to communicate genetic risk.
The last point I want to make with the EO example is we have other effects
that we are concerned with. EO does have reproductive and developmental
problems. There are reports of neurotoxicity problems. No risk assessment have
been made for that, but these effects may have impact on a total assessment.
As a final note, I think we need to communicate out there that genetic risk
assessment is not just for cancer anymore. All these genetic effects by ethylene
oxide, or whatever chemical, can cause a whole spectrum of effects, and we have got
to make sure that people out there understand that. That is why genetic toxicology
and associated research is so important.
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We cant allow genetic toxicrty to be subsumed into these other fields either,
because I think, as a basic mechanism, we can contribute to whatever field versus
just being part of that field. We can collaborate with these people examining adverse
effects.
This is the sort to perspective I have. Right now, we can easily identify
genotoxic concerns, but I think it has come to the point where we have to understand
how to communicate genetic risk. We have gotten to the point in our testing where
we are generating numbers, and we are asking to spend a lot of resources to
generate those numbers. We have got to decide on what those numbers mean in
terms of real risk out there for the people who may be exposed to those chemicals.
Thank you.
Discussion
DR. NOLAN: Thank you. I will take a couple of brief comments on that. Udo?
OR. EHLJNG: I agree, of course, with the tendency you were saying, but I
have one question and one remark.
The question is I am very impressed with the testing scheme of EPA, but does
the Agency enforce it? Who does the third tier test? I am very interested in getting
this answer.
Okay, and the statement is about the animal experiments. We know from the
Russian example that is used, that in the offspring, you couldn't show a significant
difference between the exposed and the unexposed. No one in radiation protection
would say okay, there is no effect. We didnt see it in the man. There, we accept the
animal data.
109
-------
The same is true in the Sellafield case. There, you have human data, but the
final judgment is based on animal experiments, and the case is decided that there is
no cause relationship between the leukemia and the radiation exposure, but again,
the essential data comes from animal experiments.
Why should it be in genetic toxicology so different to point out these cases?
DR. DEARFIELD: I think that is a good point. It shouldn't be different. Let me
answer your first question. Under FIFRA anyway and under TSCA, we do have the
legal clout, if you will, to require those particular tests, all the way up to the top tier
of the battery. If we have sufficient reason to move up to the next tier, we will require
the companies to go ahead and have their chemical tested.
Now, up to the second tier, there is no problem. Anybody can do those tests,
and most contract labs have that capability, and there are a lot of labs in the industry
that can do it in-house as well.
The problem comes down to the specific locus and heritable translocation or
any other of those top tier tests. I thing the heritable translocation can be done a little
bit more easily than the specific locus. We have almost come up to a specific locus
problem where we are trying to contract through Oak Ridge. The problem we had
was with GLPs.
You have to remember when you do a regulatory test that things have to be
done with the GLPs, and that is a real different consideration than just doing a straight
test in a research laboratory. There is a difficulty in that, and the best answer I can
give you right now is it a case-by-case basis. I depends on the capabilities of the
laboratories involved and who else is willing to do it.
110
-------
I know, in some cases, some of the industrial laboratories are contacting
themselves out now, and I would love to be able to see if we could do the same type
of thing with some of the national laboratories, for example, with your group in
Munich, because I think it would be a perfect place to try to do something or at Oak
Ridge, because that is not a trivial test. It is a problem, but we can require them to
do it if we believe it is necessary.
DR OSTROSKY-WEGMAN: From your talk, the impression is that we need
more human studies, and the main problem remains that usually in the First World,
we have people that are exposed to very low levels of dangerous substances. As an
example, there is a wonderful study by Marja Sorsa on workers exposed to
cyclophosphamide, and she couldn't find anything, because the levels were low and
the protection was good.
Now, we do have people in the Third World exposed to high doses of
substances, and I do think we should promote a little bit more to do collaborative
studies in humans exposed to substances without protection.
Da DEARFIELD: I think you are absolutely right. As a matter of fact, we have
been talking with different people about trying to begin sampling of some of the highly
exposed populations in Eastern Europe, for example, and also in China. For
acrylamide example I have here, there is a highly exposed group of people in the
United States.
You are right, we need to get some highly exposed human populations on, I
don't think, every chemical but at lease enough chemicals to give us enough
confidence that we can extrapolate back and, hopefully, validate the animal studies.
111
-------
So that when we do animal studies in a prospective sense, we can make better
judgments on the human exposure without actually having to expose the humans.
112
-------
Research Background for EO
Dr. Lars Ehrenberg provided the research background for the ethylene oxide
assessment. Dr. Ehrenberg is from the Department of Radiology at Stockholm
University, Stockholm, Sweden.
DR. EHRENBERG: Mr. Chairman, ladies and gentlemen, I will speak about the
data base of doses of ethylene oxide I was asked to give you for this meeting and
then say something about the limitations and critiques of the use of Hemoglobin
adducts. I will use the other adduct dose-rate effects which have been referred to by
several speakers here, and I will say something, in case of time, about the importance
of ethylene oxide, not only as an example but also, in general, about sources of this
compound (Figure 2-40).
Ethylene Oxide (EO)
In Vivo Doses: Data Base
Hemoglobin Adducts
Limitations and Critique
i
Dose-Rate Effects
EO: Sources, Importance
Figure 2-40
113
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The Use of Hemoglobin Adducts
We use the measurements of adducts in order to estimate in vivo doses, but since our
data available, (for instance, for epidemiologic studies and what we want to control
by various risk limiting measures) is based on exposure we have to establish (this
relationship between exposure and adduct level//n vivo dose, (Figure 2-40). Also, the
risk produced and risk to exposure relationships need a good exposure assessment.
In the case of a precursor of an ultimate mutagen such as ethylene oxide (A
in Figure 2-41), we have also to know the rate the conversion to the ultimate mutagen,
and we have the reaction of a specific mutagen with a specific site J in DNA for the
formation of an adduct (Figure 2-42).
There is a relationship between the cumulative degree of the specific
alterations in DNA and the risk, and this cumulative alkylation is a function of the rate
of formation of the adduct and the time integral of the concentration of the ultimate
mutagen. This is what we call the dose.
So, we express the dose in concentration times time, for instance, in mg/kg
body weight times hours. In short-term experiments where we can allow for the rate
of repair, and may determine dose from an injected or ingested amount from the value
of this rate constant (kg) and measuring the adduct level. With the hemoglobin as a
surrogate dose monitor, it has the advantage that the molecules are not repaired.
They are more long-lived, and from the steady state level of adducts from long-term
exposed persons, we can estimate the contribution to the adduct level (a) per unit of
time and then evaluate the dose as for DNA.
There are several experiments, though, that show the similarity between the
levels, at low doses, of adducts to DNA and to hemoglobin. This is from the study
114
-------
Exposure **->- Adducts •< - >• Risk
In Vivo Doses
RX — >- detoxified product
Figure 2-41
R,X -f DMA, —*> R, - DMA, + X
[B-DNAJcum
[DMA]
j_ ^ [R, -DNAJ
k, * g DNA
D,=
i -HbJ
gHb
Figure 2-42
115
-------
of Potter and colleagues (Figure 2-43). It has to be pointed out that this is a short-
term experiment where you can allow for repair.
For other data bases available, I will not go into much detail. I show here the
data for adduct levels in rats (Table 2-1), with respect to adducts to N-terminal valine
in hemoglobin. In some cases, we calculated that from measurement of some other
adduct to hemoglobin and give data for cases where we have measurements of DMA
adducts.
These are now expressed per ppm hour, that is, per unit of exposure dose,
and that gives a rather consistent figure of about 15 pmol per gram per ppm-hour in
rats and a figure which is approximately the same or a little higher with respect to
DMA adducts. It should be about twice as high because of a difference by a factor
of 2 in the rate constants. So, there is still some uncertainty.
Another important thing is that here, the lower figure (index c in Table 2-1) here
is from the experiment of Potter and colleagues, and this is from the studies of Walker
and colleagues where very high levels were used; but you see that in the gonads, the
dose is about half of that in the average of the body. There are similar data for the
mouse. I will come to that.
When we look at the human data (Table 2-2), we have the great problem of
the exposure assay. When we make the corresponding calculations of pmol per gram
per ppm hour, we see a variation of the adduct level, from about 2 up to about 14.
From various deliberations, we believe that the higher figures are the correct ones.
I cannot go into all the arguments for that here, but there is a tendency to
overestimate exposure doses with the effect that the calculated in vivo dose per unit
of exposure dose becomes too low. I think that is enough for that. The tests where
116
-------
Relationships between Alkylation of Hb and DNA by Ethylene Oxide
<
z
Q
LU
X
I I
I I I I I
n.moles N -HEH/g Globin
Relationship between alkylation of DNA (n.mol N7-HEG/g DNA) and
of globin (n.moles N'-HEH/g globin) in Fischer 344 rats exposed (6
h, inhalation) to rC]EO (1,10 or 33 ppm)
Figure 2-43
117
-------
we get the higher figures are from those studies where we have the most careful
measurements of exposure dose.
Table 2-1. Adduct Levels in Rats Exposed to EO.
Strain, exposure condition HOEtVal
Inhalation
7-HOEtGua
IP injection
F344
-------
Table 2-2. Human Data, Hb Adduct Levels from Ethytene OxkJe
HOBVaJ
cxpoHure
situation (na of
exposed/no, of
controls)
Stsrftzation
(5/4)
(10/10)
(11/3)
Dally (7/9)
Occasional
(8/9)
Nurses (9/8)
(4/0)
Petroleum
industry
uonunntnon
TWASh/d
(pom)
6-30
0.02-0.3
<0.1-0.7
17
<17
0.15
0.05
0.1
1-ca 2
(nmovgj
5-20
0.05-0.4
low-2
13.2
±2.6
2.7
±1.9
0.1
0.15'
0.23
1.7-
ca 10
{pmovyj/ppmn
2
7
. 7 (3-15)
(20(pmol/g)/
(prnoi/kg)]
2
2
8a
6
5-ceL 14
12 (10-14)
P2(pmovg)/
(MmoVkg)]
i minimi ii
Calleman et al.
1987
Sarto et al 1991
Duus et al. 1989
Tates et al. 1991
Mayer et al. 1991
Hagmar et al.
1991
Osterman-Golkar
and Bergmark, 1
van Slttert al. to be
published
' Adjusted for difference in calibration method (Tdrnqvlst et al. 1992)
In order to sum up this data base (Figure 2-44), we have to consider that the
dose in blood of humans or animals is a constant times the alpha, the fraction of the
inhaled amount which stays in the body, which is not exhaled or excreted; the
alveolar ventilation (V.J, the exposure dose; and in the denominator, the volume of
distribution (A'), a basic pharmacokinetic concept; and lambda, the rate of elimination
of the compound.
119
-------
1 -h) - const A'*-25 (L mol'1)
Figure 2-44
Now look at the various estimates we have of these constants, in Table 2-3,
where we compare these parameter values for mouse, rat, and man.
We see here that the target doses of the valine adduct level per ppm-hour, that
is, per unit of exposure are approximately the same if we stick to these higher human
values, as I have said before. We have also data here for hydroxyethylated guanine
in testis. This is the mouse value from Dr. Sega's experiments a few years ago, and
we can see that there is generally a lower level of adduct in the products.
In this equality of values per ppm-hour, the equality in level of adducts per
ppm hour is due to two effects which operate in opposite directions: the longer
lifespan of adducts with the increasing lifespan of erythrocytes, (that is, of hemoglobin
when you go from mouse via rat to man), and the decrease in alveolar ventilation,
(that it is the amount of air inhaled per minute per kilogram of body weight), and the
decreasing rate of detoxification when you go through the species.
i
The data give thus a rather consistent picture. I would say they are consistent
within about a factor of 2, and they might, therefore, be used for risk estimation.
In the recent studies of Walker et al, they have criticized the use of hemoglobin
adducts for several reasons. Here is data for rats and mice (Figure 2-45), the rats
exposed to 300 ppm for four weeks. This is the expected rate of disappearance in
120
-------
the 60 days lifespan in the rat, and this is the found one (which decreases much
faster).
Table 2-3. Best Values for Parameters and for Increments of Adduct Level or
t.r (days)
(L g'V1)
V^ (L kg'V1)
A' (L kg'1)
[HOEtVal],
increment from
1 ppmh
(pmol g"1 ppmh'1)
[HOEtGua],
increment from
1 ppmh
(pmol g"1 ppmh'1)
Dose from 1 ppmh
ppmh'1)
Dose from
1 /jmo1/kg
* ^»i h^^r^r*r*« **
Mouse
40
3.2-10-5
80-100%
60
-1
4.5
•^ww «ri VIMFW^^I Mr^^^fl •
Rat
60(55-65)
4.5 -10"6
-80%
36
<1 (0.5?)
3
^ «^«* ^
Man
120 (126)
5(4.5) -10-5
-80%
6-9
?'
1 (if A'-1)
12
20 (testis)
0.5 (blood)
0.2 (testis)
0.5 (liver)
0.2 (testis)
16
12-24
10 (testis)
0.35 (blood)
0.10 (testis)
0.30 (blood)
0.12 (testis)
12
.(?0.05-)0.3
(blood)
121
-------
A 40°
£ 300
0
O)
ra 200
| 100
0
B 140
120
c
•§ 100
CD
E 80
[5 60
X
I 40
20
0
"2\^^-—
; V_^
_ —
0 24 6 6 10 t
-S^^.
1 V ^^D -
1^^ ^
— —
— —
— ' —
0 24 6 8 10 1
400
300
200
100
0
2
140
120
100
80
60
40
20
0
2
Time (Days postexposure)
Comparison of the projected loss of N-(2-hydroxyethyl)valine to the
observed loss of adduct after 4 weeks of exposure of rats (A) to 300
ppm ethylene oxide and mice (B) to 100 ppm ethylene oxide.
Projected ( °,a); observed (•,•). Data points are mean ± SE(n=5).
The derivation of the curves for the projected loss of
N-(2-hydroxyethyl)valine is discussed in Walker et al. (23) and
Figure 2*45
122
-------
However, these very high levels of adducts are something we have never
encountered before. We see a toxicrty to the erythrocytes develop, and this is
something one should be careful about when working with very high levels.
Something similar was seen in our study (Figure 2-46), a study carried out
mainly by Dr. Osterman-Golkar, on the Bushy Run rats which had been exposed for
two years to 10, 33 or 100 ppm of ethylene oxide, 30 hours per week. There is a
drop in the curve at the highest dose level, the very highest concentration, which
could be due to a toxicrty of this kind. We have not seen that kind of effect in rats
following acute exposure or short-term exposures. They are not expected to be there
at low levels of exposure, that is those which are most interesting (Figure 2-47).
One should say, however, that there are mammalian species where the kinetics
of zero-order kinetics of hemoglobin turnover is not at hand, but where there is a first-
order of decay after exposure.
Another rather remarkable statement in one of the papers by Walker and
colleagues is that you cannot use hemoglobin adducts to predict, at any time after
an exposure, the DNA adduct level. Here is the expected level of hemoglobin adduct,
and you will see that the DNA adducts in the different organs decay at different rates,
because of repair (Figure 2-48).
Who has asked to be able to use hemoglobin adducts as something
completely parallel to the DNA adducts? You can use each of these. You can
calculate the cumulative dose if you determine first the kinetics of the change of the
quantities.
I will jump over some of the figures to make it a little shorter. There are other
pieces of criticism which could be easily handled.
123
-------
100 •
:§>
10 33 100
ppm
F344,6h/d, 5 d/wk, 2y
Figure 2-46
Hb Adduct Levels in EO-Treated Rats
4 x 26 mg EO/kg
40
30
20
10
„ HOE + Val
nmol/g
6 weeks
Figure 2-47
124
-------
350
< 300
^250
o>
§200
HI
I 150
o
50
-^^ HG
^ ^_
0246
35
< 30
•••^M
2468
Time
(Days postexposure)
Persistence of 7-(2-hydroxyethy!)guanine in exposure of rats (A) to
300 ppm ethylene oxide and ethylene oxide. Brain (A), lung (O),
leukocytes (*), sp liver (A), testis (X). Data points are means ±
SE(n)
Figure 2-48
125
-------
I should like to say a few words on dose and effect. Here is what everybody
has in our documents, I think, for dominant lethal mutations as a function of ethylene
oxide exposure, discussed from Dr. Generoso's work, and we have a strong increase
upwards (Figure 2-49).
What could be the causes of this convexity? If we look at possible causes of
non-linearity (Figure 2-50), we have that there could be saturation of the detoxification
and saturation of DNA repair which we suppose make the curve turn upwards like
that. There could be induction of detoxification that will turn the other way around,
induction of repair which could affect the curve at very low doses, thereby increasing
the risk at very low doses or decreasing risk at very low doses (Figure 2-51). ft could
be a true two-hit effect which is involved, ft could also be a loss of damaged cells.
I may come to that later, but you see often that curves drop at very high doses
because of the damage to sensitive cells.
Here (for EO), we don't discuss the additional effects of saturation of
bioactivation and so on. These ones are valid for an ultimate mutagen, such as
ethylene oxide, and they are all causative to the influence on mutation rate of dose
rate.
We see such effects if we measure hemoglobin adducts as a function of
injected amounts, for example, in a rat study in cooperation with Dr. Tates. At about
25 mg/kg, there is a switch upwards (Figure 2-51).
One can now look at the data from Walker et al, we have this bend upwards
here (Figure 2-52). The line I have drawn here is to see what a linear curve would be.
It varies at the highest dose in the mouse exposed for four weeks; here it is higher
than expected. For DNA adducts, the deviation is still higher.
126
-------
JO
CO
f
CO
I
0 50 100 150 200 250 300
EO Concentration (ppm)
Concentration-response curves for induction of dominant lethal
mutations in male germ cells. A, matings with T-stock females;O,
matings with (SEC x 101)F, females.
i
Figure 2-49
127
-------
Deviations from Linearity
Potential Causes: F
- Saturation of detoxification
- Saturation of DMA repair
- Induction of detoxification
- Induction of repair
Error-free
Error-prone
True two-hit effects
- Loss of damaged cells
(For pre-mutagens:
- Saturation of bioactivation
- Induction of bioactivation)
All these effects are influenced
by dose rate
Figure 2-50
128
-------
Hemogtobin adduct (HOEtVal) increment per
injection, in experiment with rats
Relation between Hb Adduct Level
and Dose of EO in Rats
100
JO»
50
26
50 60
mg/kg
80 100
Non-linearity; saturation of
detoxification at high doses
Rgure 2-51
129
-------
- o
0 20 40 60 80 100 120
Exposure Concentration (ppm ETO)
Comparison of the dose response of N-(2-hydroxyethyl)valine in
hemoglobin and 7-{2-hydroxethyl) guanine in DNA of rats (A) and
mice (B) exposed to ethylene oxide for 4 weeks (6 hr/day, 5 days/
week). Globin (D), brain (A), lung (O), spleen (•). Data points are
means ± SE(n=3 to 5). Note the scale of the X-axis is different in A
and B and that the scale of the Y-axes in A are adjusted so that the
data points for N-(2-hydroxyethyl)valine and 7-(2-hydroxyethyl)
guanine overlap at 3 ppm ethylene oxide.
Figure 2-52
130
-------
If you now calculate from the elimination kinetics a kind of joined effect of the
two curves, using the data of the curve from Generoso's study (Figure 2-53). Let me
recalculate here the real dose we have got at the DMA level where we also consider
the prolongation of the akylated condition in the DNA as an increased DMA dose, that
is, looking at the DNA dose over time as a measure of the degree of alkylation. So,
the curve becomes much less steeply changing upwards which should be taken into
consideration when you apply a formula for estimating the risk at very low doses from
such a curve.
When I was here, I think, six years ago, there was a discussion of ENU as a
mutagen (Figure 2-54). This is a most effective point mutagen in the mouse with
respect to induction of specific locus mutations. In this rather complicated figure
(Figure 2-54), I have drawn the curve for mutations per locus per rad of radiation as
a function of dose rate in rad per minute and then the curve for ENU from the data
of Bill Russell and colleagues where I put equality between number of alkylations of
guanine-O6 giving rise to the same mutation frequency as one rad of ionizing radiation
at high dose rates.
Here is the data of Russell with 20 times 5 mg/kg injected, so what I did was
to make an extrapolation of this curve, calculating the linear component of the
function for that curve.
Ethylene Oxide Data
Here is now Liane Russell's ethylene oxide data; the question was whether or
not you got an increase of specific locus mutations following the treatment with
131
-------
60
50
40
30
20
10
0
-r*
w
t O Dominant lethals in the study
r of Generoso et al.
x With doses estimated from
500
1000
1500
Figure 2-53
Mx107
per locus
perrad
or rad equ.
w •/
X T /
T T^ JP5*— 20X6|M
i i 1^1 i i i
lENU
20 x 5 ng/kg
-3 -2 -1E°0 -1-1 -1-2 -t3
Log (rad/min)
Figure 2-54
132
-------
ethylene oxide at very high doses. The dose rate in that study is here, and the
question was are these levels (of EO and the expectation form ENU) significantly
different. They are at about the 5 % significance level, that is, on the edge of being
significantly different.
Here (at lower dose rates) are effects which are of a different kind. The
bending upwards here is due to effects found for both radiation and ethylene oxide
for inducibility of repair. So, at very low dose rates, we may have a higher mutagenic
effectiveness.
In the work on plants 25 years ago, we saw that when hydroxyethylating
compounds were compared to methoxyethylating compounds, they were
approximately equal in mutagenicity (Figure 2-55). However, there was an enormous
difference with much stronger frequency of chromosomal aberrations when you
introduced hydroxyethylations.
In efforts to understand this, there was a model developed (Figure 2-56) where
you have hydroxyethylation of phosphate groups in DNA that leads to the formation
of a cyclic phosphate compound with pentavalent phosphorus which then breaks to
give rise to a chain break.
Furthermore, this end of the strand carrying the adduct is alkylating. ft is a
phospho-triester, which is alkylating and could alkylate the other strand. So, you have
the possibility, perhaps, of a double strand break being formed.
This is a mechanism which we are studying further. I will finish with one table
(Table 2-4), of importance, I think, to epidemiological studies. I think of Dr. Brusick's
presentation here and so on, and we have quite well limited ourselves to the industrial
exposure (to ethylene oxide), and that is the reason for the discussion.
133
-------
M CA
75 15 -
50 10 -
25 5 -
Chrom.
Aberrations (CA)
100 200 300
mM,24hat18°C
A* HOCH2CH2
AO MeOCH2CH2
(From Gichner et al. Heroditas 59,1968,253)
Figure 2-55
134
-------
dR-O-f-O-dR-
? r
CH2-CH2
9
-dR-O-P-O-dR- -> -dR-Of + HO-dR-
CH-CH
CH-CH
(dR, deoxyribose; B1, B2, bases.)
Model Alkylators:
CH3SO2OCH2CH2OH
CH3SO2OCH2CH2OCH3
CH3SO2OCHCH2OH
CH3
CH3SO2OCHCH2OCK
I * *
CH,
H2NCON-CH2CH2OH
NO
HoNCON-CHoCHCH,
NO OH
Figure 2-56
135
-------
Table 2-4. Annual Dose of EO in the 8.5 Million Population of Sweden
Source
Chemical and
pharmaceutical
industry
0.1-1 ppm EO
Ethene in air,
average 2 ppb,
mainly from burning
organic matter
Endogenous
production of
ethene (partly from
gut flora and unsat.
dietary lipids)
Cigarette smoking
10cig. cf1;0.25mg
ethene/cig
Individual dose
of EO (g a'1)
0.2-2
0.0003
0.007
0.03
(N)
Collective dose of EO
(kg a'1)
250
8.5-106
8.5-106
2-106
0.05-0.5
2.5
60
60
I have here in the last column summarized for the Swedish population of 8.5
million, the amounts, in kilograms, of ethylene oxide from various sources in the whole
population annually, and at 1 ppm of ethylene oxide during working hours. In
approximately 250 workers we have in the ethylene oxide industry in Sweden, there
will be 0.5 kg. This is a level to the whole population.
If you take ethane inhaled, 2% of the inhaled amount of which is converted to
ethylene oxide, that gives 2.5 kg. If we take the endogenous production of ethylene
oxide, partly from the intestinal flora, and partly from the diet, we think due to
unsaturated fatty acids, it is 60 kg per year. That is 100 times more than the industrial
contribution.
136
-------
So, this is something which we might find to give mutations collectively of
importance. This corresponds to the contribution from ethylene in the cigarette
smoke from smoking by about 2 million cigarette smokers which is about a quarter
of the population smoking cigarettes.
Perhaps this ethylene oxide to the population would be one of the mutagens
operating in these curves which show the frequency of mutations of new dominant
mutations, as a function of father's age, from, Friedrich Vogel's work (Figure 2-57).
Now, these are autosomal; none of them is x-chromosome inherited where the
maternal grandfathers play a role.
We calculate that endogenously produced ethylene oxide could be the initiator
in 1 % of the cancers in knowingly unexposed persons, and the question is which are
the others.
Thank you.
137
-------
4x
O—O Achondroplasia
• • • -• Achondroplasia
(new series from USA)
I—• Apert's Syndrome
3x
co
2x
1x
Population
• •» •• ^ ^ ^ «D
average
24 29 34 39 44 47
Father's age
Figure 2-57
138
-------
EO Mutagenicity Risk Assessment
Dr. Vicki Deliarco presented an Ethylene oxide mutagenicity risk assessment. Dr.
Dellarco is from the Human Health Assessment Group, Office of Health and
Environmental Assessment at the EPA in Washington, D.C., U.S.A.
DR. DELLARCO: The previous speakers have laid down a very good
background for me to present this ethylene oxide genetic risk assessment.
As Dr. Dearfield mentioned, this is something we did as an example risk
assessment a couple of years ago. This assessment was published in Environmental
and Molecular Mutaoenesis in 1990 (Vol. 16, pages 104-125), and is in your handout.
It was not a formal risk assessment per se, but we wanted to develop a framework of
thinking for quantitative genetic risk assessment to highlight the issues and
uncertainties (many of those were pointed out by Dave Brusick); furthermore, we
wanted to promote discussion on these uncertainties and issues and suggest
approaches for genetic risk assessment and eventually identify data requirements and
future research directions.
Why did we choose ethylene oxide? It is not a bad model. Also, it is a
reactive alkylating mutagen, it is widely distributed to all tissues, its pharmacokinetics
are relatively simple, there is human exposure, and more so, there is a large data
base on its genetic toxicology. Within that data base, we chose the heritable
translocation test in the mouse, because ethylene oxide was evaluated at several
dose levels, and it was given by a relevant route of human exposure, namely
inhalation.
139
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Before I get to the assessment, I just want to point out that we were not the
first to attempt to look at the genetic risks of ethylene oxide. Dr. Ehrenberg published
a risk assessment back in 1974 where he used the radiation dose equivalent
approach, taking mutation data from barley and molecular dosimetry in the mouse.
Then, shortly before we published our assessment, Thomas Smith published
a genetic risk assessment based on pharmacokinetic modeling using DMA alkylation
in the mouse testis, Dr. Generoso's dominant lethal data, and Dr. Sega's DNA single
strand break data to come up with a risk calculation.
This morning, Dr. Wright pointed out some of the steps that we have to take
in genetic risk assessment, and so what I am going to do is apply many of those
steps to this example. The first thing you have to do is define response which I have
already done; it is "heritable translocations" in mouse germ cells.
Then you have to define the dose. This is critical not only to this approach but
the parallelogram approach and involves deciding what the actual target is, selecting
the dosimetrics, and trying to establish a relationship between external exposure to
dose at the target (which Dr. Ehrenberg has gone over). It involves some concepts
concerning the window of the response, where the adducts accumulate, and dose
rate issues.
Once you have established that, you have to determine equivalent doses in
experimental animals and humans; in other words, what is the dose in mouse and
human that will cause similar risks. Then you take the heritable translocation data
and try to establish mathematical relationship; in other words, define the dose-
response. This involves considerations of mechanisms of action, how heritable
translocation are produced, and a concept called "additivity" which I will come to later.
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Then, once you do all of that, the final step in a genetic risk assessment is to
determine what the potential human health impact is; in other words, what is adverse.
Dr. Dearfield emphasized how important that is for us as a regulatory agency.
This is the translocation study in the mouse and the sequential method was
used to identify translocation carriers. Male mice were exposed to ethylene oxide for
six hours a day, five days a week for six weeks and then daily for ten more days.
As you can see from this figure (Figure 2-58), the dose response is very steep,
and it flattens out at lower exposures. There is some background, but it is very low.
All doses tested were significant over the background.
So, the question is how you take these data and estimate the risk. You must
consider the various extrapolations that are going to be necessary to do this.
Because we are using mouse data, we are going to need to do a species
extrapolation: the mice experiment was conducted at high exposures, so we are going
to need to do a high to tow dose extrapolation: and the last calculation is a mutation
to disease extrapolation.
Now, the species extrapolation involves a couple of assumptions. The first
assumption we make up front is that the biological mechanisms that link levels of
ethylene oxide exposure to risk of heritable translocations are common in mouse and
man; in other words, the mechanisms that are causing, the induction of the breaks
that lead to the formation of a translocation that would be passed on to the F1
offspring are similar in mice and man. So, we are going to have to make assumptions
about DMA repair, cell cycle kinetics, and transmission to the offspring.
This isn't so unreasonable to do despite repair differences among mammalian
species. If you look at all the in vivo data that you have for inhaled ethylene oxide in
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30 -r
25--
20--
15--
10--
5--
ppm Incidence % Translocations
0
165
204
250
300
1/2068
32/1143
52/1021
88/812
109/427
0.05
2.79
5.09
10.84
25.53
50 100 165 204
EtO Exposure (ppm)
250
300
Figure 2-58
142
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various species, you can see there is a commonality of genotoxic responses in test
animals and humans.
So, because you see this concordance of different responses, it gives you
confidence that, in general, similar mechanisms are operating. The similar data that
we have in humans, are micronuclei induction in exposed humans and in the mouse;
we have sister chromosome exchanges being observed across several species,
including humans; and chromosome aberrations are also observed in sites in humans
and the monkey. Although the rat cytogenetics are negative, in every system ethyiene
oxide has been tested, it has been positive. That is the exception.
So, in essence, our species extrapolation Is going to become one of an
interspecies dosage calculation; in other words, we try to determine equivalent doses,
but we have to define dose, and there are the questions that need to be addressed:
What is the biological effective target? Is it DNA? Is it protamine?
We went with the hypothesis that the target is DNA, however, one cannot rule
out protamine as the target because of the work that has been done by Gary Sega.
Dr. Sega has shown that there is a concordance with protamine alkylation and
dominant lethal mutations. Despite that, we think it is still reasonable to assume DNA
is the target because ethyiene oxide produces mutations and cytogenetic damage in
somatic cells where you dont have protamine, and furthermore, DNA adducts are
detected in germ cells. Nevertheless, this is a major assumption that can affect the
modeling, but we are going to go with DNA.
The next question is what is the biologically effective dose? In other words,
how do adducts accumulate in germ cells? When you talk about dose, you just can't
143
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talk about dose in nominal units like parts per million. You really have to epitomize
dose and generalize it over time.
So, is there a window that is critical for adduct accumulation? We used
dominant lethal data to determine this. I We have to define the window over which
adducts accumulate.
This involves looking at different germ cell stages throughout spermatogenesis,
and, as you know, there are stem cells, spermatocytes, spermatids, and mature
sperm. Ethylene oxide produces a post-stem cell effect and, looking at the next figure
(Figure 2-59), you will see the germ stage sensitivity pattern from the mouse dominant
lethal test for ethylene oxide. This is just the germ cell stage pattern. At the bottom
is the post-mating schedule in days, and Y axis is dominant lethal frequency, and in
the boxes at the top represent the germ cell types that are being sampled.
As you can see from this study, ethylene oxide dominant lethals are induced
in mid to late spermatids to early spermatozoa. So, the mouse window is 10 days.
So, we are going to assume that there is not a stem-cell effect, where adducts would
accumulate over the reproductive life of the entire exposure history of the individual.
We are fairly confident in this assumption. Most of the chemical mutagens that
have been evaluated to date basically show post stem-cell effects, but we cant rule
out stem cell effects unequivocally.
There was a study that Dr. Waldy Generoso did where he exposed mice to
ethylene oxide for two weeks and then eleven weeks, and he found a higher dominant
lethal response for the eleven weeks of exposure. Now, that could have been due
to a stem cell effect, or It could have been due to some pharmacokinetic factor.
144
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30
Oi
o-
Ol
_ 20
ID
O
o
I * I »*jl I
S 10 IS 20
Post-Exposure Mating Schedule (Days)
Figure 2-59
145
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So, we are going to assume in the mouse that a ten-day window exists. The
next question is what is the delivered dose? We are using the dosimetric ppm»hour,
and this involves looking at the relationship of external exposure to target dose. If the
delivered dose is proportional to external exposure, then we can use external
exposure as a surrogate. So, we are looking for proportionality.
We didnt do any sophisticated pharmacokinetics modeling or analysis. We
basically relied on the work of the modeling done by Dale Hattis in 1987 which
assumes linear pk. Although Hattis' model doesn't include predictions of
concentrations of ethylene oxide in testis, the model results for other tissues might
predict that.
During inhalation exposure, as has been discussed, ethylene oxide is readily
absorbed and it reaches a fairly rapid steady state. The tissue concentration is in
balance with the constant external air concentration.
So, assuming proportionality at low to moderate exposures isn't such a bad
assumption. Also, Smith's model had low dose predictions, and concentration of
ethylene oxide in the testis is proportional to external air concentration after a brief
equilibration period. The assumption of proportionality is a very critical one in risk
assessment. For the dosimetric ppmehour, we have to make the assumption that
there is no dose rate effect.
Where our approach fails is looking at risk to high episodic exposures,
because there is some non-proportionality at very high concentrations of ethyiene
oxide.
146
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So, for the ppm»hour dosimetric, we have to assume that response is a
function of exposure multiplied by time and this is not true if there is a dose rate
effect.
As Dr. Ehrenberg discussed, there are dose rate effects. This is shown in a
study that Dr. Waldy Generoso did for dominant lethal mutations (Figure 2-60). Dr.
Sega looked at DNA strand breaks and UDS and yes, he saw a dose rate effect, too;
this effect was less as the total exposure was lowered. Gary went down to a 300 ppm
hour exposure looking at sperm head DNA alkylation and saw a very slight dose rate
effect.
So, like radiation, it appears that as you reduce the total dose, the dose rate
effect diminishes. In rabbit somatic cells you dont see a dose rate effect.
Now, what we did is we summarized all the mouse dominant lethal data that
we had on ethylene oxide (Figure 2-61). The X axis is the total hours of inhalation
exposure, the Y axis is percent dominant lethals, and the numbers here are the total
ppm«hours.
If there is no dose rate effect these lines would be horizontal. The 7200 ppm
total hours is the dose rate study that Waldy did for dominant iethals. You can see
from these limited data that short intense exposures produce a dose rate effect lower
than longer exposures totalling the same ppm»hour. The dose rate effect appears
to diminish with lower total exposures and longer times. So, we think it is reasonable
to assume no dose rate effects when you are looking at low constant, continuous
exposure scenarios.
Now that we have determined our dose and we are using external exposure
as a surrogate for target dose, we have to determine the species equivalence of dose
147
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ETO DOSE-RATE STUDIES
CELL TYPE
HOUSE GERM CELLS:
HALE
POSTHEIOTIC
RABBIT SOMATIC
CELLS:
Peripheral
Lymphocytes
END POINT
DOMINANT
LETHALS
DMA Strand breaks
G Unscheduled ONA
Synthesis
SPERM HEAD
-ONA Alkylation
Sister Chromatic!
Exchange
TOTAL
EXPOSURE
7200 ppn/hr
1800 ppm/hr
300 ppn/hr
48. 000 ppn/hr
EXPOSURE
RATE
300ppn X 6hr X 4days
600ppn X 3hr X 4days
1200ppa X l.Shr X 4days
4SOppn X 4hr
900ppn X 2hr
ISOOppn X Ihr
75ppm X 4hr
ISOppn X 2hr
300ppn X l.hr
200ppn X 6hr X 40days
400ppn X 6hr X SOdays
ISOOppm X .5hr X 64days
DOSE-RATE
EFFECT
YES
(6X)
YES
(4X - 2X)
SLIGHT
(2X)
NO
Figure 2-60
4V 6U
Hours of Inhulution
Figure 2-61
148
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in mouse and man. The assumption that you have to make here is that the
probability of inducing a heritable translocation is a function of adduct load
irrespective of the species; also, one must assume that ppm»hour of external
exposure to the concentration multiplied by time product in the testis is the same for
mice and man.
Therefore, external exposure is a surrogate for the biologically effective dose,
and we believe that the Hattis and Smith models support the species equivalence of
steady state blood concentration. Also in support of this is the hemoglobin alkylation
data. Here we have plotted all the human hemoglobin alkylation data that we had
(Figure 2-62). Many of these studies came from Ehrenberg and Osterman-Golkar.
You can see that even though this is a log-log plot, for ppm»hours, there
appears to be proportionality among different species. Even for comparable
ppm«hours, the rate of hemoglobin alkylation among these different species is
proportional to exposure level even at quite high air concentrations. So, we feel this
is an indirect support for the proportionality assumptions that we are making.
In the next figure (Figure 2-63), we took all the sister chromatic exchange data
that we had at the time and we then plotted it in ppm«hours. We think that this also
helps legitimize the species extrapolation that we are making in that even though it
is a log-log plot, we feel there is fairly good agreement between ppm*hours and
genotoxic damage. This is a very crude plot, because we are dealing with human
data and with very gross estimations of exposure.
So, therefore, we can go ahead and scale our dose across species just using
external exposure as our surrogate. The 10-day window in the mouse would equate
to about a 21-day window in humans. For humans, there is a more prolonged period
149
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10,000
1.000
s*
0
X
^ 100
•S?
"o
3 10
1
9
UkJ
i
0
^_
O
—
A Mouse *
• Rat A
— O Rabbit
A Human AA
A * A
A
A
A **
• A
•—
A*
1 1 1 1 i 1
10 100 1,000
EtO Exposure (ppm-hours)
10.000
100,000
Figure 2-62
10
a
^. is
I
•o
a
•o
c
• Rat
O Rabbit
A Monkey
A Human
100 1.000 10,000
EtO Exposure (ppm-hours)
100.000
1.000.000
Figure 2-63
150
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to accumulate adducts. So, when you calculate your ppm»hour exposure, you would
just multiply It out over 21 days even if the humans were exposed for 2 years.
Now I will go to the dose-response modeling. We simply could take a ruler
and just draw a straight line, but that is overly conservative. It ignores the information
being provided by the higher dose points, and you would like to factor in biological
considerations when you extrapolate your dose response curve to the low dose
range. Also, it doesn't make any sense, because you would have a sharp break
between here and the straight line and that would be hard to explain biologically.
So, what we tried to do here (Figure 2-64), is to fit the data on a empirical
basis using curve fitting models, and we chose the multi-stage model, which is a
modified polynomial model that has a great deal of flexibility.
So, we are just looking for the best fit to the curve, and that is represented as
"M". Again, that approach is somewhat unsatisfying, because we are not taking any
biological considerations in. So, we looked for another model to use where we would
find some biological reasoning to apply to the selection of the model.
As shown in the next figure (Figure 2-65), simply to get heritable translocation,
you have to have breaks in two non-homologous chromosomes that are in close
proximity and we are selecting a model on the basis of that.
We went to the Weibull model which has been used in cancer; and we think
that the logic that is applied in cancer is somewhat similar to the logic that you would
apply in this case in that, in cancer, you have many cells that are at risk, and you
have to have certain rare events coinciding before that cell becomes transformed. In
this case, the nucleus is divided up in little boxes, as shown in Figure 2-66, and you
151
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0.25 -
0.20 -
VI
O
O
•55
o
O>
O>
0.15 -
5,000
10,000
15.000
ETO Exposure (ppm«hours)
Figure 2-64
152
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Biological Consequences of Rearranged Chromosomes
Infertility/low fertility
Spontaneous abortion/miscarriage/stillbirth
Developmental effects (e.g., skeletal, neurological)
Figure 2-65
MECHANISTIC CONSIDERATIONS
TWO CHROMOSOME "BREAKS"
IN CLOSE PROXIMITY
Figure 2-66
153
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have to have these two chromosome breaks occurring in close proximity; once that
happens, you get a transiocation-bearing sperm.
In the next figure (Figure 2-67), we used the Weibull model. A linear quadratic
model does not fit the data. It has to be a model with a fourth power, and the Weibull
is represented here by a "W. I am not going to go into these models too much. We
probably could have chosen some other models for curve-fitting purposes, but I would
like to make a point about additivity.
P(d) = 1 - e
Weibull (Extreme Value Distribution)
Figure 2-67
The issue came up about the background level of genetic damage. Does the
so-called spontaneous noise level of genetic damage, participate with the ethylene
oxide-induced damage to give rise to heritable translocation risk? You have to have
two chromosomes, and you have to have two breaks arising in dose proximity. One
is induced by ethylene oxide, but can the other be induced by this so-called
background level?
If the case is the background damage does not contribute to the risk, in other
words, independent background, then you can see that the curve does move down
very steeply; in other words, the two chromosome breaks both have to be induced
by ethylene oxide.
154
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We tended to favor the assumption of additive background, in other words, the
background damage contributes to the risk, and I think what supports this are the
data that were presented on ethane; that there is this endogenous and exogenous
exposure to ethane which produces ethylene oxide. So, it is reasonable to assume
that you dont have to have both chromosomes broken by the ethylene oxide
exposure. However, this is an issue that needs to be discussed further by this
workgroup. Some individuals think the background is dependent. If we were wrong
about this assumption, you could see that the independent background essentially
just lays on its axis, and K shows essentially no risk or very little risk at all at lower
exposures.
Table 2-5, to shows you, again, the big difference between an additive
background versus an independent background assumption. You would predict that
at 1200 ppm*hour total, about 7 translocation carries for 10,000 offspring versus 0.1,
respectively. So, that assumption makes a big difference in the risk assessment.
Table 2-6. Excess Risk per 10,000 LJve Births
EXTRAPOLATION
METHOD
Linear extrapolation
from lowest dose
-Multistage Moder
Weibull Model
Additive Background
Independent Background
OSppm
OOppnvftr*
2.1
0.79
0.21
-------
For the purposes of this assessment, we just chose a factor of 1, that every
translocation carrier would have some adverse effect.
We know that is really conservative, and, in fact, that the factor is probably less
than 1, but how off are we? I believe that Julian is going to get into some of these
issues later on in his talk.
Another assumption that we are making that I consider a major weakness in
this assessment is that in the population both males and females are at risk and the
data we are using comes from male mice only; and we know that the male is not a
good surrogate for female risk, but we have to assume that the female is at a similar
risk. We certainly need more studies on the female. Because we know
gametogenesis between males and females is very different, metabolism of the
chemical in the female germ cells is different, chromosome configuration or
condensation is different, and the biochemistry is different. That is a lot of differences.
Also, this assessment is limited, because we are only looking at heritable
translocations, and we know that ethylene oxide causes other types of genetic effects.
There have been some studies looking at the effects of ethylene oxide on inducing
specific locus mutations.
The ultimate germ cell stage in the female is the zygote, and there have been
some studies by Dr. Waldy Generoso looking at exposure of ethylene oxide to the
zygote; and in Table 2-6, you see that there is a high frequency of a congenital
malformations that are induced in addition to mid to late gestational deaths.
This fetus has some limb defects. This one is the normal. This one is growth
retarded, and this one has hydropy. In fact, there was quite a high incidence of
156
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hydrops after ethytene oxide exposure. This may not be a transmissible effect;
nevertheless, it is an effect on the offspring.
— - -»
TreejiimiK
EO
EMS
DES
MMS
ENU
TEM
X-ray**
Control
noauipUoii
bodteefK)
53
31
24
25
49
57
58-66
5
Mid-andtate-
nnarftrflnn
DHBtaQQli
deaths (K)
26
31
37
2.5
2
Z2
1.2-2.0
1.5
LVefatuaas
exarmnad
87
231
157
451
33
112
326
3,436
UveMuaee
WffJl
dofuct (K)
37
29
24
3.8
15
10
4.6
1.3
* fi hr rvMatmatlnn ovrar* Inr Y-rau whtoh tuaa 9 It hr
b Data for 150 R and 200 R doses were pooled.
Dr. Dearfield was pointing out that the cancer risk assessment for ethylene
oxide seemed to be the driving factor in regulatory decision making. It only takes 900
ppm for 1.5 hours, to get the zygote.effect. This could be much more sensitive than
the cancer effect, particularly for short-term exposures.
This is a big issue because there is the thinking that the cancer risk calculation
is more protective given the non-threshold mathematical approach, and because
nobody has shown that there is a germ cell mutagen that doesn't cause cancer; but
we dont have a large data base on germ cell mutagens, so we don't know if that is
true, yet that is the thinking that we have to deal with.
157
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Ethylene oxide might not be the best case for germ cell risk, but what about
acrylamide? Is the cancer risk more protective or the genetic risk? I dont know. We
need to look at that. So, in some cases, the cancer risk may protect for the genetic
risk, and in other cases, it may not at all; but we won't know that until we start
generating some genetic risk assessments and doing those comparisons.
Okay, so let me just summarize by going back to the outline I had about the
steps that we took, because many of the assumptions that we made in this risk
assessment are relevant to the parallelogram approach and I just want to go through
this again.
Nature of the target: we are assuming Is DMA. If it is protamine, there may
be a threshold response. Maybe you have to accumulate a certain adduct level
before you get a break. If so, that, you would take a different approach for modeling
the data. So, we could be wrong there.
I don't think we are so wrong in assuming proportionality for low to moderate
exposures; and true, we didn't do any pharmacokinetics modeling, but that wouldn't
improve upon this assessment. The window of response; we are assuming a post-
meiotic effect, and there may be a stem cell effect, and that could be argued. That
would have a tremendous impact on the dosimetric that we used and the dose
calculations that we did.
We are assuming no dose rate effect, and that is okay, I think, as long as we
are looking at risk to low-moderate continuous exposures; but if you are looking at
high episodic exposures, that is where this assessment fails, and maybe
pharmacokinetics analysis can explain that non-proportionality and correct for it. This
158
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is also an issue in the parallelogram approach when you extrapolate across species
and there are the acute exposure scenarios.
The additivity assumption; there is disagreement, that the background is not
additive, but independent resulting in a lower risk.
What is adverse? Well, we are using a factor of 1. We need to discuss that
later in the workshop.
So, maybe through the course of this meeting, we can identify the approaches
that should be taken for genetic risk assessment and what sort of data we should
generate.
Discussion
DR. NOLAN: Thank you very much. Any short comments?
DR. DELLARCO: Well, I was going to say that since Julian is going to critique
this assessment, some of the issues, we can open it for comment after that and just
maybe a few questions if there are urgent questions now.
DR. NOLAN: Good idea. Just the one comment, then, from Lars Ehrenberg.
DR EHRENBERG: In this, the formations in the effects, was that following
exposure of the teammates of the conception?
DR DELLARCO: Right, it was 6 hours, 6 hours after mating.
DR. EHRENBERG: There were spontaneous abortions in females exposed
to ethylene oxide during pregnancy, but they were exposed also during the period of
conception. It is difficult to say what the consequences are of it.
I think this story is important, because it was published ten years ago, and
nobody has tried to repeat it.
159
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DR DELLARCO: That is why I wanted to emphasize it in this talk, because I
think it is a critical effect of ethylene oxide.
DR. NOLAN: One last question, then.
DR ANDERSON: I think you can use the males in just the same way showing
the pups, because you get very nice data after low dose exposure, continuous
exposure, and I think that is very relevant to this sort of assessment, because it is a
disease state which may afflict males.
160
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A Reconsideration of EO Risk Assessment
The final Plenary session was a reconsideration of EO Risk Assessment by Dr. Julian
Preston. Dr. Preston is from CIIT in Research Triangle Park, North Carolina, U.S.A.
DR. PRESTON: I would like to explain some points to ponder on the risk
assessment model that you just heard.
This is my version of a parallelogram (Figure 2-68), and one I wanted to
emphasize since the four corners for response have been talked about- these various
associations with the dotted lines and the full lines. It should be noted that this figure
is only for reciprocal translocations, and what I will mention in a little bit is a rather
hefty distinction, between mutations and heritable translocation, when you are looking
at risk assessment models.
In two places, I have added underlying mechanism, and that is the underlying
mechanism for the formation of translocations in somatic cells and in germ cells in
humans and in rodent species which, I think, form a major component of the means
of extrapolation. You can extrapolate by effect, and you can extrapolate by
mechanism. I am going to try and throw in a little bit of mechanism to add to the
effect for our extrapolation considerations.
We have heard quite a lot about radiation responses and the parallelogram
approach for radiation effects.
This figure is to remind me of two or three items (Figure 2-69). This is a dose
response curve for reciprocal translocations induced in spermatogonial stem cells and
analyzed in primary spermatocytes. These are some data from Grant Brewen and
myself some years ago. It is to remind me that we have been looking at
161
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GENETIC RISK TO HUMANS BY EXTRAPOLATIONS
Example for chromosomal translocations
Reciprocal translocations
In rodent germ cells
• -> Reciprocal translocations
in human germ cells
r
Reciprocal translocations <-
in rodent lymphocytes
-> Reciprocal translocations
in human lymphocytes
V
1
underlying mechanism <-
-> underlying mechanism
J
Reciprocal Translocations Induced in Mouse Germ Cells by
EO (Data from Generoso et al., 1990, Environmental Molecular
Mutagenicity 16,126-131)
Figure 2-68
r * LOB • io~4o » 5.00 - tcr'o2
0.20
0.10-
200
400 600 600 1000
DOSE(R)
1200
Dose Response Curve for X-Ray-lnduced Reciprocal
Translocations in Mouse Spermatogonial Stem Cells (from
Preston and Brewen, Mutat. Res., 19,215-223,1973)
Figure 2-69
162
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parallelogram approaches for a long period of time; but it is also important to
remember that here, as we have heard several times, the dose is very easy to
measure.
We know something about dose, dose to the target tissue, if not necessarily
to the target cells, and with radiation, the effect is produced in the cell of interest at
the time of the irradiation. The translocation is produced, in this case, in the
spermatogonial stem cell at the time of exposure to radiation.
That makes for a very different way in which you can consider the response
curve, and here you can see a nice dose response formula which has a linear dose
component and a dose- squared component. You can define it, because you know
something about dose and dose distribution.
Thus it is (Figure 2-69) to remind me that radiation is simple, and chemicals
are a lot more difficult to assess risks from exposure. The reason is that there is a
very different mechanism of induction of the end-product. A translocation induced by
radiation is induced in the cell type exposed and close to the time of exposure, and
it is a feature of errors of repair. Repair errors will be the mode by which
translocations or mutations are produced by radiation.
That is not the case with chemical exposures, as most of you are well aware,
but it certainly affects the type of modeling that we do.
Translocation Mechanisms and the Stages of Spermatogenesis
So, understanding radiation effects is relatively simple; chemicals are a lot more
difficult. Why do we have to consider mechanism, and why do we have to consider
all the different stages in Spermatogenesis?
163
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Figure 2-70 illustrates the steps of spermatogenesis. This is my linear version
of spermatogenesis, but what I want to emphasize is not only the different stages in
spermatogenesis, but also where I have indicated what I think are the critical
components, that is, cell division and DMA synthesis.
Let me return to the saga here and explain that for radiation, the effect is
produced in the target cell in any of the stages of spermatogenesis, but for chemical
exposures, it is necessary to have a replication phase in order to produce your end
product of interest, either a translocation or a mutation. Of importance here also is
to note for replication errors that will be required to produce a translocation, you have
to have two events on separate chromosomes. For a mutation, you need a singular
event where an error of replication will produce your mutation.
So, the sites of DMA synthesis are the critical components, and they will relate
to the probability of getting your endpoint of Interest. For example, this is why I think
any risk assessment model must consider separate components of, in this case,
spermatogenesis, because they will all have very different probabilities for risk
assessment.
Here, for example, is the stem cell, and I would like to just deal with that briefly,
because I really do think it is a critical component of the spermatogenic cycle when
considering risk assessment You must understand that although a number of studies
have been performed with the mouse to look for effects in stem cells, they have
largely been hefty doses and rather acute exposures, and I will just demonstrate why
I think it is important to consider long-term chronic exposures for the stem cell.
These are just points to ponder. Figure 2-71 shows the average somatic cell
or differentiating spermatogonial cell cycle divided up into the G,, S, Gg, and mitotic
164
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SPERMATOGENESIS
Stem Cell
Spei
Differentiating
Spermatogonia
•4f
SpermMocyles
I
irmatids
I
Spermatozoa Q
Fertilization
I
Zygote
Mitosis
(S^hase)
Mitoses
(S-phases)
Melosls
(pre-melollc S-phese)
Mitosis
(S-phase)
A Diagram of the Various Stages of Speratogenisis Showing
the Points at Which Cells Undergo an S Phase
Figure 2-70
CELL CYCLE
Average Somatic Cell
Differentiating Spermatogonia
Spermatogonial Stem Cell
Mitosis
24 Hours
8 Days
A Comparison of the Cell Cycles for a Somatic Cell and a
Spermatogenial Stem Cell
Figure 2-71
165
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phases. The cell cycle duration is given as 24 hours, which is a very mean number
for a somatic cell. You could consider it less or greater than that, but let's use 24
hours as an average.
Now, if one is interested in alterations to the DNA, either adducted DNA or
strand breaks (in this case for ethyiene oxide), we will consider adducted DNA; this
is the critical component for producing a mutation or a translocation. Damage
induced in the S phase has a reasonably high probability of being involved in a
replication error. The probabilities for cells in other stages of the cell cycle will be
significantly lower because of repair that can take place prior to replication.
So, the probabilities for a differentiating spermatogonial cell for producing a
mutation or a translocation will be related to the total cell cycle and the proportion of
that cell cycle which is a DNA synthesis phase. So, for differentiating spermatogonia,
there will be some probability of having the opportunity to produce an error.
How about the stem cell? Well, it has approximately an eight-day cell cycle
in a mouse. I don't know the cell cycle time in the human. I would presume it is
rather long. Note here that the DNA synthesis phase represents a very small
component of that cell cycle. So, a large proportion of the cells will be subject to
repair prior to replication.
Similarly, if you use a high exposure situation, you have a high probability of
killing the cell that is in DNA synthesis because of associated lethal responses. So,
although you have a higher probability of producing a mutation or a translocation, you
have a higher probability of killing that same cell because of a secondary event.
The probability of having coincident events in the stem cell is a lot smaller,
especially under conditions of chronic exposures. The probability of producing a
166
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mutant cell either with a translocation or a mutation is rather high relative to cell killing
for the stem cell, under chronic exposures.
I believe that you really should, especially under chronic exposure conditions,
consider responses in the stem cell.
I want to return to spermatogenesis for a second. So, I have done what I
would call the abbreviated comparison between a stem cell and a differentiating
spermatogonia, but how about considerations of post-meiotic germ cell stages, the
ones that were utilized in the risk assessment model that Vicki Dellarco just presented
which, incidently, is a very good attempt to model risk as a first shot.
But here we have a completely separate consideration for post-meiotic germ
cells, and it is a point that is often forgotten or not recognized, and that is that the
next S phase for these cells is following fertilization. So, there is a fairly hefty period
of time here for cells to accumulate damage for several days, and there is no repair
taking place in these cells. The repair takes place after fertilization. So, there is sort
of a race between repair of damage induced in this cell stage and replication at the
S phase.
So, there is a completely separate set of probabilities of producing a mutation
or a translocation for post-meiotic germ cell stages. You can't consider probabilities
of effect similar in stem cells, spermatogonia, or post-meiotic cells. You have to divide
up, I think, the spermatogenic cell cycle into the different components and then relate
exposure or effective dose to those particular cell stages based upon duration within
the spermatogenic cell cycle.
For risk assessment, I think you have to consider a lot more than just the post-
meiotic stage of the cell cycle, and the probabilities will be quite different; but also,
167
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there is a strong possibility that those probabilities will be different across species.
We know very little about the human. We know something of the macro mechanism,
the fact that DMA synthesis is required will cross species, but the probabilities that are
found for the different stages or the different regions of the cell cycle could be quite
different for the different species.
So, I think it is very important to try and learn something about those
probabilities for the species that one is comparing.
Translocation Mechanisms and the Stages Oogenesis
I wanted to present a similar consideration for oogenesis to show that in the
female, again, a quite different situation exists that would indicate, in fact, that for
translocations and for mutations, there would be a rather low probability of induction.
The higher probability, one would anticipate, would be for aneuploidy events.
The reason for that is that here in Figure 2-72, I have identified again the
periods of S phase; note that there is a very long period of time in here between this
S phase (oogonia) and the next S phase which is after fertilization. So, there is a
hefty period of time for repair to take place with a rather low probability of getting any
adverse outcome in terms of a translocation or a mutation. There is a very long
period of time between the two S phases.
Here (zygote) is the S phase that is important for mutation production by most
chemical agents other than those that are truly radiomimetic. Again, this contrast with
radiation will be quite obvious, because radiation does not require the S phase. The
event of interest is produced in the cell type of interest at the time of exposure. So,
168
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OOGENESIS
Stem celt
i
Oogonia
Mitoses
(S phase)
1st polar body
1° oocyte
(arrests)
matures
I
2° oocyte
Mature ovum
Fertilization
Zygote
Meiosis I
(pre-meiotlc S-phase)
Meiosis II
Mitosis
S-phase
A Diagram of the Various Stages of Oogenesis Showing the
Points at Which Cells Undergo an S Phase
Figure 2-72
169
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I think there is a very different set of considerations that one has to have for the
different cell types based upon the mechanism of production.
I had several other points that related to hemoglobin adducts and dose. Dr.
Ehrenberg raised these as they related to the importance of considering dose as
measured by hemoglobin adducts based upon the lifetime of the erythrocyte. I think
this is a singularly important consideration.
Implications for Risk Assessment
The only other point I would like to make here that I think we should consider during
this workshop, and Vicki Oellarco certainly alluded to it, and that is the question of
what is genetic risk. There are different interpretations of genetic risk. I wanted to
just present one on the assumption that tt is not the only interpretation of genetic risk.
What we have to decide is what is the health impact of induced translocations.
Here is just a simple reminder of what happens to chromosomes and translocations
during meiosis. On the left of the figure is the normal situation. There are two
homologous chromosomes depicted, white and black. These will pair up at meiosis
and then segregate in two different ways (1,4:2.3 and 2,4:1,3). There will be four
different products and, of course, in this case, they will all be normal, normal haploid
genotypes.
In the right hand case (Figure 2-73) there is a translocation between
chromosome number 2 and number 3, and because of the propensity for
chromosomes to want to pair up accurately, for homologous regions to pair, in
meiosis, you get a configuration involving all four of the chromosomes; there are two
ways that these can segregate, giving four different products.
170
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normal
chromosomes
pairing
at meiosis
normal complement
translocated
chromosomes
pairing
at meiosis
I
2 4/13
segregation
IN
• o •>•
J
J
translocation 1 mostly spontaneous
carrier abortion
(semi-sterility) 2 sometimes severe
congenital abnormalities
Induction of a translocation in a stem cell spermatogonium with the
pairing configuration in the primary spermatocyte and the most common
segregation products normally and in the event of a translocation; A and
B are translocation breakpoints.
Figure 2-73
171
-------
Without following this in any detail, all I need to point out is that the four
possibilities are one normal, one translocation carrier, and two unbalanced. The
unbalanced products have duplications for the black chromosome and a deficiency
for the white chromosome or, of course, the reciprocal of a duplication for the white
chromosome and a deficiency for the black chromosome.
So, these are unbalanced genotypes, and in the vast majority of situations,
these are lethal events. So, what we have to consider here for genetic risk, and I
consider genetic means transmission from generation to generation, we have to
consider whether a translocation carrier represents a genetic risk and whether
unbalanced genotypes represent a genetic risk.
Does a translocation carrier have, per se, a risk, such that it might be that it
has some decrease in fitness? There are certainly some adverse outcomes being a
translocation carrier, and certainly there is a component of the unbalanced genotypes
which can be viable.
Do we consider all of these as adverse genetic outcomes? I argue no. I
consider only the ones that don't result in spontaneous abortions as being a
component of genetic risk.
If you take that proportion or that group of unbalanced genotypes and do a
calculation, it comes out as shown in Figure 2-74. I took this out of the UNSCEAR
report (1986, p. 102), and it is not very complicated and I don't need to go through
it. tt is simply a way of representing the proportion of conceptions that result in live
birth multiplied by the unbalanced complement in those versus the total number of
those unbalanced complements in a population of the live born and the aborted
group, assuming that 85 % of conceptions result in live births.
172
-------
If you go through the calculation, you can calculate that unbalanced offspring
arising from reciprocal translocations are about 9% of the total unbalanced genotypes.
So, 9% of the unbalanced genotypes result in a viable but genetically unbalanced
offspring.
Pi*,
p1 is the proportion of conceptions that result in
live birth (0.85).
a1 is the frequency of unbalanced complements
in live-born arising from reciprocal translocation
carriers (0.00012).
p2 is the proportion of conceptions that result in
abortions (0.15).
ag is the frequency of unbalanced complements
in abortions arising from reciprocal translocation
carriers (0.00681).
Unbalanced offspring arising from RT = 9%
Calculation of Total Unbalanced Genotypes that are Present
in Live-Born Individuals
Figure 2-74
So, we have to consider here, when we talk about genetic risk, do we wish to
consider 100 % of translocation frequencies? That means that all reciprocal
translocations present a genetic risk. Or should we consider that it is a 9 % outcome
173
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that is of significance in these cases? I have argued that the latter is the correct
approach.
Also, I would like to point out that, again, there are two different components
to translocation transmission. You should realize that for stem cells, translocations
produced in a pre-meiotic cell appear in the F, individual by transmission. For the
post-meiotic germ cells, that translocation is actually produced in the F, offspring, the
DNA damage is transmitted.
There are two very different considerations here. One, the translocation is
transmitted from the parent to the offspring. In the other case, it is produced in the
offspring. I think these are important considerations which will really affect the genetic
risk assessment. So, they are really additions to what you heard in the previous talks.
We obviously need to consider what doses and what components there are
to dose. The situation for chemical agents is not like radiation. We really don't know
what the important ONA lesion is that leads to the replication errors that I am talking
about here. We dont know what the real measure of dose is. We ought to consider
that here.
Also, we really need to consider the different responses of the cells in the
different stages of the cell cycle, and we have to relate those to the DNA replicative
phases and the probabilities of making errors on a damaged DNA template.
So, I think there are several additional components to the EPA model that are
of importance, and there is one other that relates to the modeling that is used. I
would argue that at low exposures, it would really be most applicable for
translocations to apply something which is very much a dose squared component
curve. This is in contrast to the various models fitted in the EPA risk assessment, one
174
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of which is shown in Figure 2-75, a strictly linear extrapolation from high exposure
values.
That does not imply the same mechanism as the rest of the curve at high
exposures. However, at low exposures, you need two lesions to produce a
translocation, and you can well argue that those are produced as independent events,
because you need replication errors on two chromosomes simultaneously. You can
assume those are independent events unless you wish to assume that one alteration
on one DMA strand is sufficient to initiate a process of recombination which I would
rule out at this point with the available data.
So, I think at low exposures, (and as I say, this might well not be the case at
the high exposures which is the mode that we are trying to extrapolate from very
often), just on mechanistic considerations, I think that a two-track fit, the radiation two-
track type of curve, would be the appropriate one to consider.
These are some of the points that bring in the macro mechanism into genetic
risk assessment and that is really a very simple look at mechanism. Basically, It just
involves DMA replication and its fidelity. There are also micro mechanisms involved.
Whether you have a GC to AT base pair change resulting in a mutation is what I
would call micro mechanism, which I don't think we are ready to or need to consider
at this point. However, I do think we need to consider macro mechanism in our
deliberations of which type of parallelogram approach to use and how can you fill in
the probabilities around that parallelogram.
175
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10 —
II-
M-
u-
10 —
I —
too
EtO Eipeture (ppm)
M< «0 MO
The Induction of Translocation in Mouse Post-Miotic Germ
Cells by EO (from Generoso et al, 1990, Environ. Mol
Mutagen. 16,126-131)
Figure 2-75
176
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Discussion
DR. EHRENBERG: Just a very brief comment. If you have dependence of
dose squared, the deliberative of that curve would be zero at very low doses, but if
it is additive to a background of some kind, there will be a linear component.
DR. PRESTON: A linear background or an interaction with the background?
DR. EHRENBERG: Yes, if you have additivity of background.
DR. PRESTON: I am suggesting that this is probably a very low probability
event, as background, and the probability of interaction between induced and
background events is equally low or even lower.
DR. BISHOP: Julian, just quickly. If a chemical is causing apurinic sites or is,
say, interacting with some protein component causing stress and thereby causing
breakage, would that chemical have a radiomimetic profile or a non-radiomimetic
profile or something intermediate?
DR. PRESTON: An apurinic site would have a non-radiomimetic profile. An
apurinic site would behave as a potential site for a replication error. It can result in
the A rule, that is, what do you incorporate opposite a gap. You know, the tendency
is to stick an A opposite a gap from replication of an apurinic site.
DR. BISHOP: What did you say about the protein?
DR. PRESTON: You could argue either way, and I think it would be whether
the torsion or stress of a protein effect is more likely when the DMA is unwinding
during replication or when it is stationary or when it is transcribing. Since I don't
know the answer, one could argue either way.
DR. BISHOP: Or it could be intermediate?
177
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DR PRESTON: Well, if the stress is greatest when the DMA is unwinding
during replication, it would be S phase dependent. If it is transcription which is driving
the torsional stresses on DMA, it could be a G1 event.
DR. NATARAJAN: In Drosophila, when you treat the flies with the alkylating
agents and then you store the sperm, you get a storage effect.
DR PRESTON: Right.
DR. NATARAJAN: This storage effect increases the frequency of chromosome
aberrations with time.
DR PRESTON: Transiocations.
DR. NATARAJAN: If you assume that model, you could expect without an S
phase dependency, also S independent chromosome aberrations arising from the
alkyiation at the activity sites following ethylene oxide treatment.
DR. PRESTON: I agree. In fact, some years ago, I produced non-S
dependent effects with chemicals by inhibiting DNA repair, which is essentially what
you are doing in the Drosophila storage effect. Yes, I agree. I think that both S-
dependent and S-independent processes might be equally important in assessment.
DR GENEROSO: And, Julian, in follow-up on that, the translocations we
produced with ethylene oxide or EMS which are very much the same, there is no
evidence for heterozygosis with these translocations; to me, this means they are
chromosome type aberrations which, means that these rearrangements must have
been completed before the S phase after fertilization.
DR. PRESTON: It might be very different in the case of the post-meiotic germ
cell stage. I did point out it is a race between repair and replication. If repair is rapid
178
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in that stage, then it may be more a chance of producing a chromosome aberration
by a repair process at that stage.
DR GENEROSO: That is what I think is possible.
DR PRESTON: Yes, but again, it is a mechanistic consideration, I think, that
we need to predict these probabilities.
179
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180
-------
Data Summaries of Compounds
This section contains the following compound summaries:
• Ethylene Oxide
• Acrylamide
• 1,3-Butadiene
• Cydophosphamide
181
-------
182
-------
Ethylene Oxide
Dr. AT. Natarajan presented the data summary for Ethylene Oxide. Dr. Natarajan is
from the Department of Radiation Genetics and Chemical Mutagenesis, State
University of Leiden, The Netherlands.
DR. NATARAJAN: The parallelogram approach for estimation of genetic risk
in man due to exposure to genotoxic agents utilizes the available data on tissue dose
in somatic cells of experimental animals and man, induced chromosomal aberrations
and point mutations in somatic cells of experimental animals and man, and induced
chromosomal aberrations and mutations in germ cells of experimental animals.
Thus, we have to utilize the data from the animal systems, particularly the
mouse, and all the available data in man except the germ cells in man to which we
don't have any access at the moment.
In the following, results will be discussed that have recently appeared in the
literature and that have recently been generated in our own laboratory on genetic
effects induced by ethylene oxide (EO) in rodents and in human populations. These
data pertain to somatic cells because, according to Dr. Generoso (pers. comm. 1993),
there are no recent new data available for germ cells.
From the previous presentation by Dr. Ehrenberg, It has become apparent that
DNA and hemoglobin adducts can be used to measure EO exposure in human sub-
jects and experimental mammals. Hemoglobin adducts can be used as a surrogate
for DNA adducts. As was pointed out earlier, the problem with DNA adducts is -
especially in humans chronically exposed to low levels of EO - that the DNA adducts
measured may not represent the real tissue dose, because adducts can constantly
183
-------
disappear through chemical hydrolysis or they can be repaired by intracellular DMA-
repair processes. On the other hand, hemoglobin adduct measurements reflect
exposure in the last three or four months of the individual because the half-life of
erythrocytes is about 120 days.
EO adducts in DMA are uniformly distributed in the body except for the liver
where there are higher levels of adduct formation, and in the testis where adduct
levels tend to be lower (for example, due to the presence of a blood-testis barrier).
As was shown earlier by Ehrenberg and Rhomberg et al., (1990), the frequency
of EO adducts, especially the hemoglobin adducts per a given dose, appears to be
very similar in mice, rats, rabbits, and humans. This makes a comparison between
experimental animals and humans valid, at least in the case of EO.
When we consider chromosome aberrations in somatic cells, there is an old
study by Ribeiro et al. (1987), which has been already referred to in an earlier review
by Deliarco et al., in 1990. Ribeiro et al., found that inhalation of 200,400, or 600 ppm
for six hours, and 200 or 400 ppm for 6 hr/day, 5 days/week for 2 weeks, resulted in
dose-related increases in aberrations in bone-marrow cells and spermatocytes of
mice.
Acute exposure was found to be more efficient in inducing aberrations than
chronic exposure. More recently in our laboratory, Farooqi et al. (1993) carried out an
in vivo experiment in which mice were treated i.p. with four different doses in the
range of 0-4 mmol/kg (= 0-150 mg/kg of EO). For bone marrow cells, they found a
linear increase in the frequency of chromosomal aberrations per cell: aberrations/cell
= 0.08 (±0.06) + 0.267 (± 0.03) D (mmol/kg).
184
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Another endpoint which has been studied is the frequency of micronuclei in
polychromatic erythrocytes bone marrow. In the early study by Appelgren et al.,
(1978) mice were treated i.v. with two i.v. injections of 50-200 mg/kg, 24 hr apart. A
dose-related response was obtained. Rats were treated according to the same
regimen and here also there was a dose-related increase of the frequency of
micronuclei. Positive, dose-related responses in mice were also reported by Conan
et al., (1979) and Jenssen and Ramel (1980). In our own study with mice, (Farooqi et
al., 1993) the frequency of micronuclei in polychromatic erythrocytes from bone
marrow increased linearly with dose in the range of 30-150 mg/kg. There are no new
data for induction of micronuclei in rats.
If we look at the data on induction of sister chromatid exchanges, in the bone
marrow cells of mice treated with 30-150 mg/kg (four doses), we obtain a very good
linear dose response: SCEs/cell = 0.50 (± 0.07) + 0.94 (± 0.03) D (mmol/kg). Also
in rats, Kligerman et al., (1983) reported a concentration-dependent increase of SCEs
in peripheral blood lymphocytes of rats exposed to EO by inhalation (0, 50,150, or
450 ppm for 6 h/day for 1 or 3 days).
After the publication of the review by Dellarco et al., 1990, several reports were
published on the induction of genetic damage in lymphocytes from workers
occupationally exposed to EO. These papers have been reviewed for the present
meeting by Tates (see separate document provided for the meeting; this document
did not carry author's name; the name of the document was "Genetic Damage in
Human Cells Exposed In Vivo to Ethylene Oxide").
In some of these papers the induction of chromosomal aberrations was
studied (Hdgstedt et al., 1990; Mayer et al., 1991; Tates et al., 1991 and Lerda and
185
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Rizzi, 1992) (see Table 3-1). One of the problems with the studies by Hogstedt et al.,
(1990) and Mayer et al., (1991) is that the fixation times for cultured cells were quite
long (72 and 66 hours, respectively). Thus the lymphocytes would have gone through
more than one cell division, thereby reducing the frequency of aberrations in
lymphocytes because of the dilution with second division cells. Thus, this could result
in an underestimation of the aberration frequency and therefore to the absence of an
effect in both studies. Another reason for the negative results is that the exposure
levels were very low compared to the studies by Tates et al., (1991) and Lerda and
Rizzi (1992). In our laboratory we looked at two EO-exposed groups (Tates et al.,
1991). One with a low exposure, and another with a high exposure. We detected a
dose-related increase in the frequency of aberrations in both groups of workers. In the
study by Lerda and Rizzi (1992), the frequency of aberrations was also significantly
enhanced.
Several groups of investigators studied induction of micronudei in lymphocytes
from EO-exposed workers (see Table 3-2). Sarto et al., 1991 could not detect micro-
nucleus induction in lymphocytes of sanitary workers exposed to low doses of EO.
The same investigators studied micronucleus induction in nasal cells and buccal cells
(Sarto et al., 1990,1991) but, again, there was no proof for induction of micronuclei
in such cells. In the studies of Mayer et al. (1991) and Perrera et al., (1992), the
exposure level was low, as in the previous study, and here again no induction of
micronuclei could be established. A similar result was obtained in the study by
Schulte et al., (1992) where exposure levels were also quite low. Thus it appears that
at low levels of exposure there is no significant induction of micronuclei. In our own
study with hospital workers where the exposure level was low, we found a significant
186
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3-1- Cytogenetle studies and Point Mutation Studies In Celts of
Chromosomal aberrations in peripheral blood lymphocytes
Exposed to EthyUne Oxide
Population
Hospital
workers 8
Hospital
workers 2-6
Factory
workers 3-27
Hospital
workers 3
Factory
workers
tears Exposure information X Chromosomal Result
exposed (TUA values are In ppn) aberrations
Exp. Contr.
A: Contr. (23)c Smokers*
•: Exp. (34) TUA-8 h S 1 2.3 1.3 Ma
range: 0.008 - 2.4 Monsmokere
2.5 2.9 N
A: Contr. (8) . 48
•: Exp. (9); TUA-8 h « 20 4.6 2.0 Pb
with bursts of 22 - 72
C: Contr. (15)
D: Exp. (15); TUA-8 h - 17 - 33 6.9 1.9 P
with bursts of 14 - 400
A: Contr. (10) 48
•: Exp. (10); TUA-8 h - 60 - 69 8.7-10.9 1.2-7.9 P
A: Contr. (23) propylene-oxide 72
exposed workers
I: Exp. (6); sterilizers TUA-8 h • 2 5.8 4.7 N
C: Exp. (12); assemblers 4.8 4.7 M
Culture Reference
time (h)
ooMayer et el. (1991)
Tates et al. (1991)
Lenta and Rizzi (1992)
Hogstedt et al. (1990)
• * C
5.0
4.7
187
-------
Table S-2. Nicronuetw atudlee in peripheral Mood l>a*hocytee or other type, of coll*
Population
Hospital
worker* 5
(0.5 - 12)
Sanitary
workers 2
8.6
5.3
Hospital
workers 8
Hospital
workers
Year* Exposure Information
exposed (TUA values are In pp»)
A: Contr. (27)
B: Exp. (5); TUA-8 h - 0.025b.
C: Exp. (4); TUA-6.5 h * 0.38°
TUA-30 • « 4.4
B «• C
B:
C:
B + C:
B:
C:
B * C
A: Contr. (23)
B: Exp. (34); TUA-8 h • S 1
range: 0.008 - 2.4
A: US: Contr. (8)
B: US: 0-32 ppVh (32)
C: US: > 32 pp*/h(11)(TWA-8 h-0.16)
A: Hex: Contr. (1)
B: Hex: 0-32 ppw/h (9)
C: Hex: > 32 pp»/h(12)(TUA-8 h-0.54)
O.J7
0.60
0.63
13.34
13.00
15.60
NonsMok
12.62
480"
1060
3020
1810
NN /
Exp.
Masai cat la
0.44
Buccal eel la
i!oo
1.00
IvnohocYte*
12.65
11.00
11.00
S*oker»d
16.50
,eff
13.90
5108
2150
1000 Result Method Reference
Contr.
Old
(no Cyt-B Sarto et al. (1990)
or BrdU)
«c
OldSarto et al. (1991 )"
1.00 H (no Cyt-B
N or BrdU)
N
Cyt-B
11.00 H
N
N
BrdUNayer et al. (1991)
N Perera et al. (1992)
N
Old Schulte et al. (1992)
N (no Cyt-B
N or BrdU)
N
N
188
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Table 3-2. Nlcronucleus stadias In peripheral Mood lymphocytee or other types of cell* (continued).
Hospital A: Contr. (8)
workers 2-6 B: Exp. (9); TWK-8 h « 20"
bursts of 22 - 72
Factory C: Contr. (15)
workers 3-27 0: Exp. (15); TtM-8 h « 17 - 33
bursts of U - 400
29
27
Cyt-B
Tstes ct si. (1991)
Factory A: Contr.: propylena-oxlds
workers
B
C
B
exposed workers (23)
: Exp.: stsrl Users (6); TtM-8 h » 2
: Exp.: assemblers (12)
* C:
6
5
5
.0
.4
.7
2
2
2
.6
.6
.6
P
P
P
Ndgstedt et al. (1990)
Old
(no Cyt-B)
(no BrdU)
So effect
"Statistically significant difference between control and exposed subjects
Sunber between brackets indicates muter of subjects per group
"current smokers; data on subdivision into current and former smokers are not included in the table
'cells with high frequency of SCEs
Cumulative exposure in ppVh during 4 months prior to blood sampling
"Frequencies indicated in Table 3 of Schulte et al. (1992) seem to be unreal 1st leal ty high (error?)
Lower values were found when internal exposure was measured via Nb-adduct formation.
189
-------
induction of chromosomal aberrations but no evidence for induction of micronuclei
(Tates et al., 1991). Thus for low exposures to EO, the micronucleus frequency does
not seem to be a sensitive Indicator for EO exposure. In more heavily exposed
workers from a factory involved in the production of sterilized disposables, we found
a statistically significant induction of both chromosomal aberrations and micronuclei
(Tates et al., 1991). Finally, Hogstedt et al., (1990) reported a significant increase in
the frequency of micronuclei in factory workers exposed to rather low doses of EO.
Thus, when the exposure is high, it is possible to detect micronucleus induction.
Chromosomal aberrations seem to be a more sensitive indicator of induced damage
than micronuclei under chronic low exposure conditions.
Several recent reports describe results of investigations into induction of sister
chromatid exchanges in lymphocytes from EO exposed workers (see Table 3-3).
Mayer et al., (1991) looked at smoking as a confounding factor and could
demonstrate induction of SCEs in smokers but not in non-smokers. In our own study
(Tates et al., 1991), we found that both low and high exposures induced statistically
significant amounts of SCEs. Lerda and Rizzi (1992) also found a positive response.
Finally, positive effects were also reported by Sarto et al., (1991) and Schulte et al.,
(1991). The latter two groups of investigators did not find induction of SCE's in
workers that received relatively low doses of EO.
From the foregoing, it appears that SCEs represent a very sensitive endpoint
following chemical exposure such as EO and, as expected, it comes out to be positive
in most of these studies.
190
-------
Table 3-3. Sister chroMtid
(MX) studies In peripheral Mood lymphocytes
Population
Reference
Sanitary
workers 2
8.6
5.3
Hospital
workers
8
Hospital
workers 2-6
Factory
workers 3-27
Hospital
workers
Hospital
workers 3
Years Exposure information
exposed (TiM values are in pan)
A: Contr. (10)
•: Exp. (5); TlM-8 h > 0.025
C: Exp. (4); TtM-6.5 h - 0.38
TIM-30 m « 4.4
B * C:
Contr. (23)
Exp. (34); TMA-8 h « 5
range 0.008 - 24
A: Contr. (8)
•: Exp. (9); TUA-8 h * 20 - 25
with bursts of 22 - 72
C: Contr. (15)
0: Exp. (15); TUA-8 h « 17 - 33
with bursts of 14 - 400
A: US Contr. (8)
I: US 0-32 ppm/h (32)
C: US > 32 pp*/h<11) 32 ppn/h(12)(TUA-8 h-0.54)
A: Contr. (10)
B: Exp. (10); TWA-8 h « 60 - 69
10.0
12.4
11.2
13.0
10.2
6.0
12.2
5.9
6.9
6.5
6.5
13.3
10.8
10.8
10.8
Smokj
VT6
ponsmifri
9.8
5.0
6.3
4.6
4.6
5.6
5.6
6.1
SCE/cell
Exp. Contr.
H
P
N
t£i
P
ma.
N
P
P
P
P
H
N
P
Result HFCs* Result
Exp. Contr.
Sarto at al. (1991)
Smokers Mayer at al (1991)
6.9 4.0 N
Monsisokers
1.8 1.2 N
Tates et al. (1991)
12.8 4.8 P
79.4 4.2 P
Schulteet al. (1992)
Lerda and Rlizl
-------
When one carefully analyzes data on sister chromatid exchanges in exposed
workers, one can often recognize a subpopulation of lymphocytes with a relatively
high frequency of SCEs (Mayer et al., 1991 and Tates et al., 1991) (see Table 3-3).
Therefore, the number of high frequency cells, along with haemoglobin adducts, can
be used as very sensitive indicators of exposure.
In addition to cytogenetic studies on effects of EO exposure, there are several
publications on induction of point mutations by EO. Very recently, Walker and Skopek
(1993) reported a dose-dependent induction of HPRT mutations in splenocytes of
preweanling mice (12 days old) exposed to fractionated doses of EO (cumulative
doses of 200, 600, or 900 mg/kg). In this study an expression time of 8 weeks was
used and the mutant frequency was increased respectively 5-, 15-, and 24-fold
(control frequency: 3x10"6). The dose-effect curve was found to be linear. In another
experiment with doses ranging from 150-600 mg/kg, an expression time of 20 weeks
was used. In the latter case, the increase in mutant frequency was much lower (range;
1,4-5 fold). Thus, the proper selection of the expression time for mutations is critically
important for demonstrating a positive effect of exposure. The optimal expression time
seems to be about 10 weeks. It is of interest to note that evidence for mutation
induction in EO-exposed mice could only be obtained in preweanling mice and not
in adult mice.
In Leiden, we have extensively studied induction of HPRT mutations in lym-
phocytes from spleen and cervical lymph nodes from adult rats (Dam van, et al.,
1992). Rats were given 1 x 80, 3 x 80, 4 x 60, or 4 x 26 mg/kg EO with one day
intervals between fractions. Splenocytes had higher spontaneous mutant frequencies
than lymphocytes from cervical lymph nodes. Despite the fact that several expression
192
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times were used, there was generally little or no evidence for mutation induction by
EO in these experiments. However, statistical analysis of our data by L Ehrenberg
suggests that a 50 percent increase of the mutant frequency over the control value
cannot be ruled out. These unpublished data are in agreement with those of Walker
and Skopek (pers. comm.) who also could not detect mutation induction by EO in
adult mice. The difference in mutagenic effects of EO in preweaniing and adult mice
may be explained in two ways that mutually support each other. Firstly, there are
more dividing lymphocytes in preweaniing mice than in adults so that the conditions
for fixation of pre-mutational lesions during S-phase, and consequently induction of
mutations, are better in preweaniing mice than in adults. Secondly, the detoxification
capacity of preweaniing rodents is lower than in adults, so that more pre-mutational
damage persists for mutation fixation in preweaniing mice.
The above results with mice and rats do not exclude the possibility that EO
may still induce mutations in adult mice or rats. If, for example, EO is administered
chronically over longer periods of time one increases the probability that lymphocytes
will be exposed to EO when undergoing DNA-synthesis so that pre-mutational
damage can be converted more effectively into mutational damage. To test this possi-
bility we are presently performing mutation studies with rats that are chronically
exposed to EO in their drinking water. Preliminary results indicate mutation induction
by EO in adult rats.
It is known that hydroxy ethyl nttrosourea (HOENU) induces similar adducts
as ethylene oxide (Segerback, 1990). The ratio of O6/N7 guanine adducts for the two
chemicals is very different (for EO O6 constitutes 0.5% of the alkylation of guanine-N-
7, whereas for HOENU O6-(2-hydroxyethyl) guanine was found to be 63% of N-7-(2-
193
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hydroxyethyl) guanine). At present, we are conducting experiments with HOENU in
adult rats, and found already that 75 mg/kg leads to a 30-fold increase in mutant
frequency compared to the control value. So, in principle, one would expect that with
EO we should get also induction of mutation at the HPRT. As mentioned above, we
indeed found induction of mutants in rats treated for one month with EO in the
drinking water.
We would now like to go back to our study where workers were occupationally
exposed to EO (Tates et al., 1991). This study involved a multilaboratory investigation
on workers from the former German Democratic Republic. Blood samples were
analyzed for frequencies of chromosome aberrations, micronuclei and sister chromatid
exchanges (Leiden, Bad Elster), HPRT mutations (Leiden) and hemoglobin adducts
(Stockholm and London). In addition, we sequenced in Leiden a collection of mutants.
Among the different endpoints studied, their sensitivities to detect an exposure was
in decreasing order: Hb adducts, high frequency SCEs, chromosomal aberrations,
SCEs, micronuclei, and HPRT mutations. The relative insensitivity of the HPRT
mutation assay to detect exposure is expected, because in the case of all the other
endpoints, we are looking at the whole genome, whereas with HPRT, we are looking
at a DMA target of about 43 KB only.
The spectrum of mutations induced by EO was not found to be different from
that in smoking and non-smoking controls. When we look, however, at the distribution
of mutants over the HPRT coding region, a hot spot for EC-induced mutants was
found.
Coming back to the parallelogram method for risk estimation, K is of interest
to note that mutants induced by EO and ENU in somatic cells in vitro predominantly
194
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involve changes in guanine, but in the case of in vwo-exposed germ cells and somatic
cells, the mutational changes frequently involve AT base pairs. When comparisons are
made of the spectrum of mutations in germ cells and somatic cells of mice, rats and
monkeys exposed in vivo to ENU, one finds a striking similarity. These findings are
very reassuring and indicate that we can extrapolate with confidence from in vivo data
with mice, rats and monkeys to the human situation.
Discussion
DR. SORSA: Thank you, Nat, for this very comprehensive background
information concerning genetic effects of EO. I would now like to invite a discussion
on the various points raised in the presentation by Natarajan.
DR. VOGEL Nat, in the last slide which you showed on the ENU spectrum,
you had data on the monkey and rat?
DR NATARAJAN: Mouse, monkey, and rat.
DR VOGEL Did your data pertain to somatic cells or also to germ cells?
DR NATARAJAN: Somatic cells.
DR VOGEL I am asking you, because you also showed Drosophila data
which are quite different. The germ cell mutation data for the specific locus test of the
mouse are for pre-meiotic germ cell stages whereas the germ line data for Drosophila
pertain to post-meiotic germ cell stages. In Drosophila the mutations induced are pre-
dominantly GC -> AT transitions whereas in the mouse AT -> GC changes prevail.
However, Dr. Maria Serra (pers. comm.) recently collected mutation data (vermillion
gene) for pre-meiotic germ cell stages of Drosophila treated with ENU. In that case
the mutation spectrum corresponded very well with the mouse spectrum for mutations
195
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in pre-meiotic germ cell stages, also showing a preponderance of changes at AT base
pairs. In other words, in Drosophila the mutation spectra shifted from GC -> AT
transitions to changes involving the AT base-pairs, when comparing postmeiotic and
premeiotic germ cells. Thus, when you compare the same germ-cell stages in different
species, you apparently find similar types of mutational changes.
DR PRESTON: I just wanted you to clarify. When you were showing the early
data on chromosome aberrations, you had linear dose response curves. Were those
just deletions mostly?
DR NATARAJAN: Yes.
DR PRESTON: Okay. You mentioned that the curves for chromosomal
exchanges and deletions were non-linear. What was the power of the dose in these
curves?
DR NATARAJAN: The power of the dose {= n) in these curves could be 2,
3 or perhaps 4.
DR PRESTON: You said "n" is greater than 1. I wondered what that meant
or what the number exactly was. Just for clarification.
DR GARNER: Nat, in connection with the ethylene oxide exposed individuals,
you mentioned that you could detect hemoglobin adducts, but that DMA adducts
weren't detectable. I just wondered what the limits of sensitivity of the two assays
were, because I would expect, on the whole, DMA adducts to be a more sensitive
measure than protein adducts.
DR TATES: Very restricted efforts were made to measure DMA adducts. Rob
Baan's group, in Rijswijk the Netherlands, made preliminary attempts to measure ring
open N7-ethylguanine adducts by means of a specific antibody developed in their
196
-------
laboratory. Unfortunately the technique was not yet standardized at the time these
measurements were performed. There was insufficient biological material available to
repeat these measurements at a later date.
197
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198
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Acrylamide (AA)
Dr. Kerry Dearfield presented the data summary for Acrylamide. Dr. Dearfield is from
the Health Effects Division, Office of Pesticide Programs, EPA, in Washington, D.C.,
U.S.A.
DR. DEARFIELD: Before I start, I would like to relate a short story on how I
met Vicki Dellarco, who you heard earlier. When I was in the former Office of Toxic
Substances at the EPA, acrylamide was a major compound that I was asked to
examine in terms of regulatory action. The Office was looking at grout workers who
work with acrylamide as a sealant for sewers that have cracks in them. That was and
still is a major action that the EPA is examining for acrylamide.
We examined all the data at that time and wrote up our data assessment
which culminated in the review article that is in your package. At the time we were
looking at the acrylamide data, we had some dominant lethal data from Waldy
Generoso. We were trying to decide if we could do a genetic risk assessment for
acrylamide with just dominant lethal data. This was about 1986,1987. We had just
gone through the material on the dominant lethal, and we were thinking maybe we
could give a genetic risk assessment a try.
Around this time, we heard that the EPA's Office of Research and Development
(ORD) had been discussing ideas for genetic risk assessment. So, on the advice of
my senior science advisor, I went over to the ORD, knocked on Vicki Dellarco's door,
and asked what have you done on genetic risk assessment.
She said they tried something with ethylene oxide, but the effort essentially
died. I said it is time to resuscitate it, because genetic risk assessment is the coming
199
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thing. We were beginning to receive a lot of data that I talked about this morning.
The regulatory schemes are pointing toward obtaining this type of data from the
quantitative tests, so we needed to know how to do a genetic risk assessment,
especially since the EPA is now requiring companies to perform these tests. In other
words, if we couldn't do an assessment with the data, then why are we requiring
companies to spend hundreds of thousands of dollars to do these tests?
So, we went ahead and resuscitated the ethylene oxide assessment effort that
culminated in the report that is in your background materials. I like to think that
acrylamide was responsible for the birth of the ethylene oxide risk assessment!
That was about 1988. Since we put together that particular review with
acrylamide, there has been an incredible amount of data that has been generated.
We now have heritable translocation data, and we have specific locus data.
We have looked at many different questions that were highlighted in that
particular review. At that time, we had thought that maybe acrylamide might be a
special compound, that is, it might have been a specific germ cell mutagen versus a
somatic cell mutagen. We have since found that not to be true, but there was some
discussion about that at that time.
There was also a discussion of whether it was the parent compound versus
a possible metabolite that might need consideration in a genetic risk for acrylamide.
That was very unclear at the time, and studies showed that acrylamide appeared to
have a direct acting capability. We were also trying to figure out what is the primary
genetic activity of acrylamide. We had some discussion about that, and I would like
to talk about it some today.
200
-------
So, there has been a tremendous amount of research on acrylamide. Many
people looked at pharmacokinetics and other aspects. We have also done much
exposure analysis for acrylamide. One of the risk assessments for acrylamide
culminated in the contract that Dave Brusick talked about earlier with ICPEMC and
Health Canada.
We are going to work with that assessment in our break-out group later. So,
let's meet the players (Figure 3-1 A).
Acrylamide is a fairly simple compound. It has an alpha, beta unsaturated
bond and this imparts much of its activity. The main use for acrylamide is actually to
make polyacrylamide, and there are many uses for this. Most obvious to us are the
polyacrylamide gels you see many researchers use in laboratories, but there is a lot
of industrial use for acrylamide. Probably one of the largest uses for acrylamide is as
a flocculent in drinking water.
So, we have a lot of exposure to polyacrylamide and some form of unreactive
monomer in your drinking water. This is the main reason why the EPA's Office of
Drinking Water is very concerned with acrylamide, because millions of people drink
water that has been treated with polyacrylamide.
The main reason why I was interested in acrylamide was through the efforts
of the EPA's Office of Toxic Substances, the name has recently been changed to the
Office of Pollution Prevention and Toxics (OPPT). Acrylamide is used heavily by grout
workers. What they do is mix it like cement and use it as such. The workers use big
bags of monomer, mix it like cement, and then go down into sewers for repairs. They
trowel it into cracks, and when it polymerizes, it results in a seal to fix the crack in the
sewer line.
201
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A. ACRYLAMIDE
O
H,C=CH— C— NH
B. GLYCIDAMIDE
Figure 3-1
202
2
O
/ \
H2C — CH -
O
II
-c-
-NH2
-------
These workers are covered with acrylamide, so there is a large amount of
exposure. This is the work practice that the OPPT is examining for regulatory action.
I have some exposure numbers from this.
Now, the other player I want to discuss is glycidamide (Figure 3-1B). When
you first look at the parent compound, acrylamide, you automatically assume an
epoxide is going to form. That is one of its apparent natural metabolites. However,
the data at the time about 1988 did not suggest much of a role for glycidamide. It
was questionable whether that glycidamide was a natural in vivo metabolite of
acrylamide. We now know that, most likely, it is.
There is a second reaction for acrylamide. It is called a Michael-type reaction
in which the beta-carbon reacts with a nucleophile. This is a kind of alkylating
reaction. This is a direct-acting type of mechanism, and is generally the type of
activity we have seen in many of our studies with EPA; for example, it was seen in the
study with Martha Moore and Jim Rabinowitz. This is the type of reaction that this
class of compounds, these Michael-type addition compounds, generally undergo.
These reactions are important because we will discuss in a few minutes what
is the primary target of acrylamide and glycidamide; so it is important to understand
the chemistry here.
First of all, I would like to give you an idea of some of the data. I put together
an extensive table for this workshop effort. I think it is about six or seven pages of
just the studies that have been done since 1988. This table does not include any of
the studies that were in the earlier review I mentioned.
It is just amazing how much has been done. For example, at the time of our
earlier review, we were hustling to get Mike Shelby et al.'s paper on the heritable
203
-------
translocations as a footnote! But since then, look at all these studies just on
dominant lethal assay, for example, that have been done (Figure 3-2).
An important point is that acrylamide has been tested for dominant lethal
effects via different routes of administration. You have i.p. injection, gavage, via
drinking water, and dermal exposure.
in the drinking water test, we have the lowest dose found yet in which a
positive response for a dominant lethal effect was observed, 9.2 mg/kg/day. Most of
the studies we have seen are generally up around 40 to about 100 mg/kg/day. And
I know even more data is being generated as we meet this week.
The point is that acrylamide can be incorporated via many different routes of
administration and cause an effect.
Also, once acrylamide gets into the system, it is distributed almost everywhere.
We also know that it is cleared very quickly as well. A major reaction is the adducts
it forms from Michael addition to a lot of protein in the blood, particularly, hemoglobin.
We will talk about that in a minute.
The only two places it appears to accumulate after the first few hours is in the
skin and in the testis. The testis accumulation was another piece of evidence from
earlier that suggested acrylamide may be a unique germ cell mutagen. It may yet turn
out to be more preferential to the germ cells than to somatic cells.
Another thing I want to point out, not from this slide, but when we were first
thinking about whether it was a unique germ cell mutagen or not, Ilse-Dore Adler et
al. came out with their micronuclei data to show that in the bone marrow, it was a
somatic cell mutagen. The data however showed, generally speaking, those
responses in the bone marrow were not that dramatic. There is a dose-response, but
204
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Assay
Dominant lethal
assay
Heritable
translocations
Reciprocal
translocations
Test System
Male (C3H X 101)
F, mice
Male Fischer 344
rat
Male Pzh:SFlSS
mice
Male (102/E1 X
C3H/EDF, mice
Male CD-I mice
Male (C3H/R1 X
101/RDF, mice
Male (C3H X '
I01)F, mice
Male C3H/E1
mice
Concentration/
Dose1
5 X 40 mg/kg/day;
i.p. injection
5 X 30 mg/kg/day;
gavage
75-125 rag/kg;
i.p. injection
50-125 mg/kg:
i.p. injection
0.72-9.2 mg/kg/day;
drinking water for
20 weeks
5 X 25-125 mg/kg/
day; dermal
5 X 40-50 mg/kg/
day: i.p. injection
5 X SO mg/kg/day:
i.p. injection
HID or
LED2
40 mg/kg/
day
30 mg/kg/
day
125 mg/
kg
75 mg/kg
9.2 mg/
kg/day
25 mg/kg/
day
40 mg/kg/
day
50 mg/kg/
day
Assay Result
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Reference
Shelby et al.,
1987
Working et al.,
1987
Dobrzyiiska et
al.. 1990
Ehiing &
Ncuhiuser-
Klaus. 1992
Fail et al.. 1992
Cutierrez-
Espelcta et al.,
1992
Shelby et al.,
1987
Adler. 1990
Figure 3-2
205
-------
the response was not real large compared to some of the responses you see with the
germ cell observations (for example, dominant lethal effects).
The other thing I wanted to add is that many of the other studies in germ cells
for aberrations generally turn out to be negative. But there are not very many studies
in the germ cells for aberrations. There are also some for micronuclei. These are just
some things in the general data base I wanted to point out for our assessment later.
One thing that was of interest to me is how can we sort through the pieces of
information and get to an understanding of how acrylamide and glycidamide may be
interacting?
In a lot of discussions I had with Gary Sega in the workgroup and others, we
came up with this working hypothesis, if you will (Figure 3-3). I just want to present
this for the workgroup to consider. We will talk about this in the break-out sessions
and whether this has impact on our genetic risk assessment or not.
First we have acrylamide and we now know it does convert somewhat to
glycidamide. The first thing we notice in working with acrylamide is that it binds quite
avidly to protein. It goes after thiol-containing compounds very well. This is a
component of the pharmacokinetics one has to take into account. Once acrylamide
gets into the system, it is binds extensively to protein.
Another consideration is that, early in our first assessment, there were some
in vitro studies looking at DMA adduct formation with acrylamide. However, the
problem with this observation was it took acrylamide something like 40 days to obtain
detectable binding, only then was found weak activity.
206
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converted
AA
weak binding ' 1 binding to
to DNA / I proteins
AA-DNA adducts
AA-protein adducts
o AA is SAL negative
o long time to detect DNA
binding after exposure
o delay in maximum UDS after
'exposure, suggests conversion
o similar sensitive germ cell cycle
stages as seen for DLM & HTT
(suggests protamine binding)
o synaptonemal complex effects
(proteinaceous structure)
o spindle poison?
o P450 inducer (affects proteins?)
GLY
binding to / x some binding
DNA/ N to protein
GLY-protein
adducts
GLY-DNA adducts
o GLY is SAL positive
o no delay in UDS after exposure
o epoxide less discriminant .'. may
contribute to gene mutations in
spermatogonia
o predominant adduct in DNA in
vivo rodents was GLY-DNA
adduct; major adduct, as
expected for epoxide, is at
N-7 of guanine
o DNA adduct level in vivo is
similar in several tissues
.'. GLY evenly distributed
Figure 3-3
207
-------
So we wondered, if acrylamide is an avid alkylator of DMA, why would it take
so long in a pure DNA-acrylamide environment to cause adducts? This posed several
questions, for example, is acrylamide itself really the DMA adduct moiety?
When you look down these lists on this slide, you begin to sort out activities
between acrylamide and glycidamide. Another thing that perplexed us for a long time
was that acrylamide was Salmonella negative with and without activation. What we
didnt know for awhile is that glycidamide is Salmonella positive.
In the study that Gary Sega performed with unscheduled DMA synthesis (UDS),
there was a delay in UDS response after exposure. He suggested there was some
conversion to some metabolite before eliciting a response. Gary started to examine
glycidamide. The work is unpublished, I think as of now. But it was found that there
is no delay in UDS after exposure to glycidamide. This suggests that it might be quite
appropriate to consider a glycidamide-DNA adduct.
The next point addresses the similar sensitive germ cell cycle status for the
dominant lethal and heritable translocation studies in post-spermatogonial cells. I
think in the slide that you saw from Vicki Deliarco earlier this morning, acrylamide had,
apparently, from the dominant lethal studies, the same window of susceptibility in the
mouse studies and in the rat studies.
Gary, at that time, suggested that the most sensitive time for dominant lethal
effects was the same time as protamine appearance in germ cells. Unlike ethylene
oxide where you can get some detectable DMA adducts, again, it was very difficult to
find acrylamide-DNA adducts in the germ cells. Whereas we made the assumptions
that the DMA adducts were probably the more important adduct for ethylene oxide,
because of the known affinity for acrylamide to proteins, this aspect of protamine may
208
-------
weigh a little bit more in the acrylamide-induced dominant lethal and the heritable
translocation effects, according to the Sega hypothesis I am pretty sure you are all
familiar with.
Other pieces of evidence about the protein affinity of acrylamide came from
some other work. We did some work with Jim Allen on the synaptonemal complex.
We were looking for aberrations and other types of events in the synaptonemal
complex. We didn't find any aberrations, but did find some interactions with the
complex itself in the form of irregularities. Since this is a very proteinaceous structure,
again, this suggests a binding mechanism to a protein. In the material that Ilse-Dore
Adler was reporting in some recent publications, there is some disturbances of the
spindle apparatus, possibly even leading to aneuploidy. Again, the spindle is a
proteinaceous structure.
The last point here is from an article that suggests that acrylamide might
induce P450. It might have some type of interaction with the protein, but I am not
dear on the mechanism.
All this evidence suggests that this big arrow (Figure 3-3) represents that the
major property for acrylamide is more likely for acrylamide to form protein adducts
versus DMA adducts. This does not rule out the possibility that DMA adducts may
form from an acrylamide parent.
Now examining the glycidamide evidence (Figure 3-3), epoxides, by inference,
are generally positive in Salmonella tests. This gives you an idea that there is some
DNA reactivity here. Again, we talked about the timing of the UDS response earlier.
We know that epoxides are less discriminate in what they tend to interact with, and
209
-------
we also know in general they cause gene mutations. This may contribute to the gene
mutation effect in spermatogonia.
What I am going to suggest as a hypothesis is that the glycidamide-DNA
adducts may be more responsible for the gene mutation aspect of this compound,
whereas the acrylamide protein adducts such as the protamine binding may explain
some of the clastogenic outcomes that we see in the germ cell tests.
In an unpublished study performed by Dan Segerback et al., glycidamide DMA
adducts were found in vivo. The predominant adduct in DMA was the glycidamide
adduct, and the alkylation was found in N-7 of guanine, which is expected for
epoxide. So, this is direct evidence that glycidamide adducts are forming in vivo.
He also found a similar DMA adduct in vivo in several tissues. So,
glycidamide, although you would think an epoxide would be rather reactive, does
seem to distribute rather evenly. Acrylamide itself also distributes rather evenly. That
is another consideration for when we do a risk assessment for this compound. So,
these are different pieces of information that give you an idea of the clastogenicity
versus the gene mutation activities of acrylamide.
I just might mention that there is some suggestion that this may be a
mechanism for neurotoxicity. Acrylamide binding to protein can occur for example to
proteins in myelin sheaths.
By the way, other toxicities besides neurotoxicity helped drive the regulatory
concern for this compound. Since the beginning of our efforts, the EPA has had two
cancer studies submitted. Those two cancer studies probably reproduce each other
almost as well as any two cancer studies we have seen; they were done
210
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independently at different times. Now we are getting all the genetic data as well as
reproductive and developmental data.
When assessing these various effects, we started to see effects at doses that
were below doses you would see for overt neurotoxicrty. Neurotoxicity from
acrylamide is a result of cumulative exposure. You have to be exposed for several
days, but some of these other effects are seen at doses lower than you would see in
a cumulative approach and in lesser time frames. So, these other toxicities are
becoming as important as neurotoxicrty for regulatory concerns.
DR. SHELBY: Can you say anything about the relative affinity for acrylamide,
glycidamide, and ethylene oxide? We just got a lot of ethylene oxide protein binding
data but none on acrylamide. I must ask is there any difference. Is there reason to
think there is a difference between ethylene oxide and glycidamide or acrylamide with
regard to their protein binding characteristics?
OR DEARFIELD: No, I have not looked at that yet or thought about ft. This
hypothesis I'm presenting has only crystallized in the last month, so I haven't really
done any comparisons to ethylene oxide.
Since this is a meeting on the parallelogram approach, I wanted to show some
parallelogram ideas. I apologize here, I put arrows on each end of every
parallelogram. I know this is not appropriate, but since I had to hand draw this, I just
thought I had better put as many arrows on as I could!
I think this particular parallelogram on Hb adducts is very useful (Figure 3-4)
because ft also helped us in discerning whether glycidamide was actually formed in
the body. I think this is one of the uses of a parallelogram, to give you qualitative
information.
211
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Hb adducts in vitro
animal
Hb adducts in vivo
animal
Hb adducts in vitro
human
Hb adducts in vivo
human
Figure 3-4
212
-------
This Hb work was done by Bergmark et al. and I've discussed this with Carl
Calleman. They were looking at hemoglobin adducts in in vitro systems to calibrate
essentially how they would detect these particular hemoglobin adducts from
glycidamide. They first calibrated their machines and for the levels they were trying
to detect. So, they were using an in vitro situation as their standard.
They then looked in mouse and they found the same Hb adducts in the
animal. So, they were using this experience to see if you can detect hemoglobin
adducts in in vivo situations.
These researchers obtained access to a cohort of workers in China who were
in the manufacturing process of making acrylamide. They went in and tested their
erythrocytes for hemoglobin adducts. They found glycidamide-hemoglobin adducts
in vivo. That is evidence that there is metabolism from acrylamide to glycidamide
which is detected in vivo.
In 1988, we didnt have any of these kinds of information, so we really weren't
sure about the role of glycidamide. Now I think we are pretty sure that glycidamide
will be a major player in the risk assessment for acrylamide.
I want to show a couple more parallelograms to reinforce what I was saying
about the gene mutation versus the clastogenidty activities (Figure 3-5). For gene
mutation, we have the somatic cell in vivo animal tests from the big table we put
together, for example, the mouse spot test and the transgenic mouse somatic cell
data. From the germ cell in vivo animal data, we have the specific locus test. We can
try to extrapolate to human germ cell in vivo situations using the techniques that were
talked about this morning.
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Gene Mutations
Somatic cell in vivo Germ cell in vivo
animal *—* animal
(e.g. spot test, transgenic) (e.g. specific locus)
I
Somatic cell in vivo Germ cell in vivo
human *—» human
Clastogenicity
Somatic cell in vivo Germ cell in vivo
animal «—» animal
(e.g. MVM, abs) (e.g. DIM, HTT)
1
Somatic cell in vivo Germ cell in vivo
human «—» human
Figure 3-5
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For clastogenicity (Figure 3-5), the somatic cell data we have is micronucleus
and aberration tests. For me germ cell data, we have the dominant lethal and
heritable translocation tests. Again, we will try to extrapolate to the human germ cell
situation.
This presents a question. We are probably going to derive two different types
of genetic risk assessments, one for gene mutation based on one set of studies, and
one for clastogenic events based on another set of studies, each with their own
assumptions. Is it possible to combine these into one assessment, or is it too
difficult? These are things we will need to discuss. What is the total overall risk? I
don't know if you can combine or not yet, but I suggest that someone is going to ask
what is the overall risk to a human being? Because there are so many endpoints that
this chemical or any of the other chemicals we are looking at can cause, we may have
to address an overall risk at some time.
To just present one of the assessments from one endpoint may be thought as
an underestimation of the risk because there are so many other things that can be
caused by that particular chemical. So how are we going to integrate all these
different risks from the various specific endpoints into one assessment, or should we?
Now, to do a genetic risk assessment for humans, we need to have some
human exposure figures. Generally speaking, the EPA is interested in exposure from
inhalation, ingestion, and skin absorption for acrylamide (Table 3-4).
For inhalation exposure, examples include exposure from the manufacture of
the monomer, and from the sewer line maintenance workers that I mentioned earlier.
The OSHA permissible exposure level, PEL, is 0.03 mg/m2 for an 8-hour
average. When the EPA became concerned about the grout workers, this was one
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Table 3-4. Acrylamide Exposure
Exposure occurs via inhalation, ingestion, and skin absorbtion
Inhalation: e.g., manufacture, sewer line maintenance
• OSHA permissible exposure limit (PEL) is 0.03 mg/m3
(8 hour average)
• EPA sampling of line maintenance workers found 8-h
time-weighted average exposures of 0.0008 -
0.12 mg/m3
Ingestion: e.g., in drinking water from floccujent-treated water,
eating sugar (polyacrylamides used in refining process)
•Assume 100% exposure from drinking water
• FDA limit of residual monomer in food processing not
exceed 0.05% (500 ppm) (e.a. sugar refining limit)
• Estimated exposure level in flocculent-treated water is
0.01 ug/l
Skin absorbtion: e.g., grout workers for sewer repair, research
laboratories using polyacrylamide gels, manufacture
• Assume -25% of dermal exposure is absorbed via skin
• EPA sampling of sewer repair workers found dermal
contact values estimated up to 5.0 mg/h
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of the few times EPA itself has actually gone out on their own, sent their own people,
and did all the monitoring. The Agency monitored a group of line workers, and did
a sampling of these people for inhalation and for skin absorption exposures. What
they found with workers who have an 8-hour time weighted average, was exposure
from 0.008 to 0.12 mg/m2. The top value is about four times the PEL for inhalation.
These are some of the numbers we can use in our break-out sessions. It would be
very interesting to see how our calculations for government-regulated levels compares
with the actual exposures. For ingestion, as I mentioned, you can get exposure from
drinking water treated with acrylamide. And here is something that I didn't realize until
recently. You can also get exposure from sugar. Polyacrylamides are used in the
refining process. So, for ingestion, it is not just a cohort of 50 or 60 people
working on the sewers we are looking at. There are millions of people being
exposed.
For ingestion, we are going to assume 100 percent exposure for drinking
water. From one of the drinking water documents that came from the EPA's Office
of Drinking Water, estimated exposure level for flocculent treated water is 0.01 mcg/1.
When you run these numbers, the assumptions for drinking water are like 2 liters a
day of drinking water for, I think, a 70 or 75 kg person. So, we need to perform these
conversions when we do our risk assessment via this particular route of exposure.
From the dominant lethal studies we know acrylamide can be absorbed and
cause effects by all these routes of exposure. So, we are more confident these
exposure routes are appropriate for human genetic risk assessment
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Now, for the monomer in sugar, I just thought I would put this up (Table 3-4)
because it is of interest. The FDA limits the residual monomer in food processing
which should not exceed 500 ppm.
The last exposure shown here is skin absorption (Table 3-4). This is the one
exposure scenario we may have some problems with because when these sewer
workers are working, they are supposed to be wearing protective equipment. But the
EPA has found they do have detectable exposures via the dermal route.
Another major skin route is in the research laboratories since a lot of people
use polyacrylamide gels. How many of you know people who have come back with
tingling fingers and skin peeling off their hands? Those are some of the signs of
acrylamide exposure.
What the EPA has assumed is that 25% of dermal exposure is absorbed via
the skin. We need to take the assessment figures we get and divide by 4 as one of
our risk extrapolation factors. The EPA sampling of sewer repair workers found
dermal contact values up to 5 mg/hour.
You can see that these workers are getting measurable exposures. That is
one of the main reasons why we think there are problems with a high exposure like
this, and the EPA is considering a ban on this use for acrylamide. The Agency is
using section 6 of TSCA which permits banning of adverse practices. This is one of
the few times this section is being invoked.
This slide is just a reiteration of what Dave Brusick discussed earlier (Figure
3-6). These are the formulas for the doubling dose and the direct method using the
assumptions that the ICPEMC workgroup have presented on both the ethylene oxide
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DOUBLING DOSE METHOD
DD= Spont MR
Induced MR / Unit Exposure
RISK = SponHumiin. x D / DD x N x REF
Assumptions: UNSCEAR (1986) estimate of spon mutation
rate-1.5 X10"3
Sankaranarayanan (1982) estimate of spon
chromosomal aberrations rate = 6.2 X 10"8
DIRECT METHOD
RISK = MMou" x LHunwn x D x N x REF
Assumptions: Dominant single gene diseases = 1,000
Dominant chromosomal diseases -1-
For point mutations, Ehling and Neuhauser-Klaus specific locus test
For heritable translocations, Shelby et al. reports and Adler reports
Figure 3-6
and acrylamide assessments they performed. Some variables for the methods are
shown in Figure 3-7.
In particular for acrylamide, we have the Ehling and Neuhauser-Klaus specific
locus test data to use for point mutations, and for heritable translocations, we will
probably use the Shelby et al. reports and the Adler reports.
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REPS FOR ACRYLAMIDE
Parameter REF
Locus Specificity 2
DNA Repair Variability 0.1
Metabolic Variability 1
Dose Rate Variability 1
Exposure Route 1
Germ Cell Stage Specificity 1
Dose Response Kinetics 1
REF = 0.2
Figures-?
Now, the problem with the heritable translocations data is that, generally, they
are point estimates. I know Shelby's material has two dose points, but they were so
close together they are essentially one dose point.
Last night, Ilse-Dore Adler told me she actually has dose response data now
for heritable translocations. So, we will obtain that data from her and also use that
in our assessment.
For the point estimates, we weren't able to use any of the mathematical
modelling to describe a curve. Now, with the newer data, we might be able to do
some of the modelling we did with ethylene oxide and, hopefully, use the data to help
extrapolate to lower doses.
I think that is essentially it. I expect we will have a lively discussion and I look
forward to the break-out sessions. Thank you.
220
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Discussion
DR. SORSA: Thank you very much. This is especially important to get this
new information right now, because IARC is evaluating acrylamide next February.
DR DEARFIELD: That is for cancer. Right?
DR. SORSA: Yes, that is for cancer, but they are considering, of course,
other....
DR. DEARFIELD: Cancer data is available.
DR. SORSA: Yes, and I was wondering. You didn't cite any human biomarker
data. You just commented on the exposure levels. So, there is as yet no data?
DR. DEARFIELD: There is some hemoglobin adduct work that was done on
the Chinese workers, but there is not a whole lot more biomarker data available with
acrylamide.
DR. SORSA: Yes, and then Udo.
DR. DEARFIELD: His lab might actually have more on that.
DR. EHUNG: You were asking the question what should we do with the
different endpoints. I think the answer is quite clear from the last figure you showed.
Keep the different endpoints separated, because risk assessment starts with
an endpoint, and for translccation, you get a different answer than for specific locus
mutation, and if you use a doubling dose or the direct method, that you have to keep
separated. But what UNSCEAR has done for many years is summarize the data.
Here was the different method, and then for translocation, for gene mutation, and so
on, and I think that is the proper way to approach it.
DR. SORSA: I think most of us, at least, seem to agree with this.
DR DEARFIELD: I don't think I entirely agree.
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DRSORSA: Aha, you don't?
DR. DEARFIELD: The problem is, again, I go to my risk managers, and they
ask, which one is more important, or should we add this all together. They don't
understand there are differences between genetic endpoints. So I am not really
arguing with you, but emphasizing how we communicate the assessments.
DR. EHUNG: UNSCEAR went one step further not only to estimate the
mutation frequency you can expect, but what does it mean in clinical terms. That, of
course, I mean risk estimation is a very risky business, and to try to translate it in
clinical terms is more difficult, but it can be done, and I think the more brain you put
into that work, the better the outcome.
DR. DEARFIELD: That is the same message from this morning.
DR. GENEROSO: Let me just bring in a few more points. I would like to first
emphasize that acrylamide is the very first compound that we have shown to be
mutagenic in germ cells by way of dermal exposure. There was a good dose
response, and it is almost a linear dose response where the lowest dose is fairly low.
If you used that dose and tried to detect carcinogenesis or neurological
syndromes, I think you would find it very difficult to find any effect of the lowest dose
that produced a positive response a dominant lethal mutation system by dermal
exposure.
Second, acrylamide also is effective when you expose the mother during the
zygote stage of her conceptus and produces malformations very much similar to that
of ethylene oxide and ethylmethane sulfonate.
222
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Third, you discussed a great deal the compound glycidamide and the
possibility that it is the active metabolite of acryiamide in germ cell mutagenesis. We
have new dominant lethal data in mice for all of the spermatogenic stages.
DR DEARFELD: I have been trying to get it.
DR. GENEROSO: And gtycidamide is an effective inducer of dominant lethal
mutations. The pattern of germ cell stage sensitivity is exactly the same as that of
acryiamide. The difference in mg/kg between acryiamide and glycidamide is very little.
Lastly, glycidamide also produces the same malformations that acryiamide
produces when the mothers were exposed during the zygote stage of the conceptus.
So, the similarities between glycidamide endpoints effects and acryiamide is
incredibly the same.
223
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224
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1,3-Butadiene
Dr. Ilse-Dore Adler presented the summary for 1,3-Butadiene. Dr. Adler is from the
Instttut fur Saugetiergenetik in Neuherberg (Munchen), Germany.
DR. ADLER: I am supposed to introduce this figure (Figure 3-8). There is a
categorization for butadiene as a carcinogen in animal experiments. There is a TWA
level of 22 mg/m3 which is equivalent to 10 ppm. Butadiene is metabolized to the
mono- and the diepoxide.
Marja gave me this overhead (Figure 3-9) that shows the activation to the
epoxybutene, and then the deactivation and excretion in the urine as mercapturic acid
and butenediol.
There is also activation from the monoepoxide to the diepoxide and then from
there on deactivation. There are probably a lot of other metabolites that Siv
Osterman-Golkar might be able to tell us more about.
The world production of butadiene comes to about 4 million tons. Butadiene
is mainly used in the production of synthetic elastomers and polymers. There is a
growing market in paper lamination and styrene-butadiene rubber production. About
65,000 U.S. workers are exposed. That is an estimate made that Marja got from the
literature. Urban air contamination can reach up to 15 |tg/m3. Automobile exhaust
is also a source of environmental contamination with butadiene, up to 44pg/m3, and
a side-stream tobacco smoke component is about 24jjg/tigarette.
Concerning the genotoxicity in vitro data (Table 3-5), again this is a figure that
Marja gave me, and was more or less positive if S-9 mix was provided for metabolic
activation in Salmonella tests and in CHO cells for SCEs. In human lymphocytes for
225
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1 ,3-butadiene
H2C-CH-CH-CH2
MAK Category IIIA2 (carcinogenic in animal experiments)
TWA 22 mg/m3 (10 ppm)
Metabolized to mono- or di-epoxides
CH2-pH-CHOH-CH2OH
CH2^pH-CH-CH
Figure 3-8
Activation.
Butadiene
Epoxybutene
Activation^
Dlepoxybutane
Deactlvatlon.
Deacttvatlon and
excretion In urine
OH
+ mercapturic acids
OH
Butenediol
Figure 3-9
226
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SCE induction, S-9 mix was not required. Yet in mouse lymphoma cells, for gene
mutations the results were negative even with S-9 provided.
Table
GENOTOXICfTY OF 1 ,3-BUTADIENE IN VITRO
TEST SYSTEM
Salmonella
CHO cells
Human lymphocytes
L5178Y mouse
lymphoma cells
ENDPOINT
reversion
SCEs
SCEs
gene
mutations
RESULTS
+ (with S9 mix)
+ (with S9 mix)
+ (without S9 mix)
or
- (with S9 mix)
In the animal, there is a striking difference between species. These are two
graphs from the paper of Cunningham et al, 1986 (Figure 3-10). You can see in the
upper graph that the toxicity to the erythro-poietic system is similar in mice and rats.
The dashed line is the rat The solid line is the mouse. Only at the highest dose is
there a difference in cytotoxicity, which is higher in the mouse than in the rat. if you
look for micronucleated PCEs in the lower graph, again, the solid line is the mouse,
and the dashed line is the rat; there is no effect in the rat, and there is a strong effect
from 100 ppm upwards in the mouse.
In the same paper, data were provided for SCE-induction in bone marrow, and
you see on the upper graph (Figure 3-11) in the mouse a linear increase with dose
followed by a plateau at higher doses. On the lower graph A, in the low dose range,
there seems to be a slight increase in SCEs also in the rat. If you go to higher doses,
there is virtually no increase, and in the repeat experiments, this small initial increase
here was not confirmed.
227
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Bone Marrow Micronucleus Test After
1,3-Butadiene Exposure for
(6h/d on 2 d, nose only)
Cunningham et al., 1986
£ 1.0
s
111
o
0.5
10 100 1000 10,000
1,3-butadiene (ppm)
of BD in bone marrow cells. PCE/NCE ratios of
five B6C3F, mice, •, and five Sprague-Dawley rats, O, in
each dose group were measured 24 hours after treatment.
Error bars represent the standard error of the mean.
100 1000 10,000
1,3-butadiene (ppm)
Induction of micronucleated PCEs in bone marrow of
B6C3F, mice (•, •) and Sprague-Dawley rats (O, D) from
two independent experiments. Error bars represent the
standard error for the mean of five animals.
Figure 3-10
228
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Bone Marrow SCE Test After
1,3-Butadiene Exposure for
(6h/d on 2 d, nose only)
Cunningham et al., 1986
100 300 500
1,3-Butadiene (ppm)
Induction of SCEs in bone marrow cells of B6C3F, mice. Error bars
represent the standard error of the mean of five animals
300
500
B
f
2000 6000 10,000
1,3-Butadlene(ppm)
Induction of SCEs in bone marrow cells of Sprague-Dawley rats. Error
bars represent the standard error of the mean of five animals. (A) lower
exposures; (B) higher exposures.
Figure 3-11
229
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Basically, in the mouse, there is a good micronucleus response with various
protocols and various strains of animals as was compiled by Tom Skopek in his
summary paper on somatic in vivo effects (Figure 3-12). The fold increase is given
on the Y-axis here, and accumulated ppm times days as accumulated doses is given
on the X-axis. You can see that there was virtually a positive response for all sorts of
strains of mice.
A little bit more scanty was the chromosomal assay data (Figure 3-13), and the
chromosomal aberrations don't respond as sensitively. That is the general picture
one gets from this chart as compared to micronucleus induction.
This was about the state of the art when we started with our germ cell project
which was funded by the European Commission (EC), Environmental Programme, in
Brussels; Frits Sobels convinced us to take up butadiene as one of the chemicals to
be studied in germ cells.
The project was initially called "Detection of Germ Cell-Specific Mutagens", and
then we felt that we were looking for a needle in a haystack. After long discussions,
it was renamed to "Detection of Germ Cell Mutagens", leaving out the specific.
because until today, we have not found a chemical that only acts in germ cells and
not in somatic cells.
We coordinate that project, and there are, altogether, six laboratories involved
in the studies (Figure 3-14). Additionally, there is a lot of collaboration going on with
other groups (Figure 3-15). Cooperation with Johannes Riser at the Toxicology
Institute of the GSF provided inhalation chambers and the knowledge of how to
expose the animals. Also within the GSF, we collaborated with Michael Nusse who
has performed the measurement of micronuclei in peripheral blood reticulocytes.
230
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O
30
25
20
10
5
-B-B6C3F1.PCE.Ref. 1
•4- B6C3Fl.NCE.Ref. 1
* B6C3F1.PCE.Ref.3
-»-B6C3Fl.NCE.Ref.7
e- B6C3F1.PCE. Ref. 7
•> NMRI, PCE, Ref. 8
•*- 102XC3H.7. Ref.9
-*- 102XC3H.PCE.Ref.9
-+-B6C3Fl.BM,Ref.9
-»-B6C3Fl.PCE.Ref.9
10 30 100 300 1,000 3,000 10,000 30.000 100,000
ppm x days
Figure 3-12
10
NIHSwisa,CA, Ref.2
B6C3F1.CA,Ref.2
BALBxDBA.LacZ.Ref.6
B6C3F1,CA,Ref.7
102 x C3H, HPRT, Ref. 9
B6C3F1.HPRT, Ref. 10
20 SO 100 200 500 1,000 2,000 5,000 10,000
ppm x day*
Figure 3-13
231
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STEP-CT91-0144
Detection of Germ Cell Specific Mutagens (1991)
Detection of Germ Cell Mutagens (since Estoril, 1992)
Coordinator:
Project Leaders:
I.-D. Adler, Neuherberg, Germany
U.H. Ehling, GSG, Neuherberg, Germany
R. Benign!, ISS, Roma, Italy
D. Anderson, BIBRA, Carshalton, UK
A.D. Tates, MGC, Leiden, The Netherlands
F. Pacchierotti, ENEA, Roma, Italy
J. Laehdetie, University of Turku, Finland
Figure 3-14
Johannes Riser,
Neuherberg
Michael Nusse,
Neuherberg
Marja Sorsa,
Helinski
Hans-Gunter
Neumann, Wurzburg
Siv Osterman-
Golkar, Stockholm
Inhalation exposure of animals to 1,3-butadiene
and ethyleneoxyde
Micronucleus analysis in blood (flow cytometry) of
1,3-butadiene- and ethyleneoxyde-exposed mice
DMA adduct measurement in tissues (testes, heart,
lung) and micronucleus analysis in blood of
1,3-butadiene-exposed mice
Hemoglobin adduct measurement in blood of
1,3-butadiene-exposed mice
•
Hemoglobin adduct measurment in blood of
1,3-butadiene- and ethyleneoxyde-exposed
mice (rats)
Figure 3-15
232
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Marja Sorsa collaborated with us from another EC project on butadiene
biomonitoring. She also exposed animals in the facility of Johannes Riser. Hans-
Gunter Neuman and his coworker Olaf Albrecht from the Toxicology institute at the
University of Wurzburg collaborated by performing hemoglobin-adduct measurements
for both the experiment with Marja and the experiment with our group. And I think that
Ad Tates sent specimens from mice exposed in the same setup at the GSF to Lars
Ehrenberg's group in Stockholm for a collaboration there with Siv Osterman-Golkar.
Now I would like to show you the micronucleus data that we got after
butadiene exposure for 6 hours per day on five consecutive days (Figure 3-16). This
graph contains the data from both experiments, the one carried out in Marja Sorsa's
Institute but exposed at the GSF, and the one from our lab. There seems to be a
strain-related difference in the absolute yields of micronudei, but the shape of the
dose-response curve is pretty much the same, namely a linear increase up to 500
ppm followed by a plateau up to 1300 ppm. Circles designate bone marrow
experiments, squares designate experiments with peripheral blood. The open
symbols represent bone marrow and peripheral blood data from Helsinki. Both of
these curves for peripheral blood were obtained with the conventional technique of
microscopic scoring, and we have started to do a flow analysis of peripheral blood
micronudei (closed triangles). We have now obtained the data for the highest dose
also, and they all fall in the same range of the dose-response curve. The micronucleus
responses in the same strain are pretty much the same for bone marrow and for
peripheral blood.
Some of the exposed animals were pregnant females, and Agelika Neuhause-
Klaus at the Institute did a mouse spot test (Table 3-6). The animals were 500 and
233
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Micronucleus-Test Results 18-24 h after 1,3-Butadiene-
Inhalation Exposure (6h per day on 5 consecutive days)
200
400 600 800 1000 1200
Exposure (ppm)
1400
Circles = bone marrow micronucleus test.
Squares = peripheral blood micronucleus test, manual scoring.
Triangles = peripheral blood micronucieus test, flow cytometry.
Bars represent 95% confidence limits.
Hollow symbols, dashed lines = M. Sorsa Helsinki
Filled symbols, solid lines = I.-D. Adler, Neuherberg
Figure 3-16
234
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Tabte3-6. Testing of 1,3-butadiene in the spot test with OxHTJF, mouse embryos
(500 ppm for 5 h/d on the 8th, 9th, 10th, 11 th and 12th day of gestation)
Treatment
Control'
Butadiene
Females
treated with
litter
37/34
19/17
Birth
No. of Ft
(8V.I.S.)
271 (8.0)
164(9.6)
Weaning
No. of F,
(av.l.s.)
257 (7.6)
156(9.2)
Offspring with
coat color spot
No. %
6 2.3
15b 9.6
'Untreated control in SPF (2/133) and controls kept under similar conditions as
treated animals (4/124), P = 0.16 (2-tailed).
bP + 0.0056a
exposed on the 8th, 9th, 10th, 11th, and 12th day of gestation, and Angelika looked
for genetic relevant spots in the fur of the progeny about three weeks after birth. You
see here a statistically significant increase of animals with .genetically relevant color
spots in the fur. The background rate was 2.3%, and the observed rate in the
experimental group after 500 ppm was increased by about four-fold to 9.6%.
This result indicates that butadiene-inhalation exposure of females has a trans-
placental effect, first of all, and, secondly, it caused gene mutation. Even though
there are other possible genetic origins for these color spots, they are probably
mostly gene mutations, because the genetic markers (coat color genes) that are
looked at in the mouse spot test are partly the same as in the specific locus test.
Then, Tates performed the spermatid micronucleus test, and he got animals
from us that were exposed to 500 and 1500 ppm (Figure 3-17). He sampled the
sperm 2, 5,11, and 15 days after exposure. As you can see, there is an increase at
235
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Micronuclei in Early Spermatids of Mice
A.D. Tates
M-SPTD/1000SPTD
5 11
Sampling Days
15
-*- Control • 500 ppm CH 1300ppm
Figure 3-17
236
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1300 ppm 15 days after the end of exposure. With the higher dose of 1300 ppm a
slight effect was also seen after 5 and 11 days. The samples taken after 15 days
correspond to treated spermatocytes around the time of the last DNA synthesis
period. Interestingly, though, the dose-response seems to be inverted. The higher
effect is seen at 500 ppm, and cytotoxicity at the higher dose might be the reasons
for this observation.
We also did a dominant lethal experiment, and another dominant lethal
experiment was done by Diana Anderson at BIBRA (Figure 3-18). We exposed our
animals to 1300 ppm of butadiene for 6 hours per day over 5 days, and then mated
the animals at the end of that exposure time at weekly intervals over a period of 4
weeks. As you can see from the hatched bars, the increase of the dominant lethal
effect was significant in the second week after treatment, and then it came down to
control levels at the fourth week. Diana treated the animals with 1250 ppm, 6 hours
per day, 5 days per week for 10 weeks, and then she mated the animals for one week
after that. As you can see from the striped bar, she got about 29% dominant lethals.
I think that was the early calculation including late death. Later on, it came down to
more like 28% early dead implants in the uteri of the females. And this is interesting,
if you sum up the values obtained during the first three weeks in our experiment
(hatched bars) after one week of exposure, you will come to about 23%, as compared
to the 21% in Diana's experiment with 10 weeks of exposure. So, I would conclude
here that what she observed after 10 weeks of treatment were actually only the effects
of the last 3 weeks of treatment that have accumulated.
Now, both of these experiments were done at a very high dose, but they do
show that dominant lethality was induced, and together with the spermatid data we
237
-------
Dominant lethal test after Inhalation exposure
to 1,3-butadiene
CO
f
30
25
20
CO
I 15
o
Q
xP
* 10
* *
1
1250 ppm, 6h/d, 5d/w, 10w
(BIBRA)
1300 ppm, 6h/d,5d(GSF)
* *
Mating Week
Figure 3-18
238
-------
demonstrated a mutagenic effect of butadiene-inhalation exposure in mouse germ
cells.
In her chronic experiment, Diana did not only look for the early dead
implantation rate, but she also looked at late deaths, and at abnormal fetuses (Table
3-7). This middle column here does not represent the high dose level but the 12.5
ppm dose given for 5 days per a week on 6 hours per day over 10 weeks. So, at this
stage, remember the TWA levels with 10 ppm and the experimental exposure of 12.5
ppm, which was very dose. At this low dose, Diana observed a significant increase
of late deaths and also a significant increase of malformed fetuses. Both of these
effects were also seen at the high dose. These values for the high dose are lower
than for the lower dose, but nevertheless they are significantly above the control.
Again, there seems to be a reversed dose-response similar to the spermatid
micronudeus data. Probably, the early embryonic deaths prevailed at the high dose
and thereby reduced the malformed fetuses and late deaths.
So, this is where we stand at the moment (Table 3-8). Genotoxicity of 1,3-
butadiene inhalation exposure in vivo and DNA/hemoglobin adducts in the mouse are
positive. Single strand breaks in DMA extracted from treated mice is positive. SCEs
is positive. Micronudeus induction is positive both in bone marrow and in spermatid.
Gene mutations is positive in mouse spot tests. Chromosomal aberrations are positive
both in bone marrow and in germ cells, in terms of dominant lethal induction, and we
do get congenital malformations induced in male germ cells.
Contrastingly, in the rat, adduct measurements did not show an effect. Single
strand breaks in DMA is the only positive observation. For SCEs, there is a positive
and a negative response in the two experiments I showed you.
239
-------
Table 3-7.
Number of males
Number (and %) of males mated to at
least on* female
Number of females
Number (and%) of pregnant femeJee
Number of pregnant female* used In
dominant lethal assay"
T
O
T
A
L
S
M
E
A
N
S
NtlfflD^f Of IfflpMtflfll
Number of early death*
Number of late death*
Number of lei* death*
Including dead foetuses
Number of abnormal foetuee*
Implantation*
Early death* per Implantation
Late death per Implantation
foetuee* per Implantation
Abnormal foetuses per
ifHpwnuoof)
Control
25
23<92%)
SO
41 (82%)
23
278
13
0
2
0
12.00*1.276
0.050*0.0587
0
0.007*0.0222
0
i reainienT group
12.5 ppm
25
25(100%)
50
45(90%)
24
306
16
7
6
7*
12.75BS±Z507
O.OS3M±0.0561
0.023**±0.038
0.026*±0.0424
0.024**0.0620
1250 ppm
48d
43(90%)
96
74(77%)
36
406
87
6
7
3f
10.68**±3.103
0.204** ±0.1611
0.014*"*±0.032
4
0.016°' ±0.0339
0.011**±0.0439
6 hr per day, 5 day* per week, 10 week*
Each male housed win 2 females for up to one week
Not more than one from each mala. (The other female* were allowed to litter • data to be reported
2/50 make died during the treatment (one at week 5, one at week 7)
4 Exenoephalle* (3 In litter), 2 runt* (575% of mean body weight of remainder of litter), 1 foetus with blood
In the amntotic sac but no obvtoue gross malformation (stgnlfloano* of difference not altered If tht* foetus
1 HydreoepheJy, 2 runt* (1 runt was In a litter of 2)
not significantly different from control
*, **, ••*, significantly different from control, pSO.05; psO.01, sO.001
Statistical analysis by analysis of variance and least significant different tost on transformed data (arc-sine)
ns
240
-------
Table 3-8. Species comparison of dose of expoxybutene in blood, calculated from
hemoglobin adduct levels, and predicted dose in the body of expoxybutene following
exposure to butadiene
Sp«ciM
Mo.
B6C3F1
CD2F1
ftato
Wtotv
Spr.-Dmv.
HumM.
Adduct
L*v*l
(pmol/g
porppmh)
0.5
0.3
0.00
0.3/0/1**
0.004
DOM
(nMn pot
ppmh)*
17
10
2.3
7.5/2.5**
0.30.510.
15
rlvwHlVv
DOM
7.0
4.1
0.9
3/1**
Predicted
DOM for
*6h
Expouir*
to 100
ppmBD
10.5
2.2
DOM
(nMh p»r
ppmh)
n-^-ii.g-.J
rnKUCwO
17.5
3.6
FMattv*
DOM
4.8
1
* calculated from adduct levels,
** determined at 10ppm and 100ppm, respectively
"Osterman-Golkar et al., 1993
BKohn and Melnick, 1993
Micronuclei were not induced in the human population biomonitoring studies;
likewise, SCEs were not found to be increased, neither were micronuclei nor
chromosomal aberrations.
So, there is a real difference between these three mammalian species.
Although I might get into trouble if I explain this table here in detail which was put
together by Siv, I wanted you to just look at this last column. This is a species
comparison of dose of the monoepoxide in blood calculated from hemoglobin adduct
levels. The relative dose differs vastly between mice, rats, and humans. So, the
species difference in metabolism of butadiene is the take-home message. And we
241
-------
have to discuss in the individual butadiene working group the following questions:
Do we really have a good basis, a firm basis, for extrapolating mouse data to humans,
or is the distinct difference between the two rodent species a cause for assuming that
the rat is a better model for man than the mouse?
Discussion
DR. OSTERMAN-GOLKAR: I compared dosage of the monoepoxide in these
species based on adduct measurements and based in pp-pk modeling, and these
two methods compare well, but the monoepoxide is probably not the important
epoxide. Probably, the diepoxide is much more important. So, you cant really base
risk estimations on these comparisons.
The adduct levels are very low of the monoepoxide, and the difference of a
factor of 2 to 5 between mice and rats is much less than the difference In other tests
for genotoxicity.
DR. ADLJER: Does that adduct level reflect the amount of monoepoxide formed
or present before ft is going to be metabolized into something else?
DR. OSTERMAN-GOLKAR: This is the dose. It is the concentration of the
epoxide times the time the epoxide is there.
DR. ADLER: But even if the diepoxide was the more genotoxic agent, it could
only be formed by the monoepoxide.
DR OSTERMAN-GOLKAR: Yes.
DR. ADLER: So, if there is more monoepoxide, there is a better chance that
the diepoxide is formed.
242
-------
DR. OSTERMAN-GOUCAR: There is only one study where the DMA adducts
have been measured. This is from the study by the Institute and co-workers, and they
looked at different types of DMA adduct, and here you have the adduct of the
diepoxide, and this is the adduct of the monoepoxide, and this is from the mouse.
You can see that these peaks are of the same amount of time, and the
diepoxide is so much more effective than the monoepoxide. I think this indicates that
the diepoxide is the important metabolite. In the last one of these peaks, we are not
seeing that.
DR. SORSA: Well, butadiene is a very interesting compound, as you will all
leam to know, and I think one of the problems which we are facing now is the
differences which were seen already in the somatic genotoxicity endpoints.
So, we have in the mouse in the liver the main metabolic pathway towards the
diepoxide which is the more potent also for genetic toxicity endpoints. In the rat, the
major metabolic pathways goes towards the diol formation. Obviously, the human
pathways are more towards the diol formation; but I think, here again, it is important
to point out that the polymorphism in human metabolism as well seems to play a role,
but we just don't know enough about that yet.
For instance, the cytochrome 2E1 polymorphic enzyme system is responsible
for the butadiene metabolism. So we might find individuals among the human species
which have a pathway, which is nearer to the mouse pathway.
So, I think taking butadiene as an example for the parallelogram approach has
many possibilities and it is certainly an important compound, and highly genotoxic.
It is a carcinogenic compound, and, therefore, it is of importance as well concerning
the genetic risk information.
243
-------
Certainly, I think during these days, we will pick up the genotoxlclty knowledge
and which are the important steps and information which need to be made and
reached during the next couple of months.
DR. ANDERSON: Just to take up on that point of Marja's at the end there,
where she was saying that it might be too early; but if you do recall, Frits' original
parallelogram had mouse data in it. Now, if we didn't know about the rat, we would
be doing a very nice risk assessment with the mouse.
So, we have to bear in mind, I think, that rats could be equally important in this
methodology that Frits has suggested, the parallelogram.
DR SHELBY: Agreed. I don't think it is premature to work with butadiene.
I think it presents a unique data set that we will have to deal with In the future, and
we are just fortunate here to know the species differences.
DR. EHRENBERG: I wanted to say first that Ilse-Dore put my name in the table
at the bottom. She talked about my group. I am an old man. This was a long time
ago, but I will make the proper references.
Since I am an old man, I would like to show just some data. The first here is
on the mutagenicity per alkylation of DMA from ethylene oxide and epoxybutane in
mutation in the 1000 loci. As you see, it is a factor of 100 in favor of the diepoxide.
This is from my own publication in 1957.
It is correct that I am involved in some work but more as an advisor in
independent study rather than the one you referred to on comparisons of the various
metabolites of butadiene and efforts to measure mutation, measure doses of the
diepoxide.
244
-------
This shows here the relative mutagenidty by a factor of 100 between the
butadiene monoepoxide and the diepoxide, in CHO cells. If you use the CHO cells
which are deficient in UV damage repair this increases to a factor of 10,000, about.
So, as in bacteria, there is a relationship between repair of the damage caused
in this type of cells and the UV damage which is one type of damage. In 3,4,6,7, you
have also facts by about a factor of 100.
I see that the paper referred to Skopek, or was it Moore?
DR SHELBY: Cochrane and Skopek.
OR EHRENBERG: Cochrane and Skopek, yes, which are submitted to
carcinogenesis which we have not seen so far with similar data for differences
between the epoxides related. It is certainly true, as she has said here, that it is very
difficult to make the risk estimation without proper knowledge of the doses in the
target tissues of the diepoxide.
DR SHELBY: Thank you, Lars. Are there other questions or points to make?
Martha, is there an obvious explanation for the L51 negative data that she
referred to?
DR MOORE: I don't know. There may be someone who can address this.
DR SHELBY: I think Marja has something else to say.
DR SORSA: Thank you. One obvious explanation is that it is very difficult to
work with butadiene, because you have to have it in liquid form, and you can only
have it in liquid form at minus 4 degrees. So, I think this is one of the reasons for
discrepancies.
Since we do have some time, maybe I will continue with a further discussion
about the importance of the monoepoxide as compared to the diepoxide.
245
-------
These are our lowest effective concentrations in humans in vitro, in human
peripheral blood lymphocytes and in germ cells, so that you get the activation. The
difference in focusing on the dose effect relationship of the diepoxide as compared
to the monoepoxide in vitro in human lymphocytes is 20 times, in the germ cells 10
times.
So, I think even though the diepoxide is more potent as a genotoxic agent
and, obviously, as an alkylating agent, (and, actually, it is used as a diagnostic tool
to diagnose for lymphoma in patients), still, the monoepoxide is important, because
it is certainly genotoxic.
Da SHELBY: Martha Moore?
DR. MOORE: Actually, Mike, in response to your question, can someone tell
me whether the lab that did the mouse lymphoma data had good recovery of the
small colony mutants? Do you know? That may be the answer to the question,
because it appears that butadiene is positive in other in vitro assays, so that may be
the problem.
246
-------
Cyclophosphamide (CP)
Dr. Diana Anderson presented the data summary for Cyclophosphamide. Dr.
Anderson is from BIBRA Toxicology International, Department of Genetic and
Reproductive Technology in Carshalton, Surrey, United Kingdom.
Da ANDERSON: Cyclophosphamide, N,N-bis(2-chloroethyl)tetrahydro-2H-
1,3,2-oxaphosphorin-2-amine,2-oxide monohydrate, has a molecular weight of 279.1.
It is a fine, white, almost odorless crystalline product. It is soluble at 20 degrees
centigrade in 25 parts of water, 1 part ethanol in benzene, chloroform, dioxane, and
glycols. Aqueous solutions may be held for two hours at room temperature, and
hydrolysis occurs at about 30 degrees centigrade with removal of chlorine atoms.
During its production, it is treated with propenalamine in the presence of
trimethylamine and dioxane, and 1000 kg are produced annually.
It is used only as a drug and particularly widely so in the treatment of various
cancers. It is an immuno-suppressive agent used in a variety of non-malignant
diseases including rheumatoid arthritis, systemic lupus, erythematosus, scleroderma,
glomerulenephritis, chronic intestinal pneumonia, psoriatic arthritis, multiple sclerosis,
and chronic hepatitis; and it is often used as an immuno-suppressive agent following
organ transplantation.
It has various special physiological properties. It has an intravenous \JDX of
160 mg/kg body weight for the rat, 400 for the guinea pig, 130 for rabbits, and 40 for
dogs, and it has an oral LD^, of 180 mg/kg body weight for rats.
Leucopaenia is the predominant hematological effect in mice, rats, dogs, and
man. It requires metabolic activation and it is rapidly absorbed and metabolized in
247
-------
animals and man. It has a half-life in plasma of 6.5 hours. It is excreted in the urine,
but not in air or in the faeces, and CP and its metabolites are found in bile, milk,
sweat, saliva, and cerebral spinal fluid.
Much of the work I shall refer to from now on has been provided to me as
mini-reviews on DMA adducts (Colin Garner and McConnell), mammalian cells in
culture (Patricia Ostrosky), and germ cells (Jack Bishop).
Cyclophosphamide is used extensively as a therapeutic agent, used in several
regimens for the treatment of cancer. It is a cyclic derivative of the alkylating agent
nor-nitrogen mustard and consists of a phosphoramide ring linked to a bifunctional
moiety containing two chloro-ethyl groups (Figure 3-19).
Tumor cells have been shown to contain high activities of phosphoramidase
and phosphatase enzymes. It is a pro-drug, and it becomes selectively activated
within tumor cells by phosphoramidase cleavage of the ring, and this results in the
release of the toxic bifunctional alkylating species at the target site.
It needs to be metabolized before it becomes active and the primary activation
occurs in the liver. Hepatic microsomal cytochrome P-450 monooxygenase enzymes
metabolize Cyclophosphamide, producing the proximate metabolite
4-hydroxycyclophosphamide (Figure 3-20). This product equilibrates with its ring
opened tautomer aldophosphamide which is further metabolized by aldehyde oxidase
and aldehyde dehydrogenase to the metabolites aldophosphamide, 4-keto
phosphamide and carboxyphosphamide. Aldophosphosphamide may undergo
spontaneous p elimination yielding the cvtotoxic products acrolein, and
phosphoramide mustard (Figure 3-21 a and 3-21 b). Carboxyphosphamide is
enzymatically cleaved to nor-nitrogen mustard (Figure 3-21 c). Chloroacetaldehyde
248
-------
/CH2CH2CI
P-N
o/ ^CH2CH2CI
Cyclophosphamide
Figure 3-19
CyclophosphamM* 44
-------
a) CH2=CH-CHO
Acrolein
b) CI-CH2-CH2 /NH2
N-P=0
CI-CH2-CH/ OH
Phosphoramide Mustard
c) CI-CH2-CH2
CI-CH2-CH
Nomitrogen Mustard
Cytotoxic metabolites of cyclophosphamide metabolism
Figure 3-21
250
-------
is also formed by N-oxidation. Phosphoramide mustard is the main ultimate
alkylating species generated by cyclophosphamide metabolism.
Adducts In Vitro
Cyclophosphamide in the absence of metabolic activation fails to bind to ONA.
The reactions of phosphoramide mustard with guanosine and deoxyguanosine at 37
degrees centigrade at pH 7.4 was investigated by Mehta et al. (1980, 1982). The
products were separated by HPLC and identified by UV spectroscopy and field
desorption and revealed as N7guanine derivatives. The main adducts were labile
with a half-life of 2.3 hours and were readily transformed into imidazole ring-opened
derivatives, suggesting that they would readily undergo secondary reactions with
biological systems.
Vu et a/. (1981) found that phosphoramide mustard reacts with guanosine-5'-
monophosphate forming three types of adduct: a cross linked dimer
N,N-bis(2-[5'phospho-7-guanosinyl)-ethyl]phosphorodiamidic acid, (Figure 3-22c) two
monoalkylated products, N-[2-(N7-guanosine-5'-monophosphate)ethyl]
phosphorodiamidic acid (Figure 3-22a) and 2-chloro,2'-(N7-guanosine-5'
-monophosphate) diethylamine (Figure 3-22b).
More recent work (Macubbin ef a/., 1991) has revealed that phosphoramide
mustard forms a phosphoester adduct with 2'-deoxyguanosine 3'-monophosphate and
DMA. A modification of the ^P-postlabelling technique (Macubbin, 1991) was used
to detect adduct levels of one adduct for every 100,000 normal nucleotides. Early
work investigating the biological properties of cyclophosphamide within microsome
enzyme systems demonstrated the generation of genotoxic alkylating species (IARC,
251
-------
CHaCH,CI
CH,CH2CI
B
The structures of guanine adducts formed by the reaction with phosphoramide mustard
•RP = ribose phosphate
Figure 3-22
252
-------
1987). Work by Hemminki (1985) studying the binding of (chloroethyl-3H)
cyclophosphamide to DMA revealed 3 different N7 substituted guanine adducts.
Adducts In Vivo
DNA adducts have been formed following the treatment of mice and rats with
3H labeled cyclophosphamide; the work has been carried out by Hemminki (1985)
and Benson ef a/. (1988), respectively. 3H-cyclophosphamide to mice produced
maximal DNA binding between 2 and 7 hours after injection.
As in in vitro studies, in vivo studies show that cyclophosphamide was
metabolized to phosphoramide mustard. This reacted with guanine, producing a DNA
adduct undergoing spontaneous dephosphoramidylation forming the
hydroxynornitrogen mustard. Adducts were present at the levels of 4 adducts x 107
normal nucleotides for mice and 2 adducts x 107 for rats.
Macubbin ef a/. (1991) detected a cyclophosphamide derived phosphodiester
DNA adduct in mouse liver. DNA was at a level of 3 adducts/108 normal nucleotides.
Adducts In Humans
As to adducts in humans, molecular dosimetry work by Dr. Gamer showed that
DNA adducts increase an individual's risk of developing cancer for a number of
different DNA adducts, but the use of molecular techniques for CP derived alterations
in humans have yet to be carried out successfully.
The ^P-postlabelling method developed by Macubbin et al in 1991 represents
the best prospect for examining adducts. Monitoring of adduct formation in patients
253
-------
may help to optimize therapy and detect individual differences in metabolism and
excretion. Thus, it would be useful to get a such measurement in humans.
Somatic Mammalian Cell Genotoxicity In Vitro and In Vivo
Dr. Ostrosky has examined over 200 papers on the somatic genotoxic effects
in vivo and in vitro of cydophosphamide in mammals, including humans.
These papers have confirmed that cydophosphamide shows positive effects
in the presence of metabolic activation in all endpoints evaluated in somatic cells. It
has been employed as a positive control in many studies, in fact, where activation of
the compound is evaluated.
Only one negative result has been found in humans, and this was in a
monitoring study designed to evaluate the adequacy of protective measures among
the workers; and this study was performed by Marja Sorsa and colleagues.
Twenty-eight percent of the positive data without exogenous metabolic
activation have been reported in vitro and some authors daim their cells have a
capacity to metabolize CP when there is enough time to induce it. So, positive effects
can be obtained even in the absence of metabolic activation when there is a sufficient
period in culture. However, metabolism may not be due to the usual route in the liver,
but to some spontaneous hydrolysis products and in the presence of metabolic
activation; the effects are several times more potent than in the absence of metabolic
activation.
Different sensitivities to cydophosphamide have been reported in animal
spedes, strains, and individuals. It has been found that mice are more sensitive than
rats. These are more sensitive than hamsters, which are more sensitive than humans.
254
-------
Two HPRT gene mutation studies have been carried out with exposed human
beings, and only one study in animals. These are positive.
In general, the gene mutations in vitro have been performed in V79 HPRT and
L5178Y mouse lymphoma cells. Nine positive results have been found with metabolic
activation and one negative without.
Dose-related effects for all the endpoints have been found with CP and there
appears to be a maximum dose and an optimum time for the detection of effects.
This is due to toxicity, since very high doses of CP will affect cell division in vivo and
in vitro, and genotoxic effects will not be detected if the doses are too high. DNA
repair and adaptive responses also influence the outcome of the studies.
The tables summarize the results. While there are some weakly positive and
negative results most of them are, in fact, positive (Table 3-9).
Tabie 3-9. ECAJS Workshop on Hsk Assessment F>aralletogranVChernicaI Matrix
Somatic Genotoxic In Wfiro Effects of Cydophcspnamide on Mammals
Including Humans
No. Assays Results
NM 77 22 8 47
M 59 54 5 0
M with exogenous metabolic activation
NM without
+ positive
(+) weakly positive
negative
Also different endpoints, cells and tissues have been examined including:
body fluids, host mediated assays, DNA damage, sister chromatid exchanges,
255
-------
micronuclei, chromosome aberrations, lymphoma cells, lymphocytes and gene
activation. The number of assays conducted and the results are tabulated (Table 3-
10). Again, there are very few negative results; there are some equivocate, but in fact,
most are positive. So, there is a great deal of data on cydophosphamide in mammals
and human somatic cells.
Tabte3-10. EC/US Workshop on Risk Assessment Parallelogram/ Chemical Matrix
Somatic Genotoxic A? vim Effects of Cvctophosphamide on Mammals
and Human Somatic Cells
Endpoint
No. Assays
F
H
MAMMALS
D
S
M
CB
CL
HUMAN
S
C
G
12
13
4
29
35
23
6
7
6
1
F Body fluids
H Host-mediated
D DNA damage
S Sister Chromatid exchange
M Micronuclei
C Chromosomal aberrations
CB Chromosomal aberrations in bone marrow
CL Chromosomal aberrations in lymphocyte
G Gene mutation
+ positive
(+) weakly positive
negative
Results
10
9
2
28
31
22
6
5
5
1
1
4
2
1
4
1
0
1
1
0
1
0
0
0
0
0
0
1
0
0
256
-------
Table 3-11 examines the cell type, the dose range, the results, and the
numbers of studies in vivo, in mice, rats, hamsters, and humans. Nearly all results are
positive, and there are a vast number of studies.
There is also a large continuing table (Table 3-12) from a review paper that
was produced by Heddle, (1982). The first part shows £ coli and Salmonella
bacterial data, the types of responses that were found, and whether activation or not
was required. The result column shows the lowest positive result obtained or the
highest dose tested and not found positive. Mostly, results represent positive
responses.
The other parts of Neddie's review table move from the prokaryotic to
eukaryotic systems including yeasts and plants, Drosophila, mouse, human
lymphocytes, rabbit bone marrow, mouse bone marrow, Chinese hamster and all the
way through to humans. This paper was published in the early '60s, and there have
been more data since, but even then, there were already many studies. Again, the
doses that were used are listed, with comments about the shape of the dose
response curve (for example, describing whether dose responses are simple or
complex, and the sampling times used and so on).
Work is described on the more complex animal systems involving not only
somatic cells but germ cells as well, including the mouse spot test, mouse oocytes,
and aneuploidy systems. More recently, some work has been done by Dr. F.
Pacchierotti in Italy with aneuploidy and spermatocyte aberrations, Chinese hamster
spermatogonial aberrations, mouse sperm treated at meiosis or later stages, and the
mouse translocation test. Heddle (1982) also describes dominant lethals in pre- and
257
-------
post-meiotic males, various other stages in the males, and effects on females, as well
as mouse sperm abnormalities, mouse-specific locus mutations.
Table 3-11. Examples of Somatic in vivo Effects of CP in Marnmals Including Man
ASSAY END-POINT CELL TYPE DOSE RANGE RESULTS NO. STUDIES
(mg/kg)
MICE M
C
S
RAT M
C
D
HAMSTER M
C
S
HUMANS M
C
S
G
* Environmental
M Mteronuclei
C Chromosomal
ERYT
EPYT
ERYT
ERYT
ERYT
ERYT
ERYT
LYS
ERYT
LYS
LYS
LYS
LYS
LYS
LYS
LYS
exposure
aberrations
(BM)
(COLON)
(PB)
(UV)
-------
Table 3-12. Activity of Cydophosphamide Detected In Assay Systems"
Test system
Activation
Result6
Comments
g. coll. forward mutation
g. coH, fm, ip HMA
(mouse, SC)
g. cofipolA*/
P01A"
Disc
Liquid
B. subtllte.
RecvRec"
S tvphlmurium
bm
IP HMA (1000
1.4 rev/nmote
870 mg/kg
200 mg/kg
200 mg/kg
870 mg/kg
870 mg/kg
250jig/ml
10,000 jig/ml
Base pair
substitutions
Under reexamlnatlon
TA1535 base-pair
substitution
G46
TA1950
Dose to mouse;
TA1535
Strain a21
Strain a13
Activation required
Active In 03,05 but
not 04
Increasing activity as
the compound aged
In buffer at 30-C
Gene conversion
IP HMA (mouse,
PO)
Gene conversion
testesHMA
(mouse, SC)
Gene conversion
In vitro
urine, rat, PO
Gene conversion
In vitro
urine, human, IV
500 mg/kg
380 mg/kg
340 mg/kg
3.1 mg/kg
Renal excretion
blocked
-------
Table 3-12. Activity of Cydophosphamide Detected in Individual Assay Systems'
(continued)
Test system
Activation6
Result0
Comments
V. faba. chromosomal
chromosal aberrations
D. mejanogaster,
sin
Mouse (ymphoma TK*
Human lymphocytes,
In vitro wtth rat
plasma, IV
Heta cells, c.
aberr.
Human lymphocyte,
SCE
Human lymphocyte,
SCE
Mouse spermato-
gonla,SCE
34pg/ml
Adult feeding
m/s/pg/ml/hr
500-1000 mg/kg
84 /ug/ml, 48 hr
500pg/ml
3.7 at
5x20 mg/kg
Renal excretion
blocked
Postsper-
matogonial
Actual data not
available
Diluted 10-SOx
Rabbit bone marrow, SCE
Mouse bone marrow, aberr.
Mouse bone marrow
Mouse bone marrow,
mteronudel
2.4/cdl at 20
mg/kg, 24 hr
5 mg/kg IP
5 mg/kg IP
200 mg/kg,
96 hr
Curve probably
bending up
Curve approxi-
mately linear
Position at
earlier times
260
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lame a-i£. Mcnvny or Vsyoopnospnanwae uoieciea m aiamauai *vroay oymmiiB
(continued)
Twt system Activation6
Chinese hamster, bone
marrow aberrations +
Chines hamster bone
marrow
Human bone marrow
+
Human leukocytes, In vivo,
aberr.
Human leukocytes
+
Human leukocytes, SCE
+
+
Mouse spot test
Mouse oocytes, +
aneuptoioy at Meta II
Result6
20 mg/kg.
10x64 mg/kg
daily
40 mg/kg IV
24 hr
3-5 mg/day
4-6 weeks
500mo-44g
total dose
3xat100
mg/day x 13
30x at S mg/kg
74% at 145
mg/kg IP
Comments
Complex dose
response curve
72 hr after
last treatment,
positive results
atChr
-0.065 mg/kg/
day « -2 mg/kg
total 74 hr
culture
72 hr culture
No Increase prior to
13
days; decrease
to Dover 2
wvvKS
Spontaneous 14%;
12 hr post treatment
Mouse spermato-
gonla/spermatocytes
aberrations
Chinese hamster
spermatogonia,
aberrations
Mouse spermato-
gonJa/spermatocytes
treated as
stem cells
20% abnormal
ceHs at 97 mg/kg.
oral
2 x 96 mg/kg,
oral
0/400at200
mg/kg IP
Metaphase: 1, 13
days post-
treatment; 3
transkxaUons
m 80 cells
treatments
261
-------
Table 3-12. Activity of cydophosphamide detected in Individual assay systems*
(continued)
Test system
Activation6
Result6
Comments
UDS mouse sperm,
treated at meiosis
or later stages
Mouse transtocatlon
test
Sperm
Spermatlds
Spermatocytes
Spermatogonial
stem cells
Dominant lethals, mouse
Postmelotlc
males
Various stages, males
Various stages, males
Premelotlc males
Premetotic males
Premelotlc females
Premelotlc females
Mouse, sperm
abnormality
Mouse, specific
locus spermatogonla
3-6xat200
mg/kg IP
5/35 AT 350
mg/kg IP
34/84 at 350
mg/kg IP
Z/287at350
mg/kg IP
1/1350 at 350
mg/kg IP
40 mg/kg, IP
80 mg/kg, oral
0.005% In drinking
water for 4 weeks
0.01% In
drinking water
for 20 days
210 mg/kg, IP
210 mg/kg, IP
210 mg/kg, IP
200 mg/kg, oral
2x at 5 x 130
mg/kg daily IP
3/(3642x7) at 350
mg/kg
'Abbreviations: fm = assay for forward mutations; bm = assay for back mutations; co = assay for
crossing over; c = chromosome; aberr. = chromosomal aberrations; uds = unscheduled DMA synthesis;
hma « host-mediated assay; slri = sex-linked recessive tathals.
''Shows the use of In vitro activation by +.
cResult column shows the lowest positive result obtained or the highest dose tested and not found
positive. In some cases many other studies have been carried out at other doses In the same test
system.
262
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A review of cyclophosphamide data has not only been performed by Heddle
but by Anderson and Sram (1982) (Table 3-13) in the same book which was edited
by Drs. Fred de Serres and Mike Shelby. All the dominant lethal data were examined
and doses were stated at which effects were produced. Dr. Sram has also done a
great deal of work with dominant lethals, and he has also used fractionated doses
(Figure 3-23).
It is possible to obtain very good dose response curves with different doses
of cyclophosphamide. One of the aims in our laboratory was to do so and various
other laboratories have also done so. We have examined acute dose-response
relationships and others have examined the effect of dose-fractionation on the
frequency of dominant lethals, pregnancy index, and mating weeks. The most
positive responses for cyclophosphamide are obtained in post-meiotic weeks 1 and
2, and sometimes in 3. Sotomayer and Gumming (1975), however, found a random
distribution of translocations in mouse germ cells.
Thus just about every conceivable type of endpoint has been investigated for
cyclophosphamide and with this vast array of data, it should be possible to carry out
some risk assessments.
Dr. Udo Ehling has reviewed different germ cell stages and examined the
doubling dose of cyclophosphamide in mg/kg bodyweight for different germ cell
endpoints in mice (specific locus mutations, heritable translocations, and dominant
lethals) and has determined different doubling doses for the post-spermatogonia and
the spermatogonia. He has compared them with doubling doses for somatic
mutations and sperm abnormalities. The doubling dose was based on the regression
263
-------
Table 3-13.
Pn> Poat-
npiantation implantation Dominant
loe»eec loMa«c lethality0 Comment*
150 - + + Firat 10 day* after doalng
150
100 - + + Pro-aatrua
100 + + +
50 + + + Poatmeiotic
100 + + +
50 o o o PoatfMiotic fertility reduced
atalldoaaa
100 o o o
2.5 (x25) ooo
5 ^(25) o o o
10 (x25) ooo
3.5
6
9
4.4 + + +
60 + + +
210 + + +
«0 + + + Poatmaiotic
210 + + + (Pramatotlcloaaaaat210
mg/kg); 420 mg/kg group
did notaurvlve
420 ooo
264
-------
A
0.4-
0.3-
0.2-
0.1 -
^.:Nx:rAv.-
* --^V_l^
246
B
0.8-
0.6-
•* 0.2-
\f
• M
*
•
\ .*
1 1 I 1 1
8 2468 week
.7. Cyclophosphamide: effect of the dose fractionation on the
frequency of dominant lethals induce mice. (A) Dominant lethals; (B)
pregnancy index. ( — ) 100 mg/kg b.w.; ( ) 20 x 2.5 mg/kg
( — ) 20 x 5 mg/kg.
Figure 3-23
265
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coefficient; the other values are on-point estimates (Ehling, 1981) (Table 3-14). Dr I.D.
Adler has also examined spermatogenesis and Dr. A. Wyrobeck investigated sperm
abnormalities, and he found increases. In contrast, we didn't find sperm abnormalities
with cyclophosphamide, so we must have been using the wrong dose or showing
strain differences.
Table 3-14. Doubling Dose of Cyclophosphamide in mg/kg for Different Genetic
Endpointsin Mice
GERM CELL SPECIFIC HERITABLE DOMINANT SOMATIC SPERM
STAGE LUCUS TRANS- . LETHALS MUTATIO ABNOR-
MUTATIONS LOCATIONS NS MAUTY
POST (a) NT (b)
SPERMATOQ 4 6 88+ 130
ONIA
SPERMA- 320 - - NT
TOQONIA
+ Doubling do** la baaad on tha ragraaalon eoaffioient; tha other valuea are on point eatimatea.
(a) I.-D. Adler, Terat,Carc.Mutag. 1 (1980,7546.
(b) A.J. Wyrobek end W.R. Bruc«7 Chemical Mutag. 5 (1978,257-258.
Dr. Ehling showed doubling doses for specific locus mutations ranging from
4 to 320 mg/kg. These are the actual values he produced in his paper, but he has
done a more recent paper in 1988 with Dr. Angelica Neuhauser-Klaus (Ehling and
Neuhauser-Klaus, 1988), and he has got even more values now for specific locus
mutations, but that is what he did at that time.
Now I am moving on to risk estimates made by Meddle (1982). There follows
an outline of his approach.
The specific locus mutation rates observed in the mouse after a dose of 350
mg/kg body weight is 1.2 x 10** mutations per locus per gamete. Allowing 1 mg/kg
body weight to be one unit of dose (p), this rate can be expressed as 3.4 x 10'7
mutations per locus per gamete per mg/kg body weight (or \i). Since the average
266
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person is about 70 kg, one unit of dose equals 70 mg per person, that is, 70 mg
equals one person per mg/kg body weight. Using the annual production figure of
about 1000 kg (109 mg), is equivalent to 109 mg divided by 70 mg per person/)*
equals 1.4 x 107 person p in post-spermatogonia.
If there is a linear response curve, the distribution of these units among people
will not influence the number of mutations produced, and using the mouse rate
produces 3.4 x 10~7 mutations gamete 11 times 1.4 x 107 person \i which equals 5
mutations per locus per gamete times per person. Allowing a reproductive rate of
nominal gametes per person, there will be six nominal gametes per mutation per locus
produced.
Assuming that these mutations will arise in a population of about 500 millions,
that is approximately that of Western Europe and North America, (appropriate for this
meeting, because it is EC and U.S. EPA meeting), the spontaneous number is 500 x
10* persons times nominal gametes times 7.5 x 106 mutations per locus per gamete,
and that is equivalent to 3.8 x 103 nominal mutilations per locus.
Taking into account the annual production of cyclophosphamide this is
equivalent to 0.13% of a spontaneous rate. If this rate of exposure continues for 30
years, and this is an estimate of the average reproductive lifespan, (it is recognized
that men generally reproduce for a longer time span than women), this would be 30
times 0.13%, and that value equals 4% of the spontaneous rate.
This 4% might well be an upper limit because not all of the cyclophosphamide
is used for human exposure and many of the exposed persons may be beyond the
reproductive age or may not have children because of their disease status or for other
reasons. Also the population at risk may be smaller now that manufacturers are
267
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recommending its use only in life-threatening situations, and physicians are warned
that a genetic risk may exist. This is happening of late, and the estimate depends on
the similarity between mouse and man in the induced and spontaneous mutation
spectra.
Thus the doubling dose as outlined by Ehling has been considered as well as
the population approach of Neddie. Now I wish to consider exposures of the male
which produce abnormalities as varied as those produced by females exposed in
utero. These are for example runts, which are 75% or less of the body weight of the
normal size fetus, fetuses with gastroschisis where the intestine hangs outside the
body, exencephaly where the brain comes outside of the head, and anasarca which
is a total bloating of the fetus. There are others also. All are thought to be truly
genetic events, since they are transmitted through the exposed male.
We examined the incidence of grossly abnormal fetuses that we found after
treating males with chronic low doses of cyclophosphamide given at the therapeutic
dose rate of 5 mg/kg for 30 weeks. Mating took place at weekly intervals for a long
time over this period. There was a saline control and ally! alcohol treatment also.
Ally! alcohol is involved in the metabolism of cyclophosphamide and was of interest
to the U.K. Ministry of Agriculture, Fisheries, and Food who funded the project.
We found that we got large numbers, up to 10%, of the runted and grossly
abnormal fetuses after cyclophosphamide treatment, but not with allyl alcohol and
saline treatment, after about week 7; and this response plateauxed up to week 30.
We also examined alizarin stained fetuses for skeletal abnormalities with the
following gross abnormalities: anasarca, exencephaly, hydrocephaly, other skull
268
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defects, spleen abnormalities, gastroschisis, abnormal placenta, and growth-retarded
fetuses.
There were large numbers of genetically transmitted congenital malformations.
These can be closely correlated with genetic defects in humans. Man would not want
his children to have such defects, and similar abnormalities can be identified in human
offspring. This makes the comparison between malformations in animals and man
very meaningful and tangible.
Thus, this may well be a very good model for using in genetic risk assessment,
but it is also a good model for chronic low dose exposure. We used
cyclophosphamide and butadiene in this way.
MuMhill did some work with exposed populations, that is, patients who had
been on therapeutic doses of cyclophosphamide, and he found that when they mated
after these doses, there were no abnormal children. However, he believed the
numbers examined may not have been sufficiently large to detect if there truly was an
effect, or it might simply be that the patients did not mate immediately after treatment.
If they had waited, and it is extremely likely that they did, by that time the
spermatogonia which were not affected would have formed sperm.
So, for a chemical that just affects the post-meiotic stages, provided a sufficient
length of time, elapses after treatment then there will not necessarily be a risk to the
germ cells.
However, CP is a mutagen which produces point mutations, cytogenetic
damage, and DMA adducts in several organisms. In mice, the active form reaches the
gonads in both males and females, and it also does so in rats. Thus similar effects
might be expected in exposed people. It is known that active metabolites are
-------
excreted in the urine and chromosomal aberrations are present in peripheral
lymphocytes in blood as well in exposed people.
According to Heddle, CP is likely to produce less than 4% of the spontaneous
mutation rate, but Dr. Ehling may have some thoughts on that. There is a significant
risk of a chromosomally abnormal child if conception occurs within a short time of the
end of treatment, and that is a very important point to bear in mind.
With regard to Professor Frits Sobels' parallelogram, there are positives for
mouse somatic cells, including mutations and adducts. There are also positive
responses in mouse germ cells and positive human somatic cell responses, which are
mutations; but there are no positives for human germ cell data. It is not known,
however, whether MuMhill's data represent a true response.
There are thus many areas of the parallelogram almost complete, so we should
be able to make some predictions, perhaps in our workshop deliberations this week.
Discussion '
DR. SHELBY: Dr. Ehling, would you like to consider this presentation before
we take questions?
DR. EHUNG: First, you were asking about the 4% where I have some doubts,
because it would end up as 1/1000th of the spontaneous mutation rate. If you make
such a calculation, then John Heddle had a 'hit'.
The other point is, of course, the MuMhill study. The numbers you cited were
from the Proceedings of the International EMS Meeting in Japan. It just indicates a
big difference between post-spermatogonia stages and other germ cell stages.
270
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Mulvihill published a large study from offspring from parents treated with
cyclophosphamide without considering the date of conception.
I think in doing such a thing, we can get a false sense of security, because, of
course, you debated that cyclophosphamide may not be mutagenic in man, because
you mentioned the human data from Mulvihill; but his study, I want to emphasize once
more, was without consideration of the conception date and in fact cyclophosphamide
may well be a human germ cell mutagen.
To make the story a little bit more complicated, I would like to present a figure.
Here you see radiation and the frequency of cyclophosphamide is the same
as the control.
You see here treatment due to X-irradiation. That is with 3 rads and with 6
rads. ft is a very conventional mutation rate. The same was observed in Oak Ridge
and by Hemminki in Harvard.
But if we pre-treat for 24 hours the animals with cyclophosphamide, then we
double the mutation rate in these animals. The types of mutations observed are
exactly the types of mutations that will be observed in the radiation experiment.
Very likely, what cyclophosphamide does is to block the repair mechanism, so
enabling a doubling of the mutation rate. So, it is not that simple that we do not
observe mutations in spermatogonia, but in such a situation, an actual mutation rate
with cyclophosphamide can be observed in spermatogonia.
DR BISHOP: Dr. Ehling, is that only in spermatogonia, or is that post
spermatogonia as well?
271
-------
DR. EHUNG: We were not so much concerned about post-spermatogonia in
this experiment, because we wanted to see the effect in spermatogonia, so I cannot
answer that question.
DR. SHELBY: Dr. Sorsa?
DR. SORSA: The exposure assessment is very important, and I think that the
idea of having 4% of the spontaneous mutation rate related to cyclophosphamide is
not a sound one, because not everybody is exposed to cyclophosphamide.
We have some measurements, like many other people, on the potential work
exposure of nurses when they are diluting anti-cancer agents, and, of course,
cyclophosphamide still is in most countries, as the most often used anti-cancer agent.
If you dont have any protection, it is about 0.5 mg/m2 which are the measurements
of levels to which humans are exposed.
However, there are certain occupations involved in the manufacturing of the
drug, cyclophosphamide, where the exposure levels might be as high as 800 mg. So,
there is a huge difference in exposure due to the manufacturing process, up to 400
mg. If no protection is used, this is a severe exposure.
So, one might calculate that a nurse without protection working for 2 hours per
day gets about 1 mg of cyclophosphamide 5 days a week and 5 mg, 40 weeks per
year, that is 0.2 mg. In 10 years' time working in this same situation, this equals, 2
mg.
Of course, this amount is nothing by comparison with the amount which the
patients are receiving. The patients are generally receiving about 4000 mg.
We should not be talking about cancer rates; but in this case, it might be easy,
because we do have patient studies where cyclophosphamide therapy has increased
272
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the risk of secondary cancers, namely, secondary acute non-leukocytic leukemia, and
the risk is about 1% in two of the published studies.
If the dose has been about 4000 mg, we can extrapolate that in this situation
of the nurses working, in 20 years' time, there would be 1 acute non-leukocytic
leukemia in 100,000 exposures. So, the risk is minimal.
But if we would just use these groups, of course, the risk clearly becomes, very
soon a much higher one, and I think that maybe this kind of calculation could be
performed also for the general population risk.
DR ANDERSON: I have a comment. This relates to the mechanisms involved.
There would appear to be a lestes barrier" that protects the germ cells. For this or
some other reason, germ cells are relatively protected, but we have already seen an
example of a chemical today such as butadiene that reaches the germ cells at a very
low level.
If a chemical affects spermatogonia, it could be a lasting problem and Dr. Vicky
Dellarco was earlier highlighting this. If a chemical only affects post-meiotic cells, that
is sperm and spermatids, it might be of less concern because the cells will cycle and
there will be some non-affected stages. If man waits until the non-sensitive cells are
cycled in order to mate, there may not be a problem at all.
The area where I believe there is a real cause for concern, is from chronic low
dose exposure in the workplace or from low level environmental exposure, because
man is continually exposed and mating. Since such exposure occurs all the time, if
a chemical only ever affects the post-meiotic cells, it will matter because sensitive
stages are continually cycling and being affected. Thus mating will always take place
with damaged sensitive cells.
273
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Many of the arguments concerning risk to man are based on acute high
exposures (for example, as occurred after the bomb at Hiroshima and Nagasaki).
Such extrapolations do not take into account the chronic low dose exposures
encountered in real life situations. It is like comparing apples and oranges instead
of apples and apples and I think this is a real issue we ought to be concerned about.
274
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Rapporteur's Reports
This section contains the rapporteur's reports for the following compounds:
• Ethylene Oxide
• Acrylamide
• 1,3-Butadiene
• Cyclophosphamide
275
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276
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Ethylene Oxide (EO)
Dr. Julian Preston was the rapporteur for the group discussing Ethylene Oxide. Dr.
Preston is from CUT is Research Triangle Park, North Carolina, U.S.A.
Initial Report on Ethylene Oxide
DR. PRESTON: What I have are five figures, that represent some of our
conversations and discussions this morning. The first thing that we discussed was
the heritable effects in germ cells and the heritable translocation risk estimate; so, we
started with germ cells and what we wanted to discuss was the applicability of using
the particular data set used by EPA in their genetic risk assessment for ethylene oxide
and seeing what additional data we would need to make such a risk estimate as that
presented yesterday appropriate. We decided there were two factors that were of
importance, that led us to a specific conclusion on the utility of the heritable
translocation assay. One was Lars Ehrenberg's brief calculation of dose; we talked
a lot yesterday about what one could use as dose along the abscissa. What he
pointed out was that what was appropriate, was the dose to the DMA; that is, what
was the target dose. What I plotted here (Figure 4-1) was the reciprocal translocation
frequency; first you will see data, I plotted as ppm, from the original Waldy Generoso
study and then an approximation of the recalculation of the doses to DNA, which will
shift all the points over to the right What that will do is, in fact, flatten out the curve
at the tow exposure levels, so that will reduce the effect, also, for reciprocal
translocations as a function of dose to the DNA; that will further reduce the risk
estimate from the linear extrapolation, on the ppm curve, that was discussed
277
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0)
o
(0
g
o.
'8
DC
x ppm (Qeneroso data)
o Dose to DMA (estimate)
Reciprocal Translocations Induced in Mouse Germ Cells
by EO (Data from Generoso et al., 1990,
Environmental Molecular Mutagenicrty 16,126-131
Figure 4-1
278
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yesterday. Also we discussed in some detail, at the beginning, as to whether to use
an additive assessment of slope; that is, do the spontaneous lesions interact with the
ethylene oxide induced ones or are those independent events. We concluded that
because of the very low probability of having a spontaneous event, we would
consider those as additive and not interactive and you will appreciate that that will
reduce a linear response, for either of these, to something that is convex downward.
Thus, it will reduce again, the effect at low exposure levels. Our conclusion from
these two points and others we discussed both yesterday and this morning, but
particularly based on these two, is: genetic risk from reciprocal translocations at low
exposures and for post-meiotic cells, for which this data set was collected, will be
extremely low. We indicated that further experiments really would not change that
situation. We could not see that collecting more data was really warranted to improve
the genetic risk estimate, for post-meiotic cells and for reciprocal translocations.
There might be reasons for considering other germ cell stages, for additional
information. This does refer to reciprocal translocations also. I will move on to
mutations in a minute.
DR GENEROSO: Do you mean independent, rather than additive?
DR. PRESTON: I meant the lesions are additive, the responses are simply an
additive response. The spontaneous will be present at all of the dose levels; it will
be an equal contribution. I do not consider the two responses as interacting. I was
thinking in terms of mechanisms, and independent and additive are basically the
same. To return to our discussion session. That was point number one. Then we
had a presentation from Waldy. His presentation related to the work that he has both
published and has in unpublished form, on effects on the zygote, where he has
279
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shown that there is a very sensitive stage, somewhere around from one hour to six
hours after mating, that is sensitive to the production of congenital abnormalities with
ethylene oxide. He had additional information on retinoic acid for other
developmental stages, but what we have concentrated on here are the discussions
on effects on the zygote. First of all, we want to decide what sort of effect this was.
Well, we agreed it was not a genetic effect, according to what was being defined
yesterday. It is obviously not a component of genetic risk because it is not
transmitted from an individual to the offspring. Of course it is transmissible from cell
from cell, because you get an effect in the zygote and a response in the embryo; so
it is clear you have transmissible effect, but it did not fall under the category of genetic
risk. Waldy discussed the possibility that it was a so-called epigenetic effect; that is
a word that I feel covers a multitude of sins, if you do not know quite what it is. We
could not decide what the mechanism was, which suggests some confusion in the
end. We do have a transmissible effect, and because it is such a clear response and
there is certainly a sensitive period of response, I think we felt that there was need for
separate risk assessment consideration here. A completely separate one from the
genetic risk, this is zygotic risk, maybe exhibits a broader period of action for other
chemicals that covers a several day period. We do think there is a good case for
making a separate risk assessment. Waldy in particular noted that there was a higher
sensitivity here, with the female pro-nucleus in the zygote, considerably higher than
the male, which was essentially insensitive. So here is a female effect, that I think
bears further consideration; it is quite a clear response and you appreciate that in the
pronucleus stage the two nuclei, the original sperm and egg, are separate until the
first cleavage division, so you can in fact look at effects independently on the two
280
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nuclei. There are a number of reasons why that might be the case and we had only
touched upon those; but our conclusion here was that it was a very important area
of research and that further study, in particular, mechanistic information was needed
and indicated from these original, rather exciting studies that Waldy described, hie
did have a little bit of new data, which I probably will be able to present tomorrow; I
did not put it on an overhead today. He attempted to produce a dose-response
curve, but what Waldy pointed out was that he used the six hours after fertilization
period for treatment, with a one and a half hour treatment regime; that certainly was
not ideal. This was not the most sensitive stage, and so he got a no response at 600
ppm, whereas that probably would not be true at a more sensitive stage, because the
six hour treatment, (that is, the six hour treatment start time), with an hour and a half
treatment, takes you into a rather insensitive period of seven hours after fertilization;
so he would like to move the treatment time back towards fertilization. But I think
mechanistic information is certainly needed here, in order to help to produce a risk
estimate.
Then we did a series of dubious calculations on a data set; I will present them
as we did them. Now, we have moved to consider mutational data, having covered
reciprocal translocations and then the zygotic effect and the effect on the conceptus
back into germ cells. There were no new data on mutations in germ cells that Waldy
could indicate, but we returned to some data that was published a few years ago by
Susan Lewis and her colleagues, on the mouse. These were for electrophoric variants
and also dominant visible mutations. What Susan did in these experiments was to
look for some 20 defined phenotypes (assumed to be 50 genetic loci), for dominant
visible mutations (Figure 4-2). For these 20 phenotypes, she found four dominant
281
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Dominant visible mutation data for the mouse following ethytene oxide exposure
(Source: Lewis et al., 1986, Environ. Mutagen. 8, 867-872)
• 4 dominant visible mutations (including 2 heritable translocations)
Number of offspring 1891 (assume 2000)
Average exposure for mutation induction 100,000 ppm h
• Visible mutations for 20 phenotypes that are assumed to be
representative of 50 genetic loci
• The overall mutation frequency calculates as
2 x 10"8 mutations/ppm h
i.e., 4 x 10~10 mutations/locus/ppm h
Figure 4-2
visible mutations in a study were there was a long, several week exposure. In fact,
the animals were not mated until seven weeks into the exposure regime, thus, the
exposures covered the whole speratogenetic cycle. Of these four dominants, two
were heritable translocations and I think this is in approximately 2,000 offspring. I
think it was between 1,800 and 1,900.
DR LEWIS: 1,891.
DR PRESTON: That is 4 mutations in 1,891 offspring, and the average
exposure, at the time of mating, for these four mutations, was 100,000 ppm hours; so
these mutations showed up at different times after the start of exposure. We just
simply took an average exposure time for the four mutations; in fact, the first one
showed up around 60,000 ppm hours. So there were 20 phenotypes (50 loci) scored
and we did a calculation that assumed that there are 2,000 similar dominant loci in
the human, which might be an over estimate for dominant visibles, but it is a
reasonable estimate of dominant mutations overall, if you just do a simple calculation,
282
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you would find you have 160 dominant visible mutations in humans in a study similar
to the mouse one, assuming all sensitivities are equal. That would be 160 mutations
x 10"5 per ppm hours. That would work out at one mutation per 1,600 individuals per
year of exposure, based upon a one ppm exposure (Figure 4-3). Now, there is a lot
of flop in that calculation; I mean, do you use all four dominant visible mutations,
when two of them were heritable translocations? We assumed that amongst the
offspring in humans, there would be similar numbers of heritable translocations. We
just wanted to see what the situation would be if you simply take mouse mutational
data and convert it into human data. That is the sort of approach one might take,
without putting an enormous amount of reliance upon the end product; I think it is a
Calculation of dominant mutation rate in humans following ethylene oxide
exposure
• Mouse data (Figure 4-2) give 2 x 10* mutations/ppm h
• Assume 2000 dominant loci in humans
Equivalent sensitivity
• Mutation frequency is
160 x KT5 mutations/ppm h
Le., 1 mutation/1600 persons/ppm h
(based upon a 1 ppm exposure)
Figure 4-3
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ball park type of number. We progressed from that germ cell calculation on the only
data that we found for which we could do any manipulative calculation, and moved
into somatic cell effects, to try and link sensitivity of mouse with man, by another
approach. Here, we just took a direct approach, mutation for mutation. What we
shown for a 20 week expression time and the reason that this was selected although
there were two expression times, an eight week and a 20 week, was that this would
be more appropriate for a human exposure, which is presumed to be continuous. So
have tried to do now is see what the relationships in sensitivity between the mouse
and human might be, for mice, when you look at the same endpoints in both
organisms. Somatic cells, HPRT mutations are in splenocytes. I have presented just
part of the data set from Skopek and Walker because it is one that you might be able
to use to compare with the human. The data are presented in Figure 4-4. This is we
took the longer expression time. That is the time you sample the cells, the
splenocytes for mutation analysis after exposure. These exposures are in milligrams
per kilogram and they were from more than one IP injection; for this particular set, I
assume this is like 4 times 150 (600 mg/kg) and other triple combinations, double
combinations. They were for more than one day for the higher exposures. Here the
mutation frequency is times 1CT6 (Figure 4-4). The control is 3.6, and up to 15.5 at
600, it is approximately linear. So we did a doubling dose calculation. This is
approximately 200 milligrams per kilogram, which converts to, with Lars Erhenberg's
help, to 4,400 ppm hours, which is about 110 ppm hour per week. That is a doubling
dose, based upon the mutation frequency in the mouse. There are some human data
available of the same sort, Ad Tales' study (Tales et al., unpublished). We considered
the highest exposure group; there were two exposure groups in this particular study.
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HPRT mutants in mouse spienocytes
(Source: Walker, V.E., and Skopek, T.R., 1993, Mutat Res. 288,161-162)
20 week expression time
• Doubling dose - 200 mg/kg
• This converts to 4400 ppm h or 110 ppm h/week
• Using a doubling dose approach, the mutant frequency is
4.0 mutants/4400 ppm/h
i.e., 1x10"° mutations/locus/ppmh
HPRT mutants in human lymphocytes
(Source: Tales et al., unpublished)
• Control - 8.6 x 10"6 mutations/locus
• Exposed - 13.8 x 10"6 mutations/locus
(internal dose 5 ppm h)
• Using a doubling dose approach (doubting dose about 200 ppm
h/week)
Mutation frequency is
9.0 x 10* mutations/200 ppm h/week
i.e., 1.1 x 10"* mutations/locus/ppm h
Figure 4-4
We took the control value for the humans, 8.6 x 10"8. Now, these are mutations, the
mouse ones are mutants. There is an important distinction with the spienocytes
because of donal expansion of induced mutants in progenitor cells. However, for the
mouse, there was a short exposure, where clonal expansion would be less likely to
be a problem, so we actually got mutants, but their frequency would be very similar
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to mutations. For the human, because of the long exposures, mutants or mutations
require some separation - the value used was for mutations and is 13.8 x 106. They
were sequenced. The internal dose is estimated from the hemoglobin adducts as
5 ppm. If you simply do a doubling dose calculation where here the exposed group
were approximately double the control group. I say approximately because we came
up with a doubling dose of approximately 200 ppm hours per week, which is rather
similar to that for the mouse data. Now, again there is some flop in there, but I think
that is the sort of approach that would be appropriate and we get reasonably similar
sensitivities for these two data sets for mouse and human, both on HPRT mutations.
One more quick point. We looked at somatic cells, again a comparison
between what we hope would be mouse and human for chromosome aberrations in
peripheral blood lymphocytes, because there is quite a large human data base.
When we considered the mouse data, there were none, and so we could not do a
mouse to human comparison. There are SCE data, but there are no aberration data.
For the rat, it is rather interesting, and this presents quite a fascinating story, in that
for the rat, it is negative for aberrations as shown by Andy Kligerman; I have single
data point which was also negative for chromosome aberrations. Natarajan also
reported that ethylene oxide was negative for mutations In the rat, from their study.
So, we had this interesting data set for the rat, but unfortunately no mouse data to
compare it with. Clearly there are gaps in the available information; one of the gaps
is data for the mouse, and the other is trying to understand what is going on in the
rat. There was some data for the monkey, which is positive, especially for dicentrics;
but we felt that was a rather short extrapolation to make and also it would be rather
dead-ended, because the only data you will get for the monkey are for the
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lymphocytes, since you probably will not get the germ cell data. There are a lot of
gaps in the aberration data, which was a bit of a surprise, and we could not do any
of our parallelogram comparisons. We did have some somatic cell comparisons for
mutations; we feel that the germ cell data are what is needed in the mouse for
mutations at different stages in order for us to go around the parallelogram. I think
I have approximately represented our morning discussions; if I am not here tomorrow,
it means I did not accurately represent the group.
Final Report on Ethylene Oxide
DR. PRESTON: It is a significant pleasure to be able to hand all these
overheads over to the distinguished Chairman, who will then make sense out of them.
He has not seen them yet, because I wrote them last night, but I think they are
representative of what we discussed. But what I will talk about today is really an
extension of our discussion yesterday. I am not going to cover the same material,
except where it is important, to show where we have gone in the last two of our
discussions sessions.
We have tried to address the questions, but hopefully not in a generic way, but
in a rather Ethylene Oxide specific way. I should spread the responsibility around and
let you know that Vicki Dellarco, Susan Lewis, Waldy Generoso, Lars Ehrenberg, and
Ad Tates, Natarajan and myself were the Ethylene Oxide group.
What I was trying to do yesterday, on behalf of the group, was to use the
parallelogram approach for estimating potential genetic effects, having defined genetic
effects yesterday as being transmissible effects from germ cells to the offspring, and
not including the zygotic effects. We decided to apply the parallelogram approach
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for those pieces of data that were available. So question one: which are the best
available data? Clearly, we feel the ones we are using here are the best available
data, that is why we are using them. They are rather limited, but I think they do, at
least in our estimate, allow some initial attempt at predicting genetic risk from ethylene
oxide exposure.
Now, yesterday, the germ cell data that we discussed was from Susan Lewis,
and we made a mini-adjustment to the data that I presented yesterday. Following
discussions with Susan, although 20 different phenotypes were assessed for
predominant visibles, she feels that the actual number of genetic loci that are
responsible for those 20 phenotypes, appreciating that these are relatively gross
phenotypes, was more like 50. So, it is not a one-for-one relationship between
genotype and phenotype. So, we recalculated using the 50 tod and that, instead of
one mutation per 700 persons per year to an exposure of one ppm we come up with
one per 1,600 (correction already made in Figures 4-5 and 4-7).
Dominant Visible Mutation Data lor the Mouse
Following Ethylene Oxide Exposure
(Source: Lewis et al., 1986, Environ. Mutagen. 8,867-672)
4 dominant visible mutations (including 2 heritable translocattons)
Number of offspring 1891 (assume 2000)
Average exposure for mutation induction 100,000 ppm h
*
Visible mutations for 20 phenotypes that are assumed to be representative of
50 genetic loci
The overall mutation frequency calculates as
2x10"* mutations/ppm h
i.e., 4 x 10'10 mutations/tocus/ppm h
Figure 4-5
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We have moved away from this particular approach for expressing the data in
order to include it in our parallelogram. I just wanted to point out that we had made
a slight reassessment. And because 50 loci came into the calculation, I used 2,000
dominant loci for the humans so that my calculations were easier (Figure 4-6).
Calculation of Dominant Mutation Rate in Humans
Following Ethylene Oxide Exposure
Mouse data (Figure 2) give 2 x 10* mutations/ppm h
Assume 2000 dominant loci in humans
Equivalent sensitivity
Mutation frequency is
160 x 10"5 mutations/ppm h
i.e., 1 mutation/1600 persons/ppm h
(based upon a 1 ppm exposure)
Figure 4-6
And again, just to remind you of the somatic cell data that we used; this is
exactly the same set as yesterday (Figure 4-7). The mouse data are from, Tom
Skopek who has shown up this morning, so he gets to see, some of his own data on
the screen. So, if he has any concerns about the way we used them, he can express
them.
This was, as I expressed yesterday, a 20-week expression time, for the set of
mutations, and we calculated yesterday a doubling dose. Again, we have departed
from this approach to make comparison between our various data sets a little more
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HPRT mutants in mouse sptenoctyes
(Source: Walker, V.E., and Skopek, T.R., 1993, MutaL Res. 288,
161-162)
20 week expression time
• Doubling dose - 200 mg/kg
• This converts to 4400 ppm h or 110 ppm h/week
• Using a doubling dose approach, the mutant frequency is
4.0 mutants/4400 ppm/h
i.e., 1 x 10"8 mutations/locus/ppmh
B. HPRT mutants in human lymphocytes
(Source: Tales et al., unpublished)
• Control- 8.6 x 10"6 mutations/locus
• Exposed - 13.8 x 10"6 mutations/locus
(internal dose 5 ppm h)
• Using a doubling dose approach (doubting dose about 200 ppm
h/week)
Mutation frequency is
9.0 x 10"8 mutations/200 ppm h/week
i.e., 1.1 x 10*8 mutations/locus/ppm h
Figure 4-7
sensible for the parallelogram approach. The human data are the same as yesterday
from Ad Tales' data set
It should also be noted, because that comes up later, that the human data
does have hemoglobin adduct information for an internal dose. This particular mouse
mutation data set does not; these are exposure data from IP injection, but we felt H
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that was not unreasonable to equate the IP injection data to internal dose
measurements.
So, how did we go about doing some sort of parallelogram calculations? I am
going to start in the top left-hand corner of my parallelogram (Figure 4-8), which is
with the mouse germ cell data. All we have done here is simply to convert our data
sets, using the various assumptions, and we have done this with all three data sets
that you will see, into mutants or mutations per locus per unit of dose. In this case,
in the mouse, it is mutations per locus, per ppm hour (Figure 4-5). It was just so that
we had some point of comparison. And it is the number of mutations, per locus, per
ppm hour. As in the previous day's presentation, we arrived at a value of 2 x 10"8
mutations per ppm hour. I can return to that calculation if necessary, but it is based
on this set of assumptions. The 4 mutations, in approximately 2,000 offspring, at the
50 loci, were assumed to arise at an average exposure of 150,000 ppm hour. That
is just an average, rounded off. This converts to 4 x 10*10 mutants per locus, per ppm
hour. All right, that is how we used the germ cell data for the mouse.
DR. BISHOP: I am sorry, Julian, I missed that, what germ cell stage was
created for the mouse here?
DR. PRESTON: Because of the nature of exposure, it covers the whole of the
spermatogenic cycle. The first matings were done at seven weeks, and I do not know
the exact dates after that that those animals, or the mutants arose, but, it was from
exposure of the whole spermatogenic cycle. So, you cannot define which cell stage
would be affected, or the germ cell's phase relevance. That is important, but we felt
that this was a particularly useful data set, because it approximates a human exposure
scenario.
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A parallelogram for predicting mutation induction in
human gene cells following ethylene oxide exposure
Mouse Human
Germ Cells ^ Germ Cells
Mutation Frequency Mutation Frequency
Somatic Cells ^ ,. Somatic Cells
(splenocytes/lymphocytes) ^ ^ (lymphocytes)
Mutation Frequency Mutation Frequency
Figure 4-8
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But remember, also, that there are a number of assumptions. I am trying to
minimize those, but I think these are going to have to be made, whatever data set is
available. So, with the somatic cells for the mouse, and it is based on the doubling
dose that we had calculated in the previous figure I showed (Figure 4-7A), there are
about four mutants per 110 ppm hour per week. This simply converts to 1 x 10*9
mutants, per locus, per ppm hour, for these mouse somatic cells. These are mutants,
rather than mutations but we felt that, again, this was a small compounding factor.
And here (Figure 4-7B) for the human somatic cells, again, based on a
doubling dose calculation, we come up with an estimated mutation frequency of 1.1
x 1O"8, mutations per locus, per ppm hour. I put the 1.1 in just to make it marginally
different from that for the mouse. It was rather remarkable; rather I think that it is a
fluke, not necessarily a measure of the absolute sensitivity equivalence between the
two.
What we used these data for was not for absolute numbers, but rather to
suggest that in order to extrapolate from mouse to human a number, a sensitivity
factor of one would be appropriate. We have to make some assumptions for
converting from mouse germ cell to human germ cell. So, we assumed that from the
somatic cell data, for the HPRT mutation, under these particular treatment regimes,
and the various assumptions, that we could use a sensitivity factor of one. So, if you
feed that into a human germ cell, the unknown in our parallelogram box, we assume
that equivalence of sensitivity, as I just pointed out, with the mouse germ cells, based
on the somatic cell mutations.
For the human, we assume 2,000 dominant loci, and of those 2,000 we had
to assume something about the phenotype. So, we assumed that each one was
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giving an independent phenotype. So that our loci represent 2,000 different
phenotypes. That might well be inaccurate, but that was the best that we could do.
These loci, identified as dominants, are represented in McKusick, for example, as
independent gene mutations.
So from the mouse data that I presented in Figure 4-7, we had a mutation
frequency of 4 x 10'10 per locus, per ppm hour. And so the human would be
equivalent, for per locus frequency, for the total number of dominant mutations. You
have 8 x 10'7 dominant mutations per ppm hour.
That is just, a "flavor type" of number; it is not to be taken as an extremely
accurate number, but I think it gives the types of data sets that are available, and what
you can do with those. I think that we felt that that was a reasonable approach to
take.
Lars Ehrenberg did a quick comparison with radiation numbers, and it would
indicate that the ethylene oxide number here for dominant mutations might be of the
order of ten to a 100 times higher than a radiation equivalent mutation frequency on
a per rad basis.
As I say, the approach that we took, based upon the data we had available,
realizing that we had four dominant mutations that we utilized in the mouse study, and
the HPRT mutation in somatic cells, we finished up with a decision that the
parallelogram approach could be utilized on those data. And of course we are
certainly open to discussion on the approach.
We were trying to see whether there were other methods that would be better
used to make comparisons between somatic cell mutations in humans and mice, but
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we were not totally happy. Thus, we used the HPRT mutations but were not sure that
this was truly representative of somatic mutations as a whole.
We had a fair amount of discussion on the feasibility and appropriateness of
using the relationship between tumor incidence as a measure of relative sensitivity on
the assumption that the initiation of a tumor, at least, is modeling mutation induction
in somatic cells. So, if you assume that the initiation event for tumors is a mutational
event, can you use tumor incidence? Specifically, we discussed the use of leukemias
on the assumption that that same tumor type, the leukemias that you would see in
humans, say from an ethylene oxide exposure, really is the same tumor type in a
mouse. Just because it is called a leukemia does not mean they are exactly the same
tumor type. We made the assumption that they might be, and we chose leukemias
because the mechanism of formation of leukemias, in terms of number of steps, is a
lot simpler than many of the solid tumors.
We discussed this approach and we came up with one that is really a form of
a direct method, using the rad equivalents as the dose term. We discussed the
appropriateness of doing this and, we think it might have some merits. We did not
feel, in our discussions, that we really wanted to replace our somatic cell mutations
with the tumor incidence, at this point, because I think, in general, as this is our
consensus, we felt there were maybe more assumptions than we would have to make
to the somatic cell mutations approach.
However, we wanted to present this, as it was a fairly lengthy discussion, and
we think it is worth looking at further, because you will see, as an answer to a couple
of the questions that were posed, maybe tumor incidence, or tumor formation might
be an appropriate endpoint to address other issues.
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We did discuss the topic of the spontaneous abortions in hospital workers
raised by Dr. Sorsa yesterday. The only data that were available to us, in fact, were
the 1982 publication of Hemmlnki, we did not have the other data that Marja Sorsa
presented yesterday, or rather mentioned yesterday. So, we discussed the
appropriateness of that particular data set on spontaneous abortion for risk
assessment. We all agreed that it is clearly an important component of genetic risk;
but as to its utility in genetic risk assessment, at this point, we could not establish
what component of the endpoint was genetic, what component might be zygotic, a.
la Generoso discussion yesterday, and what component is what I described as a toxic
response, a cell killing response.
So, we felt that there was need for further information to be able to incorporate
such data into one component of our risk assessment. And if it is not a genetic
component, then you still need the model, as we suggested, for the zygotic data, as
a separate assessment of risk, and take It out of the genetic risk department. We
would like to establish these factors a little more clearly and maybe utilize other data
sets that might be available.
Then we entered into a discussion around the questions that were posed by
the organizing group. Without going through all of them, since we clearly addressed
a number of them, by utilizing the parallelogram approach, by selecting a particular
data set, by pointing out assumptions: What additional data do we need? Well, we
want to remove the assumptions. What data do we need? We need more mutation
data. What data do we need for somatic cells? We need additional mutational
endpoints. So, we obviously answered some just by the assumptions that we had to
make.
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However, we obtained some additional information that relates to the
questions. First, what was needed to enhance dosimetry data, and what were the
major uncertainties? And with ethylene oxide, there is dearly a need for better
exposure data; there is not only a need for dose data, but exposure data needs to be
much better defined. That is a very important component to the sort of extrapolations
we were doing, because we were basing our frequencies of mutational endpoints on
ppm hours.
Also there was a need for establishing a relationship between DNA dose,
which one could equate with adducts, maybe specific adducts, and a response in the
target cells. We have very little information on the germ cell stages. This is not only
unique to the dosimetry information because we have a lot of gaps in biological
endpoints in the targets cells. But we did identify those as areas that clearly would
help in the genetic risk assessment process.
A second question relates to the assumptions that are made for genetic risk
assessment approaches. Again, I used a number of those in our calculations, but
these were sort of the highlight ones. A major one is the equivalence of somatic to
germ cell for sensitivity. I pointed out that we decided that the mouse and the human
had equivalent sensitivity to HPRT mutation induction. In somatic ceils we simply took
that as an equivalence in germ cells, while we do not really know whether that is true
at all. We had to use some approach, and there are no data to impact negatively
upon the utility of equal sensitivity.
Are the mutations assayed representative of the genome? Well, the mutations
in the somatic cells were just HPRT mutations. We have a lot of information on HPRT,
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and it is appropriate for this type of study, but you need to know if it is representative
of other regions of the genome.
In the germ cells we considered dominant visible mutations. Clearly it would
be important to have other types of mutations, not just that small set. And maybe
something that has a dearer definition of genotype to phenotype. This is what we
had available, and we are not sure that the 50 loci is a good firm figure. I think Susan
Lewis would say it has got quite a bit of slop in it. We would like to have something
that was better defined as a relationship between number of genes and phenotypes.
And then at low doses and low exposure rates, the cell stage of importance
for a cumulative dose estimate will be the stem cell. And we think that it is important
to look at stem cell effects under these other exposure conditions, particularly low
dose and low exposure rates.
Moving to the next question: What research is required to address the major
assumptions? Well, they almost fall out of our parallelogram approach, but there were
some additional ones that relate to this data set and other assumptions that might be
made.
One issue that is important is to look at some retrospective dosimetry, and that
is an area in which Natarajan is particularly interested. Because we are now in a
position to look at stable chromosomal aberrations, particularly reciprocal
translocations, using the in situ hybridization method, retrospectively. These are
stable aberrations which are transmitted through multiple somatic cell generations.
So the opportunity to do retrospective dosimetry allows for the endpoint, one of the
endpoints in germ cells, the translocation, to be related to the somatic cell event
rather directly.
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We felt that rather little attention is being paid to aneuploidy, which clearly is
a genetic risk feature. We really did not have the data sets upon which to work. Part
of that is due to the fact that it needs to be determined if the mouse is a reasonable
model for aneuploidy. It is pretty tough to demonstrate aneuploidy in the mouse, in
the offspring, certainly from exposure of the germ cell. There was some discussion
of this, and whether other models were more appropriate. In particular we had
discussions of: What is the mechanism of aneuploidy here, and in what areas did we
think that chemicals such as ethylene oxide would impact upon induction of
aneuploidies? So, we reached a generally, what I would describe as a holding pattern
on aneuploidy. We think it is well worth considering additional studies from
reconsidering the mouse as a model, or to utilize Natarajan's favorite, the Djungarian
hamster. There is rather little data available on the offspring of the Djungarian
hamster.
Several mutations, rather mutational assays, should be used, including, I
emphasize, relevant ones. I do not mean to imply that the type of information we
used from HPRT was irrelevant information; it is Just that there is a question whether
one should assess mutations which would have a specific human impact in our germ
cell assessment. One needs to look at additional mutational assays to see whether
the sensitivity is equivalent between the human and mouse somatic cells as the HPRT
approximately was.
We need to look at more mutational data in germ cells at all stages of
spermatogenesis and oogenesis. This impacts upon the last question, which is:
What is available for assessing genetic effects in female germ cells? We did not have
any data set to work with in the female germ cell. We could make estimates of a
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lower sensitivity than the male germ cell, and anywhere up to equal sensitivity. For
heritable translocations, as I said yesterday, the female germ cell is probably rather
insensitive, certainly in the oocyte.
What is needed to fill in these assumptions, or data gaps that we have? There
is very little, little or nothing on chronic low-dose, chronic exposures and low-dose
exposures, for somatic or germ cells. And that is clearly needed. That is the
exposure scenario we are interested in. You really cannot model, with chemical
agents, from acute exposures to chronic exposures. It is a little easier with radiation,
because of the definition of dose and the physical characteristics of radiation.
Another area that really impacts very heavily upon both extrapolation and
genetic risk is repair in germ cells, and how it impacts upon the effective dose and
the response. The repair of the appropriate lesion in DMA, a DMA adduct or strand
break, or whatever one is interested in, the repair of that will impact upon the effective
dose, and the response, be it a chromosomal event or a mutational event.
An oft-repeated question is: There have been no human germ cell mutation
inducers demonstrated: how or what are you going to do about it? The question
really is: How do you demonstrate an effect? We had some discussion about the
difficulty of actually demonstrating an effect, since you are looking at a low frequency
event amongst a fairly high frequency background. And we discussed the likelihood
that the response one would look at in an F,, is probably a cancer one, that we would
have the greatest chance of being identified, rather than a birth defect. We can
expand on that if appropriate.
There are some indicative studies, particularly from epidemiology and these
need to be pursued. And I think that I am not going to really enlarge upon those too
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much; I think Mike Shelby is discussing a little bit of that this afternoon. They are only
indicative, but we think that those are well worth looking at a little more closely to see
whether we really have an opportunity to demonstrate germ cell effects in humans,
or identifying germ cell mutagens.
So, the final question on our list for consideration was: What would be the
ideal data set for assessing genetic risk? Although the ideal data set is not the one
we utilized in our calculations, it is the ideal one for us because it was the one
available. It is not very difficult to come up with the ideal data set, as we all agreed.
The ideal data set is one that has all four corners of the parallelogram in it (Figure
4-9), and whether you have the germ cell and the somatic cell data, either direct or
by using epidemiology as a surrogate. Obviously if you have direct data for all four
points, it is easy to put arrows on both ends of the extrapolation arrow. So, I put
arrows here on both ends, because I felt that if you had all four corners you could do
that.
So, we know what the ideal data set is, we know that if you remove all the
assumptions, and collect all the data that was indicated, I suppose you finish up with
this (Figure 4-9).
That is a summary of our deliberations. We felt with ethylene oxide that we
had at least a data set that we could take some liberties with for extrapolations, and
we do appreciate the fact that there were liberties taken. But I think the end product
was quite enlightening to us. It certainly has perked my interest. That is my
presentation on behalf of ethylene oxide.
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The "perfect" data set for genetic risk assessment
by the parallelogram approach
Rodent
Human
Surrogate
epidemiology
Figure 4-9
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Discussion
DR. LEWIS: I just thought I would mention among those data which we did
not really consider from our experiment, that one of the loci that was affected by
ethylene oxide was one that does give rise to a human genetic in-born error. So, with
certain systems you can actually measure induction of human related disorders with
chemicals, and this was one of them.
DR. PRESTON: I should have mentioned in that same sentence that Susan
mentioned her own data, there are two other mutations that were observed. These
were electrophoretic variants that we did not consider. If they were seen in the same
number of offspring, and that, maximally two, were observed, then the germ cell risk
would be half that which we have presented for the dominant visibles. But we did not
consider these because we thought to use four was a bit brave. We felt that
estimates of risk from two was perhaps a little foolhardy.
DR. ANDERSON: I was very interested in what you said about the cancer
endpoints. Would you consider that for genotoxic carcinogens, that cancer could be
considered as an endpoint in the germ cell risk assessment?
DR. PRESTON: We were particularly considering the Nomura data on the
process that he has presented for radiation and for chemicals, where exposure of the
parent did result in increased cancer instances in the offspring. Yes, we felt that
maybe, it would be simpler to detect Ft cancer outcomes, or F, sensitivity to cancer
production than detecting an induced birth defect.
DR. EHRENBERG: There have been experiments, you know very well, all of
you know, Muller's experiments, there are several Russian studies published and
ongoing, proving increased cancer incidences in the offspring, second, third, fourth
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generation, after treatment with radiation, mostly radiation, but occasionally mutagenic
chemicals. Considering diagnostic techniques to detect some effect, leukemia and
some other cancers for example, the technique is so uniform and well diagnosed. I
think this is one of the endpoints one could really look for in humans. This pertains,
in a way, to the Sellafield discussion.
Besides that, if you compare the risk estimates, the ICPEMC group made
some six or seven years ago, with respect to magnitude of a genetic risk, it is a very
large response, per unit dose, which you have found in the animal tests, looking for
cancer related induction. It is much larger than the other genetic endpoints specific
locus mutations, etc., etc.
DR. PRESTON: That might be on the edge of the view that we took, but I think
it is representative of our discussion.
DR. SELBY: I would certainly be of the view that cancer endpoints would not
be the useful ones to look at. The major problem is that for many of them, heritability
is very low. If you look, for example, at what is known about human genetic disease,
there are very large numbers of hereditary disorders that have high heritability. And
many of the cancers would have low heritability, which would make the studies more
complicated. I would tend to think a much better approach would be to use a cohort
study of an exposed and unexposed pattern, somewhat like what was done in
Hiroshima and Nagasaki. I do not think here is the proper place to go into long
detailed discussion of the application of the high frequencies found by Nomura as to
what this might imply as far as future research directions.
If some of you would be interested in seeing Court transcripts, or the Judge's
summary they can be distributed.
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DR PRESTON: I think that is an important point of discussion. I would point
out that the induction of cancer is not necessarily what was meant here. It is the
induction of genetic alterations that increase the propensity to get cancer. So,
Nomura is one end of that. Mutated genes, any repair genes mutated in any of those
increase the propensity. I think that might show up as a cancer at this point not a
genetic birth defect.
DR SKOPEK: You mentioned that HPRT might not be a relevant biomarker,
and I agree, but there is also another layer of complexity with our studies as well.
And that is, as I mentioned in our working group, the actual number of mutations you
get at the HPRT locus in the animal model is very strongly a function of the age of the
animals when they are treated and the time of sampling. And just by changing those
two variables you can adjust those numbers by an order of magnitude, easily.
DR. PRESTON: We did select the 20-week expression time because we felt
that was more representative of the human situation, and it might not change much
beyond 20 weeks. And we took that rather than the eight-week exposure.
DR. SKOPEK: But if you treated young animals the numbers would be higher.
DR PRESTON: That is right, I agree.
DR. GENEROSO: I do not mean to contradict the conclusions of our group,
but I think it is important to do so because the issue is very important. And I think it
is important to express my dissenting opinion in our discussion.
First of all, what I said was, my gut feeling, that it is going to be extremely
difficult in any human population, considering our experience with Hiroshima and
Nagasaki, and with the myriad of studies that have been filed, the most recent one
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being Sellafield, to come up with cause and effect for increased mutations in human
populations.
The other point that I made, and of course Lars has one opinion on this, and
I would like to hear other opinions, is that the cancer rate in the children of Hiroshima,
Nagasaki exposed population seems to be an obvious thing to measure. What is the
experience in this? This seems to be a good cohort to start with the possibility for
using cancer in our genetic epidemiology.
DR. SELBY: The extensive studies in Hiroshima and Nagasaki looked at many
endpoints, one of which was cancer in children, and specifically leukemia. And there
was no indication at all that there was an increase. In fact, if you looked at the area
that is considered to have people that were exposed, and look at their children
compared to those outside of that circle, the point estimate is slightly lower for the
exposed.
DR. PRESTON: We could discuss that, cause and effect, and all of these
comments were ones that did arise. We did also point out that the exposure, of
course, was an acute exposure.
DR. GENEROSO: Just one more point that I think is important to bring up.
And I think, while I do not believe in our lifetime that we are going to be able to come
up with a smoking gun in genetic toxicology, I think we ought to really think and
develop another strategy. We all know that genetic toxicology is important. We all
know that chemicals can induce mutations that have some effect. And I think we
ought to develop another strategy, and I do not know what that should be. We need
to answer, or to counter, the question of genetic risk in humans from chemical
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exposures without resorting to discussions of the significance or importance of
genetic toxicology.
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Acrylamide (AA)
Dr. George Douglas was the rapporteur for the Acrylamide group. Dr. Douglas is from
Health Canada, Environmental Health Center in Ontario, Canada.
Initial Report on Acrylamide
DR. DOUGLAS: The acrylamide group had the advantage, I suppose, of
having a look at the ICPEMC Report on acrylamide that was prepared for Health
Canada, and, in brief, we decided that there were appropriate studies that we could
use in modeling separately risk for both gene mutations and heritable translocations.
The Ehling and Neuhauser-Klaus paper was used for gene mutation risk assessment.
There were two groups producing heritable translocation data: Shelby et al. and llse-
Dore Adler. The ICPEMC Report will be used as a basis for the risk assessment
modeling. Although, in some cases we used different values for exposure. We used
both the doubling dose approach, and the direct method. I think one thing that
became evident from our discussions was that the doubling dose approach really
assumes a linear response, because in the formula, there are two points, the
spontaneous and a data point, and so it results in linearity. In the case where there
is obvious curvilinearity in the dose response, which appears to be the case in Use-
t
Dore's data, we are limited by using single data points from that curve. Therefore, we
have elected to derive different doubling dose estimates for the different data points
in the dose response. In addition, we are using a modification of the direct method,
which we are going to call the modified direct method. We not using precisely the
direct method as applied in the UNSCEAR reports, where there are dominant mouse
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mutation data available in the calculation. In our case, we only have recessive mouse
data with which to estimate dominant human data, so there is a broad assumption
that we are making, and so it is not, strictly speaking, the direct approach, In
addition, later, when Kerry Dearfield returns to the EPA office, he is going to use their
computer to derive some curve fitting models to apply to the risk assessment
calculations. We are going to use the default risk extrapolation factors that you heard
about in Dave Brusick's talk. The exposure data are those that were outlined as well
in Kerry's presentation, in particular the occupational exposure by inhalation in sewer
grout workers, that the EPA collected. As well, we are going to be making risk
calculations for ingestion and skin absorption. There was some talk of whether or not
there was a partitioning of effects involving different metabolites, and whether
glycidamide was the metabolite causing gene mutation effect versus acrylamide
causing heritable translocation effects. We have not really worked that one to any
satisfactory conclusion. With regard to the parallelogram approach, there is really no
human data available, other than the Chinese hemoglobin adduct study, that will
permit us to use the parallelogram approach. I think we will be using the
parallelogram model to delineate what additional tests are required to understand the
risk involved from acrylamide. We are going to do a lot of different calculations. Have
I missed anything?
Discussion
OR WATERS: How are you planning to address the issue of the contribution
of glycidimide versus acrylamide and the two types of endpoints?
OR DOUGLAS: We do not know.
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DR. DEARFIELD: I just have one question from what Julian presented. This
is a comment that related to what Udo said yesterday, as well, talking about the
number of loci that we need to consider in our calculations, whether we are going to
be using the default value of 1,000 that the ICPEMC paper is presenting; but I noticed
that you all put down 1,800, so, we all are going to be trying to do the same
calculations. I think as a whole group, we need to figure out which type of default
values that we are going to use, if we are going to do similar calculations.
DR WATERS: I think Dave pretty much Indicated yesterday, that that 1000
figure was just the beginning of their thinking and Udo's point yesterday was pretty
clear.
DR. EHUNG: Yes, but I mean for the calculations, for the exercise, 1,000 was
a wonderful figure.
DR. WATERS: Okay, scratch that, we will make it 1,000, but somewhere in
there, put a footnote, I think.
Final Report on Acrylamide
DR. DOUGLAS: I must say it is always a pleasure to work with the group of
people present here today, and the extended group that you represent. Our group,
the acrylamide group, consisted of Kerry Dearfield (Chairman), Martha Moore, Udo
Ehling, Dave Brusick, Fred de Serres, Gary Sega, and myself. I think, as you recall
from Dave Brusick's talk, and in my preliminary report of what we were doing in the
Acrylamide group, that we are going to rely on the risk paradigm developed in the
ICPEMC report; nevertheless, we diverged from that considerably, in that we used
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other exposure estimates to factor into the equation. This table summarizes the
information.
Table 4-1. Proposed Scaling Factors (REF) for Mouse to Human Risk
Extrapolation
Parernetefs
Locus Specificity
DNA Repair Variability
Metabolic Variability
Dose Rate Variability
Exposure Route
Germ Cell Stage Specificity
Dose Response Kinetics
Experirnentsiy DuUMiiikMKJ^
Compound Specific
Compound Specific
Compound Specific
Compound Specific
Compound Specific
Compound Specific
Compound Specific
Defauk Factor
2 to 5 (assumes human sensitivity
0.1 (assumes mouse sensitivity)
1(70*)
0.1
.05
1
1
• Each factor that is experimentally determined for the compound under assessment will be used.
If compound specific Information Is not known, the appropriate default factor will be used.
* If metabolism and detoxification are dependent upon glutathtone transferase (Bell et al., 1992).
We have exposure through three different routes: 1) ingestion through drinking
water; 2) two sets of exposure data on inhalation (the EPA grout worker's study with
low and high exposures, and also the OSHA PEL for aery lam ide). We have a dermal
exposure estimate, as well, from the same grout workers study conducted by EPA.
All the exposure numbers have been converted to milligrams per kilograms per
day, for convenience of comparison. We calculated risk numbers for gene mutations
and chromosomal aberrations separately. The gene mutation numbers were derived
from the study by Udo Ehling, and are based on a single dose point (which is one
thing that we regard as a serious deficiency in the kind of data that is available). We
have some germ cell data, which are hard to obtain because funding is definitely
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inadequate. We really need to have appropriate dose-response data for gene
mutation, similar to the heritable translocation data.
There are two estimates for gene mutation: the doubling dose estimate, and
the modified direct estimate. At the 100 mg\kg dose, the gene mutation doubling
dose was 53. This was used with the different routes of exposures to derive risk
estimates.
The direct estimate for gene mutation risk does not bear much resemblance
to the doubling dose method estimate. There seems to be, in this case, a wide
discrepancy in the estimates. And, as you will see later, for the chromosomal
numbers, the direct and doubling dose estimates are much closer. One reason for
this difference could be that the number of mutant human mutations leading to
measurable dominant detrimental effects, which is a key variable in the direct method
equation, may be over estimated, leading to an over estimate of the risk. The
magnitude of this variable, and the equivalent one for chromosomal disease, needs
to be considered carefully.
For chromosomal aberrations we made three different sets of estimates. One
is based on the Shelby et al, study which gave a doubling dose of 0.1. As you can
see, the risk number are amazingly close in comparison to the direct method estimate.
We made two estimates from Ilse-Dore's data. We used a single point
estimate of 50 mg/kg, which yielded a doubling dose of 3.3, and with the associated
risk numbers. And then we also used a doubling dose number that Ilse-Dore gave
us that was based on curve modelling of the three point dose response curve. As
you would expect, the people at the highest exposure level have the highest risk. 531
new genetic diseases per million people exposed is a very high number. If we are
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going to start using these numbers we are going to have to explain them, and be able
to justify them. We have to be able to tell regulatory managers that the numbers are
meaningful. I, personally, am not totally comfortable yet with these large estimates
of risk.
Now, I should point out that not included in these numbers is any factoring of
tissue accessibility, which is something that was a consideration in the ethylene oxide
risk analysis that has been published by EPA. As well, female exposure was not
considered at all. The numbers are based on daily exposure, they are not based on
time-weighted averaging, or reproductive life span of the people exposed. However,
I would say the default factors that we used are, as assumptions, entirely valid. They
are based mostly on assumptions that are fairly well established in the toxicological
community, at least the ones that relate to exposure route and dosage and things like
that. Obviously we have some work to do in the genetic related areas here, in
particular.
We made an attempt to answer the questions posed to all groups, and as all
the other groups probably found, did not have enough time to go into detail for some
questions.
Which are the most important studies available to support genetic risk
assessment activities? We found, and I alluded to this earlier in my presentation, that
tests which give quantitative data, and dose-responses are required. I think that we
all recognize this need, but I think that we also recognize that the costs of the germ
cell tests are increasingly expensive, and that the budgets available are diminishingly
small. So, I think we need to ensure that funding continues, and increases, because
if we are really going to do modelling we need dose-response data. And in the
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acrylamide data set, we are really lacking data on human exposure to permit us
empirically to determine what these extrapolation factors are. This is really PB/PK
modelling, which just means using toxicology data to fine tune risk extrapolation
factors and minimize assumptions.
And we really should not forget about in vitro data, because it really helps us
model methods in a cost effective way that really cannot be done otherwise.
However, we have to keep in mind the limitations of extrapolating to in vivo
conditions.
What endpoints, including, other than genetic are considered adverse affects?
Obviously gene mutation and chromosomal mutation, Including aneuploidy, are the
key events. I think that other indicative endpoints in the reproductive effects field are
also relevant; but if you are thinking of these kinds of effects In a genetic context, you
have to, in your studies, elaborate the exposure/conception history of the people you
are studying. In other words, you have to show a transmitted effect that is genetically
based. If you are studying exposed males, you have to ensure that the male is
exposed prior to conception, that the chemical to which he was exposed to was not
transmitted in the semen, and that he does not cany it into the home and expose the
spouse post-conception and create a teratogen effect. I have talked on this subject
previously, and described the concept in a paper in which nobody was interested.
DR. DEARFIELD: What was the name of that journal we used?
OR DOUGLAS: It was in Mutagenesis, 5:421-423, 1990, if anybody is
interested.
DR. DEARFIELD: Have you got any reprints, George?
DOUGLAS: I have lots!
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Data gaps? We really think that in the parallelogram paradigm, the most
under-represented part of the model, in terms of data, is the human somatic cell
corner. It is essential, in these kind of studies, to have the appropriate dosimetry,
preferably target tissue dosimetry. And also, where possible, to relate this to germ
cell responses. These need not necessarily be heritable endpoints, but could be
indications of biological response in germ cells.
Is the necessary dosimetry available? In the case of acrylamide, no. To fulfil
the parallelogram model you really need to have the same biological endpoint
dosimetry in both animals and human somatic cells, or germ cells in vivo.
Unfortunately, this is something that in most cases is really unachievable. And of
course, the biggest deficiency in any risk assessment is human exposure.
What are the major assumptions in genetic risk assessment? Probably the
only one that really matters is: mouse equals man. If we do not have the answers,
we make a lot of assumptions, in toxicology we may think we know a lot, but we
know less related to the genetic endpoints. This is where the upper line of the
parallelogram can help us.
What are the major uncertainties in quantitative, in risk assessment? After a
short discussion, we came up with the following. We understand a bit about the
process of mutation, and can make some fairly comfortable assumptions about risk
extrapolation factors; but we know very little about the complexity of the responses
of different loci, and how different individuals, human and animals, respond to toxic
insults at the organ and cellular level. These are all critical if one is really going to
make informed estimates of risk.
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What research is required? Research on finding a human mutagen is needed.
Frankly, it is seemingly an impossible task, and I think we need to look at this in new
ways. I think Diana Anderson had the right idea in terms of rethinking our concept
of what is a suitable endpoint. Are there other things we can look at? You might
even want to read my Mutagenesis paper mentioned earlier. Probably the biggest
problem we have is funding, and I think this is a perception problem among funding
bodies. There is some inertia there among those that provide funding, because they
do not see ft as a problem.
Now, I would say that in my Department the lack of a human mutagen is more
of an annoyance than a complete roadblock. It is not the problem. For classification
of Priority Chemicals, germ cell mutation is a parallel endpoint to carcinogenesis. We
have a rating scheme similar to the (ARC scheme for carcinogenesis, and one with
virtually parallel wording for germ cell effects. The only difference is that if we have
a Class I or Class II carcinogen (a human carcinogen, or probable human
carcinogen), the chemical will be declared "toxic" under the terms of the legislation.
Whereas, if it is a Class I or Class II germ cell mutagen, ft mav be declared toxic.
There is a distinction between "will" and "may" that indicates the impact of the lack of
a human mutagen.
What is the priority? Well, to find a human mutagen is a priority. But we
should not stop at that. I think that we need to not let the lack of this human
mutagen inhibit us from moving ahead.
Is the parallelogram approach applicable to acrylamide? Well, yes and no.
Conceptually ft is a very useful tool to focus our efforts; ft has become so much a part
of the way we think that we use ft all the time, without calling ft a parallelogram, ft is
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an ideal to strive for, and it tells you where your data gaps are, but lack of all the data
needed to fill 3 of the corners does not prevent you from estimating human genetic
risk from animal data (that is, the fourth corner).
How would you address the problem of human cell mutagens? Just let me
say that I think that one would be cynical to think that humans are that much different
from animals in the way the germ cells are affected.
However, I think that one could do studies short of human epidemiologies!
studies to detect increases in genetic effects in offspring in order to sort that out.
Human germ cell studies on sperm, such as the work of Andy Wyrobek will, perhaps,
give us some clue to the effects that are transmissible in human germ cells.
Nevertheless, I think the assumption that mouse equals man is a reasonably
comfortable one for me.
Discussion
DR ADLER: Since George has mentioned Andy Wyrobek's work, and also
Renee Martin's is important in this respect, I thought that we all have to think about
an extension of the information that we get from the parallelogram. And particularly
in terms of genetic risk assessment, we need to get away from the somatic cells as
we try to use the data as they are available. We found all sorts of problems with
relating somatic cell mutation data from rodents to humans, splenocytes, the other is
using lymphocytes, and all sorts of problems. But if we use sperm samples, and
particularly in the exposed populations, both of animals or humans, as long as the
samples are taken within the exposure period, we are likely to be able to measure
adducts, mutations, aberrations, aneuploidy; and when we have a common cell type
318
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that we look at, we can relate sensitivity correlations. The parallelogram, then, would
boil down to the lower part of this chart where we compare sperm to sperm, and we
estimate, or we experimentally determine the effect in progeny in rodents, and we
estimate the human risk.
So, that gets us a bit closer to where we actually need to be, including germ
cells.
319
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320
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1,3-Butadiene(BD)
Dr. Marja Sorsa was the rapporteur for the 1,3-Butadiene group. Dr. Sorsa is from the
Finnish Institute of Occupational Health in Helsinki, Finland.
Initial Report on 1,3-Butadiene
DR. SORSA: Well, our small group was eagerly discussing the problems, but
I think we very quickly ended up in realizing that plainly we have to be discussing
data gaps. There are special problems with butadiene. Most of the data is just
coming in and therefore after a couple of years, we would certainty know much more.
A special problem with butadiene, that already has been discussed here, is its
metabolism and the specific metabolic features in the animal species as compared
to humans (Figure 4-10). The genetically important metabolites, if we begin from 1,3-
butadiene here, are circled in the figure here. We have mainly been discussing the
monoepoxybutene and diepoxybutane, the activity of which in various test results
seems to be from 10 to 100 times higher than that of monoepoxybutene. There are
additional genetically active metabolites, as well, (for example, crotonaldehyde, and
also the 1,2-dihydroxy-3,4-epoxybutane). Dr. Siv Osterman-Golkar informed us that
there is an additional suggested metabolite, obviously also active genetically, which
must be there, since in humans exposed to 1,3-butadiene, this specific M-1
metabolite, the 1,2-dihydroxy-4-(N-acetylcysteinyl)butane has been observed. So when
going to the parallelogram possibility, we realized, right from the beginning, that we
are dealing with a compound with a specific metabolism. The parallelogram approach
321
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Specific differences in the metabolism of butadiene in vivo
CHj-CtWH-CH,
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Compounds endosed in boxes have been identified in vivo as
metabolites of butadiene; those that have been identified only
tentatively are enclosed with broken lines. 3SH, glutathione
s-transferase; EH, epoxide hydrolase
Metabolism of 1,3-Butadiene (Henderson et al., 1993)
Figure 4-10
322
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can only be taken using similar endpoints. So we first went to see what is known
about the cytogenetic endpoints, concerning rodents and concerning humans. We
realized that so far, and there is one published and two unpublished studies, which
all seem to confirm that at the exposure levels of the manufacturing industry, which
are generally below 3 ppm level, none of the cytogenetic endpoints gives response.
So, this is difficult then to continue with the chromosome damage parallelogram. We
went through the available cytogenetic data, which clearly suggests that in mice, the
lowest effective doses are surprising low (Figure 4-11), so that both for micronucleus,
as well as the sister chromatid exchange endpoints in vivo only 6.25 ppm for a week's
exposure is needed to get a positive response. Micronucleus is obviously the most
sensitive endpoint. In rats in vivo, the only data which suggests a positive response
so far, seems to be the sister chromatid exchange, which is giving an effect at 500
ppm level, but going as high as 1300 ppm with chromosome aberrations or
micronudei; there is no positive response in rats.
DR. EHUNQ: How long was the time?
DR. SORSA: Five days, two weeks, six hours per day. Then to the germinal
data, which actually were important in picking up this compound for the exercise for
parallelogram. We have dominant lethal mutation data from mice, from two exposure
levels and two different laboratories, so that this data is .obviously true. The heritable
translocation experiment is ongoing, at the same exposure level as Dr. Adler's
laboratory.
DR. SORSA: I think, at present, it is too early to really judge the significance
of the male mediated congenital malformations; it is a warning sign and I think this
experiment certainly is something which must be repeated. From the cytogenetic
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Mice: LED 6.25 ppm
- Lung most sensitive
- Heart angiosarcomas
Rats: LED 1000 ppm
- Mammary gland tumors
Humans * Cohort studies showed a slight
excess of lymphatic/
hematopoietic cancers or no
excesses
* Nested case-control study
showed large excess of
leukemia
(ARC: 1987:Group2B
1992:Group2A
Figure 4-11
endpoint, I think it is going to be, at present, extremely difficult to continue the
parallelogram. Point mutations in vivo, give a little bit better chance. In rodents, we
have two studies, (Figure 4-12). Both are just going to be published, but fortunately
we had Dr. Cochrane's data available in the working group and then we have data
from Dr. Tates; both are positive in mice for HPRT mutations (Figure 4-13). In Dr.
Skopek's and Cochrane's data, the lowest effective dose was also the lowest dose,
and actually could be even lower, but the points where the effect was measured, i.e.
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625 ppm. There is also positive mouse spot test data at 500 ppm and what is
important, as Dr. Adler was pointing out, was that in general, the experience tells us,
that there is a very high correlation with positive mouse spot test data, and specific
locus mutations. This would also urge the importance of specific locus tests being
carried out.
INTERSPECIES DIFFERENCES
• Metabolic rate of elimination of BD Is greater in mice than in rats and
that of BME in rats than in mice.
At steady state the concentration of BME is greater in mice than rats.
Tissue differences exist: a very high level of BME is detected in mouse
bone marrow.
Role of other metabolites?
Role of human variability and polymorphisms?
Major detoxification pathway in mice in glutathione conjugation.
Major detoxification pathway in rats, and obviously in monkeys and
humans, is epoxide hydroxylase hydrolysis.
Figure 4-12
325
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HPRT Mouse Splenocytes
Cochrane & Skopek 1993
1U
8
Or
i i
0 600
Dose (ppm)
Figure 4-13
326
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in the limited human studies (Figure 4-14), so far, the only possibility for us to
use the same genetic endpoint in mice and man seems to be the point mutations in
HPRT locus. This study by Legator, et al, from butadiene manufacturing is the only
study in humans; it is in press now. The problems with the study are that the number
of workers is very small and the additional problem is that the methodology used here
is the autoradiographic detection method (Figure 4-15). The good thing in this study
is, that the exposure assessment is quite well performed; the data base of ambient
concentrations in that facility suggest that the time weighted average values, do not
increase 3 ppm, so that the general ambient levels are between 1 and 3 ppm.
Additionally the biological monitoring measurements of the specific M-1 metabolite
were performed on the same individuals. This allowed the individual correlation to be
made, on the amount of the specific butadiene metabolite in the urine samples of the
workers and the HPRT mutation frequency measured as variant frequencies. The
correlation is extremely high.
HUMAN BIOMONITORING OF BD
HPRT mutations increased in correlation to urine metabolite
concentration.
Chromosomal aberrations, sister chromatid exchanges or micronuclei
are not associated with exposure at <3 ppm level.
HB-valine adducts of BME detected at level 1-10 pmol/g from BD
exposures 1-3 ppm; no adducts below 1 ppm.
Figure 4-14
327
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HPRT Humans
Legatoretal.,1993
10
6
,.1 I
pi f<
!§>"§ i
W (0
O-
- 2
130
1300
Metabolite (ng/ml)
Low number of persons
Autoradiographic method
has to be repeated!
13,000
Figure 4-15
328
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DR. SORSA: These are control persons from outside the facility and the other
symbols are the persons working in the butadiene factory in the production. All of the
subjects were non-smokers; this is also an additional strength of this study. The third
possibility for the parallelogram, which we discussed in detail, is the possibility to use
adducts; however, the DNA adduct studies are not yet available from humans. There
is a monoexpoxide adduct which has been Identified by post-labelling in experimental
animals in mice; diepoxide adduct standards exists, and we have good hopes to be
ready with the results in a short while. The possibilities which are available
concerning adducts of 1,2-epoxybutene with the N-terminal valine in hemoglobin, were
already discussed by Dr. Osterman-Golkar, and so the results suggest that in humans,
the adducts can finally be measured. Adduct levels in the range of 1-3 pmol/g globin
were detected among the same persons in the Texas manufacturing plant as the
HPRT mutations. No adducts were detected if the exposure levels are below 1 ppm.
Adducts have been detected also in mice and they are three to five times higher in
mice than in rats. I think I will stop now and go at this point, to any further
discussions.
DR BISHOP: Questions? Lars?
DR. EHRENBERG: Can you show the first or second overhead? One of your
overheads. What I wanted to discuss is these minus signs; they are discussed here
in terms of yes or no. They are all figures with a statistical uncertainty and a non-
significant effect or absence of a significant effect could always give you an upper
level of a possible effect at a certain probability level and then it is a question whether
this, under current exposure conditions leads to an acceptable risk or not. The risk
is below an acceptability level; this means that throughout, I think, and one should
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also draw the conclusions from the absence of significant effects. They are quite
useful.
DR. SORSA: But it does not help in parallelogram calculations, if we do not
have the levels which would give us the equivalent response in humans and animals.
DR. EHRENBERG: You might, under certain conditions, use the upper 95%
confidence level of a possible effect for a parallelogram comparison and in that way
arrive at the estimate for humans, which you may ask, is it acceptable. This concerns
all this problem.
DR. EHUNG: Of course you can go one step further and say there is no
negative, there is only a certain probability to exclude, and mostly with a very high
percentage in effect.
DR SORSA: Yes, I think this should be cited as a non-positive effect.
DR. BISHOP: Diana, you do not want to comment on the male mediated?
Da ANDERSON: We discussed the mechanistic thinking behind the male
mediated; we had quite a discussion in our group this morning, but it is possibly a
dominant effect, which is present in all dominant lethal studies, if we had looked in the
past and we have not and it occurs in a window. It does not occur at very high
doses, because the level of lethality is so great that you cannot pick up the response.
If you come down to a low enough dose and you dissect the animals just before they
come to term, you will pick up these congenital malformations; and to me, these are
effects which the human population would recognize, because you would not want
any of your children to have these abnormalities. They persist, children are born
sometimes with exencelphy or definitely with spina bffida; some children are bom as
dwarfs and you can pick this up. So those are real effects that persist into adulthood
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or certainly dwarfism persists into adulthood and spina bffida children exist for some
time, so those are real genetic events transmitted through the male. They are not the
same as Waldy is talking about, although I accept all that Waldy said about his being
special epigenetic, but also indicative, maybe not genetic in the way we are thinking
about it, maybe genetic in the terms of switching on, switching off genes from cell to
cell; but this is transmission through the male germ cell and I just do not think we are
thinking enough about those sorts of issues. Look at how the cancer people and
other areas of toxicology make their risk assessments; I know it is not our business
at the moment, but I think we ought to think about it and this is a real mechanism
which we can use and the public would identify with. They identify with tumors, so
why cannot they identify with congenital malformations? They would, and in terms
of genetic effect it comes through the sperm and therefore it is a true inherited event.
OR. DOUGLAS: I wrote a discussion paper on this topic, mutagenesis to stir
up the pot a bit, that this should be considered in a human population study.
DR. ANDERSON: I just do not think we are taking it seriously enough; it has
been there for years in our data and we just have not explored it; it was just by
chance that we got interested in it. But It is a narrow window, that is all, because
otherwise you get death in dominant lethality, or you do not get death, you get these
malformations.
DR. DOUGLAS: I think that from a population epidemiology perspective, that
this endpoint is something that has been overlooked. I think that in some so-called
reproductive studies, where males have been able to partition exposure of males and
look at reproductive outcomes, that they sort of got at this idea, but they did not get
there with that intent and that end in mind; it was just part of a study that they broke
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up. I wrote a discussion paper in mutagenesis just to stir up the pot a little bit and
so far, it has been over a year, I have not gotten any reprint requests; so obviously
this is not foremost in peoples' minds and it did not stir up anybody's pot. But I think
that there is a lot there that we are not looking at that may be helpful if we are ever
to get data on populations, because I think, based on the numbers, the spontaneous
levels, that the studies are probably a little easier to do anyway.
DR. ANDERSON: May I make just one more point?
DR BISHOP: Sure.
DR ANDERSON: We are losing credibility as a population, in terms of doing
risk assessment, I think, because the public cannot identify with what is a heritable
translocation; I am not decrying anything that has gone before, but they do not
actually understand what it means, they only understand what it means if they see
things. If they see things like tumors, they know ft can effect them; if they see things
like congenital malformations, they know it can effect them. You talk about a
translocation or a specific locus, recessive mutations can take many years to come
about; I know if you got a coat color change, but we do not have those sort of things
in the human population. That is not meant to be funny or flippant, it is just what the
public can identify with. Now, I think we really ought to think about this as something
that we can handle. And if you see other areas of toxicology and what Kerry was
saying yesterday, our manager wants to know what this means, but predictions are
made all the time using numbers. And we are so pure, such purists as geneticists;
we do not have to be purists, we ought to be able to tell the public, "you ought to be
concerned about this", but yet nobody, as yet, listens to us properly. People listen
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in the field of radiation, because there have been methodologies worked out for years,
but not for chemicals and I think it is our responsibility to do something about it now.
Final Report on 1,3-Butadiene
DR. SORSA: Well, our working group was very small. It was chaired by Dr.
Ilse-Dore Adler, and the participants in the group were Dr. Ekkehart Vogel, Dr. Siv
Osterman-Golkar, then Drs. Cochrane and Skopek, who today appeared, and myself.
But the smallness of the group, I think, was reflected by the fact that there is not very
much material available to do a proper genetic risk estimation on 1,3-butadiene.
Since the previous Rapporteurs made some announcements, advertisements, maybe
I should also do the same. Much of the material which is available now was
presented in a meeting on butadiene and styrene on April 1993 and in a proceedings
publication will appear in the beginning of November 1993 with the (ARC Scientific
Publications number 127. Also there is a report of the happenings and the highlights
of the meeting in the November issue of the Journal of Occupational Medicine.
Very briefly, I will give background material from that meeting. The major
reason why the meeting was convened on Butadiene was that it is carcinogenic, and
I will very quickly go through that evidence.
Mouse is a very sensitive species, the lowest effective dose for cancer being
only about 6 ppm. Lung is the most sensitive target organ and there are some
extremely unusual cancers, also, for example, the hemangisarcomas of the heart.
Rats are far less sensitive, and that we already discussed yesterday. The main target
organ for cancer is the mammary gland.
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Human cohort studies have either shown or not shown a slight excess in the
lymphatic and hematopoietic cancers. The heavily discussed studies are the new
nested case-control studies, which seem to show a very large excess of leukemia.
And as you know, the updating of Butadiene by (ARC took place in 1992 when it was
upgraded into category Group 2A, a probable human carcinogen.
Also concerning Butadiene, as was discussed in the meeting, the species
differences were much discussed. There were several presentations on that, and the
evidence seems to be quite convincing concerning the metabolic rate of elimination
being the key issue and the biggest difference between mice and rats. And I think
we can safely state that the butadiene monoepoxide level is higher in mice than in
rats. Also, what is quite interesting, and personally I fee! should be a reason for future
studies, is that there seem to be metabolic differences between different tissues. Very
high levels of butadiene monoepoxide have been reported, for example, in the mouse
bone marrow.
The role of the other metabolites is still quite uncertain, and we discussed that
yesterday. Human variability and the polymorphisms involved are also certainly
^
something about which we know extremely little. And then, there is variability in the
detoxification pathway, as we already discussed. It seems to be so that the
resemblance of humans in this respect is more related to rats than mice.
Concerning the human biomonitoring data, and this is what we are going to
have as the basis in our extrapolation, there is only one study which shows that there
is an HPRT mutation increase among workers exposed to Butadiene, as I told you
yesterday. None of the cytogenetic studies shows any response.
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There are at least two research groups who have been studying the
hemoglobin adduct levels in relation to butadiene exposure: the group of Dr. Siv
Osterman-Golkar and the group of Dr. Hans-Gunther Neumann. The detection limit
of adducts seems to be at exposure levels exceeding 1 ppm, while the detection of
adducts at lower than 1 ppm levels has not been reported.
Now we move to the very brave and very inaccurate estimates of our working
group, which are based on the HPRT mutations in somatic cells, which is the only
endpoint available both in humans as well as in mice. The data (Table 4-2) derives
from a human study by Dr. Legator et al. (1993) and the mouse study by Dr. Skopek
and Dr. Cochrane in an article submitted for publication. Both of latter authors are
here so we have the possibility to discuss their data with the authors themselves.
In the butadiene worker study, as I showed to you yesterday, the number of
exposed workers was very small, only 13; and this consisted of workers in the low
exposure category and workers in the higher exposure category, with mean exposure
time of 7.8 years. And as has been reported in the publication (Legator et al. 1993),
the overall average exposure level has been in the range of 1 ppm. We can calculate,
if the workers work about 1,600 hours per year, that the ppm hours for the exposed
worker category was 12,480 ppm hours.
The HPRT mutation frequency levels, as I mentioned yesterday, were detected
by the autoradiographic method. Mutation frequencies in the exposed group were
2.5 x 10"6, and for the controls very low, only 1.03 x 10"*. And we can then calculate
that the doubling dose of HPRT mutations would be about 10,000 ppm hours in the
lymphocytes of these workers. The mouse studies have been performed on
splenocytes of very young animals; altogether the number of animals was 8. The
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exposure was 625 ppm, for 2 weeks time, 6 hrs per day, total of 10 days. Total
exposure in ppm hours was 37,500. The HPRT mutation frequency in the controls was
1.3 x 10*, and in the exposed group it was roughly five times higher, so that the
doubling dose of butadiene, if we expect a linear dose response is about 15,000 ppm
hours. Surprisingly similar to the value in humans.
Table 4-2, Estimate of Comparative Doubling Dose of HPRT Mutations by
••"f— "••
Reference study
Study group
Size
Exposure data
Estimate of
ppm hrs
HPRT mutation frequency
in controls
In exposed
Estimate of
doubling dose
MICE
Cochrane & Skopek 1993,
In press
B6C3F1
8
625 ppm
6 hrs/d, 5 d/w,
2 weeks
60x625 = 37500
1.3 x 10*
6.0 x 10*
15 000 ppm hrs
HUMANS
Legator et al. 1993, In press
Butadiene production workers
13
TWA1 ppm
mean exposure 7.8 yrs
1600x7.8x1 =12480
1.03 x Iff6
2.5 X10"6
10000 ppm hrs
But since we had the problems in the metabolic differences between mice and
humans and in the type of the tissue examined, our estimations in general are based
on butadiene and not the metabolites; this estimation cannot be considered relevant,
only a brave attempt.
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Among the other interesting results on human biomonltoring, which in general
is among the future research priorities, the hemoglobin adduct monitoring studies
performed by Siv Osterman-Golkar have produced results which show that there is
some dose related effect on the adducts level. These adducts measured are now the
monoepoxide adducts but we are still uncertain how well these are representing the
genetically significant adducts. Future research certainly should try to focus on the
other metabolites as well.
But anyway I think this is reassuring, and it has even been repeated in another
study population exposed to roughly similar levels of butadiene but in another
country, in Portugal. Also these so far unpublished results show that there is an
exposure related effect in the workers, as compared to the control levels.
Our working group actually was discussing these human biomonitoring results
and the urgent research needed for better understanding of genetic risks with
Butadiene. The confirmation of the worker study concerning the HPRT mutation
frequency needs to be performed, preferably with another methodology. Confirmation
is also needed on the important findings from Diana Anderson's group which were
discussed yesterday. The male mediated malformations at the low dose of 12.5 ppm
were observed and urgently need to be confirmed.
The metabolism studies, especially towards better understanding of human
metabolism and in relation to the genetic polymorphism of metabolic enzyme systems
in humans, are among the research priorities.
DMA adduct studies are urgently needed, and especially, I think, this would be
important for the tissue specific adduct determinations. One example of a need is in
the testicular tissue for the genetic risks estimation. In our group, Ekkehart Vogel was
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suggesting mutation sequence analysis for butadiene studies, and I think this is
certainly a way which the research should be focused on. Mutation spectrum analysis
could also give us a better understanding of the Importance of the different
metabolites of butadiene, and this should be performed for germinal mutations.
Heritable translocation data is especially needed if we want to have some confirmation
in our parallelogram approaches.
Ladies and gentlemen, I am not going to go repeat all the discussions which
we already have had with the other two groups concerning the questions which we
were given. I will maybe just take some highlights, because to a large extent our
discussions have already been presented by the other Rapporteurs.
The first question is the important studies needed to support genetic risk
assessment activities. Of course, we cannot deny that we do need the germinal cell
studies, and I think especially the heritable translocation data. It was pointed out that
concerning human risk, the balanced transiocations are important because also the
carriers show impairment and reduced fertility.
There is now test data available on some 30 chemicals concerning the genetic
heritable effects. When we discussed which might be the important priority chemicals
for risk assessment efforts, benzene came up because of its importance as a human
exposing agent, but also because of its obvious in vivo genetic toxicrty activity.
The Questions 6, 7 and 8 were the ones which were actually already
answered. I think just one additional point concerning the future prospectives in
addressing the problem of "no human germ cell mutagens", which theoretically, does
not exist. An "ideal data set" is not available, since we are not living in a dream world.
The way to go would be to try to do the more biomarker studies, because this would
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enable, a better focusing of epidemiologic studies. This would give a better possibility
to identify the internal, biologically relevant exposures in individuals. Exposure
assessment is very often a flaw in many epidemiologic studies. Proper biomarker
studies would enable the possibility for earlier prevention. We do not need dead
bodies for biomarker studies, so this would enable better focusing of epidemiologic
research, and in that sense it would be also cheaper.
Discussion
DR. DOUGLAS: I just want to make the observation that I think that when we
express risk numbers we should do it with a common unit of exposure. I do not
necessarily know what it is, but in some cases we are talking about ppm hours, other
cases we are talking milligrams per kilograms per day, and so on. I think we really
need to work on that, in particular for the numbers that come out in this report; if they
are going to come out as numbers, they ought to be based on a common
denominator.
DR. SORSA: So, is there a consensus?
DR. ANDERSON: For drugs you cannot do quite the same thing.
Da DOUGLAS: Of course.
DR. SORSA: Well, ppms are, of course, for gases are the most easily available
numbers to relate to real life situations.
DR. ANDERSON: Just to take an example, ppm hours is very relevant in an
exposure for a work force; but if you have got a drug, let us just take
cyclophosphamide in milligrams per kilogram per animal data studies, then when you
get up to the human studies they have this other sort of exposure where they have
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millimeters squared, which is related to the skin surface area. So, if you are trying to
equate the animal data, you have to then convert it to a unit for the clinical situation.
So, it is not as easy as all that.
OR DOUGLAS: But there are units that are used in particular application. But
if you are going to put all the numbers together, you should as well make them as
much in common as possible.
DR. ADUER: You just have to produce conversion term to table.
DR.SORSA: I think so.
DR. DOUGLAS: We do that routine with gases.
DR. WATERS: I think we will make an attempt to do that, although I do not
know that this is a major point.
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Cyclophosphamide (CP)
Dr. Paul B. Selby presented the report on Cyclophosphamide. Dr. Selby is from Oak
Ridge National Laboratory, Biology Division, in Oak Ridge, Tennessee, U.S.A.
Initial Report on Cyclophosphamide
DR. SELBY: We have not yet gotten very far in trying to work out
parallelograms for Cyclophosphamide. How far we will get in this task is questionable,
but we will make an honest attempt to develop parallelograms. The group working
on Cyclophosphamide consists of Dr. Diana Anderson as chairperson, and of Drs.
Jack Bishop, R. Colin Gamer, Pat Ostrosky-Wegman, and myself. Thus far we have
spent most of our time discussing reports prepared by these members and identifying
gaps in knowledge. We have not yet prioritized the gaps in knowledge, but my list of
those identified will give you some feel for how things are moving.
It seems clear that metabolism needs to be better understood for
Cyclophosphamide. Peculiar findings have been found after certain treatment
regimens. Some of these peculiar findings might result from differences in metabolism
over time, and this possibility needs to be pursued. The Information on adduct
formation for this chemical is extremely complex. It is not clear what adducts should
be measured in humans. Apparently there are no clearly defined data available on
adducts in humans. Adducts have been studied in mammals, but curiously they have
not been studied in reproductive cells. One peculiar thing about Cyclophosphamide
is that while there are voluminous amounts of data in the literature, rather little of it is
recent. It might be worthwhile to see if results from alkaline-elution or unscheduled
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DMA synthesis studies might serve as a surrogate for data on adduct formation.
There is some question how useful information on adducts will be. Probably more
than 200 studies have been carried out on somatic cells, but most of them have
focused on clastogenic effects. Some studies show effects of metabolic activation,
but many more do not. Incidentally, in this regard, there was an error in the
viewgraph that was shown yesterday related to this. The lines "with metabolic
activation" and "without metabolic activation" got switched, which caused confusion
about the importance of metabolic activation.
There is relatively little information available for cyclophosphamide on induction
of gene mutations in somatic cells in vivo; one study in humans and one in
experimental mammals gave positive results. There is also very little information
available on the persistence of gene mutations after treatment ends. More information
is needed on induction of point mutations in In vivo studies, using hemoglobin, HPRT,
and giycophorin.
Jack Bishop described the extensive information available on germ-cell effects;
many studies have shown very clearly that there is induction of dominant lethality.
There are good data available for induction of heritable translocations and
specific-locus mutations. Heritable translocations were induced primarily in the
second and third weeks following treatment, and specific-locus mutations were
induced in the first three weeks following treatment. There is no suggestion of any
induction of specific-locus mutations in a large study on exposed spermatogonial
stem cells.
In regard to what was said in the prior presentation, there is a Jenkinson and
Anderson paper that gives results on induction of congenital malformations in
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progeny of exposed rats. Even though it is difficult to know how to apply those
results, that paper is important because it strongly suggests that there are mutations
induced that have effects in heterozygotes. UNSCEAR (United Nations Scientific
Committee on the Effects of Atomic Radiation) has considered whether congenital
malformation data can be applied in risk estimation, perhaps in a way similar to the
direct method. For various reasons, it has thus far never applied those data in risk
estimation. We will give some more thought to the possibility of doing so for
cyclophosphamide since there are no other data available that can be used to make
a direct estimate of genetic risk. Perhaps, if we use sufficient caveats, we can apply
those data in quantitative risk estimation.
Clearly, for a chemical such as cyclophosphamide, pharmacokinetic
information is essential to the process of making any genetic risk estimate. Stage
specificity must also be taken into consideration. There are special problems for this
chemical in making extrapolations from high dose to low dose. Most people exposed
to cyclophosphamide are exposed in many fractionated exposures during their
therapeutic treatment regimen. As a result, real low-level exposure would be
extremely uncommon in the human population.
Our work group had a fairly long discussion of what could be done in applying
available data in a case like this, in which the heritable-translocation and
specific-locus data show clearly that mutations are induced. I mentioned briefly some
findings from my laboratory that I will develop more fully in my presentation tomorrow.
Some of you know that we are conducting a series of long-term experiments using
the Assessment of Dominant Damage (ADD) method, in which the two most important
endpoints evaluated in first-generation progeny are skeletal malformations and
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cataracts. Our results show that while ethylnitrosourea (ENU) is very effective in
inducing dominant mutations with phenotypic effects, chlorambucil is surprisingly
ineffective. Both treatments induce very high frequencies of specific-locus mutations,
with the frequency being about twice as high for ENU. Our samples are too small to
exclude the possibility that chlorambucil may be inducing some dominant mutations.
However, if it does, there is still a much smaller effect than would be expected based
on the findings for specific-locus mutations. These results are mentioned here
because they may have relevance to cyclophosphamide. Cyclophosphamide and
chorambucil both induce dominant lethals, heritable translocations, and specific-locus
mutations at about the same times following treatment in the male. Unfortunately,
there is much too little known presently to judge whether these similarities might also
suggest that the mutations induced by cyclophosphamide are also quite unlikely to
cause serious dominant effects.
There was discussion as to what we might hope to accomplish in estimating
genetic risk for cyclophosphamide. There was speculation that populations may be
so infrequently exposed to low concentrations of cyclophosphamide that there may
be little need for risk assessment for the population at large. If the exposed
population is primarily the group of people being given chemotherapy, the question
is whether there is sufficient reliability in any genetic risk estimates that might be
made for them to be applied in any useful way. Something that must always be kept
in mind when estimating genetic risk is that the human population already has a large
genetic burden. One quite accurate figure, discussed in detail in the 1993 UNSCEAR
report, is that approximately 8% of liveborn humans will manifest a serious genetic or
partially genetic abnormality by the time they are 25 years old. In the presence of
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such a large current burden, even if genetic risk estimates suggested that a patient
might have a 1% chance of having a child with an induced effect, it must be
questioned how such an estimate would be applied. I doubt that it would be justified
to suggest that such a patient not have children as a result of his treatment because
his total risk is now 9% instead of 8%? Furthermore, any estimate of 1% induced risk
might be a considerable overestimate.
I have briefly described some of the findings and uncertainties that we must
deal with when attempting to estimate genetic risk for cyclophosphamide. This is as
far as we have gotten. I hope that we can accomplish much more by tomorrow.
Thank you.
Final Report on Cyclophosphamide
DR. SELBY: Thank you, Ekkehart. The cyclophosphamide group consisted
of Diana Anderson, Jack Bishop, R. Colin Gamer, Pat Ostrosky-Wegman, and myself.
Much of what we did was to identify gaps in knowledge. I mentioned almost all of the
major gaps in knowledge yesterday. I may touch on a few of them this morning, but
I will not take time to review those gaps in knowledge today. We have not yet made
enough progress to make a parallelogram calculation, but we intend to make one
when working up a review.
Some of the questions that we discussed were as follows: What data are
available? Are they acceptable data? How can they be applied in quantitative
genetic risk estimation? Ideally there would be data for relevant doses, at least for the
germ-cell data. Ideally we could apply linearity of dose. There is probably no
alternative but to assume that humans and rodents are approximately equal in
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sensitivity to mutational damage, or at least that any differences between them can
be corrected for by using known differences found for somatic data. It would be nice
to have the same cells on both comers of the parallelogram, but oftentimes the
desirable data are lacking.
With cyclophosphamide we have a somewhat peculiar situation regarding the
doses applied in some experiments. The doses were split into many fractions
administered on many successive days. For this reason, at first sight, the treatments
may seem to be chronic exposures somewhat similar to the situation in which Dr. W.L
Russell found that when ENU was administered in many small fractions, there was a
reduction in the mutation frequency. However, with cyclophosphamide, it must be
kept in mind that just because a dose is fractionated into smaller amounts does not
mean that one is dealing with a low-level exposure. It turns out that the individual
doses used in the fractionated cyclophosphamide experiments are rather similar to
the individual doses used in fractionated therapeutic exposures for people. As a
result, the findings apply to high-dose therapeutic treatments instead of to long-term
chronic exposures.
We discussed the questionnaire supplied by the organizers, and I will not take
time to recite our responses now. They are very similar to those given by other
working groups. Although we have not yet been able to make any quantitative risk
estimates for cyclophosphamide, when we do so, we shall attempt to use a
parallelogram approach. However, in some cases, in order to make a risk estimate
it may prove necessary just to look at the germ-cell data and make certain
assumptions about relative risk.
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Most of the rest of my comments will relate to the corner of the parallelogram
that deals with germ-cell effects measured in mammals. Needless to say, one man
who has contributed much to knowledge about induction of mutations in germ cells
of mammals, by radiation and chemicals, is W. L Russell. Bill Russell and Frits
Sobels were good friends with a common Interest in genetic risk estimation for several
decades. I no longer see Bill very often, but I happened to see him the day before
I came to this meeting. He asked me to mention one thing for him. He told me that
he had always hoped to attend a meeting on the parallelogram approach with Frits.
He would have liked to have had a chance to tease his friend about the parallelogram
approach by suggesting that it is really the paralogism approach. Paralogism is
defined in the Shorter Oxford English Dictionary as (1) a piece of false reasoning, or
(2) a fallacy, especially one of which the reasoner is himself unconscious. For various
reasons, some of which have been discussed at this meeting, Bill has always been
skeptical about the validity of applying the parallelogram approach; however, I am
sure that he appreciated the importance of what Frits was trying to accomplish by
promoting the parallelogram approach. Many of the uncertainties that must be dealt
with when trying to apply the parallelogram approach must be dealt with in any
attempt to extrapolate from data collected in controlled experiments on laboratory
animals to humans.
Without question, one of the important sources of data that we have for
cyclophosphamide is specific-locus data. The specific-locus method offers a precise
means of counting mutations induced in mouse germ cells. Another important feature
of those data is that knowledge is rapidly expanding on the molecular genetics (that
is, what is actually happening at the DMA level) near these seven genes. There is
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also a vast amount of historical data available for comparison. But there are also
some problems with specific-locus data. A major difficulty concerns how to
extrapolate from recessive mutations found at only seven genes to estimations of
clinical effects in first-generation offspring. Another important difficulty is that
mutations are only scored at seven genes per animal, and it is now becoming very
expensive to raise large numbers of mice for observation.
If one is dealing with clastogenic effects, the most sophisticated method for
counting transmitted mutations induced in mouse germ cells is the heritable
translocation test. This method also has the advantage of having much historical data
available for comparison. However, as mentioned by Dr. Julian Preston yesterday,
there is the difficulty of how to relate induced translocations to damage found in
first-generation offspring. One figure that has been used to make such an estimate,
in the 1986 UNSCEAR Report, was that 9% of the unbalanced segregants of heritable
translocations will survive birth and have serious handicaps. Dr. Udo Ehling and I
took part in the discussions that led to the use of that figure of 9%. The data on
which that figure was based were extremely soft; many of them were observations
made studying human pregnancies, and we could easily have used those data to
estimate that a much smaller percentage of unbalanced segregants would have
serious health problems, perhaps only 2%. Not only is this a very uncertain figure,
but it is unknown whether the same percentage applies to different mutagens and to
different germ-cell stages. It would be useful to obtain experimental data to explore
these questions. At the present time, the best that can be done is to use the data
obtained for heritable translocations following treatment with cyclophosphamide and
make various assumptions recognizing these difficulties.
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The most precise data that we have for cyclophosphamide are (1) the data on
induction of heritable translocations, for which there is induction predominantly in the
second and third weeks after a single exposure, (2) specific-locus data, and (3) data
on induction of dominant lethals. A recent paper from Dr. Udo Ehling's group shows
that, following single exposures, there is induction of specific-locus mutations for
matings made in the first three weeks after treatment. We shall use these data when
we attempt to make a risk estimate for cyclophosphamide.
Once you get away from mutations that can be precisely counted, such as
heritable translocations and specific-locus mutations, and try to move in the direction
of risk estimation, it becomes necessary to deal with the difficult question of what
mutations really mean in terms of human health. How many induced mutations have
clinical relevance? The types of data that relate more directly to health effects deal
with mutations that cannot be counted as precisely. I will briefly discuss one
interesting set of data of this type. These are the data of Jenkinson and Anderson
referred to yesterday. They exposed rats to cyclophosphamide in a fractionated
treatment that was spread out over many weeks. Individual exposures were to 3.5 or
5.1 mg/kg per day (at times it was 3.5 and at other times it was 5.1). They found that
the percentage of dominant lethality increased until it reached a plateau of about 70%
after about 7 weeks. The explanation for this plateau is unknown. It could be that
an equilibrium is reached and that they are mainly seeing the additive effects from the
first few weeks of treatment. Alternatively, perhaps the long continued fractionation
leads to a change in metabolism of cyclophosphamide that thereby produces the
response that was observed. Of course, this finding is for dominant lethality, which
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does not relate to health effects seen in humans that survive birth. Risk estimates, at
least for radiation, usually only apply to abnormalities found in liveborn humans.
Interestingly, following the same exposure regimen, Jenkinson and Anderson
found a very similar pattern of response for fetal abnormalities. Such abnormal
fetuses are often referred to as congenital malformations in the literature, and they are
abnormalities found during uterine examinations made shortly before the time of birth.
For this endpoint, the plateau was reached by the seventh week of continuous
treatment, at which time about 10% of the fetuses were abnormal. An estimate of the
induced frequency of abnormal fetuses can be made by subtracting the control
frequencies from the experimental ones. The data seem to show convincingly that
about 9% of the offspring had induced abnormalities. This study seems to be free of
the difficulties present in some other studies of congenital malformations.
The problem with such data, however, comes in deciding how to apply them
in making a quantitative risk estimate. One of the difficulties is that the experiment
deals with only a rather small array of different abnormalities. Another difficulty is that
about 60% of the fetuses said to have congenital malformations had only growth
retardation. Some investigators call the small fetuses "dwarfs", but they are clearly not
dwarfs in the clinical sense. In the Jenkinson and Anderson experiment, about a third
of the animals with congenital malformations were shown to have gross chromosomal
abnormalities. Some of the fetuses with malformations may thus be aneuploid
segregants for translocations. It is hard to know what clinical significance, if any, to
attach to many of the malformations. Some of the smaller fetuses might just be
toward the lower end of the normal distribution of size and might develop normally
after birth.
350
-------
The Jenkinson-Anderson experiment deals with rats observed just before the
time of birth, at which time they would probably be roughly equivalent in stage of
development to a human at about four months of pregnancy. Already at this time, 12
of the 41 mice with congenital malformations were dead. Thus, many of the fetuses
said to have congenital malformations clearly do not relate to birth defects seen in
liveborn humans. Probably the abnormal fetuses relate partly to anomalies with no
clinical relevance and partly to late miscarriages, still births, and some gross
congenital abnormalities present at the time of birth. We will attempt to make a risk
estimate using these data, but in doing so it will be necessary to make assumptions
to cover the above uncertainties.
For radiation risk, by the direct method of genetic risk estimation, for one cGy
(that is, 1 rad) of paternal exposure, an estimate of risk that I consider reasonable is
shown in Figure 4-16. You will find this estimate to be similar to the risk estimate for
paternal exposure that will be in the 1993 UNSCEAR Report, which will be published
soon. Incidentally, that report provides a very useful discussion of the uncertainties
in genetic risk estimation, and it describes the types of information needed to improve
risk estimates for hereditary effects of radiation. That report also provides a valuable
discussion of risk estimation from a clinical perspective. The main consultant in the
development of that report was Dr. Judith Hall, who is the President-elect of the
American Society of Human Genetics.
To better understand how risk estimates can be made for chemicals, it is
useful to consider how risk estimates are made for radiation, for which quantitative
risk estimates have been made by committees for several decades. In the direct
method, as applied in Figure 4-16, the range of from 10 to 20 per million liveborn is
351
-------
based on damage detected in first-generation offspring of exposed males. The 10 is
based on cataracts and the 20 on skeletal malformations. The estimate of 20 is
unchanged since it was first made in 1977 by UNSCEAR. The second part of the
estimate, of from 5 to 10 per million liveborn, is based on early deaths, that is, on
deaths that occur before mice grow up so that they can be examined for other effects.
That estimate is explained in the 1986 UNSCEAR Report and in the upcoming 1993
report. Part of the induced damage estimated in both rows of the figure is expected
to result from induced translocations.
For radiation we have much more data on damage in first-generation offspring
to use in making a rough estimate of genetic risk. For chemicals, comparable data
is scarce or nonexistent.
Genetic risk estimates for 1 cGy of paternal exposure
to X or gamma radiation made by the direct method
Expected number of seriously affected
children in the first generation per
million live births.
Estimate based on phenotypic changes 10-20
seen in mice living 3 weeks or longer
Estimate based on frequency of mice 5-10
dying between birth and 3 weeks of
age
Total 15-30
Figure 4-16
352
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Crosscutting Papers
This section contains the following crosscutting topics:
• Differential Repair of Chemically-induced Damage:
Somatic vs. Germ Cells
• Chemically-induced Mutation:
Comparative Outcome in Somatic vs. Germ Cells
• Qualitative and Quantitative Results from Short-term Tests
• Chemicals for Future Study of Heritable Genetic Damage in Human
Populations
353
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354
-------
Differential Repair of Chemically-induced Damage: Somatic vs.
Germ Cells
The first crosscutting paper was delivered by Or. Ekkehart W. Vogel. Dr. Vogel is from
the Department of Radiation Genetics and Chemical Mutagenesis, State University of
Leiden, The Netherlands.
DR VOGEL At this point, Dr. Natarajan and I had been asked to deal with
differential repair of chemically induced damage in somatic versus germ cells. The first
thing we did was to delete the term "differential", because germ cell mutagenesis itself
is so decidedly complex that it seemed impossible to contrast on one side genetic
damage in somatic cells with mutagenic effects seen in germ cells on the other. The
next question, then, was what might be relevant for the purpose of this meeting. We
first had in mind to just compare in mammals mutagenesis in somatic tissue versus
germ cells. At the same time we felt inclusion of a non-mammalian species such as
Drosophila would not be appropriate, because a general view seems to be that
Drosophila is not a model system for the mouse. However, when checking the
literature we became surprised and fascinated by a number of similarities seen in both
species in the responses to mutagens. Thus in the following, experimental data with
respect to some major principles of germ-cell mutagenesis in male mice vs.
Drosophila males is discussed against the mechanistic mode of action of genotoxins
assayed in germ cells. The chemicals with data for germ cells are predominantly
alkylating agents.
355
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The Mate Germ-cell Cycle
The meiotic cell cycle is associated with changes from diploid to haploid. Of most
interest for our understanding of genetic damage formation in germ cells is a period
of repair incompetence which lasts 3-4 days in Drosophila and 14 days in the mouse
(from differentiation of late spermatids onwards). The efficient functioning of the
maternal repair system, which operates in the egg after fertilization, is therefore of
utmost importance for removal of DMA damage carried by male germ cells. The total
duration of spermatogenesis is about 35 days in the mouse and 11 days in
Drosophila. Another characteristic feature of germ-line mutagenesis is germinal
selection prior to meiosis (Muller, 1954). Thus, the interaction of various complex
biological processes, (that is, the efficiency of repair enzymes, the capacity of the
germ cells for metabolism, and germinal selection during and prior to meiosis), largely
determine the mutational spectrum finally observed in each individual cell stage.
For a comparison of mutation spectra in mouse versus Drosophila, there is
data from specific-locus experiments for both species. In addition, for Drosophila there
is information on multi-locus recessive lethal assays (Lee et al., 1983); that is, there
is information from the X-linked recessive test, where activity is measured in 700
genes, and from the lind chromosome recessive lethal assay, a system covering
about 1400 to 1500 genes located on the second chromosome.
DNA Damage and Repair in Drosophila
For processing of chemically induced DNA lesions in Drosophila, two loci, namely the
mei-9 (X-chromosomal) and mus(2)201 (iind chromosome), are of particular interest.
Both mutants have been shown to block nucleotide excision repair (NER) and to have
356
-------
no UDS (Boyd et al., 1976; Lee and Kelley, 1986). Mutants of these two loci have
extensively been used to study the consequences of a deficiency in NER for (Figure
5-1) alkylation-induced mutagenesis. In the example shown in Figure 5-1, wild-type
(exr+, Base) males treated with 0.15 mM MMS (methyl methanesulphonate) for 24
hours were mated to either exr (mei-9L1) or exr+ (Berlin-K) females, and X-linked
recessive lethal (RL) induction was measured at 10 successive broods, corresponding
to the entire spermatogenesis cycle. The MMS dose was such that it would induce
approximately 0.4 to 1% RL (« 2- to 5-times the spontaneous rate) in the cross with
wild-type genotypes. The time-related potentiating effect seen for the postmeiotic
stages, and the subsequent decline mutational yield in meiotic and pre-meiotic stages,
supports the notion that DMA repair functions lose their efficiency as spermatogenesis
progresses. With MMS, up to 95% of the mutations seen in the exr group do not
occur when the maternal excision repair is functioning (Figure 5-1). That this decline
in mutation induction by MMS in the premeiotic stages is due to repair but not a result
of germinal selection is indicated by the low yield in premeiotic stages of both X-
chromosomal and of ll-chromosome RL The RL^,: RL^ ratio (RL,p and RL^
denotes recessive lethals in spermatozoa and spermatogonia, respectively) is 23:1
for the X and 19:1 for the second chromosome.
The next point I want to discuss is the response of different germ-cell stages
of Orosophila to END (N-ethyl-N-nitrosourea), MMS, and EMS (ethyl
methanesulphonate) because for these three alkylating agents there is also
information for the mouse specific-locus test. Forward mutation induction in
Drosophila by ENU, MMS, EMS was measured in both the lind and the X
chromosome. The iind chromosome (40% of the genome) has about twice the length
357
-------
0)
'w
CO
V
o
4)
oc
1 3 5 7 9 11 13 15
Days
Haploid —> —> Diploid germ-cells
% Recessive lethal mutations (RL) induced in Base males mated to either
(exr-) or to wild-type (exr+) females. Brood pattern analysis of 10 successive broods
(total 14 days). The enhanced induction of RL seen for the exr- groups indicates the
amount of DNA damage (N-alkylation) normally repaired after fertilization by the
maternal repair system (From EW. Vogel, unpublished).
Figure 5-1
358
-------
of the X chromosome (20%). Three germ-cell stages were compared: the
postmeiotic spermatozoa and spermatids versus the premeiotic spermatogonia.
Important for this analysis is that premeiotic male cells have two copies of the second
but only one copy of the X chromosome. With ENU (and also with DEN, N-
nitrosodiethylamine) high incidences of mutations are found for the X and the second
chromosome in both post- and premeiotic germ-cells, providing no indication for a
significant germinal selection (Figure 5-2). For EMS the ratio of lind- to X-linked lethals
was 1.8 for spermatozoa and 14:1 for spermatogonia, indicating a selection against
mutations located on the X-chromosome prior to meiosis. It was already mentioned
that with MMS a decrease in RL is seen for both the second and the X chromosome.
Thus, for three of the five alkylating agents depicted in Figure 5-2, a substantial
portion of lesions induced on the X-chromosome do not pass through the filter of
meiosis.
Another analysis made was to compare the ratios of lind-chromosome RL in
spermatozoa versus those in spermatogonia stages (Figure 5-3). In this case germinal
selection should not have a big impact. While there were no significant changes in the
RL.p:RL^ ratios for ENU and DEN, the low yields of RL in spermatogonia for MMS
and (less so) for EMS indicate efficient error-free repair of alkylation-induced damage
in premeiotic cells. Surprisingly, the cross-linking agent DEB was very active in all
stages (Shukla and Auerbach, 1980).
In summarizing the data for Drosophila, the following can be concluded:
basically, efficient paternal and maternal repair systems are available for DMA damage
removal; there is, however, accumulation of premutagenic damage from N-alkylation
during a period of repair incompetence (3 days). For the purpose of a general
359
-------
II:X Spermitoioi
Spcrmtto(oni>
20
Germinal selection: MMS, EMS. DEB
MMS EMS ENU DEN DEB
Ratios of second chromosome recessive lethals (RL,,) to X-linked recessive lethals
(Ri-x) for spermatozoa and spermatogonia. For spermatozoa the ratios are as
expected (• 2:1) because the lind chromosome is twice the length of the X
chromosome. The increase in the RL^R!^ ratios seen for spermatogonia indicates
germinal selection for MMS (methyl methanesulphonate), EMS (ethyl
methanesulphonate) and DEB (1 ,2;3,4-diepoxybutane), but not for ENU (/v-ethylW-
nitrosourea) and DEN (A/-nitrosodiethylamine). Data for MMS, EMS and ENU from
Vogel and ZiJIstra, 1967; data for DEN and DEB from Shukla and Auerbach, 1980.
Figure 5-2
Repair in Sg:
MMS and EMS
ENU??
MMS EMS ENU DEN DEB
Comparison of Induced lind-chromosome (autosomal) recessive lethal mutations In
spermatozoa (and some spermatids) versus Induction In spermatogonia. The high
RL.piRL^ ratio obtained for MMS and (less so) for EMS Indicates efficient DMA repair
in spermatogonia. RL.p:RL^ ratios are not significantly different from 1 for ENU and
DEB. A decrease is found ror DEN because mature spermatozoa cannot activate
promutagens. For abbreviation and references see Figure 5-2.
Figure 5-3
360
-------
comparison, three distinct categories of alkyiating agents can be identified. Category
1 chemicals, prototypes are MMS, DMS (dimethyl sulfate), EMS, and EO (ethylene
oxide), are very efficient for ring-nitrogen alkylation. These agents show major activity
in postmeiotic cells. Both DNA repair and germinal selection seem to be responsible
for the low mutation yield from premeiotic male germ cell stages. To category 2
belong agents with preferential ability for alkylation at oxygen atoms in DNA (Singer
and Grunberger, 1983). Thus, ENU, DEN, and MNU (A/-methyl-A/-nitrosourea), are all
highly genotoxic at all germ cell stages; a major role of germinal selection is not
evident. Category 3 (DEB, 1,2;3,4-diepoxybutane; BCNU, bis[chloroethyl]nitrosourea;
CHL, chlorambucil; HMPA, hexamethylphosphoramide) represents cross-linking
agents which, in addition to their property to form mono-adducts, can crosslink DNA.
Although germinal selection was demonstrated for this category (for example, for DEB,
see Figure 5-2), they are highly genotoxic to all germ-cell stages. They are expected
to mainly produce large deletions in postmeiotic cells, as was recently demonstrated
for HMPA (Aguirrezabalaga et al., 1993; 1994). The data further suggest for premeiotic
cells less efficient repair of cross-links in comparison to N-alkyl mono-adducts.
DNA Damage and Repair in the Mouse
The response of different male germ-cell stages in the mouse to the induction of
visible specific-locus mutations (data from the laboratories at Neuherberg and Oak
Ridge) show striking similarities to Drosophila. (Table 5-1). ENU (category 2) is
predominantly mutagenic in spermatogonial stages whereas MMS, EMS and DES
(diethyl sulfate) (category 1), which induce relatively high levels of N-alkylations
361
-------
Chemical
Acrylamfde monomer*'"
Category ]
1,2-Dibramoethane*
Ethylene oxide?
Methyl methanesulfonate*'d
Ethyl methanesulfgnate*fd
Di ethyl sulfate*'*
MMS+EMS+OES
Cateaory 2
g-Nethyl -N.-nf trosourea*'9
Procarbazine 'a
M- Ethyl -Jl-nl trosourea*^'"
MMHPRC+EMJ
Cateaory 3 .
TriethylenemelMlne*' '
Ch I orambuc 1 1
Nelphalan ' .
Nechlorethamlne1
Nitomycin C*'°
PlatinoP
Cyclophosphamide''*
Trophosphamide
IEN+CHL+NLP+CPP
AA
DBE
EO
PRC
TEN
CHL
NLP
NNC
CPP
TPP
Abbrev. Chemical
properties
s • 0.96
NNS s * 0.84; Nt
ENS s • 0.67; Et
DES s • 0.64; Et
MM s « 0.42; Nt
UkeNNU
EMI s • 0.26; Et
cross- 1 inking
cross- I Inking
cross- 1 inking
NEC cross- I Inking
cross- I Inking
PLA cross- I Inking
cross- 1 inking
cross- I Inking
Post-stem cell,
Resp. LL
* 3
I
15
:
5
* 1
* 11
* 13
*
?
*
* 3
28 9
OL UC
2
10 4 3
0 1 15
6 20
2 1 1
23 0
42 3
4
2 1
3
2
2
1
Resp. LL
*
-
:
n
+ i
+ 0
+ 1
•
* 0
2 21
Stem- cell snermatogonla
OL UC
1 3
2 44
8 246
WS 131
4 1
2 2
3 3
12 1
7
0
9
122
' L.B. Russel et al., 1990; b L.B. Russell and Rlnchlk, 1993; c U.L. Russell, 1984; also negative with mouse dominant lethal assay and the electrophoretJc specific-
locus test (Barnett et al.. 1992); d Ehling and Favor. 1984; Ehling and NeuMuser-Klaus. 1984, 1989b. 1990; * Ehling and Neuhauser-Klaus. 1988«; r Ehling and
Neuhltuser-Klaus, 1988b; • U.i. Russel and Nunsicker, 1983; ,
H W.L. Russell et al.. 1979; rCattanech, 1966; * L.B. Russell et al.. 1989; l Ehling and Neuhauser-Klaus, 1989s; " Ehling and Neuh«user-Klaus, 1994; n Ehling and
NeuhCuser-Klaus, 1992; ° L.B. Russel et al., 1992a, 1992b; p L.B. Russel et al., 1984 (positive for mouse dominant lethals and heritable trans locations (Generoso
et al., 1980; ICPEMC, 1983); q Favor et al., 1994.
Abbreviations: Et, ethylating; LL, large (multi-locus) deletions; Nt, methyl sting; OL other lesions (Intra-locus); UC, unclassified; although the germ-cell stage
is a major determinant of the nature of mutations recovered, the type(s) of initial DMA lesions induced also have an impact.
362
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(predominantly 7-alkylguanine), are much more mutagenic in post-meiotic stages. This
may be largely due to N-alkylation products leading to DMA breaks and the
accumulation of apurinic sites in these stages which have lost the capacity of repair.
This hypothesis assumes that mutations are fixed following fertilization. In view of their
preponderance for ring nitrogen alkylation, there should be a smaller chance for MMS-
type mutagens to display a genotoxic effect in the early stages. This has indeed been
found, as is evident from the negative test results in stem cells for MMS, EMS and
OES (Table 5-1). An intermediate position is taken by MNU and PRC (procarbazine)
which produce specific-locus mutations in both spermatogonia and post-gonia cells.
Thus a positive correlation seems to exist between increasing efficacy for alkylation
at oxygens in DNA (category 2), in particular at oxygens of pyrimidine bases, and
mutation induction in premeiotic cells. The specific-locus test results summarized in
Table 5-1 also contain 8 chemicals known to form cross-links with DNA. Interestingly,
4 of these 8 mutagens displayed genotoxic effects in premeiotic male germ cells of
the mouse. Clearly, the general pattern emerging for the 3 categories in the mouse
is very similar to what is seen in Drosophila.
Retrospective analysis by Dr. Lee Russell of mutations recovered in chemical
mutagenesis experiments revealed very many more large deletions (LL) than other
lesions (OL; unknown but most likely all intragenic alterations such as base-
substitutions and intralocus deletions) in postspermatogonial stages compared U>
spermatogonia (Russell et al., 1990; Russell and Rinchik, 1993). If, however, the three
agents MMS, EMS, and DES (all negative in premeiotic cells) are again regarded as
a group (group 1) distinct from MNU, PRC, and ENU (group 2), and all the
monofunctional agents to be different from the sub-set of cross-linking agents (group
363
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3), It appears that the ratio of LL versus OL is not the same for the three groups. The
LLOL ratio is as high as 2.5:1 (15 LL, 6 OL) for group 1, approximately 1:1 (5 LL, 6
OL) for group 2, and again very high, 3:1 (27 LL, 9 OL) for group 3. This suggests
that although the germ-cell stage is a major determinant of the nature of mutations
recovered, the types(s) of initial DNA lesions induced also have an impact.
Comparison of Mouse and Drosophila
This study suggests that a classification of alkylating agents into sub-groups based
on their mode of DNA modification, rather than a consideration of individual agents,
may be worthwhile. In fact, when data analysis is based on a consideration of initial
interaction patterns with the DNA, three distinct categories of alkylating mutagens can
be distinguished for both Drosophila and the mouse.
The prototype of the first category is MMS, an agent predominantly reacting
with ring nitrogens in DNA. Broadly speaking, efficient, error-free repair in
spermatogonia appears to be the protective mechanisms responsible for the low
mutation yield in Drosophila and the absence of significant numbers of mutations in
germ cells of the mouse. By contrast, there are no problems with most of category
1 mutagens to be detected in postmeiotic cells of Drosophila. For three of theses
agents (MMS, EMS, and DES) molecular mutation spectra have become available
(Table 5-1). MMS and EMS are quite efficient deletion makers in Drosophila. In
postmeiotic germ-cells of the mouse, a high LLOL ratio of 2.5:1 is found for MMS,
EMS and DES (Table 5-2). Strikingly, for MMS, DMS, EO, and DBE (1,2-
dibromoethane) high TDg, values have been estimated for rodents (Barbin and
364
-------
Table 5-2. Genetic activity profiles of three categories of alleviating agent.
comparison with tumor 1 genie potency in rodents.
postmetotle cells
US XIGR
XLL
CEA weakly * for RL
EO"
VkBhS*
DBE
MMS
DES
ENS
* responses In RL test
positive for RL
None 78
45 23
93 7
89 6
22
32
no test
15
Mouse
XBS+IGR
inactive 7
inactive ?
inactive*
29
117]
mutants not
In visible specific-locus assays of Orosophila C vermilion locus) and the mouse (7 loci) In
Mouse
DOBtmeiotlc cells Stem-eel I en
XLL
71
83
specified
BS+I6R LL
inactive 7
inactive 7
inactive
inactive
inactive
inactive
Inactive
prmatooonia
high 7
13700
10100
1650
1240
[100]
000]
Comments/predictions
mg/kg bw DD (mg/kg)
efficient repair expected
like CEA
like CEA
DO - 2 (PSG)
DO • 17 (PSG)
DO « 9 (PSG)
Category 2
NNU
PRC
DMN
NNNG
DEN
MN
ENU
52 32
positive in RL test
56 20
30 40
89 11
100 0
16
24
30
no test
0
P3]
[60]
no test
no test
inactive
[67]
[67]
[40]
(331
[75] [25]
96 4
no test
no test
inactive
97 3
28
262
139
27
56
11
DO - 110 (SG)
active In sperms t Ids for
DO - 4 (SG)
Category 3
DEB
CHL
MLP
NEC
positive for RL
positive for RL
positive for RL
positive for RL
cJsDDP positive for RL
MMC
TEN
positive for RL
positive for RL
positive 7
21
13
positive
Inactive
Inactive
[67]
79
87
[331
positive ? tow?
100 0
[75] [25]
inactive
inactive
100 0
[80] [20]
92
67
17
20
0.68
T
DD - 0.7 (PSG)
DO « 0.6 (PSG). 3.9 (SG)
DD « 0.1 (PSG)
DD « 1 (SG)
TD-n's: 5, trsnlmon; 110,
TEPA
HEMEL 0 33
HMPA 6 39
CPP
positive for RL
67
55
no test
7
[40]
[60]
no test
Inactive
Inactive
no test
27
2080
DD - 6 (PSG)
positive for mouse dominant lethaIs and heritable translocatlons (ICPEMC, 1983);
Chemicals: CEA, 2-chloroethylamine; CPP, cyclophosphamlde; cisOOP. cisplatln; DBE, 1,2-dibromoethane; DEB, 1,2:3.4-diepoxybutane; DEN, K-nitrosodlethylamine; DES, diethyl
sulfate; DMN,
JJ-nitrosodimethylamine; ECH, epichlorohydrin; EO*, ethylena oxide; ENS, ethyl methanesulphonate; ENU, Jl-ethyl-JJ-nitrosourea; HEMEL, hexamethylmelamine; HMPA, hexamethyl-
phosphoramide; NEC, mechlorethamine; MLP, melphalan; MMC, mitomycin C; MMS, methyl methanesulphonste; MNNG, N.-methyl-NJ.-n1trojJ-nitrosoguen1dlne; NNU. M-methyl-N/nitrosourea;
PRC, procarbazine; TEN, triethylenemelemine.
Abbreviations: BS, base substitutions; DD, Doubling Dose in mg/kg (from Favor et al., 1994); IGR, intralocus rearrangements; LL, large (multi-locus) deletions; MN, micronuclei;
PSG, postspermatogonia; RL, X-linked recess ice lethal mutations; SG. apermatogonia; TD5Q, median life-time dose (mg/kg body weight) producing in SOX of animals (rodents) tumors
(Bar-bin and Bartsch, 1989; Vogel et al., 1990).
Nutation spectra at the vermilion aene. DBE: Ballering et al., 1994; HNS: Nivard, 1991; Nlvard et al., 1992; MNU, DMN: Nlvsrd, 1991; EMS: Pastlnk et al., 1991; MLD for EMS,
estimate based on sterile and male-lethal vermilion mutants; ENU: Pastlnk et al.. 1989; DEN: L.M. Sierra (personal communication); HEMP A, HEMEL: Aguirrezabalaga et al., 1993;
1994.
Nouse-SLT: For references see Table I.
365
-------
Bartsch, 1989; Table 5-2); these values support the hypothesis of a generally low
genotoxic efficiency of N-alkyl DNA-mono-adducts.
To the second category belong MNU, PRO, DMN (A/-nitrosodimethylamine),
DEN, MNNG (/V-methylW-nrtro-N-nitrosoguanidine), and ENU (Table 5-2). These
agents in common have a considerable ability for modification at oxygens in DNA
(Singer and Grunberger, 1983). Category 2 mutagens are monofunctional agents of
low nucleophilic selectivity, which are expected to show activity in a wide range of
both post- and premeiotic germ-cell stages. Base-pair substitutions were the
predominant type of mutations found in postmeiotic germ-cells of Drosophila with
ENU and DEN (note the higher proportion of deletions by MNU, DMN, and MNNG).
In agreement with a high mutation induction by these agents in Drosophila, a relatively
low LL:OL ratio of 1:1 was observed for the mouse specific-locus test for MNU, PRO,
and ENU (Table 5-2). A number of these agents have been demonstrated to be
among the most potent carcinogens in rodents, as is evident from their generally
lower TDgo estimates compared to class 1 chemicals (Barbin and Bartsch, 1989; Vogel
etal., 1990).
Agents capable of cross-linking DNA form category 3. The most unexpected
finding of this study was the realization that the cross-linking agent DEB (1,2:3,4-
diepoxybutane), with regard to the production of autosomal mutations, is equally
active in both post- and premeiotic Drosophila germs cells (Shukla and Auerbach,
1980). In accordance with that observation, four cross-linking agents (TEM,
triethylenemelamine; CHL, chlorambucil; MLP, melphalan; and MMC, mitomycin C)
display activity in stem-cell spermatogonia of the mouse (Table 5-2). The negative
data for c/sDDP (cisplatin), CPP (cyclophosphamide), and MMC is not necessarily
366
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against the argument of cross-linking agent being a potential risk factor for the entire
spermatogenic cycle. Classification of 11 mutations induced by either chlorambucil
or melphalan in spermatogonia revealed that 10 (91 %) of the 11 were either intragenic
(IG) or other lesions (OQ; no large deletions, LL). By contrast, 24 (83%) of the 29
mutations induced in postspermatogonial stages were of the LL category (Russell et
al., 1990; Russell and Rinchik, 1993). The predominant occurrence of the LL category
in postmeiotic germ-cells seems to be a general characteristic of the activity of cross-
linking agents: 75% of all visible specific-locus mutations were of this type (Table
5-2). However, since the OL category should include a considerable fraction of
intragenic deletions, the total yield of rearrangements may reach more than 90%, as
has recently been shown for mutations induced by HMPA at the vermilion locus of
Drosophila (Aguirrezabalaga et al., 1993; 1994). Thus cross-linking agents are highly
effective in inducing deletion mutations in postmeiotic stages of both Drosophila and
the mouse. Regarding their tumorigenic potency in rodents, (direct-acting) cross-
linking agents have generally lower TDg, values than their monofunctional
counterparts. It therefore seems to us that the considerable genotoxic potential of
cross-linking agents for both tumor induction and incidence of heritable genetic
damage is often under-estimated, because the emphasis is usually more on their
cytotoxicity - the desired effect for the use as cytostatic agents of chemicals such as
cisplatin, melphalan, and chlorambucil.
With all the caution indicated when trying to summarize the very complex
pattern of alkylation-induced mutagenesis in the two species, the following trends in
responses of germ cells occur:
367
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Category 1: MMS, EMS, DES, and EO.
Active in postmeiotic germ-cells In both mouse and Drosophila
Efficient repair of ring-nitrogen alkylation in spermatogonia
Better deletion makers than category 2 chemicals.
Category 2: MNU, PRC, DMN, MNNG, DEN, and ENU
Activity expected for a wide range of germ-cell stages
O-alkylation adducts (at pyrimidines) seem responsible for
mutation induction in premeiotic cells because changes
involving AT basepairs are the major event seen with ENU in
both Drosophila and the mouse.
Category 3: DEB, CHL, MLP, MEC, JEM, HEMEL, and HMPA
Activity expected for a wide range of germ-cell stages
Poor repair of cross-links in spermatogonia ?
Most efficient deletion makers in postmeiotic cells.
Conclusions and Perspectives
The most unexpected but encouraging outcome of the study is the identification of
shared genotoxic activity profiles for three vastly different biological indicators of
genotoxictty: mutations in Drosophila vs. mutations the mouse vs. tumors (TO^
estimates) in rodents. It should be noticed that the distinction into two categories of
monofunctional agents has a purely operational purpose; it facilitates the recognition
of some common genetic action principles in the two germ-line systems.
Based on the above 3-category classification scheme the following tentative
conclusions can be drawn. Monofunctional agents belonging to category 1 display
genotoxic effects in male germ-cell stages that have passed the meiotic division. We
believe the reason for the rather impressive resistance of premeiotic stages and the
generally high TDg, estimates observed for this class in rodents is efficient error-free
repair of N-alkylation damage at low exposure levels. When accepting the hypothesis
of efficient error-free repair of N-alkylation damage in vivo, then the increased
368
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carcinogenic potential in rodents, seen when passing from category 1 to 2, along with
their ability for genetic damage in premeiotic stages, must be due to the enhanced
ability of category 2 type of chemicals for alkylations at oxygens in DNA; it is this
property which distinguishes the two groups from each other.
Both in terms of hereditable genetic damage and the initiation of tumors (low
TDggS) cross-linking agents form a considerable genotoxic hazard. Doubling doses
have been determined for 4 cross-linking agents not requiring metabolic conversion
and in all four cases they were lower than those for MMS, DES, and EMS (Table 5-2).
Four members of this class display activity in stem-cell spermatogonia indicating that
this type of agent is active in a wide range of germ-cell stages. The negative test
results obtained for MEC (mechiorethamine), c/sDDP, HMPA, CPP, and TPP
(trophosphamide) in stem-cell spermatogonia is not in disagreement with such a
conclusion because the cause could be rather trivial (for example, metabolic
clearance). In contrast to what was observed in unicellular systems (Yamada et al.,
1992) and in mammalian cells in culture (Nivard, 1991; Spelt et al., 1992), both cross-
linking agents and MMS-type mutagens (high s value) predominantly produce deletion
mutations in postmeiotic male germ-cell stages. This is the uniform picture found for
both Drosophila and the mouse. It is concluded that in vitro systems, in contrast to
Drosophila germ cells, fail to predict this very intriguing feature of mouse germ-line
mutagenesis. In addition to their potential for induction of deletions and other
rearrangements, cross-linking agents are among the most efficient inducers of mitotic
recombination in Drosophila (Vogel and Nivard, 1993). Thus there are several
mechanisms by which cross-linking agents may cause loss of heterozygocity,
generating genetic damage and/or cancers.
369
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We feel much more effort should be spent to learn about the time course of
repair in germ-cells in vivo in relation to the types and amount of DMA lesions. This
study suggests that DMA-crosslinks are not well repaired by premeiotic cells. This
problem could be examined by studying pairs of structurally-related agents in repair-
proficient and -deficient premeiotic cells of Drosophila, and in repair-competent mice.
For example, it would be interesting to compare the monofunctional agents 2-
chlorethylamine, ethylene oxide, and ethyl methanesulphonate with their respective
crosslinking counterparts nitrogen mustard, 1,2;3,4-diepoxybutane and chloroethyl
methanesulphonate. The prediction would be a considerably higher genotoxic activity
of the latter sub-group compared to the agents only producing mono-alkyi adducts,
for which efficient DNA repair is anticipated.
370
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Chemically-induced Mutation: Comparative Outcome in
Somatic vs. Germ Cells
Dr. Susan Lewis presented "Chemically-induced Mutation: Comparative Outcome in
Somatic vs. Germ Cells." Dr. Lewis is from Research Triangle Institute in Research
Triangle Park, North Carolina, U.S.A.
DR. LEWIS: What Jim and I are trying to do is point out some of the attributes
special to germ cells that make human germinal risk calculations difficult. Taking into
account the basic biology of both the male and female germ cell, perhaps versus the
same somatic cells that are commonly used. A lot of the relevant germ cell biology
was covered in Banbury Report #34 edited by Jim Allen, Lee Russell, Mary Lyon,
Montrose Moses, and Bryn Bridges. This Banbury volume has a lot to do with the
correlation of mutagenic responses and the biology of developing germ cells at all
stages.
Special Attributes of Germ Cells
I want to emphasize the primary differences between germ cells and somatic cells of
all kinds. Germ cells and somatic cells both go through mitosis cell cycles at various
stages, but meiosis is unique and special to germinal tissue. I do not mean to make
the somatic cells appear less complex as opposed to more complicated germ cells.
The variety of differentiation that various somatic cells do go through is considerable.
For example, certain cell types can become polyploid.
I want to remind you of a concept derived from work in carcinogenesis. If you
examine the NTP database on carcinogens, you find that different somatic tissues
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respond differentially to different carcinogens. Thus, I am not sure you can
extrapolate mutation data from various somatic cells to possible consequences in
germ cells. At the very least, I think you have to be careful. But, just remember,
effects may vary according to mutagen, and exact correlation may be very difficult.
So, you cannot assume exact correlation between the somatic cells that are usually
used for in vivo mutagenesis studies and germ cells.
Female Germ Cells
We will start with a consideration of the female germ cell. As a matter of fact, I would
like to remind you that there is such a thing as a female germ cell, and it is very
different in its biology and response to mutagens from that in the male. The germ cell
stages progress in a very different manner in males and females. The female germ
cell is arrested in meiotic prophase. In contrast, germ cells in the male go through
a continual cycle of spermatogonial renewal through its entire mature life span.
Cell Environment
Germ cells at different stages are in different environments that could significantly
affect the access of mutagens. Udo Ehling has often reminded us more than once
that the mature sperm, which is a mutagen-susceptible gtage of spermatogenesis, is
no longer in the testes at all, but is in the epididymis. Also, the spermatogonia are
also outside what is now called the Sertoli cell barrier. Both meiotic cells and
spermatids are within this barrier but in different positions relative to the barrier itself.
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Sex Specificity of Mutagens
There are so called sex-specific mutagens in germ cells. There are a number of
recent studies that show that there are some chemicals that are mutagenic in late
oocytes but not in any male germ cell stage. Of course, there are a number of
mutagens that are effective in males but not females. So, extrapolating from the male
to the female, which is often done, may not be valid. For purely logistical reasons,
it is much more difficult to do mutagenesis experiments on females than males.
Induction of Mutations Resulting in Inborn Errors of Metabolism
Finally, in regard to the contribution of chemical exposure of germ cells to the human
disease problem, there have been several inborn errors in metabolism parallel to
those in humans or induced by mutagens in mice. The phenylalanine hydroxylase
seems to be an extremely susceptible loci to mutagens.
In summary, the following table (Table 5-3) presents some of the aspects of
mammalian germ cell, developmental biology, and genetics that should be taken into
account when assessing germinal risk.
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Table 5-3. Factors to be Considered in Assessing Gene Mutations in Germ Cells
• There are sex-specific mutagens in germ cells. These act either in the
mate or female but not in both.
• Germ cells at different stages of gamelogenests are in different
xits that could affect access of mutagens to the largest cells.
Stage-specific susceptibilities are important both for mutation
frequency and for spectra of induced mutations.
There are stage-specific differences in sensitivity to induced mutations
in females as well as in males. The conditions of exposure differ
according to developmental stage. As a general rule, large lesions are
induced in postgonial stages and smaller ones in spermatogonia.
Effects of differential gene expression
Expression of a gene may alter repair and thus induction of mutation.
Coordinated changes in gene expression occur during the
development and moderation of germ cells of both sexes.
Condition of the chromatin in germ cells
The mature sperm looks like an impossible target for mutagens, but
mature sperm respond to a significant number of mutagens.
Lack of cell cycle in germinal tissue during stages of gametogenesis
sensitive to mutation
Spermatids through mature sperm in males and oocytes are arrested
in meiotic prophase during the entire reproductive lifetime of females.
Repair-deficient, germ-cell stages in males
Fixation of mutations induced in late male germ cells occur in the egg
after fertilization. The genotype of the mother affects frequency of
mutations induced in the male.
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Chemically-induced Mutation: Comparative Outcome in
Somatic vs. Germ Cells
Dr. James W. Allen gave the Second Part of the talk on "Chemically-induced Mutation:
Comparative Outcome in Somatic vs. Germ Cells." Dr. Allen is from the Genetic
Toxicology Division of the EPA in Research Triangle Park, North Carolina, U.S.A.
DR. ALLEN: There are a number of factors modulating germ line
mutagenesis. I have organized them as: 1) those which affect the distribution of
mutagenic substances to the target molecules-such as physiological barriers in the
testes, 2) germ cell properties which mediate the induction of damage-such as cell
cycling, gene expression, various chromatin states, and 3) germ cell properties which
may mediate the fate and potential recovery of damage-such as repair and selection.
As many of these factors are operative at meiotic prophase, I would like to
focus on this particular stage. It encompasses many of the elements that are
considered to be protective of the germ line, as well as those which may involve
unique targets for damage to the germ line. Some factors relate to accessibility of the
mutagen to the meiotic prophase cells, and others to the crucial genetic processes
that may be affected, or to the various outcomes. The Sertoli cell barrier is generally
appreciated as being protective of germ line cells. It has been suggested several
times in this conference that, perhaps one of the reasons why spermatogonial cells
are showing relatively lower responses to mutagens, as compared with some other
cell types in spermatogenesis, is that they might be protected by the Sertoli cell
barrier. Actually, this should not be the case. Lonnie Russell has pointed out that a
chemical in the vascular supply should have access to the spermatogonial and
375
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prelepotene cells, which are in the basal compartment (Figure 5-4). It is not until
leptotene-zygotene stage cells are moved through the tight junctions of the Sertoli
cells to reach the adluminal compartment, where pachytene and later stages develop,
that these cells become protected by the barrier. Sperm leave this relatively sheltered
environment upon passing to the epididymis.
There is not very much known about how protective this barrier is against
different mutagens. In general, it is believed to be effective against chemicals with
high molecular weight, low fat solubility and/or high particle size. However, a number
of potential limitations of the barrier, even for these types of chemicals, have been
pointed out (by Lonnie Russell and Irving Fritz). Germ cells may take up the chemical
on one side of the barrier (in the basal compartment) , and then cross the barrier
where that chemical may have a delayed effect on meiotic chromosomal processes.
Or, damage may be carried across the barrier; that is, spermatogonial or preleptotene
stages may suffer the chemical exposure effects and then carry aberrations across
the barrier to further interfere with meiotic chromosomes, for example, their
segregation at cell division. Furthermore, some chemicals may directly damage the
Sertoli cells to gain access to the germ cells, and perhaps also to secondarily inhibit
nutrient supply to the germ cells. Several agents have been found to specifically
damage Sertoli ceils.
In some collaborative studies, Ilse-Dore Adler and I studied the effects of
bleomycin, which has a high molecular weight (>1400), on different cell types in the
testis. We determined that this chemical is very damaging to basal compartment
cells, as evidenced by subsequent detection of synaptonemal complex aberrations
in meiotic prophase. Even after bleomycin exposure of zygotene and pachytene
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SERTOLI CELL BARRIER
Adlumlnal Compartment
pachytene
Sertoll Cell
preleptotene
spermatogonial
Basal Compartment
mutagen in vascular supply
(Based on LD. Russell, 1977; 1990)
Figure 5-4
377
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stages of prophase, extensive induction of synaptic errors and breakage was
apparent in synaptonemal complexes. This agent clearly caused damage evident in
adluminal compartment cells.
It was expected that breakage events in synaptonemal complexes would also
be detectable as breakage events when those cells progressed to meiotic metaphase
stages. However, in both Ilse-Dore's lab and ours, only control levels of structural
damage were observed. Similar patterns of higher levels of damage observed in
prophase (SC aberrations), as compared to metaphase (chromosome aberrations)
have been found for cyclophosphamide, acrylamide, and other chemicals tested.
Apparently, much of the damage that is evident In synaptonemal complexes at
prophase disappears by the time those cells reach metaphase. The extent to which
repair and/or selection are operative in late prophase to reduce the amount of
damage being transmitted further is unknown. Some references made during this
conference to germ cells appearing less sensitive; to "induction* of damage may, in
fact, reflect instances in which the cells were very sensitive, but once damaged, they
did not survive long enough to be recovered at meiotic metaphase. Imperfect
synapsis is believed to be implicated in cell loss, and in aneuploidy of surviving cells.
Electron microscopy studies of synaptonemal complex formation can be used to track
homologous chromosome pairing through early prophase stages to complete
synapsis in pachytene stage of mid-prophase. The process of crossing-over
accomplishes chiasma formation, which holds the homologues together and helps to
regulate their separation in cell division. Figure 5-5 shows synaptonemal complexes
at pachytene from a mouse treated with cyclophosphamide. A normal-appearing
autosomal synaptonemal complex, with lateral elements of two homologues held in
378
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Synaptonemal complexes at pachytene from
a mouse treated with cyclophosphamide
Figure 5-5
379
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continuous register, is indicated by the asterisk (*). All of the remaining synaptonemal
complexes in the cell reveal various types of structural and/or synaptic errors. An
important question is-which types of damage will cause the cell to die, and which
types may be compatible with cell survival but may undermine recombination and
disjunction?
It is generally believed (with evidence from mutant mouse strains and humans)
that improper synapsis and recombination of the XY chromosomes can lead to cell
death. In this cell, the XY pair is sufficiently abnormal to be unrecognizable. In
surviving cells, failed recombination and chiasma formation in any of the bivalents may
lead to homologue nondisjunction. This concept is supported by the recent work of
Warren and Hassold and others studying individuals with trisomy conditions,
specifically Down syndrome and Klinefelter syndrome. They have reported evidence
of failed parental meiotic recombination in the extra chromosome of aneuploid
offspring.
I think it is important to understand the risks associated with mutagen effects
on processes of synapsis and recombination. However, it is also important to
consider this in light of new views about meiosis. We have all been taught that
homologous chromosomes synapse so that they can cross-over. Recently, there
have been some very interesting findings in yeast (as reviewed by Hawley and Arbel)
that indicate this perceived order of events in meiosis may, in fact, not be correct. It
was found that double-strand breakage serving to initiate recombination actually
precedes synapsis. The alignment of chromosomes in synapsis is believed to
function in the maturation of recombination intermediates necessary to complete the
exchange and form chiasmata. There is increasing evidence to suggest that proper
380
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disjunction depends upon crossing-over in connection with synapsis and SC
formation. Consequently, mutagen-induced interference with any of the steps in this
chromosome orientation and exchange process may pose an aneuploidy risk. It is
very apparent that synapsis is severely perturbed following mutagen exposure.
However, it is not clear which types of abnormalities are significant for undermining
recombination. It could be that they all are-we would like to know more about the
importance of such damage.
In the Armenian hamster XY bivalent, homology and synapsis is limited to the
short arms. We know from earlier bromodeoxyuridine labelling-differential staining
studies that most cross-overs occur in a proximal segment of the synapsing region.
This provides an animal model for analyzing synaptonemal complex anomalies that
may impact, or otherwise involve, crossing-over. We have found that there are, in
fact, some curious types of abnormalities that occur in that region. For example,
breakage across both lateral elements to result in complete scission of the SC has,
thus far in our analyses of XY chromosomes, only been observed in the cross-over
region. Our interpretation is that both homologues may be broken at the same site
as a result of disruption to cross-over exchange-leaving both chromosomes involved
in the exchange with double-strand breakage. If this type of abnormality predisposes
aneuploidy, it represents a germ-line specific mechanism; there is no regular
counterpart process in somatic cells subject to such interference. The same may be
said of induced alterations to synapsis, for example, "point synapsis", in these
chromosomes which can result from radiation or chemical exposure. It could cause
recombination to fail, and signify a germ-line specific mechanism predisposing
aneuploidy.
381
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The unique biology of germ cells affords many possibilities for germ cell-
specific pathways of mutagenesis. The consequences may be equally specific for
the germ line; some examples are infertility, germ cell tumors including those
characterized by aneuploidy, and abnormal sexual differentiation due to anomalous
meiotic exchange. These fundamental points are not always taken into account in
efforts to extrapolate from mutagenic mechanisms and outcomes associated with
somatic cells to those associated with germ cells.
382
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Qualitative And Quantitative Results From Short-term Tests
Dr. Michael D. Waters presented "Qualitative and Quantitative Results From Short-term
Tests." Dr. Waters is from the Health Effects Research Laboratory at the EPA in
Research Triangle Park, North Carolina, U.S.A.
DR WATERS: The main source of the information that I will discuss is the
EPA/IARC Genetic Activity Profile (GAP) Database, which appears in the (ARC
Monographs. The figure is an example of a genetic activity profile (Figure 5-6).
We are looking at lowest effective doses for positive test results along the top,
and the highest ineffective doses for negative test results along the bottom (positive
up, negative down). That is the way the profiles are plotted. In this particular plot,
the tests are organized in a phylogenetic sequence; we also do an endpoint plot.
Kerry Dearfield alluded to the EPA Office of Pesticide Programs Mutagenicity
Testing Guidelines in his presentation. What we were interested in doing was to look
at the performance of the short term tests, in particular, those being required by the
EPA under these Guidelines.
\
We also have been considering international testing requirements and together
with Bryn Bridges recently published a paper that is an assessment of the
performance of short-term tests relating to the UK guidelines.
This is the scheme that is being used in the U.S. for pesticide registration
(Figure 5-7). The initial three-tier test is required for all pesticide chemicals. It is
actually four tests, if you consider the L5178Y gene mutation assay as being
performed with an assessment of small and large colonies, or in conjunction with an
in vitro cytogenetics assay.
383
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OPP MUTAGENICITY TEST GUIDELINE
Salmonella + In Vitro
Gene Mutation
+ In Vivo Bone
Marrow Cytogenetics
• Aberrations
or
• Micronuclei
In Vitro Gene Mutation (choice):
(a) Mouse lymphoma L5178Y cells, TK locus,
small and large colonies
(b) Chinese hamster ovary cells strain AS52
(c) Chinese hamster ovary or lung fibroblasts
with the appropriate in vitro test for clastogenicity
Figure 5-7
384
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By way of example, to follow up a positive response for chromosomal effects,
the dominant lethal test or the heritable translocation test would be performed. Or for
positive gene mutation, one would undertake studies to look at the interaction of the
chemical with gonadal DMA, to be followed by a specific locus test.
This is the battery (Figure 5-8), that is required by the EPA Office of Toxic
Substances to be used by industry in the evaluation of Industrial chemicals according
to specific test rules.
Our germ cell database was assembled primarily from the EPA/IARC GAP
database; this was supplemented with data from the EPA Gene-Tox database. Gene-
Tox, was established to review test systems, whereas GAP was set up to facilitate
evaluation of chemicals. Sometimes either of the two of these databases may be
somewhat out of date. So, by combining the two, we hoped that we would get most
of the available data on germ cell mutagens.
We started out with 64 chemicals (Figure 5-9) and reduced the number to 30
confirmed positives. We subsequently reviewed the literature for putative germ cell
nonmutagens, in order to create a negative data set. There were 26 of those. This
overhead just describes how that process occurred. With the germ cell data in hand,
we looked at the corresponding mammalian somatic cell and in vitro data and then
we updated all of it.
These are the test codes for the germ cell assays (Figure 5-10), including the
specific locus tests, dominant lethal tests, and chromosomal aberration assays, in
various germ cell stages.
This is the confirmed germ cell positive database (Figure 5-11). This is just to
give you a sense of how much information is in the database. Here is bacterial
385
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OTS MUTAGENICITY TEST SCHEME
Salmonella + In Vitro
+ In Vivo Bone
Gene Mutation Marrow Cytogenetics
• Aberrations
or
• Micronuclei
Dominant Lethal
(DLM, DLR)
Interaction with Gonadal DNA
(CCC, CGC, CGG, COE)
Specific Locus (SLP, SLO) Heritable Translocation (MHT)
• Visible
• Biochemical
Figure 5-8
386
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HOW WAS THE DATABASE ASSEMBLED?
• Data were reviewed for 64 chemicals with evidence of germ
cell mutagenicity; 40 from the EPA/IARC GAP database and
24 from the GENE-TOX database.
The germ cell data were updated and the list of chemicals
was reduced to 30 confirmed positives.
• Data for the GENE-TOX chemicals (10) were adapted to the
EPA/IARC GAP format and the datasets were combined.
1 Chemicals without data for specific somatic cell tests were
updated from the recent literature.
1 The updated dataset was used for the analysis presented here.
1 An anatogus procedure was followed for preparing the dataset
for the 26 germ cell nonmutagens.
Figure 5-9
GERM CELL MUTAGEN TESTS
TEST CODE DEFINITION
Mouse Specific Locus Tests
SLP Post-Spermatogonial
SLO Other Stages
MHT Mouse Heritable Translocation Test
Dominant Lethal Tests
DIM Mice
DLR Rats
Chromosomal Aberration Tests
CCC Spermatocytes Treated In Vivo, Spermatocytes Observed
CGC Spermatogonia Treated In Vivo, Spermatocytes Observed
CGG Spermatogonia Treated In Vivo, Spermatogonia Observed
COE Oocvtes or Embryos Treated In Vivo
Figure 5-10
387
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Short-term test results for agents positive in more than one germ cell assay
CTCLOPHOSPHAUIOE
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Figure 5-11
388
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mutation, here are the mammalian cell assays, both mutation and chromosomal
aberration, and here are the bone marrow assays, the mouse micronucleus test, and
chromosome aberrations assay. The positive responses are in black, open circles are
negative, conflicting calls are the half-shaded figures. "P is a positive response in a
female dominant lethal assay, which is in the DIM column.
This is the positive data set (Figure 5-11), where you have a confirmed positive
response in more than one germ cell assay. The next illustration (Figure 5-12) shows
a confirmed positive response, but only in one germ cell assay, and again you see
the corresponding bacterial, mammalian cell and bone marrow data.
To segregate this information for your evaluation (Figure 5-13), we have
ordered the chemicals most-to-least tested. Please look at the consistency of the
black dots. It is immediately apparent from this kind of assessment that the short
term in vitro tests do well in picking up the confirmed germ cell mutagens. I will show
you the actual numbers in a later slide. One exception, ethanol, is not picked up.
But, for the most part, the short-term tests are quite sensitive. Another notable
exception, hexametapol, is not detected in the in vitro tests but is picked up in the
bone marrow assay. It may be hard to appreciate, but the bone marrow assay is
doing quite well here, and as we look into the other negative data, you will see that
it is doing even better.
Now examine the negative data set (Figure 5-14). It became critically important
if one were going to evaluate the performance of these short-term tests to build a
negative germ cell data set. As far as I know, this has not been done previously. It
is a challenging task, because, one has to review a lot of data and confirm that the
data is negative. We used the Gene-Tox criteria to call these results negative. There
389
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Short-term test results for agents confirmed positive in one germ cell assay
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390
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POSITIVE GERM CELL VS. SOMATIC CELL TESTS
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HMS
DBCP
6-MERCAPTOPURINE
CISPLAT1N
BLEOMYC1N
ETHANOL
B(A)P
AORIAMYQN
N. MUSTARD
MTX
HEXAMETAPOL
METEPA
METHYUCL
GERM
CELL
H NH
BONE
MARROW.
MM CA.
IARC
EVAL
Positive In specific-locus tests and MHT
Positive in MHT or in nonherltable tests
Single germ cell test confirmed positive
2A
2A
2A
3
2B
3
* 1: Carcinogenic to humans; 2A: Probably carcinogenic to humans; 2B: Possibly
carcinogenic to humans; 3: Not classifiable as to its carcinogenicity In humans
Figure 5-13
391
-------
Short-term test results for agents negative in more than one germ ceJI assay
MCHLORVOS
PCS
ETHYLENEMBROMIOE
ACRYLONtTTVLE
CYCtOHEXYLAMINE
MTROFURANTON
2.4-OlNrmOTOLUENE
FORMALDEHYDE
CALOUM CYCLAMATE
CAFFEINE
PARATMON
2.4.S.T
CADMIUM
ARSENIC +3
BENOMYL
PROPYLENEOXnE
VINYL CHLORIDE
METHYL PARATHON
GERM CELL
MUTATION
SS CCCC DDM
LL COQO LLH
PO CCQE MHT
OOO O
o o
oo
o o
oc o
o o
oo
o o
o ooo
ooo
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o oo
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o o
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BACTERIAL
MUTATION
SSSSSSSSS
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234 5 7 »» OS
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•0 O*
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MAMMALIAN CELLS
• MUTATION ABS.
QOQQGQQQQG CCCCCCCCCC
CO »M55 MH 1 1 1 1 1 1 HHH 1
LOHOLT1AHT CMRSTAFLTH
O • • • O
O •
• • • •
• *e** • o o
o • e
• o o
o • o
• • ••
o o •
ooo 00*0 •••
• o •
•
• • o •
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• • •
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MARROW
MMMMMCC
VVVVVBB
MRCAHAH
0 0
O O
0 O
O O
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o
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o» o
o e
ooe e
e
• o
o •
e
• e
• e
• e
e
• ••
•• •
• Positive
e Conflicting
O Negative
Figure 5-14
392
-------
are a number of compounds that are surprisingly negative, but this is the negative
data set. Again, we are looking at the black dots. That is, what the short term tests
are doing wrong? We are also interested, especially, in this bone marrow column.
Here is the residual negative data set (Figure 5-15). These are the chemicals
that only have a single confirmed negative response. In terms of the strength of the
evidence, you see that the negative data set is much weaker. We do not have any
heritable assays represented, and we have much less data for the non-heritable
assays, but all of these test results are in agreement. We have a consistently negative
data set.
Now we have reduced the negative data set (Figure 5-16), hi order to give you
the ability to look quickly at the germ cell column. This is the standard by which we
are measuring the other short-term tests. And now we want to examine the
performance characteristics of the various short term tests. So, (in the darker shaded
area) we have bacterial gene mutation, mammalian cell gene mutation and
chromosome aberration and then bone marrow micronucleus, and chromosomal
aberrations. Finally, in the last column there is the IARC evaluation of the
carcinogenicity of these compounds. Many of them are quite clearly animal
carcinogens.
The horizontal line that is drawn across the middle, of this figure is to show you
where the bone marrow assays agree with the negative germ cell response. Then,
in the bottom half, you have either a bone marrow call that is either in question, or a
clearly positive call. If you look at the bone marrow data, I think the results are a bit
more encouraging, than in the case of the in vitro data, where you have quite a few
black dots, at the top and at the bottom of the slide.
393
-------
Short-term test results for agents confirmed negative in one germ cell assay
MEUMN
t.l-OIMETHYUmMAaNE
EPtCHLOWXYDWN
CHLOKMNE
KXPET
HEXACHLOHOBENZENE
GERM CELL
MUTATION
SS CCCC DDM
LL CGOOLLH
PO CCOE MR T
o
o
o
o
o
o
BACTERIAL
MUTATION
SSSSSSSSS
AAAAAAAAA
234 57«»OS
oooooo
o 000*00
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ooooo
••oo »o
oo oo
MAMMALIAN CELLS
MUTATION ABS.
QQQQOQOQaQ CCCCCCCCCC
CC89MS5 1 IH 1 1 1 1 1 1 HHN 1
LOHOLT1AMT CMRSTAFLTH
•
•O
•• ••• • •
o» • oo
• • ••
o
BONE
MARROW
MMMMMCC
V VVVV BB
MRCAH AH
O
O
o e
• Positive
e Conflicting
O Negative
F - Positive in females
Figure 5-15
NEGATIVE GERM CELL VS. SOMATIC CELL TESTS
PCB
CAFFEINE
CADMIUM
DIELDRIN
CYCLOHEXYLAMINE
2.4-DINrTROTOLUENE
NrTROFURANTOIN
1.1-DIMETHYLHYDRAZ1NE
FORMALDEHYDE
ETHYLENE DIBROMIDE
ACRYLONrmiLE
DICHLORVOS
PARATHION
CALCIUM CYCLAMATE
EPICHLOROHYDRIN
ISONIAZIDE
^4.5-T
ARSENIC +3
THEOBROMINE
BENOMYL
PROPYLENE OXIDE
VINYL CHLORIDE
METHYL PARATHION
HEXACHLOROBENZENE
CHLORDANE
FOLPET
Figure 5-16
394
-------
And now, we come to the performance characteristics of the short-term tests
(Figure 5-17). Since we have 30 agents positive in germ cell assays and 26 agents
negative, we are in a good position to compare these performance characteristics.
I am going to point out the numbers for sensitivity and specificity. These numbers
are of primary concern, but since prevalence was not bad, we also calculated
predictivity and accuracy.
Sensitivity and specificity are as follows: 1) bacterial mutation, 79% for
sensitivity, specificity 50%, 2) sensitivity for mammalian cell gene mutation, 96%,
specificity 21 % and 3) chromosomal aberration, sensitivity 92%, specificity 11 %. It has
been a concern, on the part of many people, that in vitro chromosomal aberration
assays are giving too many false positive responses, and this evidence indicates that
their specificity is quite low.
If, on the other hand, you examine the in vim bone marrow assays, you have
high sensitivity (96%), whether you use micronucleus test, the chromosome
aberrations assay or either of the two. Specificity is 63% for the micronucleus test,
64% for chromosomal aberrations, and if you use either, it is 55%. The number of
chemicals tested is small, but the indication is that clearly the bone marrow assays,
either the micronucleus or the chromosomal aberration assays, are performing much
better, in terms of specificity than are the in vitro systems.
Since we had the lowest effective dose (LED) information, we wanted to
examine how the tests under investigation were performing, relative to one another,
in a quantitative sense. We decided to regress comparable bone marrow tests, and
then to regress comparable germ cell tests to see whether the two data sets would
395
-------
Performance of Somatic vs. Germ Cell Tests
for Agents Confirmed in Germ Cell Studies
IN VITRO TEST SYSTEMS
GERM "*"
cat
MUTATION _
PreetlctMty
GERM +
ffl I
MUTATION _
BACTERIAL
MUTATION
+ " -
23
13
6
13
64% 68%
MCRONUd£US
+
22
7
1
12
Pndletmty 78% 92%
Sens*
78%
Specil.
50%
Accur.
65%
INVIV
Sensit
96%
Specif.
63%
Accur.
81%
MAMMAUAN
MUTATION CHROM.ABS.
+ - H- -
22
15
1
4
59% 80%
0 BONE MARROV
CHROM. ABS.
+ -
22
5
1
9
8f% 90%
Sensit.
96%
Specil.
21%
Accur.
62%
(V TESTS
Sensit.
96%
Specil.
64%
Accur
84%
22
16
2
2
58% 50%
EITHER
+ -
27
a
1
11
75% 92%
Sens/I.
92%
Specif.
11%
57%
Sensit.
96%
Specil.
55%
Accur.
79%
Figure 5-17
396
-------
be in quantitative agreement in terms of LEDs. This (Figure 5-18) was the regression
of the mouse micronucleus test LEDs versus the mouse bone marrow cytogenetics
assay LEDs. The data base has been culled for only mouse data, and only IP
injections. So we are looking at a common route, ail in the mouse and the correlation
coefficient is 0.77. There are only 11 chemicals in common between the two tests.
We do not have a large data set here, but for other chemicals not tested in germ cell
assays, we still have a good quantitative correlation between the bone marrow
chromosome aberration and micronucleus tests.
These are compounds for which we do not have germ cell data (Figure 5-19)
and the correlation coefficient is still good, so I think we have some degree of
confidence that these two types of bone marrow assays are giving us the same sort
of quantitative LED data.
Here we are looking at the dominant lethal test and the mouse heritable
translocation test (Figure 5-20) - two tests that you would expect to correlate quite
well in terms of LED, and, in fact, they do. The correlation coefficient is 0.92, and the
slope is 0.98. So, I think this gives one confidence that the dominant lethal test and
mouse heritable translocation test are yielding similar data, in terms of LED.
We look now at the dominant lethal test in the mouse, versus the micronucleus
test in the mouse, performing the same regression (Figure 5-21). This solid line is the
actual regression line, the dotted line is what you would expect for a one-to-one
relationship. So, you see that the mouse micronucleus test is responding at a slightly
lower dose, on the average (as indicated by the Y intercept), than the dominant lethal
test. For a screening assay (such as the micronucleus test), this is quite desirable.
There are 18 chemicals represented in this pair-wise regression.
397
-------
LEDs of Germ Cell Mutagens Tested in CBA vs. MVM
MVM Dose (log(mg/kg))
Correl. Coeff. = 0.77
Slope = 0.88
Y Intercept = 0.11
N = 11
(D
(2)
TEM
A Triaziquc ne
A
PRO
MMC
AA
EM:
MMS
A EMU
(1) 0 1 23
CBA Dose (log(mg/kg))
Figure 5-18
LEDs of Agents Tested in CBA vs. MVM
(Not Tested in Germ Cell Assays)
MVM Dose (log(mg/kg))
4
3
Correl. Coeff. = 0.75
2
Slope = 0.89
1
Y Intercept = 0.05
0
N = 19
(1)
(2)
P
>
XI
i
X
t
XJ
Y
14
A
-s
' A
A ./
A S
7*.
k
!) (1) 0 1 2 3 4
CBA Dose (log(mg/kg))
Figure 5-1 9
398
-------
LEDs of Germ Cell Mutagens Tested in DLM vs. MHT
MHT Dose (log(mg/kg))
4
Correl. Coeff. = 0.92
Slope = 0.98
Y Intercept = 0.05
15
/
TEPA
/ATE
/A Tnajiq
z
L/*ThioTE
»
one
CPA
IMS
INU* Y
/A
/MMS
W
'A
IMP
PRO ./
y
^/ENU
EMS
AEO
^/
DLM Dose (log(mg/kg))
Figure 5-20
LEDs of Germ Cell Mutagens Tested in DLM vs. MVM
MVM Dose (log(mg/kg))
Correl. Coeff. - 0.72
Slope = 0.98
Y Intercept = -0.32
N = 18
-2^
ThioTEP. A X
A Tnazit jone
MylemA.*
/ MMC
EMS ^
-------
What we did next (Figure 5-22) was to select the most sensitive stage of the
specific locus test and regress those results against the results of the mouse heritable
translocation test, and this is the line that you see. These two tests are quantitatively
quite comparable in terms of LED even though they represent different endpoints.
In summary, if one wants high sensitivity, short-term in vitro tests provide that
sensitivity at low cost. Obviously, you can test a lot of chemicals with these assays.
But if you want to discriminate potential germ cell mutagens, you need to move
quickly into the bone marrow tests. They seem to be quite useful in a qualitative
sense, and, from the present data, also in a quantitative sense in predicting what the
response might be in the germ cells. And then the germ cell assays themselves seem
to be correlating quite well, in terms of lowest effective dose. This argues for the
value of the dominant lethal test as a relatively inexpensive confirmatory germ cell
assay.
What kinds of agents would we want to examine in the future. I think we
would want to add more data particularly for those chemicals that were positive in a
single germ cell study (Figure 5-23). Mike Shelby is going to talk about some of the
agents on this list. He will go into some of the details about why we might want to
look further at some of these agents.
400
-------
LEDs of Germ Cell Mutagens Tested in MHT vs. SLT
SLT Dose (log(mg/kg))
^
3
Correl. Coeff. = 0.92
Slope = 0.88 2
Y Intercept - 0.27 1
N = 9
0
(D
(1
/ATEM
X
xL
MMS EMS
*
X
CPA ./
A >/
kX PRO
«NU ENU
/
) 0 1 234
MHT Dose (log(mg/kg))
Figure 5-22
401
-------
AGENTS POSITIVE IN MAMMALIAN GERM CELL TESTS
Heritable (H) NonHeritable (NH)
• Positive
O Negative
e> Conflicting
F Female Pos.
CYCLOPHOS.
MYLERAN
ETHYLENEOX.
MMS
EMS
MYTOMYCINC
ENU
MNU
ACRYLAMIDE
feu
ICM
PROCARBAZINE
HHYL SULPHATE
THIOTEPA
TRIAZIQUONE
IMS
TMP
TEPA
HMS
DBCP
=RCAPTOPURINE
aSPLATIN
BLEOMYON
ETHANOL
B(A)P
ADRIAMYCIN
N. MUSTARD
MTX
HEXAMETAPOL
METEPA
METHYL CL
S S M
L L H
POT
C C C C
C G G O
C C G E
Positive in specific-locus tests and
• *
• O
0 •
• O
• o
0 •
• •
•
• •
: • •
• O • •
0 •
• O •
• 0 • •
O •
• 0 •
D D
L L
M R
MHT
• •
• •
• •
•
•
•
•
•
• •
:
Positive In MHT or in nonheritable tests
e
e
•
•
0
e o
O 0
o o o
• • •
o •
o •
o •
•
•
• e •
• • •
o •
•
•
•
•
•
F •
0 •
•
F
F
• •
Single germ cell test confirmed positive
0
• F
Figure 5-23
402
-------
Chemicals for Future Study off Heritable Genetic Damage In
Human Populations
Dr. Michael Shelby presented "Chemicals for Future Study of Heritable Genetic
Damage in Human Populations*. Dr. Shelby is from the National Institute of
Environmental Health Sciences in Research Triangle Park, North Carolina, U.SA
DR. SHELBY: One of the concerns that has been expressed throughout this
meeting is the fact that there are no known human germ cell mutagens. In fact, we
do have evidence of human germ cell mutagens. One of them is ionizing radiation.
Brewen, Preston, and Gengozian published a paper in 1975 on cytogenetic analysis
for translocations in irradiated testes of humans. Rene Martin's work using human
sperm and hamster egg fusion experiments has also shown that structural and
numerical chromosome aberrations result from testicular radiation.
More recent studies, again using Rene Martin's basic technique in cancer
chemotherapy patients, have clearly shown chromosomal aberrations in human
sperm. So, we do have evidence of human germ cell mutagenicity, not the
transmission of genetic damage expressed as genetic disease in subsequent
generations, but there can be little doubt that such effects will result.
The purposes of animal germ cell mutagenicity studies include the simple
identification of germ cell mutagens. Secondly, we study the mechanisms by which
these mutations are induced, and finally, we generate data for risk estimation. Many
people lose track of these different types of studies and they try to do risk estimation
based on experiments that are conducted to identify germ cell mutagens. I think that
is a problem many of you have run into here today.
403
-------
To do germ cell mutation risk assessments, there are several factors that have
been brought up that are typically not a part of germ cell mutagen identification
experiments. One is the gender that is affected; we know now that there are specific
mutagens for each gender. The cell stages affected, the dose-response, dose rate
effects, and the types of mutations induced whether you want to think of these as
deletions versus base pair substitutions or dominant lethal mutations versus specific
locus mutations, are all critical when one is trying to estimate genetic risk. As was
discussed some yesterday, I think it is important that we look at reproductive outcome
whenever possible. Our standard mutational endpoints are very informative but
reproductive outcomes provide an additional, medically relevant, endpointthat we can
review.
What chemicals should we study in humans in our efforts to toy to identify
germ cell mutagens? I have come up with three exposure situations that seem
reasonable to me.
First are the chemotherapy agents in which you have a population of
individuals who have been exposed to high, acute, defined exposures to known
mammalian germ cell mutagens. A study is now underway to investigate the health
outcomes of some 25,000 people who have survived childhood cancer, who were
diagnosed with cancer before the age of 21 and have survived more than five years.
The primary objective of this study is to look at the health outcomes in these 25,000
individuals. But, there is a big opportunity to do studies on the offspring of these
people where we have both the parents and the offspring available for evaluation.
The second situation involves high occupational or accidental exposures
because they provide unique opportunities to study human populations. We hope
404
-------
those do not occur; but if they do, we should take advantage of them. Finally, we
should study large populations exposed to low doses of potent germ cell mutagens,
a situation which, theoretically has the greatest potential for affecting the human
population. Fortunately, as far as we know, there are no potent germ cell mutagens
to which large segments of the population are exposed.
These are the chemicals that I would suggest are the primary ones we should
study in human populations. They are all cancer chemotherapy agents. Chlorambucil
is positive in all the endpoints we have looked at. Post-meiotic stages in male mice
are affected. It is biologically active in humans. In addition, these are all human
carcinogens. That sometimes detracts people's interest in germ cell mutagens but
from our point of view, it provides evidence of their biological activities in humans.
Because they are human carcinogens, they must be metaboiically activated or direct
acting in humans. Chlorambucil is used in breast and ovarian cancer chemotherapy,
and we think it would be very important to study it in females.
The second chemical, melphan, is positive again in dominant lethal, heritable
translocations, specific locus, and affects all germ cell stages in the male and is
positive cytogenetically in humans. All of these compounds down to tresulphan,
which is potent in in vitro and in vivo mutagen tests and an alkylating agent, is very
similar to myleran and busulphan. We have been studying these chemicals in our
program, mainly through the work of Waldy Generoso and Lee Russell, for the reason
that we think if we have a sound mouse germ cell mutagenesis database, we will be
prepared when the time comes for the studies to be done in humans. We will know
whether the female or male is affected, and what germ cell stages are most sensitive.
These are factors that are sometimes ignored by epidemiologists. If you look at the
405
-------
papers in the literature, they often give no consideration to what germ cell stage is
affected and how long a period has elapsed between exposure and conception. But
in this case I hope that we are at least partially prepared to work in support of the
human epidemiology studies.
So with regard to the title of the talk here today, that is the one slide I have,
chemicals for future studies in humans. There is another interesting group of
compounds, none of which are chemotherapy agents except for azathioprine. Again,
it is a human carcinogen. It is a rodent clastogen and there is a single report of
mouse dominant lethal positive. All these results were thanks to Mike Waters and the
massive database he has on chemical mutagens. Benzene, to which there is the
most widespread exposure to a known potent clastogen and human carcinogen, is
virtually lacking in animal germ cell mutagenidty data. Benzene is, I think, well worth
studying and it simply has not been done. N-butyl glycidyl ether and diazepam, are
potential germ cell mutagens. There are reports of bone marrow micronucleus
positive results in animals with diazepam so here may be one case of widespread
exposure in the population of what might be a potent germ cell mutagen; we just do
not have the data to say whether it is or not.
Ziram is in the same situation. There is also a smattering of information in the
literature that ethanol may affect genetic integrity of germ cells and there is certainly
widespread chronic exposure to this chemical. So, there is a another partial list of
compounds on which I think we need to extend the animal data base before we
consider these for studies in humans. If not before, at least concurrently with the
human studies that might be conducted.
406
-------
There are few special topics that I had to throw in here that are not part of my
title. DBCP remains enigmatic in the fact that it sterilizes humans, it induces dominant
lethals in male rats, and it seems to have no effect on the male mice. DBCP is one
issue that should be sorted out in our literature I believe.
We have Dr. Generoso's female specific germ cell mutagens with regard to
dominant lethal induction. Additional work certainly needs to be done there.
Aneuploidy work is going ahead very well I think. A big study is being conducted in
Europe. There are such chemicals as chloral hydrate which are used to sedate
children, and trichlorfon which I have another slide on here, that was reported to
induce a high frequency of abnormal birth outcomes in a small village in Hungary.
I do not know if you all have seen that paper or not; I have a couple of copies with
me, but it is some of Andrew Czeizel's work in Hungary and some very impressive
data.
Another topic involves the triplet repeat mutations that have been shown now
to be associated with at least six human genetic diseases. We do not know if these
are chemically inducible because, as far as I know, we do not have a mouse or an
in vitro model to study it and a mouse model would be very convenient to have to
study this type of genetic alteration. Finally what role should transgenic animals play?
There has been a world of hype about what the transgenics could do for us and I
think it is a great deal, but their role in studying germ cell mutations has not yet been
defined and I think this is desperately needed at this time.
I will show you one set of Rene Martin's data because she refers to it in her
paper as important to those who are considering use of the parallelogram in
assessing genetic damage. This is simply the percent of aberrant cells. It is from 13
407
-------
patients on radiation therapy where she looked at chromosome aberrations in these
patients' lymphocytes and in their sperm using fusion of sperm and hamster eggs.
These are the months after radiation therapy running from one month to five years.
These men were azoospermic for about a year after they were exposed to radiation
therapy. So, there was no sperm to do cytogenetic analysis on, but if you look at the
24 month to five year samples, you see that the data are not real tight, but they seem
relatively steady in the sperm through that four year period while in the lymphocytes,
the frequency goes down with each year of sampling. If you try to use this kind of
data in a parallelogram, just an example, at this point it looks like you have a one to
one ratio of aberrations in lymphocytes and sperm. If you look out here you have five
times as many sperm with aberrations as you do in the lymphocytes. It is the kind
of data that simply raises a red flag that you have got to think about using the
parallelogram to understand what is happening to the lesions you are looking at.
This is the Czeizel study, the paper on environmental trichlorfon and the
cluster of congenital abnormalities. In this small village over a two year period, there
were 15 births. Eleven of them or 73% had congenital abnormalities. There were
three sets of twins in these 15. The number of Down Syndromes is very high. The
number of births was lower than the expected number, so there may also have been
sterility or abortions.
Anyway, it is interesting because this effect was attributed to consumption by
the mothers of trichlorfon-contaminated fish, trichlorfon they used to eliminate
parasites from the fish on the fish farms. They sometimes used ten times more of this
chemical than recommended. It killed the fish, but they were picked up and eaten
408
-------
because food was short. We have this chemical on study in the continuous breeding
protocol for reproductive effects and for the induction of dominant lethals.
Discussion
DR. EHRENBERG: I have one big question to each of the Mike's...any
qualification on that. What are the criteria used for positive and negative results?
DR. WATERS: As I mentioned we followed the Gene-Tox criteria so that we
went through each study to match the Gene-Tox criteria for a positive or negative
response and I cannot quote you all those criteria right now but they are as published
in Mutation Research. It is an obvious question. It is the sort of thing that we are
very concerned about, but we felt that in order to get some handle on the
performance of the short-term test we needed to create that data set. I do not really
have any apologies for it. I accept your criticism and we are aware of it.
DR EHRENBERG: It is a question I think of historical data that we have. The
question to you Mike, is why not include nitric oxides and nitrogen dioxide as
chemicals in the test. With the nitric oxide you have both the endogenous and
exogenous exposure. The endogenous exposure varies very much between
conditions of the human - ten fold over production in certain allergies.
DR SHELBY: Very good idea. We will include it. I would also like to answer
part of the question you asked Mike Waters. Ilse-Dore Adler is very aware of this
question and so much concerned about it that she recently organized a meeting in
Neuherburg to address this issue of the analysis of a few of these tests. Their
recommendation was that the power be clearly stated in manuscripts so that when
you see a negative result you know the level of effect ruled out by the study.
409
-------
DR. ADLER: That is future music. I mean the literature results, in particular
the negative ones. I still need to look at them and It is fine if you find a key to the
positive effect criteria for that is very simple and straightforward; and I have done or
I have reviewed all the positive data and confirmed that they were really truly positive
but with the negatives there is a lot of work ahead of me.
DR WATERS: I was going to say that Ilse-Dore quite kindly has gone through
all of the positive agents for us, and she is hopefully going to agree to look at the
negative data set also. But that has not been done yet so we are a little premature
in presenting this. We thought it would give you some idea of how the information
seems to be sorting out so far. I think at least the bottom lines to me are very
interesting with respect to the performance of the in vitro tests versus the bone
marrow assays.
Da NATARAJAN: Mike, you have a table from Renee Martin, the sperm
chromosomes and the lymphocytes.
DR. SHELBY: Right.
DR. NATARAJAN: I think this is what one would expect, I mean nobody has
adequately compared the numbers if you have been in the field of radiation biology.
DR BISHOP: And you are a cvtogenetidst.
DR NATARAJAN: Yes, because the lymphocyte population is renewed, the
half life of lymphocytes in the human body is about 200 days or 220 days; so that is
what you expect, that the population depleting and you are comparing the sperm then
they are coming from the stem cells, and it is good through meiosis and the selection,
and then what do you get. So, you get this big difference in what is expected. This
can be combatted, too.
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DR SHELBY: Right, if you are not a cytogeneticist and you do not know
about cell cycles then you are a risk estimator.
DR SHELBY: But that does not keep them from doing it.
DR ANDERSON: Do we now all accept from what Mike has just put up that
there are in fact human germ cell mutagens, so we could all stand up with our hands
on our hearts and now say we believe in x-rays, germ cell mutagens, human germ cell
mutagen, or a human mutagen, and also that chemotherapeutic agents are; so if we
are asked, do we know any human germ cell mutagens, or human mutagens we will
say yes. Is that right? Does everybody accept that?
DR ADLER: If we start to do more sperm studies we might come up with
better examples even than that.
DR. ANDERSON: Do we accept that x-rays are then, human mutagens.
DR. BISHOP: I think the discrepancy, Diana is that mutation is a heritable
change and these chromosome aberrations that are being induced; these studies are
not looking at a heritable change, they are not looking at factual transmission so I am
not sure everybody would automatically accept that those are human mutagens
because we are not showing a heritable change.
DR. ANDERSON: But sperm changes can be transmitted, sperm morphology
changes can be transmitted, some of them.
DR PRESTON: I would very quickly say we saw reciprocal translocations in
spermatocyte in humans; you find exactly the same alteration in the mouse and in the
mouse you can show that it is transmitted to the F, so I think that is probably fairly
solid.
DR ANDERSON: Do we accept it?
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DR. ADLER: Any more questions, comments? If not, thank you all again and
I think it is has come up to right about that time, who is going to do the wrapping up?
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Wrap-up And Concluding Remarks
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414
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Wrap-up
Dr. Brusick gave the wrap-up presentation representing his views on the scientific
sessions. Dr. Brusick is from the Hazleton Labs America, Inc., located in Vienna,
Virginia, United States.
DR BRUSICK: It appears to me, after spending two or three days talking
about this, that we certainly do not have an absence of information to do risk
assessment. If anything I would say that we have too much information available to
us. If you go to other areas of toxicology, the cancer risk assessment people are
very happy if they have one good study to do their risk assessment on, and they do
their risk assessment and then live and die by that risk assessment, until someone
else comes along and argues with them that there is another study.
We have compounds here where we say, "well we have got one specific locus
test, we have two heritable translocation tests, and maybe three or four dominant
lethal studies. We need some more data to do a risk assessment." And I do not
think that that is really true.
I think in fact we know too much about what we are doing, and as a result of
knowing too much and too many of the subtleties about the process of mutation, we
end up with two problems.
One is, I think a function of paralysis by analysis (Figure 6-1). What that
means is that you are analyzing so much that you fail to act; you, in a sense, paralyze
yourself because you spend so much time down among the trees, that you lose the
forest, and I think that we have done that to some extent.
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CATCH 22 SITUATION
REGULATOR: WE CANT PREDICT HOW TO REGULATE ON THE BASIS OF
GENETIC RISK UNTIL WE HAVE ENOUGH DATA (EXPOSURE, ROUTE OF
ADMINISTRATION, PK, DOSE RATE) TO KNOW EXACTLY HOW TO APPLY THE
RISK ASSESSMENT.
REGULATED: WE ARE NOT OBLIGATED TO SUPPLY ANIMAL GERM CELL
DATA UNTIL THE AGENCY CAN TELL US HOW IT WILL BE INTERPRETED AND
USED.
Figure 6-1
The second problem (Figure 6-2) that I think has happened as a result of
knowing too much, is that we have intimidated potential users of this information. It
is so complicated and so intimidating, with the information that we have, and
acrylamide was a good example. Every time we begin a discussion, we would talk
about beginning to do an estimate of risk for a certain type of mutation, and someone
would throw out something more that we know about the window of opportunity for
CONSEQUENCES
NEW DATA MUST BE DEVELOPED FROM R&D PROGRAMS WHICH
ARE POORLY FUNDED OR HAVE MISSIONS
INCOMPATIBLE WITH THE AGENCIES
Figure 6-2
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mutation or heritable translocation, because ft is this kind of mechanism; these are
very, very fine details which may or may not have any relevance to the overall number.
The variance on these numbers is so big that some of these little fine things just really
do not contribute that much, but they intimidate anyone trying to understand what is
going on and I think to some extent they intimidate us to the point that we are afraid
to sort of lay this out. Because if I do not make it complicated, then I am going to be
criticized by my peers for trying to simplify this process too much. So in a sense, we
are sort of losing because we know too much, I believe.
So it is not surprising that when we go through all this, and you give it to the
regulatory agencies, that one of the things that happens is that they run right back
and adopt the Ames test because it is simple, you understand it and boy, I could take
the Ames test and I can make all the conclusions. I can extrapolate it all over the
world because of the perception of the simplicity and the way it is explained.
So I think that there is really the core of the problem that I see. Taking into
account that we do not want to oversimplify or throw out numbers at people are going
to grab, and I really realize that. But I think that what we have is that genetic
toxicology to some extent is a technology in search of a problem.
We do not have the corpse and that has been alluded to, many different times.
We have a lot of smoking guns, I think. We have got animal data, which is a smoking
gun. We have got all the information that Mike talked about, which in a sense is
indirect information; but we still do not have the corpse, and we may never have the
corpse. So I do not think that we ought to try to hang this field particularly on finding
the corpse.
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Another problem we have is the Catch-22 situation and this is between the
regulators and the regulated. The regulator says we cannot predict how to regulate
your compound because we really do not have enough information on the dosimetry,
dose-effect rates, dose, route of exposure, PK information, et cetera, please give us
that information so we will understand how to use this technology. The regulated
community says we are not going to give you this information until you can show us
how you are going to use it.
So we have got a dilemma in that nobody has access to information, to
develop a way to use the information, and therefore we cannot actually solve the
problem of the regulated, by telling him what we are going to do, because we will
never get the information. As a consequence, data are generally developed out of
our R&D program. And most of those, as we know, are not funded to this same
extent. They do not have the buying power, the data development power, that
industry has in developing products, if that information were made available. In many
cases, without apologizing, these R&D programs are not directed necessarily at the
missions of the agencies like EPA, or some of the others. They want to go and study
some other information, where in fact EPA needs some very fundamental information,
which is not particularly a scientific interest.
So I would suggest that we might want to try another meeting, and in a
second meeting talk about some issues that relate to how we communicate this,
rather than how we do it. Because I do not think there are really any problems on
how to do it.
It became reasonably clear that the four work groups (Figure 6-3), even though
they approached each chemical a little bit differently, had no problem coming up with
418
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SUMMARY
1. GENERAL CONSENSUS IS THAT THE TIME IS UPON US TO INCREASE
THE VISIBILTTYAND IMPORTANCE OF GENETIC RISK ASSESSMENT
-CREDIBILITY OF SCIENCE IS AT RISK
-IMPACT OF THE RISK MAY BE UNDERESTIMATED
-SURVIVAL OF FIELD IS TIED TO INTEREST OF GOVERNMENT
AND PRIVATE INDUSTRY
2. EXAMINE HOW WE VIEW GENETIC RISK
-CONGENITAL ANOMAUES FROM TREATMENT OF ZYGOTES
-MALE TRANSMITTED CONGENITAL ANOMALIES
-INCREASED CANCER INCIDENCE IN F1 AND F2
-FERTILITY AND ASSOCIATED ENDPOINTS
3. EXAMINE HOW WE COMMUNICATE GENETIC RISK
-SIMPLIFY THE DESCRIPTION WITHOUT OVER-SIMPLIFICATION
-EXPRESS RISK IN TERMS OF ADVERSE OUTCOMES RATHER
THAN INCREASES IN SPECIFIC ENDPOINTS
(TRANSLOCATIONS,
DELETION ETC.)
-ATTEMPT TO REACH CONSENSUS ON BASIC RULES FOR THE
PROCESS
4. EXAMINE HOW WE EXPRESS RISK
-POPULATION OR INDIVIDUAL
-INCLUDE REPRODUCTIVE BEHAVIOR: RISK PER
MATING/CONCEPTIONS/F1
-COMBINE INDIVIDUAL RISKS OR NOT
-INCLUDE CUMULATIVE EXPOSURES OR ONLY ACUTE
Figure 6-3
some numbers and we can argue for days and days about whether those numbers
are good numbers or bad and you know, you saw George, he had a page full of
419
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numbers that were risk numbers for the F, population. So generating the numbers is
not a problem, and I think here is where we have to look.
We have to then look at trying to find out how we can increase the visibility,
and demonstrate the importance of genetic risk assessment. We have got credibility
at risk, we have got an impact issue; survival in the field, I think is tied to how well we
can convince people in the government, and private industry, that this is an important
issue. We have got to examine how we view genetic risk. Are we going to make ft
very narrow, or are we going to broaden it to include congenital anomalies in the
zygotes, male transmitted congenital anomalies, or are we even going to look at the
issue of cancer in the Fv F2, as an indicator and even broaden it maybe into fertility,
and other associated reproductive developmental problems. That is another issue
that we should talk about.
The third, is how are we going to communicate the risk numbers that we
develop. Number one, we have got to simplify the description without oversimplifying
it, and that is a very difficult thing to do, but it is absolutely essential. If we are not
going to take an approach to begin to simplify the process, and explain it as a
relatively simple process, we will never sell it, we will never, ever sell it.
We have got to be able to express risk in terms of adverse outcomes, and not
genetic endpoints, and I think that was made pretty clear by most of the speakers and
we have got to reach a consensus on basic rules for the process. I think just some
very simple basic rules like what kind of data you need, what are certain assumptions
that we are going to make. Without these kinds of basic rules, we will not fit in again,
good or bad, to the mainstream of toxicology. And the mainstream of toxicology
when you look at other risk analyses, analysis processes, they have basic rules that
420
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they follow. Some people diverge from those rules and are quickly beaten around a
little bit, and are brought back into tow.
Then examine how we are going to express it; is It going to be a population
risk? Are we going to talk about individual risks? That is very important. Are we
going to include reproductive behavior, we are going to talk about risk per mating
opportunity, risk per conception, risk per F.,? All of these things may become
important. If you want to counsel individuals who are concerned about an exposure
on the job, they might want to know risks such as what is the risk per conception, or
what is the risk every time I have sex; and we can get numbers like that, they are not
totally out of reason, and can we combine individual risks.
We have risks for chromosome damage, we have risks for gene mutation. Is
tt possible, once we have calculated these, to then come up with the single number
or we will always be faced with this matrix of risk numbers?
And finally, I think we should discuss cumulative exposures, versus acute, and
that came up over and over again; that in a sense what we do is take the animal data
and we calculate a risk for acute exposure then, in a sense, in many cases with
occupational exposures, especially for spermatic gonial mutagens, you accumulate
that risk on a daily basis to that spermatogonia population. And therefore, what is the
dose? Is it a cumulative dose, over 20 years or is it a dose for a week, or whatever
it may be.
So my suggestion te to think about that for future meetings, before we then
and go back in and do more number crunching, and then finally application of the
parallelogram to risk assessment (Figure 6-4).
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APPLICATION OF PARALLELOGRAM TO
RISK ASSESSMENT
ADVANTAGES
• METHOD IS INTUITIVE AND SIMPLE TO EXPLAIN
• SOME EXTRAPOLATION FACTORS ARE INTRINSIC
LIMITATIONS
• WITHOUT ADEQUATE DOSE:EFFECT DATA,
PARALLELOGRAM WILL HAVE TO BE CONSTRUCTED
FOR EACH POTENTIAL HUMAN EXPOSURE
• MAY BE DIFFICULT TO COMPARE ACROSS
COMPOUNDS UNLESS DATA OF THE THREE CORNERS
ARE SIMILAR (I.E. MAKE ONE OR TWO HUMAN SOMATIC
ENDPOINTS STANDARD)
• HUMAN SOMATIC CELL DATA MAY BE DIFFICULT TO
OBTAIN
Figure 6-4
The advantage of it is, I think, that it is a method that is pretty intuitive. If you
look at it, it makes sense, it is fairly simple, and it has a strong appeal from that
standpoint It also, because you are only looking at two endpoints in the same
animal, and then in human, some of the extrapolation factors are automatically built
in. We do not have to worry about extrapolating necessarily from the somatic cell to
the germ cell, because that is built into the same animal. So some of those factors
are sort of taken into account, but the limitations probably are more important than
the advantages.
422
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If we do not have dose effect data, every time you want to look at the
parallelogram for a different dose exposure, you have to go back and redo the
parallelogram again, because you do not know the shape of the curve so if it is linear
that is one thing; but if it is not linear, then you do not know that the relationship
between somatic and germ cells in the animal is going to hold up over a range of
doses. It is going to be for that particular exposure, and then the same relationship
will hold only for that particular exposure in the human, and it is difficult to compare
across compounds.
I think it would be nice if we are going to use this as an approach to try and
standardize, maybe the somatic cell determinations in humans, to one or two
endpoints. Maybe hemoglobin abducts would be a good one, dosimetry, or SCEs or
something of that sort, so you can then begin to look at one chemical versus another.
Now we are only able to look essentially at a compound in isolation and not
necessarily compare it because we do not know the relative sensitivities of these
comparisons across different compounds.
So if you are looking at SCEs with one compound, as a somatic thing and you
make those chromosome aberrations or abducts in another, there are some
differences and that is difficult to get human somatic cell data.
So I think from my perspective it is certainly a model that we can use, but I
think that at this point we do not have to go to models like parallelograms, if we do
not want to; I think we have got enough power in using the direct, or modified direct
or whatever analysis we would like to use for doing a risk assessment to go right
ahead and do that. That is it.
423
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424
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Concluding Remarks
Dr. Canice Nolan gave the concluding remarks. Dr. Nolan is from the European
Commission located in Brussels, Belgium.
DR NOLAN: It falls on me to give the wrap up of the wrap up. Going back
to the objectives of the meeting - to identify the methodology, data requirements, and
mechanistic research needed to understand the health impact of germ cell mutagens,
I think we have gone a long way towards doing just that. When we chose the four
chemicals we knew already that we would have problems with one or two, because
there were data missing. Problems were identified in applying the parallelograms to
each of these four compounds. However, I do not think you should throw out the
method because of problems with the data. We have identified needs for data and
shown that the parallelogram does have advantages over other methods. I am not
pro- or anti-parallelogram or indeed any other assessment methods but I think that
it should be given a chance, with a full set of data and I look forward to the day that
we can do so. That is about as much as I am going to say on that.
We are quite pleased with the meeting and we have what we wanted. We also
had some good overview papers and cross-cutting papers. I have not been able to
sit in on all the working group meetings and I await getting the full transcripts from
those. I would hope, and I hope I speak for Dr. Ott as well, that this will not be a one-
off meeting and that we will have follow-up meetings.
I would like to thank you all for participating but I would like to thank
particularly our hosts here at Research Triangle Park, especially Mike Waters, who I
know has done a lot of work putting this meeting together. I would also like to thank
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Frits because he really did a lot of the preparatory work on the European side for this.
It is a real pity that he could not be here with us.
DR. WATERS: I would also like to thank my counterpart, Canice, because I
failed to do that In the beginning, which I had intended to and he certainly has done
his share to make this meeting happen. Hopefully it will not be the last, and we will
look forward to the next one.
DR. NOLAN: Thanks Mike and thank you all.
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439
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Appendix A
Final List of Participants
A-1
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A-2
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EC/US WORKSHOP ON RISK ASSESSMENT
Omni Durham Hotel and Convention Center
201 Foster Street, Durham, North Caroina, USA
October 11-14,1993
FINAL LIST OF PARTICIPANTS
Dr. Ilse-Dore Adter
GSF - Forschungzentrum fur Umwelt
und Qesundheit GmbH
Institut fur Saugetiergenetik
Ingotetradter Landstrasse 1
D - W 8042 NEUHERBERG (MOnchen)
GERMANY
Dr. James W. Allen
Mutagenesis and Cellular
Toxicology Branch
Genetic Toxicology Division (MD-68)
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
USA
Dr. Diana Anderson
BIBRA Toxicology International
Dept of Genetic & Reproductive
Toxicology
Woodmansteme Road
UK - CARSHALTON, SURREY SMS 4DS
UNITED KINGDOM
Dr. Jack Bishop
A2-10
National Institute of Environmental
Health Sciences
P.O. Box 12233
Research Triangle Park, NC 27709
USA
Dr. David Brusick
Hazteton Labs America, Inc.
9200 Leesburg Pike
Vienna, VA 22180
USA
Dr. Larry Claxton*
Genetic Toxicology Division
MD-68
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
USA
Ms. Judi Cochrane
Pathology Department
355 Rosenau Hall 201H
University of North Carolina
Chapel Hill, NC 27599
USA
Dr. Kerry DearfiekJ
Health Effects Division
Office of Pesticide Programs (H7509C)
U.S. Environmental Protection Agency
401 M St, SW
Washington, DC 20460
USA
Dr. Vicki Dellarco
Human Health Assessment Group
Office of Health and Environmental
Assessment (RD-689)
U.S. Environmental Protection Agency
401 M St, SW
Washington, DC 20460
USA
Dr. Frederick J. de Serres*
Research Triangle Institute
P.O. Box 12194
Research Triangle Park, NC 27709
USA
A-3
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Dr. George R. Douglas
Health Canada
Environmental Health Center
Tunney's Pasture
Ottawa, Ontario K1A OL2
CANADA
Dr. Udo Ehling
Instrtut fur Genetik Gesellschaft
fOr Strahten- und Umweltforschung
Neuherberg/
Ingoistradter Landstrasse 1
D - 8042 OBERSCHLEOSSHEIM
GERMANY
Dr. Lars Ehrenberg
Department of Radiobtotogy
University of Stockholm
S -10691 STOCKHOLM
SWEDEN
Dr. R. Colin Gamer
The Jack Birch Unit
of Environmental Carcinogenesis
University of York
Heslington Road
UK-YORKY015DD
UNITED KINGDOM
Dr. Walderico Generoso
Oak Ridge National Laboratory
Biology Division
P.O. Box Y
Oak Ridge, TN 37831-6050
USA
Dr. Susan E. Lewis
Research Triangle Institute
P.O. Box 12194
Research Triangle Park, NC 27709
USA
Dr. Barry Margolin*
Dept. of Biostatistics
CB# 7400 Rosenau Hall
University of North Carolina
Chapel Hill, NC 27599
USA
Dr. Martha Moore
Mutagenesis and Cellular
Toxicology Branch
Genetic Toxicology Division (MD-68)
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
USA
Dr. A.T. 'Nat* Natarajan
Dept. of Radiation Genetics
& Chemical Mutagenesis
State University of Leiden
Wassenaarseweg 72
NL - 2300 RA LEIDEN
THE NETHERLANDS
Dr. Canice Nolan
Commission of the European
Communities
DG XII Science, Research
and Development
200 Rue de la Loi
B -1049 BRUSSELS
BELGIUM
Dr. Siv Osterman-Golkar
Department of Radiobiology
University of Stockholm
S -10691 STOCKHOLM
SWEDEN
Dr. Patricia Ostrosky-Wegman
Institute de Investigaciones Biomedicas
UNAM
P.O. Box 105-285, PoJanco
Mexico D.F. CP 11581
MEXICO
Dr. Heinrich Ott
Commission of the European
Communities
DG XII Science, Research
and Development
200 Rue de la Loi
B -1049 BRUSSELS
BELGIUM
A-4
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Dr. Julian Preston
GIFT
P.O. Box 12137
6 Davis Dr.
Research Triangle Park, NC 27709
USA
Dr. Lawrence W. Reiter
Health Effects Research Laboratory
MD-51
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
USA
Dr. Gary Sega
Oak Ridge National Laboratory
Analytical Chemistry Division
4500 South Building
P.O. Box 2008
Oak Ridge, TN 37831-6120
USA
Dr. Paul B. Selby
Oak Ridge National Laboratory
Biology Division
Bldg. 9210, MS-8077
Oak Ridge, TN 37831
USA
Dr. Michael Shelby
E4-03
National Institute of Environmental
Health Sciences
P.O. Box 12233
Research Triangle Park, NC 27709
USA
Dr. Thomas R. Skopek
(represented by Ms. Judi Cochrane)
Pathology Department
355 Rosenau Hall 201H
University of North Carolina
Chapel Hill, NC 27599
USA
Dr. Marja Sorsa
Finnish Institute of Occupational
Health
Topeliuksenkatu 41 a A
F1 - 00250 HELSINKI
FINLAND
Dr. A.D. mMT Tales
Department of Radiation Genetics
and Chemical Mutagenesis
State University of Leiden
Wassenaarseweg 72 / P.O. Box 9503
NL - 2300 RA LEIDEN
THE NETHERLANDS
Dr. Ekkehatt W. Vogel
Dept. of Radiation Genetics &
Chemical Mutagenesis
State University of Leiden
Wassenaarseweg 72
NL - 2333 AL LEIDEN
THE NETHERLANDS
Dr. Michael D. Waters
Health Effects Research Laboratory
MD-51A
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
USA
Dr. Alan S. Wright
Tunstall Laboratory
Shell Research Limited
Sittingboume Research Centre
UK - SITTINGBOURNE, KENT ME9 SAG
UNITED KINGDOM
*lnvted Observer
A-5
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A-e
-------
Appendix B
Agenda
B-1
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B-2
-------
EC/US WORKSHOP ON RISK ASSESSMENT
Omni Durham Hotel and Convention Center
201 Foster Street, Durham, North Carolina, USA
October 11-14,1993
Monday. October 11
8:00-10:00 p.m. Fteceptk>n at the Shef aton LMiversity Center Hotel in the
Executive dub, 2800 MkJoMon Avenue, Durham
(Compliments of Hazleton Labs America, he. -
Dr. D. Brustek)
Tuesday. October 12
Morning Session (8:45 am. - 12:30 p.m.) Room 107 - M. Waters and C. Nolan,
Session Co-Chairs
8:45 - 9:00 Introductory Remarks H. Ott, L Reiter
9:00 - 9:25 Parallelogram Concept A. Wright
9:25 - 9:30 Discussion
9:30 - 9:55 ICPEMC Efforts D. Brusick
9:55 -10:00 Discussion
10:00 -10:30 Break, Compliments of the Omni Durham Hotel and
Convention Center
10:30 -10:55 Regulatory Perspective K. Dearfield
10:55-11:00 Discussion
11:00 -11:25 Research Background for EO L Ehrenberg
Assessment
11:25-11:30 Discussion
11:30 -11:55 EO Mutagenkaty Risk V. Dellarco
Assessment
11:55 -12:00 Discussion
B-3
-------
12:00 -12:25 Critique of EO J. Preston
Assessment
12:25 -12:30 Discussion
12:30- 2:00 Lunch
Afternoon Session (2:00 - 5:30 p.m.) Room 107 - M. Shelby and M. Sorsa, Session
Co-Chairs
Data Summaries of Chemicals (Effort to apply
parallelogram and/or alternative methods)
2:00 - 2:30 Ethytene oxide A. Natarajan
2:30 - 2:45 Discussion
2:45 - 3:15 Acrylamide K. Dearfield
3:15- 3:30 Discussion
3:30- 4:00 Break
4:00- 4:30 1,3-Butadiene I-D. Adler
4:30- 4:45 Discussion
4:45- 5:15 Cydopnospnamide D.Anderson
5:15- 5:30 Discussion
Evening (6:00 - 9:00 p.m.)
6:00 - 9:00 Social and Dinner, Parizade Restaurant, 2200 W. Main St.,
Durham
(Meet in hotel lobby at 6:00 p.m. for transportation to the
restaurant)
Wednesday. October 13
Morning Session: Chemical Work Groups (9:00 a.m. -12:00 p.m.)
Data Presentations (parallelogram component contributions-see matrix)
Parallelogram Calculations (chairpersons)
B-4
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Identification of Data Gaps (chairpersons)
Alternative Approaches (rapporteurs)
10:00 -10:30
Break, Compliments of the Omni Durham Hotel and
Convention Center
Etnylene oxide
Chair: A Natarajan
Rapp.: J. Preston
Aaylamide
Chain K. Dearfield
Rapp.: G. Douglas
1.3-Butad»ne
Chain I-D. Adter
Rapp.: M. Sorsa
cyctopnospnamKie
Chain D. Anderson
Rapp.: P. Selby
Mouse Germ
Gets
W. Generoso
U. Ehling
I-D. Adler
J. Bishop
Adducts
L Ehrenberg
G. Sega
S. O.-Golkar
C. Gamer
Mouse and
Human
Somatic
Cefe
A. Tates
M. Moore
T. Skopek
P. Ostrosky
Room No.
and
Weekday
Boardrm. I
(Wedmiur)
Boardrm. II
(Wed/Thur)
Rm. 108
(Wed/Thur)
Rm.106
(Wed) and
Rm. 105
(Thur)
Afternoon Session (12:30 - 5:00 p.m.) - J. Bishop and U. Ehling, Session Co-Chairs
12:30- 2:00 Lunch
2:00- 3:00 Status Reports to Plenary-Rapporteurs (see above)
Room 107
3:00- 3:30 Break
3:30- 5:00 Chemical Work Groups-Meet lor further discussion, report
preparation
B-5
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Evening
6:00 - 9:30 Excursion and Dinner, Angus Barn, Ltd., Highway 70 East
& Airport Boulevard, Raleigh
(Meet in hotel lobby at 5:30 p.m. for transportation to the
restaurant)
Thursday. October 14
Morning Session: Chemical Work Groups - Prepare Written Reports (9:00 am. -12:30
p.m.) - J. Allen and A. Wright, Session Co-Chairs
9:00 -10:00 Finalize Reports
10:00 -10:30 Break, Compliments of the Omni Durham Hotel and
Convention Center
10:30 -12:30 Rapporteurs' Presentations of Work Group Reports to
Pierian/- Room 107
EO J. Preston
Acrylamide G. Douglas
1,3-Butadiene M. Sorsa
Cydophosphamide P. Selby
12:30- 1:30 Lunch
Afternoon Session (1:30 - 4:30 p.m.) Room 107 - M. Moore and I-D. Adter, Session
Co-Chairs
Cross-cutting Papers Focusing on Several General Topics
Pertinent to Somatic and Germ CeN Mutation
1:30- 2:00 Differential Repair of E. Vogel and
Chemically-irKluced Damage: A. Natarajan
Somatic vs. Germ Cells
2:00 - 2:30 Chemically-induced Mutation: J. Allen, S. Lewis
Comparative Outcome in Somatic M. Moore and
vs. Germ Cells U. Ehling
B-6
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2:30- 3:00 Qualitative and Quantitative M. Waters and
Results from Short-Term Tests I-D. Adier
3:00- 3:30 Break
3:30- 4:00 Chemicals for Future Study of M.Shelby and
Heritable Genetic Damage M. Waters
in Human Populations
4:00 - 4:30 Wrap-up D. Brusick
C. Nolan
B-7
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B-8
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