United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-99/081
September t998
Summary of the U.S. EPA
Workshop on the
Relationship Between
Exposure Duration and
Toxicity
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EPA/600/R-99/081
September 1998
SUMMARY OF THE U.S. EPA WORKSHOP
ON THE RELATIONSHIP BETWEEN
EXPOSURE DURATION AND TOXICITY
Sheraton Crystal City
Arlington, Virginia
August 5-6,1998
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC 20460
Printed on Recycled Paper
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NOTICE
This document has been reviewed in accordance with U.S. Environmental Protection
i i *, , ' I
Agency (EPA) policy and approval for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
N. ' i i
This report was prepared by Eastern Research Group, Inc. (Contract No. 68-D5-0028)
as af general report of discussions during the Workshop on Relationship Between Exposure
Duration and Toxicity. As requested by EPA, this report captures the main points and highlights
of discussions held during plenary sessions. The report is not a complete record of all details
discussed nor does it embellish, interpret, or enlarge upon matter that were incomplete or unclear.
Statements represent the individual views of each workshop participant; none of the statements
represent analyses by or positions of the National Center for Environmental Assessment or the
EPA.
11
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CONTENTS
SECTION ONE: BACKGROUND '..1-1
1.1 Background j_j
1.2 The August 1998 Workshop j.j
SECTION TWO: OPENING PLENARY SESSION 2-1
2.1 Introductory Presentations 2-1
2.2 C x T: Historical Perspectives, Current Issues, and Approaches 2-2
2.2.1 The Risk Assessment Context 2-2
2.2.2 Haber's Law : 2-3
2.2.3 Dose Metrics 2-7
2.2.4 Historical Perspective on Dose-Response Assessment 2-12
2.2.5 Harmonization of "Noncancer" versus "Cancer" Endpoints 2-13
2.2.6 Variability and Uncertainty 2-17
2.2.7 Discussion Questions < 2-17
SECTION THREE: PLENARY PRESENTATIONSENDPOINTS OF TOXICITY 3-1
3.1 Developmental Toxicity: The Effects of Temperature and Exposure on In Vitro Development
and Response-Surface Modeling of Their Interaction 3_1
3.2 C x T and Dermal Toxicity 3.7
3.3 Neurotoxic Effects of Trichloroethylene Inhalation as a Function of Exposure Concentration,
Duration, and Target Tissue Dose 3_10
3.4 Respiratory Toxicity: Coherent Response Models of Ozone Injury in Humans and Animals .3-15
3.5 Observer Comments 3_2g
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SECTION FOUR: PLENARY PRESENTATIONSSTATISTICAL APPROACHES 4-1
l
4.1 What Can Mechanisms Tell Us About Modeling Dose-Time Relationships? 4-1
I,
4.2 C x T Issues Related to National Ambient Air Quality Standards (Eco Effects) 4-6
4.3 Statistical Models for Assessing Dose-Rate Effects 4-9
4.3.1 Background 4-9
4.3.2 Ethylene Oxide Study 4-12
SECTION FIVE: PLENARY PRESENTATIONSDOSIMETRY AND
MECHANISTIC MODELING 5-1
5.1 Dosimetry: Mechanistic Determinants of Exposure-Dose-Response 5-1
5,2 Dosimetry and Mechanistic Modeling 5-11
l
SECTION SIX: PLENARY PRESENTATIONS: IMPLICATIONS FOR
RISK ASSESSMENT 6-1
6.1 Implications for Risk Assessment 6-1
6.2 Integration of Approaches 6-9
SECTION SEVEN: FUTURE DIRECTIONSWHAT SHOULD BE ACCOMPLISHED
IN THE NEXT 5 YEARS? 7-1
i
SECTION EIGHT: SUMMARIES OF BREAKOUT GROUP DISCUSSIONS 8-1
8.1 Summary of Breakout Group One Discussions 8-1
8.1.1 Relationship Between Concentration and Exposure Duration (C x T) and
Toxic Endpoint 8-1
,j
8.1.2 Mechanistic Modeling 8-3
8.1.3 Statistical Modeling 8-5
8.14 Dose Metric , 8-5
IV
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8.1.5 Risk Assessment 8-6
8.2 Summary of Breakout Group Two Discussions 8-7
8.2.1 Endpoints of Toxicity 8-7
8.2.2 Statistical Approaches 8-9
8.2.3 Dosimetry and Mechanistic Modeling 8-10
8.2.4 Implications for Risk Assessment 8-10
8.3 Summary of Breakout Group Three Discussions 8-11
8.3.1 Dosimetry and Mechanistic Modeling 8-11
8.3.2 Risk Assessment 8-12
APPENDIX A:
APPENDIX B:
APPENDIX C:
WORKSHOP AGENDA
CHARGE TO PARTICIPANTS
LIST OF INVITED PARTICIPANTS AND BIOGRAPHIES, LIST OF
EPA PARTICIPANTS, AND LIST OF OBSERVERS
APPENDIX D:
ISSUES PAPER
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SECTION ONE
BACKGROUND
1.1 BACKGROUND
Current default risk assessment procedures typically define "dose" as an averaged daily exposure with an
emphasis on "chronic effects" observed from long-term exposures or in chronic bioassays to characterize
potential lifetime risks. However, emergency response scenarios and other regulatory implementation
activities require characterizing acute exposures, and it has always been recognized that real-world exposures
constitute intermittent regimens at a variety of concentrations. Contemporary toxicology-and recently
proposed U.S. Environmental Protection Agency (EPA) guidance are now placing emphasis on how more
mechanistic data can help to inform default approaches. Toxicity can depend on not only on the magnitude
but also on the duration, frequency, and timing of an exposure. Mechanistic determinants of chemical
disposition (absorption, distribution, metabolism, and elimination) as well as pharmacodynamic
considerations of toxicant-target tissue interaction (e.g., repair and proliferation rates) include both
concentration and time-dependent processes. An "acute" exposure duration may result in "chronic" toxicity
if the chemical or its damage accumulates. Thus, the choice of an appropriate measure of a "dose metric"
must be defined by characterizing the exposure-dose-response continuum.
EPA's Risk Assessment Forum (RAF) is beginning to examine how dose-duration relationships are or can be
incorporated into the risk assessment process for less-than-lifetime exposures. This is an extension of efforts
within EPA, as well as collaborative work carried out with researchers from the Harvard School of Public
Health. As part of this effort, a workshop was held on August 5 and 6,1998, in Arlington, Virginia. The
objective of the workshop was to discuss our current understanding of dose-duration relationships and their
underlying mechanistic basis, the approaches that can be used in modeling these relationships, their inclusion
in risk assessment, and future directions in this area. Appendix A presents the meeting agenda and Appendix
B presents the charge to the participants.
1.2 THE AUGUST 1998 WORKSHOP
The RAF invited scientists with expertise in toxicology, biostatistics, and risk assessment, and epidemiology
from both within and outside the Agency, to participate in the workshop (Appendix C). The workshop was
designed to identify and to discuss areas of common understanding, as well as areas of differences. Prior to
the workshop, each invited participant received an issues paper (Appendix D) intended to explore issues in
the assessment of dose-duration effects in order to identify where the current risk assessment approach may
be improved and to identify gaps in our knowledge and methodology in order to suggest areas of further
research. (The paper included in Appendix D is the original draft distributed prior to the workshop; it may be
revised in the future to reflect comments provided by the participants.) During the workshop, plenary
presentations provided specific examples of the various issues that are defined in the paper.
Louise Ryan, Professor of Biostatistics at Harvard University's School of Public Health and Dana Farber
Cancer Institute, served as the workshop facilitator. The workshop was structured as a series of alternating
plenary sessions and breakout group discussions. Each participant was assigned to one of three breakout
1-1
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groups. In making the group assignments, EPA sought to ensure a mixture of expertise and Agency
representation in each group. Breakout group participants are listed in Sections 8.1,8.2, and 8.3. A group
leader helped to facilitate discussions in each group and a rapporteur captured key discussion points for the
report back to the plenary sessions. The agenda included specific times for workshop observers to contribute
comments and questions. The final workshop session addressed future directions for work on this issue over
the next5 years and beyond.
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SECTION TWO
OPENING PLENARY SESSION
2.1 INTRODUCTORY PRESENTATIONS
To open the workshop, Louise Ryan, Professor of Biostatistics at Harvard University's School of Public
Health, welcomed the participants and observers. She explained the purpose of the meeting and EPA's
charge to the participants. Each of the invited experts then introduced themselves briefly, indicating their
affiliations and describing their interest in the topic. The experts included:
William Boyes
Chief, Neurophysiological Toxicology Branch
National Health and Environmental Effects
Research Laboratory
U.S. Environmental Protection Agency
Harvey Clewell
Senior Project Manager
K.S. Crump Division
ICF Kaiser, International
Rory Conolly
Senior Scientist
Chemical Industry Institute of Toxicology
Dan Costa
National Health and Environmental Effects
Research Laboratory
U.S. Environmental Protection Agency
Dale Hattis
Research Professor
Center for Technology, Environment, and
Development
Clark University
Annie Jarabek
National Center for Environmental Assessment
U.S. Environmental Protection Agency
GaryKimmel
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Allen Lefohn
President
ASL & Associates
James McDougal
Senior Scientist, Geo-Centers, Inc.
Operational Toxicology Branch
AF Research Laboratory
Resha Putzrath
Principal
Georgetown Risk Group
Stephen Rappaport
Professor of Occupational Health
Department of Environmental Sciences and
Engineering
School of Public Health
University of North Carolina
Lorenz Rhomberg
Assistant Professor of Risk Analysis and
Environmental Health
Harvard University
Center for Risk Analysis
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Harvey Richmond
Office of Air Quality Planning and Standards
U.S. Environment Protection Agency
George Rusch
Director of Toxicology and Risk Management
AUiedSignal, Inc.
Louise Ryan
Department of Biostatistical Science
Dana Farber Cancer Institute
Harvard University
School of Public Health
Paige Williams
Associate Professor of Biostatistics
Harvard University
School of Public Health
Ronald Wyzga
Senior Program Manager
Health Studies Program
Electric Power Research Institute
Appendix C contains the lists of contact information for invited and EPA participants, as well as biographies
of the invited participants.
22 C x T: HISTORICAL PERSPECTIVES, CURRENT ISSUES, AND APPROACHES
Annie Jardbek, EPA National Center for Environmental Assessment
The purpose of Dr. Jarabek's presentation was to:
Introduce the risk assessment context
Clarify terminology
Review assumptions in current approaches
Identify conceptual commonalities and differences
Engender rnterdisciplinary dialogue to help improve applications
22.1 The Risk Assessment Context
It must first be appreciated that the topic of exposure-dose-response is embedded in an overall scheme of
regulatory risk assessment and management. This scheme includes inherent aspects of duration, notably
assumptions in characterization that must be informed by data all along its cycle, from source
characterization to transport and transformation description as it is used to define air (or water or soil)
quality, which is then evaluated by exposure-dose-response assessment. This assessment is then used to
inform health or ecological risk estimates that form the basis of regulatory standards that in turn dictate
control technologies on the source. The intent of this workshop is to focus on the dose-response assessment
aspect according to the 1983 National Academy of Sciences scheme for risk assessment versus risk
management, but the broader context must be kept in mind when discussing how this information might be
communicated or used in other arenas.
The concept of scale provides a framework for both human and ecological risk assessment, since spatial and
temporal aspects are unifying concepts. Scale includes implicit issues of time as it relates to spatial
distribution, the development or degree of severity, and when observation takes place.
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A number of important exposure issues are raised when attempting to interpret the exposure-dose-response
continuum. It is important to accurately characterize activity patterns (how long a person stays in an activity,
where a person is relative to a certain concentration profile). Measurements must be defined with respect to
time (e.g., as a daily or annual average) in order to characterize exposures. Spatial representativeness must
also be considered (personal or area samples and where the sample is relative to exposure). It is also
important to consider variability in measuring concentration and time when characterizing exposures.
Underlying assumptions in risk assessment and risk management should be explored. The assumptions may
differ when the objective is dose-response assessment versus risk management and they typically dictate
derivation approaches. In particular, we should examine underlying assumptions with respect to the exposure
scenario, the effect severity, the population to be considered, the database utilized, the use of safety versus
uncertainty factors, and the use of dosimetry adjustments.
Currently, exposure scenarios (acute or chronic) direct risk characterization without regard to its interface
with toxicology or health outcome characterizations. Acute exposures that require regulation include
emergency releases or intermittent start-up/shut-down processes, periodic contaminations, or occupational
exposures. Chronic exposures are defined as "lifetime ambient," typically 70 years (with exposures or
consumptions of 24 hr/day or 2 L/day). Types of toxicity data used to. address these exposure scenarios
include:
Acute: 1- to 24-hour exposures, single or few oral administrations, up to 14-day exposures
Chronic: 90-day studies, 2-year bioassays
However, these categories are based on chronologic time with no consideration of the toxicity mechanisms or
appropriate ways to express the dose metric.
Default equations for duration adjustment of exposure are shown in Figure 2-1 (with the exception that in
current practice these do not apply to exposures associated with developmental toxicity). The default
duration adjustments assume that the internal dose is equivalent to exposure concentration. Further, it is also
assumed that toxicity is related linearly to the C * T product so that equivalent C x T products are predicted
to cause the same toxicity. The basis for these default equations is in Haber's Law.
2.2.2 Haber's Law
According to this "law," a constant, in this case a fixed effect level (i.e., a constant severity or incidence of a
health endpoint), is related to exposure concentration and duration by the equation shown in Figure 2-2. The
relationship is described by the hyperbola and the arms converge asymptotically toward the axes of the time
and concentration coordinates. Because Haber examined only extremely short durations at relatively high
concentrations of irritant gases, a C x T relationship appeared to hold because concentration was the
dominant determinant of the observed toxicity in that limited time window. Haber's Law was extended to
characterization of long-term exposures on the basis of considerations of potential accumulation of the
chemical or its damage so that the toxicity observed after chronic exposures was again thought to possibly be
due to the C x T product However, Figure 2-3 illustrates how the use of this relationship might give
erroneous estimates when extrapolating from an 8-hr exposure effect level to shorter and longer durations. If
a single 8-hr experimental concentration is used as the basis of a C x T product calculation to estimate a 24-
hr equivalent level, then the use of Haber's law results in an estimate that is conservative (presumably
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the single 8-hr experimental concentration was extrapolated to a 1-hr estimate, however, assumption of
Haber's law (B) results in an overestimate of the effect level when compared to an estimate assuming
concentration alone (A) was the determinant.
The problem with this default, as with any other, is that it relies on a rudimentary description of a complex
and dynamic system. As illustrated in Figure 2-4, toxicity depends on the magnitude, duration, and frequency
of exposure. Thus, the choice of a metric to characterize this toxicity should depend on some knowledge of
the mechanism by which the toxic effects are induced. Current research in toxicology and modeling allows
us to now define alternative dose metrics that begin to better embody considerations of these underlying
mechanisms.
2.2.3 Dose Metrics
Alternative potential dose metrics include:
Blood concentration of parent chemical
Area under blood concentration curve (AUBC) of parent chemical
Tissue concentration of parent chemical
Area under tissue concentration curve (AUTC) of parent chemical
Tissue concentration of metabolite
AUTC stable metabolite
AUTC reactive metabolite
Toxicologists recognized early on that detoxication could occur and that expressing C x T relationships on
the basis of exposure was inadequate. Hayes restated the C x T relationship in 1975 to address dosimetry
(Figure 2-5). Likewise, the pharmaceutical industry looked at physiologic time rather than chronologic time
as a way to normalize the plasma half-life of drugs across species (Figure 2-6).
For accurate risk assessments, it is ultimately desirable to have a comprehensive biologically based dose-
response model that incorporates the mechanistic determinants of chemical disposition, toxicant-target
interactions, and tissue responses integrated into an overall model of pathogenesis (Figure 2-7).
Incorporating a comprehensive description of the exposure-dose-response continuum can allow us to be more
quantitative and to move from estimates presumed to be protective to actual predictive estimates.
Mechanistic models have been demonstrated to be particularly effective, particularly with respect to more
accurate description of chemical disposition.
Mechanistic models can account for both time- and concentration-dependent parameters and processes,
including chemical disposition (deposition, absorption, distribution, metabolism, and elimination) and
endpoint (severity, window of observation).
Along the exposure-dose-response continuum, exposure factors that govern absorption include concentration,
contact duration, frequency, dosing pattern, dosing vehicle, fed or fasted state of test species, occlusion,
release from vehicle, and transit/residence time. Factors governing absorption also include physicochemical
characteristics such as dissociation state, molecular size, molecular weight, partition coefficient, pKa,
reactivity, solubility, volatility. Portal-of-entry factors that govern absorption include the barrier capacity,
particularly as it is related to variability in species and individuals, blood flow rate, cell turnover, cell types
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and morphology, contact site, contact area, contact duration, diffusion to blood, intactness of organ,
metabolism, pH of portal of entry, recirculation, specialized absorption sites, and storage in cells. Factors
governing target tissue dose include metabolism/clearance rates, tissue binding, tissue blood flows,
tissue/blood partition coefficients, and tissue coefficients. Many of these aspects will be discussed in the
following plenary presentations.
It is important to remember that mechanistic inferencing is a key consideration for the choice of dose metric
and to describe it when developing any dosimetry or PBPK model. For example, the model structure and
metric will be dictated by the effect of irritants on the portal of entry, and the volume of distribution and half-
life of a chemical with remote toxicity.
EPA now uses some form of mechanistic descriptions of dosimetry in its reference concentration methods and
this is being adopted to other approaches in the Agency. It must be remembered that these approaches are
applied to a database of information on the effects of a chemical and that the objective is to evaluate the
potential for a chemical to induce toxicity at any of the critical life stages from conception through geriatrics,
including development and reproduction. This translates into data requirements. For example, the minimum
database for derivation of an RfC includes two chronic inhalation bioassays in two different species, one two-
generation reproductive study, and two developmental toxicity studies in two different species. These will
capture toxicity across the life span and species sensitivity. If interaction only occurs with the respiratory
tract, the two-generation or developmental study may not be necessary. Uncertainty in the estimate results if
these data are not available and there are no mechanistic data by which to better understand the mechanisms
of potential toxicity. Consideration of this uncertainty has an important historical component that may be
useful to the workshop.
2.2.4 Historical Perspective on Dose-Response Assessment
Historically, risk assessment procedures focused on uncertainty because of limitations in experimental design,
measurement techniques and animal models. Because of the limited understanding of underlying
mechanisms, separate approaches for cancer and noncancer endpoints were developed. But contemporary
toxicology is much more informative. Bioassays and databases are more comprehensive. Measurements are
more sophisticated. There is a growing understanding of mechanisms at the molecular level. Animal models
of susceptibility are used, and enhanced computational capacity exists to describe processes quantitatively.
Recently, the National Research Council has emphasized the use of mechanistic data, saying that analyses
will improve as we learn more about biology, chemistry, physics, and demography. That has been reflected in
an emphasis on mechanistic information in the proposed cancer guidelines. In hazard assessment, not only
tumor data are considered, but also the mode of action, as well as structural-activity relationships,
toxicokinetic/dosimetry studies, toxicity and pathology findings, and physical chemical properties.
This hazard assessment informs the likelihood and conditions of human exposure and mode of action
conclusions to help determine how one extrapolates in the dose-response assessment. Biologically based
dose-response modeling is preferable; short of that, linear and non-linear default choices exist for
extrapolating in the low-dose range.
An emphasis on precursor lesions and mechanistic information in the proposed EPA cancer guidelines
mirrors some of the tenets in molecular epidemiology: that early biological effects are more prevalent than
later events in the population at risk; that later events (disease) have been of historical interest but earlier
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events may be more specific to the exposure; and that technological advances allow xenobiotics to be directly
quantified in the body or indirectly measured by identification of a dose-related biologic response.
As in molecular epidemiology, construction of characterization of the exposure-dose-response continuum
involves the following progression: exposure, internal dose, biologically effective dose, biological effect,
altered structure or function, clinical disease, prognostic significance. A marker can be of exposure and effect
or susceptibility, depending on how the dynamic relationship plays out One-to-one sequential linkage
between each biological markers does not need to exist; correlations are useful for dose-response assessment
even if the mechanistic underpinnings are unknown (Figure 2-8). These linkages can be informative for risk
assessment and should be explored. They are also useful to frame future research.
It is possible to extend dose-rate models by adding parameters (e.g., duration, gestational days, means of
responses, severity categories). The key question is where these approaches interact with respect to levels of
biological organization (i.e., biochemical/molecular, subcellular/organelle, cellular, tissue or organ, whole
organism, population) and how this influences the choice of the dose metric as well as the endpoint.
2.2.5 Harmonization of Noncancer and Cancer Endpoints
Focusing on mode-of-action information might provide a means whereby approaches to noncancer and cancer
assessments begin to converge since some of the same cellular events (e.g., erosion and atrophy with
subsequent cellular proliferation) may be underlying events to various lesions in common. As an example,
EPA has participated in a public-private partnership with the Vinyl Acetate Task Group to look at how
mechanistic information might harmonize cancer and noncancer approaches and change the dose metric for
the inhalatioirrisk assessment for vinyl acetate.
In a 2-year bioassay for vinyl acetate, tumors were observed only at last sacrifice at highest concentration
(600 ppm); "noncancer" toxicity endpoints included olfactory degeneration, basal cell hyperplasia (strong
dose-response above 50 ppm, sometimes at interim sacrifices ). Metabolism by carboxylesterase of vinyl
acetate to acetaldehyde and acetic acid is thought to be the underlying mechanism of its cytotoxicuy.
The default dosimetry model for a Category 1 gas, one that interacts intimately with the respiratory tract,
describes parent uptake in the entire upper respiratory tract region as a function of the overall mass transfer
coefficient. Although species-specific, this mass transfer approach currently does not provide localized
description of dose to, for example, in this case, the olfactory tissues. The default model also does not
provide for description of other dose metrics such as the amount of acid formed or the resultant change in
intracellular pH. A PBPK model was developed by researchers at DuPont Haskell laboratory that describes
species-specific airflow and then metabolism in the olfactory tissue compartments. This model allowed full
description of the exposure-dose-response continuum for vinyl acetate as shown in Figure 2-9, and shows the
relationship between the "noncancer" cytotoxic events and subsequent tumor formation. The dose-response
can now be constructed using alternative dose metrics such as tissue concentration of acid and the biological
responses can include loss of pH control or some other precursor lesion such as degeneration or cell
proliferation. Development of such a model is consistent with the Agency's hierarchy scheme shown in
Figure 2-10. The models differ in their ability tp characterize dominant determinants of the disposition (e.g.,
localized versus regional compartment; mass transfer versus tissue concentration; parent versus metabolite)'
and endpoint. These more comprehensive descriptions must be accommodated in how uncertainty factors are
currently applied with a change to consideration of the degree of confidence in the description to capture
critical events along the exposure-dose-response continuum.
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2.2.6 Variability and Uncertainty
Variability (heterogeneity in time and space) and uncertainty (lack of knowledge) are important to consider
for human health dose-response assessment Currently, uncertainty factors are not mechanistically motivated;
they are derived looking across chemicals and across species. One of the challenges is to see how mechanistic
modeling might improve the framework for uncertainty analysis.
Mechanistic models explicitly identify sources of variability and uncertainty (e.g., airflow patterns, partition
coefficients, and tissue-specific clearance rates) and illuminate data gaps. Model validation reduces
uncertainty about processes governing toxicokinetics.
EPA is developing mode of action motivated dosimetry. This includes approaches for inhalation, oral, and
dermal uptake and disposition. It addresses toxicity (i.e., harmonizes "noncancer" and "cancer" approaches).
It includes structures to address temporal and spatial considerations. It provides a framework for considering
mechanistic data and uncertainty more explicitly.
Updating risk assessment with the state of the science will take time, and will involve the following:
Expanding the role of mechanistic information
Emphasizing full characterization
Developing more sophisticated (semi-empirical and mechanistic) models for dose-response analysis
Acknowledging a lag time before the new science is accepted in regulatory application
ป Enhancing computational capacity to describe processes quantitatively
The challenge is that the quality of a prediction is a function of several things: the data, an understanding of
the biology, the modeling assumptions, and the modeling methods.
2.2.7 Discussion Questions
Key discussion issues to address this framework include the following:
What biological level can or should be assessed in establishing a toxic exposure?
Is it necessary to have an understanding of the mechanism of toxicity?
How do such tissue and cellular functions as metabolism, repair, and bioaccumulation factor into
defining the relationship?
Is it possible to adequately describe a concentration-duration relationship without measuring effects
at biologic levels below the whole animal-systemic?
2-17
-------
Many of the guidelines reflecting the endpoints in Table 1 of the discussion paper (Appendix D, page
7) are based on exposures of different durations than the guideline, and have used Haber's law for
extrapolation purposes. Are these guidelines reliable? How can development of these guidelines be
unproved?
Are the models developed for assessing the joint effects of exposure level and duration reliable
enough to allow extrapolation to doses not considered in fitting the models? How can these models
be verified? How can we summarize uncertainty when extrapolating a model beyond the range of
duration included in the data to which it was fit?
How can mechanistic information and exposure metrics derived from PBPK models be better
incorporated into models that evaluate dose-rate effects?
How can study designs be improved to use information from previous exposures within the same or
other studies to determine the most efficient subsequent exposure?
What is the appropriate metric to use for reflecting exposure levels and durations in dose-rate
studies? How can the type of endpoint and the mechanism of action be utilized to choose the
appropriate metric?
What are the most important areas that require strengthening in the assessment of dose-rate effects?
2-18
-------
SECTION THREE
PLENARY PRESENTATIONS: ENDPOINTS OF TOXICITY
3.1 DEVELOPMENTAL TOXICITY: THE EFFECTS OF TEMPERATURE AND EXPOSURE
ON IN VITRO DEVELOPMENT AND RESPONSE-SURFACE MODELING OF THEIR
INTERACTION
Gary Kimmel, EPA National Center for Environmental Assessment
Dr. Kimmel described a study carried out in collaboration with Harvard University School of Public Health
Department of Biostatistics and the U.S. Food and Drug Administration's Center for Devices and Radiologic
Health. The environmental exposure used to study C * T was hyperthermia. Effects were observed that could
not be accounted for only by the temperature, so duration of exposure was examined as well. This work
relates to a very specific aspect of developmental toxicity within a limited range of what is considered
developmental toxicity. Developmental toxicity is not a system-related event, but a time of exposure-related
event. Many endpoints in development are examined.
The in vitro model is a useful tool for looking at C * T relationships in development The embryo is taken
out of the mother, isolating the individual unit, taking it out of any metabolic system, and allowing exposure
of the embryo to clear "blocks" of exposure (i.e., raising temperature within 2 minutes to any level and
lowering it back to the control level).
In this experimental design, embryos are explanted, equilibrated at 37ฐ C, allowed to develop over 4 hours,
then exposed to a variety of temperatures (up to 42."0) and exposure time (up to 60 minutes). They are then
cultured overnight at 37ฐ C and evaluated for viability, growth, and morphology. Only at a discrete period of
development (day 10 of gestation in the rat) is considered. Table 3-1 shows the number of embryos cultured
in each temperature-duration combination.
Moving from the more extensive to more subtle exposures, it is important to consider whether data can best
be obtained by using a balanced design, in which all combinations have an identical number of animals, or by
not using a balanced design.
The percent of affected animals (showing changes in growth, morphology, or viability) is shown in Table 3-2.
The data show a relationship between effects and the combination of temperature and duration. Figure 3-1 is
a plot based on Haber's law to constrict the model of these dataan equal response for any similar
combination of the C * T multiple. Figure 3-2 is an expanded model, looking not only at lie combination of
concentration and times, but the additional effect of time or concentration individually on the model. It shows
that shorter durations at higher concentrations (temperatures) produce greater effects than comparable lower
temperatures at longer durations of exposure. The same relationship holds for non-viability (Figure 3-3).
Growth parameters and morphologic parameters examined generally showed the same effect.
Objectives addressed in the study included:
To develop dose-response models that reflect the interrelationship between exposure intensity,
duration, and effect, incorporating multiple outcomes.
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time.)
Additional long-term objectives include:
To develop methods for extrapolating to untested exposure intensities or durations
To apply the models to evaluation of experimental designs
3.2 C x T AND DERMAL TOXIOTY
James McDougal, Gee-Centers, Inc., Wright-Patterson Air Force Base
This presentation focuses on the impact of C x T on the dermal route. Two types of dermal toxicity exist:
skin as a target organ (local toxicity: irritation, sensitization, corrosion) and skin as a barrier (systemic
toxicity).
The skin is the largest organ in the body, made up of the epidermal layers (including the tightly packed
stratum corneum, thought to be the barrier) and the dermal layers.
Flux (milligrams per surface area exposed per time) is
P =
. PK,
5
where
D is diffusivity (cm2/hr)
K,,, is membrane partition coefficient (unitless)
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C is concentration (mg/cm3)
P is permeability coefficient (cm/hr)
Experimentally, skin is isolated in a diffusion cell (Figure 3-4). The cumulative mass absorbed over time is
shown in Figure 3-5, showing that linear absorption occurs after a lag time of 30 to 45 minutes before any of
the chemical comes through the skin in to the receptor solution.
How is Haber's law used in the risk assessment? For systemic toxicity, if we look at mass that comes across
the skin, the fixed law relationship is:
3-7
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t is exposure time (hr)
However, this does not take lag time into account. EPA has recommended the following approach, which
includes the chemical in the skin as well as the chemical that comes through the skin:
M3N-AC
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where Kin = membrane (skin) partition coefficient
8 thickness of skin
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For systemic toxicity, C * T ignores lag time and may be very inaccurate (Figure 3-6). For local toxiciiy, we
assume that the toxicity is related to the concentration of chemical in the skin (Figure 3-7). Lag time is not as
important; C * T would be inaccurate if the skin is saturated. The way to address toxicity from the dermal
route is to use a mechanistic approach, such as PBPK modeling.
With C x T, we always focus on the external concentration, the time of exposure (minutes to years), and some
constant toxicity related to the product of the two. But many nonlinear processes occur between external
concentration and the response, as shown in Figure 3-8, making C x T inaccurate.
i .
3.3 NEUROTOXIC EFFECTS OF TRICHLOROETHYLENE INHALATION AS A FUNCTION
OF EXPOSURE CONCENTRATION, DURATION, AND TARGET TISSUE DOSE
William JBoyes, EPA National Health and Environmental Effects Research Laboratory
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This project examined concentration-duration relationships for inhalation exposure to trichloroethylene
(TCE) as a model volatile organic solvent, and examined the tissue dosimetry as a better predictor of the
outcome.
The study looked at three different outcome measures of neurotoxicity: hearing loss (a permanent effect, due
to damage to the cochlea, following exposure to high concentrations for an extended duration) as well as two
acute, reversible effects: visual function and signal detection behavior. Tissue dose was estimated using a
classically based PBPK model.
Figure 3-9 shows some of the results. These data show that Haber's rule is underprotective when going from
long- to short-duration exposures and overprotective when going from short- to long-duration exposures.
3-10
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Tissue dosimetry might allow better predictions. Figure 3-10 shows the four exposure situations in the
experimental design, which allows evaluation of what the proper dose metric is for evaluating outcomes (area
under the curve, peak concentration, or C * T).
Visual effects showed poor relationship to area under the curve (AUC arterial [TCE]) but a good relationship
to peak concentration (Figure 3-11). Figure 3-12 shows that it doesn't matter what concentration or duration
is used; the magnitude of effect in the brain can be predicted as a function of the blood concentration. This
shows that the momentary or peak concentration of the compound is the critical determinant of the effect.
Figure 3-13 shows predictions of PBPK model; concentrations in blood versus brain (target tissue); and brain
concentration versus outcome. One can go from any combination of atmospheric data through a series of
mathematical relationships to predict quantitatively the magnitude of effect in the brain. Figure 3-14 is a
simplified version that could be used for risk assessment to see when effects on brain function are likely to
occur. This figure is based on values determined in rats, and would have to be converted into equivalent
human values before being used for risk assessment.
The study concluded that:
The linear form of Haber's rule led to inaccurate extrapolations across durations.
The tissue dose estimated from the PBPK model predicted well across exposure conditions.
Peak tissue concentration was an appropriate measure for acute TCE effects.
Other mechanisms of action may require other tissue-dose metrics.
3.4 RESPIRATORY TOXICTTY: COHERENT RESPONSE MODELS OF OZONE INJURY IN
HUMANS AND ANIMALS
Dan Costa, EPA National Health and Environmental Effects Research Laboratory
Haber's law as we use it is probably somewhat of a misrepresentation; Haber used it specifically for lethality
resulting from pulmonary effects, and he said it was only useful when comparing two toxicants but not when
looking within a toxicant for C x T relationships.
For ozone, percent mortality versus concentration of ozone shows a reasonable C * T relationship. Ozone
reacts with lung tissue immediately. In inhalation toxicology studies, we are limited hi design, and are
generally dealing with a square wave type of exposure. In environments such as Los Angeles, ozone
concentrations go up and down along with other toxicants. A year ago the ozone standard was reconsidered.
It was historically established using a 1-hour peak concentration, but concern exists about possible
cumulative effects from an 8-hour exposure.
This study looked at a direct measure of toxicity: protein leakage from the vascular side to the pulmonary
side. In looking at a number of species, modeling the pulmonary tissue dose using the Miller-Overton model,
a linear response exists. A C x T matrix exposure was done with animals. For the rat and guinea pig, an
exponential model fit the data pretty well:
3-15
-------
Estimated Blood [TCE]
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3-16
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3-18
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3-19
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Estimated Arterial [TCE] as a Function
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3-20
-------
F-344Rat
In BALP = 4.59 + 0.47eai47CT
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R2 = 0.86
This was applied to other endpoints where human as well as animal data were available. A major database
exists on reduction of forced vital capacity, from which the 1-hour ambient standard for ozone was
established. The FVC falls with increasing concentrations of ozone (Figure 3-15). Three primary human
models describe this data set (Figure 3-16).
The exponential model (Response = Aec x *) is preferable because it is simple, yet fits the data well. In
addition, it:
Describes first-order chemical processes conceptually similar to O3 substrate reaction
Describes many concentration-related events (e.g., uptake, elimination in PK models)
Describes population responses (e.g., growth)
Utilizes C x T as simple exposure parameters
The application of the exponential model to animal and human data is illustrated by the relationships
expressed in Figure 3-17 and shown graphically with human data from previously published studies in Figure
3-18. The one place where the exponential model begins to fail is at high concentrations and high levels of
exercise where one might assume high doses. In chronic studies looking at thickening of distal airways, a
simple C * T relationship works pretty well when a-dosimetry model is incorporated that accounts for losses
of ozone down the airways.
The study concluded that in the case of ozone:
C x T appears to be a reasonable approximation for dose, especially at lower concentrations.
For a single-day exposure, the exponential relationship of response for C x T appears to describe
adequately across species.
The adequacy of the C x T dose metric may be endpoint dependent.
Where the endpoint can be mechanistically linked to a cumulative injury/stimulus, C x T may be
appropriate for chronic outcomes.
For a particularly corrosive inhalant such as phosgene (or one with diverse mechanisms/responses),
concentration may overshadow the impact of exposure duration on the magnitude of response.
3-21
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3.5 OBSERVER COMMENTS
Dr. Chen from OSHA discussed the importance of having a description of rate (what are the
circumstances and assumptions?). Clearly communicating the end result of risk assessment to the
public is critical. With regard to gaining more transparency in the risk assessment, it is has been
stated that it is important to conduct more research on metabolism and mechanism. Good examples
are butadiene and methylene chloride, for which this research has been done in the last few years but
the risk assessment is not better.
Dr. Strickland from NCEA presented categorical regression as a way to look at C * T. The
software/documentation for the CatReg model is available from EPA (EPA/600/R-98/052 and
EPA/600/R-98/053).
i
Ernest Falke noted that when doing Cn x T = K, one should consider the impact on risk assessment.
The higher the value of n, the flatter the dose-response curve. If n = 1, an interpretation could be that
there is no repair. Finally, some acute studies present data in terms of CT products; this is
transformed data that is difficult to look at
Carol Kimmel raised a question about the models presented in which the effect of duration at low
doses is much less than at higher doses. Since we are using the lowest dose or NOEL for
extrapolation to lower levels; correction on a C x T basis will result in overcorrection. This should
be taken into account in default situations.
3-26
-------
SECTION FOUR
PLENARY PRESENTATIONS: STATISTICAL APPROACHES
4.1 WHAT CAN MECHANISMS TELL US ABOUT MODELING DOSE TIME RESPONSE
RELATIONSHIPS?
DaleHattis, Clark University
Mechanistic models are important and potentially helpful because:
They are potentially true (capture some real, albeit simplified aspect of the toxic system), allowing
projection to unmeasured circumstances of dose, time, etc.
They are productive of experimental studies/hypotheses in the form of measurable intermediate
parameters between external dose and effect.
When they fail, the way they fail can yield useful mechanistic insights for revising the theory, and for
further experimental study.
Using the example of chlorine, the story of the Michaelis-Menten enzyme equation illustrates mechanistic
reasoning. The basic Michaelis-Menten framework for saturation of either activation or detoxification
processes is:
Reaction Rate =
[C]
This is not an empirical equation, but is derived from fundamental mechanical principles. This is a reaction
catalyzed by a few large molecules. At the limit of low doses, the reaction rate is governed by rare chance
meetings between the substrate and the active site of the enzyme, so at low doses C in the denominator
becomes low relative to K,,, and the reaction becomes first order. At high doses, C becomes high relative to
K,,, and the reaction proceeds at a maximum rate. This can influence dose-time relationships in different
ways, depending on whether the damage or the effect depends on C at some relevant receptor site or the
integral of C x T (area under the curve).
How would different levels of administration affect the C x T product where you have a Michaelis-Menten
detoxification process? Figure 4-1 shows Michaelis-Menten metabolism starting either well below K,,,
(Haber's law applies, repair is first order) or well above (concentration declines at rate of V,^ linear over
time, leading to a C2 dependence for the area under the curve).
If linear repair of damage occurs, Haber's law fails at lower doses at the high time end. The rate of effect
reversal is critical in understanding these dynamics. Figure 4-2 shows the effects of pulses of exposure
building up to Cmgx. Spacing the pulses far apart and compressing the dosing increases C,,^ significantly.
Spacing the pulses closer together relative to the biological half-life of the damage or the toxicant does not
have the same effect, and C^ is proportional to C x T. (Figure 4-3). Therefore, knowing something about
4-1
-------
Michaelis-Menten Metabolism Starting Either Well
Above or Well Below the Michaelis Constant
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4-2
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Effects of Pulses Spaced At Intervals of 3 Half-Lives
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4-3
-------
Effects of Pulses Spaced At Intervals of 1 Half-Life
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Figure 4-3
4-4
-------
the dynamics of reversal for reversible processes help in predicting when Haber's law will apply and how
much adjustment is needed
It is important to know the biological mechanisms, and they need to work at multiple levels of biological
organization, depending on where the important dynamic processes are occurring. This is not a statistical
problem, but a problem of understanding biology quantitatively. At the biochemical/molecular level,
examples include:
Enzyme inhibition or unproductive reactant yielding too much or too little of some vital function.
This can be irreversible (e.g., phosphate anticholinesterase) or reversible (e.g., uncoupling of
oxidative phosphorylation by arsenic).
Receptor agonism, i.e., too much of a signal at a key developmental point
Receptor antagonism, i.e., too little of a signal at a key developmental point
At the organelle or cellular level, examples include:
Depletion of a key cofactor (e.g., glutathione) or energy resource (ATP) leading to temporary loss of
function (such as neurotransmission) or cell death
Death of nonreplicating cells (e.g., neurons) as opposed to loss of cells with a normal replacement
cycle
Disturbance of appropriate differentiation pathways
Inappropriate triggering of apopstosis (or inappropriate inhibition of programmed cell death, which
may be involved in producing teratogenic effects)
At the tissue/organ level, long-term adaptive responses can occur, including:
Proliferation of goblet cells leading to chronic bronchitis
Irreversible loss of neurons hi the substantia nigra, leading to inadequate internal concentrations of
dopamine and loss of control function
Irreversible loss of lung support structural proteins and alveolar septa, leading to
emphysema/decreased lung function
At the systemic level, problems include:
Inadequate oxygen transport by carbon monoxide inhibition of oxygen binding to red cells, leading to
impaired function and death of the brain and other organs
Dynamic consequences of enzyme induction, including enzymes that induce activation or inactivation
of the inducing toxicant itself, and enzymes that repair or compensate for various types of damage
caused by the toxicant
4-5
-------
4.2 C x T ISSUES RELATED TO NATIONAL AMBIENT AIR QUALITY STANDARDS (ECO
EFFECTS)
Allen Lefohn,ASL& Associates
In 1981, in discussing EPA's proposal to use long-term average concentration as the form of a standard to
protect vegetation from the effects of ozone, a question arose about why all concentrations should be treated
the same, as opposed to weighting differentially the higher, mid-level, and lower hourly average
concentration. No evidence was cited in the literature to support this regarding growth reduction for crops.
This began an area of research that has continued to the present
One of the first questions was what types of ambient exposures are actually occurring? Figure 4-4 shows
different types of exposure for ozone (same average and different distributions). If higher concentrations
should be given greater weight than mid-level and lower, one has to be concerned about the episodic
exposures and can't use average concentrations over time.
In 1996, EPA's conclusions concerning exposure indices for vegetation effects included the following:
Exposure indices that weight the hourly ozone concentrations differentially appear to be the best
candidates for relating exposure with predicted plant response.
Peak-weighted, cumulative indices appear to have major advantages over the mean (e.g., 7-hr
seasonal mean), peak indices (e.g., 2HDM) and the index that cumulates all hourly average
concentrations (i.e., SUMOO).
OAQPS continues to believe that the selection of an appropriate integration time of interest should
take into account the cumulative impact from repeated peaks over an entire growing season.
In 1982, another group performed an experiment assessing the importance of peaks (ambient versus
uniform). They examined two exposure profiles: a 6-hr episodic profile of varying peak frequency,
concentration, and duration, and a 6-hr profile of equivalent peak concentration and duration but constant
concentration. They found that bean plants exposed to ozone with a simulated episodic ambient
concentration distribution showed significantly more injury, less growth, and lower yield than those exposed
to an equivalent dose of ozone with a uniform concentration distribution (Musselman et al. 1983. J. Amer.
Hort Sci. 108:347-351).
The EPA Corvallis group (Hogsett et al. 1985. Atmos. Environ. 19:1135-1145) compared a 30-day episodic
profile of varying peak frequency, concentration, and duration, and a daily peak profile of equivalent peak
concentration and duration each day. They found that with regard to expression of ozone exposure, the 7-hr
means for these two profiles did not reflect the observed growth response. The episodic profile has the
smaller seasonal means, yet caused greater yield reduction.
With regard to the SUM07 (the sum of all hourly average concentrations greater than or equal to 0.07 ppm)
this group concluded that the same SUM07 exposure values for the two profiles did not reflect the observed
growth response. The episodic profile caused greater yield reductioa
i
Given the problem that the higher concentrations elicited more of an adverse effect than the mid-level or
lower concentration, over an ozone season (April-October), how does one cumulate the ozone exposure to
come up with a metric that represents the potential impact? Figure 4-5 shows a sigmoidal weighting function.
4-6
-------
Different Types of Exposures
ฃ
LL
ฃ
2000 n
1500-
1000-
500-
20 00-,
1500-
1000-
500-
Cusfer National Forest
Montana
1979
M7= 0.043 ppm
W126 = 13.2 ppm-h
25 45 65 85 105
Concentration (ppb)
Jefferson County
Kentucky
1965
M7= 0.042 ppm
W126- 31.1 ppm-h
5 25 45 65 85 105
Concentration (ppb)
Figure 4-4
4-7
-------
Differential Weighting
W126 Sigmoidal Weighting
0.000 0.015 0.025 0.035 0.045 0.055 0.065 0.075 0.085 0.095 0.105 0.115 0.125 0.135
Concentration (ppm)
Figure 4-5
4-8
-------
This W126 exposure index focuses on the higher hourly average concentrations, while retaining the mid- and
lower-level values. To determine the W126 index, the sigmoidal weighting value at a specific concentration
is multiplied by the concentration and then summed over all concentrations:
where M and A are arbitrary positive constants (4403 and 126 ppm'1 respectively)
Wj = weighting factor for concentration C,
C, = concentration 1 in ppm
The design of the weighting function was based on:
Truncating hourly average concentrations below 0.4 ppm
Having an inflection point near 0.065 ppm
Providing equal weighting of 1 for hourly average concentrations at approximately 0. 10 ppm and
above
Figure 4-6 shows how this index differs on an accumulation basis from the SUMOO, which is directly linked
to the average. The SUMOO spent too much time accumulating at the lower end of the distribution, and is not
weighting the area of interest biologically (peaks).
On the health side, Hazucha et al. (1992, Am. Rev. Respir. Dis. 146: 1487-493) found a greater effect for the
triangular shape compared to the square wave (Figure 4-7), showing the relevance of the mathematics and
developmental work on differential weighting. Hazucha et al.'s work illustrates the importance of the high
concentrations with respect to the mid- and lower level values.
43 STATISTICAL MODELS FOR ASSESSING DOSE-RATE EFFECTS
Paige Williams, Harvard School of Public Health
This presentation addresses the following question: If we have data for different combinations of exposure
duration and exposure levels, how do we extend the concept of a benchmark dose to account for duration of
exposure?
43.1 Background
Use of a benchmark dose approach has become popular in risk assessment for non-cancer endpoints. The
idea is to chose a dose-response model that reflects the probability of a toxic endpoint as a function of dose.
Because these models have been adapted from cancer risk assessment, they are usually just a function of
dose. For many non-cancer endpoints (e.g., in developmental toxicity studies), however, both the timing and
the duration of exposure are crucial to the levels and even types of outcomes. Because regulatory agencies
want to set exposure standards for varying lengths of exposure, the benchmark dose approach needs to be
extended to incorporate duration and possibly timing of exposure.
4-9
-------
Averages Versus W126
1.0
0.9
0.8
0.7
0.6
OJS
(U
O3
0.2
0.1
0.0
lilt
SUMOO
A W126
* Both
* ** A A A
I I I 1 I I
I 1 I
*
0.00 04)1 0.02 0.03 OJH O05 OOS 0.07 0.00 0.09 0.10 0.11
LeveJfppm)
Figure 4-6
4-10
-------
Square Wave Versus Triangle Exposure
105
103
g
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EXPOSURE TIME (hours)
Hazuchaetal. 1992. Am RevResp Dis 146:1487-1493.
Figure 4-7
4-11
-------
4.3.2 Ethylene Oxide Study
i
The goals of this study-were to:
Assess effects of EtO on developmental toxicity
Evaluate applicability of Haber's Law
Develop risk assessment methods for dose-rate studies
Improve approaches for designing dose-rate studies
. ,i !!! . : ; ' ; f : . i1"1
The study design included three multiples of C * T; within these three multiples (control, 2100 ppm hours,
and 2700 ppm hours) the researchers looked at various combinations of C * T that gave the same overall
cumulative exposure, this allowed an assessment of any departures from Haber's law, e.g., the percentage of
deaths was much higher for short acute than longer chronic exposures, for the same C * T value..
!j . .
Figure 4-8 shows the four steps usually used for calculating a benchmark dose (without regard to duration).
The two methods for computing the lower confidence limit are shown hi Figure 4-9. Figure 4-10 shows the
four steps for extending these models with information on combinations of dose levels and duration of
exposure.
The extended models fit better than Haber's model; the coefficients for time were always negative, indicating
that short acute exposures were causing more damage than longer chronic exposures (Figures 4-11 to 4-13).
I
Calculating the effective dose/duration contour is described in Figure 4-14. Figure 4-15 shows that the
extended model allows for extra effects of duration in addition to cumulative exposure. At very high levels of
exposure, the allowable duration is much shorter for the same level of toxicity than under Haber's model.
To calculate the lower confidence contours, either the lower effective dose or benchmark dose approach can
be extended to account for duration of exposure (Figures 4-16 and 4-17). One problem with this is the
simultaneous inference problem, described in Figure 4-18.
A final topic is accounting for multiple outcomes found in many non-cancer studies. Figures 4-19 and 4-20
outline an approach that takes into account fetal death and fetal malformation and extending that to a
situation where both dose and duration information is available. With multiple outcomes and multiple
covariates, one has to solve for the effective dose iteratively by computer. The extended dose/duration
contours (Figure 4-21) show the need to be much more conservative hi the risk assessment than just choosing
the most sensitive endpoint.
Other applications have included ordinal outcomes, continuous outcomes, and joint assessment of discrete
and continuous outcomes. Further research needs include deriving lower confidence contours on EDDC and
issues of optimal study design.
4-12
-------
'August 1998
C x T Workshop
Risk Assessment for Non-Cancer Endpoints
Benchmark Dose Approach
(1) Choose a dose-response model
Logit:
Probit:
In General: P(D) = T(a +.(3D)
P(D) =
P(D) = $(a + {3D)
(2) Fit model, using GEE's if necessary to account for litter effect
and/or correlation between .multiple responses
(3) Estimate effective dose-(ED) - the dose which results in a
specified increase in risk over background.
Excess Risk Functions:
Additive: r(D) =
Relative: r(D) =
P(D) - P(0)
~ P(0)
1 - P(0)
Ex: For a 5% excess risk of an adverse response (EDos), r(D) = 0.05.
\
(4) Compute lower confidence limit on effective dose to get the
benchmark dose (also referred to as BMDL)
Figure 4-8
4-13
-------
August 1998
C x T Workshop
Computing the Lower Confidence Limit
Proportion of
prenatal death
P(0) + .05
P(0)
Upper confidence limit
P(d): fitted dose-response
LED BMD ED
05 05 05
Dose (mg/kg)
II ' ,'
Two Methods:
i , , ' . '
LED - Lower Effective Dose
!' . ij
estimate the variability of the predicted dose-response function to cal-
culate upper confidence limit on the entire curve, then find dose on
upper confidence limit curve corresponding to the excess risk.
ji
BMD - Benchmark Dose
estimate the variability of the predicted dose ED (eg. ED 10), and
calculate a lower confidence limit (proposed by Crump, 1984). One
potential problem is that it is possible to get negative values for the
BMD.
Figure 4-9
4-14
-------
Auzust 1998
C x T Workshop
Dose Response Models for Dose-rate Studies
(1) Dose-response models - a function of exposure level (D) and
duration of exposure (T).
Haber's Model:
logit(P(D,T)) -
Extended Model:
logit(P(D,T)) -
(2) Fit models to get "response surface"
If necessary, use GEEs to account for litter effects
(3) Effective dose/duration contour -
all possible combinations of D and T that jointly lead to a certain
increase in risk over background.
Additive Excess Risk Function:
r(D,T) = P(L>,T)-P(0,0)
(4) Calculate the "lower confidence contour" - i.e., the lower
confidence limit on the effective dose-duration contour
Figure 4-10
4-15
-------
August 1998
C x T Workshop
Example: Ethylene Oxide Study
Dose Response Models estimated using GEEs:
MATERNAL MORTALITY:
; , ' ' '' i
1 ' ' ' !
Haber's Model
logit(P(D, T}\ = -2.02 + 4.03xlO~4 D * T
" ,',:'' ' 'i 3
!l '
Extended Model
logit[P(D,T)] = -1.08 + -0.36T + 5.97xKT4D * T
i
Constrained Model
logit[P(D, T)] = -1,99 + -0.48 T5D + 11.6xlO-4 D * T
. . i
FETAL DEATH:
Haber's Model
logit[P(D, T)] = -1.99 + 3.88xlO~4 D * T
i ;
Extended Model
logit[P(D, T)] = -1.18 + -0.31 T + 5.64xlQ-4 D * T
', ..' !!
Constrained Model
logit[P(D, T)] = -1.99 + -0.48 T8D + 11.6xlQ-4 D * T
Figure 4-11
4-16
-------
August 1998
C x r Workshop
Ex: Ethylene Oxide Study
Fitted Response Surfaces for Fetal Death
under Haber's Model and Extended Model
Fetal Death
100
1200
Dose (ppi
Fetal Death
.100
1100 Ull
Dose (ppi)'
Habers Model
j'l.5 > Duration (krsl
Extended Model
4.5
1.5 J
-------
August 1998
C x T Workshop
Ex: Ethylene Oxide Study
Fitted Response Surfaces for Fetal Malformation
under Haber's Model and Extended Model
Habers Model
lalforiatioH
iflfl
Dose ippi)
jl.5 ' Duration (i'rsl
Extended Model
Halfonation
Dose (ppi)
(jl.5 ' Duration (hrs)
Figure 4-13
4-18
-------
August 1998
C x T Workshop
Calculating the Effective Dose/Duration Contour
In general, the excess risk function is
r(D,T) = P(D,T)-P(0,0)
4- faD + /32T 4- foD * T) -
and the effective dose/duration contour is the set of all
points (D,T) in the design space such that r(D,T) = 7
By fixing one of the two variables, we can solve for the other to get all
points on this contour (isobol); i.e., for a given T:
D(T) =
+
, (D,T)<=R
True contour: D(r,/?,7) Estimated contour: D(T,J3,~f)
Figure 4-14
4-19
-------
August 1998
C x T Workshop
Effective Dose-Duration Contours for Fetal Death
f L " , , , . . ,j| ,<
under Haber's Model and Extended Model
Effective Exposure/Duration Contour
Excess RisH.05
o -
?!
a1
Habers lodel
Extended Mode]
500 1000 1500
Exposure Level (ppm)
Figure 4-15
2000
4-20
-------
August 1998
C x T Workshop
Calculation of Lower Confidence Contours
for Combinations of Dose and Duration
Both the LED and the BMD approaches can be extended to account for
duration of exposure.
I. Extending the LED approach:
Find the upper 95% limit on the dose-duration response surface, and then
determine where this surface intersects .the 7-level increase in risk.
II. Extending the Benchmark Dose approach:
^^ !
Fix D, and calculate the.standard error of the estimated time point T on
the contour using the delta method
T f
/\
^
\
0=0
Repeat this for many values of D to get the lower contour:
(Or fix time, and calculate the standard error of -the estimated dose D on
effective dose/duration contour.)
Figure 4-16
4-21
-------
August 1998
C x T Workshop
Lower Effective Contours (LEC's)
using "fixed time" approach
Lower Effective Contours (LEG)
(Habersiodel)'
(]
V
1 1
1 50(1
i
lilt
i i
1500 ^ 20
Exposure Level (ppm)
, '' ,':: , "v ; i ;ii
Contour: Death: ED05 Death: LEC;"
Half: ED05 Half: LEC
Figure 4-17
4-22
-------
August 1998
C x T Workshop
Simultaneous Inference Problem
Usually we set z=1.645 for a one-sided 95% confidence interval.
However, the coverage probability of the lower confidence contour must
correspond to the entire true contour, and not just to -a single point.
Using z=1.645 will give coverage probabilities under 95%.
This was confirmed in simulation studies:
Type of Lower
Confidence Contour
Lower Effective Contour
Benchmark Fixed-Dose
Benchmark Fixed-Time
Mean Standard
Crossing Rate Error
19.2%
14.6%
5.1%
0.7%
0.7%
0.4%
How do we choose the correct critical value, z?
An approximation has been developed using Hotellings' Theorem on the
volume of tubes (Johansen and Johnstone, 1990):
a.
2 V2 ' - \w
dw)
This approximation can be used to:
'estimate z for a particular dataset
, estimate true Type I error (a) for a given z
Figure 4-18
4-23
-------
August 1998
C x T Workshop
Accounting for Dose-rate Effects
on Multiple Developmental Outcomes
Ryan (92) considered the hierarchical endpoints:
fc fetal deatli
" i
8 fetal malformation
If we define probabilities as a function of dose, D:
i
PFD(D] = Pr(fetal death)
: ! ' ' " / ',,!,' :,
PFM(D] Pr (malformation | no fetal death)
;,'.' '.' J , i |'
then Ryan showed that the overall probability of an abnormal event can
be written as:
i
Pabn(D} = l-[l-PFM(D}}(l-PFD(D}}
This same principle can be applied to assess dose-rate effects on abnormal
Outcomes, as follows:
Pabn(D, T) = 1 - [1 - PFM(D, T)} [1 - PFD(D, T)}
Figure 4-19
4-24
-------
August 1998
C x T Wbrfcsfiop
Effective Dose-Duration Contours
with Multiple Developmental Outcomes
Single Outcome, Single Covariate:
P(d) = F(a + (3d]
'- + (3d)~
r(d] = P(d) - P(0) =
ED =
7
Single Outcome, Multiple Covariates:
P(d,t) =
r(d,t) =
_ .F-1 [7 + ^(q)] - (q + A t)
Multiple Outcomes, Multiple Covariates:
P(d, t) = 1 - [1 - PFA/(d, t)] [1 - PFD(d, t)]
r(d, t) = P(dt t) - P(0, 0)
t + c * -
*t) FM(am + /3lmt + /32m d*t)
Effective dose cannot be expressed analytically, but can be calculated using an
iterative approach.
Figure 4-20
4-25
-------
August 1998
C x T Workshop
Ethylene Oxide Study
Effective Dose/Duration Contours: Habers Model
H
III 'Ylll
' " ' "
. . ED1J " '"""
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De?elw)ieital"6utcoie'"~' Fetal Death "-lalioriatioE
Figure 4-21
1881
Abaonal
4-26
-------
5.1
SECTION FIVE
PLENARY PRESENTATIONS:
DOSIMETRY AND MECHANISTIC MODELING
DOSIMETRY: MECHANISTIC DETERMINANTS OF EXPOSURE-DOSE-RESPONSE
Annie Jarabek, EPA National Center for Environmental Assessment
Often we don't have the database on the anatomical, physiological, and biochemical parameters needed to do
a truly comprehensive and mechanistically based dosimetry model, so we are often looking at correlations or
empirical descriptions of processes we think are important. The dosimetry model is central (Figure 5-1) to in
informing the dose-response model. This dosimetry modeling approach is iterative and often points to
additional data that might be needed and thus can be used for experimental design. Case studies with
particles and gases will be presented to illustrate the approach.
Figure 5-2 presents another way to look at the exposure-dose-response schema, as presented in Section Two,
and identifies parameters and processes familiar to experimentalist. Note that defense mechanisms are
described at various levels of organization from molecular to whole organ. The pharmacokinetics component
needs to be linked to the pharmacodynamic component. With respect to the vinyl acetate model discussed
previously, it was generally accepted that acetic acid was the important metabolic product of metabolism, but
one could say that the pH could be the response or a dosimeter; clearly more insight is needed in the
pharmacodynamic arena.
Figure 5-3 shows the HEC default approach for particles. The fractional deposition is important because the
same aerosol distribution will deposit in a human versus a rat in a much different fashion (Figure 5-4). The
deposited fraction is an integration of deposition efficiency and inhalability. hi rats, the deposition fraction is
about .45 whereas it is almost 1.0 in humans. This has important implications for interspecies extrapolation
and biologic plausibility. For example, rats may have to be exposed to much higher concentrations in order
to get an inhaled dose sufficient to manifest toxicity. The model currently used for upper respiratory tract
model in the rat is empirical, as is the International Commission on Radiation Protection dosimetry model for
humans
Figures 5-5 through 5-7 show how mechanistic models can start to impart important nonlinearities (e.g.,
nonlinear pattern of the deposition fraction) that might not be considered when just looking at the exposure.
Note the influence of activity pattern on ventilation rate or differences in environmental aerosol particle
distributions (Phoenix versus Philadelphia) on the resultant mass deposition fraction hi each region. Mass
deposition fraction may be an appropriate metric to characterize acute effects, but differences can be
introduced by how it is normalized. Figures 5-8 and 5-9 show mass of particles deposited per surface area or
mass normalized to lung tissue. Figure 5-10 shows particle number fraction deposited versus aerodynamic
diameter, a different relationship than shown with mass. Mechanistic insight needs to help inform the choice
of these various dose metrics. For chronic endpoints, we need to incorporate additional parameters in the
model, i.e. those that characterize clearance, to characterize retained dose (Figure 5-11). The simulation
showed the importance of getting a handle on the clearance parameters, and that one of the key uncertainties
of the model is the dissolution-absorption rate.
5-1
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Figure 5-6
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Figure 5-10
Retained Dose
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(b) Phoenix
Total
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Figure 5-11
5-10
-------
To illustrate similar considerations for gases, a simulation exercise was conducted exploring C * T
relationships for dichloromethane (DCM) and perchloroethylene (PERC). These two were chosen because
they differ in both key physicochemical parameters (e.g., fat-blood partition coefficients of 6.19 versus 121.0
for DCM versus PERC) and metabolic parameters (e.g., Vm of 11.54 versus 0.18 for DCM versus PERC)
that would likely influence dose-duration relationships of different metrics. Figures 5-12 to 5-16 illustrate
output for seven different simulations that have an equivalent C * T exposure product For example, a 0.5-hr
exposure at 400 ppm, a 4-hr exposure at 50 ppm, and an 8-hr exposure at 25 ppm are simulations that have
an equivalent C * T exposure product of 200 ppm-hr. If "Haber's law" held men the plot of C x T products
versus T would be a straight horizontal line.
Mechanistic modeling will inform default approaches in the future by serving as templates to determine the
key processes and parameters and influence the next generation of dosimetry modeling.
The advantages of the PBPK/dosimetry modeling approach include the following:
It allows integration and extrapolation using diverse data.
It predicts complex kinetic behavior.
It has the capability to "lump" or "split" model structure to explore dose-response.
It enables interspecies dosimetric comparisons.
It allows parameter scaling across species.
It facilitates hypothesis generation.
It identifies needed areas of research.
5.2 DOSIMETRY AND MECHANISTIC MODELING
Harvey Clewell, ICF Kaiser
The role of mechanistic modeling is to define the relationship between external concentration or dose and an
internal measure of biologically effective exposure in both the experimental animal and the human. Tissue
dose equivalence is an underlying assumption of dosimetry in risk assessment:
Effects occur as a result of tissue exposure to the toxic form of the chemical.
Equivalent effects will be observed at equal tissue exposure/dose when measured by the appropriate
metric.
Dosimetry provides an adequate basis for identifying pharmacodynamic differences.
Reasons for understanding dosimetry in looking at concentration-time relationships include:
Nonlinear uptake, metabolism, or clearance
Toxicity associated with products of metabolism rather than the parent chemical
Tissue interactions (depletion of critical resources, induction of clearance/repair)
To demonstrate the importance of understanding dosimetry when trying to interpret concentration-time
relationships, the following example shows the impact of saturable metabolism on the apparent
concentration-time (C x T) relationship for the production of carbon monoxide (CO) from two
dihalomethanes: dichloromethane (DCM) and bromochloromethane (BCM). The PBPK model used in this
5-11
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example1 not only tracks the parent chemical but also the CO produced by metabolism. Two metabolic
pathways are described: a saturable oxidative (P450) pathway which produces both carbon dioxide (COj) and
CO, and a first-order glutathione conjugation pathway which produces only CO2. The metabolite model
includes a fairly complete description of the fate of the CO produced and is able to predict the fraction of
hemoglobin bound up as carboxyhemoglobin at any time, as determined by the current rate of CO production
(and inhalation of CO, if appropriate), the competition of oxygen and CO for hemoglobin, and the rate of
exhalation of unbound CO. This PBPK model has also been successfully applied to a variety of exposure
routes: inhalation, intravenous, and oral.
The ability of the PBPK model to describe the kinetics of CO exposure is shown in Figure 5-17, which shows
the model predictions (lines) and experimental data (points) for blood carboxyhemoglobin (HbCO) levels
from human volunteers exposed to CO at various concentrations. Figure 5-18 shows the concentration-time
profile for CO exposures associated with an HbCO level of 40%, as predicted by the model (solid line). The
profile for C x T=800 ppm-hours is also shown (dotted line) for comparison. It can be seen that
extrapolation on the basis of C x T is reasonably accurate, although it tends to be overconservative at long
durations and under conservative at short durations.
The situation is quite different, however, for the production of CO from dihalomethanes. Figure 5-19 shows
the PBPK model predictions (lines) and data (points) for short duration (4-hr), high concentration exposures
ofratstoDCM. The shaded area indicates the exposure period. Due to saturation of metabolism,
approximately the same blood HbCO levels are achieved at concentrations of 200 and 1014 ppm; however,
due to post-exposure metabolism of the unmetabolized DCM stored in the fat, this HbCO level was
maintained for a period of time after the exposure was terminated, particularly at the higher concentration.
Thus the C x T relationship for CO toxicity from DCM exposure is greatly distorted by the impact of
nonlinear metabolism.
Similarly, Figure 5-20 shows the predicted (lines) and experimental timecourse for blood HbCO levels in rats
following a very short (1/2 hr), very high concentration (5000 ppm) exposure to BCM (triangles) or DCM
(circles). Again, the shaded area indicates the exposure period The higher peak HbCO level for BCM
reflects the higher capacity of P450 metabolism for this compound, while the longer post-exposure
metabolism of BCM reflects its greater tissue solubility (resulting in greater storage). Thus the extent of the
impact of nonlinear metabolism on the apparent C x T relationship is chemical dependent
Figure 5-21 shows that for sufficiently high concentrations of another compound, halothane, the amount of
metabolism which takes place after the cessation of a 4-hr exposure (as measured by the production of
bromide) can actually equal the amount that takes place during the exposure. Figures 5-22 and 5-23 show
PBPK model simulations of the same scenario for two different compounds, vinyl chloride (VC) and carbon
tetrachloride (CC14). Note that in the case of VC (Figure 5-22), the low tissue solubility of the chemical
prevents significant post-exposure metabolism, so that the amount metabolized 24 hours after the beginning
of a 6-hr exposure is not much greater than the amount metabolized immediately after the 6-hr exposure. At
high concentrations of CC14, however, the amount metabolized in one day for a 6-hr exposure is almost as
much as the amount metabolized for a continuous exposure.
Andersen ME, Clewell HJ, Gargas ML, MacNaughton MG, Reitz RH, Nolan RJ, McKenna MJ.
1991. Physiologically based pharmacokinetics modeling with dichloromethane, its metabolite carbon
monoxide, and blood carboxyhemoglobin in rats and humans. ToxicolAppl Pharmacol 108:14-27.
5-17
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200 PPM MM INHALATION RAT
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240 4.W
HME-HOUHS
TIME-HOURS
Figure 5-19
5-20
-------
SOOO PPM DCM-BCM INHAUTION BAT
1S.P-1
to
3.75-
0.00
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TIME-HOURS
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5-21
-------
PCSSt-EXPdSURE METABOLISM
24 hr
200 4OO 6OO BOO 100O 120O 140O 1600 18OO 2OOO
Figure 5-21
5-22'
I
-------
VINYL CHLORIDE MONOMER
15.0-1
0.00-
(6 HR: 24 HR}
(6 Hfl: 6 HR)
12500
EXPOSURE CONCENTRATION
(PPM)
2500O
Figure 5-22
5-23
-------
CCI
4.OCH
(24 HR: 24 HR)
(6 HR: 24 HB)
(6 HR: 6 HR)
1500 3OOO
EXPOSURE CONCENTRATION
(PPM)
Figure 5-23
5-24
-------
Finally, it should be noted that the impact of post-exposure metabolism will, in general, be different for
human exposures as compared to similar rodent exposures. Figure 5-24 shows the PBPK simulation for the
case of CC14; the longer time-constants for chemical kinetics in humans lead to a very different timecourse for
the chemical compared to the rat
The appropriate dose metrics will vary depending on the toxicity and the chemical:
Stable chemicals: area under the curve (C * T)
Acute toxicity: peak concentration or Cn * T
Reactive intermediates: amount produced/tissue volume
U.S. EPA RfD guidelines: mg/kg body weight
U.S. EPA RfC guidelines (Category 3 gas): duration-adjusted exposure concentration (x ratio of
partition coefficients if >1)
Mode of action is important in choosing an appropriate measure of tissue dose:
Parent chemical vs. stable metabolite vs. reactive intermediate
Reactivity vs. physical effect vs. receptor occupancy
Cumulative vs. rapidly reversible
As depicted in Figure 5-25, for the case of trichloroethylene (TCE), multiple dose metrics may be required for
different toxicities associated with the same chemical. For example, in the case of TCE, acute toxicity might
be produced by the parent chemical and the best metric might be either the maximum concentration (C^ or
area under the concentration curve (AUC) for TCE. However, other toxicities of TCE might be the result of
the activity of one of the stable metabolites of TCE, such as dichloroacetic acid (DCA), trichloroacetic acid
(TCA) or trichloroethanol (TCOH). The best metric in such cases might be a measure of exposure of the
target tissue to that metabolite, whether the Cmax, AUC, or time above a critical concentration (TACC).
Finally, some toxicities might result from reactive species produced during metabolism, in which case an
appropriate measure might be the rate of production of the reactive species divided by the volume of the
tissue into which they are produced.
Dosimetry for developmental toxicity is even more complicated due to the need to consider the relationship of
the exposure period to the time course of pregnancy and gestation. The importance of this relationship can be
demonstrated in the case of methylmercury (MeHg). Figure 5-26 shows the ability of our PBPK model of
MeHg to simulate the time course for maternal and fetal MeHg levels associated with an acute exposure of
the mother to MeHg from contaminated grain.2 Figure 5-27 shows simulations performed with this model to
evaluate the impact of the timing of the exposure with respect to the pregnancy. As can be seen in this figure,
the third-trimester fetal exposure to MeHg resulting from the same maternal exposure can vary by as much as
a factor of 3 depending on the relationship between the exposure and gestation.
2GearhartJM,CleweUHJ, Crump KS,Shipp AM, Silvers, A. 1995. Pharmacokinetics dose
estimates of mercury in children and dose-response curves of performance tests in a large epidemiological
study. Water Air Soil Pollut 80:49-58.
5-25
-------
Rats
Time (Days)
Figure 5-24
5-26
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Figure 5-26
5-28
MI i llf liihi
-------
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500
3rd Trimester Fetal
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100 200 300
Days Since Conception
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5-29
-------
.
To demonstrate the importaoce of selecting an appropriate dose measure based on the mode of action for the
toxicity of concern, PBPK models for TCE and VC were exercised to determine general expectations for the
crbfs-specics dosinetry for one class of chemicals, the volatile lipophilic solvents.3 All three of these
chemicals would Be considered Category 3 gases (relatively water-insoluble chemicals which achieve a
steady-state during inhalation exposure) in the EPA (1994) dosimetry guidelines. In a standard risk
assessment, the animal-to-human dosimetry adjustment for each of these chemicals would be performed in
exactly the same way, based on time-weighted average (TWA) exposure concentration for inhalation and on
mg/kg/day administered dose for oral exposure, regardless of the nature of the toxic endpoint or the
mecljanism of tojacity. As shown below, the correct relationship for cross-species dosimetry depends on
whether die toxicity is due to the parent chemical or a metabolite, and in the case of toxicity from a
me|abqlite, .whether the metabolite is reactive or stable. Moreover, the nature of the cross-species
relationship for each ol 'these possibilities is different for oral exposure than for inhalation. Therefore,
pharmacokiiietics modeling is required to provide accurate cross-species extrapolation that considers the
nature of the toxic entity.
, ,: i|
For Inhalation exposure:
Acute toxicity due to parent chemical (TCE, VC)
PBPK HEC similar to default
Chronic tpxicity due to reactive metabolite (VC)
- to 25-fold higher than default
Chronic toxicity due to stable metabolite (TCE)
PBPK similar to 10-fold lower than default
For oral exposure:
Acute toxicity due to parent chemical (TCE, VC)
PBPK human dose 10- to 100-fold lower than RfD default
* Chronic toxicity due to reactive metabolite (VC)
PBPK human dose similar to RfD default
Chronic toxicity due to stable metabolite (TCE)
PBPK human dose 15- to 60-fold lower than RfD default
Conclusions regarding use of mechanistic modeling in dosimetry include:
"I ,, 1 ,11' ' , ,' ' i - , -
Advantage: Provides broader selection of potential dose metrics (including stable and reactive
metabolites).
3Cleweli HJ m, Gentry PR, Gearhart JM. 1997. Investigation of the potential impact of benchmark
dose and pharmacokinetics modeling in noncancer risk assessment. J Toxicol Environ Health 52:475-515.
5-30
-------
Greatest impact: Accounts for nonlinearities in uptake, metabolism, and clearance that alter the C
T relationship.
Challenge: Selection of dose metric^/- each toxic endpoint is based on presumed mechanism of
action and available experimental data (whether using default or mechanistic dosimetty).
Caution: When incorporating dosimetty, it is important to considering pharmacodynamics
(progression, compensation, repair) to produce the appropriate duration adjustment
5-31
-------
11111,,, 'I'll
-------
6.1
SECTION SIX
PLENARY PRESENTATIONS:
IMPLICATIONS FOR RISK ASSESSMENT
IMPLICATIONS FOR RISK ASSESSMENT
Steve Rappaport, University of North Carolina
Risk assessment represents the quantification of the exposure-risk relationship. It can be done in a variety of
ways (epidemiology, toxicology with extrapolation, deterministic models, guessing). We should use human
data for risk assessment whenever possible. Poor human data is arguably more valuable than really good
animal data for risk assessment.
Exposures in human beings vary widely. Figure 6-1 presents data from a longitudinal study over 1 year of
occupational exposure to styrene. Levels varied 100- to 1,000-fold between workers and within workers over
time.
The C x T debate relates to the time series of exposure and tissue levels. Do peaks of exposure influence the
rate at which the chemical is absorbed into the body, metabolized, etc.? Haber's law relates to the saturable
processes. At low levels of exposure, these tend to be linear and Haber's law is approximately true; Haber's
law is never true at high levels.
We can also distinguish between acute and chronic effects. Doing risk assessment for acute effects is
problematic. The episodes that generate very high levels (excluding irritation as an endpoint) are likely to
result from unusual accidents in occupational settings or catastrophes in ambient settings, and it is difficult to
model the behavior in such events. In these situations, risk assessment becomes surreal because nonlinear
models are unstable since the output depends largely on initial conditions. Therefore, the focus should be on
preventing the accidents and catastrophes (the source rather than the receptor).
For chronic effects, the C * T debate becomes more interesting. We can investigate it with a combination of
mechanistic and kinetic models plus biomarkers. Interindividual variability becomes important, because the
rates of uptake, bioactivation, detoxification, repair, etc. tend to be specific to particular individuals. Figure
6-2 presents a simple model relating exposure to tissue burden. Figure 6-3 shows this relationship in a
hypothetical example of a realistic exposure scenario. Substances producing chronic effects include slowly
eliminated substances, such as lead (Figures 6-4 and 6-5) and rapidly eliminated substances such as styrene
(Figures 6-6 and 6-7). In both of these examples, evidence of linearity between tissue levels and external
exposure is evident.
In summary, we should focus the Haber's law debate on substances that produce chronic effects. We also
should differentiate between substances that are slowly eliminated (for which the C * T debate only exists
only over very long time periods) and those that are rapidly eliminated (for which the C x T debate becomes
more interesting, and for which we should use human data and biomarkers to get at the linearity of these
processes).
6-1
-------
Exposure varies over time and between persons
Example: Workers exposed to styrene in a boat plant
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Figure 6-1
6-2
-------
RISKS OF CHRONIC DISEASE
If Hater's Law holds: Dose and risk are proportional to cumulative
exposure
For one person exposed to time series {X<}:
Uptake Elimination, metabolism, etc.
{Zero Order) (First Order)
KQ K-J
> Subsequent time series
EXPOSURE BURDEN
RK0
t ~~tL
Increment from * Residual burden from
current exposure prior exposures
Cumulative Exposure (CE):
o
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Note that for a population exposed to {Xt}, ko and ki are random
variables!
Figure 6-2
6-3
-------
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6-4
-------
Exposure To Inorganic Lead Among Alkyi-Lead Workers
Rappaport, SM . Ann. Occup. Wyg, 35, 61-121, 1991). data
from Cope et al. A1HAJ 40, 372-379, 1979.
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Figure 6-4
6-5
-------
UPTAKE OF LEAD AMONG BATTERY WORKERS
RSR Rappaport SM (1995) TcodcoL Letter* 77: 171-182, data
from Hopkins DG, eฑ *L (1992) Be. J. lad. Mod, 49:241-248.
15 2t 25
In Air
Figure 6-5
6-6
-------
STYRENE-EXPOSED BOAT WORKERS
(Est mean values and SEs shown for logged data)
Exposure
8*
I
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Styrene in Breath
m , , i
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REfENTJOM OF STYRENE
TLV4WA ซ 85 mg/m*
TLV-STEL ซ170 mg/m*
w
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f: Rappaport SM Htfs} forfco/. tetfew 77rt7i.1ซi. Based upซi data from Pซfrซซซ >ซ.
al {iiซl M Areft. eceaa. ฃปซ*ปซ. ซซซป 6727-34, ซnd Yagtr J, ป ซซ (ซ93) Mutat
Figure 6-7
6-8
-------
6.2 INTEGRATION OF APPROACHES
Louise Ryan, Harvard School of Public Health
Given data from an ethylene oxide developmental toxicity showing strong effects of duration for a given C x
T fixed value (0,2100, and 2400), how can one fit a model that describes the data well and allows
extrapolation to regions not represented in the experimental design? Statistical approaches include:
Fit a generalization of Haber's law model, e.g., Y = 00 + ptC *T+ @2T
Fit series of models using different dose metrics. Pick the "best" according to some goodness of fit
standard (e.g., peak exposure, AUC, AUC above a threshold, etc.)
Attach weights to different concentrations and sum over time (Allen Lefohn's approach)
The limitations of statistical approaches include the need for a lot of design points and the lack of reliability
for extrapolating beyond the experimental design. A clear analogy exists with the classic problem of low-
dose extrapolation.
Mechanistic approaches emphasize the following:
Chemical properties
Understanding metabolic and elimination pathways
Identifying mechanisms of action
Accounting for factors such as breathing rates
Identifying the appropriate dose metric (blood or tissue concentration of parent chemical or
metabolite, either peak or AUC)
Limitations of mechanistic approaches include the need for extra information that is not always measurable
on the same animals, and the fact that mechanisms are not generally well understood.
To reconcile these perspectives, we can:
Use biological information to inform decisions about the appropriate class of statistical knowledge.
Use the biomarker paradigm.
To develop heuristics, considei: the simpler problem of using a biomarker to develop a more accurate model
for extrapolation to low dose. (Biomarkers or "quanta! intermediates" are things in between the pathway
from exposure to response; they can be indicators of the amount of exposure to the tissues, or of the amount
of response in the tissue itself.) Figures 6-8 though 6-12 show a hybrid biological/statistical model using the
biomarker paradigm.
Unifying the statistical and mechanistic models under the biomarker paradigm combines the best features of
both approaches. Statistical methods to incorporate biomarkers into risk assessment are relatively
undeveloped. Needs include a theory to properly account for uncertainty in predicting dose from exposure.
Aspects of the measurement error framework apply.
6-9
-------
Biomirkers in risk assessment
Consider
Pr(Adverse event|exposure X) =
I
Let Z be a bJomarker (say GSH level) and
.p(X, Z) = Pr(Adverse eventjX, Z\
* ',.'.. '
\- :,'. i If'
Ti lli.li
Function of interest, p(X\ obtained by inte-
II'
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A special case
Consider version of the 1-hit model
p(X,
- exp(a0
Integrating over distribution of Z yields
K*) = I
f(z\X}dZ
/
ซ/
where
is a function of X.
Depending on the relationship between Z and
J*T, the function modifies the dose response
model p(X) to a generalization of the simple
1-hit model.
Figure 6-9
6-11
-------
Maximum likelihood estimation
l contributions for three data settings:
i "i a
A: Just X arid y (outcome)
II
;,: B:'Just'3:'and Z
.: C: X, Z and Y
"* -ii1;
Classical measurement error formulation. Can
estimate model parameters from (1) A alone
or (2) A and B or C. Ideally all three, and the
more C the better!
Figure 6-10
6-12
-------
Notes and generalizations
Statistical approach is A only
Unlike biologically based models, does not
assume mechanism fully explained.
If outcome is independent of X given Z,
then mechanism is explained through Z
(biological model)
If Z is independent of X, then Z is unre-
lated to mechanism (statistical model)
X can be multidimensional (e.g. C and T)
Z can be multidimensional
Other methods besides maximum likelihood
- regression calibration from measurement
error context, Bayesian methods
Figure 6-11
6-13
-------
T"', I'flilliiil"1!! 'i'!'11! ,11 '"I!*: "IPIfi
Mpre on C x T
:,: ::: ;v; ' . i : ; : ' " . :/ ' ;' , '.,j
Suppose we want to use information on GSH
levels (Z) to construct p(C, T), the dose re-
sponse surface as a function of C and T
, !!
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Assume:
' '
il, i : " '"Ji;"
p(C,T,Z) = l-
Integrating over distribution of Z yields:
, T)' =
) T)
If c*i and a2 are zero, then Z "explains" the
dose-rate effect. If a3 is zero, then Z ex-
plains nothing and we are back to the statisti-
cal model
Figure 6-12
6-14
-------
SECTION SEVEN
FUTURE DIRECTIONS: WHAT SHOULD BE
ACCOMPLISHED IN THE NEXT FIVE YEARS?
The group discussed the merits of a range of steps to further clarify C * T issues. These included the
following:
Develop case studies, including some with less information but looking at a variety of mechanisms.
Consider whether the dose metric could change with the same endpoint, the same mechanisms and
the same chemical; consider where the point of activity is binding and the molecule has sites for
multiple ligands that bind cooperatively. One might finds a model that fits but has nothing to do
with the underlying mechanism. Hemoglobin-carbon monoxide is a possible choice, where the
mechanism is understood.
As we go below the whole-animal level, clearly designate what adverse effects are for mechanistic
modeling.
Look at pharmacodynamic modeling issues, thinking about how time works through those models,
and about what principles are at work and how they can be simplified to more widely applicable
pharmacodynamic models.
Find ways to encourage researchers doing mechanistic work to routinely collect data related to C * T
issues (e.g., examine endpoints at more than one dose or time). Place databases from published
research on the Internet. A lot of mechanistic data exists but the studies are not designed to look at
C x T issues.
Show that EPA will use time-dependent data in extrapolations rather than the default (as is occurring
with the HAPS rule); this will encourage industry to generate useful data in testing compounds.
Encourage industry to expand their notion of product stewardship, so that in evaluating the cost-
benefit of doing C x T for a certain chemical, determine if it will impact other chemicals in the same
category.
Ask those reviewing grant proposals to encourage researchers to generate C x T-related data.
Encourage this research (e.g., case studies) in ORD solicitations.
Determine how to incorporate these needs with constrained resources (e.g., one can gain dose groups
by using 28 animals per dose group rather than 30, but if the required dose group size is still
considered 30 in the risk evaluation, the tradeoff isn't worth it). EPA should consider balancing the
C x T issue against statistical power of the number of animals at each dose.
7-1
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.it t . ,JL; i i,,!i!i i, i, ', ii , ,i : , 'if: v ป. / ;|;,t., i,:,,!:-.1!,. ",.: , :,.. ,. , I,
Think about data we alreacfy have in different ways (as in William Boyes's worksee Section 3.3,
Figure 3-11); a lot can be learned by doing studies out of the literature.
..;. ' . ""'.,:'' ,""::: ;' ' - : " " " ;v' -, - , ^ , ' ' '" ii"":":':'1 -.' ., ^f11111',"" ' " "" ' '
Ask industry to publish data already available. It would be important to identify the kinds of
ฃj^ฃjjatj[oti neede
-------
SECTION EIGHT
SUMMARIES OF BREAKOUT GROUP DISCUSSION
This section summarizes the discussions that occurred in the breakout groups during one workshop. The
groups were encouraged to discuss the topics without attempting to reach a consensus. Therefore, individual
members of a group may agree or disagree with some of the specific statements in these summaries.
8.1 SUMMARY OF BREAKOUT GROUP ONE DISCUSSIONS
Resha Putzrath, Discussion Leader; Rory Conolly, Rapporteur; William Boyes; Harvey Clewell;
Gary Kimmel; Paige Williams
8.1.1 Relationship Between Concentration and Exposure Duration (C * T) and Toxic
Endpoint
The group discussed what issues should be considered in examining the relationship between the pattern of
exposure and response, i.e., whether the C x T = constant response relationship is valid (or sufficiently
valid). The following were considered potentially important:
Whether the endpoint was reversible or not reversible can affect the relationship.
Reversibility (of phannacokinetics or due to repair of damage) implies mat the exposure
history doesn't matter. This may mean that the momentary concentration at the target tissue
is more important than the exposure history.
- If the dose rate is greater than the repair rate, C * T = constant may apply.
- Above saturation or below a threshold, C * T = constant is not interpretable. The
assumption that C * T = constant implies a no-threshold model.
Accommodation or tolerance by the animal or system is likely to change the relationship between
exposure and toxicity.
Whether the chemical is rapidly cleared or biopersistent will affect whether some patterns of
exposure depart from C * 71' = constant.
Different relationships among exposure, concentration, and response might exist at different dose
levels.
Different relationships among exposure, concentration, and response might exist for acute exposure
versus chronic exposure to the same chemical.
Any nonlinearities in the dose-response relationship are important in evaluating the potential for
departure from C * T = constant.
8-1
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I
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11'-
The duration of exposure itself might affect the dose-response function. Over 8 hours during a
developmental study, the biological entity can be changing rapidly, i.e., major changes in the structure and
function of the organism can occur. Thus, during one exposure the entity has significantly changed.
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What does it mean to average exposure over those 8 hours? Should the comparison be to a 1-hr
exposure at; eiglit times the concentration at the beginning middle, or end of this time?
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Do all 1-hr intervals need to be explored (cf. limitations on funding and other resources, below)?
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Even though the organism changes, the mode of action may or may not be affected.
The group discussed the changes in experimental design that would be necessary to explore the relationship
between duration or pattern of exposure and toxicity. Procedures that might enhance our understanding prior
to additional experiments were also addressed.
* Experimental conditions for exploring the relationship between exposure duration and toxicity
require examination of numerous different combinations of concentration and time, including where
C x T - constant.
Such research is expensive, and funding is a concern.
Usually, it will be too expensive to determine empirically the shape of the complete C x T
surface for adverse endpoints.
The ejgperimental design might differ depending on whether the goal is understanding the
potential toxicity of the chemical over a full range of doses or establishing a regulatory limit
S"|oul| intermittent exposures be examined hi addition to continuous exposures?
We should consider using our resources to define mechanistic endpoints that determine
relationships between exposure, concentration, and response. Mechanistic data can be used
to develop models of the relationship between C and T.
We should encourage editors of journals to encourage authors to publish data that they
already have from their experiments which would assist in resolving these issues.
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0 What information is likely to already be available from existing experiments that
would be most useful for risk assessment?
o Shpulji theงe data, be available through the Internet?
What are the implications of archiving data electronically, given changing
i ::il
* software/hardware?
How could scientists be encouraged to include some additional data points hi their
experiments that would help resolve some of the issues regarding exposure pattern and
toxicity? Is there guidance we can provide prior to experiments as to what (limited)
additional information would be most useful for resolving this issue?
J
8-2
j|
-------
- The relationship between pattern of exposure and response will be a larger issue in the
future. We need to think about the issues now.
We might look to the pharmaceutical literature, in which it has been speculated that there might be
more examples of relationships between exposure duration and concentration resulting in specific
toxic effects.
- Pharmacologists, however, are typically concerned with a rather narrow range of responses.
The lower response levels of interest for regulatory purposes may not have the same
relationship.
- Some data exist in the literature, but full sets are unlikely for most chemicals.
- One observer reported that for one database of 3,900 papers on 40 chemicals, only 6 had
experiments that varied both concentration and time.
- Are data available from industry, such as the data obtained from the successful call for data
for examining the benchmark dose procedure?
What might we do given limited resources?
- Start with a conceptual model and design experiments to test predictions.
Use information about related chemicals that have been more extensively studied.
Characterize those areas of dose-response surface of regulatory interest and/or of use to
validate preliminary model, not whole C * T grid.
With regard to establishing relationships between exposure duration and time, the use of very high
doses in laboratory animal experiments may be of questionable relevance for human exposure.
- Relationships seen at high doses may not hold for doses of interest in risk assessments.
- Models may predict unexpected effects in low-dose region, e.g., the preliminary model for
the heat shock protein appears to predict responses below the control level at some low
temperatures.
8.1.2 Mechanistic Modeling
The group discussed how mechanistic models can significantly enhance our understanding of the interaction
between exposure duration, concentration, and response, as rate constants are usually built into mechanism-
based models.
The distinction between statistical and mechanistic models is not absolute. We are always limited by
data gaps and will use empirical or statistical procedures to fill those gaps. Similarly, the choice of
statistical model is often based on some assumptions about mechanism.
8-3
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Experimental data are needed to develop and validate the models. Developing mechanism-based
models depends on an understanding of the mechanism. It would be useful to develop guidance for
the, data needs of mechanism-based models.
PBPK H$deting allows us to sort out the pharmacokinetics. This is an essential basis for sorting out
thepharmaoxiynamics. Both are needed, and we need to start developing both, and iteratively work
toward the center.
Mechanistic research and development of models is expensive. If we want to be able to use it in risk
assessment, someone will have to pay for it.
Mechanistic models don't fully describe mechanisms, so they have correlational components where
parameErs are adjusted so that the model fits the data.
How do we determine the confidence or reliability of the values produced by PBPK modeling? What
How do we 4itermine what: is "similar" for scientific and/or regulatory purposes?
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How do we incorporate information from human exposure, including epidemiology?
How do we incorporate human variability into mechanistic models?
We need to consider positive effects as well as negative effects, e.g., tamoxifen, essential elements).
What is the threshold for scientific peer acceptance of mechanistic modeling for risk assessment? Is
it the same for regulatory acceptance?
i
The group discussed the differences between mechanism of action and mode of action. Without attempting to
craft definitions for both terms, the group settled on a functional use of mode of action, i.e., that knowledge of
it would provide sufficient information about the essential features of mechanism to be useful for risk
assessment. The goal is sufficient information from the mode of action to define the overall shape of the
dose-response curve (or surface).
The mode of action determining the dose-response curve might change from the exposure where data
are available to the exposure range of interest
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* Biochemical endpoints might be used to assist in evaluating the mode of action and thus toxicity.
j
Among biochemical changes, we have to define what is bad (adverse).
We need to recognize that there is a range of normal and that increases and decreases are
possible.
-' It is necessary to determine that the endpoints we can measure are accurately correlated with
the endpoint of interest.
8-4
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8.13 Statistical Modeling
Increased availability of computational resources allow exploration of more "what if' questions. Results
from such studies can enhance our understanding of when significant departures from C x T = constant may
occur and how to design experiments to determine the relationship between exposure duration, concentration,
and toxicity.
Computational resources are not a barrier. In contrast, experimental resources, e.g., number of
animals, can be an issue.
Statistical modeling can be used to assist in the design of experiments for the more efficient use of
animals (e.g., Williams, Kimmel). Pilot studies can be used in conjunction with statistical modeling
to target the best use of resources, e.g., where on the dose-response surface interesting changes may
occur.
Statistical modeling suggests that just using the most sensitive endpoint may be anticonservative.
Therefore, models that include all data (i.e., simultaneous effects) should also be considered.
- The dose-response for each endpoint should be evaluated before attempting to combine the
information.
- Only statistically and lexicologically significant changes should be considered for
combination.
- More endpoints are becoming of interest to regulatory agencies. How should we determine
how many (and which) should be combined into one risk assessment?
8.1.4 Dose Metric
Whether the C x T = constant relationship holds may depend on the metric by which both are measured.
f Dose metric can vary for the same chemical with a change in endpoint due to different modes of
action.
The dose metric can change with dose or duration, e.g., the accumulation of manganese over time
results in a change in mode of action.
Are we concerned with evaluating the relationship between exposure duration and concentration at
the level of exposure or at the concentration at the target tissue?
Should we consider models/metrics where there is an exponent on time, e.g., instead of Cn x T, why
not Cxi"?
Dose metric might change with dose, for example, if the mode of action involved (cooperative)
binding of more than one ligand to an active molecule, where number of ligands bound affected V,,,^
8-5
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A PBPCmodel might suggest the appropriate dose metric.
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'arjgui Jose metrics be used as surrogates for modeling when data are limited, e.g., when we
have information on dose metric for a related chemical?
Use of the area under the curve (AUC) above a critical concentration implies a threshold
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Various dose metrics should be considered in statistical models. The results of such evaluations can
aid in the design of future experiments to explore the relationship between C and T.
Use of the same dose metric from laboratory experiments in animals for human exposure assumes
that humans and animals are qualitatively similar with regard to pharmacodynamics.
Selection of a particular dose metric in a statistical exposure-response model has the advantages and
disadvantages of both the mechanistic and statistical modeling approaches. This approach can be
viewed as midway on the statistical-mechanistic continuum.
It is important to acknowledge that there may be alternative plausible models.
We need to examine many models, as the same data may fit various models if different dose
metrics are used
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Assumptions about dose metric, given sufficient fit of the data, may influence future
experimental design. Prior assumptions can affect models, e.g., models where the planets
moved around the Earth also fit the data sufficiently well.
I
It can be particularly difficult to distinguish among models when data are limited.
Pilฐt studies may assist by eliminating some models from further consideration.
8.1.5 Risk Assessment
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The current default assumption by EPA is that C*T= constant. Given that a model exists and is being
use"d, what is required to change the model? Regulators tend to be comfortable with what is being done and
often require a high level of proof for change. The level of proof may be greater if higher levels of exposure
would be aBowed, consistent with the desire to err on the side of protection We should consider an a priori
determination of what proof will be required, e.g., to demonstrate that the current model is not valid or that
anojher model .is more valid.. For the purpose of changing a default in general or in a particular case, is there
a neM to distingjiish between the new model being "correct" and its being "less wrong" than the current
mojjel, i.e., is it sufficient to establish that the proposed model is very likely to be more accurate than the
current model?
The C x 7! -constant adjustment should be recognized as a default with advantages but limitations.
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When can changes from the C * JT = constant assumption be justified \ฃ we don't have, at a
minfmimv, a good dosimetry model?
8-6
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The premise of the workshop, the draft paper, and most of the participants is that C x T = constant
is the current procedure used. Some potential exceptions that may be already employed were
mentioned.
- Exposure may be adjusted to internal dose based on amount absorbed across a barrier (e.g.,
skin) or presence of other materials (e.g., food for oral exposure). This returns to the issue
of whether the concentration is measured as exposure or dose to the target tissue.
- Interspecies extrapolations may be based on internal dose.
- . RfDs and RfCs assume that a threshold exists. How do we accommodate the C * T =
constant adjustment below a threshold?
When should we invest resources to investigate C x T relationships?
- Generally, we don't need to worry about deviations from the default assumption if the
extrapolation away from the data is small. The question was then raised as to how to define
"small".
- Clear evidence for departure from linear metabolism might be the basis for assuming a
departure from standard C * T = constant.
How can we provide guidance for experimental design and/or reporting of available data that would
be useful for other risk assessment purposes than those of interest to the experimenter?
A narrative, such as that being proposed for cancer risk assessment, might be useful for providing
information about issues on how exposure-patterns can affect toxic responses.
- A discussion could indicate the effect on the risk estimate of selecting among various models
or dose metrics (and their associated assumptions).
- The amount of confidence in various choices could be presented.
Many of the issues raised might be better examined with some case studies to deliberate.
8.2 BREAKOUT GROUP TWO
George Rusch, Discussion Leader; Edie Wetter, Rapporteur; Dan Costa; Dale Hattis; Harvey
Richmond; Louise Ryan
8.2.1 Endpoints of Toxicity
To generate discussion, the discussion leader described an experiment of dimethyl-carbonyl chloride in which
animals were exposed via inhalation for 180^6 ppm-hr, 30x6 ppm-hr/5 days and 1x6 ppm-hr/1 year. The
lethal concentration for each of these was 180 ppm-hr, 150 ppm-hr and 220 ppm-hr, respectively. Most
animals at 180 ppm-hr died of irritation whereas most animals at 220 ppm-hr died of cancer. Therefore, we
have two different endpoints of toxicity depending on the exposure pattern, while the total dose is almost the
same.
8-7
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This example generated discussion not only about the endpoints of toxicity but also about the mechanism of
actlgOj rnpdelinjkand"the relationship of Habeas Law to the endpoints. The group discussed whether a
tiii^oirf'esaste^ti^'scttmg;. It "was proposed that if there is irreversible damage one would not expect a
thr^ojki biologicalty, whereas if there was reversible damage a threshold could potentially exist. It was also
pro|k)ged that ifthere was no saturation in the system, one would expect Haber's model to hold. Members of
the group expressed several opinions on these questions.
The group discussed endpoints for respiratory toxicity. It was proposed that body temperature and heart rate
may be reliable endpoints for monitoring the effects from exposure over time because they reflect the direct
stress on lie animal. It was also mentioned that in inhalation studies it is important to account for ventilation
rate when computing the internal dose. It was mentioned that the type of analysis used for these studies could
be complicated if the animals are exposed repeatedly over time contrasted to a single high-level exposure,
since in the first case one would have to consider the body's potential to adapt. An example was provided
using sunburn as"the endpoint. People initially exposed to the sun will burn. If they wait a few weeks, they
will bum just as badly. If they are exposed to the sun again immediately, they will not bum as badly. The
body's ability to adapt changes the toxicity response to the exposure. This means one may not have the same
effect on the endpoint if exposed repeatedly over timeespecially if these time points are close. This is an
important characteristic to consider when defining the endpoints of toxicity.
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The group also discussed endpoints for developmental toxicity. The endpoints that would be expected to be
observed depend on the timing of the exposure during the gestational period. Therefore, the specific day of
exposure often is more important than the total dose received over several days.
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11111 ' '.'.'.:":: , ;, ' , '; ' , '' "" '. '"' , 'i;! ;l '" ', ! .,'!;' ',, :
Based on the discussions the group proposed a family of equations that would explain the concentration-time
relationship. The type of equations used will depend upon the type of exposure (e.g., repeated/continuous
exposure versus one time) and the timing of exposure (e.g., timing during the gestational process in a
developmental toxicity study). It was pointed out that the form of the model is specific to the endpoint
considered. In addition, the group discussed the importance of mechanistic information for low dose
extrapolation.
In discussion about the appropriateness of Haber's model, R = aC1 x 7* there was disagreement within the
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group as to whether this truly holds for a specific biologic phenomenon. The exponential model was
suggested as an alternative, potentially more biologically based model. An example with ozone was
mentioned where the diffusion process tends to follow the exponential model
The key points mentioned during the session included:
ป Toxicological endpoint is important in determining the model
Points to consider when choosing the endpoint
: ซ " threshold
saturation
- developmental
' -f systemic
ion
reversibility
8-8
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Importance of mechanistic information for low-dose extrapolation
Appropriateness of the R - aC? x T model versus the exponential model
Need for a family of equations to explain the C x T relationship
Generally, brief high-level exposures are more harmful than long-term exposures with the same total
dose.
8.2.2 Statistical Approaches
The ethylene oxide experiment done at the Harvard School of Public Health (Weller et al., submitted 1998)
was used as a guide to discuss statistical approaches and how best to incorporate the mechanistic information
into the models. In this study, animals were exposed via inhalation to various concentration and durations of
EtO on gestational day 7 (see Table 8-1 below). The maternal and developmental effects were assessed using
dose-response models to assess whether there was a difference in effects between short duration/high
concentration exposures and long duration/low concentration exposures.
Table 8-1
Experiment Design For EtO Experiment
ppm-hr
0
2,100
2,700
Concentration (Q
0
0
0
1,400
700
350
1,800
900
450
Duration (T)
1.5
3.0
6.0
1.5
3.0
6.0
1.5
3.0
6.0
Assuming that the mechanism of action and the correct mechanism of action were defined, the family of
equations^ andf2 were proposed. The first equation relates the concentration and times along with the other
mechanistic information to the appropriate internal dose metric as
8-9
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and the second equation
fi(C,T, other variables) ~* Dose Metric
f2 (Dose Metric) ~* Toxic Endpoint
relates the dose metric along with other potential covariates that might affect a toxic response allowing for the
uncertainly in the dose metric. This approach would use concepts similar to the measurement error
methodology. The group discussed whether specific mechanistic information could be chosen that would
most influence i|e amounj: of chemical that is ultimately absorbed in the body, and therefore, potentially be
associated with I toxic response. The consensus from tite group was mat this approach seemed reasonable;
however, there was some disagreement about the appropriate mechanism of action. The issues of statistical
design were discussed briefly. The design characteristics that were preferred were those with the most
precision of the coefficient of the dose metric in model^ and those with the most accurate risk assessment.
8.2.3 Dosimetry and Mechanistic Modeling
During this session the importance of selecting the correct dose metric, and understanding the mechanism of
action was stressed. An issue was raised about, whether there is a need to understand everything, i.e., the
whole black box, or whether some of the mechanistic information could be used in the approach proposed in
Section 8.2.2. This could include, for example, information on the absorption and distribution of the
chem|ca| and the receptor activation, DNA damage, and cell death due to the chemical. The group agreed that
it might not be necessary to understand the complete mechanism. An understanding of the details that would
allow an understanding of time/dose dependency would be adequate. Again, the ethylene oxide study was
used as a guide in these discussions.
8.2.4 Implications for Risk Assessment
The difficulties in risk assessment with respect to low-dose extrapolation and across-species extrapolation
were discussed. The, question was raised again as to whether all of the mechanistic information needs to be
known to provide more information to predict what to expect in the low-dose region of C*T design. The
following paradigm was proposed:
EXPOSURE
CORRELATIVE
INTERMEDIATES
TOXIC
RESPONSE
where all the mechanistic information may not need to be known but that some correlative/causal
intermediates would be known. These intermediates would potentially provide adequate information about
the effect of exposure on the toxic response and provide reliable estimates of the predicted toxic effect hi the
low-dose region.
8-10
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" .' ,; 'It1 i!1! ..: ."iiil1 ,::' :.
-------
Most of the subsequent discussion focused on a recommendation for immediate action that could be proposed
to the workshop participants to resolve some of the issues related to studying the OT relationships. The
group developed the following recommendation:
Look carefully at research that has been done on chemicals that are fairly well studied, such as EtO,
formaldehyde, and ozone.
Determine what needs to be done to fully study the OT relationship for these chemicals.
Apply the methodology proposed in the Statistical Approaches session (Section 8.2.2) to the data
from these chemicals.
Establish the appropriateness of the proposed approach.
The group stressed the importance of interdisciplinary communication in determining the appropriate
correlative intermediates and the importance of focused research.
8.3 BREAKOUT GROUP THREE
LorenzRhomberg, Discussion Leader; Ronald Wyzga, Rapporteur; Annie Jarabek; Allen
Lefohn; James McDougal; Stephen Rappaport
8.3.1 Dosimetry and Mechanistic Modeling
Mechanistic models should demonstrate qualitative agreement with biological understanding and quantitative
agreement with existing data. It is important that application of these models provide some quantitative
indication of how well they fit all of the existing data. Some measure of uncertainty should be given to the
estimated parameters and projections made from these models. Often applications of these models do not
distinguish between interpolation and extrapolation; it is important that such distinctions be made because of
the greater uncertainty associated with extrapolation. Mechanistic models can be particularly valuable in
undertaking sensitivity analyses to help indicate those variables/parameters that may be of greatest
importance in influencing model results. Future experiments can then focus upon obtaining more and better
data/estimates for these variables/parameters.
To date these models have largely been built using animal data. There is a strong need to develop human data
that can be used to define these models. The result will be more confidence in using these models for health
risk assessments.
There are two major components of these models: pharmacokinetics models and pharmacodynamic models.
To date there has been considerably more experience in developing and applying pharmacokinetics models,
and the value of these models is well-established. Pharmacodynamic models have not been well explored.
There is no reason to believe that such models are intractable, and resources need to be made available to
encourage the development and application of these models. This can only facilitate and improve the risk
assessment process.
There are two modeling approaches: statistical/empirical models and mechanistic models. Each has its
virtues and its disadvantages. It will be important to take advantage of the positive aspects of each type of
8-11
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model and the many them to the fullest extent possible. Close collaboration between biologists and
statisticians'
types of researchers.
Cross-species extrapolation is likely to vary with both concentration and duration; i.e., the same extrapolation
factor may not be equally valid for different exposure-duration combinations. These factors can also vary by
level of organization (e.g., cellular level vs. organ level) within a species; moreover, the phannacokinetics
and pharmacodynamic components associated with dose response may involve very different cross-species
extrapolation factors. An understanding of these, however, may facilitate the choice of factor for different
dose-response combinations and levels of organization.
' "l""l|li ll"1" ' " '" ' |M ' " ! ' "
It should be noted that the choice of dosimeter used in experiments or in modeling does imply some judgment
about mode of action. In cases of greater uncertainty alternative metrics should be considered.
We weed human exposure profile data. These data are very limited. The presence of these data could greatly
influence the choice of exposures in experimental protocols. Models could also be run (see the presentation
by Ifattis in Section 4.1) to indicate how their results may vary with alternative exposure profiles. These can
be vsry complex; for that reason, it is important to ensure that model considerations reflect reality and that
ensuing regulations are indeed protective (and not overly protective) for the exposures likely to be
encountered by real people in the real world.
Human ambient exposures are likely to be relatively low. At these exposures, phannacokinetics are likely to
be linear; hence these considerations may not be particularly important. Pharmacodynamic considerations,
such as tolerance, may be particularly important at these exposures. Hence these need special consideration
in risk assessments to be applied to ambient conditions.
83.2 Risk Assessment
i
Clearly more research is needed on the concentration-duration issue. There fs general agreement that we want
more accurate assessments, these need to lead to regulations protective of public health, but not overly
protective to the extent that large costs are incurred with little marginal public health benefit. It is assumed
that current regulatory approaches for risk assessment are conservative and protective of public health. If
anything, it is assumed that conservative assumptions are made to account for uncertainties associated with
our understanding of the exposure-health response relationship. This may not necessarily be true. In some
easel, particularly', when extrapolating from chronic exposures to acute exposures, the current approach may
^|g^pr^|ec^^^SU]^e<|i m iny case better d^ta/models wffl 1
and to regulations based on more science and less judgment More accurate risk assessments and less
uiKeXtainly will, in general, lead to less need for regulatory agencies to apply conservative safety factors or
models to set regulations, yet regulations can be equally protective. The increased information will simply
indicate that conservative assumptions need not be applied because there is less uncertainty. The possibility
of less onerous regulations should then provide incentives for those being regulated to support the generation
of better data and models. To ensure that this cycle operates well, participating parties need to operate in
good faith. Regulatory agencies need to be willing to relax regulations when less uncertainty allows them to
and they need to ^^^j^k ^s j^. ^ ^ regulated community, and the regulated community needs to
take steps to ensure that their research is of the highest quality and can withstand peer scrutiny.
8-12
-------
If C * T, pure and simple, does not work (is not appropriate), then resulting risk assessments become more
problematic. Current default methods often assume a simple C * T relationship. The inappropriateness of
this assumption can result in both overestimates and underestimates of risk, with the latter being more likely
when extrapolation is toward periods of shorter duration. It is important that the definitions of C and T be
clear. Regulations target external or ambient levels of C; biological definitions can vary considerably from
the concentration at the point of intake to the target organ concentration. The validity of the formula clearly
will depend upon the definition used, and when the best definition of C is not the regulatory definition,
appropriate adjustments need to be made. Similarly, alternative definitions of Tare possible. Some
regulations allow complete flexibility in the time average for a regulation (e.g. NAAQS); in these cases a
regulation can be set for the time averaging period that demonstrates adverse response in health research.
Other regulations do not appear to be as flexible (this could be by tradition or practice); flexibility is urged.
It is better to have regulations set for time averages for which we have health data than to set a regulation for
one time period with the intention of protecting for effects that occur at a very different time period.
Alternatives to the simple use of C * T require more information, which in turn can require money and more
time. If additional information is available, clearly it should be used to derive appropriate improvements to
the duration extrapolation procedure. If time and money are not of the essence, more data can be generated to
increase the confidence and precision of duration extrapolations. In some cases "reasoned guesses" can be
made about the appropriateness of the simple C * T relationship. For example, if a great deal is known about
one chemical, it may be possible to make inferences about another chemical. In other cases it may be possible
to suggest how pharmacokinetics may alter the relationship at the exposures of concern. In all cases,
including the default assumptions, it will be important to articulate the assumptions and uncertainties
associated with the resulting risk assessment.
8-13
-------
1 * if
1 I , II ,1 ' lU'lim !
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APPENDIX A
WORKSHOP AGENDA
-------
i"',. f,:,;
-------
tori
Unfed States
Environmental Protection Agency
Office of Research and Development
Workshop on the Relationship Between
Exposure Duration and Toxicity
Sheraton Crystal City
Arlington, VA
August 5-6,1998
Final Agenda
Workshop Chair: Louise Ryan, Harvard School of Public Health, Cambridge, MA
WEDNESDAY, AUGUST 5, 1998
8:30AM Registration/Check-ln
9:OOAM Welcome Remarks and General Introduction
Louise Ryan
9:10AM Introduction of Invited Experts
9:30AM "CxT": Historical Perspectives, Current Issues, and Approaches
Annie Jarabek, EPA/National Center for Environmental Assessment (NCEA),
Research Triangle Park, NC
10:30AM BREAK
10:45AM Plenary Presentations: Endpoints of Toxicity
Developmental Toxicity
Gary Kimmel, EPA/NCEA, Washington, DC
Dermal Toxicity
James McDougal, Geo-Centers, Inc., Wright Patterson Air Force Base (AFB), OH
Neurotoxic Effects of Trichloroethylene Inhalation as a Function of Exposure
Concentration and Duration, Target Tissue Dose
William Boyes, National Health and Environmental Effects Research Laboratory (NHEERL),
Research Triangle Park, NC
Respiratory Toxicity
Dan Costa, NHEERL, Research Triangle Park, NC
11:45 AM Observer Comments
12:00 NOON LUNCH
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WEDNESDAY, AUGUST s, isls (Continued)
1:00 PM
Breakout Group Discussions: Endpoints of Toxicity
!l 'ii'if Lii '! ilull L Jil 'i'i' U ill!1 Si Ml i"1.' ,1" !, '' ,iul,i ' ปซ, "ii!!,1 ', ' ,>!l!,,,!1|! '','ii1 I/M. Ill ill,'1' ' vili l'1'1 I1 Pill,"!'1:, ' 'i. if ':!','l!,',n,:, " ป. ' ', i
Group 1 Discussion Leader: Resha Putzrath, Georgetown R/s/c Group, Washington DC
Group 1 Rapporteur: Wory Conolly, ClKemical Industry InstituteoYfoxicolpgy (Ctit),
Research Triangle Park, NC
' ' iu ' ' ' |l
Group 2 Discussion Leader: George Rusch, AlliedSignal, Inc., Morristown, NJ
Group 2 Rapporteur Edie Weller, Harvard School of Public Health, Cambridge, MA
Group 3 Discussion Leader: Lorenz Rhomberg, Harvard School of Public Health, Cambridge,
GroupS Rapporteur: Ronald Wyzga, EPRl, Palo Alto, CA
III ill i Mil, 'Mir!
MA
'^jiljilfT,,,11111 ILilir'i1 ,''p,,'
'
2:30 PM Plenary Presentations: Statistical Approaches
What Can Mechanisms Tell Us About Modeling Dose Time Response Relationships?
'' OhiversSy, Worcester, MA [[[
' ,,' ........ , "n'liii: ' ' i'"'1". ii'1 :.,:' irfl "'!:' i : /I1,1, , ' ...... '; ..... : ' 'v,s| <;, iป ,[.'.;: " (.1 ' '<
CxT Issues Related to National Ambient Air Quality Standards (Eco Effects)
Alien Lefohn, ASL & Associates, Helena, MT
Statistical Models for Assessing Dose-Rate Effects
Paige Williams, Harvard School of Public Health, Cambridge, MA
II-III!
13 i il'-ii
3:30PM Breakout Group Discussions: Statistical Approaches
, I, ii i ill ii i ; , .iij"!!!'111!,,:;!,;:!!!;, ..""nn ; ", ., ii t > r i ' .. ' > , ; '..<.' ':,.,",< ..
Group 1 Discussion Leader: Resha Putzrath
Group 1 Rapporteur: Rory Conolly
Group 2 Discussion Leader: George Rusch
Grpup 2 Rapporteur: Edfe Welter
Group 3 Discussion Leader: Lorenz Rhomberg
Group 3 Rapporteur: Ronald Wyzga
5:OOPM ADJOURN
t H U R S DA Y, A U G U S T 6 1 99 8
I", 8:30AM^ Plenary Review of Breakout Group Discussions
9:30AM Plenary Presentations: Dosimetry and Mechanistic Modeling
Dosimetry: Mechanistic Determinants of Exposure-Dose-Response
i::"i:" AnnieJarabek '" \ " , _'" \ ' '^"' ..." 1^ , j
/' ;:",;,, , /,;::"" ':::; ,' ' :,,l":":l'. ' ; :,;;'":"::' '>.", , ' ;!,; '.|.,; "
Dosimetry and Mechanistic Modeling
-------
THURSDAY, AUGUST 6, 1 9 9 3 (C o n t i n u e d)
10:45AM Breakout Group Discussions: Dosimetry and Mechanistic Modeling
Group 1 Discussion Leader: Resha Putzrath
Group 1 Rapporteur: Rory Conolly
Group 2 Discussion Leader: George Rusch
Group 2 Rapporteur: Edie Weller
Group 3 Discussion Leader: Lorenz Rhomberg
Group 3 Rapporteur: Ronald Wyzga
12:OONOON
1:OOPM
LUNCH
Plenary Presentations: implications for Risk Assessment
Implications for Risk Assessment
Steve Rappaporf, University of North Carolina, Chapel Hill, NC
Integration of Approaches
Louise Ryan
1:45PM Breakout Group Discussions: Implications for Risk Assessment
Group 1 Discussion Leaden Resha Putzrath
Group 1 Rapporteur: Rory Conolly
Group 2 Discussion Leader: George Rusch
Group 2 Rapporteur: Edie Weller
Group 3 Discussion Leader: Lorenz Rhomberg
Group 3 Rapporteur: Ronald Wyzga
2:45PM Observer Comments
3:OOPM Plenary Review of Breakout Group Discussions
3:30PM Future Directions: What Should be Accomplished in the Next 5 Years?
Moderated by Louise Ryan
4:OOPM Closing Remarks
4:15PM ADJOURN
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APPENDIX B
CHARGE TO PARTICIPANTS
-------
:;;{:,.! i, , at
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&EPA
Unfed States
Environmental Protection Agency
Office of Research and Development
Workshop on the Relationship Between
Exposure Duration and Toxicity
Sheraton Crystal City
Arlington, VA
August 5-6,1998
CHARGE TO THE PARTICIPANTS
Background
Current risk assessment procedures are typically based on overall daily exposure levels, and tend to
emphasize effects resulting from continuous exposures over a lifetime. However, there has been an
increasing realization that exposures are more likely to be experienced as bursts or spikes, or
intermittent exposures of varying levels. The Agency's Risk Assessment Forum is beginning to examine
how dose-duration relationships are or can be incorporated into the risk assessment process for less-
than-lifetime exposures. As part of this effort, the Risk Assessment Forum, together with the Harvard
School of Public Health, is organizing a workshop to discuss our current understanding of dose-duration
relationships, the approaches that can be used in their modeling, the inclusion of these relationships in
risk assessment, and future directions in this area.
Charge to the Invited Participants
The objective of the workshop is to provide a forum for open discussion and to identify areas of
consensus, as well as areas of difference. Approximately one week prior to the workshop, each
participant will receive a working draft of an issues paper and a list of breakout groups. The paper is
intended to explore issues in the assessment of dose-rate effects in order to identify where the current
risk assessment approach may be improved and to identify gaps in our knowledge and methodology in
order to suggest areas of further research. During the workshop, several presentations will be made to
provide specific examples of the various issues that are defined in the paper. Every invited participant is
asked to read the issues paper prior to the workshop, and be prepared to discuss it and the issues
addressed in the presentations during the breakout group and plenary session discussions.
Each participant will be assigned to a specific breakout group. In making the group assignments, the
organizers seek to ensure a mix of expertise in each group. Each breakout group will have a discussion
leader to facilitate the discussion and a rapporteur to capture the discussions of the group. It is
important that each of you participate in the breakout group to which you have been assigned.
We look forward to your input and to a productive and enjoyable workshop.
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UP"!|
r !|p , n ll'i,
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APPENDIX C
LIST OF INVITED PARTICIPANTS
INVITED PARTICIPANTS' BIOGRAPHIES
LIST OF EPA PARTICIPANTS
LIST OF OBSERVERS
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Hi; .''|,!!..i !;'" I"""':!1!! 'i-'W ' 'I'ifWI I '
-------
AEPA
United States
Environmental Protection Agency
Office of Research and Development
Workshop on the Relationship Between
Exposure Duration and Toxicity
Sheraton Crystal City
Arlington, VA
August 5-6,1998
Final List of Invited Participants
Harvey Clewell
Senior Project Manager
K.S. Crump Division
ICF Kaiser, International
602 E. Georgia Avenue
Ruston, LA 71270
318-242-5017
Fax: 318-25^4960
E-mail: hclewell@linknet.net
Rory Conolly
Senior Scientist
Chemical Industry Institute of
Toxicology
6 Davis Drive
Research Triangle Park, NC 27709
919-558-1330
Fax: 919-558-1404
E-mail: rconoily@ciit.org
Dale Hattis
Research Professor
Center for Technology, Environment,
and Development
Clark University
950 Main Street
Worcester, MA 01610
508-751-4603
Fax: 508-751-4600
E-mail: dhaffis@clarku.edu
Allen Lefohn
President
ASL & Associates
111 North Last Chance Gulch
Suite 4A
Helena, MT 59601
406-443-3389
Fax: 406-443-3303
E-mail: ASL_Associates
ฉcompuserve.com
James McDougal
Senior Scientist, Gee-Centers, Inc.
Operational Toxicology Branch
AF Research Laboratory
2856 G Street (AFRL7HEST)
Wright-Patterson AFB
OH 45433-6573
513-255-5150 ext. 3182
Fax: 513-255-1474
E-mail: mcdougalj
ฉfalcon.al.wpafb.af.mil
Resha Putzrath
Principal
Georgetown Risk Group
3223 N Street, NW
Washington, DC 20007
202-342-2110
Fax: 202-337-8103
E-mail: rmputzrath@mindspring.com
Stephen Rappaport
Professor of Occupational Health
Department of Environmental
Sciences and Engineering
School of Public Health
University of North Carolina
C.B. #7400
Chapel Hill, NC 27599-7400
919-966-5017
Fax: 919-966-4711
E-mail: stephen_rappaport@unc.edu
Lorenz Rhomberg
Assistant Professor of Risk Analysis
and Environmental Health
Harvard University
Center for Risk Analysis
718 Huntingdon Avenue
Boston, MA 02115-5924
617-432-0095
Fax: 617-432-0190
E-mail: rhomberg@hsph.harvard.edu
George Rusch
Director of Toxicology and Risk
Management
AlliedSignal, Inc.
101 Columbia Road
Morristown, NJ 07962
973-455-3672
Fax: 97^455-5405
E-mail: George. Rusch
@AlliedSignal.com
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Louise Ryan
Professor of Biostatistics
Department of Bfostatfetical Science
Dana Farber Cancer Institute
Harvard University
School of Public Health
44 Binney Street
02115
EdieWeller
Research Associate
Department of Biostafetical Science
Dana 'F^rSer Cancer,, Institute ,
Harvard'gnjversfty ...................
Schoojioff'UDliciHea!& ..... |_"
44 Biriney"S"lreet ...............
Boston, MA 0211 5
617-632-2445
Fax:,6l7332-2444 ,,;:;:' ..... " ..... ; ; '
E-mai: ewdfer@jlnimy.harvard.edu
Paige Williams
Associate Professor of Btostafetfcs
Harvard University
School of Public Heafth
S77 Huntiington Avenue
Boston, MA 02115
; '817-432-3872
"'Fix:
Ronald ..... Wyzga
Senior Program Manager
Health Studies Program
Sectifc Power Researchlnstitute '
3412 Hillvfew Avenue
Palo Alto, CA 94304
650-855-2577
ill
rwyzfla@epri.c6m
I:!r -.( :V Uill
!
-------
United States
Environmental Protection Agency
Office of Research and Development
Workshop on the Relationship Between
Exposure Duration and Toxicity
Sheraton Crystal City
Arlington, VA
August 5-6,1998
Invited Participants' Biographies
HARVEY CLEWELL
As Senior Project Manager and Director of the Health Assessment Group at ICF Kaiser International Dr. Clewell is
reponsible for directing all pharmacokinetic modeling activities, as well as providing expertise on the application of
physiologically-based pharmacokinetic modeling in chemical risk assessment and pharmaceutical safety
assessment. Prior to working for ICF Kaiser, Dr. Clewell was the Director of the Risk Assessment Program for
ManTech Environmental Technology, Inc. at Wright-Patterson Air Force Base in Ohio. In this position, Dr. Clewell
was responsible for directing all risk assessment activities in support of Air Force and Navy requirements, as well as
providing expertise on chemical risk assessment to other corporate programs. Dr. Clewell has done graduate
course work in computer science and mathematics at the Air Force Institute of Technology. He received his MA. in
physical chemistry from Washington University and his B.A. in chemistry from Bradley University. Dr. Clewell has
authored numerous publications including: Applying Simulation Modeling to Problems in Toxicology and Risk
Assessment - A Short Perspective; Reanalysis of Dose-Response Data from the Iraqi Methylmercury Poisoning
Episode; Tissue Dosimetry, Pharmacokinetic Modeling, and Interspecies Scaling Factors; Use of Physiologically-
based PharmacokineSc Modeling to Investigate Individual Versus Population Risk and PharmacokineSc Dose
Estimates of Mercury in Children and Dose-Response Curves of Performance Tests in a Large Epidemiological
Study. Dr. Clewell is a member of the American Chemical Society and the Society for Risk Analysis. He is a
recipient of the Air Force Scientific Achievement Award and the Air Force Meritorious Service Medal.
RORYCONOLLY
For the past nine years Dr. Conolly has worked in the Risk Assessment Department and the Department of
Inhalation Toxicology and Biomathematical Modeling of the Chemical Industry Institute of Toxicology (CUT). Prior to
his work at CUT, Dr. Conolly worked in the Toxic Hazards Research Unit of NSI Technology Services Corporation.
Dr. Conolly received his Sc.D in physiology (toxicology) and his B.A. in biology from Harvard University. Dr. Conolly
has received the Outstanding Presentation in Risk Assessment Award at the Annual Meeting of the Society of
Toxicology. He is the Vice President of the Risk Analysis Specialty Section of the Society of Toxicology. Dr.
Conolly's publications include: A Physiologically-Based Pharmacokinetic Model Describing 2-Methoxyacetic Acid
Disposition in the Pregnant Mouse; Cancer and Non-Cancer Risk Assessment: Not So Different If You Consider
Mechanisms; A Strategy for Establishing Mode of Action of Chemical Carcinogens as a Guide for Approaches to
Risk Assessments; Improvements in Quantitative Noncancer Risk Assessment and Implementation of EPA Revised
Cancer Assessment Guidelines: Incorporation of Mechanistic and Pharmacokinetic Data.
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DALEHATT1S
i: 111' 1
I F.I!
Dr. HatBs is a Research Professor at the Center for Technology, Environment, and Development at Clark University.
He received his Ph.D. in genetics from Stanford University and his B A in biochemistry from University of California,
Berkeley. Dr. Haffis' research interests include methodologies for quantitative health risk assessment for cancer
and non-cancer health effects, pharmacokinetic and Monte Carlo simulation modeling, and health and economic
implications of alternative regulations controlling exposures to noise and lead. Dr. Hattis has authored over 1 00
articles Including \fajfebfijfy fa Susceptibility-How Big, How Often, For What ^g^^gg jo whaf Agents?; The
Importance of Exposure Measurements in Risk Assessment of Drugs; Uncertainties in Risk Assessment; and
Analysis ofDose/Time/Response Relationships for Chronic Toxic Effects - The Case ofAcrylamide. He is a
councilor of the Society for Risk Analysis, a member, editorial board for Risk Analysis, and a member of the
Massachusetts Department of Environmental Protection/Department of Public Health Advisory Committee on
Health Effects.
, , , . , . ,,; ....... ,... r .... . . ..... , , ::, . ...... . , , ;: ::,
Dr. Lefo|n, as president and founder of A.S.L. & Associates, directs a muiti-disciplinary staff which focuses on
providing solutions to a wide variety of critical environmental problems, such as the identification of the effects of air
pollutants on the terrestrial environment; the development of mathematical exposure/dose-response relationships
that describe agricultural crop yield and forest seedling growth as a function of air pollutant exposures; and the
characterization of air quality arid wet chemistry databases for eva)^^
environmental relationships. His research is directed at better uride^nding the quantification
between pollutant exposures and naturally occurring processes and the possible effects of air pollutants on human
health and the ecosystem. In addition, he was the lead author for Chapter 4 (Environmental Concentrations,
patlernii'and'"Exposure JEsfimates)',"He~wa's"aiso"th'e" co-author of a "section" in Chapter 5 focusing on ...... exposure- ...............................
response of the U.S7 EPA Air Quality Criteria for Ozone and the Photochemical Oxidants. From 1 971 through 1 979
Dr. Lefohn worked in a variety of capacities for the EPA in their offices located in Research Triangle Park,
\IVas,hjngton, DC, Corvallts, Oregon, and Helena, Montana. Dr. Lefohn is the chairman of the Science Advisory
Committee of the Center for Ecological Health Research at the University of California, Davis. Dr. Lefohn has
published over a hundred articles including The Difficult Challenge of Attaining EPA's New Ozone Standard;
Establishing Ozone Standards to Protect Human Health and Vegetation: Exposure/Dose-Response
Considerations; Developing Realistic Air Pollution Exposure/Dose Criteria for Ecological Risk Assessment(\n
Press); and Estimating Historical Global Sulfur Emission Patterns for the Period 1850-1 990 (Submitted). Dr. Lefohn
receive]! his PED. from trie University of California' , Berkeley and his "BJS.froni i the University of California, Los
Angeles. He is an executive editor of Atmospheric Environment.
JAMES MCDOUGAL " ' ' " ' '''' " ' ' ''' ' '"": ............. ......
- As Senfcr Scientfet In the Toxicology Division of Geo-Center's AF Research Laboratory at Wright-Patterson Air
Force Base, Dr. McDougal has done extensive research on dermal toxicology exposure and its relationship to
human health risk assessment. Significance of the Dermal Route of Expos'ureto pjsk Assessment, Physiologicaily-
based Pharmacokinetic Modeling inDermatotoxicology (chapters in the 4th, 5th and 6th editions), Comparison of
D^r^aiand Inhalation .Routes of Entry for Organic Chemicals, and Mechanistic Insights Aid in the Search forCFC
Substitutes; Risk Assessment ofHCFC-123 are among the numerous articles Dr. McDougal has authored. Dr.
McDougal received his Ph.D. in pharmacology from the University of Arizona Health Science Center. He received
his M,S, in addiction studies, and his B.S. in zoology/chemistry from the University of Arizona. Dr. McDougal is a
fully affiliated associate professor at the College of Medicine, Wright State University. He served as chairman of the
EPA Human Exposure Peer Review Committee, Human Exposure and Atmospheric Sciences Division, National
Exposure Research Laboratory. Dr. McDougal also served as consultant to the Air Force Surgeon General on
Toxlcolggy, as well as Air Force Technical Representative to the Committee on Toxicology, of the Board on
Environmental Studies and Toxicology, National Research Council.
Stir !
ft .'i1
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RESHA PUTZRATH
As Principal at the Georgetown Risk Group, Dr. Putzrath has developed innovative methods for toxicology
evaluations and risks assessments. She has evaluated toxic hazards and prepared risk assessments for hazardous
waste sites, consumer products, and other regulated chemicals including: methods for combining toxicity from
mixtures of chemicals and multiple routes of exposure for accurate characterization of total risk; analysis of
uncertainties; development of more accurate site- or chemical-specific risk estimates. Dr. Putzrath received her
Ph.D. and M.S. in biophysics from the University of Rochester, and A.B. in physics from Smith College. Dr. Putzrath
is also an Associate of the Department of Environmental Health Sciences, School of Hygiene and Public Health at
the Johns Hopkins University, where she developed and teaches a course on advanced topics in quantitative risk
assessment. Dr. Putzrath is a member of the Society for Risk Analysis (SRA) where she is the current President as
well as a founding member of the Dose-Response Specialty Group. She also co-chaired and organized a session
at the SRA annual meeting Time: The Forgotten Dimension of Risk Assessment". Dr. Putzrath is a member of the
Society of Toxicology and a Diplomate of the American Board of Toxicology.
STEPHEN RAPPAPORT
Dr. Rappaport is currently conducting graduate-level teaching and research in occupational and environmental
health. His research interests include the relationship between chemical exposure and disease (especially cancer)
and the development and evaluation of statistical approaches for evaluating exposures. Dr. Rappaport received his
M.S. in public health and a Ph.D. in air & industrial hygiene from the University of North Carolina. He received his
B.S. in chemistry from the University of Illinois. Dr. Rappaport has published.numerous articles, some of the articles
relevant to this project are: Compliance Versus Risk in Assessing Occupational Exposures to Chemicals; A
Lognormal Distribution-Based Exposure Assessment Method for Unbalanced Data; A Model to Estimate the
Delivered Doses of Substances in Liquid and Gaseous Phases; and Assessment of Long-Term Exposures to Toxic
Substances in Air. Dr. Rappaport authored the chapter Exposure Assessment Strategies in Exposure Assessment
for Occupational Epidemiology and Hazard Control. Dr. Rappaport is a member of the American Association for
Cancer Research and the American Industrial Hygiene Association. He is the North American editor for Annals of
Occupational Hygiene and he sits on the editorial boards of Occupational Hygiene: Risk Management of
Occupational Hazards and Biomarkers: Biochemical Indicators of Exposure. Response and Susceptibility to
Chemicals. Dr. Rappaport has been a consultant to EPA's Environmental Health Committee, Science Advisory
Board.
LORENZ RHOMBERG
Dr. Rhomberg is an assistant professor of risk analysis at Harvard School of Public Health. Prior to this, Dr.
Rhomberg spent ten years with the Environmental Protection Agency as a biostatistician in the Health Risk
Assessment Division. While at the EPA Dr. Rhomberg was chair of the Interagency Pharmacokinetics Group, chair
of the Pharmacokinetics Focus Group, member of the Federal Liaison Group's Committee on Risk Assessment
Methodology, National Academy of Sciences and co-chair for Pharmacokinetics, Research to Improve Health Risk
Assessment Program. Dr. Rhomberg is a consultant to the Presidential/Congressional Commission on Risk
Assessment and Management. Dr. Rhomberg has co-authored the book Low-Dose Extrapolation of Cancer Risks:
Issues and Perspectives, as well as writing several articles, including, A Survey of Methods for Chemical Health
Risk Assessment among Federal Regulatory Agencies; Use of Quantitative Modeling in Methylene Chloride Risk
Assessment; Risk Assessment and the Use of Information on Underlying Biologic Mechanisms: A Perspective;
Physiological Parameter Values for Physiologically-Based Pharmacokinetic Models; and Carcinogens and Human
Health.
-------
GEORGE RUSCH
As Director of Toxicology and Risk Management for the last 10 years, Dr. Rusch has been responsible for the
Toxicology program at AlliedSignal Corporation, including program development and management as well as long-
farm forecasting of corporate needs in toxicology. Dr. Rusch received his Ph.D. in organic chemistry from Adelphi
University, his MA In Biochemistry from The City College and his B.S. in chemistry from Hobart College. Dr. Rusch
hasauiiored numerous publications, including: The Determination of Permissible Exposure Limits for the Alternate
Fluorocarbons; The Use of Acute Data to Set Exposure Standards; Subacute and Subchronic Inhalation Toxicity of
Chtorofriiuoro&thyiene; and Quantitative Exposure of Humans to Hydrochlorofluorocarbon HCFC-141b (in press).
Dr. Rusch is the chair of the National Advisory Committee on Acute Exposure Guidance Levels for Hazardous
Substances. He also sits on the editorial board of Human and Ecological Risk Assessment Dr. Rusch was a part of
the U.S. EPA Review of Acute Risk Assessment Methods Workshop.
LOUISE RYAN
i i I'll!11!!". f : "!( , HI I,,:,, f'i! , Si; IMF",, '' ' ', :" :. ... !,, " '. ' " .'.:' ... , "I ,.,; ... !!. :' ... I" , 1.. '.I,
Dr. Ryan fe a professor in the Department of Biostatistics at Harvard School of Public Health and in the Department
of Biostatistics at the Dana-Farber Cancer Institute. She received her Ph.D. in statistics from Harvard University and
her B A in statistics from Macquarie University in Australia. Dr. Ryan's major research interests are statistical
methods in toxicology, multiple outcome models, repeated measures models, survival analysis and environmental
epidemiology! She is a fellow of the American Statistical Association and a member of the International Biometrics
Society and trie Teratology Society. Dr. Ryan is editor of Biometrics Shorter Communications and for six years she
was an associate editor of Journal of the American Statistical Association: Applications and Case Studies. Dr. Ryan
has authored numerous articles, some of her articles relevant to this task are Statistical Issues in Assessing Human
Population Exposures; Dose-response Models for Developmental Toxicity; Use of Historical Controls in Time-
ad/usted Trend Tests for Carclrtogenidty; A Semiparametric Approach to Risk Assessment for Quantitative
Outcomes', and Developmental Toxicity Modeling for Risk Assessment.
i
EDIE WELLER
Dr. Weller is a Research Associate, Department of Biostatistics, Harvard School of Public Health and Department
of Blostattstical Science, Dana-Farber Cancer Institute. Dr. Weller received her B.S. in mathematics from the
University of Delaware and her Ph.D. in biostatistics from Medical College of Virginia, Virginia Commonwealth
University. Her current major research interests are risk assessment and design in development toxicology,
environmental statistics and Nonparametric methods for dose-response studies. Dr. Weller has authored
numerous articles including: Statistical Variability: What Does It Mean in Risk Assessment?; Implications of
Developmental Toxicity Study Design in Quantitative Risk Assessment; Statistical Issues in Assessing Human
Population Exposures; and An Adjustment in P-Valuesfor Multiple Endpoint Testing. Dr. Weller is a member of the
American Statistical Association and Biometric Society (Eastern North American Region).
PAIGE WILLIAMS '. 1 , , ,' ' ' ' ' , .,.'" ' ." ' 1
^.\^A^.h^^^.','^-^'t.:^..^P^^']n Biostatistics from the Oniversrty of North Carolina at Chapel Hill. Dr.
Williams is currently an associate professor in biostatistics at Harvard School of Public Health. Dr. Williams is
developing statistical methods for incorporating duration and timing of exposure into risk assessment for
developmental toxictty. Her major areas of research interests are risk assessment for cancer and non-cancer
endpoints, environmental statistics, survival analysis, analysis of AIDS data, and clinical trails. Dr. Williams is the
author of many reports and scientific publications including Dose-response Models for Developmental Toxicity;
Developmental Toxicify of Short Term Exposure to Ethylene Oxide; The Effects of Temperature and Duration of
Exposure on In Vitro Development and Response-surface Modeling of Their interaction; and Incorporating Duration
of Exposure in Risk Assessment for Developmental Toxicology. Dr. Williams is a member of the American
Statistical Assocjatipp, the Society for Risk Analysis, and the International Biometric Society.
-------
RONALD WYZGA
As the senior program manager of the Health Studies Program at Electric Power Research Institute (EPRI), Dr.
Wyzga is responsible for research on health effects of air pollution and toxics in the environment and workplace, as
well as on health risk assessment methods. Dr. Wyzga received his Sc.D. in biostatistics from Harvard University,
his M.S. in statistics from Florida State University and his A.B. in mathematics from Harvard College. Prior to
working for EPRI, Dr. Wyzga worked at the Organization for Economic Cooperation and Development (OECD) in
Paris, France where he was responsible for statistical and economic research and analysis on environmental
problems, as well as authoring a book on estimating the economic value of environmental damage. In addition, Dr.
Wyzga has authored numerous publications including Uncertainties in Identifying Responsible Pollutants in
Environmental Epidemiology; Health Risk Models and Compartmental Models; Quantitative Risk Assessment
Concerns and Research Needs; Towards Quantitative Risk Assessment for Neurotoxicity; and The Role of
Epidemiology in Risk Assessments of Carcinogens. Dr. Wyzga is on the Advisory Board of the Journal of
Environmental Statistics, as well as being a member and serving on many committees of the National Academy of
Sciences. He is a member of the EPA's Science Advisory Board, Environmental Health Committee.
-------
In:! I!'1 1 ' IT'"
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-------
&EPA
United States
Environmental Protection Agency
Office of Research and Development
Workshop on the Relationship Between
Exposure Duration and Toxicity
Sheraton Crystal City
Arlington, VA
August 5-6,1998
Final List of EPA Participants
William Boyes
Chief, Neurophysiological Toxicology Branch
National Health and Environmental Effects
Laboratory
U.S. Environmental Protection Agency
ERC Building
86 T.W. Alexander Drive
Room P-319B, Mail Drop 74B
Research Triangle Park, NC 27711
919-541-7538
Fax:919-541-4849
E-mail: boyes.wiliiam@epa.gov
Dan Costa
National Health and Environmental Effects
Laboratory
U.S. Environmental Protection Agency
ERC Building
86 T.W. Alexander Drive
Room - M201, Mail Drop 82
Research Triangle Park, NC 27711
919-541-2531
Fax:919-541-4849
E-mail: costa.dan@epa.gov
Annie Jarabek
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Catawba Building, Mail Drop 52
3210 Highway 54
Research Triangle Park, NC 27709
919-541-4847
Fax: 919-541-1818
E-mail: jarabek.annie@epa.gov
Gary Kimmel
National Center for Environmental Assessment
U.S. Environmental Protection Agency
401 M Street SW (8601-O)
Washington, DC 20460
202-564-3308
Fax: 202-565-0078
E-mail: kimmel.gary@epa.gov
Harvey Richmond
Office of Air Quality Planning and Standards
U.S. Environment Protection Agency
Mail Drop 15
Research Triangle Park, NC 27711
919-541-5271
Fax:919-541-0840
E-mail: richmond.harvey@epa.gov
I Printed on Recycled Paper
-------
-------
AEPA
United States
Environmental Protection Agency
Office of Research and Development
Workshop on the Relationship Between
Exposure Duration and Toxicity
Sheraton Crystal City
Arlington, VA
August 5-6,1998
Final List of Observers
Hans Allender
Statistician
Office of Pesticide Programs
SAB
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703-305-7883
E-Mail: allender.hans@
epamail.epa.gov
Marie Ambrogil
Progam Staff Assistant
Scientific and Policy Programs
American Industrial Health Council
2001 Pennsylvania Avenue, NW
Suite 760
Washington, DC 20006
202-833-2131
Fax: 202-833-2201
Katherine Anitole
Biologist
Office of Prevention, Pesticides, &
Toxic Substances
Office of Pollution Prevention &
Toxics
U.S. Environmental Protection
Agency
401 M Street, SW (7403)
Washington, DC 20460
202-260-3993
Fax: 202-260-1279
E-Mail: anitole.katherine@epa.gov
Jack Arthur
Environmental Health Scientist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703-305-4075
Ayaad Assaad
Scientist
Office of Prevention, Pesticides, &
Toxic Substances
Office of Pollution Prevention &
Toxics
U.S. Environmental Protection
Agency
401 M Street, SW
Washington, DC 20460
703-305-0314
Leila Barraj
Manager Statistical & Data Analysis
Services
Novigen Sciences
1730 Rhode Island Avenue, NW
Suite 1100
Washington, DC 20036
202-293-5374
Fax: 202-293-5377
E-Mail: lbarraj@novigensci.com
Ted Ban-era
Barrera Associates
733 15th Street, NW - Suite 1120
Washington, DC 20005
202-638-6631
Fax: 202-638-4063
E-Mail: ban~erainc@aol.com
Dave Bartenfelder
Environmental Scientist
Office of Solid Waste Emergency
Response
Economics, Methods, & Risk
Assessment Division
U.S. Environmental Protection
Agency
401 M Street, SW (5307W)
Washington, DC 20460
703-308-8629
Fax: 703-308-8609
Ambika Bathija
Toxicologist
Office of Water
Health & Ecological Criteria Division
U.S. Environmental Protection
Agency
401 M Street, SW (4304)
Washington, DC 20460
202-260-8864
Hsieng-Ye Chang
Environmental Engineer
U.S. Army Center for Health
Promotion and Preventive Medicine
5158 Blackhawk Road
MCHB-TS-EHR
Aberdeen Proving Grounds,
MD 21010-5422
410-436-2025
Fax: 410-436-8170
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-------
i'r11 ill,!',,,1,'!' lii*1"1 ,"!:''Wir'l,'1'1!
Chia^Chen
Senior Industrial Hygienist
Office o^ Health Standard Programs
Occupational Safety and Health
Administration
200 Constitution Avenue, NW
Washington, DC 20210
;ri ,_ 202:219^74117
E-Mafc chJa.chen@osha-noiosha.gov
ii Uf ill
Health Scientist
Office of Children's Health Protection
U.S. Environmental Protection
i Agency
401 M Street, SW (1107)
Washington, DC 204iO
202-260-7778
Fax: 202-260-4103
E-Mal: chen.david@epa.gov
Senfor lexicologist
LS "'I ' .1 ""' "S '"! S
Corporate Drive - Suite 680
111 liiiir ,,ii i' i '"nil i"ri,iiiliiiiiiii;::i:|i' ป n,.., ,
" Landover, MD 20785
3Q1-S77-7830
,;, fax: 301-577-0005
,|,,, ,,,,,,.,.,, ... E-Mail: connoifc@exppnentcom
Vaishali Deshpande
(CF Consulting Group
M, Filrfax,VA 22031
Fax: 703-934-3740
E-Mal: vdeshpande@Jcfkatser.com
Keyo Did ier [[[
Regulatory arid information Analyst
/^erlcin Petrbleurh
1220 L Street, NW
Washintbri; ...... 136" " SOOQ5
202-682^320
Fax: ......... 202-6823031
E-Ma8; oMer@api.org
[,'. ''"i,;!" is ::;
III , I ...ilr1 .JlOli
ill i'< J.
Sanjivani Diwan
Biologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washingtpn, DC 20460
^03:305.5992
Fax: 703-305^5529
;i E-Mail: diwan.sanjivani@
eplmatjle'p'agov
Julie Du
Office of Water
U.S. Environmental Protection
Agency
401 M Street SW (4304)
Washington, DC 20460
202-260-7503
E-Mail: du.julie@epa.gov
PaulDugard
Director of Scientific Programs
Halogenated Solvents Industry
Alliance
2001 L Street, NW- Suite 506A
Washington, DC 20036
202-775-0232
'Faxr202-833-0381 " "_| ''
E-Mail: pdugard@hsia.org
Elizabeth Easton
Manager of Environmental Issues
Farhana Fathimulla
Environmental Engineer
U .S. Army Center for Health
Promotion and Preventive Medicine
5158BlackhawkRoad
MCHB-T^EHR
Aberdeen Proving Grounds,
MD 21010-5422
Fax: 410-436-8170
William Fayerweathier
Corporate Epidemiologist
Owens Coming
One Owens Corning Parkway
(43659)
Toledo, OH 43659
419-248-6460
Fax: 419-325-1460
E-Mail: wil!iam.fayetweather@
owenscoming.com
Chemical Manufacturers Association
1 300 Wilson Boulevard
Arlington, VA 22209
'"703-741-5234 ............................
....... Fax: 703:741 -6234 ' ' ............................. .................
E-Mail: efeabeth_easton@
mail.cmahq.com
Ernest Falke
Senior Scientist
Risk Assessment Division
Science Support Branch
U.S. Environmental Protection
Agency
401 M Street, SW 7403
Washington, DC 20460
202-260^3433
n; ..if.. '.;.,' ,,i t-,i i- '! ,; . ", i. ?
John Festa
Senior Scientist
U.S. Environmental Protection
Agency
111 19th Street, NW
Washington, DC 20460
202-463-2587
Fax: 202-463-2423
Mary Fox
Student
School of Public Health
John Hopkins University
i24 North Broadway - Box 736
Baltimore, MD 21205
410-614-2188
Fax: 410T614-2797
E-Mail: mfox@jhsph.edu
Marcie Francis
Senior Director Science Policy
Chlorine Chemistry Council
1300 Wilson Boulevard
Arlington, VA 22209
703-741-5872
Fax: 703-741-6093
E-Mail: marcie_francis@
mail.cmahq.com
E-Mail: falke@erols.com
.'.".. ', ., L ;- '' ',ซ!*. .''I1-!"'1 ",.!" hi " f !',r,/:'(t! !' :'' "'! !'!. \ sr,..'
,: .ii , ,:;. . . i , . , :, i: ". ' ,; lh; .. , ir ',:. ":; ii../. , .,,111,111, ,, '|: |
-1,,:;;,"!' iii"i'ปi"viiji'. '"is!1;*:;1* '.^v'. < ^i;'.'.;^'1/ -. ^"v!*
,:":..,;,;',: "jt: v;i' '.. '":ii I:,/;,:,. :'(!:,,>; L ;.'I;,:,;1''.;::: '..'-,>.*
.-'ijllf' ,,..1 ;.ซ;; ifiji;" ,11 i, ",,! 'ii :..:'"' ,; ;,1!:, Mi1/ ,- ,i !l|i|:l,;i/,,.
'.Ji !-:-:
-------
Robert Fricke
Toxicologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW
Washington, DC 20460
703-308-2728
Bonnie Gaborek
Environmental Scientist
U.S. Army Center for Health
Promotion and Preventive Medicine
5158 Blackhawk Road
MCHB-TS-EHR
Aberdeen Proving Grounds,
MD 21010-5422
410-436-5212
Fax: 410-436-8170
Franklyn Hall
Chemical Engineer
Office of Pollution Prevention &
Toxics
Economics, Exposure, and
Technology Division
U.S. Environmental Protection
Agency
401 M Street, SW (7406)
Washington, DC 20460
202-260-9596
E-Mail: hall.franklyn@epa.gov
Karen Hamemik
Pharmacologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703-305-5467
Fax: 703-305-5147
Anita Hanson
Environmental Protection Specialist
Office of Pesticide Programs
Biological & Economic Analysis
Division
U.S. Environmental Protection
Agency
401 M Street, SW (7503C)
Washington, DC 20460
703-308-7228
Fax: 703-308-8091
E-Mail: hanson.anita@epamail.epa.gov
Karen Hentz
Senior Staff Scientist
Karch & Associates, Inc.
1701 K Street, NW - Suite 1000
Washington, DC 20006
202-463-0400
Fax: 202-463-0502
E-Mail: khentz@karch-inc.com
Lee Hofman
Environmental Hearth Scientist
Office of Solid Waste & Emergency
Response
Office of Emergency & Remedial
Response
U.S. Environmental Protection
Agency
401 M Street, SW (52026)
Washington, DC 20460
703-603-8874
Fax: 703-603-9133
E-Mail: lee.hofmann@epamail.epa.gov
Karen Hogan
Statistician
U.S. Environmental Protection
Agency
401 M Street, SW
Washington, DC 20460
202-260-3895
E-Mail: hogan.karen@epa.gov
Ching-Hung Hsu
Pharmacologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703-305-7043
Jennifer Jinot
Office of Research & Development
National Center for Environmental
Assessment
U.S. Environmental Protection
Agency
401 M Street, SW (8623D)
Washington, DC 20460
202-564-3281
Fax: 202-565-0079
E-Mail: jinotjennifer@epa.gov
Ann Johnson
Office of Solid Waste
Economics, Methods, & Risk
Assessment Division
U.S. Environmental Protection
Agency
401 M Street, SW (5307W)
Washington, DC 20460
703-308-0498
Fax: 703-308-0511
E-Mail: johnson.ann@epamail.epa.gov
Elizabeth Julien
Novigen Sciences
1730 Rhode Island Avenue
Washington, DC 20036
202-293-5374
E-Mail: bjulien@earthlink.net
Myra Karstadt
Environmental Protection Specialist
Office of Pollution Prevention &
Toxics
Environmental Assistance Division
U.S. Environmental Protection
Agency
401 M Street, SW (7408)
Washington, DC 20460
202-260-0658
Fax: 202-401-8142
E-Mail: karstadtmyraฉ
epamail.epa.gov
Patrick Kennedy
Chemist
Office of Pollution Prevention &
Toxics
Economics, Exposure, & Technology
Division
U.S. Environmental Protection
Agency
401 M Street, SW (7406)
Washington, DC 20460
202-260-3916
Fax: 202-260-0981
E-Mail: kennedy.patrick@
epamail.epa.gov
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Carole Kimmel
1 Senior Scientist
NlafioarilCenter for Environmental
Ass^ssjjjent
gjfggg'lgg'yjtj^agon1 a"n(j
Characterization Group
U.SjEi3|fonmentat Protection
Agency
401 M Street SW (8623)
Washington, DC 20460
202-564-3307
Fax: 202-565-0078
j^-Mail: WmmeJ.carote@epa.gov
Sasha Kpo-Oshima
Senjor ScJenfst/Project Manager
EAErig1!n1eerlngril'Sclence; &
Technology
8401 Cojesville Road - Suite 500
Sliver Spring, MD 2W1M312
" '' '
Matthew Levinson
Environmental Engineer
Marasco Newton Group, Ltd.
2801 Clarendon Boulevard
E-Mal: sk@eaest.com
John Kordalski
EnvJronmentel Engineer
Veterans, HeaK Mmjnjstratibh
U.S. Department of Veterans Affairs
2402 Cavendish Drive
Alexandria, VA 22308
^202-273^6001,, , ,
Stephen Kroner
Environmental Scientist
Economics, Methods, & Risk
Assessment Division
U.S, Enyjronmental Protection
Agency
401 M Street, SW (5307W)
Washingtoh.bC 20460
703-308-0468
Fax 703-308-0511
E-Mafc kroner.stephen@
epamaS,epa.gov
t Tim Leighton
| ^nvtrogmentil,, Healtfi" Scje,^! " 'r",_'"_,""
"Toxic Sutetences
Health Effects Diyfeiorj
U.S. En^rbhmehtai Protection
Agency
401 M]
Washwigtoh, DC
703-305-7435
Arlington, VA 22201
703-516-9100
Fie' Yos.g-i'e.gioQ .............................
E-Mail: mlevinso@marasconewton.com
Girvin Liggans
Environmental Health Scientist
Technical Resources international
, ' 32 02 'Tower' bales ...... Boulevard' ............................
Rockville.MD 20852
301-231-5250
Fax: 301-231-6377
E-Mal: gliggans@tech-res.com
Amal Mahfouz
Office of Water
U.S. Environmental Protection
Agency
Washington.DC 20460
2021260-9568
Fax: 202-260-1036
E-Mail: mahfouz.amal@
epamail.epa.gov
Mary Marion
Statistician
Office of Water-
SAB
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington.DC 20460
703-308-2854
E-Mail: marion.mary@epa.gov
Alec McBride
Office of Solid Waste
Economics, Methods, & Risk
Assessment Division
y.S. Environmental Protection
...... Agency
401 M Street, SW (5307W)
Washington, DC 20460
703-308-0466
Fax: 703-308-0511
E-Mail: mcbride.alexander@
epamail.epa.gov
Pat McGinn
Policy Analyst
TRW
5113 Leesburg Pike - Suite 200
Fills Church, VA :
703-824-4218
Fax: 703-578-6501
E-Mail: prncglnh@trw.cpm
Rashmi Nair
Manager of Product Safety
Environmental Safety and Health
Department
Solutia Inc.
10300 Olive Boulevard
Creve Coeur, MO 63141
314-674-8817
Fax: 314-674-8808
E-Mail: rashmi.nair@solutia.com
Kelly p'Rourke
Biologist
Office of Prevention, Pesticides, &
Toxic Substances
Health Effects Division
U.S. Environmental Protection
Aosncv
401 M Street, SW (7509C)
Washington, DC 20460
703-308-9548
E-Mail: keUy.orourke@epa.gov
Richard Paul
Manager, Environmental Health
Vehicle Environment Department
American Automobile Manufacturers
Association
7430 Second Avenue - Suite 300
Detroit, Ml 48202
313-871-5344
Fax: 313-872-5400
E-Mail: paulrt+aDET01%7332724@
mcimail.com
William Pepelko
Toxicologist
National Center of Environmental
Assessment
Effects Indentification and
Characterization Group
U.S. Environmental Protection
Agency
401 M Street, SW (8623D)
Washington.DC 20460
- 202-564^3309
"Fax: 202-565-0078
E-Mail: pepelko.william@
epamail.epa.gov
I IF
iilKlli
'jilill iijiaiiiijiiilK ;4!i:>ilili '
-------
Karen Pollard
Office of Solid Waste
Economics, Methods, & Risk
Assessment Division
U.S. Environmental Protection
Agency
401 M Street, SW (5307W)
Washington, DC 20460
703-308-3948
Fax: 703-308-0511
E-Mail: pollard.karen@epamail.epa.gov
Kathleen Raffaele
lexicologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703-305-5664
John Redden
Senior Scientist
Office of Prevention, Pesticides, and
Toxic Substances
Registration Division
U.S. Environmental Protection
Agency
401 M Street, SW (7505C)
Washington, DC 20460
703-305-1969
Rick Reiss
Senior Scientist
Jellinek, Schwartz, & Connolly
1525 Wilson Boulevard - Suite 600
Arlington, VA 22209-2411
703-527-1670
Fax: 703-527-5477
E-Mail: rickr@jscinc.com
Cindy Lynn Richard
Senior Scientist
Achieva
5570 Sterrett Place - Suite 208A
Columbia, MD 21044
410-964-9900
Fax: 410-964-0008
E-Mail: clrich@esri.org
William Richards
Senior Toxicologist
Syracuse Research Corporation
1215 Jefferson Davis Highway #405
Arlington, VA 22202
70^413-9372
Fax: 703-418-1044
E-Mail: wlrichards@vasyr.com
Mohsen Sahafeyan
Residue Chemist
Office of Prevention, Pesticides, &
Toxic Substances
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW
Washington, DC 20460
703-305-6872
E-Mail: sahafeyan.mohsen@
epamail.epa.gov
Val Schaeffer
Toxicologist
Health Standards Programs
Occupational Safety and Health
Administration
200 Constitution Avenue, NW
RoomN3718
Washington, DC 20210
202-219-7105131
Fax: 202-219-7125
E-Mail: val.schaeffer@
osha-no.osha.gov
Brendan Shane
Project Manager
Associate State Drinking Water
Administrators
1120 Connecticut Avenue, NW
#1060
Washington, DC 20036
202-293-7655
Fax: 202-293-7656
E-Mail: asdwa@erols.com
Debbie Smegal
Toxicologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703-305-7457
Judy Strickland
Toxicologist
National Center for Environmental
Assessment
U.S. Environmental Protection
Agency
Catawba Building 52
3210 Highway 54
Research Triangle Park, NC 27711
919-541-4930
Fax: 919-541-0245
E-Mail: strickland.judy@epa.gov
Clark Swentzel
Toxicologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703-305-5064
Seyed Tadayon
Chemist
Office of Pollution Prevention &
Toxics
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509)
Washington, DC 20460
703-305-5238
E-Mail: stadayon@epa.gov
Sara Thurin Rollin
Reporter
Bureau of National Affairs
1231 25th Street, NW
Washington, DC 20036
202-452-4584
Fax: 202-452-7891
E-Mail: strollin@bna.com
Steve Via
Regulatory Engineer
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW
Washington, DC 20460
Frank Vincent
Director Product Regulatory Affairs
Fort James Corporation
P.O. Box 899
Neenah.WI 54956
920-729-8152
Fax: 920-729-8089
E-Mail: frank.vincent@
fortjamesmail.com
Phil Wakelyn
Senior Scientist
National Cotton Council
1521 New Hampshire Avenue
Washington, DC 20036
202-745-7805
Fax: 202-483-4040
E-Mail: pwakelyn@cotton.org
Wilhelm Wang
Managing Director
Transnational Regulatory Group
P.O. Box 1015
Westwood, NJ 07675
201-666-1218
Fax: 201-666-1218
E-Mail: wilwang@earthlink.net
-------
Biologist
Office of Pesticide Programs
Health Effects Division
U.S. Environmental Protection
Agency
401 M Street, SW (7509C)
Washington, DC 20460
703^305-0313
Wright i/" "^ "', ;
|entor Scientist
"Oow'Afjjro Sciences
9330 Zfonsville Road
Indianapolis, IN 46268-1054
317-337-3509
:ax: 317-3374214
' "Llf !l "I1!'1
1 INI ' 'S"i
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APPENDIX D
ISSUES PAPER
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ISSUES IN EVALUATING THE
RELATIONSHIP BETWEEN EXPOSURE
DURATION AND TOXICITY
Sheraton Crystal City
Arlington, Virginia
August 5-6, 1998
Submitted by:
Paige Williams
Department of Biostatistics
Harvard School of Public Health
677 Huntington Avenue
Boston, Massachusetts 02115
Submitted to:
Eastern Research Group, Inc.
110 Hartwell Avenue
Lexington, Massachusetts 02421
Prepared under EPA Work Assignment No. 98-07, Contract No. 68-D5-0028
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-------
NOTE
Mention of trade names or commercial products does not constitute endorsement
or recommendation for use. Statements are the individual view of the author; the
statements in this preliminary paper do not represent analyses or positions of the
Risk Assessment Forum or U.S. Environmental Protection Agency.
This paper was prepared by Paige Williams under contract to Eastern Research
Group, Inc., an EPA contractor. It is a work in progress.
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,74'
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Issues in Evaluating the Relationship
Between Exposure Duration and Toxicity
Paige L. Williams
Department of Biostatistics, Harvard School of Public Health,
'677 Huntington Avenue, Boston, Massachusetts 02115
SUMMARY
Regulatory agencies have become increasingly aware of the need to develop exposure
standards for varying lengths of exposure. Historically, the strategy for comparing responses
among exposures of different durations has often relied on a relationship attributed to Haber
(1924) that response levels should be equivalent for any constant multiple of dose times
duration, i.e., whenever the cumulative exposure remains constant. This premise is widely
recognized to be an over-simplification, yet may provide a good starting point for consid-
ering toxic effects of varying durations of exposures. To fully understand the complexities
of exposure effects on toxic responses, one should ideally take into account the entire expo-
sure profile, including the timing, duration, and intermittent nature of exposures reflecting
realistic scenarios encountered in practical settings. The proper dose metric to characterize
an exposure will be highly dependent on the pharmacokinetic properties of the chemical or
exposure in question, and the toxic effects considered in models must likewise be carefully
chosen to reflect the relevant endpoints based on the exposure and dosimetry characteristics.
Models have been developed over the last few decades which begin to address the effect
of duration of exposure in addition to exposure levels; however, many of these models do
not incorporate mechanistic information. In addition, only limited work has been done on
developing efficient designs for studying dose-rate effects, and these designs tend to be sim-
plistic. The purpose of this paper is to explore issues in the assessment of dose-rate effects on
toxic endpoints, by summarizing current defaults and identifying gaps in our knowledge and
scientific methodology so that further research in this area can be focused and productive.
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1 Historical perspectives and current default approaches
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Current risk assessment procedures are typically based on overall daily exposure levels, and
tend to emphasize effects resulting from low-level continuous exposures' over a lifetime. How-
ever", there has been an increasing realization over the last several decades that exposures
are inore likely to be experienced as bursts of higher concentrations over shorter time peri-
ods, or intermittent (recurring) exposures of varying levels. In addition, data collected from
'siibchronic"studies are often extrapolated to develop estimates of noncancer risk, such as No
deserved Adverse Effect Levels (NOAELs), for lifetime chemical exposures. Traditionally, a
10-fold uncertainty factor is applied when a subchronic exposure is used to estimate a chronic
NOAEL. However, some feel that these extrapolated lifetime values may be overestimated
(Crofton et al, 1996). Due to the fact that exposures are often experienced over shorter
durations and at higher levels than the regulatory standards are intended to represent, un-
derstanclingthe underlying biological relationship between timing and duration of exposure
a|icl subsequent toxic effects is important in developing risk assessment strategies for various
durations of exposures.
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As Jarabek (1995) notes, much of the health assessment procedures have been based
OH a 1983 National Academy of Science (NAS)/National Research Council report (NRG,
1983) which recommends utilizing exposure scenarios as close as possible in practice to the
standards they are meant to reflect. Thus, standards for acute toxic effects, such as 15-minute
occupational time weighted average ceilings, are derived from animal experiments which are
also classified as acute, such as 1 to 24 hour single inhalation exposures. Similarly, subchronic
siandards such as those resulting from periodic contaminations are usually defined on the
basis of 90-day subchronic animal studies. Default assumptions are used in determining
siandards, so that the standards apply to a 70-kg male who drinks 2 liters of water and
Inhales 20 cubic meters of air per day. While the NRG report establishes a paradigm for
letting standards based on "different durations, it does not specifically address the issue of
dose-rate effects.
In the context of incorporating duration of exposure, the use of" animal studies to de-
fine exposure standards for human healtfi assessment suffers from all of the usual limitations
Of cross-species extrapolation, pharmacokinetic differences in metabolism and distribution
5f the environmental agent",' sensitivity and variability in response for different endpoints,
etc. In addition, accounting for duration of exposure requires an additional assumption that
toxic effects resulting from an animal experiment comprising a certain percentage of that
species' lifetime would be comparable to the effects resulting in humans over the same per-
centage of lifetime. For example, the 2-year chronic animal bioassay study is considered to
be representative of the effects over a 70 year human lifespan, and standards for a 7-year
subchronic exposure in humans would be based on a 3-month subchronic animal study, each
.: representing approximately 10% of the total lifespan. However, the timing of exposure is not
' considered in these default assumptions. While this may not present too many difficulties
In assessing lifetime exposures, the point in chronological development may be a key factor
in the response of an animal subjected to an acute or subchronic exposure.
There are a number of different regulatory statutes whose implementation activities
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require consideration of exposure durations varying from as short as 15 minutes to an entire
lifetime. These include the Clean Air Act Amendments of 1990, the Safe Drinking Water
Act, the Clean Water Act, the Comprehensive Environmental Response, Compensation and
Liability Act, the Resource Conservation and Recovery Act, and the Occupational Safety and
Health Act. Some of the exposure limits set in response to these statutes are summarized in
Table 3 of Jarabek (1995). For example, three different levels of "Emergency Response Plan-
ning Guidelines" (ERPG's) given a one-hour exposure duration are defined by the American
Industrial Hygiene Association (AIHA) aimed at effects ranging from mild transient health
effects to life-threatening outcomes. In some cases, the requirement for setting standards for
specific exposure durations has led to a situation where data collected under one exposure
scenario is utilized for exposures of a very different duration and level. For example, Weller
et al. (1998a) note that exposure-response relationships for 189 air toxics have been derived
primarily from acute lethality studies, but these results must be extrapolated to provide
standards for ambient air environmental exposure levels, which tend to be much lower in
magnitude. The lack of data from a range of exposure durations combined with the man-
date to set realistic exposure standards has led to an increased need for more sophisticated
quantitative models.
The development of quantitative risk assessment models has followed a premise gener-
ally attributed to Haber (1924) that toxicity levels should be linearly related to the product
of dose level times duration, or "C7 x T" (concentration x time). In its simplest form Haber's
Law would imply that, for example, a one-hour inhalation exposure at 800 ppm should pro-
duce the same toxic response as an 8 hour exposure at lOOpprn. In fact, Haber proposed
this relationship in the context of evaluating very short-term effects of gas warfare, and did
not suggest that it be used for extrapolating effects of exposure from very short term to long
term. However, Haber's law has been used implicitly by some agencies like the Agency for
Toxic Substances and Disease Registry (ATSDR) to adjust effect levels like NOAELs from
animal studies to those of human exposures. For example, if animal studies for a particular
exposure are conducted using 8 hour exposures and the human exposure of interest is 24
hours, then the effect level from the animal study would be divided by 3. In other words,
it would be assumed that an exposure of NOAEL/3 for 24 hours would result in the same
toxic effects as exposure to the NOAEL for 8 hours.
Bliss and James (1966) suggested that Haber's law tends to be most appropriate when
evaluating either very short exposures to high concentrations or long chronic exposures to
low concentrations. When Haber's law is violated, use of this premise for extrapolation pur-
poses can lead to either underestimates or overestimates of the exposure levels associated
with a particular toxic effect. While a great deal of attention has been addressed at docu-
menting exceptions to Haber's Law, relatively less effort has been focused on extension of risk
assessment methods, especially dose-response models, to account for duration of exposure.
Direct references to Haber's law have been cited most often in the context of animal
experiments, yet this framework has also been used extensively by epidemiologists to account
for the effects of exposures over various durations on human health outcomes. A typical
strategy for representing exposure level is to use the concept of "person-time" exposure,
which accounts for the length of time at risk for each individual and then sums these times
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over all individuals in a cohort. The cumulative duration of exposure at a certain dose
level is then assumed to be related to risk of adverse events. In a notable exception to
this framework, epidemiologists studying the risk of radiation exposure have noted that long
exposures at low dose levels generally produce lower risks than short intense exposures of
the same total dose for low-linear energy transfer (LET) exposures, while for high-LET the
reverse may be the case (Thomas, 1990; BEIR reports of 1980,1990). In the area of radiation
carcinogenesis, there have also been several investigations which support the dependence of
cancer risk oh the timing of exposure (i.e., age at exposure), time since exposure, and the
duration of exposure.
For most exposures of interest, there is little epidemiological evidence available and
risk assessment tends to be based primarily on experimental studies conducted in laboratory
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animals. These animal studies are conducted to characterize the dose-response relationship,
but rarely address the effects of exposure over a range of exposure durations. There are some
notable exceptions to this general statement, including work that has been conducted to in-
vestigate the relationship between exposure duration and toxicity on several different types
of endpointSj including dermal toxicity (McDougal), neurotoxicity (Crofton et al, 1996;
Bushnell, 1996 (abstract), 1997 (abstract), 1997; Bercegeay, 1997 (abstract), Boyes, 1996
(abstract), 6'Shaughnessy and Losos, 1986; Hattis and Shapiro, 1990; Miller et al, 1983;
Tilson and Cabe, 1979; and Yoshimura et al, 1992), developmental toxicity (Kiinmel
et al, 1993 (abstract), 1994 (abstract), Scharfstein and Williams, 1994; Williams, Molen-
berghs, and Lipsitz, 1996; and Geys, Molenberghs, and Williams, 1998), inhalation effects
[Jarabek, 199o; Weller et al, 1998a, i998b), and vegetation effects (Lefohn and Jones,
1986; Lefohn and Runeckles, 1987; Musselman et al, 1994). Further details of some of these
studies will be discussed in the following sections.
I A
ill,
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1 Toxic Endpoints
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The biplogical level of endpoints ranges from the whole animal and systemic endpoints
to cellular-subceliular-molecuiar effects. Traditionally, regulatory agencies have relied pri-
marily on effects at the whole animal/systemic level to define toxicity. This is not to say
that examples of regulatory assessments which have considered endpoints at the cellular-
subceliular-molecuiar level are non-existent, but rather that systemic effects have become
the primary basis for most regulatory standards. In this context, exposure is defined on the
basis of the whole animal rather than at the target issue (tissue/cellular), let alone the target
1||ง (cellular/molecular). Likewise, the endpoint (effect) is generally measured at the level
of a systemic occurrence or event, rather than further distinguishing the cellular-subceliular-
molecuiar effects. These approaches are in part a natural extension of test systems which have
been developed and accepted with time. However, when considering concentration-duration
relationships, an issue for discussion is what biological level can or should be assessed in
establishing a toxicologic exposure? Is it necessary to have ah understanding of the mech-
anism of toxicity? How do such tissue and cellular functions as repair, metabolism, and
bioaccumulation factor into, defining the relationship? Is it possible to adequately describe
a, concentration duration relationship without measuring effects at biological levels below
whole animal-systemic?
: 4
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w
The type of toxic endpoint of interest often depends on whether the exposure duration
is acute (single dose), repeated in frequency, or chronic. However, the endpoint itself may
not parallel the type of exposure. For example, single dose exposures may result in some
immediate acute effects, such as irritation, hearing loss, CNS depression, or even lethality.
However, they may also lead to chronic health effects if the single acute exposure leads to
irreversible damage. Similarly, chronic lower-level exposures may result in both immediate
acute effects after each exposure in addition to long-term chronic effects. .For repeated
exposures, the duration between exposures may allow for repair of damage and elimination
of toxic metabolites, resulting in toxic effects of milder severity than those of single acute
exposures. However, repeated exposures may also lead to bioaccumulation or may not allow
sufficient time between exposure for repairing damage, so that chronic effects develop. Again,
the choice of endpoints depends on an integral understanding of the mechanism of action of
the agent in question, and the possibility that an exposure may result in several different
types of endpoints must be considered.
Numerous examples exist of CxT studies carried out' at the whole animal-system
level. For example, several investigations have been conducted to evaluate the effects of
repeated exposures to toxins. Investigations comparing the effects of repeated exposures of
acrylamide with a single acute exposure have been conducted by Miller et al (1983) and
Tilson and Cabe (1979). Burek et al (1980) have also studied possible repair mechanisms
after exposure to acrylamide under various recovery periods. Bushnell et al (1997, abstract)
examined changes in tolerance to repeated inhalation of trichloroethylene (TCE) in rats,
and found that behavioral changes existed even after accounting for changes in metabolic
tolerance.
Other whole animal or systemic studies have been conducted to address both qual-
itative and quantitative differences in toxic endpoints for short-term high-dose exposures
versus longer term low-dose exposures. In the area of neurotoxicity, O'Shaughnessy and
Losos (1986) compared CNS lesions in rats following acute high-dose exposures to. low-dose
chronic exposures. Yoshimura et al. (1992) investigated a number of different chemicals, and
found that in many cases the same chemical led to different types of neurotoxic endpoints
when administered to dogs and rats in high doses for short periods as compared to low doses
for longer periods. Numerous studies conducted in the 1980's by Musselman et al (1983,
1986), Hogsett et al (1985), and Lefohn and Jones (1986) indicated that peak exposures to
ozone and sulfur dioxide were more important in determining effects on vegetation growth
than the total cumulative exposure. These studies led to development of exposure indices
which placed greater weight on higher concentrations of ozone than on lower levels (Lefohn
Benedict, 1982; Lefohn and Runeckles, 1987; Lee et al., 1988). Rappaport (1991) has also
noted the importance of peak exposures in occupational exposure assessment.
In some instances, the endpoint of interest has been related to the administered
exposure, while in other cases, correlations with target tissue levels have been found to be
greater. For example, Crofton et al (1996) investigated the effect of dose rate on neurotoxicity
of acrylamide by exposing rats to 1 day, 10 day, 30 day, or 90-day exposures, and found that
the behavioral effects (motor activity, grip strength, and startle response) of acrylamide
depended on both the level and duration of exposure. However, the recovery of behavioral
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tllili/
a
function was found to be independent of duration of dosing, and indicated that the toxicity
was not due to an accumulation of acrylamide in the target tissue. Similarly, Boyes et al
(1996), Bushnell (1996, abstract), Bushnell et al (1997), and Bercegeay et al (1997, abstract),
have found that Haber's Law did not predict the relationship between TCE inhalation and
changes in behavioral function in rats as measured by visual evoked potentials (VEPs),
response time, or sensitivity index, but noted that the peak arterial concentration of TCE
estimated by a physiologically-based pharmacokinetic (PBPK) model was a good predictor
of all three of these neurologic endpoints, and tended to be more highly correlated with
concentration than with the C x T product.
in i1
These and other studies have expanded our understanding of the limitations of Haber's
conjecture, But the question remains as to how this understanding can result in predictive
models of concentration-duration relationships that can estimate the potential for a given
CxT multiple to be of concern. Moreover, the appropriateness of specific approaches to both
acute-short term and chronic-long term exposures must be established. While risk assessment
procedures have historically been developed to reflect lifetime exposures and have therefore
emphasized chronic endpoints such as tumor development, a number of guidelines have been
more recently developed for acute and shorter-term exposures. For example, within the EPA,
the Office of Drinking Water develops health advisories for 1-day and 10-day consumption
levels, and the Office of Prevention, Pesticides, and Toxic Substances addresses emergency
r/esponses to accidental releases of toxic substances and episodic exposures to pesticides. The
guidelines have often been derived for specific populations; for example, the drinking water
advisories are developed separately for adults and children, and some guidelines such as
"EEGL's" have been developed with military personnel in mind. A summary of guidelines
adapted from Kimmel (1995) and Jarabek (1995) and their relationship to various toxic
endpoints is shown in Table 1.
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As mentioned above, these guidelines and the test systems used to establish current
exposure limits focus for mainly on the whole animal-systemic level of biological organization.
This is in part due to a reliance on the traditional test systems that have been used in
toxicology and the wealth of historical comparative data that is available. These test systems
tend to have a clear definition of what response is considered to be a sign of toxicity, and
they tend to be "apical", i.e., they examine the overall effect of an exposure on the biological
system. As models of concentration-duration relationships are developed, it will be important
to establish how compatible or complimentary they are with current approaches in risk
assessment, , '
S Dosimetry/Mechanistic Modeling
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The issue of dosimetry is intrinsically linked to the effect of interest and the mechanism or
, lotion"of,tHeej^ironmentar agent. The appropriateness of applying Haber's Law, which in
furii^ implies use'of'"the'cumulative exposure C x T" as the the "dose "metric,' depends on the
pTiaxmacbicinetics of the particular agent and the dose/duration patterns under consideration.
If the administered dose results in saturation, then higher doses may not have any effect on
Response rates regardless of the duration of exposure. On the other hand, if the pathway is not
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Table 1: Basis for Short-term Exposure Guidelines
Guideline1
TLV-STEL
TLV-EL
IDLH
ERPG
EEGL
SPEGL
CEGL
CEEL
HA
Organization2
ACGIH
ACGIH
NIOSH
AIHA
COT
COT
COT
COT
COT, EPA
Basis
Irritation, impaired work,
irreversible tissue damage
3 times TLV-STEL
Death, irreversible health effects
Levels: (1) life threatening
(2) irreversible, impair response
(3) mild transient effects
Acute effects in military
impairing emergency response
Irreversible health effects
Irreversible health effects
Irreversible health effects
Adverse health effects
Duration
15 min
30 min
<30 min
1-hour
1-hour
1-hour
1-24 hours
1-24 hours
90 days
1-8 hours
1-10 days
Frequency
<4x daily
recurrent
one-time
constant
constant
constant
constant
one time
1 TLV-STEL: threshold limit value, short-term exposure level
TLV-EL: threshold limit value, excursion limit
IDLH: immediately dangerous to life and health
ERPG: emergency response planning guideline
EEGL: emergency exposure guidance limit
SPEGL: short-term population exposure guidance limit
CEGL: community exposure guidance limit
CEEL: community emergency exposure level
HA: health advisory
2 ACGIH: American Conference of Governmental Industrial Hygienists
NIOSH: National Institute of Occupational Safety and Health
AIHA: American Industrial Hygiene Association
COT: Committee on Toxicology of NAS
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DRAFT:July 27, 1998 C xT Issues Paper
Saturated and the dose delivered to the target organ, depends on the metabolic rate through
the area under the curve (AUCJ), then Haber's Law may provide a reasonable starting point
for examining dose-duration response relationships. The latter condition will typically hold
as long as the overall rate of elimination of the toxic substance follows a first-order process
at" the tissueof Interest. Andersen ei al. (1987) suggested that toxicity for most industrially
important gases and volatile liquids would probably be related to the AUC rather than peak
blood concentrations; in this case, use of C x T as the dose metric might be an acceptable
basis for extrapolation. Jarabek (1995) notes that an alternative to using C x T as the
rSetric is to use concentration alone regardless of duration, and points out that this might
be appropriate for irritants for which damage does not accumulate with duration.
Figure 1 (adapted from Jarabek, 1995) reflects the various levels to which the exposure-
dose-response continuum can be explored, and lists some of the mechanisms which might
be elucidated to address relationships between external or administered exposure, dose at
the tissue level, and response at the systemic, tissue, cellular or subcellular level. Level I
reflects, what If referred to as a "black-box" dpsimetry model, in which the relationship be-
tween administered exposure ana! internal dose is unknown, but systemic toxic effects can be
evaluated and related to the administered exposure. Level II reflects the inclusion of phar-
macokinetic (disposition) models, which relate the exposure to the internal tissue dose by
considering absorption, metabolism, distribution, and elimination. Levels III and IV include
not only pharmacokinetic models, but also pharmacodynamic models, indicated in Figure 1
as "Toxicant-Target Models".
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Mathematical models of the mechanistic determinants of the disposition of a parent
compound and/or its metabolites, such as PBPK or dosimetry models, have been useful in
describing the relationship between exposure concentration and target tissue dose. These
disposition models can be linked to other models that address the mechanistic determi-
nants of the toxicant-target tissue interaction and tissue response, respectively. These latter
models refine the designation of response. The tissue dose is linked to determinants of target-
tissue interaction (e.g., critical mechanistic events such as cytotoxicity and rebound cellular
proliferation), which, in turn, may then be related via other mechanisms to the ultimate
production of lesions or functional changes that are typically defined as the disease (patho-
genesis) outcome. To the extent that these events are explanatory of the disease outcome,
they can be used to quantitate important nonproportionalities or as replacement indices of
IHe response function (U.S. EPA, 1994). It is important to emphasize that the integration of
the mechanistic determinants may not necessarily be achieved By linking respective models in
a series" (.e.7 the output of one model becomes the input to the next) but may require simul-
taneoussolution (e.g., the mechanistic determinants of disposition are dynamically related
?lmoment-by-mbment" to mechanisms of toxicant-target interaction). Eventually, causality
of the critical mechanistic toxic effect can be correlated to the internal toxic moiety as the
jfose surrogate" rather tEan relating the exposure concentration to the "black box" of the
within a population.
|=:; !;;:t, TChe characteristics of exposure which become important in understanding the exposure-
2pse-resp6nse continuum are not only the magnitude, duration and frequency of exposure,
also agent-specific characteristics such as the reactivity, solubility, and volatility. Char-
; ti >ii;
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C x T Issues Paper
Figure 1: Comprehensive Exposure-Dose-Response Continuum
Level
Exposure =ป Dose ==ป Response
?? Dose ??
Tissue
Dose
Disposition
Models
Toxicant
Tissue
Interaction
Disposition
Models
Toxicant-Target Models
T-i- "
Tt r -ri JL 1SS 116
IV. Exposure =>
Dose
=>
Toxicant
Tissue
Interaction
Disposition Toxicant-Target Models
Models
Tissue
Response
Models
Response
(Adapted from Jarabek, 1995)
acterizing attributes of the portal-of-entry (e.g., barrier capacity, cell types and morphology,
specialized absorption sites, pH) and its interactions with the agent are important to denn-
ing dose, both for "local" target toxicity, as well as how it modulates systemic delivery.
Systemic parameters that modulate target tissue dose include metabolism, clearance, tissue
binding, blood flows, tissue volumes, and partition coefficients. An understanding of how
the exposure is translated into a "dose" also depends on whether the important determinant
of toxicity is the concentration of the parent compound or some metabolite, and the stability
or binding of the parent compound or metabolite. Finally, the response depends on repair
processes, cytotoxicity, cell proliferation, altered gene expression, and adaptation.
There is a striking similarity between the exposure-dose-response paradigm shown
in Figure 1 with that proposed by molecular epidemiologists for considering the role of
biological marker components in sequential progression between exposure and disease, as
9
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Figure 2: Biological Marker Components in Sequential Progression Between Exposure and
Disease (adapted from Schulte, 1989)
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Exposure
Effect
(Non-cancer / Cancer)
''!
Exposure
-
|l .||l| .,1 ^
Internal
Dose
=>
1r ' "'
Biologically
Effective
Dose
=>
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Structure/
Function
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=>
1
Clinical
Disease
1
. ,,'i , . t .
ii .,.
=>
Prognostic
Significance
! lii i IIIMir , ' > <
shown in Figure 2 (SchulteT 1989; tfS EPA, 1994)' As denned by the National Research
Council (NRG) Board on Environmental Studies and Toxicology, a "biologic marker" is any
cellular or molecular indicator of toxic exposure, adverse health effects, or susceptibility. The
markers may represent signals - - generally biochemical, molecular, genetic, immunologic, or
physiologic - - in a continuum of events between a causal exposure and the resultant disease.
It should be emphasized that the components shown in Figure 2 are not necessarily discrete
events or the only events in the continuum; there may be a series of other components (steps
or stages) between or in parallel with those shown that have yet to be discovered.
The molecular epidemiology approach is based on the combination of two biologi-
cjd tenets: (1) Early biologic effects from a toxic exposure are far more prevalent in the
population at risk than the later events which have historically been of direct interest such
as disease, and may sometimes be more specific to the exposure than the outcome itself;
Snd (2) Given technological advances, most xenobiotics can either be directly quantified in
the body or indirectly measured by identification of some predictable, dose-related biologic.
Thus, the historic analytic epidemiology approach may be supplanted by a more in-depth
approach which identifies intervening relationships more precisely or with greater detail than
in 'the past. As a result, health events are less likely to be viewed as dichotomous phenom-
ena (presence or absence of disease) but rather as a series of changes in a continuum from
Epmeostatic adaptation, through dysfunction, to disease and death (Schulte, 1989).
(|l|| I | I ' ' .''..,..:. , If1 i ,1'Jj '!. t. :,S;".*-ปiir .I'.M, ','.! I i'",!. .: ' Si ' I. ' 1 M '... t! " I'*
The similarity of the dosimetry and molecular epidemiology paradigms emphasizes
that quantitative consideration of the events in the exposure-dose-response continuum has
implications for dose-response assessment; insights may be gained on (1) how to extrapolate
from high to low exposure levels, (2) the reliability of extrapolation from laboratory species
to humans, (3) the relevance of certain physiologic events to disease outcome, and (4) an
index of human interindividual variation (U.S. EPA, 1994).
In the most general case, we can view the exposure profile as involving a continuous
curve representing exposure levels over time. This is illustrated in Figure 3, adapted from
jfarabek (1995). This figure suggests that there are many possible metrics for summarizing
the effects of exposure on a toxic outcome. Some possible metrics are listed in Table 2.
In some cases, more than one of the metrics might be required to fully characterize the
'Ix^osule-rllpo'ffse relationship. For example, Williams et at. (1996),found that the effects
in
in 11 iii i ii
I Ill I I il Ilillllllllllllllllil IIHII ill liil'lliiliililiilliiiill'ill'il Ill i1
i i
illiiiiii i i in ii nil mil in nil nil in Hi
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C x T Issues Paper
Figure 3:
General Exposure Profile
o
%
W
it 129 180 2*0 m ]ft 429
Duration of Exposure (minutes)
21,
C represents the ueas coaceatntioa HI, t reflects the duration of exposure i
I corresponds to a threshold level, and letric !5! is shosn as the darkened area above I.
P. indicates the recovery period (7] between exceeding the threshold, and the iK H! is
represented by tie'unioa of the gray and black solid areas.
Table 2: Dose Metrics Based on Exposure to Parent Compound or Metabolites
Description
(1) average concentration level, C
(2) duration of exposure, t
(3) time-weighted average concentration, C x t
(4) area under the curve, AUC
(5) area over a certain threshold x
(6) area under the curve when threshold x is exceeded
(7) duration of recovery periods between exposures exceeding threshold x
of heatshock exposures on developing embryos were best modeled by including both the
C x T metric of cumulative exposure and an additional effect for duration of exposure (i.e.,
metrics (2) and (3)), to account for the fact that shorter exposures at a higher level of
heat stress resulted in greater morphological impairment than longer exposures yielding the
same cumulative exposure. Both ozone and sulfur dioxide exposures show characteristic
episodes of ambient-type exposures, i.e., increasing throughout the day to a peak and then
.decreasing. For ozone, short-term, high concentration exposures were identified by many
researchers as being more important than long-term, low concentration exposures (see U.S.
EPA, 1996a). Acknowledging this fact, Lefohn and Benedict (1982) introduced an exposure
parameter that accumulated all hourly concentrations equal to and above a threshold level
of 0.10 ppm, which corresponds to metric (5) in Table 2.
For exposure to chemical compounds, mechanistic properties of the chemical (deposi-
tion, absorption, distribution, metabolism, and elimination) lead to the question of whether
11
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^^^^^
'{'Ml
"S*,,
DRAFT: July 27, 1998
"Cx Tissues
ii
the important dose metric should be measured on the basis of the parent compound or an
important metabolite, and whether they should be measured based on an external exposures
or concentrations at some target tissue. The dose metrics denned in Table 2 could poten-
tially refer to either the administered or ambient exposure level of the parent compound, a
blood or tissue concentration of the parent chemical, or a blood or tissue concentration of
an important metabolite. It should also be noted that the metrics listed in Table 2 are very
generally described and would require additional specification for use in practice, such as the
starting point in time after which exposures are measured.
Physiologically based pharmacokinetic (PBPK) models have been employed with
some success to estimate levels of chemical disposition at target tissues. By appropriate
sealing of mechanistic parameters, such as metabolic rates via the mixed function oxidase
(MFO) or glutathione (GST) pathway, a more accurate estimate of target exposure levels in
Humans can" be obtained. In some cases, it is likely that application of a PBPK model will
provide an exposure metric which removes the need to subsequently account for duration of
exposure through Haber's law or a more flexible dose-response model. Unfortunately, PBPK
models have only been developed for a small minority of compounds of interest. In addi-
tion, estimates of mechanistic parameters like lipid partition coefficients obtained from other
sludies are treated as if they are "known", which results in underestimates of population
variability.
While the exposure profile shown in Figure 3 attempts to illustrate the possible com-
plexity of exposure profiles, controlled experiments rarely collect data at this level of detail.
In situations where the exposures are controlled by the investigator, intermittent and highly
variable exposure patterns are difficult to simulate and control, and the number of such
patterns that would need to be considered would be impractical. Instead, investigators have
considered a much more limited range of exposures, such as holding concentration steady
over a certain duration and comparing to other constant CxT exposure blocks (see Figure 4).
Another possible simplified exposure scenario which would represent repeated bursts of ex-
posure relevant to many occupational settings could be to compare exposure patterns such
is those illustrated in Figure 5. For example, hospital employees involved in sterilization of
surgical instruments may be exposed to high concentrations of ethylene oxide over periods of
a few minutes" when opening hoods to load and unload equipment, and such exposures may
Ofic'ur repeatedly throughout tKe day. In other situations, investigators have not attempted
to "control" exposure patterns, but have instead collected observational data by measuring
Sllll'lil11 ' . ; . "I1, If'-WIUII- ifer , fr I " i -a, . 'i . "" "r:".a . i i , ., r'li i, - i ;. , .: T , .
both the exposure pattern over time and observed effects. These types of studies can give
gome sense of association between exposure patterns and toxic endpoints, but are not able
to address causal hypotheses due to the limitations of observational studies.
In contrastrto the complexity of possible exposure patterns which we might expect to
observe in nature, the exposure scenarios exhibited in Figures 4-5 are simplified scenarios for
Controlled studies; they may not be ideal, but allow an initial assessment of dose-rate effects.
The design of such studies will be discussed further in Section 4.2. Both in the design of
studies and the modeling of the effects of exposure levels and duration on toxicity, there is
pften a gap between the underlying variability in exposure levels over time and the extent
to which current methods allow modeling of such variability.
ii Iiii iiiii
ill l ill 1 iliiillli ill
1 , . . ,t .' it i1!,; ........ I,'!'*' ' '::!..;*,' ..... t!ft. '".'' t Iltll Jilt1; Trf ' , I1 .:.'., ! .' ..... " ....... Ji
i ,1 ..... iii illliiK i ..... M ..... itilOir ........... ' , ii 111 ...... IjiLIM^^ ..... lnMI ..... ...... III. ill ....... IJgM^^^^^ ..... i.'.
Jf''i, .( 'ill!!'" llj'liifc ...... \''!i!
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C x T Issues Paper
I
W
1233!
a
I,.
s aj
gt
Figure 4:
Constant Blocks of Exposure
0 ppa x 1 fiour
ppa x 3 hours
ppn x 5 hours
PP'E x 12 hcurs
1 2 3 I 5 6 1 8 5 13 11 12
Duration of Exposure (hours)
Figure 5:
Spiked Patterns of Exposure
1 2 3 i 5 5 1 I S 13 11 12
Duration of Exposure (hours)
13
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n r.< nriir )ซ"ซ" mart mm
DRAFT: July 27, 1998
"'jini! 11
1"' ini'llMf ;!li4!ll"T.i! U
C x T Issues Paper
An additional issue related to the dosimetry of exposure is the timing of exposure.
TJie timing of exposure can affect the magnitude of toxic effects resulting from a given
exposure due to periods of increased sensitivity. For example, for developmental toxicity
outcomes, an exposure during the early part of the gestational cycle may result in effects of
a much different type and severity than an exposure to the same agent and at the same level
and duration but later in the gestational cycle (Daston and Manson, 1995). It is possible
that the timing of exposure can be incorporated into the dosimetry of the exposure if the
metric of interest is concentration of the parent compound or metabolite at a target organ,
since the timing of exposure may impact the transport of the agent to the target tissue.
However, in cases where less mechanistic information is known about the agent in question,
the timing of exposure becomes an additional covariate that must be accounted for, leading
to a multivariate vector of exposure covariates rather than a single summary metric.
; 41 ^.StMlsticsil, Approaches for Assessment of Dose-rate
- Effects ' '
r
i-;,! '. ..i- SUM,: in :; ~ \ ui
4.1 Models for Dose-Rate Effects
I,-!!1!!1' : :,
IMF 4'
'I'l,,* !l,
Vfll'"!!" i ,
ป! '|."i|j: ' ' , I ' f I , i iป J II I
$&ere, a and, V are^,bot|i empirically defined coefficients. The key implication of this model
is that any pair of values (C,'t) which yield the same multiple YC1 x ti would be assumed
!'!ซ ' ] lif ,: ITT'IJ. &. v,",, ,'1 v.Yi, ;..,< sj; .y.i,., ,,_ ', ,..:.,,. ซ .A.-.. .ซ ' ..; .'-, ." r .
to produce the same level of toxic effect. This relationship can be summarized by an isobol
of constant effect level as shown in Figure 6 (model 11. For example, if the effect level of
', nErxri j^" ]7, iTriiih 'i|i"niiiT,,i, TT 'i;nn .ป, ,in \f :: ป:in, : T:" ,ป i'j, , u, i ' , , O \ / M, A, , ,
Inte'rest was the EJDos for fetal death resulting from a reproductive study and the exponent
^equalled" 1, then one could say that the ED05 level was proportional to the cumulative
exposure. This implies that equivalent values of the ED05 might be a 6-hour exposure at
'}
I"
14
. jij ....... Mmt ,.j;i^^^ ..... Iliill^^^^^^^^ ...... )!i^^ ..... i:*l!'ia ..... i'
..... I I
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Figure 6:
Effective Exposure/Duration Contour
Excess SisbC'.tS
ง
g>
1.
Q
i
Exposure Level (ppm)
300 ppm and a 9-hour exposure at 200 ppm. Based on 1-hour lethality studies, ten Berge
et al. found that 19 of the 20 chemicals they evaluated had estimated values for n of 1-3.5.
Thus, use of the relationship given by (1) assuming n = 1 for these 20 chemicals will tend to
overestimate the exposure level corresponding to shorter durations, and underestimate the
exposure level associated with the same effect level at longer durations.
These comments point to two important considerations. One is the need to account
for chemical specific information on the same endpoint to establish an exposure-response
relationship. Most of the 20 studies considered by ten Berge were lethality studies, and even
if a given study yielded data which satisfied (1), this would be tenuous grounds for assuming
that endpoints of milder severity such as the NOAEL would also follow this relationship.
The other is that it is often tempting to use a relationship such as (1) to estimate the effect
levels for durations of exposure that were not considered in the experimental design of the
study conducted. Without a great deal of external 'mechanistic information, extrapolation
of effect levels to durations of exposure not contained within the range of durations for a
given set of studies is on very shaky ground. As noted by Bliss and James(1966), Haber's
law tends to apply to either end of the exposure-response curve spectrum, i.e., at very high
concentrations and short exposures or at low concentrations for longer durations, but not in
between.
The relationship given by (1) above does not account for variability between animals
or individuals in response levels. In order to estimate the probability of toxicity at combina-
tions of exposure levels and durations, a dose-response model which reflects inter-individual
variability must be fit to experimental data. Standard dose response models are a function
of dose only and do not include a mechanism to account for various durations of exposure.
Scharfstein and Williams (1994) extended dose-response models to account for duration and
dose-rate effects as follows. First, under Haber's law the important metric is the cumulative
15
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DRAFT: July 27, 1998
IF ! ! j.';'
Ir" ;li
liV.llI I I,'
1; j
iiii,!=.i In' Iiii.
l. i
.'IE; I "lit!
C x T Issues Paper
exposure, C xT, so the dose-response model under Haber's law becomes:
p(C,T) = F[a
(2)
wHefe p(C, T) is the probability of an adverse event at concentration C and duration of
exposure T, an3 3- is a cumulative distribution function, such as a logistic or probit link
."function."1 TC'Eese models are fit using maximum likelihood methods, which yield parameter
estimates for a and 0 along with estimates of their standard errors. To reflect departures
from Haber's moctel, an extended model can be fit which includes an extra effect of duration
of exposure in addition to the C x T metric:
P(c,T) : =
;;
,1
In some instances, it may be desirable to modify this model so that duration of exposure
fias "no" effect at the control dose (C = 0), as follows:
"p(C,TJ" = F[a + '(C xT)]' ''". '"' ' ' (4)
V-ilC !!liK!,M.iซ 'illl'lllitt
where 8c = 0 if C = 0 and Sc = 1 if C > 0. The suitability of this model depends on
the length and conditions of the control exposure. For example, to evaluate morphological
impairment from heat stress of less than one hour, submersion of embryos in a water bath at
body temperature was expected to have the same effect regardless of duration. In contrast,
based on an inhalation study of ethylene oxide (Weller et al, i998b), it was possible that
exposure of pregnant mice to air for up to 8 hours could impact adverse effect levels such as
fetal weight, since mice were deprived of food and water during the exposure period.
Based on fitting either of the extended models (3) or (4) above, one can conduct a
test of whether Haber's model holds by testing whether 7 = 0. Significant negative estimates
of 7 would imply higher toxicity rates for short high level exposures than for longer low level
exposures given the same C x T multiple, while significant positive estimates of 7 would
Imply the reverse. After fitting such models, one can also construct a '"response surface",
qr the predicted probability of toxicity at each (C, T) combination (see Figure 7). The best
fftting model can also form the basis for estimation of the effective dose-duration contour,
i.e., the set of all (C,T) combinations that lead to an effect level of interest, such as the
EDo3. This is an extension of the isobol for Haber's model shown in Figure 6.
I", ' .Models such as (2) - (4) have been applied with some success' to data resulting from
.^teto^F'^lSJCWilHajms'et a/., 1996; Kimmel "et al., 1998, Geys'e* ai, 1998) and" a ""
dose-rate oleveiopmental toxicity study of ethylene oxide (Weller et al., i998b). In these
Contexts, the clustering of the animals into litters was accounted for by using generalized
equation (GEE) approaches" These models" could' also be easily extended to;
For example, in analysis of developmental
gestation ""day (GD), as follows:'
(5)
.ijlll i fin ... .,i ;., uppLiiu ,| .IT \ ~ J / ~ L~~ ' f t \ / J * '
lill'llll 'I . , |.:. .1, '' i i, jiijili .III'! 11 .'. ' ' . : 'P.i. I I :, ./'ill,.,:, , n /i ', .'i '... '.II. .:.'ii ,..'!,.w, ,.11,1,;... ,1 ,,. ii, it "11111111 iiirpinir n,n I II T, t, "Mil ;; in' .. , .'. u.'
" " iiiilti! ji; , ';, afl'li; , iilltii,. Jilli1 Jii'l'ii.;1! : i< rif-HK: if a ''K:* ,>'..:', i/jjk.V.l'1,' ?-* 11.' ",,|.1''p.i!JH"ii'>i'iiป1*ll.'!Kiill1 ii'i.'i iii'i'LKiiiin'fSjrjwi.faS' i ' ir'..f. vi.'ii1 . ;....i;r v f ';.'...
t?wever, the amount of .data that would be needed to fit such a model would be much larger
ajithat SmoSels"^
gestation day and C x T effects.
::"'::- ' ' : : ",:::::: ''. i1::1:::;1:::!: ' " . .' .' '.. ~~ ' : H c
:, , : ".::; ;,:_::: ., : :: ,;:,. ; : , lo
w.. 'UNI,./.. :'ii I
i .iliiii::!!!;.,;!;1,:1!!!!! , id :!! at; ill
t; J 1,3 ..iiii I'Pilii I 'ij.ll..1.1, I!
tu ...... id;;, v, |: ; i .....
:, iฃ,ii ..... ;;m ....... iiaiiii!! ..... amf,! ',
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Figure 7: Response Surfaces for Heatshock Data
Actual Percentage of Malformed Embryos Percentage of Malformed Embryos
Halted
Response Surface using Sioothed Spline Interpolation
Halted
Exposure Level 1
Excosure Level
The conceptual approach behind the models given in (2) - (4) can easily be extended
to address both continuous and categorical outcomes, in addition to the binary toxicity
outcomes considered above. For example, if the outcome of interest was fetal weight from
a developmental toxicity study, a general model for estimating the mean fetal weight as a
function of concentration and duration could be expressed as:
A*(C7,r) = a + 7T + 0(C x T)
(6)
and under Haber's law 7 would be set to 0. This type of model could be fit using generalized
estimating equations to account for clustering, or by maximum likelihood in cases where
data were not clustered. Such models are illustrated in the evaluation of fetal weight in the
EtO study (Weller et al, 1998b) and a set of six growth parameters, including crown rump
length and yolk sac diameter, in the heatshock study (Kimmel et al, 1998). Modeling a
continuous endpoint as shown above has the advantage of taking the actual measurements
into account, and should thus be more informative in terms of identifying possible dose
rate effects. However, approaches for risk assessment still typically depend on specifying a
lower cutoff value (eg. for fetal weight, 2 standard deviations between the mean weight of
control animals), below which animals are classified as "affected". This issue continues to
be controversial in the assessment of continuous endpoints and will not be examined further
here.
Similarly, the general model for binary outcomes given by (3) can be extended to
ordinal outcomes, such as the severity of a malformation into 3 levels (normal, moderately
affected, severely affected), by fitting an ordinal logistic regression model with covariates for
concentration and duration:
Pr(Y
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DRAFT: July 27, 1998
C x T Issues Paper
where Y reflects the severity level, and a separate intercept ak is estimated for each severity
level but a proportional odds assumption is made for the effect of exposure on the ordinal
outcome. These models can be fit to single outcomes, and have also been extended to allow
for joint modeling of multiple outcomes. These models have the advantage that they may
provide for greater power in exploring dose-rate relationships.
A disadvantage of the models described above is that they treat the exposure data
at the level of administered or external exposure only, and do not attempt to reflect the
underlying mechanism of toxicity. Just as there is a shift in the area of cancer risk assess-
ment to account for more mechanistic information in the risk assessment process, there has
been a parallel shift in risk assessment for non-cancer outcomes. One such attempt is a
generalization of Haber's law to account for detoxification:
D = [CVm- De] tR/w
(7)
where D is the dosage received during time t, C is concentration, Vm is the respiration rate
(m3/min), De is the detoxification rate, w is body weight, and R is the retention coefficient.
If the chemical is not detoxified at all (De = 0), then the relationship D = aC x t holds,
with a = VmR/w. However, if the detoxification rate is high, the dose received decreases.
While this equation is a step in the right direction in terms of accounting for mechanistic
information, most of the mechanistic parameters are unknown and not routinely estimated
ia toxicological studies.
4.2 Experimental Design
As noted previously, it is often impractical to design studies which evaluate the same types of
highly variables exposures that are known to occur in the environment. Not only would the
types of exposures shown m Figure 3 be difficult to control, but an evaluation of the effects of
exposure as a function of such exposure patterns would require comparison with a seemingly
unlimited set of exposure profiles. As a result, experimental designs for dose-rate studies, at
least in the area of animal bioassays, have focused on exposure patterns such as those shown
in Figure 4. There are again some exceptions to this general statement. For example, both
Musselman et al. (1983) and Hogsett et al. (1985) developed experimental designs which
allowed them to compare peak exposures to ozone versus mid-level exposures. Musselman
et al. (1986) extended these earlier studies by further comparing exposure patterns with two
different peak concentrations and two different exposure distribution curves (square wave
an,d ambient wave). . .
. |" '*. ;';*!,; ' ''I:;" 'Is '.: :' ; ' '~'''^ ' ' ' '. j^;.' '"' .' | '''.V !,,"":' '' ,C l" """'. I"1 'z^' '''., |;V ""f!'; . , I1.''1'','," '''"''i'':;''' ',' '" :".' '."f ''
:: While designs have been developed on an ad hoc basis to test the hypothesis of
Haber's Law, there appears to be very little in the literature on optimal design of dose-rate
< i|jjjjjj||;i.' I 'i;:'"! i". , ,: M'I" i >",. " i1 >, ""'H',' I,' |U iMIII II' ,ป!; , "i*i , ,,ปป|l V , ซi < .< ,< <
studies. In other words, if the goal is to employ a whole animal-systemic approach and
obtain the "best" characterization of the pattern of responses resulting from various dose-
duration combinations, or have highest power for detecting departures from Haber's law,
there is little guidance to draw from. Much of the work that has been done has focused on
design of dose-rate studies for assessing developmental toxicity.
' ' ' ' "'"' ' '"' ' '' " '
Using the modeling framework presented in the previous section, Scharfstein and
I
,, ..., 18
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Williams (1994) consider the issue of optimal design for dose-rate studies. It should first be
emphasized that the optimal design might depend on whether the goal of the study was to
test for departures from Haber's model, or whether the objective was to obtain as accurate
an estimate as possible of the combinations of (C, T) that lead to a certain effect level,
such as the ED^. In the first situation, the optimal design would be one which minimized
the variance of the parameter 7, so that a test of 7 = 0 would have high power. For the
second objective, Scharfstein and Williams based comparisons of the efficiency of various
designs on the distance between the "true" effective dose-duration contour, as based on
a known dose-response model, and an "estimated" dose-duration contour based on Monte
Carlo simulations of data under that true model and .other possible models.
Initially, one .might consider a design which investigated the combination of t duration
levels TI, ...,T4 and c concentration levels C\,..., Cc, and assess the response level at all t x c
combinations of duration and concentration. However, in many toxicological studies, it is
likely that exposure for the highest duration Tt and concentration Cc would be associated
with too high a level of mortality. In some cases, the range of exposures which lead to
sufficient incidence rates for model fitting without excessive mortality can be fairly narrow.
In addition, while this type of design might allow a better construction of the response
surface, it would likely lead to a less efficient test of departure from Haber's law. Instead, it
makes more sense to design studies which investigate several different C x T multiples and
vary the duration of exposure within multiples. Comparison of toxicity rates within a given
C x T multiple across the various durations of exposure thus gives a direct intuitive feel for
whether Haber's law is met or violated.
Scharfstein and Williams considered the situation where 9 (C, T) combinations rep-
resenting 3 multiples of C x T were to be evaluated, by specifying 3 different durations of
exposure (1, 3, or 6 hours) at each multiple. The lowest multiple was assumed to apply to
control animals (C = 0) and the highest multiple was fixed at 1200 ppm-hours in the context
of an inhalation study, so that the issue of identifying optimal designs was reduced to the
question of how best to choose the middle C x T multiple and how to allocate the animals to
the 9 (C. T) combinations given a fixed number of pregnant dams. Within this framework,
Scharfstein and Williams found that the optimal design for both of the objectives mentioned
above tended to be the same. There were two designs which appeared to be robust to model
departures and provide good efficiency for a wide range of dose-response relationships:
(1) equal allocation of animals to the 9 (C, T) groups, and specification of a middle multiple
as 70% of the highest multiple, or
(2) allocation of twice as many animals to the 6 exposed groups as the 3 control groups,
with specification of the middle multiple as 30% of the highest multiple
The first design described above was implemented in a study to investigate the dose-
rate effects of ethylene oxide exposure on developmental toxicity. In this context and in many
other types of toxicological studies, it seems essential to conduct a well-planned pilot study
in preparation of the main study to investigate effects of timing. If resources prevent use
of sufficient animals to include timing of exposure into the analysis based on a model such
as (5), then the timing must be concentrated within a narrow enough range to isolate the
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toxic endpoints of interest during that range. For example, in the EtO studies conducted at
Harvard,, we found that exposure during gestation day 6-8 yielded the most informative data
on malformations of the type we were interested in, while exposures during earlier periods
tended to result in greater fetal resorptions and fetal death.
Another complication of dose-rate studies which needs to be addressed in pilot the
p, nu ill 1, ,, ,! Ill lihi,', ill! i llliiiiiKINI1 , ' l|r ii'
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DRAFT: July 27, 1998
C x T Issues Paper
reliable enough to allow extrapolation to doses not considered in fitting the models?
How can these models be verified? How can we summarize uncertainty when extrap-
olating a model beyond the range of durations included in the data to which it was
fit?
4. How can mechanistic information and exposure metrics derived from PBPK models be
better incorporated into models which evaluate dose-rate effects?
5. How can study designs be improved to use information from previous exposures within
the same or other studies to determine the most efficient subsequent exposure?
6. What is the appropriate metric to use for reflecting exposure levels and durations in
dose-rate studies? How can the type of endpoint and the mechanism of action be
utilized to help choose the appropriate metric?
7. What are the most important areas that require strengthening in the assessment of
dose-rate effects?
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References
Andersen, M.E., MacNaughton, M.G., Clewell, H.J.III, and Paustenbach, D.J. (1987). Adjust-
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Atherley, G. (1985). A critical review of time-weighted average as an index of exposure and
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Bliss, C.I. and James, A.T. (1966). Fitting the rectangular hyperbola. Biometrics 22, 573-602.
: II
Burek, JU. Albee, R., Beyer, J., Bell, T., Carreion, R., Morden, D., Wade, C., Hermann, E.,
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Crofton, K.M. and Zhao, X. (1997). The ototoxicity of trichloroethylene: extrapolation and
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Crofton, K.M., Padi'lla,S., Tilson, H.A., Anthony, D.C., Raymer, ""j.EL and MacPhail, R.CT
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istered dose, target tissue concentrations, tissue damage, and functional effects. Toxicology
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Geys, H., Molenberghs, G., and Williams, P. (1998). Analysis of toxicology data with covariates
specific to each observation, (submitted)
' Haber, F. (1924). Zur Geschichte des Gaskrieges (On the History of Gas Warfare), in: Funf
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