Review of Office of Research and Development (ORD) Draft Assessment

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON D.C. 20460
OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD
December 21,2007

EPA-SAB-08-004

Honorable Stephen L. Johnson
Administrator
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, N.W.
Washington, DC 20460
Subject: Review of Office of Research and Development (ORD) draft assessment
entitled, "Evaluation of the Carcinogenicity of Ethylene Oxide".

Dear Administrator Johnson:

In response to a request from EPA's Office of Research and Development (ORD), the
Science Advisory Board (SAB) convened an expert panel to conduct a peer review of EPA's draft
assessment entitled, "Evaluation of the Carcinogenicity of Ethylene Oxide". EPA last published an
assessment of the potential Carcinogenicity of Ethylene Oxide (EtO) in 1985. The current
assessment evaluates the more recent database on the Carcinogenicity of EtO and focuses on lifetime
cancer risk from inhalation exposure.

The SAB was asked to comment on three issues, including carcinogenic hazard, derivation of
a cancer unit risk value for inhalation exposure to EtO, and uncertainties associated with the
Carcinogenicity assessment. The report contains a number of recommendations that are aimed at
making the assessment more transparent and improve the scientific bases for the conclusions
presented. Appendices authored by two panel members are also included to provide further
discussion of the issues where the Panel had divergent opinions, e.g., low dose extrapolation and the
healthy worker survivor effect. The Panel's key recommendations are highlighted below.

A majority of the Panel agreed with the conclusion in the draft document that the available
evidence supports a descriptor of "Carcinogenic to Humans" although some Panel members
concluded that the descriptor "Likely to be Carcinogenic to Humans" was more appropriate. There
was consensus that the epidemiological data regarding ethylene oxide Carcinogenicity were not in
and of themselves sufficient to provide convincing evidence of a causal association between human
exposure and cancer. Differing views as to the appropriate Carcinogenicity descriptor for ethylene
oxide were based on differences of opinion as to whether criteria necessary for designation as
"Carcinogenic to Humans" in the absence of conclusive evidence from epidemiologic studies were
met. The majority of Panel members thought that the combined weight of the epidemiological,

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experimental animal, and mutagenicity evidence was sufficient to conclude that EtO is carcinogenic
to humans.

The Panel concluded that the assessment would be improved by: 1) a better introduction to
the hazard characterization section, including a brief description of the information that will be
presented; 2) a clear articulation of the criteria by which epidemiologic studies were judged as to
strengths and weaknesses; 3) addition of a more inclusive summary figure and/or table at the
beginning of section 3.0; and 4) inclusion and expansion of material now provided in Appendix A of
the draft assessment to within the main body of that assessment.

The Panel identified several important shortcomings in the linear regression modeling
approach used to establish the point of departure for low dose extrapolation of cancer risk due to
EtO. The Panel was unanimous in its recommendation that the EPA develop its risk models based
on direct analysis of the individual exposure and cancer outcome data for the NIOSH cohort rather
than the approach based on grouped data that is presently used.

The Panel was divided on whether low dose extrapolation of risk to environmental EtO
exposure levels should be linear (following Cancer Guideline defaults for carcinogenic agents
operating via a mutagenic MO A) or whether plausible biological mechanisms argued for a non-
linear form for the low dose response relationship. With appropriate discussion of the statistical and
biological uncertainties, several Panel members strongly advocated that both linear and nonlinear
calculations be considered in the final EtO Risk Assessment.

The Draft Assessment characterizes the magnitude of the risk associated with EtO as "weak".
This finding is well substantiated by the epidemiologic evidence where a relatively small number of
excess cancers are found above background even among highly exposed individuals. However, the
magnitude of risk suggested by the unit risk estimate is somewhat at odds with this concept. In our
report, we provide specific recommendations for addressing this apparent inconsistency.

In accordance with EPA guidance, the draft assessment applied an Age Dependent
Adjustment Factor (ADAF) to adjust the unit risk for early life exposure. While the Panel felt that
the application of a default value by the Agency was appropriate, the description in the Draft
Assessment was not adequate, particularly for those not familiar with the EPA Guidance.

The Panel appreciates both the public health and economic significance of EPA's EtO risk
assessment. A more thorough discussion of the Panel's recommendations is included in the body of
the report. Some of the Panel's recommendations, such as the reanalysis of the NIOSH cohort using
data from individuals, will require significant effort.

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       The Panel encourages the Agency to devote sufficient resources to make implementation of
these recommendations possible. We look forward to receiving the Agency's response and
appreciate the opportunity to provide EPA with advice on this important subject.

                                 Sincerely,

             /Signed/                                /Signed/

       Dr. Stephen Roberts, Chair                Dr. Granger Morgan, Chair
       SAB Ethylene Oxide Review Panel         EPA Science Advisory Board
                                        NOTICE

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       This report has been written as part of the activities of the EPA Science Advisory Board,
a public advisory committee providing extramural scientific information and advice to the
Administrator and other officials of the Environmental Protection Agency. The Board is
structured to provide balanced, expert assessment of scientific matters related to problems facing
the Agency. This report has not been reviewed for approval by the Agency and, hence, the
contents of this report do not necessarily represent the views and policies of the Environmental
Protection Agency, nor of other agencies in the Executive Branch of the Federal government, nor
does mention of trade names or commercial products constitute a recommendation for use.
Reports of the EPA Science Advisory Board are posted on the EPA Web site at:
http://www.epa.gov/sab.
                      U.S. Environmental Protection Agency
                              Science Advisory Board
                                         11

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Ethylene Oxide Review Panel*
CHAIR
Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for
Environmental and Human Toxicology, University of Florida,
OTHER SAB MEMBERS

Dr. Timothy Buckley, Associate Professor and Chair, Division of Environmental Health
Sciences, School of Public Health, The Ohio State University

Dr. Montserrat Fuentes, Associate Professor, Department of Statistics, North Carolina State
University,

Dr. Dale Hattis, Research Professor, Center for Technology, Environment, and Development,
George Perkins Marsh Institute, Clark University

Dr. James Kehrer, Dean, College of Pharmacy, Washington State University

Dr. Mark Miller, Public Health Medical Officer, Office of Environmental Health Hazard
Assessment, California Environmental Protection Agency

Dr. Maria Morandi, Assistant Professor of Environmental Science & Occupational Health,
Department of Environmental Sciences, School of Public Health, University of Texas - Houston
Health Science Center

Dr. Robert Schnatter, Senior Scientific Advisor, Occupational and Public Health, ExxonMobil
Biomedical Sciences, Inc.

Dr. Anne Sweeney, Associate Professor, Department of Epidemiology and Biostatistics, School
of Rural Public Health, TAMU System Health Science Center
CONSULTANTS

Dr. Steven Alan Belinsky, Co-Director, Cancer Epidemiology and Control Program for the
University of New Mexico

Dr. Norman Drinkwater, Professor and Chair of Oncology, McArdle Laboratory for Cancer
Research, University of Wisconsin Medical School

Dr. Steven Heeringa, Director, Division of Surveys and Technologies, Institute for Social
Research, University of Michigan
in

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Dr. Ulrike Luderer, Associate Professor, Department of Medicine, Division of Occupational
and Environmental Medicine, University of California at Irvine

Dr. James Swenberg, Kenan Distinguished Professor of Environmental Sciences and
Engineering, Nutrition and Pathology and Laboratory Medicine, Schools of Public Health and
Medicine, University of North Carolina at Chapel Hill.

Dr. Vernon Walker, Clinical Associate Professor; Research Scientist, Molecular Biology and
Lung Cancer Program, College of Pharmacy, Lovelace Respiratory Research Institute
SCIENCE ADVISORY BOARD STAFF

Dr. Suhair Shallal, Designated Federal Officer, 1200 Pennsylvania Avenue, Washington, DC,
20450, Phone: 202-343-9977, Fax: 202-233-0643, (shallal.suhair@epa.gov)
                             TABLE OF CONTENTS
                                        IV

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EXECUTIVE SUMMARY	1
ISSUE 1: CARCINOGENIC HAZARD (SECTION 3 AND APPENDIX A OF THE EPA DRAFT ASSESSMENT)
	1
ISSUE 2: RISK ESTIMATION (SECTION 4 AND APPENDICES C AND D OF THE EPA DRAFT
ASSESSMENT)	3
INTRODUCTION	6
CHARGE QUESTIONS	6
Issue 1: Carcinogenic Hazard (Section 3 and Appendix A of the Draft Assessment)	6
Issue 2: Risk Estimation (Section 4 and Appendices C and D of the Draft Assessment)	7
Issue 3: Uncertainty (Sections 3 and 4)	8
RESPONSES TO THE CHARGE QUESTIONS	9
CHARGE QUESTION 1-HAZARD DESCRIPTOR	9
l.a. Qualitative Characterization of Epidemiology Data	9
l.b. Relevant Additional Key Studies	11
I.e. Mode of Action	15
l.d. Hazard Characterization	17
CHARGE QUESTION 2-DOSE-RESPONSE ANALYSIS	18
2.a. Selection of Epidemiology Studies	19
2.b. Methods of Analysis	22
2.c. Age-dependent Adjustment	31
2.d Low-dose Extrapolation	32
2.e. Extrapolation from animal studies	32
REFERENCES	33
APPENDIX A	36
APPENDIX B	54
APPENDIX C	62
APPENDIX D	73
ATTACHMENT 1 MEMO AND CHARGE QUESTIONS	78

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EXECUTIVE SUMMARY

EPA's Office of Research and Development (ORD) requested that the Science Advisory
Board (SAB) review its draft assessment entitled, "Evaluation of the Carcinogenicity of Ethylene
Oxide." EPA last published a health assessment of the potential carcinogenicity of Ethylene
Oxide (EtO) in 1985 (U.S. EPA, 1985). EPA'sORD completed a review of the more recent
database on the carcinogenicity of EtO, pertinent data from the 1985 assessment, and several
reviews and assessments issued by other organizations. The Agency's Draft Assessment focuses
on lifetime cancer risk from inhalation exposure. The EtO Review Panel of the EPA Science
Advisory Board met in January 2007 to deliberate on charge questions raised by ORD. These
questions focused on three issues, including carcinogenic hazard, derivation of a cancer unit risk
value for inhalation exposure to EtO, and uncertainties associated with the analysis.

This Executive Summary highlights the outcome of the Panel's deliberations. It includes
the context for the charge questions and issues raised for consideration by EPA, and the
conclusions reached by the SAB Review Panel. While the Agency requested that the Panel
respond to three separate multi-part charge questions, the Panel has presented their response to
the third charge question in the context of each of the other two charge questions. Therefore, this
report is structured so that the comments concerning Uncertainty (Issue 3) are integrated in the
responses to the Carcinogenic Hazard (Issue 1) and Risk Estimation (Issue 2) sections.

Issue 1: Carcinogenic Hazard (Section 3 and Appendix A of the EPA Draft Assessment)

1. Do the available data and discussion in the draft document support the hazard conclusion
that EtO is carcinogenic to humans based on the weight-of-evidence descriptors in EPA's
2005 Guidelines for Carcinogen Risk Assessment? In your response, please include
consideration of the following:

1. a, EPA concluded that the epidemiological evidence on EtO carcinogenicity was strong, but
less than completely conclusive. Does the draft document provide sufficient description of the
studies, balanced treatment of positive and negative results, and a rigorous and transparent
analysis of the data used to assess the carcinogenic hazard ofethylene oxide (EtO) to
humans? Please comment on the EPA's characterization of the body of epidemiological data
reviewed. Considerations include: a) the consistency of the findings, including the
significance of differences in results using different exposure metrics, b) the utility of the
internal (based on exposure category) versus external (e.g., SMR and SIR) comparisons of
cancer rates, c) the magnitude of the risks, and d) the strength of the epidemiological evidence.

A majority of the Panel agreed with the conclusion in the draft document that the available
evidence supports a descriptor of "Carcinogenic to Humans" although some Panel members
concluded that the descriptor "Likely to be Carcinogenic to Humans" was more appropriate.
There was consensus that the epidemiological data regarding ethylene oxide carcinogenicity
were not in and of themselves sufficient to provide convincing evidence of a causal association
between human exposure and cancer. Differing views as to the appropriate descriptor for
ethylene oxide were based on differences of opinion as to whether criteria necessary for
designation as "Carcinogenic to Humans" in the absence of conclusive evidence from
epidemiologic studies were met. The majority of Panel members thought that the combined

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weight of the epidemiological, experimental animal, and mutagenicity evidence was sufficient to
conclude that EtO is carcinogenic to humans.

The Panel concluded that the assessment would be improved by: 1) a better introduction to the
hazard characterization section, including a brief description of the information that will be
presented; 2) a clear articulation of the criteria by which epidemiologic studies were judged as to
strengths and weaknesses; 3) addition of a more inclusive summary figure and/or table at the
beginning of section 3.0; and 4) inclusion of material now provided in Appendix A of the draft
assessment to within the main body of that assessment.

The Panel agreed with the EPA in their reliance on "internal" estimates of cancer rates rather
than "external" comparisons (SMR, SIR) due to well recognized limitations to the latter method
of analysis.

The Draft Assessment characterizes the magnitude of the unit risk estimate associated with EtO
as "weak". This finding is substantiated by the epidemiologic evidence where a relatively small
number of excess cancers are found above background even among highly exposed individuals.
However, the magnitude of risk suggested by the unit risk estimate is somewhat at odds with this
concept. Subsequent recommendations in our report try to address this apparent inconsistency.

l.b. Are there additional key published studies or publicly available scientific reports that are
missing from the draft document and that might be useful for the discussion of the
carcinogenic hazard of EtO?

The Panel agreed that the discussion of endogenous metabolic production of ethylene oxide and
the formation of background adducts should be expanded.

The Panel believed that the description of studies of DNA adduct formation resulting from EtO
exposure appears incomplete and superficial. This discussion should be expanded - both in
terms of the number of studies cited and the depth of the discussion.

Since ethylene is metabolized to EtO, some members recommended the inclusion of the ethylene
body of literature for consideration. Most members were hesitant about adding them to the
document, but if added, they cautioned that a discussion of the caveats associated with their
interpretation relative to ethylene oxide should be included.

I.e. Do the available data and discussion in the draft document support the mode of action
conclusions?

The Panel agreed with the Draft Assessment conclusion of a mutagenic mode of action.
However, an expanded discussion of the formation of DNA adducts and mutagenicity is
warranted.

l.d. Does the hazard characterization discussion for EtO provide a scientifically-balanced and
sound description that synthesizes the human, laboratory animal, and supporting (e.g., in
vitro) evidence for human carcinogenic hazard?

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While some members of the Panel found the hazard characterization section of the Draft
Assessment to be satisfactory, a majority expressed concerns that this section did not achieve the
necessary level of rigor and balance. An issue in this characterization, particularly in the face of
epidemiological data that are not strongly conclusive, is whether the presumed precursor events
leading to cancer in animals, such as mutations and/or chromosomal aberrations, are observed in
humans. This issue needs to be addressed in greater detail.

Issue 2: Risk Estimation (Section 4 and Appendices C and D of the EPA Draft Assessment)

2. Do the available data and discussion in the draft document support the approaches taken by
EPA in its derivation of cancer risk estimates for EtO? In your response, please include
consideration of the following:

2.O, EPA concluded that the epidemiological evidence alone was strong but less than
completely conclusive (although EPA characterized the total evidence -from human,
laboratory animal, and in vitro studies - as supporting a conclusion that EtO as "carcinogenic
to humans"). Is the use of epidemiological data, in particular the Steenland et al. (2003,
2004) data set, the most appropriate for estimating the magnitude of the carcinogenic risk to
humans from environmental EtO exposures? Are the scientific justifications for using this
data set transparently described? Is the basis for selecting the Steenland et al. data over other
available data (e.g., the Union Carbide data) for quantifying risk adequately described?

The Panel concurred that the NIOSH cohort is the best single epidemiological data set with
which to study the relationship of cancer mortality to the full range of occupational exposures to
EtO. That said, the Panel encouraged the EPA to broadly consider all of the epidemiological
data in developing its final Assessment. In particular, the Panel encourages the EPA to explore
uses for the Greenberg et al. (1990) data including leukemia and pancreatic cancer mortality and
EtO exposures for 2174 Union Carbide workers from its two Kanawha Valley, West Virginia
facilities. (Also described in Teta et al. 1993; Teta et al., 1999).

The Panel encouraged the EPA to investigate potential instability that may result from
interaction between the chosen time metric for the dose response model and the treatment of time
in the estimated exposure (i.e., log cumulative exposure with 15 year lag) that is the independent
variable in that dose-response model.

2.b. Assuming that Steenland et al. (2003, 2004) is the most appropriate data set, is the use of
a linear regression model fit to Steenland et al. 's categorical results for all
lymphohematopoietic cancer in males in only the lower exposure groups scientifically and
statistically appropriate for estimating potential human risk at the lower end of the observable
range? Is the use of the grouping of all lymphohematopoietic cancer for the purpose of
estimating risk appropriate? Are there other appropriate analytical approaches that should be
considered for estimating potential risk in the lower end of the observable range? Is EPA's
choice of a preferred model adequately supported and justified? In particular, has EPA
adequately explained its reasons for not using a quadratic model approach such as that of
Kirman et al. (2004) based? What recommendations would you make regarding low-dose
extrapolation below the observed range?

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The Panel identified several important shortcomings in the linear regression modeling approach
used to establish the point of departure for low dose extrapolation of cancer risk due to EtO. The
Panel was unanimous in its recommendation that the EPA develop its risk models based on
direct analysis of the individual exposure and cancer outcome data for the NIOSH cohort rather
than the approach based on published grouped data that is presently used. The suggested
analysis will require EPA to acquire or otherwise access individual data and develop appropriate
methods of analysis. The panel recommends that the Agency allocate the appropriate resources
to conduct this analysis.

The Panel was divided on whether low dose extrapolation of risk due to environmental EtO
exposure levels should be linear (following Cancer Guideline defaults for carcinogenic agents
operating via a mutagenic MO A) or whether plausible biological mechanisms argued for a non-
linear form for the low dose response relationship. With appropriate discussion of the statistical
and biological uncertainties, several Panel members strongly advocated that both linear and
nonlinear calculations be considered in the final EtO Risk Assessment.

In conjunction with its recommendation to use the individual NIOSH cohort data to model the
relationship of cancer risk to exposures in the occupational range, the Panel recommended that
the Agency explore the use of the full NIOSH data set to estimate the cancer slope coefficients
that will in turn be used to extrapolate risk below the established point of departure. The use of
different data to estimate different dose response curves should be avoided unless there is both
strong biologic and statistical justification for doing so. The Panel believed this justification was
not made in the Agency's draft assessment.

Although the analysis based on total lymphohematopoietic (LH) cancers might have value as
part of a complete risk assessment, the rationale for this aggregate grouping needs to be better
justified. The Panel recommends that data be analyzed by subtype of LH cancers (e.g. lymphoid,
myeloid) and strong consideration be given to these more biologically justified groupings as
primary disease endpoints.

The Panel was divided in its views concerning the appropriateness of estimating the population
unit risk for LH cancer based only on the NIOSH data for males. Several Panel members pointed
out that a standard approach in cancer epidemiology and risk analysis begins by conducting
separate dose-response analyses on males and females and combining the data only if the results
are similar. Conducting separate analyses for males and females is also the standard practice
when analyzing data from animal carcinogenicity bioassays. A second approach to dealing with
the possibility of gender differences in response is to include gender as a fixed effect in the
statistical modeling of the data and determine whether gender or its interaction with other
predictors (e.g., gender x exposure) are significant explanatory variables. If so, the combined
model with the estimated gender effects could be used directly or separate, gender-specific dose-
response analysis would be performed. If not, the gender effects could be dropped and the model
re-estimated for the combined male and female data. In addition, the Agency should test whether
the male/female differences are mitigated by use of alternate disease endpoints discussed in the
previous paragraph.

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2.c. Is the incorporation of age-dependent adjustment factors in the lifetime cancer unit risk
estimate, in accordance with EPA's Supplemental Guidance (U.S. 2005b), appropriate and
transparently described?

In accordance with EPA guidance, the Draft Assessment applied an Age Dependent Adjustment
Factor (ADAF) to adjust the unit risk for early life exposure. While the majority of the Panel felt
that the application of a default value by the Agency was appropriate due to lack of data, the
description in the Draft Assessment was not adequate, particularly for those not familiar with the
EPA's Supplemental Guidance.

2.d. Is the use of different models for estimation of potential carcinogenic risk to humans
from the higher exposure levels more typical of occupational exposures (versus the lower
exposure levels typical of environmental exposures) appropriate and transparently described
in Section 4.5?

While the method was transparently described, most of the Panel did not agree with the
estimation based on two different models for two different parts of the dose response curve (see
response to 2b). The use of different data to estimate different dose response models curves
should be avoided unless there is both strong biological and statistical justification for doing so.
The Panel believed this justification was not made in the Agency's draft report.

2.e. Are the methodologies used to estimate the carcinogenic risk based on rodent data
appropriate and transparently described? Is the use of "ppm equivalence" adequate for
interspecies scaling ofEtO exposures from the rodent data to humans?

The ppm equivalence method is a reasonable approach for interspecies scaling of EtO exposures
from rodent data to humans. If the use of animal data becomes more important (i.e., the
principal basis for the ethylene oxide unit risk value), more sophisticated approaches such as
PBPK modeling should be considered.

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INTRODUCTION

This report was prepared by the Science Advisory Board (SAB) Ethylene Oxide (EtO) Review
Panel (the "Panel") in response to a request by EPA's Office of Research and Development
(ORD) to review their draft Evaluation of the Carcinogenicity of Ethylene Oxide. According to
the document, EPA last published an assessment of the potential carcinogenicity of EtO in 1985.
The current assessment reviews the more recent database on the carcinogenicity of EtO.

EtO is a gas at room temperature. It is manufactured from ethylene and used primarily as a
chemical intermediate in the manufacture of ethylene glycol. It is also used as a sterilizing agent
for medical equipment and as a fumigating agent for spices. The largest sources of human
exposure are in occupations involving contact with the gas in plants (facilities) and in hospitals
that sterilize medical equipment. EtO can also be inhaled by residents living near production or
sterilizing/fumigating facilities. The Draft Assessment describes the derivation of inhalation unit
risk estimates for cancer mortality and incidence based on human epidemiological data.

ORD identified 3 issues where they were seeking the SAB's advice and recommendations.
These included the proposed carcinogenic hazard, risk calculations and uncertainty. The SAB
EtO Review Panel was asked to comment on the scientific soundness of this risk assessment.
The Panel deliberated on the charge questions during their January 18-19, 2007 face-to-face
meeting and during a conference call on May 29, 2007. The responses that follow represent the
views of the Panel. In all cases, there was agreement by a majority of the Panel members as to a
particular recommendation. In some cases, there were some Panel members that had a differing
point of view. These instances have been noted throughout the report and are described in more
detail in appendices authored by two panel members to provide further discussion of the issues
where the Panel had divergent opinions, e.g., low dose extrapolation and the healthy worker
survivor effect (Appendix A- Discussion of the Resurgent Controversy over Thresholds for
Genetically Acting Agents and Appendix B- Illustration of a Simple Approach for
Approximately Assessing the Effect of Measurement/Estimation Uncertainties for Individual
Worker Exposures on Estimates of Dose Response Slopes and Appendix C- Framework Analysis
of Genotoxicity and Risk Assessment).
The specific charge questions to the Panel are as follows:

Charge Questions

The memo requesting this review along with the complete charge to the Panel can be found in its
entirety in Attachment 1. Below is an abbreviated version of the charge questions.

Issue 1: Carcinogenic Hazard (Section 3 and Appendix A of the EPA Draft Assessment)

1. Do the available data and discussion in the draft document support the hazard conclusion that
EtO is carcinogenic to humans based on the weight-of-evidence descriptors in EPA's 2005
Guidelines for Carcinogen Risk Assessment! In your response, please include consideration of
the following:

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l.a. EPA concluded that the epidemiological evidence on EtO carcinogen!city was strong, but
less than completely conclusive. Does the draft document provide sufficient description of the
studies, balanced treatment of positive and negative results, and a rigorous and transparent
analysis of the data used to assess the carcinogenic hazard of ethylene oxide (EtO) to humans?
Please comment on the EPA's characterization of the body of epidemiological data reviewed.
Considerations include: a) the consistency of the findings, including the significance of
differences in results using different exposure metrics, b) the utility of the internal (based on
exposure category) versus external (e.g., SMR and SIR) comparisons of cancer rates, c) the
magnitude of the risks, and d) the strength of the epidemiological evidence.

I.e. Do the available data and discussion in the draft document support the mode of action
conclusions?

Issue 2: Risk Estimation (Section 4 and Appendices C and D of the EPA Draft Assessment)

2.a. EPA concluded that the epidemiological evidence alone was strong but less than completely
conclusive (although EPA characterized the total evidence - from human, laboratory animal, and
in vitro studies - as supporting a conclusion that EtO as "carcinogenic to humans"). Is the use of
epidemiological data, in particular the Steenland et al. (2003, 2004) data set, the most appropriate
for estimating the magnitude of the carcinogenic risk to humans from environmental EtO
exposures? Are the scientific justifications for using this data set transparently described? Is the
basis for selecting the Steenland et al. data over other available data (e.g., the Union Carbide
data) for quantifying risk adequately described?

2.b. Assuming that Steenland et al. (2003, 2004) is the most appropriate data set, is the use of a
linear regression model fit to Steenland et al.'s categorical results for all lymphohematopoietic
cancer in males in only the lower exposure groups scientifically and statistically appropriate for
estimating potential human risk at the lower end of the observable range? Is the use of the
grouping of all lymphohematopoietic cancer for the purpose of estimating risk appropriate? Are
there other appropriate analytical approaches that should be considered for estimating potential
risk in the lower end of the observable range? Is EPA's choice of a preferred model adequately
supported and justified? In particular, has EPA adequately explained its reasons for not using a
quadratic model approach such as that of Kirman et al. (2004) based? What recommendations
would you make regarding low-dose extrapolation below the observed range?

2.d Is the use of different models for estimation of potential carcinogenic risk to humans from
the higher exposure levels more typical of occupational exposures (versus the lower exposure
levels typical of environmental exposures) appropriate and transparently described in Section
4.5?

Issue 3: Uncertainty (Sections 3 and 4 of the EPA Draft Assessment)

1. EPA's Risk Characterization Handbook requires that assessments address in a transparent
manner a number of important factors. Please comment on how well this assessment clearly
describes, characterizes and communicates the following:
a. The assessment approach employed;
b. The use of assumptions and their impact on the assessment;
c. The use of extrapolations and their impact on the assessment;
d. Plausible alternatives and the choices made among those alternatives;
e. The impact of one choice versus another on the assessment;
f Significant data gaps and their implications for the assessment;
g. The scientific conclusions identified separately from default assumptions and policy calls;
h. The major risk conclusions and the assessor's confidence and uncertainties in them, and;
i. The relative strength of each risk assessment component and its impact on the overall
assessment.

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RESPONSES TO THE CHARGE QUESTIONS

Specific responses to each of the charge questions are presented below. The Panel has
responded to Charge Questions 1 and 2 and has tried to incorporate their comments regarding
Charge Question 3 within those responses. A separate response for Charge Question 3 was not
deemed necessary since issues of uncertainty were addressed in the responses to charge
questions 1 and 2.
Charge Question 1- Hazard Descriptor

The Agency's assessment concludes that in accordance with EPA's 2005 Guidelines for
Carcinogen Risk Assessment (U.S. EPA, 2005a), EtO was characterized as carcinogenic to humans
based on the total weight of evidence. This evidence, as assessed by EPA, included: a) strong,
though less than completely conclusive, evidence of carcinogenicity from human studies; b)
sufficient evidence of carcinogenicity in laboratory animals; c) EtO is a direct-acting alkylating
agent with clear evidence of mutagenicity/genotoxicity, and there is sufficient evidence that DNA
adduct formation and the resulting mutagenic/genotoxic effects are key events in the mode of
action of EtO carcinogenicity; d) evidence of chromosome damage in humans exposed to EtO,
supporting the inference that the same mode of action for EtO carcinogenicity is operative in
humans.

1. Do the available data and discussion in the draft document support the hazard
conclusion that EtO is carcinogenic to humans based on the weight-of-evidence descriptors
in EPA's 2005 Guidelines for Carcinogen Risk Assessment? In your response, please
include consideration of the following:

l.a. Qualitative Characterization of Epidemiology Data

EPA concluded that the epidemiological evidence on EtO carcinogenicity was strong, but
less than completely conclusive. Does the draft document provide sufficient description of
the studies and transparent analysis of the data used to assess the carcinogenic hazard of
EtO to humans? Please comment on the EPA's characterization of the body of
epidemiological data reviewed. Considerations include:

a) the consistency of the findings, including the significance of differences in results using
different exposure metrics, b) the utility of the internal (based on exposure category) versus
external (e.g., SMR and SIR) comparisons of cancer rates, c) the magnitude of the risks,
and d) the strength of the epidemiological evidence.

A majority of the Panel agreed with the conclusion in the draft document that the
available evidence supports a descriptor of "Carcinogenic to Humans" but some of Panel
members concluded that the descriptor "Likely to be Carcinogenic to Humans" was more
appropriate. The consensus of the Panel was that the epidemiological data regarding ethylene
oxide carcinogenicity did not provide convincing evidence of a causal association between
human exposure and cancer. The differing views as to the appropriate descriptor for ethylene

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oxide were based on whether all of the requirements for designation as "Carcinogenic to
Humans" in the absence of convincing epidemiological evidence were met. Panel members
favoring a descriptor of "Carcinogenic to Humans" found the epidemiological evidence for an
association between ethylene oxide exposure and cancer to be adequate, albeit not strong enough
to assert causality. Other Panel members found the epidemiological evidence to be weak, lacking
consistency across multiple studies, and they concluded that the data were currently insufficient
to conclude that key precursor events were observed in humans.

The Panel believes that the document would be improved by a better introduction to the
hazard characterization section, including a brief description of the information that will be
presented. EPA has provided a comprehensive review (when the Draft Assessment as a whole is
considered) of the existing epidemiologic evidence relevant to ethylene oxide and a fair,
transparent, and critical assessment of this evidence for purposes of classifying EtO as a human
carcinogen. Presentation of the epidemiologic evidence would be strengthened by including a
summary figure and/or table at the beginning of section 3.0. In particular, the authors should
include the material now provided in Appendix A of the Draft Assessment to within the main
body of that Assessment. These tables should also provide clearer information on the observed
endpoints, in particular any information regarding cancer type within the broad category of
lymphohematopoietic cancers.

Based on this review, the Agency's assessment that the evidence is "strong but less than
completely conclusive" is supported although a characterization of the epidemiologic evidence
as "strong" is questionable. This ambiguity and the "less than completely conclusive"
assessment is appropriate given the uncertainties and inconsistencies in the occupational
epidemiology as is accurately summarized on page 11 of the Draft Assessment "3.1.1
Conclusions Regarding the Evidence of Cancer in Humans." EPA has both appropriately
applied the Hill criteria (Bradford-Hill, 1965) to assess causality and correctly interpreted their
application to the existing data. EPA's determination of EtO as a human carcinogen is robust in
that this conclusion is sustained by the largest and highest quality study (i.e., the NIOSH study)
under a variety of approaches to exposure classification. EPA appropriately identifies Steenland
et al. as the critical study for establishing human carcinogenicity. We agree with EPA in their
reliance on "internal" estimates of cancer rates rather than "external" comparisons (SMR, SIR)
due to well recognized limitations to the latter method of analysis. The Draft Assessment
characterizes the magnitude of the risk associated with EtO as "weak". This finding is well
substantiated by the epidemiologic evidence where a relatively small number of excess cancers
are found above background even among highly exposed individuals. A more comprehensive
discussion with additional perspective can be provided by comparing EtO's unit risk to other
similar carcinogens such as benzene, 1,3-butadiene, and/or formaldehyde.

The EPA's reliance on the NIOSH studies in providing a robust basis for assessment is
well justified based on the sample size and available quantitative exposure data. In this study,
the strongest exposure response associations were found with log cumulative exposure rather
than average or peak exposure. Such a basis for exposure classification is well supported for a
chronic effect such as cancer. The Draft Assessment describes both the internal and external
cancer rates reported within the literature. This is appropriate both for providing an accurate
summary and for addressing the different dimensions of EPA's evaluation, i.e. strength of
evidence and unit risk estimate. There was a strong sense among the Panel that the EPA's risk

-------
characterization could be improved by additional analyses of the rawNIOSH data, taking into
account the individual exposure data in the dose-response model

l.b.  Relevant Additional Key Studies

Are there additional key published studies or publicly available scientific reports that are
missing from the draft document and that might be useful of the discussion of the
carcinogenic hazard of EtO?

       Although the Draft Assessment generally provides a clear and concise summary of the
literature regarding EtO, the Panel identified two areas that deserve a more expansive treatment.
First, endogenous production of EtO results in some measure of background DNA adducts and
this issue should be addressed more fully in the document. The presentation of data from a single
reference (Bolt,  1996) giving background levels of 7-HEG in unexposed humans suggests that (i)
these values are the most reliable and (ii) the potential impact of spontaneous hydroxyethylation
of DNA by endogenously formed EtO  has little to no importance in the estimation of human
cancer risk for this chemical. However, it has been known for nearly 20 years that endogenous
formation of ethylene and conversion to EtO leads to 2-hydroxyethylation of DNA yielding
background levels of 7-HEG in unexposed humans and rodents (Post et al.,  1989; Walker et al.,
1992b, 2000; Cushnir et al., 1993; Farmer et al., 1993; van Delft et al., 1993, 1994; Leutbecher,
1995; Bolt et al., 1997; Wu et al., 1999; Zhao et al., 1999). Table V in Walker et al. (2000) lists a
series of studies of background levels of these adducts in differing tissues of unexposed humans
(see references therein), showing that lower spontaneous levels of 7-HEG have been  typically
found using more sensitive detection methods than those used in reports cited in Bolt's
commentary (1996) (see references therein).  In another commentary/review, Farmer and Shuker
(1999) suggest that in order to estimate the increase in cancer risk attributable to a given external
exposure, it is clearly important to establish and consider background levels of corresponding
DNA damage so that the scale of the incremental increase can be calculated. It is mainly for this
reason that more sensitive and specific analytical methods have been developed for the
measurement of background and EtO treatment-induced levels of 7-HEG than for any single
other DNA adduct (supporting references available).  Because the levels of background 7-HEG
are fairly substantial, and there are no chemical differences in DNA damage by endogenous
versus exogenous EtO, the Draft Assessment requires a section considering  the potential impact
of endogenous versus exogenous EtO exposure that carefully lays out (i) why the current
evidence of background levels of 2-hydroxyethylation of DNA does not constitute a threshold
and (ii) whether the magnitude and variability in endogenous EtO-induced damage may
overwhelm any  contribution from exogenous EtO exposure (other than some acute high-dose
exposure).

       Second,  a more comprehensive discussion of the production of DNA adducts by EtO
exposure would be appropriate. For the last paragraph of section 3.3.1  (page 21), a report by Dan
Segerback (1990) showed that treatment of calf thymus DNA with 14C-labelled EtO resulted in
the formation of N7-HEG, N3-HEA, and O6-HEG at  a ratio of 200:8.8:1. The Draft Assessment
suggested that this ratio of DNA adducts was found in a study of EtO-exposed rats by Zhao et al.
(1997); however, N7-HEG was the only product of EtO-induced hydroxyethylation measured in
this study. Instead, Walker et al. (1992b) found that the ratio of the steady-state concentrations
of 7-HEG, 3-HEA, and O6-HEG was 300:1.2:1 following repeated exposures of rats to EtO,


                                         11

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indicating that 3-HEA and O6-HEG do not accumulate in vivo to the levels predicted by the in
vitro ratios of these adducts and 7-HEG.  The same misquoting of Zhao et al., (1997) about the
ratio of these three DNA adducts is present beginning on the last line of page 21 of the Draft
Assessment.

       Finally, some Panel members supported the inclusion of the cancer bioassay results for
ethylene exposure and believed they were relevant and should be discussed in the Draft
Assessment. However, others on the Panel were less enthusiastic about this addition and felt that,
were the ethylene results to be included, a careful discussion of the caveats to their interpretation
relative to EtO carcinogenicity would be essential. The rationale for including the bioassay
results for inhalation exposure of F344 rats to ethylene (Hamm et al., 1984) is as follows. There
were no treatment- or dose-dependent increases in the induction of neoplasms following 2 years
of exposure to 0, 300, 1000, or 3000 ppm ethylene, suggesting that the levels of in vivo
formation of EtO during exogenous exposures to ethylene were insufficient to have carcinogenic
effects. In vivo metabolism of ethylene at high exogenous exposures (>1000 ppm) is saturated
and EtO is formed at the highest rate possible in the rat, with ethylene concentrations higher than
1000 ppm corresponding to exogenous exposure to approximately 6 ppm EtO based upon N7-(2-
hydroxyethyl)valine values and a two-compartment model (Bolt and Filser, 1987; Czanady et al.,
2000; Walker et al., 2000). Measurements of N7-(2-hydroxyethyl)guanine (7-HEG) adduct
levels in rats exposed to ethylene or directly to EtO indicate that 3000 ppm ethylene exposures
yield equivalent EtO levels of 6.4 to 9.5 ppm in various tissues except for liver (Walker et al.,
2000). The resulting reactions with nucleic acids and proteins following in vitro or in vivo
exposures to EtO are purely chemical in nature. In terms of potential differences in the nature
and/or the degree of DNA damage produced by hydroxyethylcarbonium ions resulting from (i) in
vivo conversion of endogenously formed ethylene to EtO, (ii) in vivo formation of EtO
following exogenous exposure to ethylene, and (iii) exogenous exposures to  EtO - there is no
biological, chemical, or theoretical basis for believing that hydroxyethylation arising from these
three different sources is different and imposes more or less genetic risk.  Furthermore, EtO
arising from metabolism of endogeous/exogenous ethylene or from exogenous EtO exposures is
rapidly and evenly distributed to all tissues (except for testis) in vivo (Wu et  al., 1999; Walker et
al., 2000).  Thus, under standard cancer bioassay conditions using 63 to 80 rats per group, the
ethylene equivalent of approximately 6 ppm EtO appears to be below the limit of detection for a
tumor response over the spontaneous background in the F344 rat.

       For the first paragraph of section 3.3.2.1, increased frequencies ofHprt gene mutations
were also observed in lymphocytes of rats at concentrations of EtO used in cancer studies with
this species (Tates et al., 1999; van Sittert et al., 2000; Walker et al., 2000). Likewise, for the
sentence beginning on page 24, line 27, the underlined changes are suggested:  "Increases in the
frequency of gene mutations in the lung, in T-lymphocytes, in bone marrow,  and/or in the testes
have been observed in transgenic mice and in rats exposed to EtO by inhalation	" For the
remainder of this section, it should be noted that Hprt refers to the rodent gene while HPRTis
reserved for the human counterpart in discussing data about this reporter gene.
                                         12

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Relevant references which were not included in the draft on Evaluation of the
Carcinogenicity of EtO:
1
Albertini, R.A., and Sweeney, L.M. (2007) Propylene oxide: genotoxicity profile of
a rodent nasal carcinogen. Crit Rev Toxicol. 37(6):489-520.
    Applegren, L.E., Eneroth, G., Grant, C., Lanstrom, L.E., and Tenghagen, K. (1978)
    Testing of ethylene oxide for mutagenicity using the micronucleus test in mice and
    rats. Act Pharmacol. Toxicol. 43: 69-71.
    Bastlova, T., Andersson, B., Lambert, B., and Kolman, A. (1993) Molecular
    analysis of ethylene oxide-induced mutations at the HPRT locus in human diploid
    fibroblasts. Mutat. Res. 287: 283-292.
    Bolt, H.M. and Filser, J.G. (1987) Kinetics and disposition in toxicology.  Example:
    carcinogenic risk estimate for ethylene. Arch. Toxicol. 60: 73-76.
    Conan, R.A., Waggy, G.T., Spiegel, M.H., and Berglund, R.L. (1979) Study of the
    mutagenic action of ethylene oxide, ethylene glycol, and 2-chloroethanol residues in
    plastic material sterilized by ethylene oxide.  Ann. Falsif Expert. Chim. 72:  141-
    151.
    Eisenbrand, G., Muller, N., Denkel, E., and Sterzel, W. (1986) DNA adducts and
    DNA damage by antineoplastic and carcinogenic N-nitrosocompounds. J. Cancer
    Res. Clin. Oncol. 112: 196-204.
    Farmer, P.B., Bailey, E., Naylor, S., Anderson, D., Brooks, A., Cushnir, J., Lamb,
    J.H., Sepai, O., and Tang, Y.-S. (1993) Identification of endogenous electrophiles
    by means of mass spectrometric determination of protein and DNA adducts.
    Environ. Health Perspect. 99: 19-34.	
    Farmer, P.B., and Shuker, D.E.G. (1999) What is the significance of increases in
    background levels of carcinogen-derived protein and DNA adducts? Some
    considerations for incremental risk assessment. Mutat. Res. 424: 275-286.
    Farooqi, Z., Tornqvist, M., Ehrenberg, L., andNatarajan, A.T. (1993) Genotoxic
    effects of ethylene oxide and propylene oxide in mouse bone marrow cells. Mutat.
    Res. 299: 223-228.
10
Fomenko, V.N., Strekalova, E. Ye (1973) The mutagenic effect of some industrial
toxins as a function of concentration and exposure time. Toksikol Nov Prom Khim
Veshchestv 13: 51-57.
11
Golberg, L. (1986) Hazard Assessment of Ethylene Oxide. CRC Press, Boca Raton,
FL, pp 3-7.
12
Hamm, T.E. Jr., Guest, D., and Dent, J.G. (1984) Chronic toxicity and oncogenicity
bioassay of inhaled ethylene in Fischer-344 rats. Fund. Appl.  Toxicol. 4: 473-478.
13
Hurst, D.T. (1980) An Introduction to the Chemistry and Biochemistry of
Pyrimidines, Purines, and pteridines. Wiley, New York, pp 5-8.
                                         13

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14  Jenssen D., and Ramel, C. (1980) The micronucleus test is part of a short-term
    mutagenicity test program for the prediction of carcinogenicity evaluated by 143
    agents test. Mutat. Res. 75: 191-202.
15  Kelsey KT, Wiencke JK, Eisen EA, Lynch DW, Lewis TR, Little JB. 1988.
    Persistently elevated sister chromatid exchanges in ethylene oxide-exposed
    primates: the role of a  subpopulation of high frequency cells. Cancer Res.
    48(17):5045-50.	
16  Kligerman, A.D., Erexson, G.L., Phelps, M.E., and Wilmer, J.L. (1983) Sister-
    chromatid exchange induction in peripheral blood lymphocytes of rats exposed to
    ethylene oxide by inhalation. Mutat. Res. 120:37-44.
17  Lambert, B., Andersson, B., Bastlova, T., Hou, S.-M., Hellgren, D., and Kolman, A.
    (1994) Mutations induced in the hypoxanthine phosphoribosyl transferase gene by
    three urban air pollutants: acetaldehyde, benzo[a]pyrene diolepoxide, and ethylene
    oxide.  Environ. Health Perspect 102 (Suppl. 4): 135-138.	
18  LaMontagne AD, Kelsey KT. 1998. OSHA's renewed mandate for regulatory
    flexibility review: in support of the 1984 ethylene oxide standard. Am J Ind Med.
    34(2):95-104.	
19  LaMontagne AD, Kelsey KT. (1998) Ethylene Oxide. In Rom, W.N. (ed.):
    Occupational and Environmental Medicine, 3rd ed. Philadelphia: Lippincott/Raven
    Press.
20  Lin, J.-S., Chuang, K.-T., Huang, M.-S., and Wei, K.-M., (2007) Emission of
    ethylene oxide  during frying of foods in soybean oil. Food and Chemical
    Toxicology 45: 568-574.
21  Lorenti Garcia, C. Darroudi, F., Tates, A.D., and Natarajan, A.T. (2001) Induction
    and persistence of micronuclei, sister-chromatid exchanges and chromosomal
    aberrations in splenocytes and bone-marrow cells of rats exposed to ethylene oxide.
    Mutat. Res. 492: 59-67.	
22  Marsden DA, Jones DJ , Lamb  JH, Tompkins  EM, Farmer PB, Brown K (2007)
    Determination of endogenous and exogenously derived N7-(2-
    hydroxyethyl)guanine  adducts in ethylene oxide-treated rats.  Chem Res Toxicol
    20:290-299.	
23  Mayer J, Warburton D, Jeffrey  AM, Pero R, Walles S, Andrews L, Toor M,
    Latriano L, Wazneh L, Tang D, et al. 1991. Biologic markers in ethylene oxide-
    exposed workers and controls. Mutat Res. 248(1): 163-76.
24  Ong, T., Bi H.K., Xing, S., Stewart, J., and Moorman, W. (1993) Induction of sister
    chromatid exchange in spleen and bone marrow cells of rats exposed by inhalation
    to different dose rates of ethylene oxide. Environ. Mol. Mutgen. 22: 147-151.
25  Ribeiro, L.R., Tabello-Gay, M.N., Salvadori, D.M., Pereira, C.A., and Becak, W.
    (1987) Cytogenetic effects of inhaled ethylene oxide in somatic and germ cells of
    mice. Arch. Toxicol. 59: 332-335.
26  Rusyn, I, Asakura, S., Li, Y., Kosyk, O., Koc, H., Nakamura, J., Upton, P.B., and
    Swenberg, J.A. (2005) Effects of ethylene oxide and ethylene inhalation on DNA
    adducts, apurinic/apyrimidinic sites and expression of base excision DNA repair
    gene in rat brain, spleen, and liver. DNA Repair (Amst.) 4: 1099-1110.	
                                         14

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27
Segerback, D. (1990) Reaction products in hemoglobin and DNA after in vitro
treatment with ethylene oxided and 7V-(2-hydroxyethyl)-7V-nitrosourea.
Carcinogenesis 11:307-312.
28
Schulte PA, Walker JT, Boeniger M, Tsuchiya Y, and Halperin WE. (1995)
Molecular, cytogenetic, and hematologic effects of the ethylene oxide on female
hospital workers. J. Occup. Environ. Med 37:313-320.
29
Shore RE, Gardner MJ, Pannett B. 1993. Ethylene oxide: an assessment of the
epidemiological evidence on carcinogenicity. Br J Ind Med. 50(11):971-97.
30
Streklova, E. Ye, Chirkova, E.M., and Golubovich, E. (1975) Mutagenic action of
ethylene oxide on sex and somatic cells in male white rats. Toksikol Nov Prom
Khim Veshchestv 14: 11-16.
31
Tates, D., van Dam, F. J., Natarajan, A. T., van Teylingen C. M.M., de Zwart, F. A.,
Zwinderman, A. H., van Sittert, N. J., Nilsen, A., Nilsen, O. G., Zahlsen, K.,
Magnusson, A.-L., and Tornqvist, M. (1999) Measurement of//Permutations in
splenic lymphocytes and haemoglobin adducts in erythrocytes of Lewis rats exposed
to ethylene oxide. Mutation Research 431: 397-415
32
Van Delft J.H.M., van Winden M.J.M., van den Ende, A.M.C., and Baan R.A.
(1993) Determining N7-alkylguanine adducts by immunochemical methods and
HPLC with electrochemical detection: application in animal studies and in
monitoring human exposure to alklylating agents. Environ. Health Perspect. 99: 25-
32.
33
Van Sittert, N.J., Boogaard, P.J., Natarajan, A.T., Tates A.D., Ehrenberg, L.G., and
Tornqvist, M.A. (2007) Formation of DNA adducts and induction of mutagenic
effects in rats following 4 weeks inhalation exposure to ethylene oxide as a basis for
cancer risk assessment. Mutation Research 447: 27-48.
34
Walker, V.E., Wu, K.-Y., Upton, P.B., Ranasinghe, A., Scheller, N., Cho, M.-H.,
Vergnes, J.S., Skopek, T.R., and Swenberg, J.A. (2000) Biomarkers of exposure and
effect as indicators of potential carcinogenic risk arising from in vivo metabolism of
ethylene to ethylene oxide. Carcinogenesis., 21: 1661-1669.
I.e. Mode of Action

Do the available data and discussion in the draft document support the mode of action
conclusions?

The Panel agrees with the conclusion in the draft assessment that the available data
strongly support the action of EtO as a genotoxic agent producing DNA adducts as well as
cytogenetic and small-scale mutagenic effects. However, a more careful discussion of the
sequence of events that are presumed to lead to EtO-induced mutagenesis is warranted. In the
Draft Assessment, the description of the events leading to gene mutations and chromosome
damage presume that 7-HEG and 7V-alkylated bases are indirectly responsible, or primarily
responsible, for genetic changes. The section on the mode of action does not consider any other
possibilities to explain the genotoxicity of EtO, which include (but are not limited to) the
15

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potential consequences of (i) formation of minor promutagenic adducts, (ii) hydroxyethylation of
the DNA backbone, and (iii) the formation of secondary reactive species including reactive
oxygen species. The sentence beginning on line 4 of page 22 states that "HEG adducts result in
various types of cytogenetic damage, including gene mutations, which have been observed in
mice and rats".  However, there is currently limited evidence to directly support this statement.

       As discussed in a recent review by Albertini and Sweeney (2006), N7-alkylguanine
adducts formed from small epoxides such as EtO and propylene oxide do not cause distortion of
the double helix and do not interfere with hydrogen bonding; rather, they are hypothesized to
result in mutation via loss of N7-alkylguanine via depurination or the action of DNA
glycosylases, leaving an apurinic site in the DNA. The action of apurinic endonuclease indeed
creates a DNA single-strand break which, if unresolved, can lead to DNA double-strand breaks.
Furthermore, depurination of N7-alkylguanine can result in preferential insertion of an adenine
(according to the A-rule) or another base leading to mispairing/mutations.  Based upon the initial
mutational spectra data for EtO in mice (Walker and Skopek, 1993), it was hypothesized that
formation of apurinic (AP) sites might be involved in the mutagenesis of EtO. In order for these
mutagenic events to occur at a rate sufficient to result in an EtO-induced changes in mutational
spectra (including increases in double-strand breaks and changes in mutant fractions for point
mutations), then accumulation of AP sites arising from high levels of 7-HEG would be expected
to occur over time.   A  study was recently completed to test the hypothesis that EtO exposure
results in the accumulation of AP sites and induces changes in the expression of genes for base
excision DNA repair, predisposing to point mutations and chromosomal  aberrations in F344 rats
exposed by inhalation  for 4 weeks to 0 or 100 ppm EtO, or 0 to 3000 ppm ethylene, (Rusyn et
al., 2005). The  resulting data demonstrated that DNA damage induced by exposure to EtO is
repaired without accumulation of AP sites, and that the mechanisms proposed above play a
minor role in the mutagenicity of EtO. The same conclusions would apply to the accumulation of
3-HEA formed in minor amounts in EtO-exposed rats (Walker et al., 1992b), and the induction
of strand breaks or point mutations at A:T base pairs. Rusyn et al. (2005) have suggested that the
mutagenic effects of EtO were likely to be the result of minor promutagenic adducts, such as O6-
FIEG, Nl-FIEAdenine, or possibly ring-opened 7-HEG.

       Drs. Lars Ehrenberg and Timothy Fennell have independently proposed that EtO may
induce strand breaks and chromosomal alterations via 2-hydroxyethylation of the DNA
backbone. 2-Hydroxyethylation of phosphate groups introduces extreme instability into the
sugar-phosphate backbone because the resulting phosphotriester breaks down through a
dioxaphospholane ring intermediate (Eisenbrand et al., 1986). This alternative mechanism for
EtO-induced strand  breaks and chromosomal damage is not mentioned in the Draft Assessment.

       In summary, the overall genetic toxicology data strongly support  the consistent action of
EtO as a relatively weak mutagen and clastogen, but the underlying mechanisms for its mode of
action as a genotoxin are not known with a high degree of certainty. The paucity of knowledge
about the fundamental  ways in which EtO acts to induce large- and small-scale mutations is not
reflected in the mode-of-action section; rather this section is presented as if there is a good basic
understanding (which  does not currently exist).
                                         16

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l.d.  Hazard Characterization

Does the hazard characterization discussion for EtO provide a scientifically-balanced and
sound description that synthesizes the human, laboratory animal, and supporting (e.g., in
vitro) evidence for human carcinogenic hazard?

       While some members of the Panel found the hazard characterization section of the Draft
Assessment to be satisfactory, a majority expressed concerns that this section did not achieve the
necessary level of rigor and balance. As discussed above, a majority of Panel members agreed
with the overall characterization of EtO as a human carcinogen. However, a critical issue in this
characterization, in particular in the face of epidemiological data that are not strongly conclusive,
is whether the precursor events leading to cancer in animals are observed in humans at the levels
to which they are exposed to EtO.

       The mode of action for EtO carcinogenicity involves the key events of DNA alkylation
and the induction of point mutations and/or chromosomal changes. Evidence for genotoxicity of
EtO in humans is largely based on cytogenetic analyses. The frequency of cells with
chromosomal aberrations and micronuclei in peripheral blood cells are two of the most accepted
cytogenetic biomarkers used in human population studies because they were the first indicators
of effect shown to be early predictors of cancer risk. However, the micronucleus data in EtO-
exposed humans are weak, with very small increases reported, and the abundant data on
chromosomal aberrations in EtO-exposed people have not demonstrated, with confidence, the
occurrence of stable chromosome changes leading to mutations. As indicated at the bottom of
page 20 of the Draft Assessment, chromosome painting/FISH are needed to detect  and quantify
stable chromosomal aberrations, which would provide more conclusive evidence for classifying
EtO as a human carcinogen. A problem in the hazard characterization in  the Draft Assessment is
the lack of an adequate review of the cytogenetic data for EtO in exposed rodents and head-to-
head comparisons to corresponding data in humans. The sections concerning sister chromatid
exchanges (SCEs) (3.3.2.2) and chromosomal aberrations (3.3.2.3) in the Supporting Evidence
present only data from human studies and overlook contradictory or equivocal findings from
studies of EtO-exposed rodents. Furthermore, there is no discussion of findings related to
micronuclei in humans or rodents in the Supporting Evidence section.  In brief, several studies
have shown that repeated exposures of rats to high concentrations of EtO induces dose-related
increases in SCEs (Kligerman et al., 1982; Ong et al., 1993; van Sittert et al., 2000; Lorenti
Garcia et al., 2001). Treatment of rats and mice with high acute doses of EtO by i.p./i.v.
injection or oral dosing (i.e., routes of exposure not relevant to humans) also caused increases in
the frequencies of micronuclei or chromosomal aberrations (Strekalova et al., 1971; Applegren et
al., 1978; Conan et al., 1979; Jensen and Ramel, 1980; Farooqui et al., 1993). In contrast,
following inhalation exposures (i.e., a route of exposure relevant to humans), no increases in the
frequencies of micronuclei or chromosomal aberrations were found in peripheral blood/splenic
lymphocytes from rats exposed at concentrations of 50 to 450 ppm EtO for 1 or 3 days
(Kligerman et al.,  1982) or 50 to 200 ppm EtO for 4 weeks (5 days/week, 6 h/day)  (van Sittert et
al., 2000; Lorenti Garcia et al., 2001). Furthermore, two studies showed that 4 weeks of
exposure of rats to 200 ppm EtO failed to cause an increase in translocations (van Sittert et al.,
2000; Lorenti Garcia et al., 2001) (e.g., the % translocation in controls and 200 ppm rats were
0.1% and 0.09%, respectively, in the latter study). In the study by van Sittert et al. (2000), the
authors concluded that "The absence of effects on reciprocal translocations (assessed by FISH)


                                         17

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demonstrates that 4 weeks of inhalation exposure to EO at high levels does not produce
genetically transmissible chromosome aberrations in the rat." A single study reported that
repeated exposures of mice at 200 to 600 ppm EtO for two weeks induced chromosomal
aberrations in bone marrow cells (Ribeiro et al., 1987), but no studies have been performed to
assess whether this chemical causes transmissible chromosome aberrations in somatic cells in
this species.

       In contrast to lack of data supporting induction of chromosome aberrations and reciprocal
translocations at EtO concentrations used in rodent carcinogenicity studies of this chemical, there
are unequivocal data from three research groups (cited reports by Les Recio, Ad Tates, and
Vernon Walker) showing that EtO causes dose-related increases in point mutations in multiple
tissues of mice and rats exposed by inhalation to 50,  100, or 200 ppm EtO, or concentrations
used in the cancer bioassays of EtO, as well as in oncogenes and tumor suppressor genes of
various EtO-induced cancers in the mouse (Houle et al., 2006;  Hong et al., 2007). In these rodent
studies using the Hprt and/or lad reporter genes, EtO was consistently a weak point mutagen.
However, as noted in the Draft Assessment, studies of the induction of Hprt mutations in EtO-
exposed humans have been inconclusive.

       Thus, studies of both humans and rodents exposed to EtO have yielded evidence
consistent with the genotoxic mode of action of EtO, but different types of genetic alterations are
demonstrated in the two species.
Charge Question 2- Dose-Response Analysis

       The Agency's assessment describes the derivation of inhalation unit risk estimates for
cancer mortality and incidence based on the human data.  An ECoi of 44 ug/m3 (0.024 ppm) was
calculated using a life-table analysis and linear modeling of the categorical Cox regression
analysis results for excess lymphohematopoietic cancer mortality in males reported in a high-
quality occupational epidemiologic study (Steenland et al., 2004). Linear low-dose extrapolation
from the LECoi yielded a lifetime extra cancer mortality unit risk estimate of 5.0 x 10"4 per
ug/m3 (0.92 per ppm) of continuous EtO exposure. According to EPA's assessment,applying the
same linear regression coefficient and life-table analysis to background male
lymphohematopoietic cancer incidence rates yielded an ECoi of 24  ug/m3 (0.013 ppm) and a
preferred lifetime extra cancer unit risk estimate of 9.0 x  10"4 per ug/m3 (1.6 per ppm). The
preferred estimate was greater than the estimate of 5.0 x 10"4 per ug/m3 (0.91 per ppm; ECoi = 44
ug/m3) calculated, using the same approach, from the results of a breast cancer incidence study
of the same worker cohort (Steenland et al., 2003), and was recommended as the potency
estimate for Agency use.

       According to the Agency's assessment, the weight of evidence supports a mutagenic
mode of action for EtO carcinogenicity.  The Draft Assessment then concludes that, in the
absence of chemical-specific data on early-life susceptibility, an increased early-life
susceptibility should be assumed and the age-dependent adjustment factors (ADAFs) should be
applied, in accordance with EPA's Supplemental Guidance for Assessing Susceptibility From
Early-Life Exposure to Carcinogens, hereinafter referred to as "EPA's Supplemental  Guidance"
(U.S. EPA, 2005b).  Applying the ADAFs to the unit risk estimate of 9.0 x 10"4 per ug/m3 yields


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an adjusted full lifetime unit risk estimate of 1.5 x 10"3 per ug/m3, and the commensurate lifetime
chronic exposure level of EtO corresponding to an increased cancer risk of 10"6 is 0.0007ug/m3.
[Note that for less-than-lifetime exposure scenarios (or for exposures that vary with age), the
unadjusted (adult-based) potency estimate of 9.0 x 10"4 per ug/m3 should be used, in conjunction
with the ADAFs as appropriate, in accordance with EPA's Supplemental Guidance.]

In the Agency's assessment, unit risk estimates were also derived from the three chronic
rodent bioassays for EtO reported in the literature. These estimates, ranging from 2.2 x 10"5 per
ug/m3 to 4.6 x 10"5 per ug/m3, are about an order of magnitude lower than the estimates based on
human data [unadjusted for early-life susceptibility]. The Agency takes the position that human
data, if adequate data are available, provide a more appropriate basis than rodent data for
estimating population risks (U.S. EPA, 2005a), primarily because uncertainties in extrapolating
quantitative risks from rodents to humans are avoided. Although there is a fairly sizable
difference between the rodent- and human-based estimates, the assessment infers that the
similarity between the unit risk estimates based on the male lymphohematopoietic cancer and the
female breast cancer results increases confidence in the use of the unit risk estimate based on the
male lymphohematopoietic cancer results. According to the Agency assessment, the unit risk
estimates were developed for environmental exposure levels and are not necessarily applicable to
higher-level occupational exposures, that appear to be subject to a different exposure-response
relationship. However, occupational exposure levels are of concern to EPA when EtO is used as
a pesticide (e.g., fumigant for spices). Therefore, it is appropriate that EPA presents unit risk
estimates for occupational exposure scenarios.
2. Do the available data and discussion in the draft document support the approaches taken
by EPA in it derivation of cancer risk estimates for EtO? In your response, please include
consideration of the following:

2.a. Selection of Epidemiology Studies

EPA concluded that the epidemiological evidence alone was strong but less than completely
conclusive (although EPA characterized the total evidence - from human, laboratory
animal, and in vitro studies - as supporting a conclusion that EtO as "carcinogenic to
humans"). Is the use of epidemiological data, in particular the Steenland et al. (2003, 2004)
data set, the most appropriate for estimating the magnitude of the carcinogenic risk to
humans from environmental EtO exposures? Are the scientific justifications for using this
data set transparently described? Is the basis for selecting the Steenland et al. data over
other available data (e.g., the Union Carbide data) for quantifying risk adequately
described?

The Panel agreed that the epidemiological evidence is less than completely conclusive.
The data are somewhat consistent in showing a weak to moderate excess carcinogenic response.
It is not unusual for epidemiological evidence to be strong but in and of itself not provide
conclusive evidence of causation. It is appropriate in light of conclusive evidence in animals to
use sound human epidemiological studies to determine the dose response even though in and of
themselves these studies may not provide conclusive evidence of carcinogenicity.
19

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The Panel agreed that the NIOSH retrospective cohort study with observations on in excess
of 18,000 workers from 13 sterilizing facilities is the best single source of data for determining
the dose-response relationship for evaluating the risk of low level EtO exposure in human
populations (Steenland et al, 2004).

As a single source, the epidemiological data for the NIOSH cohort has the following distinct
advantages:

1) A large (18,000+) sample of workers with long periods of exposure to EtO;

2) A roughly 55%/45% female to male gender ratio, similar to the general population;

3) Multiple distinct facilities with worker exposure estimates. (Facility intra-class correlation is
never considered in any of the models applied to the NIOSH data);

4) Limited coincidental exposure in the occupational cohort to other compounds, e.g. ethylene
dichloride, that might confound the interpretation of the relationship of EtO exposure to cancer
outcomes.

5) Careful mortality follow-up using multiple sources; and finally

6) Continuity of the investigators who have been building and analyzing the data set.

A primary disadvantage of the NIOSH data , common to all retrospective
epidemiological data, is the need to apply a model to estimate the profile (time, intensity) of
individuals' exposure to EtO (Hornung, et al., 1994). The model of EtO exposure over time is
needed to support the use of different exposure metrics, e.g. cumulative (time integrated), peak,
duration. Random errors in estimating exposures are certainly present and systematic bias
resulting from errors in model inputs or model mispsecification are certainly also present to some
extent. Ideally, estimation biases are small relative to the variance of the predictions and the
assigned exposure profiles result in acceptable classifications of individual exposure levels (see
below). Given the importance the estimated exposures to the use of these data in ultimately
modeling the dose-response relationship, the Panel noted several important features of the
NIOSH exposure estimation model and the exposure predictions for individual NIOSH cohort
members.

The worker exposure observations used to fit the model were not a random sample (effect
unknown) of workers or work environments but were designed to represent the typical range of
exposure conditions that occur in the contemporary work place. A total of 2350 full-shift
charcoal tube measurements were collected from workers in twelve plants. By design, the
observations were distributed across workers involved in eight exposure activity types (e.g.
sterilizer area, production, maintenance) and five product types (e.g. spore strips, plastics, etc.).
In addition to these two main effects, the multivariate regression model for predicting exposures
for the NIOSH cohort workers includes additional covariates for age of product, year (of
exposure), and size and ventilation characteristics of the work area. A random sub-sample of
observations of the worker measurements was set aside as a cross-validation sample for purposes
of evaluating the predictive potential of the fitted model. The final model produced an R2 to the

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cross-validation exposure measurements (cross validation sample) of 0.85. There was
consensus among the Panel that the exposure model development for the NIOSH data was
conducted in a rigorous fashion and it would be difficult to improve on the exposure estimates
generated by the NIOSH exposure measurement study (Griefe et al., 1988, Steenland et al.,
1987).

In its discussion of the predicted exposure measures in NIOSH cohort data, the Panel
focused in some detail on the role of chronological time in the prediction of annual exposures for
the individual cohort members. From Table VI of Hornung at al. (1994), the year in which the
exposure occurred is highly predictive of the exposure concentration. Quoting from the paper,
"We had hoped that some combination of engineering controls would eliminate the need for
including calendar year in the model, .... However, no combination of variables could be found
to allow removal of calendar year from the model. We attributed this finding to calendar year
acting as a surrogate for improvement in work practices due to increased awareness of the
potential health effects of ETO". The effect of chronological time is highly significant and
quadratic—0.446 x (year-82) and 0.062 x (year-82)2. The model-based assignment of exposures
to individuals in the NIOSH cohort truncates this highly significant time effect on exposure
(quadratic) at 1978. That is, all exposures for work years prior to 1978 receive the same
contribution to modeled exposure as in 1978—the year at which the trend in exposures is at a
maximum for the quadratic time effect. The reason for assigning all pre-1978 exposures to the
1978 level is that prior to that time data on general work place exposures was scarce (7 activity x
product combination mean exposures based on 23 individual samples collected in 1976-1978).

Regarding the handling of time in modeling annual exposures for individual NIOSH
cohort members, the Panel's concern is less on the modeling decision but rather on how this
estimation decision may interact with subsequent dose response model fitting in which the
exposure metric is itself a function of time. For example, the 1978 truncation point is very close
to the analysis censoring exposure point of t-15, or t-20 for the Steenland et al. (2004) dose
response models that use time lagged exposure as the independent variable. Does introducing
the lag into the cumulative exposure measurement alter the quality of the effective exposure
model since the parameters for non-time factors (e.g. activity type, product type, engineering
controls, size of workplace) have been estimated in the presence of a strong and dominant
quadratic time trend? The Panel encourages the EPA to investigate potential instability that may
result from interaction between the chosen time metric for the dose response model and the
treatment of time in the estimated exposure (e.g. log cumulative, exposure with 15 year lag) that
is the independent variable in that dose-response model.

While the advantages of the Steenland data set are described, the Draft Assessment
contains no list of the criteria that were utilized to select studies for inclusion in the risk
assessment process. For example, a description of what constituted adequate sample size,
exposure assessment, minimum length of employment, length of follow-up, lag time for selected
outcomes, etc., would be helpful. It is certainly appropriate to critique all available datasets and
provide justification for excluding those who did not meet these criteria. While a review of most
studies conducted between 1985 (when the last EtO assessment was conducted) and 2004 was
included, it was not always clear why some studies were not considered in the process. For
example, Steenland's dataset was deemed most appropriate because of the larger sample size
(n=l 8,254), gender diversity (45% male, 55% female), lack of potentially confounding co-

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exposures, and more developed measures of individual worker exposures. There were
disadvantages, e.g., lack of information on age of the cohort members. The authors state that
earlier exposures decreased markedly by the mid-1980's. Did the risk of cancer decrease in later
time intervals, i.e., what is the risk for workers initiating employment in the 1980's when levels
are lower over a similar time period (20 years)? Also, age at exposure, an increasingly
recognized critical factor in environmental/occupational exposures and adverse health effects,
could not be evaluated (e.g., younger workers with similar cumulative exposures may be at
different risk than older workers).

Other cohorts consisted of smaller sample sizes, less precise measures of individual
worker's exposure levels, and concurrent chemical exposures. Some of these studies were
justifiably omitted from the risk assessment because of sufficient weaknesses in design and/or
analysis. However, it seems it would be of value to examine some of these studies to determine
the potential for interaction between EtO and other common workplace exposures.

To summarize, the Panel concurred that the NIOSH cohort is the best single
epidemiological data set with which to study the relationship of cancer mortality to the full range
of occupational exposures to EtO. That said, the Panel encouraged the EPA to broadly consider
all of the epidemiological data in developing its Draft Assessment. In particular, the Panel
encourages the EPA to explore uses for the Greenberg et al. (1990) data including leukemia and
pancreatic cancer mortality and EtO exposures for 2174 Union Carbide workers from its two
Kanawha Valley, West Virginia facilities. (Also described in Teta et al. 1993; Teta et al., 1999).

The Panel did not believe that it was necessary to use only one study to arrive at a single
potency estimate or to limit the assessment to a single modeling approach. Panel members
emphasized that the EPA's own cancer risk assessment guidelines support the consideration of
the full range of available data as well as alternatives to the default exposure models. Quoting
from the EPA's Guidelines for Cancer Risk Assessment.

Section 1.3, p. 1-8, "\T]hese cancer guidelines view a critical analysis of all of
the available information that is relevant to assessing the carcinogenic risk as the
starting point from which a default option may be invoked if needed to address
uncertainty or the absence of critical information".
2.b. Methods of Analysis

Assuming that Steenland et al. (2003, 2004) is the most appropriate data set, is the use of a
linear regression model fit to Steenland et al.'s categorical results for all
lymphohematopoietic cancer in males in only the lower exposure groups scientifically and
statistically appropriate for estimating potential human risk at the lower end of the
observable range? Is the use of the grouping of all lymphohematopoietic cancer for the
purpose of estimating risk appropriate? Are there other appropriate analytical
approaches that should be considered for estimating potential risk in the lower end of the
observable range? Is EPA's choice of a preferred model adequately supported and
justified? In particular, has EPA adequately explained its reasons for not using a
quadratic model approach such as that of Kirman et al. (2004) based? What

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recommendations would you make regarding low-dose extrapolation below the observed
range?

The Panel's discussion of this multi-part question was extensive. To simplify the
presentation here, the written discussion is divided into seven segments: 1) linearity vs.
nonlinearity in dose response modeling and extrapolation; 2) the linear regression methodology
of grouped risk ratios employed in the EPA Draft Assessment; 3) exclusion of high exposure
data points; 4) grouping lymphohematopoietic cancers in analysis; 5) using only male data; 6)
justification of approach and alternatives; and 7) statistical and computational issues.

1. Linearity vs. non-linearity of low dose extrapolation

The Panel was "philosophically" and scientifically divided on the whether low dose
extrapolation of risk to environmental EtO exposure levels should be linear (following Cancer
Guideline defaults for mutagenic MO A) or whether plausible biological mechanisms argued for
a non-linear form for the low dose response relationship. Some Panel members thought that the
data on ethylene oxide imply a non-linear response despite a mutagenic mode of action. They
encouraged the exploration of the use of non-linear models for low-dose extrapolation, such as
the quadratic and linear quadratic. Others thought that non-linear extrapolation was inappropriate
given the mutagenic mode of action. After considerable debate, the Panel was unable to arrive at
consensus. Therefore the two distinct lines of argument are presented below.

The Linear Extrapolation Argument: In general a linear no-threshold interpolation to
zero for ethylene oxide external exposure is consistent with available information about the
mutagenic mode of action in this case. General arguments are that DNA repair and other
genomic defense processes (e.g. apoptosis) are not likely to be perfect in the sense of repairing
all incremental damage before the next cycle of DNA copying which would otherwise lead to
miscoding errors or more extensive chromosome level changes including breakage,
recombination, and nondisjunction events. Genomic defense processes have costs to the cell,
and some are also known to create their own baseline damage, so that it is highly likely that the
extent of the effectiveness of such processes has been subject to an evolutionary optimization
that falls short of perfection. Thus, even with background and endogenous exposures there
should be some expectation of ongoing equilibrium damage on a cellular stochastic basis. Such
equilibrium damage is likely to contribute to the appreciable "background" of cancers of all
types that humans suffer. A detailed review of the argument for "low dose linearity" in cancer
risk assessment involving a mutagenic mode of action is given by Hattis (2007, Appendix A).

The Non-linear Low Dose Response Model Argument: Linear extrapolation of risk
below the chosen point of departure (POD) to a zero baseline is a conservative assumption, given
EtO's reactivity (which will diminish the amount reaching the nucleus), mutagenic mode of
action, and that it is generated endogenously. Some repair seems likely and some threshold
probably exists. Thus, the human risk estimates at the lower end of the observable range are
likely to be exaggerated under a linear extrapolation. Furthermore, a linear model through zero
(linear model per se at low doses is acceptable) assumes that other effects of EtO on the
development of cancer are insignificant. This seems unlikely given the reactive nature of this
compound and thus its ability to affect signaling pathways that may positively and negatively
influence the development of cancer. Measuring such effects is problematic, but they must exist

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and impact the incidence of cancer. Linear regression is for "extra" risk; but this still seems
problematic given the endogenous level of EtO and base levels of damage and repair. In other
words, is it justified to assume linear above baseline levels? At low doses, a reactive compound
like EtO will react with cellular constituents before it ever gets to DNA. Linear defaults are not
supported when a framework analysis is done of genotoxicity and this is even more strongly so
for clastogenic agents, which are quadratic in dose response (Preston, 1999). Swenberg (2007,
Appendix C) provides a framework analysis of Genotoxicity and Risk Assessment in support of
an argument for a nonlinear low dose response mechanism for EtO.

At the conclusion of its discussion, the Panel was not in agreement on the linearity vs.
non-linearity of the cancer response to EtO exposure levels in: 1) the occupational exposure data
used to estimate the point of departure for the low dose extrapolation; and 2) in the form of the
model used to extrapolate cancer risk below the POD to a zero or baseline exposure level. With
appropriate discussion of the statistical and biological uncertainties, several Panel members
advocated the consideration of both linear and nonlinear functional forms in the final EtO Risk
Assessment. These Panel members pointed out that such an approach was consistent with the
latest guidance in the EPA Guidelines for Cancer Risk Assessment. Quoting Section 1.3 p. 1-9,

"Significant risk management decisions will often benefit from a more
comprehensive assessment, including alternative risk models having significant
biological support."

2. Linear regression model for categorical data

The Panel identified several important shortcomings in the linear regression modeling
approach used to establish the point of departure for low dose extrapolation of cancer risk due to
EtO. Based on its review of the methods and results presented at the January 17,18, 2007
meeting, the Panel was unanimous in its recommendation that the EPA develop its risk models
based on direct analysis of the individual exposure and cancer outcome data for the NIOSH
cohort. The Panel understands that these data are available to EPA analysts upon request to the
CDC/NIOSH. The Panel recognizes the burden that a reanalysis of the individual data places on
the EPA ORD staff but given the important implications of the risk assessment, this burden is
well justified to achieve the best scientific and statistical treatment of all the available
epidemiological data.

The following paragraphs present the statistical basis for the Panel's assessment of the
linear regression model approach and the use of categorized exposure and outcome data.

The approach described in the Draft Assessment uses a model based on categories
defined by cumulative exposure ranges for male subjects in the NIOSH cohort. Steenland et al.
identified several models that provide a significant (p<0.05) fit to the exposure data; however,
the EPA has elected to use model-based relative rate parameter estimates for categories of 15
year lagged, cumulative exposure. In Steenland, et al. (2004) this model was not one that
provided a significant fit to the NIOSH data (p=0.15 for the likelihood ratio test
of/? = {/?1,/?2,/?3,/?4} = 0). The use of the weighted least squares regression fit of a linear
regression line through the three data points defined by the estimated rate ratios and mean
cumulative exposures for the first three exposure categories of the Steenland, et al. 15 year lag,

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cumulative exposure category model is not a robust application of this technique. The Panel
identified four weaknesses in the approach.

       a) Model-based dependent variable: The dependent variables are model-based estimates
of rate ratios for exposure categories.  The rate ratio values used in the weighted least squares
regression are derived from a cumulative exposure model (15 year lag) in which the estimated
regression parameters in the proportional hazards regression model are not significantly different
from 0 at a=0.05 (p=0.15). In Steenland et al. (2004), the only individually based (proportional
hazards) model  that fits the data for males in the NIOSH cohort is a model for log of individual
exposure through t-15 years.

       b) Grouped data regression: The weighted least squares fit applies estimates of variance
for the individual rate ratios under that assumption that these inverse weighting corrections
correctly adjust for heteroscedasticity of residuals  in the underlying regression model.
Historically, models for grouped proportions applied adjustments of this type but it  is by no
means a preferred technique when the underlying individual data are available. The "ecological
regression" model per Rothman (1998, Second edition) is subject to bias due to within group
heterogeneity of predictors and unmeasured confounders. The heterogeneity in the  grouped
model involves  the range of exposures within the collapsed categories.  The unmeasured
confounders include variables (other than gender)  that affect the potency of exposure or may
have produced gross misclassification based on the original exposure model estimation for the
individual (Hornung, et al., 1994).

       c) The model fitting does not conform exactly to the Rothman (1986) procedure:

       The 1998 (Second edition) of Rothman (Rothman and Greenland,  1998) describes the
technique for estimating this risk from grouped data in Chapter 23. In that updated  version of the
original monograph the model that is fitted is:

                  Expected (Rate \ Exposure) = B0 + B^ • Mean (Exposure)

The objective is to estimate the rate ratio (for exposure 0=no, l=yes, or equivalently for a one
unit increase in  the exposure metric).  That estimator is then:
The model estimated by the EPA method is:

                        Expected(rr  Exposure) = B* • Mean(Exposure)

In the former, the variance in the estimation of the rate ratio is a function of the variance of the
estimated slope and the variance in the estimated baseline hazard, represented by the estimated
intercept.  This variance is present in the estimation of the baseline hazard in the Steenland,  et
al. (2004) estimation of the rate ratios but is not present in the EPA adaptation to the linear rate
ratio model.  The EPA approach permits no intercept (>0) for the background exposure or any
                                         25

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allowance for an effect of true non-zero exposures in the internal control group (exposures less
than 15 years).

       In general, the use of categorical exposure ranges is not the optimal strategy for using
epidemiologic data. When continuous data are categorized and then used in dose response
modeling, it amounts to starting with a full range of exposures, collapsing that range into
somewhat arbitrary boundaries, and then deriving a continuous dose response model for an even
larger range of exposures.

Categorizing continuous variables results in a host of issues:
•    Assumption that the risk within the category boundaries is constant
•    It is not known whether a given categorization is representative of the data since there are
many ways of categorizing.
•    Loss of power and precision by spending degrees of freedom on each category
•    Misclassification at category boundaries (this can be minimized by choosing cutpoints
where relatively few observations are present)
•    Categorizations can be manipulated to show the desired results

       The Panel acknowledged that techniques such as  the linear regression method described
by Rothman (1998) or Poisson regression may be the most appropriate techniques when only
grouped or categorized data are available for estimating the dose/response model. However, the
original NIOSH cohort data are available at the individual level and this permits the use of
models such as the Cox regression models employed by  Steenland et al. (2004) that utilize the
full information in the individual  observations.  If categories of exposure (as opposed to
individual exposure estimates)  must be used, the crude rates should be computed for a large
number of equally  spaced exposure ranges and the Rothman and Greenland (1998) model fitted
to these multiple points.
3. Exclusion of high exposure groups

       In conjunction with its recommendation to use the individual NIOSH cohort data to
model the relationship of cancer risk to exposures in the occupational range (see 2.B.1 above),
the Panel recommends that the EPA analysts explore the use of the full NIOSH data set to
estimate the cancer slope coefficients that will in turn be used to extrapolate risk below the
established point of departure.  The use of different data to estimate different dose response
curves should be avoided unless there is both strong biologic and statistical justification for
doing so. The Panel believed this justification was not made in the Agency's draft report.

       In the Draft Assessment, EPA analysts have faithfully adhered to the paradigm of cancer
dose response analysis usually used for animal data in analyzing the human epidemiological data
for this case. This is a useful step toward harmonizing the treatment of animal and human-
derived information in carcinogenic risk estimation. However, while achieving operational
consistency, the Agency's current analysis does not yet take into account some important
differences between animal and human carcinogenic dose response data. These differences need
to be factored in for designing a modern set of analytical procedures for human data to achieve
more comparable types of risk inferences and a better analysis of uncertainties.

                                         26

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The most important differences in human vs. animal data that may require differences in
analytical approach are:

• Animal exposures are the result of intentional and consistent administration of the test
material at specific target levels, often reinforced with frequent empirical measurements of
differences between target exposures and actual delivered doses. Human exposures, by contrast,
are unintended, often variable over time, and at best estimated from occasional measurements of
exposures of the subjects themselves or subjects considered to have similar exposures.
Uncertainty in exposures is thus nearly always much greater for human than for animal data.
Such uncertainties in human data lead to distortions in both central estimates and uncertainties in
potency estimates that require at least discussion and preferably adjustment of ordinary dose
response model fitting and "slope factor" estimation procedures. Procedures for adjusting
traditional regression analyses for such effects are relatively well known in biostatistics under the
general heading of "errors in variables' models, but have rarely been applied to occupational
cancer data in part because, unlike this case, exemplary quantitative analyses of likely errors in
exposure estimates have not often been available. There are some examples of the use of errors
in variables models in epidemiological studies of other effects (Stayner et al. 2003; Richardson et
al. 2004; Brown et al. 2004; Choi, 2000; Carrothers and Evans, 2000; Kulathinal et al. 2002;
Siebert et al. 2001). The current case, where very extensive efforts have been devoted to
development of exposure estimates and quantitative errors in exposure estimates (Hornung et al.
1994), represents an invaluable opportunity to analyze and offset the distortion in dose response
estimates from this type of problem. The analysis presented in Appendix C illustrates a relatively
simple analytical approach to gauging the extent of the modification in the low-dose cancer risk
for male lymphoid cancers that could be indicated for this case.

• The subjects of human epidemiological studies may be subject to a variety of selection
biases, including the "healthy worker" effect, and the "healthy worker survivor" effect. The
former complicates comparisons with general population mortality data, but the internal
comparisons used for analyses of the Steenland et al. (2004) avoid these. The "healthy worker
survivor" effect (HWSE) is a known phenomenon that produces established distortions in
relationships between measured risks and measures of cumulative exposure, as shorter term
workers suffer greater mortality than workers who work at exposure-producing jobs for longer
periods of time (Steenland et al., 1996; Kolstad and Olsen, 1999; Garshick et al. 2004; Siebert et
al. 2001; Steenland and Stayner 1991). Some Panel members believed that the HWSE may be
applicable for workers exposed to Ethylene Oxide. Interestingly, in the light of the gender
differences in the current analysis, in at least one study the healthy worker effect was found to be
greater for women workers than for men (Lea et al. 1999). Adjustments for this effect are at the
cutting edge of current practice for the treatment of human epidemiological data, but they are
vital for achieving the best possible analysis of those data. The authors of some of the leading
studies documenting the healthy worker survivor effect include authors involved in the Steenland
et al. (2004) ethylene oxide mortality study (e.g., Steenland et al., 1996; Stayner et al. 2003). It
might be useful for EPA to consult with Steenland and coworkers to judge whether analytical
adjustments for this effect are possible in this case. Even if the data will not support the more
complex analyses [and analyses of this sort are notoriously complex (Robins, 1986; Arrighi and
* Ordinary regression models minimize the sum of the squares of the distance of the points to the regression line
only in the "y" dimension (representing the dependent variable), "errors in variables" models minimize the weighted
sum of squares distance as measured in terms of the uncertainties in both the dependent and independent variables.

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Hertz-Picciotto, 1996; Hertz-Picciotto, personal communication) EPA could provide at least
some discussion of how large the distortions might be by citing previous cases such as the cancer
risks from diesel particles (Garshick et al. 2004) and the approach that California risk assessors
(and possibly others) have taken to risk analysis where the healthy worker survivor effect is even
more prominent than it may be in this case. (For diesel particulates, the relative risk vs.
cumulative dose curve even had a negative, rather than a positive slope.)

Another source of the dose estimation problem that is more distinctive for this case is the
presence of a background of ethylene oxide exposure from endogenous generation. Conceivably
this could be substantial enough to limit the potency of ethylene oxide that would be compatible
with observations of "background" rates of lymphoid cancers in people without occupational
exposures. Should the EPA analysts accommodate this possibility by adding estimates of
exposure from this source (perhaps including variability and uncertainty) to the estimates of
occupational exposures for all the groups in the Steenland et al. (2004) study? If so, how should
such estimates be derived? In preliminary work, Hattis (see Appendix B) attempted to estimate
this effect using the model parameters and data from Csanady et al. (2000). Generally, the
exploratory work by Hattis (Appendix B) finds the effect to be small enough (about 1.8 ppb
occupational equivalent ethylene oxide exposure, amounting to about 26 occupational equivalent
ppm-days of cumulative exposure over a 60 year period) as not to be likely to appreciably distort
the EPA analysis.
4. Cancer groupings

Although the analysis based on total LH cancers might have value as part of a complete
risk assessment, the rationale for this aggregate grouping needs to be better justified. Certainly,
a rational grouping of cancer types with a similar pathophysiology can lead to improved power
to detect significant effects of EtO exposure. By the same turn, grouping cancers that affect a
single organ system (e.g. blood) but with very different cancer etiology could produce a spurious
and therefore misleading result. The Panel therefore recommends that data be analyzed by
subtype of LH cancers with biological rationale for any groupings that are formed.

Lymphohematopoietic (LH) cancers are diverse diseases with diverse, and often multiple,
etiologies, including exposure to ionizing radiation, viruses, and chemical carcinogens, and
genetic predisposition. The Draft Assessment argues that (a) all LH cancers are a larger category,
(b) there may be misclassification between LH categories, and (c) it could be a relevant category
because of the existence of multipotent stem cells. However, a larger category that is made up of
heterogeneous diagnoses is not desirable, one could aggregate bladder and kidney cancers
because they were both urinary system cancers, but this would ignore their different etiologies.
Even more marked differences exist between LH categories, and even between leukemia cell
types. The misclassification of disease mentioned by the Draft Assessment (unspecified
leukemias) would result in a slight loss of precision but not necessarily a bias. Also,
misclassification between lymphoid, myeloid and other cancers is minimal. In addition, this
issue exists within any organ system, e.g. there is some minimal misclassification between
kidney and bladder cancers but it is not a good reason to aggregate the two cancers. While
multipotent stem cells exist, research by Greaves (2004) suggests that the initiating event for
many haematopoietic cancers does not occur in the multipotent stem cell. In addition, these cells

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are few in number and are likely to be well-protected because of their critical function.
Steenland et al's category of lymphoid tumors is more consistent with modern
lymphohaematopoietic classification techniques (WHO 2001) and should be used as the
preferred disease outcome.
5. Males Only

       The Panel diverged in its views concerning the appropriateness of estimating the
population unit risk based only on the NIOSH data for males.

       Several Panel members pointed out that a standard approach in cancer epidemiology and
risk analysis begins by conducting separate dose-response analyses on males and females and
combining the data only if the results are similar. This approach assumes the possibility of
known and unknown gender specific differences in the cancer etiology. Indeed, rates for Non-
Hodgkin's Lymphoma, Multiple Myeloma, and Leukemias are 25 to 60% higher in men than in
women (NCI, 2007). From a risk assessment perspective, it is protective against a gap in our
biological knowledge of an underlying mode of action that is truly gender dependent. By the
same turn, it is not a statistically conservative approach if in fact no gender difference exists. In
the case of no gender effect, a sex-specific analysis will have reduced power to detect any
common effect for women and men.

       A second approach to dealing with the possibility of gender differences in response is to
include gender as a fixed effect in the statistical modeling of the data and determine whether
gender or its interaction with other predictors (e.g. gender x exposure) are significant explanatory
variables.  If so, the  combined model with the  estimated gender effects could be used directly or
separate, gender-specific dose-response analysis would be performed. If not, the gender effects
could be dropped and the model re-estimated for the combined male and female data.

       Members of the Panel who argued for the second approach were concerned that there
appears to be no genetic or other physiological basis for the  observed  differences in the trends
observed for males and females (see overall SMRs). Women comprise 55% of the NIOSH
cohort  and in general have lower average levels of cumulative exposures to EtO than the males
in the original cohort.  From Steenland, et al. (2004), Table 4 the SMRs  and observed deaths for
males and females in 5 categories of lagged (10 year) cumulative exposure are:

Table 1: NIOSH Cohort. SMRs and observed lymphohematopoietic cancer deaths.
Source: Steenland et al. (2004)
Lagged (10) Cum Exp
ppm-days
0 (prior tot- 10)
1-1199
1200-3679
3680-13499
13500
Male
(n=7645)
1.15(7)
0.63 (5)
0.87 (5)
1.10(7)
1.46(13)*
Female
(n=9885)
0.31 (2)
1.04(13)
1.38(10)
1.06(9)
0.46 (3)
* 8 of 13 are NHL
                                         29

-------
Cox proportional hazards models with a random set of m=100 controls matched by race,
birth year and gender found no significant effect of the cumulative exposure level on the all
haematopoeitic cancer hazard for a combined analysis (p=0.20) or separately for males (p=0.12)
or females (p=0.34). Steenland et al. (2004) refitted the models to indicator variables
representing four quartiles of categorical exposure and again found no significant relationship
between the cumulative exposure category and the lymphohematopoietic cancer hazard.
Introducing time lags of 10, 15 and 20 years in cumulative exposure, Steenland et al. (2004) re-
estimated the proportional hazards models using both actual estimated exposure values and the
natural log transformation. For males only, the best fitting model (log cumulative exposure, 15
year lag) achieved a marginal significance level (p=0.02). In the broad class of models
considered for females, the significance of the modeled relationship between
lymphohematopoietic cancer hazard and the various exposure metrics did not approach
significance under a nominal a =0.05 level.

Steenland, et al (2004) defend their model exploration steps against criticism of possible
"data dredging" (and thus consideration of multiple testing criteria) by pointing out that prior
work has shown latency (a lag in exposure measures) to be important in studies of cancer for
occupational exposures and that empirically the log of cumulative exposure has provided a better
fit to these types of time integrated data. To summarize, Steenland et al's work hints at a
relationship between EtO exposure and all haematopoeitic cancer hazards, but only for men and
not for women. In a statistical sense, this evidence for an exposure relationship in males is at
best marginally significant and its estimated strength is influenced by the chosen exposure
metric—the best metric being the log of estimated cumulative exposure (lagged to t-15 years).
Similar sensitivity to the transformation of the exposure metric is seen in the Cox regression
results relating breast cancer hazard to EtO exposure.

Given these results, the EPA should carefully consider the scientific justification for a
"men only" model for its assessment of the risk of lymphohematopoietic cancer hazards. There
should be a strong scientific argument for excluding the female data. Presently, the draft
document identifies no basis for excluding the female data. In the data set, women on average
have lower average levels of estimated exposure to EtO (possibly more relevant to the exposures
of interest in the risk assessment). By the same turn, the results of the extensive modeling effort
suggest that the significance of the model fit is influenced by the chosen exposure metric and the
best fitting models are nonlinear with respect to exposure. Panel members are not challenging
the statistical analyses presented in the Steenland et al (2004) paper. However, the Panel
encourages the EPA to narrow its modeling efforts to functional forms, exposure metrics, and
disease outcomes that make sense from a biological and risk assessment perspective. The
process of model building should also include a challenge to any model (e.g. log cumulative
exposure, 15 year lag) which yields results that differ substantially from a second model that
only changes the scale of the exposure metric (e.g. cumulative exposure, 15 year lag).

6. Preferred model justified, alternative analytical approaches.

As discussed in 2.b.l-2.b.4 above, the Panel recommended exploration of use of a
number of models, including non-linear models, to fit data within the observation range and
calculate a point of departure (POD). Preference for biologically-based models was indicated.

-------
7. Statistical issues

Pages 29-49 of the draft Evaluation outline the EPA's proposed approach to estimation of
the Inhalation Unit Risk for EtO. In addition to the general issues of estimation and model-based
extrapolation described above, there are a number of statistical assumptions and methods used in
this approach that deserve mention.

Conditional on the cancer slope factor results from the weighted least squares regression
analysis, the life table (BEIRIV) approach to the determination of the LEC.oi is programmed
correctly.

The life table methodology that is the basis for the BEIR IV algorithm is designed to
estimate excess mortality and is not readily adapted to modeling excess risk for events
(incidence) that do not censor observation on the individual in population under study. The
methodology for substituting the mortality slope to an excess risk computation for HL cancer
incidence requires the assumption of a proportional rate of incidence/mortality across the cancer
types that are included in the grouped analysis. This is generally not a viable assumption. The
Panel therefore discourages the use of the BEIR IV algorithm for extrapolation of the cancer
mortality algorithm to estimation of excess cancer incidence.

Several Panel members commented on the use of the upper confidence limit for the
estimated slope coefficient as the basis for estimating an LEC.oi. The Panel encourages the EPA
to present unit risk estimates based on the range of EC.01 values corresponding to the lower 95%
confidence limit, the point estimate, and the upper 95% confidence limit for the estimated cancer
slope coefficients from the final dose-response models.
2.c. Age-dependent Adjustment

Is the incorporation of age-dependent adjustment factors (ADAF) in the lifetime cancer
unit risk estimate, in accordance with EPA's Supplemental Guidance (U.S. 2005b),
appropriate and transparently described?

The Panel felt that the application of ADAF by the Agency was appropriate, but that the
description in the Draft Assessment was not adequate, particularly for those not familiar with the
EPA's Supplemental Guidance. A clear description of the ADAFs is important for the Draft
Assessment. For example, on page 57 line 15 the lifetime unit risk calculation with ADAFs is
presented. Unless the reader knows the binnings and associated uncertainty factors, this would
not be understandable to the average reader. There was also discussion of the type of
information that would be needed to address the issue of potential increased sensitivity of
children.

While the Panel recognized the role of the childhood exposure uncertainty factors in the
broader risk assessment process, it did not simply accept the defaults without first attempting to
establish the biological arguments for their application in the case of EtO. The Draft Assessment
states that because of the immaturity of detoxifying enzymes, a child's susceptibility may be

-------
increased. This should, however, be extended to include the same comment about DNA repair
enzymes being immature, and the presence of more DNA synthesis due to growth, and thus a
further increased risk if exposure occurs during development.

In general, the Panel concluded that the lack of data examining the impact of prenatal and
childhood exposure to ethylene oxide is a major limitation in the assessment of cancer risk (Kim
et al., 2006; Olshan et al., 2000; Anderson et al., 2000). Given this uncertainty, the application
of ADAFs in estimating cancer risks due to childhood exposure is warranted.
2.d. Models For Occupational And Environmental Exposures

Is the use of different models for estimation of potential carcinogenic risk to humans from
the higher exposure levels more typical of occupational exposures (versus the lower
exposure levels typical of environmental exposures) appropriate and transparently
described in Section 4.5?

The use of different data to estimate different dose response curves should be avoided
unless there is both strong biologic and statistical justification for doing so. The Panel believed
this justification was not made in the Agency's draft report.

The Panel believes that fitting separate models for occupational and environmental
exposure levels, as proposed by EPA, requires some mechanistic justification. An alternative is
to provide fits based on two known distortions from the expected linear shape of dose response
relationships: the errors-in-variables issue as analyzed in Appendix C, and the healthy worker
survivor effect discussed elsewhere in this report. Such analyses are not simple, but they do
provide mechanistically justifiable ways to analyze the data without either arbitrarily dividing the
data in to different regions of exposure or adopting a supralinear dose response form that does
not have an obvious basis in plausible biological mechanisms."

I.e. Extrapolation from animal studies

Are the methodologies used to estimate the carcinogenic risk based on rodent data
appropriate and transparently described? Is the use of "ppm equivalence" adequate for
interspecies scaling of EtO exposures from the rodent data to humans?

The ppm equivalence method is a reasonable method for interspecies scaling of EtO
exposures from rodent data to humans. If the use of animal data becomes more important (i.e.,
the principal basis for the ethylene oxide unit risk value), more sophisticated approaches such as
PBPK modeling should be considered. The PBPK models that Filser's group (cite) and Fennell
and Brown (cite) have developed are appropriate. One Panel member conducted a PBPK model
some time ago and found that it gave very similar results to the ppm equivalence approach,
although this should be revisited [in the event that animal data assume a greater role in the
ethylene oxide unit risk].

All of the animal cancer data need to be presented as survival adjusted data.
32

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Hornung R, Greife A, Stayner L, Steenland NK, Herrick RF, Elliott LJ, Ringenburg VL,
Morawetz J. 1994. Statistical model for prediction of retrospective exposure to ethylene oxide in
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Houle CD, Ton T-V T, Clayton N, Huff J, Hong H-H, Sills RC (2006) Frequent p53 and H-ras
mutations in benzene- and ethylene oxide-induced mammary gland carcinomas from B6C3F1
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Kim AS, Eastmond DA, Preston RJ. Childhood acute lymphocytic leukemia and perspectives on
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1999 Feb 15;149(4):347-52.

Kulathinal SB, KuulasmaaK, GasbarraD. 2002. Estimation of an errors-in-variables regression
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Lea CS, Hertz-Picciotto I,  Andersen A, Chang-Claude J, Olsen JH, Pesatori AC, Teppo L,
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Li CY, Sung FC.  1999. A review of the healthy worker effect in occupational epidemiology.
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http://seer. cancer.gov/faststats/sites.php?stat=Incidence&site=Myeloma&x=18&y=14
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Olshan AF, Anderson L, Roman E, Fear N, Wolff M, Whyatt R, VuV, Diwan BA, Potischman
N. Workshop to identify critical windows of exposure for children's health: cancer work group
summary.  Environ Health Perspect 2000; 108 Suppl 3:595-7.

Richardson D, Wing S, Steenland K, McKelvey W. 2004. Time-related aspects of the healthy
worker survivor effect. Ann Epidemiol. 14(9):633-9.

Robins J. 1986. A new approach to causal inference in mortality studies with a sustained
exposure period—application to control of the healthy worker survivor effect. Mathemat
Modeling 7:1393-1512.
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Rothman, KJ. 1986. Modern Epidemiology. Little and Brown Co., Worchester, MA.

Rothman, KJ. and Greenland, S. 1998. Modern Epidemiology, Second Edition. Little and Brown
Co., Worchester, MA.

Siebert U, Rothenbacher D, Daniel U, Brenner H. 2001. Demonstration of the healthy worker
survivor effect in a cohort of workers in the construction industry. Occup Environ Med. 2001
Dec;58(12):774-9.

Stayner L, Steenland K, Dosemeci M, Hertz-Picciotto I. 2003. Attenuation of exposure-response
curves in occupational cohort studies at high exposure levels. Scand J Work Environ Health.
29(4):317-24.

Stayner L, Steenland K, Greife A, Hornung R, Hayes RB, Nowlin S, Morawetz J, Ringenburg V,
Elliot L, Halperin W.  1993. Exposure-response analysis of cancer mortality in a cohort of
workers exposed to ethylene oxide. Am J Epidemiol. 138(10):787-798.

Steenland K, Stayner L, Greife A, 1987.  Assessing the feasibility of retrospective cohort studies.
AmJIndMed.l2(4):419-30.

Steenland K, Deddens J, Salvan A, Stayner L. 1996. Negative bias in exposure-response trends
in occupational studies: modeling the healthy workers survivor effect. Am J Epidemiol.
143(2):202-210.

Steenland K, Stayner L. 1991. The importance of employment status in occupational cohort
mortality studies. Epidemiology. 2(6):418-423.

Steenland K, Whelan E, Deddens J et al. 2003. Ethylene oxide and breast cancer incidence in a
cohort study aof 7576 women (United States). Cancer Causes and Control 14:531-539.

Steenland K, Stayner L, Deddens J. 2004. Mortality analysis in a cohort of 18,235 ethylene oxide
exposed workers: follow-up extended from 1987 to 1998. Occup Environ Med. 61:2-7.

Teta MJ, Benson LO, Vitale JN.  1993. Mortality study of ethylene oxide workers in chemical
manufacturing: a 10 year update. Br J Ind Med. 50:704-709.

Teta MJ, Sielken RL, Valdez-Flores C. 1999. Ethylene oxide cancer risk assessment based on
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U. S. EPA (Environmental Protection Agency) (2005a) Guideline for carcinogenic risk
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www.epa.gov/cancerguidelines .
                                         35

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                                     Appendix A

                      Discussion of the Resurgent Controversy over
                        Thresholds for Genetically Acting Agents
                                   Dale Hattis, Ph.D.
       The roots of the historical controversy can be traced to a basic difference between
different sets of disciplines in mental models of biological systems, and the ways that chemicals
and other perturbing influences can cause effects.  The disciplines of physiology, traditional
toxicology and pharmacology tend to foster a view of biological systems as complex interacting
webs of processes. These systems are seen as exquisitely designed so that perturbation of any
one parameter automatically gives rise to countervailing adaptations that, if the perturbation is
not too large, will keep the systems functioning within normal limits without serious or long
lasting harm. This mental model leads directly to a general expectation that there should be
thresholds in dose response; for any toxicant that acts by overwhelming some set of homeostatic
processes there should be a dose below which the system can handle the perturbation without a
meaningful adverse effect.

       A different vision of some fundamental life processes arose from the ex-physicists who
created the discipline of molecular biology in the decades after the end of World War II (e.g.,
Stent, 1963). This is the notion that there is a basic fragility in some functions that are central to
life. When both somatic and germ cells divide, an enormous amount of information must be
faithfully copied and distributed between the progeny cells.  Mistakes can occur in  this copying,
and a change at even a single place in the DNA can give rise to important adverse (or,  very
rarely,  beneficial) effects if by chance the mistake happens in just the wrong place in the DNA of
the wrong cell. This leads to an intuition that even a single molecule of a DNA reactive
chemical has a small but finite chance of doing lasting damage if it happens to react with the
wrong  place on DNA and if the DNA lesion is not repaired by the next time the DNA is copied.

       In the 1970s and early 1980s it was recognized that basic bimolecular reaction  kinetics
require a fundamental linearity between the concentration of DNA reactants and relevant sites on
DNA.  However it was also recognized that there were many opportunities for at least  high-dose
nonlinearities both before and after DNA reaction in the sequence of events from intake of a
DNA reactive agent (or a metabolic precursor) into the body to the ultimate manifestation of
tumors (Hattis 1990).

       In the 1970s some looked to pharmacokinetics as a potential source of threshold dose
response relationships that might intervene between toxicant intake and the delivery of DNA
reactive molecules to the nucleus of relevant cells. Figure 1 is an illustration similar to one that
was published in Science (attributed to researchers at Dow Chemical) that attempted to make this
pharmacokinetic-based threshold idea plausible. In the diagram, liquid (representing a
continuous dosage of a toxicant) flows into a tank with two triangular holes. The level of liquid
rises in the tank until some begins to flow out of the lower of the two holes (representing a high-
affinity metabolic pathway producing a "safe" metabolite).  A further rise occurs until the
amount of liquid flowing out of the tank equals the amount flowing in. If the inflow is small
enough that it can be completely balanced by flow out of the lower hole, then the liquid will not

                                         36

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                                   Figure 1

Argument for the Plausibility that Thresholds Might Arise From the Competition Between
    Metabolic Pathways Producing Safe and Dangerous (DNA Reactive) Metabolites
         Flow of
         liquid into
         tank
     Hole at the lower
     level represents
     a high affinity
     enzyme pathway
Hole at a higher level represents
a relatively low affinity enzyme
pathway
      Level of liquid in tank.
      As long as the inflow
      does not exceed the
      capacity of the lower
      hole, no liquid flows
      out the higher hole.
                                    37

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rise to the level of the higher hole (representing the lower affinity enzyme producing the
dangerous metabolite).  Thus the analogy predicts a threshold of inflow into the tank, below
which all of the metabolism is via the "safe" high affinity pathway.

Unfortunately, this representation of the competition between higher and lower affinity
metabolic pathways is not compatible with conventional Michaelis-Menten enzyme kinetics
(Hattis, 1990; Slikker et al. 2004).  Using the basic Michaelis/Menten equation, the rate of the
activating reaction (producing the dangerous metabolite, D) is:
                                    dD    V    [C]
                                             max1  J
                                     dt    Km

where [C] is the concentration of substrate (the form of the toxicant that is absorbed from the
environment), Vmax is the maximum rate of the reaction that produces the dangerous metabolite,
and Km (the Michaelis constant) is the substrate concentration at which the reaction proceeds at
half of its maximum velocity (Vmax).  Similarly the rate of the competitive detoxifying reaction
(producing the safe metabolite, S) is:

                                    dS    V    '[C]
                                            max1  J
                                    dt    Km + [C]
The [C]'s in the denominator of both equations can be neglected at low doses when they become
small relative to the K  's.  At low doses we can therefore find the ratio of the substrate [C] that
goes by the dangerous and safe metabolic pathways by simply dividing the two equations:
                           rate of D production          V    [C]/K
                                                        iTlciX     111
                            rate of S production          V    '[C]/K  '
                                                        in 3.x      m

and because the numerator [C]'s now cancel, it can be seen that we are left with a ratio of four
constants. This means that below the dose region where there is appreciable saturation of the
enzymes producing either the safe or the dangerous metabolite, the fraction of the substrate taken
by each pathway approaches a constant, independent of dose. There are no dose rate effects in
this low dose region, there can be no thresholds, and indeed the system must operate linearly at
the limit of low dosage, albeit with a different distribution of metabolism between "safe" and
"dangerous" pathways than would be observed at higher doses. At the limit of high dose, the
ratio of production of the dangerous to the safe metabolites is governed only by ratio of the two
Vmax values; whereas at lower doses the Km's become progressively more involved. If the
                                         38

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higher affinity (lower Km) pathway produces the dangerous metabolite, then the fraction of
material metabolized by the dangerous pathway will be greater than at the highest saturating
doses, resulting in a convex-upward dose response relationship for DNA damage (e.g. the pattern
seen for vinyl chloride). On the other hand, if the safe pathway has the lower Km then the
portion of the chemical processed by the safe pathway will be greater at lower doses than is seen
at higher doses. In the abstract of a paper (Gehring et al 1978) describing a process model for
carcinogenesis from electrophilic agents, Perry Gehring acknowledges that there should be an
expectation for some "albeit negligible" carcinogenic risk from genetically acting chemicals at
low doses.

       It is well to emphasize that the basic Michaelis-Menten equation applied above is not
simply an empirical formula.  It is well grounded in fundamental mechanistic considerations of
receptor association and dissociation kinetics with reasonably wide applicability (Hoel, 1985),
The maximal velocity, Vmax, arises because there are a limited number of enzyme molecules
available to catalyze the reaction, and each enzyme molecule is necessarily constrained to
operate at a finite rate in converting its substrate into its product.  The fact that the reaction
proceeds linearly at low doses (with a rate constant of Vmax/Km) arises from the fact that  the
reaction is limited by the rate  of diffusion of the  substrate molecules into the active site of the
enzyme—a rate that must be linear with substrate concentration at the limit of low doses. In the
light of this Figure 2 offers a more accurate molecular-scale vision of the competition between
enzyme-mediated activating and detoxifying processes. Each small substrate molecule has a
"random walk" through a cellular compartment as it rebounds from collisions with other
molecules.  At the limit of low dosage, when there are few or no other similar substrate
molecules around, the substrate molecule must have a finite chance of encountering the active
site of each type of enzyme (or,  similarly, a transport molecule taking it to a different
compartment). Therefore each type of enzyme or macromolecular transporter must have finite
opportunity to process the substrate molecule at the limit of low dosage.

       The basic Michaelis-Menten equation form applies with equal force to active transport
processes (in which specialized molecules utilize energy to pump specific molecules or ions into
our out of cells), and to DNA  repair processes. Thus the fundamental expectation for low dose
linearity applies similarly to these other components of the causal  chain between external
exposure and the generation of somatic mutations that are components of carcinogenesis. At the
limit of low dose the Michaelis Menten enzyme/transport reaction rates are limited by the rate of
diffusion of substrate molecules into the active sites of the enzymes/transport molecules; and
those diffusion processes, given a specific temperature, are linear functions of substrate
concentrations.
                                          39

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                                    Figure 2

A Molecular Vision of the Low-Dose Competition for Substrate between Activating and
                          Detoxifying Enzyme Molecules
                        vA/
          Activation enzyme
          molecules
Small substrate
molecule (little circle)
has random movements
in liquid -bumping in to
other molecules,
including big enzymes.
Collision with the active
site on one or the other
type of enzyme leads to
a finite chance of an
activating or detoxifying
reaction.
                                                                    Detoxification
                                                                    enzyme molecules
                                     40

-------
With this as background, we can now examine the bases for some more recent claims that
thresholds should be expected at low doses for genetically acting agents. A convenient starting
point for this examination is a special issue of Mutation Research published in 2000 by
participants at a conference on threshold mechanisms of carcinogenesis sponsored by the
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC). Without going
through the threshold claims from each of the papers in this collection individually (Kirshch-
Volders et al. 2000; Schulte-Hermann et al. 2000; Muller and Kasper 2000; Moustacchi 2000;
Parry et al. 2000; Swenberg et al. 2000; Lowell 2000; Madle et al. 2000; Henderson et al. 2000;
Crebelli 2000; Kirkland and Muller 2000; Speit et al. 2000; Parry 2000), three main types of
arguments stand out:

• Multiple targets. Some specific modes of genotoxicity are reported to depend on multiple
interactions between chemicals and target macromolecules (rather than the single-DNA-reactant-
molecule DNA adduct formation mechanism discussed above). If the number of target
interactions required to produce an effect is large, the resulting low dose dose-response
relationship can be expected to be highly upward-turning, and well approximated by a threshold.

• Multiple barriers. A molecule of a chemically reactive agent must pass multiple
transport, potential detoxification, alternative targets for reaction other than DNA, and DNA
repair hurdles in order to cause a permanent change in DNA sequence or chromosomal damage.
The multiplicity of these hurdles makes it unlikely that any single molecule could cause an actual
mutation along the pathway to carcinogenesis. If these multiple barriers do not produce an
"absolute threshold" they can at least be expected to lead to a "pragmatic" or "practical"
threshold below which exposure is of no real biological consequence.

• Inducible detoxification, apoptosis, and/or DNA repair processes. One result of exposure
to a toxicant may be the induction of the levels of a variety of cellular and genomic defense
processes. If this induction is effective enough, and occurs at low enough doses, it is possible
that the prevention "good" that results from avoidance or repair of mutagenic damage from
background processes may even be great enough to exceed the direct mutagenic harm done by
the toxicant itself. This gives rise to "hermetic" dose response relationships in which the net
mutagenesis and carcinogenesis is even reduced by some range of exposures to the toxicant
compared to background (zero dose) levels.

Modes of Genetic Action Requiring Multiple Interactions with Macromolecular Target
Molecules or Structures

The first paper in the Mutation Research special issue (Kirsch-Volders et al. 2000) gives
a reasonable theoretical mathematical account of the dependence of the shape of the dose
response curve on the number of macromolecular targets that must be "hit" in order to produce
an effect. Essentially, if a single hit on DNA, an alpha or beta tubulin structure, or
topoisomerase is sufficient to cause an effect (assuming imperfect repair) then the fundamental
math calls for a single hit Poisson process:

Probability of Effect/Target = 1 - e"m (i)
41

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where m is the average number of "hits" per target. In cases where the number of hits per target
needed to cause an effect is larger than 1 (e.g. according to the authors, where the target is the
spindle drawing chromosomes to different progeny cells during mitosis,  or the nuclear
membrane, by a mechanism that is not detailed in the paper), then the appropriate Poisson term
for n hits required per target is substituted:


Probability of Effect/Target = 1 -  e"m —                                 (2)
                                            n!

(where the notation n!, translated to English as "n factorial" means n X n-1 X .... All the way
down to 1.)

The larger the n, the more steeply upward-turning the resulting curve will be—increasingly
resembling, but not the same as a curve with a true threshold of zero probability of effect for a
finite dose.
       Later on, Kirsch-Volders et al (2000). add:

"To be able to clearly assess a threshold, the spontaneous frequency of the analysed endpoint
should be very low, ideally equal to zero; indeed a too high spontaneous background will lead to
additive effects and a difficult estimation of small increases at low dose level."

This comment undermines considerably the generality of the earlier application of multi-hit
analysis to putative multi-target mutation/chromosomal damage mechanisms at very low doses.
Essentially it says that in order for the multi-hit formulas to apply at the limit of low dosage, the
genetically active agent must cause genetic changes by a mechanism that is somehow distinct
from all the processes that cause the appreciable background of genetic changes from all other
endogenous  and exogenous  agents, as well  as the imperfections in functioning of the apparatus
of polymerases, spindle proteins etc. that maintain,  copy and transmit the genetic material.

       The essential low-dose linearity of agents that act in concert with background processes
was discussed in some of the foundational papers that derived the methods for inferring low dose
cancer risks  in the 1970s (e.g. Crump et al.  1976). This general expectation can be illustrated
with a simple example of a two-stage mutation process in which there is a background of 1
arbitrary unit and an expectation for 1 additional induced unit per 1 mg/kg continuous dose of a
mutagenic agent (Table 1, adapted from Hattis and  Smith, 1986).  It can be seen that at high
doses, the dose response relationship between excess tumors over background vs dose of the
                                                        2
inducing agent appears almost perfectly proportional to (dose)  . This  is because at high doses,
far above the background rate of the tumors, the agent predominantly acts by causing both
mutations in the two-step process.  As the dose is reduced to regions where it causes mutations
that are a small fraction of background rates, the induced mutations predominantly cooperate
with mutations that result from the background processes—leading to  an increment in tumors
over background that approaches linearity with dose of the added inducing mutagen.
                                         42

-------
                                     Table 1

Effect of Background Mutation Rates on the Carcinogenesis Dose-Response Curve at Low
           Doses, Assuming a Hypothetical Two-Stage Carcinogenic Process
Dose
1000
100
10
1
.1
0.01
0.001
Rate of 1st
Transition (1
extra unit per unit
of dose)
1001
101
11
2
1.1
1.01
1.001
Rate of 2nd
Transition (1
extra unit per unit
of dose)
1001
101
11
2
1.1
1.01
1.001
Relative No. of Tumors
(background =1)
(product of two previous
columns)
1,002,001
10,201
121
4
1.21
1.0201
1.002001
Induced
Excess Over
Background
1,002,000
10,200
120
3
0.21
0.0201
0.002001
Source: Adapted from Hattis and Smith, 1986.
                                      43

-------
One example of a process that may involve multiple targets is the action of spindle
poisons such as vinblastine and colcemid (Parry et al. 2000). Older observations by Elhajouji et
al (1995, 1997) are often cited as evidence of thresholds for agents that inhibit spindle function.
In vivo data are available in a recent report by Choudury et al. (2004). However even in this
case, it is worth compiling data on background rates and mechanisms of spindle malfunction to
assess the extent of potentially interacting background processes.

A more questionable example of a postulated threshold process that seems sometimes to
be attributed to the multiple target type of theory is the inhibition of topoisomerase (Lynch et al.
2003; Bolt and Degen 2004). From the available description of the mode of action of
topoisom erases (see footnote) it is not completely clear that multiple targets are involved in the
action of topoisom erase inhibitors to enhance single- and double-strand breaks at individual
locations on DNA. A topoisomerase enzyme molecule apparently acts by itself in stabilizing a
double strand break at a specific place in DNA. An inhibited enzyme molecule gives rise to a
delay in religation with no mention of a need for cooperativity either between inhibitor
molecules or between inhibited enzyme molecules. Nevertheless Lynch et al., after citing
Kirsch-Volders et al (2000) and other papers in the same special issue of Mutation Research
proceed to assume that because the interaction of inhibitor is not directly with DNA, a threshold
theory for the topoisomerase inhibitor mode of action is biologically justified. They then go on
to offer as experimental evidence, a particular kind of plot of dose response results for in vitro
induction of micronuclei by three different topoisomerase inhibitors (Figures 3-5, but not Figure
6). In the first three of these plots the log of the % micronuclei is plotted vs the log of the
concentration of the topoisomerase inhibitor in the culture
In the last two decades an important role has become apparent for topoisomerase II in normal DNA replication.
This enzyme gets its name from its function to change the topology of DNA during replication, transcription, and
repair. It binds covalently to DNA in such a way as to produce a temporary double-strand break, allowing another
DNA strand to pass through it. After this, normally the breaks in the two strands are rejoined (religated). However
some compounds (DNA topoisomerase inhibitors) can stabilize the usually transient state of the complex with the
double strand break unrepaired, and inhibit religation. This leads to chromosome breakage and rejoining events that
in rare cases splice together inappropriate portions of different genes leading to uncontrolled cellular growth
promotion signals.
Just such gene fusions are responsible for a key step in the development of several types of Acute Lymphocytic and
Acute Myeloid Leukemia in children (Lightfoot and Roman 2004). By examining blood spots made at birth in
children who later developed leukemia, it has been found that these events often happen before during fetal life; to
be followed by one or more subsequent steps in leukemia development after birth (Gale et al. 1997).
Epidemiological studies indicate that cases of adult leukemias occur at increased frequency after chemotherapy with
topoisomerase II inhibitors for other cancers (Greaves, 1999; Le Deley et al. 2007) compared to patients treated with
other types of chemotherapy. Topoisomerases inhibitors are also used deliberately in the control of HIV, but
activity of this type has also been reported from a metabolite of the headache remedy acetaminophen (Bender et al.
2004) and in a wide variety of foods and herbal medicines (Lightfoot and Roman, 2004). So far, there is limited
evidence that maternal dietary consumption of specific DNA topoisomerase II inhibitors increases the risk of gene
fusion related leukemias (Specter et al. 2005; Alexander et al. 2001; Ross 1998).

-------
                                              Figure 3

          Log-Log Plot From Lynch et al. (2003) Offered in Support of a Threshold in the
  Dose Response Relationship for Induction of Micronuclei in L5178Y Mouse Lymphoma
                      Cells by the Topoisomerase Inhibitor Etoposide*
                -   2
                                             t   -
                                                                      J
-2
                                          -10123

                                           log canoentutloit (uj/mL)
 Source: Lynch et al. 2003. The different colored points represent different assays. The caption to this graph is
"Broken stick model. The breakpoint was identified at ln(conc.) = -6.049 or 0.00236 ug/ml on the original
concentration scale with ln(%MN) = -.364 fitted before the breakpoint and ln(%MN) = -5.234 + .94 X ln(conc)

fitted afterwords.  The goodness of fit (R ) was 93.4%."
                                            45

-------
                                       Figure 4

         Log-Log Plot From Lynch et al. (2003) Offered in Support of a Threshold in the
  Dose Response Relationship for Induction of Micronuclei in L5178Y Mouse Lymphoma
                   Cells by the Topoisomerase Inhibitor Doxorubicin*
                  3

               i  2
               I  •
                                         '
                                   0123
                                           »no*rvtrJli»f» (ug/mL)
 Source: Lynch et al. 2003.  The different colored points represent different assays.  The
caption to this graph is "Broken stick model. The breakpoint was identified at ln(conc.) = -6.495
or 0.00151 |ig/ml on the original concentration scale with ln(%MN) = -0.135 before the
breakpoint and ln(%MN) = -5.674 +.979 X In(conc) fitted afterwards.  The goodness of fit (R )
was 89.2%."
                                        46

-------
                                       Figure 5

         Log-Log Plot From Lynch et al. (2003) Offered in Support of a Threshold in the
  Dose Response Relationship for Induction of Micronuclei in L5178Y Mouse Lymphoma
                    Cells by the Topoisomerase Inhibitor Genestein*
                                               o

                                               a

                                 O   o
                                     o
                                              -v
                   2
                                                                t
.$     -4
                                       2-10     1     2
                                         log concentration O0/mL)
* Source: Lynch et al. 2003.  The different colored points represent different assays. The
caption to this graph is "Broken stick model. The breakpoint was identified at ln(conc.) = 0 or 1
jig/ml on the original concentration scale with ln(%MN) = -0.817 before the breakpoint and
ln(%MN) = -1.117 + 1.508 X In(conc) fitted afterwards.  The goodness of fit (R2) was 88%."
                                        47

-------
                                       Figure 6

         Linear %MN vs Linear(concentration) Plot From Lynch et al. (2003) Offered in
 Support of a Threshold in the Dose Response Relationship for Induction of Micronuclei in
     L5178Y Mouse Lymphoma Cells by the Topoisomerase Inhibitor Ciprofloxacin*
               I
                   .20
20
50
 100

uj/mL)
140
180
* Source: Lynch et al. 2003.  The different colored points represent different assays.  The
caption to this graph is "Broken stick model. The breakpoint was identified at cone. =40 |ig/ml
with % micronuclei = 0.636 fitted before the breakpoint and % MN = 0.392 + 0.016 X cone.
Fitted after the breakpoint. The goodness of fit (R ) was 77.1%."
                                        48

-------
       The log log plots of Figures 3-5 are an example of how disciplinary perspectives can
shape the presentation and interpretation of evidence.  From a toxicological perspective, there are
two realms of concern - the realm of homeostasis and the realm in which homeostasis has
broken down.  Then using a logarithmic plot for the x axis seems appropriate since the logarithm
of concentration makes available a great space on the graph for the very low doses that would be
relevant if homeostatic effects were dominant. However the data in this low putative
homeostatic realm are almost always completely uninformative. From a molecular biological
perspective as well as an experimental biological perspective, the putative homeostatic region is
one for which the data are consistent with a linear extrapolation to zero or to a fixed background
as can be seen quite clearly in Figure 6 (despite the misleading line and commentary).

       Whether or not thresholds are assumed to exist, log  log plots such as 3-5 can  serve to
illustrate the state of the experimental data in that region. However, in general, because a log log
plot expands (potentially indefinitely) the curve near zero, even a linear no-threshold function
with a background level unaffected by the toxicant will exhibit a "broken-stick" appearance
(Figure 7).  And whenever there is lack of precision in measurements (giving rise to an implicit
background and obscuring the fine curvature barely discernable in Figure 6), the cosmetic effect
that gives the appearance of two lines will be enhanced. For that reason log log plots have no
force as evidence for the existence of thresholds.  Thus, contrary to the suggestions by Wadell
(2006; 2003) for tumor data more generally, anyone starting from a molecular or experimental
perspective is likely to regard such plots as distracting from a natural linear extrapolation.  [See
also the published critique of Haseman (2003).] In our view such plots may be used  (if presented
carefully) to characterize data availability in a low dose region; but any presentation  as evidence
for threshold behavior is fundamentally misleading.
                                          49

-------
ex
o
                              Figure 7

                Plot of Log(y) vs Log(x) for the Simple
                Linear Relationship, y = .392 + .016x
                With Regression Line Fit to Last 4 Points
                        y = - 0.447 + 0.205x RA2 = 0.948
                                  log(x)
                               50

-------
Arguments for Absolute Thresholds or "Practical Thresholds" from the Presence of
Multiple Transport, Detoxification, and Repair Processes

The "multiple barriers" argument appears to be mostly a rhetorical device since its
mathematical implications reinforce linearity. By reciting the series of opportunities that a
molecule of a DNA-reactive agent has to go astray rather than react with DNA and then have the
adduct cause a mutation in a gene that matters in a cell that matters for carcinogenesis, a speaker
can make it appear very unlikely that such a chain of events could occur (Parry et al. 2000). And
indeed, from the standpoint of a single molecule, the probability is necessarily very small.
However each physical barrier has some probability of being surmounted by each molecule; each
alternate reaction opportunity or detoxification enzyme will divert a finite fraction of the
molecules, each DNA repair process will repair the molecules/adducts at a finite rate and
therefore a finite fraction of the DNA lesions will persist to the time of the next passage of the
polymerase enzyme responsible for copying the DNA. Similarly not all cells with damaged
DNA will be removed by apoptosis, and cell cycle checkpoint functions will not perfectly
prevent the copying of damaged DNA. If there is a finite rate of DNA lesion generation and a
finite rate of DNA repair or removal by apoptosis, then there must be a finite rate of mutation
that, at the limit of low dosage where saturation effects are negligible, must be a linear function
of the number of DNA reactive molecules (or their precursors) that enter the system. Moreover
once an initial tumor cell is generated there must be a finite probability that it will escape
repression by its normal neighbors through gap junction communication and by other immune-
based defense processes. The presumption of some of the discussion in the Mutation Research
special issue (Herman et al. 2000) seems to be that at low doses some or many of these processes
can be assumed to be perfect; but this is just not possible. The dose response relationship for the
combined process is a simple multiplicative combination of the component processes. If all of
these are linear at low doses, then the combined dose response relationship must also be linear at
low doses.
A final refuge of this set of arguments is to distinguish between an "absolute" threshold
(a true zero response at a finite dose rate)* and a "pragmatic" or "practical" threshold. Lowell
(2000) argues:

"A 'pragmatic' threshold can be considered as a concentration below which any effect is
considered biologically unimportant (Figure 2)** (Lutz, 1998). This term is used in a somewhat
similar way to how ECETOC defines a biological threshold except that it implies that there may
be effects occurring because of treatment or exposure but these are considered below what might
be considered biologically important. An example might be increases that did not exceed the
range of responses seen in negative control material in a well-conducted series of experiments.
Such a threshold may be defined, in part, with the help of statistical tests. The distinction
* Lowell (2000) quotes a somewhat different definition of "absolute" threshold attributed to ECETOC: "... a
concentration below which a cell would not 'notice' the presence of the chemical. In other words, the chemical is
present but does not interact with the cellular target." The precise identification of such a threshold, if it exists is
difficult.
" The figure referred to is not reproduced here. It shows an upward turning dose response curve beginning at the
origin (zero response at zero dose) but a region of response shaded and labeled as "Biologically unimportant". The
point where the continuously increasing dose response curve emerges from this "unimportant" shaded region is
labeled as the "Practical Threshold".

-------
between the various classifications of thresholds can initiate a philosophical discussion but is not
relevant to regulatory risk assessment."

       It appears, therefore, that this line of argument reverts to treating an effect that may be
present but which cannot be clearly demonstrated as above "the range of responses seen in
negative control material" (with some undefined sample size and sensitivity/detection noise) as if
it weren't there. It seems to us that risk assessment methods have been created precisely to make
fair assessments of the likely magnitude of effects that cannot be directly measured but are still
potentially substantial enough that decision-makers and the public may reasonably care about
reducing them.  Or, put another way, "practical" for a biologist to detect, may not mean the same
as of "practical" concern because someone might get hurt.  Our view is that terms such as
"practical threshold" are inherently evasive and do a disservice to transparent public analysis and
discussion of likely underlying realities.
Arguments for Thresholds or Hormetic Dose Response Relationships from Possible
Inducible Detoxification or DNA Repair Processes

       In contrast to some of the arguments  reviewed above, this category of mechanisms does
have some potential to produce changes to the low dose linear expectation under some
circumstances.  Up to this point we have discussed the several processes producing high dose
nonlinearities in dose response relationships  as if their levels were static—fixed at some baseline
level of activity/efficiency in promoting or reducing damage to DNA or subsequent steps in the
carcinogenic process. In fact, however, it is  not unlikely that the levels of the enzymes that
mediate these processes are themselves regulated by feedback mechanisms that respond to
influences from the external and internal environment, as do many other components of
biological systems (Schulkin, 2003). It is certainly possible, in theory, that under some
circumstances the induction of detoxifying or DNA repair enzymes (e.g. from radiation—
Schmerold and Wiestler 1986; Chan et al. 1992) could have the side-effect of preventing or
repairing enough "background" damage as to outweigh the primary damage done by the
inducing toxicant over some range of dosage. Whether such possible offsetting effects could
extend all the way down to the limit of low dosage depends on the fundamental dose response
relationships underlying the induction process(es) and the levels and types of "background"
damage of the that are available to be prevented.

       Specifying the requirements for this helps illuminate the special nature of the conditions
that would be needed to produce a net biological benefit from a particular type  of exposure to a
genetically active agent:

    "Background damage" (e.g. from the DNA damaging free radicals produced as a byproduct
of metabolism, other endogenous DNA reactive agents such as ethylene oxide and possibly
formaldehyde, and other exogenous DNA reactive agents) must occur at sufficient rates that
offsetting prevention benefits could occur,

    The usual "baseline" state of expression  of the detoxification, DNA repair,  apoptosis, or cell
cycle check point mediators needs to be sub-optimal. Normally, one would expect that if it were
net beneficial to have higher standing levels  of a particular enzyme, then that would have been of
selective advantage during evolution. Consequently people's normal constitutive should have

                                         52

-------
been adjusted to at least approximate optimality by natural selection. However, the types and
levels of present-day exposures to mutagenic agents could conceivably be different enough from
those present during the recent evolution of modern humans that prevailing constitutive levels of
defensive enzymes are not perfectly tuned to current exposures. (For example cancer rates in
wealthier industrialized countries tend to be very substantially higher than in poorer, less-
developed countries. (Harris et al. 1985)) In evaluating such possibilities, it is important to bear
in mind that both detoxification enzymes and DNA repair enzymes can have adverse biological
side-effects themselves. For example the same P450 "detoxification" enzyme that is induced by
ethyl alcohol (Daiker et al. 2000; Feierman et al. 2003; Sato 1993) also is involved in the
transformation of vinyl chloride to the activated form that reacts with DNA to induce the
characteristic liver cancers produced by that compound. Epidemiologic data now exist
indicating that high consumers of alcohol are much more susceptible to the carcinogenic effects
of vinyl chloride (Mastrangelo et. al 2004). P450s also affect estrogen metabolism including
some to genetically active agents. DNA excision repair enzymes, which repair DNA by cutting
out small sections of DNA that has been damaged, also do damage themselves by making cuts at
some finite rate in sections of DNA that do not contain pre-existing damage (Branum et al.
2001). Thus it is likely to be beneficial, biologically, to induce these enzymes above their
baseline levels only when there is sufficient damage in a particular cell that the biological "costs"
of the excision repair enzyme itself are outweighed by the need to repair an unusual amount of
damage by a relatively rare exposure episode.

• The dose-response relationship(s) for induction of the detoxification and/or repair enzymes
has to be steep enough, and the induction long lasting enough, that the prevention benefits are
sufficient to offset the primary damage done by the inducing agent.
53

-------
Appendix B

Illustration of a Simple Approach for Approximately Assessing the Effect of
Measurement/Estimation Uncertainties for Individual Worker Exposures on Estimates of
Dose Response Slopes*

Dale Hattis, Ph.D.

There are several steps in this analysis:

A. Use the cross-validation results for the Hornung et al. (1994) exposure estimation model
to derive a preliminary quantitative estimate of the minimal likely measurement/estimation errors
in the exposure levels used for calculating cumulative exposure for individual workers.

B. Derive an analytical expression for the observed distribution of individual male worker
exposures in the reference group (that is, the non-lymphopoietic cancer cases) in the Steenland et
al. (2004) study.

C. Remove the (assumed lognormal) variance attributable to random
measurement/estimation errors from the lognormal variance of the observed worker exposures to
derive an estimate of the lognormal variance of the real worker exposures.

D. Derive adjusted estimates of the likely real mean cumulative exposures of workers in
each of the four categorical dose groups.

E. Redo the regression analyses of relative risk vs cumulative dose using the adjusted
estimates of mean cumulative dose in each exposure group; assess the effects on the results of
assuming larger estimates of measurement error than those directly derived from the Hornung et
al. (1994) cross-validation analysis.

• Implications of the Cross-Validation Results for the Hornung et al. (1994) Exposure
Estimation Model
Hornung et al. (1994), as part of the exemplary exposure assessment effort that led to the
Steenland et al. (2004) study, gathered a total of 251 annual arithmetic means of measured
ethylene oxide exposure levels in specific sets of job titles at 18 sterilization facilities based on
2700 individual full-shift charcoal tube samples. Before developing their exposure model, the
data from 6 randomly selected plants (including 46 annual arithmetic means based on 350
individual charcoal tube samples) were set aside for later "validation" analysis of model
performance. The model predicted individual annual exposures based on job/exposure category,
product type, age of product, calendar year, rear exhaust, aeration procedure, and sterilizer
volume. Table 1 summarizes the performance of the eventual exposure model—juxtaposing
these 46 values with the eventual model predictions.
* An updated version of this case study will appear in a white paper on uncertainty in cancer risk assessment that is
in process under separate EPA funding.

-------
                                        Table 1
  Hornung et al. (1994) Comparison of Model-Predicted Annual Average 8-Hour Average
 Time-Weighted Average Exposure Levels with 46 Observations Not Included in the Data
                               Used to Develop the Model

Gmean
Geom. Std. Dev.
Arith mean
Standard dev
Range
Measured Level
(ppm)
2.22
3.8
4.62
5.76
0.1-32.0
Model 2 "Prediction"
(ppm)
1.5
5.09
3.5
3.79
0.05-15.7
Measured/ Model
ratio
1.48

1.32


Bias = average of all 46 dj's where d{ = prediction - measurement =1.13 ppm
Precision = standard deviation of all dj = 3.66 ppm

Overall the precision of the model  predictions was superior (less) than the predictions of 11
different industrial hygienists with access to the same information, considered individually or
collectively.

      For purposes of this analysis it is desirable to separate the contribution of the indicated
"bias" (apparent systematic underprediction of exposures relative to the measured exposures) to
the random error represented by the "precision" measure of deviation. The minimum estimate of
the contribution of the "bias" to the variance (square of the standard deviation) represented by
the "precision" observation is

                            22                      2
Prediction variance = Precision  - Bias = 13.4 - 1.3 = 12.1 ppm

In terms of an arithmetic standard deviation, the adjusted "precision" estimate is therefore
(12.1) '  =3.48 ppm.  Dividing by the arithmetic mean of 4.62 ppm this yields a precision
coefficient of variation of about 0.75.
       It is clear from the relatively large ratio of the arithmetic standard deviations to the means
of both the measured and model predicted values that the distributions will be better described by
lognormal than arithmetic distributional statistics. (This is nearly always the case for exposure
distributions.)  Fortunately one can use a standard formula (Aitchison and Brown, 1957) to
convert the "precision" coefficient of variation to an estimate of the geometric standard deviation
(the antilog of the standard deviation of the log-transformed exposure values). Where CV is the
coefficient of variation, an estimate of the geometric standard deviation is:
                 Geometric Standard Deviation =  e{ln[Cv2+1]}C
                                         55

-------
       Applying this formula to the precision CV derived above yields a geometric standard
deviation of 1.95 (about 2)—meaning that the random error in the exposure estimates is such that
about 2/3 of the estimated values are expected to be within 2-fold of the actual values; and 95%
of the estimates are expected to be within 4-fold of the true exposures.

       In fact, however, there is reason to expect that the actual uncertainty may be larger than
this. All of the exposure measurements used to derive and test the model were from the mid
1970s and later, whereas some of the exposures that were estimated appear to go back to the first
regular use of ethylene oxide for sterilization in 1938  (Stayner et al. 1973). In the use of their
exposure model, Hornung et al. assumed that exposures prior to 1978 were equal to the values
that would apply to 1978. This creates some additional uncertainty in the exposure analysis that
is not captured in the analysis of precision of exposure estimates from the 1970s and 1980s.
Therefore  the analysis below will include a hypothetical case assuming a much larger random
error (3  times the estimated variance, corresponding to a geometric standard deviation of about
3.2) than that directly derived from the estimated imprecision in 1970s-1980s exposures.
•  Estimating the Cumulative Exposure Distribution for the Reference Group (Non-
Lymphoid Cancer Cases)

       The main inputs for estimating the 15-year lagged exposure distribution for the reference
group were the boundary lines for the exposure categories and the estimated numbers of workers
in each category of accumulated exposure (Table 2). An initial probability plot of based only on
the estimated numbers of workers with finite exposures in the different groups (Figure 1) led to
the conclusion that the data (represented by the points) are reasonably described by a lognormal
distribution (the fitted line) with a geometric mean of about 10     = 2320 ppm-days and a
                                     1 22
geometric standard deviation of about 10 '   = 16.6.
                                        Table 2
Inputs for the Estimation of the Distribution of Exposures in
Referent Workers (Non-Lymphohematopoietic Cancer Cases)
>sure Group

jged out)
>0 ppm-days
-3679
-13499
00
median exp
(ppm-days)
0
360
2093
6230
43212
mean exp
(ppm-days)
0
442
2191
7105
60269
RR

1
1.23
2.52
3.13
3.42
                                         56

-------
                                        Figure 1

  Initial Lognormal Probability Plot of the Exposure Distribution for Referent Workers
                  Based on the Estimated Numbers within Each Group
           •f
           I
           I
                          Lognormal Plot of the Implied Distribution
                          of 15-Year Lagged Exposures for the Male
                          Non-Cases in the Steenland et al (2004) Study
                          y = 3.36 + 1.22x RA2 = 0.992
                                           Z-Score
        A more extensive optimization analysis using the additional information on the within-
group means and medians for the exposure groups from Table 2 led to an adjustment of the basic
 parameters for the lognormal distribution (geometric mean = 2910 ppm and geometric standard
  deviation = 9.86) and a conclusion that the exposure estimates were likely to be truncated at
   about 337,500 ppm-days (corresponding to 45 years of 250 day/year 8 hr/day occupational
exposure at an average of 30 ppm, which would otherwise correspond to about the 98l percentile
  of a full lognormal distribution). This truncation point was chosen to avoid having key model
  parameters such as the mean of the highest exposure group being importantly dependent on
 cumulative exposures that are not likely to be present in the actual data. The resulting fit to the
  referent within-group mean and median cumulative exposure information is shown in Table 3.
                                         57

-------
                                           Table 3

 Fit of the Derived Referent Exposure Distribution to the Within-Group Mean and Median
                                    Exposure Data

Exposure
Group
ppm-days)
0 (lagged
out)
0
-3679
-13499
oo a
Observations
median exp
(ppm-days)
0
360
2093
6230
43212
mean exp
(ppm-days)
0
442
2191
7105
60269
Lognormal Model
"Predictions"21
median exp
(ppm-days)

342
2121
6805
36277a
mean exp
(ppm-days)

426
2226
7376
62371a
Ratio: Model
"Predict ons'VObservations
median exp
(ppm-days)

0.949
1.013
1.092
0.840a
mean exp
(ppm-days)

0.964
1.016
1.038
1.035a
 With truncation of the >13500 category at 337,500 ppm-days.
       Overall, although the fit to the truncated lognormal distribution is not as close as might be
hoped (particularly for the within-group median for the largest exposure group, the predictions
for the key within-group means are not unacceptable, with no "prediction" departing from the
reported observed mean by more than 4%.

•  Remove the (Assumed Lognormal) Variance Attributable to Random
Measurement/Estimation Errors from the Observed Lognormal Variance to Estimate the
Lognormal Variance of the Real Worker Exposures

       In the special case where both the distributions of the true worker exposures and the
distribution of measurement/estimation errors are lognormal, then the lognormal variance of the
observed distribution is just the sum of the real exposure variance and the error variance.  This
simplifying assumption allows us to estimate the lognormal variance of the real underlying
worker exposures as:

       Real lognormal variance [the square of the real log(geometric standard deviation)] =
Observed  lognormal variance - lognormal variance from measurement/estimation error
= [log(9.86)]2  - [log(1.95)] 2 = 0.903

       This lognormal variance implies a geometric standard deviation of about 8.92—slightly
reduced from the geometric standard deviation of the observations of 9.86. Similarly, if we
choose to assume that the measurement/estimation variance is as much as three times that
derived from the  Hornung et al. validation comparison the reduced estimate of the real variability
in exposures corresponds to a geometric standard deviation of about 7.18.

       Similar variance subtraction techniques have been used previously to  quantify likely
underlying real variability in a wide variety of parameters including survival  and fecundity rates
in ecological analyses and coal miner breathing rates  (Akcakaya 2002; Hattis and Silver 1994).
                                         58

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For the more general case where the distributional forms of the real variation and measurement
variation one needs "errors in variables" regression models (Stayner et al. 2003; Richardson et al.
2004; Brown et al. 2004; Choi, 2000; Carrothers and Evans, 2000; Kulathinal et al. 2002; Siebert
et al. 2001 or more complex "deconvolution" procedures that are not yet in widespread use.
• Derivation of Adjusted Estimates of the Real Distribution of Cumulative Exposures in
Each of the Four Categorical Dose Groups

Using the adjusted estimates of the geometric standard deviation and the same geometric
mean and upper percentile truncation point derived earlier, it is straightforward to calculate a
large number of percentiles of the indicated lognormal distributions and the mean values for
exposures in each of the worker groups. The results in Table 4 are derived from dividing the
original and error-variance corrected lognormal distributions into 3700 equal parts corresponding
to the number of referent workers at a 100:1 ratio to cases (the exact number is not critical).

Table 4
Changes in Estimates of Mean Cumulative Exposures in Previously Defined Groups Using
the Simple "Regression to the Mean" Effect
Exposure group (ppm-days
for original observations)
<1200
1200-3679
3680-13499
>13500-337500
Original
Observations
442
2191
7105
60269
Lognormal Fit
to Original
Observations
426
2226
7376
62371
Error = GSD
1.95
457
2247
7060
53629
Error = GSD 3. 2
533
2296
6433
38942
It can be seen in Table 4 that the narrowing of the distributions by subtraction of
estimation error causes a reversion toward the mid point of the exposure distribution. The
estimates of the mean cumulative exposures for the lower two groups are raised; and the
estimated means for the higher two groups are lowered, with the greatest effects seen on the
group with the largest exposures.

In addition to the regression to the mean effect shown in Table 4, there is an additional
effect that results from what is classically known as "classification error". The error in
estimating individual workers' exposures causes some estimated exposures to be 'scrambled"—
that is, misclassified from their real exposure ranges to adjacent ranges. To model this, ten
replicate Monte Carlo simulations were done of 3700 trials each for both assumed error level.
On each trial, a random draw was made from the estimated underlying lognormal distribution of
real exposures (after subtracting the variance attributed to measurement error) and then a random
perturbation was added back corresponding to the lognormal distribution of measurement errors.
The final two columns of Table 5 show the effects of this on the true mean exposures within each
group for workers classified into exposure groups using their "observed'Vestimated exposure
levels. The calculation also took into account the censoring of both the "real" and estimated
exposures at 337,500 ppm-days. Comparing Table 5 with Table 4, it can be seen that that
undoing the effects of this scrambling misclassification leads to a much larger set of changes in
59

-------
the estimated real exposures within each of the groups of workers classified by observed
exposures.
                                        Table 5
  Changes in Estimates of Mean Cumulative Exposures (ppm-days) in Previously Defined
 Groups after Including the Scrambling of Individual Exposures among Exposure Groups
Exposure group (ppm-
days for original
observations)
<1200
1200-3679
3680-13499
>13500-337500
Original
Observations
442
2191
7105
60269
Lognormal Fit
to Original
Observations
426
2226
7376
62371
Error = GSD 1.95
After Scramble
593
2822
8416
51440
Error = GSD 3. 2
After Scramble
1264
3836
8415
29290
       These shifts have predictable effects on the estimates of the linear cancer potencies
(Tables 6-7), using the same estimation methods (based on the same spreadsheet formulas) as
were used by the EPA analysts. In the case of the 3 point calculation (Table 6), the overall effects
are modest—slope factor estimates even decrease somewhat in the calculations with the
scramble effect because of the increased estimated real mean exposure for workers with in the
observed 3,680—13,499 ppm-day group. The full implications of the different amounts of
estimation error are more apparent for the full analysis of all four points (Table 7). It can be seen
that in this case the effect of the GSD  1.95 estimate of measurement error is to increase the slope
factor by about 25%; whereas the effect of the larger GSD 3.2 assumption for estimation error is
to slightly more than double the estimate of low dose risk. In the latter case, the ratio of the low-
dose risks projected using the 3- vs the 4-point  analysis is reduced to about 3-fold, compared
with about 7.5-fold using the group means of the original observations.  Thus, errors-in-variables
distortion of the high dose point in particular seems to be a reasonable candidate  explanation for
some, but not all, of the convex nonlinearity seen in the data.
                                         60

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                                                   Table 6
 Changes in Estimates of the Linear Slope Coefficient, ED10, and LED 10 for Dose Response Analyses Based on the Lowest 3
                                              Exposure Groups
Risk
Parmeter
estb
SEb
uclb
Analysis With
Group Means oi
the Original
Observations
3.47E-04
2.51E-04
7.60E-04
Analysis With Group
Means of the
Lognormal Fit to
Original Observations
3.34E-04
2.43E-04
7.33E-04
Analysis With Revised
Means After Subtracting
GSD 1.95 Estimation Error
Without "Scramble" Effect
3.49E-04
2.52E-04
7.62E-04
Analysis With Revised
Means After Subtracting
GSD 3.2 Estimation Error
Without "Scramble"
Effect
3.80E-04
2.71E-04
8.25E-04
Analysis With Revised
Means After Subtracting
GSD 1.95 Estimation Error
With "Scramble" Effect
2.92E-04
2.09E-04
6.36E-04
Analysis With Revised
Means After Subtracting
GSD 3.2 Estimation Error
With "Scramble" Effect
2.75E-04
1.93E-04
5.92E-04
                                                   Table 7
Changes in Estimates of the Linear Slope Coefficient, ED10, and LED 10 for Dose Response Analyses Based on All 4 Exposure
                                                   Groups
Risk
Parmeter
estb
SEb
uclb
Analysis With
Group Means oi
the Original
Observations
4.54E-05
3.28E-05
9.94E-05
Analysis With Group
Means of the
Lognormal Fit to
Original Observations
4.38E-05
3.17E-05
9.60E-05
Analysis With Revised
Means After Subtracting
GSD 1.95 Estimation Error
Without "Scramble" Effect
5.77E-05
4.06E-05
1.25E-04
Analysis With Revised
Means After Subtracting
GSD 3.2 Estimation Error
Without "Scramble"
Effect
7.31E-05
5.03E-05
1.56E-04
Analysis With Revised
Means After Subtracting
GSD 1.95 Estimation Error
With "Scramble" Effect
5.52E-05
3.81E-05
1.18E-04
Analysis With Revised
Means After Subtracting
GSD 3.2 Estimation Error
With "Scramble" Effect
1.04E-04
6.42E-05
2.10E-04
                                                                                                          61

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                              Appendix C
             Framework Analysis of Genotoxicity and Risk Assessment
                           James Swenberg, PhD
Slide 1
             Framework Analysis of Genotoxicity
                    and Risk Assessment
                        James Swenberg
                   University of North Carolina
Slide 2
             2005 EPA Guidelines for Carcinogen
                      Risk Assessment

             •  Linear extrapolation should be used when
               there are Mode Of Action data to indicate
               that the dose-response curve is expected
               to have a linear component below the
               POD.

             •  Agents that are DNA-reactive and have
               direct mutagenic activity
                                                                     62

-------
Slide 3
                           MOA Key Events
                                Genotoxic
                                 DMA Adducts
Slide 4
                            Mutations in surrogate genes
                             Mutations in cancer genes
                                  Cancer
                           Genotoxicity

                 A chemical is defined as genotoxic if the
                 weight of evidence is positive in a battery
                 of genetic toxicology assays.

                 This is not a quantitative data set.

                 It represents Hazard Identification, not
                 Risk Assessment.
Slide 5
                           MOA Key Events
                                Genotoxic
                               DNA Adducts
                            Mutations in surrogate genes
                             Mutations in cancer genes
                                  Cancer
                                                                                 63

-------
Slide 6
             Molecular Dosimetry of DMA Adducts
              •  DMA adducts are expected to be linear at
                low doses.

              •  An exception to this is when identical
                adducts are formed endogenously.

              •  Many forms of endogenous DMA adducts
                have been identified and measured. These
                include direct oxidative adducts, exocyclic
                adducts, AP sites and deamination
                products.
Slide 7
Slide 8
                   Linear DNA Adducts at Low Doses
                MMS  ,-
                 PO
                                     DBF
                                    DMN  /
               0  100  200  300  400  =00
                         MOA Key Events

                              Genotoxic
                             DNA Adducts
                       Mutations in surrogate genes
                        Mutations in cancer genes

                                 I
                               Cancer
                                                                          64

-------
Slide 9
Slide 10
Slide 11
                 Mutations Do Not Go Through Zero

                 In contrast to most DMA adducts, mutations do
                 not go through zero.
                 Rather, they reach a spontaneous level that
                 reflects the summation of endogenous DMA
                 damage and repair that occurs in cells.
                 The inflection point for a dose response curve
                 where the number of mutations increases above
                 the spontaneous level represents the point at
                 which the exogenous DMA damage starts driving
                 the biology of mutations.
                        Typical Mutation Dose Response
                                  200      300
                                     Dose
                   Historical Control Data for HPRT
                       and TK Mutations in vitro
                                   Cell Line                  95tn

                                   Penman and Crespi, Environ Mo/MuM 0:35-60, 1987
                                                                                  65

-------
Slide 12
Slide 13
Slide 14
SOURCES OF SPONTANEOUS
            MUTATIONS
     Endogenous Sources      Exogenous Sources
              Depurination  Polymerase  Alkylation Environment   Life Radiation
                      Errors    & ROS   Chemicals    Style
                                                I    I
                           DNA DAMAGE
                                                I    I
                            DNA REPAIR
                            MUTATIONS
             HPRT Mutation in AHH-1 cells with MMS
            •Doses above 4jig/ml MMS appear to be cytotoxic

            •A possible threshold dose at l|j.g/ml MMS at the HPRT locus exists,
            with doses above l|ig/ml inducing significantly more mutants
                 MMS-induced TK-A Mutants
                           1      2

                             MMS (ng/ml)
                •The NOEL for mutation at the TK locus is located at
                1 |ig/ml MMS - same as seen with HPRT locus.
                                                                            66

-------
Slide 15
Slide 16
Slide 17
MNU
HPRTM
in AHH-
•Linear n
threshold
(LNT) do
response.
and
utation
1 Cells
on-
>e

2500 -I _. j 140
;' 120
| 2000 L^_ 	 5 ^^<
'** '^^* \s^^ " 10° >-
fe 150°" ;^J . ^^- --so 1
-e woo- J^^^-^ 60 1
S 500 - .^*
AJ*
0 0.05 0.1 0.15 0.2
/ MNUnj^ml
/'
300 n Ti6o ""• *ig" Mutation
250 ,-HO frequencies (MF) of
g 200 . » s_____L_— 1=^-^ : 12° ^ MNU treated AHH-1

1 1

t
i.
* 5 * *
DM MMS or MNU
Mutant Frequency (TK-) in Mouse Lymphoma Cells
eated with Equimolar MMS or MNU: Linear Scale



+ MMS-MF ,


A
m.
A A
4>^
/M»^^

+ MMS-MF
^ »- AMNU-W

F i I
f
I «


-""••<""«
                                                                                        67

-------
Slide  18
Slide  19
Slide 20
                               MMS Induced Micronuclei in Polychromatic Erythrocytes of
                                 Mice Bone Marrow24 Hours Following Single Injections
                        s
                        I/I
                        I
                        O
                        IE

                        1
                        S,
                        a
                        5?
                                                                                        Control

                                                                                       -MMS
                                                     MMSmg/kg
                                                 'adapted from Kliesch et al, 1981
                                Chromosomal Abberations per 100 Chineese Hamster Ovary Cells Exposed to Colchicine
                                                     Colchicine (ug/ml)
                                                   "adapted from Ami etal, 1997
Micronucleas  Dose-response Curves of MMC, Ara-C
     and COL Using Flow Cytometry of 2 M cells
                                           s
                                                 0001  0010  0 tOO  1000  1000
                                                        1000   1000   1000
                                              1    0010   0100   1000   1000


                                                     Dose  I'm g''kg}


                                               Asano, N. et al. Mutagenesis 2006 21:15-20; doi:10.1093/mutage/gei068
                                                                                                                             68

-------
Slide 21
               DMA Repair Can Modulate Where
                   the Inflection Point Occurs
                If DMA repair is impaired or absent, the
                inflection point for mutations occurs at
                lower doses.

                This results from increased numbers of
                DMA adducts relative to a cell of
                individuals with normal DMA repair.
Slide 22
             Exposure-response for Mutagenesis in Drosophila Exposed
             By Inhalation To Propylene Oxide
               100
             - o.i
                o.oi    o.i      i      10
                        7-HPG/10 nucleotides
                                           100
                                  Nivard et al, Mut. Res. 529: 95-107, 2003.
Slide 23
                         Ethylene Oxide

                Genotoxic in many systems including DMA
                adducts and in vitro and in vivo mutations.
                Known animal carcinogen.
                IARC Category 1 human carcinogen based on
                limited epidemiology data and human genetic
                toxicolgy.
                Formed endogenously in humans and animals
                from metabolism of ethylene.
                HEG is present in all human and animal cells.
                                                                              69

-------
Slide 24
Slide 25
Slide 26
Observed N7-HEG (pmol/umol Guanine)
following low-level EO exposure for 4 weeks
Tissue ppm EO Observed N7-HEG
Spleen Rats

0 0.2
3 2.5
10 4.0
33 8.8
Mice
0.2
0.5
1.4
5.6

In vivo hprt Mutations in Mice Exposed to
Ethylene/Ethylene Oxide
o
5*4
c
-------
Slide 27
0.1
EO Drosophila Mutations
0.1 1 10 100
7-HEG/10 nucleotides
1000
Slide 28
Slide 29
MOA Key Events

Genotoxic
DNAAdducts
Mutations in surrogate genes
Mutations in cancer genes

1
Cancer
Gaps in Knowledge

Most mutation assays are done at high
doses to establish that a compound is or is
not genotoxic.

There is a real need to generate dose
response data at low exposures to
establish NOAELs for mutation in CA, MN
and surrogate genes such as hprt.

These data will further establish the
inflection points where the background
number of mutations become increased.
71
-------
Slide 30
Conclusions

As our knowledge of carcinogenesis has
expanded, concepts of "one molecule —>•
cancer" have little to no scientific support.
Mutations in genes controlling cell
proliferation and cell death appear to play
major roles in the induction of cancer.
While these genes are difficult to monitor in
noncancer tissues, surrogate mutations can
be used to examine dose response in cells,
animals and humans.
Slide 31
Conclusions (cont.)

Such mutations do not have linear
relationships with exposure. Rather, they
reach a spontaneous incidence that is driven
by endogenous biological processes.

The inflection point for mutagenesis
represents a much more strongly supported
Point of Departure for setting acceptable
exposures.

This could be accomplished by using a
Margin of Exposure approach to protect
susceptible individuals.
72
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ATTACHMENT 1 MEMO AND CHARGE QUESTIONS

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
jflto sr*t-f. National Center for Environmental Assessment
t> Washington, DC 20460

October 27, 2006
NCEA Washington Office (8623D)
MEMORANDUM
SUBJECT: Request for SAB review of the Draft Ethylene Oxide (EtO) Carcinogenicity
Assessment
FROM: David A. Bussard, Director ^afj^id ' 3& ~@
National Center for Environmental Assessment-Washington (8623D)
Office of Research and Development

TO: Sue Shallal, Ph.D.
Designated Federal Officer
EPA Science Advisory Board Staff Office (1400F)

This is to request a review by the Science Advisory Board of the draft document entitled
"Evaluation of the Carcinogenicity of Ethylene Oxide". This document is an assessment of the
carcinogenicity of ethylene oxide (EtO). The assessment was prepared by the National Center
for Environmental Assessment (NCEA), which is the health risk assessment program in the
Office of Research and Development. The document has been made available for public
comment on the Agency's NCEA web site at the following URL:
http://cfpub.epa. gov/ncea/cfm/recordisplay.cfm?deid= 157664. The assessment broadly supports
activities authorized in the 1990 Clean Air Act and is of particular interest to EPA's Office of
Air and Radiation. However, the assessment should also be applicable to the needs of all
program Offices and Regions in evaluating the carcinogenicity of EtO.

EPA last published an assessment of the potential carcinogenicity of EtO in 1985. The
current assessment reviews the more recent database on the carcinogenicity of EtO. The
scientific literature search for this assessment is generally current through June 2004, although a
few later publications are included. This assessment focuses on lifetime cancer risk from
inhalation exposure.

EtO is a gas at room temperature. It is manufactured from ethylene and used primarily as
a chemical intermediate in the manufacture of ethylene glycol. It is also used as a sterilizing
agent for medical equipment and as a fumigating agent for spices. The largest sources of human
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exposure are in occupations involving contact with the gas in plants (facilities) and in hospitals
that sterilize medical equipment. EtO can also be inhaled by residents living near production or
sterilizing/fumigating facilities. This document describes the derivation of inhalation unit risk
estimates for cancer mortality and incidence based on human epidemiological data.

Attached is a draft of a charge to the Science Advisory Board that identifies the questions
and issues we want the Science Advisory Board to address in reviewing the document.
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CHARGE QUESTIONS FOR EPA'S SCIENCE ADVISORY BOARD (SAB) REVIEW
OF THE ETHYLENE OXIDE (EtO) CARCINOGENICITY ASSESSMENT

EPA's Office of Research and Development (ORD) has requested that the Science
Advisory Board (SAB) review its document entitled "Evaluation of the Carcinogenicity of
Ethylene Oxide". This document is EPA's draft of the evaluation of the carcinogenicity of
ethylene oxide (EtO). The assessment was prepared by the National Center for Environmental
Assessment which is the health risk assessment program in the Office of Research and
Development. The assessment broadly supports activities authorized in the 1990 Clean Air Act
and is of particular interest to EPA's Office of Air and Radiation. However, this review also
should be applicable to the needs of all program Offices and Regions in evaluating the
carcinogenicity of EtO.

EPA last published a health assessment of the potential carcinogenicity of EtO in 1985
(U.S. EPA, 1985). The current assessment reviews the more recent database on the
carcinogenicity of EtO. The scientific literature search for this assessment is generally current
through June 2004, although a few later publications are included. This assessment focuses on
lifetime cancer risk from inhalation exposure.

EtO is a gas at room temperature. It is manufactured from ethylene and used primarily as
a chemical intermediate in the manufacture of ethylene glycol. It is also used as a sterilizing
agent for medical equipment and as a fumigating agent for spices. The largest sources of human
exposure are in occupations involving contact with the gas in plants (facilities) and in hospitals
that sterilize medical equipment. EtO can also be inhaled by residents living near production or
sterilizing/fumigating facilities.

The DNA-damaging properties of EtO have been studied since the 1940s. EtO is known
to be mutagenic in a large number of living organisms, ranging from bacteriophage to mammals,
and it also induces chromosome damage. It is carcinogenic in mice and rats, inducing tumors of
the lymphohematopoietic system, brain, lung, connective tissue, uterus, and mammary gland. In
humans employed in EtO-manufacturing facilities and in sterilizing facilities, the greatest
evidence of a cancer risk from exposure is for cancer of the lymphohematopoietic system.
Increases in the risk of lymphohematopoietic cancer have been seen in several studies,
manifested as an increase either in leukemia and/or in cancer of the lymphoid tissue. In one
large epidemiologic study of sterilizer workers that had a well-defined exposure assessment for
individuals, positive exposure-response trends for lymphohematopoietic cancer mortality in
males and for breast cancer mortality in females were reported (Steenland et al., 2004). The
positive exposure-response trend for female breast cancer was confirmed in an incidence study
based on the same worker cohort (Steenland et al., 2003).

In accordance with EPA's 2005 Guidelines for Carcinogen Risk Assessment (U.S. EPA,
2005a), EtO was characterized as carcinogenic to humans based on the total weight of evidence.

This evidence, as assessed by EPA, included:

a) strong, though less than completely conclusive, evidence of carcinogenicity from human studies
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b) sufficient evidence of carcinogenicity in laboratory animals
c) EtO is a direct-acting alkylating agent with clear evidence of mutagenicity/genotoxicity, and
there is sufficient evidence that DNA adduct formation and the resulting mutagenic/genotoxic
effects are key events in the mode of action of EtO carcinogenicity
d) evidence of chromosome damage in humans exposed to EtO, supporting the inference that the
same mode of action for EtO carcinogenicity is operative in humans

This document describes the derivation of inhalation unit risk estimates for cancer
mortality and incidence based on the human data. An ECoi of 44 ug/m3 (0.024 ppm) was
calculated using a life-table analysis and linear modeling of the categorical Cox regression
analysis results for excess lymphohematopoietic cancer mortality in males reported in a high-
quality occupational epidemiologic study (Steenland et al., 2004). Linear low-dose extrapolation
from the LECoi yielded a lifetime extra cancer mortality unit risk estimate of 5.0 x 10"4 per
ug/m3 (0.92 per ppm) of continuous EtO exposure. Applying the same linear regression
coefficient and life-table analysis to background male lymphohematopoietic cancer incidence
rates yielded an EC0i of 24 ug/m3 (0.013 ppm) and a preferred lifetime extra cancer unit risk
estimate of 9.0 x 10"4 per ug/m3 (1.6 per ppm). The preferred estimate is greater than the
estimate of 5.0 x 10"4 per ug/m3 (0.91 per ppm; ECoi = 44 ug/m3) calculated, using the same
approach, from the results of a breast cancer incidence study of the same worker cohort
(Steenland et al., 2003), and is recommended as the potency estimate for Agency use.

Because the weight of evidence supports a mutagenic mode of action for EtO
carcinogenicity, and in the absence of chemical-specific data on early-life susceptibility, this
assessment finds that increased early-life susceptibility should be assumed and the age-
dependent adjustment factors (ADAFs) should be applied, in accordance with EPA's
Supplemental Guidance for Assessing Susceptibility From Early-Life Exposure to Carcinogens,
hereinafter referred to as "EPA's Supplemental Guidance" (U.S. EPA, 2005b). Applying the
ADAFs to the unit risk estimate of 9.0 x 10"4 per ug/m3 yields an adjusted full lifetime unit risk
estimate of 1.5 x 10"3 per ug/m3, and the commensurate lifetime chronic exposure level of EtO
corresponding to an increased cancer risk of 10"6 is 0.0007ug/m3. [Note that for less-than-
lifetime exposure scenarios (or for exposures that vary with age), the unadjusted (adult-based)
potency estimate of 9.0 x 10"4 per ug/m3 should be used, in conjunction with the ADAFs as
appropriate, in accordance with EPA's Supplemental Guidance.]

Unit risk estimates were also derived from the three chronic rodent bioassays for EtO
reported in the literature. These estimates, ranging from 2.2 x 10"5 per ug/m3 to 4.6 x 10"5 per
ug/m3, are about an order of magnitude lower than the estimates based on human data
[unadjusted for early-life susceptibility]. The Agency takes the position that human data, if
adequate data are available, provide a more appropriate basis than rodent data for estimating
population risks (U.S. EPA, 2005a), primarily because uncertainties in extrapolating quantitative
risks from rodents to humans are avoided. Although there is a fairly sizable difference between
the rodent- and human-based estimates, the assessment infers that the similarity between the unit
risk estimates based on the male lymphohematopoietic cancer and the female breast cancer
results increases confidence in the use of the unit risk estimate based on the male
lymphohematopoietic cancer results.
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The unit risk estimates were developed for environmental exposure levels and are not
necessarily applicable to higher-level occupational exposures, which appear to be subject to a
different exposure-response relationship. However, occupational exposure levels are of concern
to EPA when EtO is used as a pesticide (e.g., fumigant for spices). Therefore, this document
also presents extra risk estimates for cancer for a number of occupational exposure scenarios.

The SAB Ethylene Oxide Review Panel is being asked to comment on the scientific
soundness of this carcinogenicity assessment. The specific charge questions to the Panel are as
follows:

Issue 1: Carcinogenic Hazard (Section 3 and Appendix A of the Draft)

1. Do the available data and discussion in the draft document support the hazard conclusion that
EtO is carcinogenic to humans based on the weight-of-evidence descriptors in EPA's 2005
Guidelines for Carcinogen Risk Assessment! In your response, please include consideration of
the following:

l.a EPA concluded that the epidemiological evidence on EtO carcinogenicity was strong, but
less than completely conclusive. Does the draft document provide sufficient description of the
studies, balanced treatment of positive and negative results, and a rigorous and transparent
analysis of the data used to assess the carcinogenic hazard of ethylene oxide (EtO) to humans?
Please comment on the EPA's characterization of the body of epidemiological data reviewed.
Considerations include: a) the consistency of the findings, including the significance of
differences in results using different exposure metrics, b) the utility of the internal (based on
exposure category) versus external (e.g., SMR and SIR) comparisons of cancer rates, c) the
magnitude of the risks, and d) the strength of the epidemiological evidence.

I.e. Do the available data and discussion in the draft document support the mode of action
conclusions?

Issue 2: Risk Estimation (Section 4 and Appendices C and D)

2.a. EPA concluded that the epidemiological evidence alone was strong but less than completely
conclusive (although EPA characterized the total evidence - from human, laboratory animal, and
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in vitro studies - as supporting a conclusion that EtO as "carcinogenic to humans"). Is the use of
epidemiological data, in particular the Steenland et al. (2003, 2004) data set, the most appropriate
for estimating the magnitude of the carcinogenic risk to humans from environmental EtO
exposures? Are the scientific justifications for using this data set transparently described? Is the
basis for selecting the Steenland et al. data over other available data (e.g., the Union Carbide
data) for quantifying risk adequately described?

2.b. Assuming that Steenland et al. (2003, 2004) is the most appropriate data set, is the use of a
linear regression model fit to Steenland et al.'s categorical results for all lymphohematopoietic
cancer in males in only the lower exposure groups scientifically and statistically appropriate for
estimating potential human risk at the lower end of the observable range? Is the use of the
grouping of all lymphohematopoietic cancer for the purpose of estimating risk appropriate? Are
there other appropriate analytical approaches that should be considered for estimating potential
risk in the lower end of the observable range? Is EPA's choice of a preferred model adequately
supported and justified? In particular, has EPA adequately explained its reasons for not using a
quadratic model approach such as that of Kirman et al. (2004) based? What recommendations
would you make regarding low-dose extrapolation below the observed range?

2.c. Is the incorporation of age-dependent adjustment factors in the lifetime cancer unit risk
estimate, in accordance with EPA's Supplemental Guidance (U.S. 2005b), appropriate and
transparently described?

Issue 3: Uncertainty (Sections 3 and 4)

a. The assessment approach employed;
b. The use of assumptions and their impact on the assessment;
c. The use of extrapolations and their impact on the assessment;
d. Plausible alternatives and the choices made among those alternatives;
e. The impact of one choice versus another on the assessment;
f. Significant data gaps and their implications for the assessment;
g. The scientific conclusions identified separately from default assumptions and policy calls;
h. The major risk conclusions and the assessor's confidence and uncertainties in them, and;
i. The relative strength of each risk assessment component and its impact on the overall assessment.
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