>
United States
Environmental Protection
Agency .
Office of Research and
Development
Washington DC 20460
NCEA-f-0835
September 2000
SAB Review Draft
Chapters. Dose-
Response Modeling
for 2,3,7,8-TCDD
Review
Draft
(Do Not
Cite or
Quote)
Exposure and Human
Health Reassessment of
2,3,7,8-Tetrachlorodibenzo-
p-Dioxin (TCDD) and
Related Compounds
Part II: Health Assessment for
2,3,7,8-Tetrachlorodibenzo-p-
dioxin (TCDD and Related
Compounds
Notice
This document is a preliminary draft. It has not been formally
released by EPA and should not at this stage be construed to
represent Agency policy. It is being circulated for comment on its
technical accuracy and policy implications.
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NCEA-I-0835
September 2000
SAB Review Draft
www.epa.gov/ncea
Chapter 8. Dose-Response Modeling for 2,3,7,8-TCDD
Exposure and Human Health Reassessment
of 2,3,7,8-TetrachIorodibenzo-/?-Dioxin (TCDD)
and Related Compounds
Part II: Health Assessment for 2,3,7,8-Tetrachlorodibenzo-/?-dioxin
(TCDD) and Related Compounds
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally released by the
U.S. Environmental Protection Agency and should not at this stage be construed to represent
Agency policy. It is being circulated for comment on its technical accuracy and policy
implications.
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document is a draft. It has not been formally released by the U.S. Environmental
Protection Agency and should not at this stage be construed to represent Agency policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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TABLE OF CONTENTS - OVERVIEW
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-/7-Dioxin (TCDD)
and Related Compounds
Part I: Estimating Exposure to Dioxin-Like Compounds (Draft Final)
(EPA/600/P-00/001 Bb, Be, Bd) September 2000
Volume 1: Executive Summary (EPA/600/P-00/001Ba) (§dI/M|iij^includea-.in this draft.)
Volume 2: Sources of Dioxin-Like Compounds in the United States (EPA/600/P-00/001Bb)
Chapters 1 through 13
Database ;pf Sources of Environmental Releases|>f Dioxin.-
'p^^ (Draft Final)
W -987002B)
Also included; on this CB--R0M:
Volume 3: Properties, Environmental Levels, and Background Exposures
(EPA/600/P-OO/OOlBc)
Chapters 1 through 6
Volume 4: Site-Specific Assessment Procedures (EPA/600/P-00/001Bd)
Chapters 1 through 8
Part II: Health Assessment for 2,3,7,8-Tetrachlorodibenzo-/;-dioxin (TCDD) and Related
Compounds (Draft Final)
(EPA/600/P-00/001Be) September 2000
Chapter 1. Disposition and Pharmacokinetics
Chapter 2. Mechanism(s) of Actions
Chapter 3. Acute, Subchronic, and Chronic Toxicity
Chapter 4. Immunotoxicity
Chapter 5. Developmental and Reproductive Toxicity
Chapter 6. Carcinogenicity of TCDD in Animals
Chapter 7. Epidemiology/Human Data
Chapter 8. Dose-Response Modeling for 2,3,7,8-TCDD
(SAB Review Draft, September 2000)
Chapter 9. Toxic Equivalency Factors (TEF) for Dioxin and Related Compounds
(SAB Review Draft, September 2000)
Part III: Integrated Summary and Risk Characterization for
2,3,7,8-TetrachIorodibenzo-/J-Dioxin (TCDD) and Related Compounds
(SAB Review Draft, September 2000) (EPA/600/P-00/001Bg)
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CONTENTS
8. DOSE-RESPONSE MODELING 8-1
8.1. INTRODUCTION ...'.. 8-1
8.1.1. Overview 8-1
8.1.2. What Is Dose? 8-1
8.1.3. What Is Response? 8-2
8.1.4. What Is Modeling? 8-6
8.1.5. Empirical Modeling 8-8
8.1.6. Mechanism-Based and Mode-of-Action-Based Modeling 8-8
8.1.7. Elements of Chapter 8 .8-11
8.2. DOSE METRICS 8-11
8.2.1. Introduction ; 8-11
8.2.2. Selection of Effective Dose Levels 8-14
8.2.3. Dose Corrections for Species Differences in Half-Lives 8-15
8.3. EMPIRICAL DOSE-RESPONSE MODELING OF INDIVIDUAL
DATA SETS 8-16
8.3.1. Introduction 8-16
8.3.2. Human Dose-Response Models 8-16
8.3.2.1. All Cancers Combined and Lung Cancer 8-17
8.3.2.2. Average Body Burden 8-21
8.3.2.3. Noncancer Endpoints 8-23
8.3.2.4. Uncertainties in Estimates From Human Epidemiology 8-24
8.3.2.5. Conclusions for Human Cancer Dose-Response
Modeling 8-26
8.3.2.6. Additional Knowledge Gaps in Human Cancer
Dose-Response Modeling 8-26
8.3.3. Rodent Dose-Response Models: Cancer Endpoints 8-28
8.3.3.1. Animal Cancer Studies for Dose-Response Modeling 8-28
8.3.3.2. Conclusions From Animal Cancer Dose-Response
Modeling 8-29
8.3.3.3. Knowledge Gaps in Animal Cancer Dose-Response
Modeling 8-29
8.3.4. Rodent Dose-Response Models: Noncancer Endpoints 8-30
8.3.4.1. Methodology 8-30
8.3.4.2. Multiple-Dose Studies 8-33
8.3.4.3. Single-Dose Studies: Adult Animals 8-34
8.3.4.4. Single-Dose Studies: Developmental Studies 8-34
8.3.4.5. Summary of the Dose-Response Modeling for
Noncancer Endpoints 8-35
8.4. MODE-OF-ACTION-BASED DOSE-RESPONSE MODELING 8-39
8.4.1. Introduction 8-39
8.4.2. Model Structures and Model Development 8-40
8.4.2.1. PBPK Models 8-40
8.4.2.2. Biochemical, Tissue, and Endocrine Response Models 8-48
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CONTENTS (continued)
8.4.3. Application of Models 8-54
8.4.3.1. Modeling Preneoplastic Lesions 8-55
8.4.3.2. Estimation of Cancer Risks 8-58
8.4.4. Knowledge/Data Gaps 8-59
8.4.5. Summary 8-60
8.5. DATA GAPS 8-62
8.6. SUMMARY 8-63
8.7. CONCLUSIONS 8-66
REFERENCES FOR CHAPTERS 8-105
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LIST OF TABLES
8-1. Estimated half-lives for species considered in the analyses to
follow and used for converting between daily exposures and
steady-state body burdens 8-69
8-2. Maximum likelihood (95% lower bound) estimates for average
body burden yielding 1% added risks for lung cancer and total
cancer response from three epidemiological studies 8-70
8-3. Doses yielding 1% excess risk (95% lower confidence bound)
based upon 2-year animal carcinogenicity studies using simple
multistage models 8-71
8-4. Noncancer endpoints used for comparing ED0j values 8-72
8-5. Ratio of ED01/lowest dose, categorized by study type and
endpoint type 8-73
8-6. Estimated shape parameters, categorized by study type and
endpoint type 8-74
8-7. Categorization of specific endpoints 8-75
8-8. Steady state ED01 values calculated using mechanism-based
dose-response models of dioxin-regulated responses 8-77
Appendix I: Multiple-dose studies 8-78
Appendix II: Single-dose adult studies 8-89
Appendix III: Single-dose developmental studies 8-96
LIST OF FIGURES
8-1. Distribution of ED0, and BB01 values inmultidose studies by endpoint 8-102
8-2. Distribution of ED01 values in single-dose studies by endpoint 8-103
8-3. Schematic representation of the linkage of current PBPK models
and biochemical/tissue response models for TCDD action. . 8-104
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8. DOSE-RESPONSE MODELING
8.1. INTRODUCTION
8.1.1. Overview
This chapter describes concepts that embody the evaluation of dose-response
relationships for the dioxins and related compounds and examines dose-response models for
2,3,7,8-tetrachlorodibenzo-/?-dioxin (TCDD). TCDD is the most potent form of a broad family
of xenobiotics that bind to an intracellular protein known as the aromatic hydrocarbon receptor
(AhR) (Chapter 2). Other members of this family, in addition to the polychlorinated
dibenzodioxins (PCDDs), include polyhalogenated hydrocarbons such as the polychlorinated
dibenzofurans (PCDFs), polychlorinated biphenyls (PCBs), and polychlorinated naphthalenes
(PCNs). In addition, there are other classes of chemicals that bind to the AhR, such as
polynuclear aromatic hydrocarbons and naturally occurring compounds. A detailed discussion of
the interactions of these chemicals and the concept of TCDD equivalence is presented in Chapter
9. The biological and toxicological properties of dioxins have been investigated extensively in
more than 5,000 publications and abstracts since the identification of TCDD as a chloracnogen
(Kimmig and Schulz, 1957). Some data sets on members of this family of compounds other than
TCDD are clearly amenable to dose-response modeling. However, this chapter focuses
exclusively on studies in laboratory animals that can be used to evaluate dose-response for
TCDD. In addition, it evaluates human data where exposure to TCDD has been estimated and
dose-response can be modeled quantitatively.
Most of the information presented in this introduction is found in more extensive detail
later in this chapter or in the other parts of this reassessment. This introduction sets the stage for
discussion of dose-response modeling of TCDD by briefly answering the questions, "what is
dose?" "what is response?" and "what is modeling?" It then goes on to describe and, to a limited
degree, compare different modeling approaches. This introduction also shows the reader the
types of data and information available for TCDD that may have an impact on the development
of dose-response models. Both in the introduction and throughout this chapter, gaps in
knowledge relating to the evaluation of TCDD dose-response are identified. Understanding these
gaps and their impact on the conclusions of this chapter can guide the design of new experiments
that will add to our knowledge of TCDD action and clarify issues related to its dose-response.
8.1.2. What Is Dose?
When performing dose-response analyses, it is critical to understand what is meant by
dose and how it applies to the response. The dose, in dose-response modeling, is an inclusive
term. Examples of dose include the amount of TCDD given to an experimental animal by some
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specific route at some specific frequency, measured tissue concentrations in laboratory studies,
body burdens attained in these studies, or daily exposure seen by workers in an occupational
setting. In general, units of dose should reflect the magnitude of the exposure and the frequency
over which it applies. Dose can be expressed in a multitude of metrics. Some of these metrics
include daily intake (ng/kg/day), total body burden (ng/kg), body burden averaged over a given
period of time, or tissue concentration. Depending on the particular endpoints to be compared,
and in consideration of the half-life of elimination of TCDD (see Section 8.2), it may be possible
to express dose in a form that allows comparison of response across various endpoints and
species. Specific issues relating to dosage and comparison across species and endpoints are
discussed in Section 8.2.
Most, if not all, of the effects elicited by TCDD are mediated by the ability of this
chemical to bind to the AhR. The activation of this protein leads to a series of molecular and
biochemical events that ultimately contribute to particular biological responses (see Part I,
Chapter 2). It is clear from the available human and animal data that TCDD can elicit many
types of responses depending on the species, the age of the animal at exposure, and whether the
exposure is acute or chronic. These responses vary from biochemical alterations such as en2yme
induction, which may require only acute exposures, to developmental effects, which may require
a level of exposure at a particular window of tissue development, to more complex responses
such as cancer, which may require prolonged exposures (Section 8.1.3). To determine what
might be the most sensitive endpoints, the species variation in sensitivity to these endpoints, and
how these differences or similarities might be extrapolated to effects in humans, requires a
comparison of the amount, or dosage, of TCDD that is present in particular tissues and/or the
whole organism.
Dose is not always a known quantity. For humans, the actual dose is rarely known and
best estimates are made on the basis of several assumptions and observations made at only a few
time points, often many years after what may be believed to be the period of highest exposures.
For these cases, models of exposure linked to response data may be used to develop a
dose-response model. However, limited knowledge of the events that control tissue distribution
(especially in humans at low levels of exposure) and those molecular and biochemical processes
that ultimately lead to particular responses contribute uncertainty in these analyses.
8.1.3. What Is Response?
Response, in this context, generally relates to an observation seen in an animal or a
human following exposure to TCDD. These responses cover a broad range of observations,
ranging from early responses such as biochemical alterations that are closely coupled to
activation of the AhR to more complicated responses such as cancer and developmental defects.
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The responses are sometimes species- and/or tissue-specific and have different degrees of
variation across individuals. However, there is some commonality across species and there are
known linkages between some responses (e.g., mRNA serves as a precursor molecule for the
synthesis of protein). Dose-response modeling can address endpoints separately, provide insight
into their quantitative similarity across species and tissues, and link responses in a
mechanistically reasonable manner.
The binding of TCDD to the AhR is similar, although not identical, to the interaction of
many steroid hormones with their intracellular receptors (Poellinger et al., 1987; Cuthill et al.,
1991; DeVito et al., 1991; Lucier et al., 1993). An overall hypothesis for the mode of action of
TCDD, put forth by several groups, is based on the transcriptional activation of expression of
specific genes. This hypothesis has been most well characterized for transcriptional activation of
the cytochrome CYP1A1 gene. There is also some evidence to indicate that activation of the
AhR by TCDD may elicit responses by mechanisms that may not involve direct transcriptional
activation of genes. The biological basis for these models of AhR action is outlined in Part II,
Chapter 2. It is accepted by most researchers that most, if not all, cellular responses to TCDD
require the initial interaction between TCDD and the AhR.
Although gaps in our knowledge remain, evidence to date is consistent with the
hypothesis that binding of TCDD to the AhR and inappropriate activation of this protein
represent the first steps in a series of biochemical, cellular, and tissue changes that define the
toxicity observed. These changes are defined as responses to TCDD. Evidence to support this
theory has been reviewed in several sections of this document as well as in the peer-reviewed
literature (Safe, 1990; Birnbaum, 1994; Poland and Knutson, 1982). Many of the known
biological activities of related PCDDs and PCDFs also appear to follow their rank order of
binding affinity of the congeners and analogues to the AhR (see Part II, Chapters 2 and 9). This
rank order holds for toxic responses such as acute toxicity and teratogenicity and for changes in
concentration of several proteins, including the induction of cytochromes P-450 1A1 (CYP1A1),
1A2 (CYP1A2), estrogen receptor, and epidermal growth factor receptor (EGFR). The direct
relationship between AhR binding and carcinogenicity of TCDD is less clear.
The AhR has been identified in numerous mammalian species including humans (Okey et
al., 1994; Roberts et al., 1985, 1986; Abbott, 1995; Manchester et al., 1987; Lorenzen and Okey,
1991; Cook and Greenlee, 1989), several nonmammalian vertebrates including chicken embryos
(Denison et al., 1986) and newts (Marty et al., 1989), and several aquatic species from whales to
teleosts and elasmobranchs (Hahn, 1998). The broad phylogenetic distribution in vertebrate
evolution (Hahn, 1998) and the phylogenetic conservation of this receptor also suggest that it has
an important role in regulating cellular function in vertebrate animals. However, the
physiologicaLrole or function of this receptor has yet to be determined.
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Although the human data are limited, there is relatively good concordance for the
biochemical/molecular effects of TCDD between laboratory animals and humans, indicating that
animal models are generally appropriate for estimating human responses. Where wide species
differences exist, understanding the relative sensitivity of human responses may not be possible
at this time. However, many of the biochemical effects produced by TCDD and its analogues in
animals also occur in humans. Data on effects of TCDD and its analogues in humans are based
on in vitro (i.e., in cell culture) as well as epidemiological studies. Placentas from Taiwanese
women exposed to rice oil contaminated with dioxin-like PCBs and PCDFs have markedly
elevated levels of CYP1A1 (Lucier et al., 1987). Comparison of these data with induction data
in rat liver suggests that humans are at least as sensitive as rats to enzyme-inductive actions of
TCDD and its structural analogues (Lucier, 1991). Consistent with this contention, the in vitro
EC50 for TCDD-mediated induction of CYP1 Al -dependent enzyme activities is ~1.5 nM when
either rodent or human lymphocytes are used (Clark et al., 1992). The human AhR appears to
have greater than a twentyfold range in TCDD affinity (Okey et al., 1994). This range is
comparable to that of the sensitive and resistant mouse strains as well as that of rats (see Chapter
2). It does appear that humans contain a fully functional AhR (Cook and Greenlee, 1989), as
evidenced by significant CYP1 Al induction in tissues from exposed humans, and that this
response occurs with similar sensitivity as observed in experimental animals.
One of the biochemical effects of TCDD that might have particular relevance to toxic
effects is the loss of plasma membrane EGF receptor. There is evidence to indicate that TCDD
and its structural analogues produce the same effects on the EGF receptor in human cells and
tissues as observed in experimental animals. Incubation of human keratinocytes with TCDD
decreases plasma membrane EGF receptor, and this effect is associated with increased synthesis
of transforming growth factor-a (TGF-a) (Choi et al., 1991; Hudson et al., 1985). Placentas
from humans exposed to rice oil contaminated with PCDFs also exhibited markedly reduced
EGF-stimulated autophosphorylation of the EGF receptor, and this effect occurred with similar
sensitivity as observed in rats (Lucier, 1991; Sunahara et al., 1989). The magnitude of the effect
on autophosphorylation was positively correlated with decreased birth weight of the offspring.
Chloracne, a well-known response observed in highly exposed humans, has also been
shown to occur hi several animal species including nonhuman primates, rabbits, and hairless
mice. However, it should be noted that in populations exposed to similar amounts of TCDD
(e.g., Seveso, Italy), some humans may exhibit chloracne while others do not. In mice,
responsiveness to TCDD and related chemicals can be modified by genes as well as the AhR.
For example, mice congenic at the Hr locus demonstrate altered sensitivity to the chloracnegenic
and tumor-promoting effects of TCDD (Poland et al., 1982). These data suggest that there may
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be multiple factors (e.g., genetics) that may contribute to the development of a particular
response both within and between species.
Several reports in the literature suggest that exposure of humans to TCDD and related
compounds may be associated with cancer at many different sites, including malignant
lymphomas, soft tissue sarcomas, hepatobiliary tumors, hematopoietic tumors, thyroid tumors,
and respiratory tract tumors. These studies are evaluated in Part II, Chapter 7a, including
discussion of confounding factors and strength of evidence. TCDD is a carcinogen in several
species of laboratory animals (mice, rats, hamsters, fish) and the tumor sites include liver,
thyroid, and the respiratory tract, as well as others.
Several noncarcinogenic effects of PCDDs and PCDFs show good concordance between
laboratory species and humans (DeVito et al., 1995). For example, in laboratory animals, TCDD
causes altered intermediary metabolism manifested by changes in lipid and glucose levels.
Consistent with these results, workers exposed to TCDD during the manufacture of
trichlorophenol showed elevated total serum triglycerides and cholesterol with decreased high
density lipoprotein (Walker and Martin, 1979), similar to results seen in Air Force personnel
following exposure to Agent Orange (Wolfe et al., 1990; Fallen et al., 1994). Another interesting
finding of these studies was a positive relationship between TCDD exposure and diabetes (see
Part II, Chapter 7b).
There are also differences between human and animal effects associated with TCDD. For
example, chloracne has been observed in exposed humans but in only some animal species.
Similarly, increases in humans of certain cancers such as soft-tissue sarcoma have not been
observed in animals (see Part II, Chapters 6 and 7). Also, immunotoxic endpoints consistently
seen in animals have rarely been demonstrated, or looked for, in humans (see Part II, Chapter 4).
The recognition of these similarities and differences is essential when using animal data to
estimate human effects. Understanding of these similarities and differences can substantially
improve dose-response analysis.
The human-to-experimental-animal comparison is also complicated by several other
factors: (1) for most toxic effects produced by dioxin, there is marked species variation. An
outlier or highly susceptible species for one effect (i.e., guinea pigs for lethality or mice for
teratogenieity) may not be an outlier for other responses; (2) human toxicity testing is based on
epidemiological data comparing "exposed" to "unexposed" individuals. However, the
"unexposed" cohorts contain measurable amounts of background exposure to PCDDs, PCDFs,
and dioxin-like PCBs. Also, the results of many epidemiological studies are hampered by small
sample size, and in many cases the actual amounts of TCDD and related compounds in the
human tissues were not examined; (3) In addition, it is often difficult, if not impossible, to assess
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in humans the same endpoints that might be determined in experimental animals (e.g., some
immunotoxic effects and altered liver enzymes).
In summary, for many of the biological responses elicited by TCDD, animal models
appear to be reasonable surrogates for estimating human risks. However, it must be kept in mind
that the animal-to-human comparison would be strengthened by additional mechanistic
information, especially the relevance of specific molecular/biochemical precursors to toxic
responses. It is also important to note that the key events leading to carcinogenesis may be quite
different at different sites (see Part II, Chapter 6).
8.1.4. What Is Modeling?
In the sciences, a model is a representation of how something works. Models are of
several types, such as conceptual (e.g., a mental image of how something works), biological (e.g.,
transgenic mice as a surrogate for a human system), physical (e.g., a three-dimensional model of
the human heart) and mathematical (e.g., a physiologically based pharmacokinetic model
{PBPK]). Any model is defined by a set of parameters that make up its key components, and
usually has inputs (e.g., dose) and outputs (e.g., response) that correspond to its real-world
counterparts. Mathematical models of dose-response generally can be classed into two broad
areas: empirical models and mechanism-based or mode-of-action models; these are described in
the next two sections.
Modeling involves the application of a mathematical model to data as a tool to allow for
analysis and prediction. Any modeling exercise requires the estimation of model parameters.
The tools used to estimate parameters range from very simple techniques, such as estimating a
slope of a straight line (linear regression), to extremely complicated approaches, such as
estimation by maximizing a statistical likelihood function comprising unknown model
parameters. In some cases, estimation of parameters in a model involves choosing a value based
upon scientific judgment. The quality of any parameter estimate is dependent on the available
data to characterize the model. The quality of the data and information used to develop a
mathematical model is the major component in determining the confidence placed in any
conclusions or predictions from that mathematical model.
Dose-response models for receptor-mediated events should use information on the
quantitative relationships among ligand concentration, receptor occupancy, and biological
response. For example, Roth and Grunfeld (1985) state: "At very low concentrations of
hormone receptor occupancy occurs but may be trivial; i.e., the curve approaches 0% occupancy
of receptors. But if there are 10,000 receptors per cell (a reasonable number for most systems),
the absolute number of complexes formed is respectable even at low hormone concentrations.
One advantage of this arrangement is that the system is more sensitive to changes in hormone
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concentration; at receptor occupancy (occupied receptors/total receptors) below 10%, the
concentration of occupied receptors is linearly related to the concentration of hormone, whereas
at occupancies of 10 to 90%, the concentration of HR is linear with log hormone concentration, a
given increase in the concentration is more effective in generating occupied receptors at the
lowest part of the curve than at the middle."
It is clear that multiple dose-response models are possible when considering
ligand-receptor mediated events. For example, when there is a proportional relationship between
receptor occupancy and biological response, occupancy of any number of receptors would
produce a response, although it would be unlikely that the response could be detected if the
number of receptors occupied was very low. Given this proportionality, a simple model,
describing the response as a linear function of dose, may be adequate. However, such a simple
relationship is unlikely to explain the diversity of biological responses that can be elicited by a
single hormone utilizing a single receptor. For example, low concentrations of insulin produce
much greater effects on fat cells than on muscle cells because fat cells have more receptors.
These differences are due to cell-specific factors that determine the qualitative relationship
between receptor occupancy and response. Similarly, it is expected that there are markedly
different dose-response relationships for different effects of TCDD.
Coordinated biological responses, such as TCDD-mediated increases in cell proliferation,
likely involve other systems, which means that the dose-response relationships for relatively
simple responses (i.e., CYP1 Al induction) may not accurately predict dose-response
relationships for complex responses such as cancer. Thus, it is necessary to consider what is
known and observed regarding a biological response before a reasonable mathematical model can
be applied to the data. Responses that include coordination of multiple steps that have linear
dose-response relationships may ultimately produce markedly nonlinear dose-response
relationships.
The goal of mathematical modeling should be to use as much data as possible to reduce
uncertainties and to identify the areas where data gaps exist. Several important concepts have
been generally accepted that may determine the types of mathematical models one might apply to
responses due to exposure to TCDD: (1) TCDD is a member of a class of xenobiotics (and
probably natural products) that is not directly DNA reactive, binds to a cellular receptor, alters
gene expression, and alters cell growth and development; (2) a significant amount of information
is available for estimating risks from exposure to this compound, and these data should be used
to their fullest extent; (3) the biology of receptor-mediated events should be included to the
greatest extent possible in any modeling exercise for TCDD, empirical or mechanism-based.
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8.1.5. Empirical Modeling
By its very nature, data applicable to dose-response modeling can generally be expressed
through groups of individuals (cells, animals, humans) exposed to a common level of a toxic
agent (TCDD) for which some response is measured. Given sufficient numbers of exposure
groups, it is possible to see a pattern arise, which indicates a change of that response as a
function of increasing dose. Empirical dose-response modeling attempts to find a simple
mathematical model that adequately describes this pattern. Empirical models generally have
little or no direct linkage to the underlying mechanisms driving a given response, but instead
focus on flexible mathematical forms that can fit a broad spectrum of data and allow
comparisons across individual data sets. However, empirical models should be interpreted in
light of information available on the biology of the modeled response and, in doing so, can
provide qualitative insights into underlying mechanisms.
Examples of empirical models include linear functions (such as those used in linear
regression), log-linear models, Poisson regression (commonly used in epidemiology), and Hill
models (commonly used to analyze ligand-receptor data). Empirical models have the advantage
of ease of use, the existence of "user-friendly" software tools capable of fitting these models to
dose-response data, and a formal framework for hypothesis testing and interpolation between
data points. In addition, empirical models can be used to estimate a point of departure for
extrapolation. The major disadvantage of empirical models is their inability to quantitatively link
multiple data sets in a mechanistically meaningful manner.
8.1.6. Mechanism-Based and Mode-of-Action-Based Modeling
In contrast to empirical modeling, mechanism-based modeling attempts to use an
understanding of the mechanistic relationship between exposure and multiple endpoints to
simultaneously describe the observed response. Mechanism-based modeling can be a powerful
tool for understanding and combining information on complex biological phenomena (Lucier et
al., 1993). Mechanism-based modeling commences from a series of experiments with a
xenobiotic agent. The experimental results (data) can indicate a mechanism supporting the
creation of a mathematical model. The predictions of that model are tested for consistency with
the existing knowledge base for the agent and effect under study. Defects in the fit can suggest
new experiments that may permit refinement of the model. On each iteration of this process, the
model either gains additional credibility by predicting the new experimental results or it is
modified to fit the new as well as previous results. In either case, subsequent iterations of this
process increase our confidence in accepting or rejecting a final model, although it may be
difficult or impossible to quantify this confidence.
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Mathematical models that incorporate parameters that correspond to actual biological
structures or processes do not automatically constitute "mechanism-based models." The types of
data available for the model and the method by which these data are incorporated into the model
determine if a model truly reflects the biology. A parameter that specifies the activity of a
xenobiotic metabolizing enzyme, for example, should have a biologically realistic value.
Without careful attention to the representation of biological detail, confidence in the model and
use of its results is reduced.
Ideally, the parameters in a mechanism-based model are derived from first principles in a
"bottom-up" fashion. In this case, the structure of the model is an accurate mathematical
representation of the known properties of the system being modeled, and the mechanistic
parameters in the model are estimated directly from data. Such, a model can increase confidence
in extrapolating outside the range of the data as long as attendant uncertainties are carefully
evaluated. In practice, it is generally impossible to completely develop a mathematical model for
biological processes. At some point, processes by which the mechanistic events elicit the
observed toxic effects must be deduced in a "top down" approach that uses some curve fitting.
The concept of mode of action has been developed in response to this difficulty in implementing
the "bottom up" approach (U.S. EPA Guidelines for Carcinogen Risk Assessment,
EPA/600/Z96001). The term mode of action is defined as a series of key events and processes
starting with interaction of an agent with a cell, through operational and anatomical changes
resulting in cancer formation and other toxicities. "Mode" is contrasted with "mechanism" of
action, which implies a more detailed molecular description of events. Operationally, the
description of the mode of action should convey enough information to characterize the shape of
the exposure-response curve. A risk assessment model based on the mode of action is preferable
to empirical modeling when making inferences outside of the range of the effects data.
Without data (as is the case with extrapolated predictions), the statistical issue of the
accuracy of a prediction cannot be easily addressed. Thus, while there may be greater biological
confidence in extrapolated results, it is unlikely that an increased statistical confidence can be
demonstrated. However, for each level and type of data, there are ranges of exposure beyond
which it is impossible to demonstrate an effect because of limitations in the sensitivity of those
assays. In general, effects can be demonstrated at lower exposures for mechanistic data (e.g.,
gene expression) than for toxicity data. Hence, use of a true mechanism-based approach should
enable reliable and scientifically credible extrapolations to lower exposures.
Risk assessment typically involves extrapolations between species, from high to low
doses, and between different patterns of exposure. Uncertainty in risk assessment is reduced to
the extent that these extrapolations are based on mechanistic considerations. For TCDD, the
mechanisms of three processes are of primary interest: (1) the dosimetry of TCDD throughout
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the body and specifically to target tissues; (2) the molecular interactions between TCDD and
tissue proteins, emphasizing the activation of gene transcription and increases in cellular
concentrations of growth-regulatory gene products and metabolic enzymes; and (3) the
progressive tissue-level alterations resulting from these interactions that lead, eventually, to
toxicity. Mechanism-based modeling for TCDD is the quantitative description of the
mechanisms that define these processes. A model based on mechanistic understanding of the
biochemistry of TCDD-induced toxicity and that accurately reproduces observed effects would
permit more confident extrapolations to low doses and more reliable resultant risk estimates. As
previously stated (Greenlee et al., 1991), "Neither the position taken by U.S. EPA or by
Environment Canada (and several other countries such as Germany and the Netherlands) is based
on any detailed mechanistic understanding of receptor-mediated interactions between TCDD and
target tissues. In addition to their use in risk assessment, models of these processes can aid in the
design of future experiments to clarify understanding of TCDD toxicity and support further risk
estimation."
Several models ranging from very simple to complex have been developed to describe the
toxicity of TCDD. It is obvious that the biology governing the toxicity of TCDD, beyond a few
initial critical events, is not straightforward. These critical events, the first of which is binding to
the AhR, are generally response-independent. The response-dependent events are species-, sex-,
organ-, tissue-, cell- and developmental stage-specific. If binding to the AhR is essential but not
sufficient for effects to occur, then the dose-response curve for this event (as well as the rate
equations) should be a better predictor of biological action than external dose as long as the
shapes of the dose-response curves for these subsequent actions are similar to those of receptor
binding curves. In general, the available data indicate that receptor involvement is necessary for
most if not all low-dose actions of TCDD. However, it is clear that for many responses, the
dose-response curves are different from receptor binding curves. Furthermore, although the AhR
has been detected in many kinds of cells, not all of these exhibit toxic responses. These data
suggest that there must be other factors that are necessary for TCDD-induced toxicity. The roles
of these cell-specific factors and how they affect the ultimate response must be elucidated before
there is a complete understanding of TCDD action. However, a model may be developed for
specific endpoints by using available data and biologically plausible assumptions.
TCDD can be considered as a prototype for exploring and examining the ability of
mechanism-based modeling to improve the accuracy of quantitative risk assessment. The
database for a mechanistic modeling approach to TCDD is extensive and contains a considerable
amount of information on low-dose behavior. In addition, there is some concordance between
human data and experimental evidence in animals (see Section 8.3). On the other hand, some
aspects of the mechanism by which TCDD induces its effects, such as binding of the AhR to
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accessory proteins, have not been modeled extensively because of lack of data. Because of this
deficiency, several alternative mechanistic hypotheses may agree with the existing data. The role
of mechanism-based modeling in this case is to identify a set of candidate biologically plausible
models, rather than to provide a final description. This outcome is inevitable for the application
of the technology of mechanism-based modeling to a new area. Reduction in the size of the
candidate set and, eventually, identification of the preferred model must await additional results
from the laboratory.
To reiterate an earlier point, mechanism-based modeling can aid in explaining and
understanding experimental results, beyond its proposed use in risk assessment.
8.1.7. Elements of Chapter 8
The following sections of this chapter discuss the underlying science related to selection
of appropriate dose metrics for dose-response modeling, empirical modeling of individual data
sets, and mechanism-based dose-response modeling for biochemical responses and tissue
responses. This modeling effort follows a natural progression related to the kind of information
available at the time these models were developed. In addition, knowledge gaps have been
identified throughout the chapter and have been consolidated in a section related to data gaps and
research needed to address critical uncertainties that remain in the dose-response modeling of
TCDD. Discussion of the strengths and weaknesses, assumptions and uncertainties, and
implications of these TCDD dose-response modeling efforts follows. Detailed tables containing
the outputs of the empirical dose-response modeling efforts are appended to this chapter for the
benefit of those readers who wish a more detailed view of the data and analyses supporting the
discussion and conclusions of this chapter. General conclusions are presented in a short
summary statement that is found toward the end of this chapter.
8.2. DOSE METRICS
8.2.1. Introduction
One of the more perplexing issues in toxicology is animal-to-human dose extrapolation.
To provide significant insight into differences in sensitivity among species, an appropriate
animal-to-human extrapolation of tissue dose is required. Chemicals can produce many different
types of responses depending on the exposure scenario and the response. Some responses are
reversible (enzyme induction) whereas others are irreversible (death, cancer). Some responses
require prolonged exposures (porphyria and cancer). Others have unique windows of
susceptibility where an adverse effect (e.g., cleft palate) occurs only after a critical window of
exposure (e.g., during development). The processes leading to particular toxic responses are
highly divergent, with some responses requiring a continued exposure over a prolonged period of
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time and some requiring an exposure over only several hours. It is unlikely that a single dose
metric will be adequate for interspecies and intraspecies extrapolation for all of these endpoints.
Estimating risk to various human populations is complicated by differences in exposure
scenarios. Human exposures to high levels of dioxins have occurred in several different
scenarios. There have been industrial accidents that have resulted in high exposures over a very
short period of time, such as the explosion at the ICMESA trichlorophenol plant near Seveso,
Italy, in 1976 (Ghezzi et al., 1982) and the BASF chemical plant in Ludwigshafen, Germany, in
1953 (Zober et al., 1990). Increased daily exposures over background to dioxins have occurred
in occupationally exposed populations using some herbicides, for example, during the Vietnam
War (Verger et al., 1994) and in agricultural workers (Kogevinas et al., 1995). Routine
occupational exposures have occurred in several manufacturing facilities around the world. The
final type of human exposure occurs in the general population, which is exposed daily to TCDD
in the diet at a dose rate of approximately 0.14 to 0.4 pg/kg/day1 (see Part I). One of the
difficulties in examining and comparing these different populations is that the actual dose or
exposure is rarely known. Estimates are often based on present serum TCDD concentrations,
with extrapolation back to the initial time of exposure based on the half-life of TCDD in humans
(Fingerhut et al., 1991; Scheuplein and Bowers, 1995).
In contrast, the exposures in animal experimentation are controlled and well defined.
Animal studies use multiple dosing regimens including single acute exposures, chronic daily
exposures, and biweekly exposures. Comparison across species sometimes requires
extrapolation from one exposure scenario to another. Large differences between species and the
half-life of TCDD, and quantitative differences in the tissue distribution of TCDD, must be
considered (van der Berg et al., 1994).
Determining the most appropriate dose metric represents an additional difficulty when
different endpoints and species are compared. Comparison of responses across species requires
the expression of dose using an equivalent metric. Dose can be expressed in a multitude of
metrics (DeVito et al., 1995) such as daily intake (ng/kg/day), current body burden (ng/kg),
average body burden over a given period of time, plasma concentration, concentration of
occupied AhR (Jusko, 1995), induced CYP1A2 (Andersen et al., 1997a; Kohn et al., 1993), and
reduced EGFR (Portier and Kohn, 1996).
Different dose metrics can lead to widely diverse conclusions. For example, the lowest
dose with an increased tumorigenic response (thyroid tumors) in a rat (NTP, 1982a) is 1.4
"Calculated from human daily dietary dose of 10 to 20 pg/day TCDD and human body weights between 50 and 70
kg; it should be noted that, on a total TCDD equivalents (TEQ) basis, total daily intake equals approximately 70
pg/day (see Part I) (see Chapter 9 for discussion of TCDD equivalents).
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ng/kg/day and the daily intake in humans is approximately 0.14 to 4 pg/kg/day. This implies that
humans are exposed to doses 3,500 to 10,000 times lower than the rat dose. However, 1.4
ng/kg/day in the rat leads to a steady-state body burden of approximately 25 ng/kg, assuming a
half-life of TCDD of 23 days and absorption from feed of 50%2. The current body burden in
humans is approximately 5 ng/kg lipid or 1.25 ng/kg body weight (assuming about 25% of body
weight is lipid), suggesting that humans are exposed to about 20 times less than the minimal
carcinogenic dose for the rat. The difference between these two estimates is entirely due to the
approximately 100-fold difference in the half-life between humans and rats. At least for this
comparison, the most appropriate metric for comparison is the steady-state body burden. (Note
that current daily intake for humans is likely lower than historical levels and is biased downward
because of unknown sources, leading to a discrepancy between body burdens and daily intake.
For example, the predicted steady-state body burden for humans given a daily intake of TCDD of
0.2 pg/kg/day, a 7.1-year half-life and 50% bioavailability is 0.4 ng/kg. (For a discussion, see
Parti).
In addition to the uncertainty in the half-life of TCDD in humans, such calculations
assume exposure to TCDD at a constant rate rather than the actual episodic exposure scenarios
generally seen in the studied populations. In principle, a reliable PBPK model for humans could
be used to compute body burden, tissue dose, or any other desired dose metric for any dosing
scenario. However, as outlined in Section 8.4, the existing data are inadequate for this
extrapolation. If time courses of TCDD in human blood were available for widely different
doses, metabolic parameters for humans could be estimated. Inclusion of these quantities in a
PBPK model would permit the calculation of a tissue dose or body burden to be used for risk
assessment.
The developing embryo represents a very different complication in choosing a correct
dose measurement. The susceptibility of a developing embryo or fetus to TCDD insult may be
dependent upon the stage of development. For example, susceptibility to TCDD-induced cleft
palate has a specific window of sensitivity. Once the palatal shelves fuse, cleft palates cannot be
induced by TCDD. These windows of susceptibility are on the orders of hours to days. One of
the difficulties is that the time span is often too short to clearly discriminate among dose metrics
such as peak concentration, steady-state body burden, or average body burden. When these types
of comparisons for TCDD are attempted, it appears that they are of equivalent utility, provided
the dose metric was determined only during the window of sensitivity. In both animals and
2 Steady-state body burden (ng/kg) = daily dose (ng/kg/day) [(half-life/ln(2)] (f where f is the fraction absorbed from
the exposure route (unitless) and half-life is the half-life in days.
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humans, the biological half-life of TCDD is much greater than the time span of the window of
susceptibility. Hence an average measurement or a peak measurement can be used as an
appropriate dose metric. The windows of susceptibility for some of the developmental toxicities
of TCDD have been identified (i.e., induction of cleft palate and hydronephrosis). Peak body
burden may be a more appropriate dose metric for developmental effects because the window of
susceptibility is undefined for several endpoints.
Ideally, the best dose metric is that which is directly and clearly related to the toxicity of
concern by a well-defined mechanism. For mechanism-based cancer modeling, instantaneous
values of a dose metric are used because these can be used as surrogates for mutational rates and
growth rates within a two-stage cancer model. For epidemiology studies of lung cancer and all
cancers combined, there is not enough information to develop a mechanistic approach. In this
case the chronic exposures generally thought to be associated with the cancer process can be
described by metrics that integrate dose over a specific time period., and an average body burden
dose metric is acceptable for steady-state conditions. However, difficulties arise when this
metric is applied to accidental high acute exposures. To allow for comparison across studies, it is
sometimes useful to find a constant daily exposure or steady-state body burden that yields the
same total exposure. Comparability of response over multiple species for a given dose metric
can be used to assess the adequacy of that metric. It should be noted that for compounds like
TCDD with very long half-lives, relative differences between doses expressed as steady-state
body burden versus those expressed as total exposure may be small for humans, although the
same may not be true in experimental animals where the half-life is much shorter.
8.2.2. Selection of Effective Dose Levels.
Comparisons across multiple endpoints, multiple species, and multiple experimental
protocols are too complicated to be made on the basis of the full dose-response curve.
Comparisons of this sort can be made by either choosing a given exposure and comparing the
responses, or choosing a particular response level and comparing the associated exposures. In
the analyses for the presentations in this chapter, responses are compared using estimated
exposures associated with a given level of excess risk or response. To avoid large extrapolations,
this common level of excess risk or response was chosen such that for most studies, the estimated
exposure is in or near the range of the exposures in the studies being compared (Murrell et al.,
1998; Gaylor and Zheng, 1996; Barton and Das, 1996; Allen et al., 1994a,b; McGrath et al.,
1995), with extra weight given to the human data. A common metric for comparison is the
effective dose, or EDp, which is the exposure dose resulting in a excess risk in the studied
population. Although effective dose reporting for the 2%, 5%, and 10% increased risks has been
the suggested approach, these latter two levels are actually higher than those typically observed
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in the exposed groups in studies in humans. To illustrate, lung cancer mortality has a
background lifetime risk of approximately 4% (smokers and nonsmokers combined), so that even
a relative risk of 2.0 represents approximately a 4% increased lifetime risk. On the basis of this
observation, and recognizing that many of the endpoints studied in the laboratory include 1%
effect levels in the experimental range, the dose resulting in a 1% effect above controls (ED01) is
presented.
Different measures can be used to present risks above and beyond the background risks
encountered in the general environment or through genetic variables. For simplicity, a common
measure will be used; the excess risk, defined as the effective dose for risk (p*100%), satisfying
the relationship in equation (1):
P = R (dp) - R (0)
R(°°)-R (0)
(1)
where R(d[) represents the response (either risk or other measure) atp at a given exposure or dose
level d, and R(°°) is the maximum response possible (Q.g.,R(<*>) =1 for quantal responses, such as
cancer). In this excercisej? is equal to 0.01.
The relative risk commensurate with a one percent excess risk can be calculated by
rearranging the above formula:
0.01
Relative Risk (ED01) = 0.99 +
Multiplying the relative risk by R(0), the background risk, gives the value of the absolute risk. If
the background risk is 0 then the absolute risk equals the excess risk.
8.2.3. Dose Corrections for Species Differences in Half-Lives
Considering the very large difference between half-lives of TCDD in various species, it is
best to compare across species using body burden rather than daily intake (DeVito et al., 1995).
Under steady-state conditions, it is possible to calculate total body burdens (ng/kg) for TCDD in
equation (2).
ED01(ng/kg body burden)=ED01(ng/kg/day)*half-life/ln(2)*f
(2)
where f is the fraction of dose absorbed and is assumed to be 50% for absorption from food
(Kociba et al., 1976) and 100% for other routes. Half-lives for converting between daily
exposures and steady-state body burden are presented in Table 8-1.
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In summary, the unit(s) of dose should appropriately reflect the magnitude of exposure
and the frequency of this exposure. Given the various types of exposure scenarios and different
types of responses, it is difficult to determine a single dose metric for TCDD that can be used to
compare all endpoints and species. Nevertheless, for several types of specific endpqints, it is
possible to express the dose of TCDD in a form that allows for a comparison of responses across
various endpoints and species. For the analysis contained in this chapter, various measures of
body burden will be used.
8.3. EMPIRICAL DOSE-RESPONSE MODELING OF INDIVIDUAL DATA SETS
8.3.1. Introduction
TCDD has been previously classified by EPA as a probable human carcinogen, and has
more recently been classified as a known human carcinogen by the International Agency for
Research on Cancer (IARC, 1997). Epidemiological data have suggested increases in soft-tissue
sarcomas, respiratory system tumors and all cancers combined (see Chapter 7 for a detailed
discussion of these findings).
TCDD is a carcinogen in all species and strains of laboratory animals tested (e.g., mice,
rats, hamsters) with tumors detected in the liver, thyroid, respiratory tract, and other organs and
tissues (see Chapter 6). Long-term rodent carcinogenicity studies have shown that TCDD is a
potent carcinogen, with the most seriously affected organ being liver in female rodents (NTP,
1982a,b; Kociba et al., 1978; Portier et al, 1984).
8.3.2. Human Dose-Response Models
Despite the increasing amount of epidemiological data available for TCDD, it is generally
difficult to find human data with sufficient information to model dose-response relationships.
Unlike laboratory studies, human data can be affected by factors that are difficult to control.
There exists the possibility of disease misclassifications, and measurements of exposure are often
imprecise. However, risks studied in human populations do not require assumptions concerning
species extrapolation and, as such, should be used maximally in studying dose-response. TCDD
is no different in this regard, with several epidemiological studies providing varying degrees of
utility for dose-response assessment. This section applies simple empirical models to the few
studies for which exposure-response data for TCDD are available in human populations.
Modeling cancer in humans uses slightly different approaches from those used for the
animal studies that will be presented later in this chapter. The modeling approach used in the
analysis of the human epidemiology data for all cancers combined and lung cancer involves
applying estimated human body burden to cancer response, and estimating parameters in a linear
risk model for each data set. A linear risk model is the simplest form that can be applied to these
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data. In all three cohorts studied there are three exposure groups and one reference group; this is
sufficient information to consider more complicated dose-response models. However,
considering the complexity of the epidemiological data, the potential impacts of bias and
confounding, and uncertainties associated with the exposure measures used, this simple model is
warranted. Evaluation of the shape of the dose-response data, for the human studies was not
done. Access to the raw data may make it possible to use more complicated mathematical forms
that allow for the evaluation of shape (Becher et al., 1998). In the one case in which this has
been done (Becher et al., 1998), the estimated shape of the dose-response curve was supralinear
(dose raised to a power <1).
8.3.2.1. All Cancers Combined and Lung Cancer
There exist three studies of human occupational exposure that provide enough
information to perform a quantitative dose-response analysis. These are the NIOSH study
(Fingerhut et al., 1991), the Hamburg cohort study (Manz et al., 1991), and the BASF cohort
study (Zober et al., 1990).
8.3.2.1.1. NIOSH study. Aylward et al. (1996) presented a dose-response, analysis using data
from a cohort study of 5,172 male workers at 12 plants in the United States that produced
TCDD-contaminated chemicals (Fingerhut et al., 1991), considering only cancers occurring after
20 years of exposure. Workers were classified into groups by length of exposure, with each
group assigned a TCDD exposure value calculated using a linear first-order elimination model
for concentration of TCDD in serum lipid. The model assumed a constant concentration of 5 ppt,
in serum lipid for all years including those before and after first exposure, constant input of
TCDD over the period of exposure, and an exponential decay during the years following
industrial exposure. Persons with serum lipid levels below 10 ppt at time of measurement were
assumed to have had no excess occupational exposure. The elimination half-life was assumed to
be 7.5 years for all subjects. Three dose metrics were derived from the reconstructed TCDD
concentration profile over time: area under the time-concentration curve (AUC, units of
ppt-years), peak serum lipid concentration (ppt), and mean serum lipid concentration (AUC/age
at time of observation). The serum measurements for 253 workers from one plant were used to
estimate the doses of 4 exposure groups consisting of workers from all 12 plants. Each of the
dose metrics was found to increase with duration of exposure. Excess risk for lung cancer death
was calculated from the standard mortality ratios from the original study (Table 8-2) (Fingerhut
et al., 1991). Excess risk for respiratory cancer increased with each of the dose metrics given.
To provide EDOI estimates for comparison in this chapter, Poisson regression was
(Breslow and Day, 1987) used to fit a linear model to these data. Table 8-2 presents the
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estimates for the steady-state body burden predicted to yield a 1% additional effect over
background. Also presented in Table 8-2 are the observed and predicted relative risks (based
upon the linear Poisson regression model) and the mean exposures used in each category of
exposure. Other analyses of exposure and response exist for this occupational cohort
(Scheuplein and Bowers, 1995; Sweeney et al., 1997; Steenland et al., 1992, 1999). None of
these studies presented estimates of the ED01s so it is not possible to obtain a direct quantitative
comparison; however, the results are similar to those presented in this chapter.
8.3.2.1.2. Hamburg cohort study. Another cohort studied consisted of 1,189 men who worked
at a herbicide plant in Hamburg, Germany (Becher et aL, 1998; Manz et al., 1991; Flesch-Janys
et al., 1995,1998a). Flesch-Janys et al. (1995) used an estimate of TCDD levels in workers in
their analysis. Levels of TCDD were measured in blood or adipose tissue for 190 male workers
in the cohort. Levels at the end of employment were estimated using a first-order kinetic model,
and the contribution of each of several job areas was estimated by regression of the TCDD level
on time worked in the job areas. The regression results were used to calculate TCDD
concentrations (ng/kg of blood fat) at the end of the occupational exposure for each member of
the entire cohort. The cohort was divided into the lower four quintiles and ninth and tenth
deciles of the calculated value. Cox regression was used to calculate relative risks for cancer
mortality. Relative risks were calculated using either an external reference group (control group
of gas workers) or the lowest two quintiles of the Hamburg cohort combined as internal
reference. Variables used in the regression were TCDD level (categorized by quintiles), total
duration of employment, age, and calendar year of first employment. A test for trend of the
relative risks with increasing TCDD concentration was conducted. In the calculations using
either reference group, the trend test was significant at/?<0.05. Standard mortality ratios (SMRs)
were calculated on the basis of the national mortality data available from the German Federal
Office of Statistics using standard methods (Breslow and Day, 1987). The SMRs for the tenth
decile of TCDD concentration were significantly elevated, whereas none of the SMRs for lower
TCDD concentration categories were significantly elevated in the comparison with the lowest
two quintiles combined. In the comparison with the gas worker controls, SMRs were 129 or
higher. The increase was significant for three of the five categories.
Flesch-Janys et al. (1998a) extended this analysis using mortality up to 1992 and
calculating time courses for TCDD concentration in blood lipid. Workers were divided into
quartiles by integrated blood concentrations over time and SMRs were calculated. For total
cancer mortality, the mortality was significantly increased for the highest quartile (SMR 173;
95% CI=121-240) and for all workers combined (SMR 141, 95% CI=117-168). The overall
cancer SMR is increased over the results of Manz et al. (1991), which included mortality only up
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to 1989. For all workers combined, lung cancer mortality was significantly increased (SMR 151,
95% CI= 107-208), but the SMRs were not significantly over 100 for any of the individual
quartiles. A linear trend test on the SMRs by quartile was significant for total cancer deaths
(p=0.01) but not for lung cancer deaths. For this chapter, these data were modeled using the
Poisson regression method applied earlier to the NIOSH data used by Aylward et al. (1996). The
results are presented in Table 8-2.
Another recent article (Becher et al., 1998) gave a dose-response analysis of the Hamburg
cohort for all cancers combined. A Cox regression was used for the dose-response modeling.
Three response models were used: a multiplicative model, an additive model, and a power
model. The response variable in the analysis was SMR for total cancer mortality. The dose
variable was the integrated blood levels for TCDD concentration as calculated by Flesch-Janys et
al. (1998a). Year of entry into employment, age at entry, duration of employment, and an
exposure metric for beta-hexachlorocyclohexane were also used as covariates in the model. The
models were calculated with latency times of 0 and 10 years. The results obtained are discussed
later in this chapter.
8.3.2.1.3. BASF cohort study. Zober et al. (1990) studied a cohort of 247 workers from a 1953
accident at a BASF factory in Germany that released TCDD into the factory. Overall cancer
mortality for all workers combined was not significantly increased. However, for the 127
workers who developed either chloracne or erythema, and for a 20+ year latent period, mortality
from all cancers was increased (SMR=201; 90% CI=122-315). There was also an increase in
cancer mortality with a 20+ year latency for a subcohort of 153 workers who were considered
most likely to have been exposed to TCDD (SMR 198; 90% CI=122-305).
Another study of the BASF cohort (Ott and Zober, 1996a) included 243 male workers.
Chloracne status and estimated TCDD concentration ((ag/kg body weight) at time of exposure
were used as metrics of exposure. The concentration was calculated by a first-order kinetics
model using a regression procedure. Subjects were divided into 3 or 4 groups by concentration.
SMRs were calculated by dose group. Standardized incidence ratios were calculated by dose
group for all cancers and for cancers at various sites. Neither total cancer mortality nor
respiratory system cancer mortality was significantly increased overall, although respiratory
cancer mortality was increased in the highest of three TCDD concentration groups (SMR 240,
95% CI=100- 500). The incidence was not significantly increased for all cancers or respiratory
cancers, either overall or in any concentration subgroup. This study also included a
dose-response analysis by a Cox proportional hazard model, which calculated relative risks, with
cigarette smoking, body mass index, exposure to asbestos, exposure to aromatic amines, age,
and date of first exposure included as explanatory variables. TCDD dose was found to be
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marginally significantly related to total cancer deaths (relative risk 1.22; 95% CI=1.00-1.50), but
not significantly related to respiratory cancer deaths or to incidence of either. There also
appeared to be a trend for increasing total cancer deaths by TCDD level in smokers and in all
workers, but not in nonsmokers or ex-smokers. These data were also modeled in this analysis
using the Poisson regression described earlier, with the results presented in Table 8-2.
8.3.2.1.4. Other studies. Hooiveld et al. (1998) studied former workers at an herbicide factory
in the Netherlands. A back-calculation and regression method was used to estimate peak TCDD
concentration for all workers. A total of 1,031 male workers were divided into groups of low,
medium, or high estimated peak TCDD level (outpoints were 7.7 and 124.2 ppt). These groups
were approximately tertiles of the TCDD level. Relative risks (RR) of mortality were calculated
for the high and medium groups versus the low group, with adjustment for age, time of
follow-up, and time since first exposure. Relative risks for total cancer deaths were significantly
increased for both medium (RR 1.9, 95% CI=1.2-2.8) and high (RR 1.9, 95% CI=1.3-2.8)
exposure groups, but with no apparent trend. Some relative risks for specific cancer types were
marginally significant, but with no apparent trend from medium to high exposure. Not enough
information is given in this study to calculate average body burden.
In the cohort of residents from Seveso, Italy (Bertazzi et al., 1993), a single episode of
exposure to TCDD occurred following an explosion at a local chemical plant. Men, women, and
children from this community have been followed for cancer mortality for 15 years. However,
this study could not be included in this analysis because the limited exposure information is not
sufficient at present to calculate average body burden.
Two other studies were also not included in this analysis for various reasons. Kuratsune
et al. (1998) reported increased lung cancer mortality in male victims (SMR = 330, based on
eight cases) from the Yusho PCB and PCDF contaminated rice-oil poisonings. Although there
are serum measurements and 37 total TCDD equivalents (TEQ) estimates available for this
cohort, there was no TCDD in the contaminants reported. Because this chapter has focused
primarily on the effects of TCDD, this cohort will not be included in the modeling effort. In
addition, Collins et al. (1993) reported increased mortality for both lung cancer and all cancers
combined for a subcohort of 122 U.S. workers who developed chloracne following exposure to
TCDD at a chemical plant during a 1949 accident. Their analysis, however, attributes this
increase in mortality to co-exposure to 4-aminobiphenyl. As that chemical plant is included in
the NIOSH study cohort (Fingerhut et al., 1991), it is discussed in Chapter 7.
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8.3.2.2. Average Body Burden
As described above, the data used in the analyses presented in Table 8-2 are from the
analysis by Aylward et al. (1996) of the NIOSH study, Flesch-Janys et al. (1998a) for the
Hamburg cohort, and Ott and Zober (1996a,b) for the BASF cohort. The limited information
available from .these studies is in the form of SMRs and/or risk ratios categorized by exposure
subgroups with some estimate of cumulative subgroup exposures. Exposure subgroups were
defined either by number of years of exposure to dioxin-yielding processes (Aylward et al., 1996)
or by extrapolated TCDD levels (Flesch-Janys et al., 1998a; Ott and Zober, 1996b). No study
sampled TCDD blood serum levels for more than a fraction of their cohort, and these samples
were generally taken decades after last known exposure. In each study, serum fat or body fat
levels of TCDD were back calculated using a first-order kinetic model. The assumed half-life of
TCDD used in the model varied from study, to study. Aylward et al. (1996) used the average
TCDD levels of those sampled in an exposure subgroup to represent the entire subgroup.
Flesch-Janys et al. (1998a) and Ott and Zober (1996b) performed additional calculations, using
regression procedures with data on time spent at various occupational tasks to estimate TCDD
levels for all members of their respective cohorts. They then divided the cohorts into exposure
groups based on the estimated TCDD levels. The information presented hi the literature cited
above was used to calculate estimated average TCDD dose levels.
The mean blood lipid levels of TCDD in the NIOSH study are given in Aylward et al.,
(1996). Body burdens used in this analysis are the mean blood lipid levels multiplied by 0.25
(assuming 25% lipid in the body). Flesch-Janys et al. (1998a) divide the study population from
the Hamburg cohort into quartiles by calculated AUC of TCDD (in ng/kg-yr) and gave cutpoints
for the quartile ranges. For the analysis in this chapter, mean concentrations for each range are
needed. It is assumed here that the AUC value's are lognormally distributed. Under that
assumption, it is possible to compute a likelihood function for the distribution of data points
among the quartiles. Lognormal parameters which maximize the likelihood were calculated, and
the mean of the AUC in each exposure range was taken to be the mean of the lognormal
distribution when restricted to that range. Time mean concentrations Cs were derived by dividing
the mean AUCs by an age of 63 (derived by subtracting the mean year of birth of the study
subjects, 1929, from the date of followup, 1992). This gives a concentration in lipids; body
burden was computed by multiplying this by 0.25 (assuming 25% lipid in the body) and adding
1.25 ng/kg (mean lipid concentration of 5 ng/kg, times 0.25). Parameters for the fitted lognormal
distribution are u=6.3617, cr=2.2212.
Ott and Zober (1996b) give data on numbers of workers in four groups, classified by
calculated TCDD concentrations (in |ig/kg body weight) at time of exposure (1953, the time of
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the BASF accident). The lognormal fitting procedure described above was used to find mean
values for each group. (The analysis for respiratory cancers used three groups, with the top two
of the four groups combined; means for the combined group were calculated using the lognormal
parameters derived by fitting to the four-group data). AUCs were then calculated for each group
by integrating the solution to the first-order kinetics equation over time 39 years (the time from
the 1953 accident to the 1992 followup). Using C0 as the initial concentration (i.e., that given in
the article), this gives
(C0/ke)[l-e-39ke] (3)
where the constant ke is ln(2)/(half-life). The time-mean concentration is taken to be AUC
divided by the age 71 years (mean age in 1954, 33 years, + 38 years from 1954 to the date of
follow-up 1992). Parameters for the fitted lognormal distribution are u.=-1.8676, a=2.2927. The
half-life used for the calculation of ke is 2593 days (see Table 8-1).
As discussed in Section 8.2, a useful dose metric for risk estimation is the time average
body burden. The body burdens obtained from Flesch-Janys et al. (1998a) were presented with
background exposure of the general population subtracted; the calculations from the other studies
do not. However, the calculated concentrations in the study cohorts are much larger than
background levels. The analysis here takes this into account and assumes a background level of
5 ng/kg in blood lipid. The data from the NIOSH cohort (Fingerhut et al., 1991 ; Aylward et al.,
1996) are taken from deaths with a 20-year latency; data from the other two cohorts used here do
not take latency into account.
Using body burden as the dose metric allows one to estimate either effective dose or
lifetime risk based on an assumption that acute exposure and continuous exposure are equivalent.
However, this may not be realistic if the effect of TCDD is related to the timing of exposure, or if
it is related to body levels attained above a threshold level that would never be reached with
constant exposure. Considering the periodic nature of occupational exposure and given the
limited amount of information available, use of body burden is felt to be the most workable
approach.
The body burden value for each exposure group was used in a Poisson regression
calculation. The number of observed cancer deaths was assumed to be Poisson distributed with
the expected number of cancer deaths assumed to be a linear function of dioxin exposure. The
constant determining the actual increase in risk with exposure (the slope constant) was found by
maximizing the likelihood function for the Poisson distribution. The predicted relative risk in
Table 8-2 was obtained by substituting the optimal value of the slope constant into the formula
for risk as a function of exposure. This procedure was repeated for each data set. ED0] values
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were found by calculating the exposure at which the formula predicts a 1% excess risk as defined
by equation (1).
The estimates derived in this analysis and presented in Table 8-2 can be compared to
those of Becher et al. (1998), who used several models to estimate dose-response for total cancer
mortality using data from the Hamburg cohort. If we assume an average lifetime daily intake
above background of 1 pg TCDD/kg/day and 100% absorption, the estimated steady-state body
burden would be 0.4 ngTCDD/kg. Using the risk calculated from the Hamburg cohort data, this
gives a total excess cancer mortality risk per pg/kg/day of 57 per 10,000 exposed. The risk
estimates of Becher et al., derived from data for male workers with a 10-year latency and taking
greater caution over other factors affecting risk, range from 13 per 10,000 to 56 per 10,000 per
pg/kg/day intake.
8.3.2.3. Noncancer Endpoints
8.3.2.3.1. Cardiovascular disease. A pattern of increased risk of cardiovascular and ischemic
heart disease mortality was observed by Flesch-Janys et al. (1995) across six exposure categories.
There was a statistically significant trend 0=0.04) in relative risk for mortality for all
cardiovascular diseases when gas workers were used as the reference population, but in no single
class of TCDD exposure was there a significantly increased relative risk. There was no
statistically significant trend for death from ischemic heart disease (p=0.l\ but the highest
TCDD group (344.7-3,890.2 ppt) showed a significant relative risk of 1.99 (CI=1.05-3.75).
When national rates were used for the reference population, there were no statistically significant
trends for either disease, and all confidence intervals included 1. Information about time-average
body burden could be obtained from Flesch-Janys et al. (1998 a,b). With these data, an excess
body burden over background (95% lower bound) for 1% excess risk was calculated as 11.2
ng/kg (3.1 ng/kg) for all cardiovascular disease, assuming a lifetime risk of 25%. No statistically
significant increase of cardiovascular diseases was observed for the NIOSH cohort (Steenland et
al., 1999) or for the BASF cohort (Zober et al., 1990, 1994).
8.3.2.3.2. Effects on infants. One major public health concern is the potential effects of
environmental chemicals on the developing fetus, infants, and children. TCDD and related
chemicals produce a broad range of effects in experimental animals exposed in utero ranging
from alterations in biochemical parameters to overt toxiciry and lethality (see Chapter 5 for a
review). Few studies have examined the effects of TCDD and related chemicals in humans
following in utero exposures. Studies in the Netherlands (Huisman et al., 1995; Koopman-
Esseboom, 1996; Weisglas-Kuperus et al., 1995) have examined infants for thyroid hormone
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status, mental and psychomotor development, and immunological status. Exposures were
assessed by determining the concentrations of PCBs, PCDFs, and PCDDs in maternal and
umbilical blood and maternal breast milk. Exposures were then categorized by total TCDD
equivalents (TEQs), Planar-PCB TEQ, nonplanar-PCB TEQ and total dioxin-PCB TEQs. (For a
discussion of the TCDD toxic equivalency concept, refer to Chapter 9.) These studies are
discussed in greater detail (design, analysis, and limitations) in Chapter 7. There is an indication
that these data would be amenable to dose-response analysis for complex mixtures of PCDDs,
PCDFs, and PCBs, but not for TCDD exposure alone.
8.3.2.4. Uncertainties in Estimates From Human Epidemiology
There are many uncertainties associated with risk estimates derived from epidemiological
studies, both in hazard identification and in dose estimation. The estimates of dose, although
based on actual body measurements, may not be fully representative or precise. Although 253
subjects were sampled in the Fingerhut et al. (1991) study, the blood samples were all taken
decades after last exposure and were from 2 of a total of 12 plants. Subjects from the larger of
these two plants had the higher TCDD levels but a lung cancer SMR=72 based on seven deaths,
whereas the smaller plant had only one death from lung cancer (SMR=155). Thus, while serum
TCDD levels correlated well with duration of occupational exposure for the 253 individuals
sampled, and cancer response correlated well with duration of exposure for the 12 plants overall,
correlation of serum TCDD levels with cancer response in this study is far less certain. Analysis
by plant in the Fingerhut et al. (1991) study would have been possible if body measurements at
these other 10 plants had been available.
The choice of half-life is another element of uncertainty. In the literature and when
necessary in this analysis, average body burden was calculated on the basis of a one-compartment
model with first-order elimination. This analysis assumed a half-life of 7.1 years; half-life
assumptions in the literature varied but were close to that. Some data, however, suggest a shorter
half-life of as little as 5.8 years (Ott and Zober, 1996b) while others suggest a longer half-life of
11.3 years (Wolfe et al., 1994). A recent study (Portier et al., 1999) suggests a half-life of 9.5
years. A longer half-life than 7.1 years would result in higher calculated body burdens and hence
lead to a reduced 1% excess risk estimate. Conversely, a shorter half-life would increase the risk
estimate. However, the assumption of a single half-life is uncertain because it is possible that in
humans the apparent half-life may be shorter at higher levels of exposure, as has been observed
in rat liver (Walker et al., 2000). If this were the case, the actual initial exposure may have been
higher than predicted using a single half-life. This would also lead to a reduced 1% excess risk
estimate. In addition, it is assumed that the apparent half-life for TCDD is independent of
exposure to other dioxin-like compounds. In the rodent, apparent half-life is in part determined
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by binding to CYP1A2, which is inducible via the AhR. In humans, while neither the dose-
response for induction of CYP1A2 by TCDD nor the effect this may have on disposition of
TCDD is known, it is likely that the half-lives for dioxin-like compounds are not independent.
Another uncertainty is possible interaction or confounding between TCDD and tobacco
smoking. In mice, TCDD and 3-methylcholanthrene (3-MC, one of the many polycyclic
aromatic hydrocarbons in tobacco smoke) have been shown to be cocarcinogenic (Kouri et al,
1978). Other studies of mouse skin tumors have shown that TCDD can have anticarcinogenic
properties when administered before initiation with either 3-MC or benzo(a)pyrene.
Furthermore, dioxin's tumor-promoting ability suggests that two-stage models would be more
appropriate if individual smoking histories were known. Smoking histories and analyses are
presented only for the Zober et al. (1990) cohort; for the 37 cancer cases, only 2 were stated as
being nonsmokers. Of the 11 men with lung cancer, only 1 reported never smoking. The Ott and
Zober (1996b) analysis, which includes smoking as a covariate, did appear to show an effect of
smoking on TCDD dose-response. Although similar SMRs from other smoking-related diseases
in the two subcohorts in Fingerhut et al. (1991) suggest similar smoking prevalence across this
multifactory cohort, the effects with higher levels of TCDD could be synergistic for cancer.
Other potential confounders in all three studies include exposures concomitant with
TCDD exposures, other chlorinated hydrocarbons in the case of Zober et al. (1990) and Manz et
al. (1991) and miscellaneous chemicals including 4-aminobiphenyl, a known human bladder
carcinogen, in the case of Fingerhut et al. (1991). These confounders raise the question of
whether the increased SMRs are due to exposure to TCDD or to the confounders. However, it is
important to note that within this context, 4-aminobiphenyl does not increase tumors overall, and
there is no evidence that TCDD induces the incidence of bladder cancers.
Another source of uncertainty is the choice of a linear model for analysis. Table 8-2
shows a strict pattern of increasing relative risk with increasing dose for total cancer mortality in
the Hamburg and the BASF cohorts, and for lung cancer in the NIOSH cohort, but for none of
the data sets in the table is the increase in risk simply linear with dose. The Becher et al. (1998)
analysis of data from the Hamburg cohort used three models for dose-response for total cancer
mortality, of which only one was linear. The risk estimates they derived using different.models
varied by as much as a factor of five.
When interpreting the risk estimates presented hi this section, a few additional caveats
and potential biases must be kept in mind.
All observed risk is attributed to exposure to TCDD, even in the presence of exposure to
other confounding chemicals. In particular, this analysis ignores exposure to PCDDs, PCDFs,
and other dioxin-like chemicals. The extent to which exposure to other agents increases the total
exposure on a TEQ basis (Chapter 9) also increases the potential bias of calculated risk estimates.
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In general, exposure to these compounds is correlated with the exposure to TCDD, although
differences in relative contribution of different dioxin-like compounds to the total TEQ have
been observed. This issue is especially important for agents with shorter half-lives than TCDD
(some will be longer; some shorter). Analysis of blood samples analyzed years after exposure
may fail to adequately measure an initial exposure to dioxin-like compounds with shorter
half-lives. For example, a current lipid level of 1 ppt for an agent with a half-life of 7 years, e.g.,
TCDD, would imply a lipid level of a little less than 8 ppt 20 years ago. On the other hand, an
isomer with a current lipid level of 1 ppt and a half-life of 2 years would imply a lipid level of
1,024 ppt 20 years ago.
In any epidemiological study, misclassification can bias estimates of risk. In this case,
recent exposures to TCDD, changes in the lipid fraction of body weight or presence/absence of
genetic differences hi humans that alter the distribution and metabolism of TCDD could cause
misclassification bias, resulting in higher or lower risk estimates depending upon the direction of
the misclassification.
Selection bias may be another factor. For example, it is possible that the subpopulation
used for the biomonitoring of TCDD levels in human blood is not representative of the entire
cohort used for risk estimation. There is also a potential bias due to a healthy worker effect in
these occupational populations.
8.3.2.5. Conclusions for Human Cancer Dose-Response Modeling
Epidemiological studies of occupational exposure suggest a TCDD-mediated increase in
all cancers and also suggest that the lung in the human male is a sensitive target for TCDD.
Smoking and other factors (discussed above) may be modifiers for these cancers. Caution should
be used in interpreting the overall risk estimates and care should be taken to understand them in
the context of the entire weight-of-evidence concerning the potential toxicity of TCDD. The data
obtained from three occupational studies were sufficient to calculate risk estimates. Estimates
derived from the human data (Table 8-2) suggest an EDOI based on body burden in the range of
6-80 ng/kg for all cancers combined and in the range of 36-250 ng/kg for lung cancer.
8.3.2.6. Additional Knowledge Gaps in Human Cancer Dose-Response Modeling
One major knowledge gap in the epidemiological data is a complete exposure history for
each individual in the cohort. This includes lack of a realistic exposure matrix (areas and their
exposure potency and time spent in such areas of occupational exposure) and TCDD
concentrations measured over time during exposure. At present, only a few measurements per
individual are available to estimate a time course ranging over many years of human life.
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Back-calculation of present TCDD body burden used assumptions to derive an individual
body burden over time, which was then converted to the dose metric of time-averaged body
burden used in this analysis. Assumptions varied from study to study. Half-lives used in the
calculations varied, and not all calculations took into account variation in weight arid in
percentage of body fat. Some of the calculations assumed that there was no nonbackground
exposure to TCDD except from the primary occupational source. The Poisson regression used
for risk calculations assumed that the dose-response is linear and proportional to background
response. Because little is known about the validity of these assumptions, no modulation of the
models used above was able to account for them. Sensitivity of the results reported so far on the
presence of extreme measured TCDD concentration values of persons from the population used
for the back-calculation, and of predicted TCDD concentration of persons from the complete
cohort, has to be considered in future analyses. The low correlation, in the range of 0.5, between
measured and predicted concentration levels adds to the uncertainty.
Different dose metrics have been discussed in Section 8.2, and others may arise if more
information about the exposure process becomes available. Neither comparisons of the dose
metrics applicable at present to available data sets nor simulation studies on artificial data sets
have been performed to clarify the strengths and weaknesses of different metrics under different
scenarios.
This dose-response analysis was restricted to a grouping of the exposed population into a
few categories of increasing TCDD levels. Analysis of individual data, making use of statistical
resampling methods, may be useful to estimate population heterogeneity.
More information is needed on factors determining individual differences in half-life of
TCDD such that these can be included into the calculation of individual time-average body
burdens. Age, sex, and portion of body fat have been discussed and used as factors of influence.
The existence of a more complex model for TCDD kinetics in humans may be possible, but no
systematic usage of these factors in risk estimation has been made so far.
Information about confounders of human carcinogenesis, such as smoking or other
behavioral cancer risk factors, was sparse in these studies. Future studies must reduce this lack
of information by use of appropriate design measures, or by inclusion of appropriate biomarkers
of coexposure. Exposure to related dioxin-like compounds clearly complicates the estimates of
the effective dose of TCDD. For example, in the Hamburg cohort, the mean TCDD
concentration for 236 males was 108.3 ppt, whereas the mean TEQ concentration based on all
other PCDDs and PCDFs (except TCDD) was 142.0 ppt. Other coexposure-based confounders
have been described above. Although TEQ values can be calculated for each person using
half-life estimates of each individual PCDD and PCDF congener, it is unclear how an interaction
of different congeners in the individual organism determines the concentration levels over a long
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time period in humans. Long-term studies, even of a small cohort of individual persons, would
have the potential to clarify basic pharmacokinetics of these complex mixtures. One question to
be addressed would be potential changes in half-life of TCDD in the presence of other
dioxin-like compounds in different concentrations.
The ED0]s presented in Table 8-2 are based on a simple dose-response model. The
analysis uses the crude endpoint of all cancers combined, or the most frequent cancer in men,
lung cancer. No mechanistic information was available for these cohorts to strengthen this
analysis. This prohibited cancer modeling using parameters other than TCDD blood serum
concentration. For a mechanism-based cancer risk estimation, such information would be
required. If such information cannot be obtained for the entire cohort, investigators should
consider statistically appropriate subcohort sampling as a possible source of information.
Risk estimates could not be calculated for infant or nonadult exposure. This is to some
extent due to insufficiencies in study design for risk estimation for the total population and
missing information in the reporting of the results. Similarly, it is not possible at present to
identify subpopulations that may be at increased risk. Effects of limited but high exposure at an
early age have not been investigated under conditions where dose-response analyses can be done.
In addition, dose-response data are almost completely missing for human noncancer endpoints.
Although the cohorts considered above are large (with a few thousand individuals), given the size
of the effects to be expected, the statistical power of some analyses is quite small and larger
studies with thorough epidemiological design consideration are required.
8.3.3. Rodent Dose-Response Models: Cancer Endpoints
8.3.3.1. Animal Cancer Studies for Dose-Response Modeling
Mathematical modeling can be a powerful tool for understanding and combining
information on complex biological phenomena. Modeling of carcinogenicity can be
accomplished using simple techniques (Portier et al., 1984) and can be improved by taking the
results of an existing mechanism-based model on receptor-based effects of TCDD within the
context of a physiologically based pharmacokinetic (PBPK) model (Kohn et al., 1993) and using
these results in a detailed multistage model of carcinogenesis (Portier et al., 1996). Both
approaches have been attempted. For a mechanism-based approach see Section 8.4.3.2.
Portier et al. (1984) used a simple multistage model of carcinogenesis with up to two
mutation stages affected by exposure to model the five tumor types observed to increase in the 2-
year feed study of Kociba et al. (1978) (Sprague-Dawley rats) and the eight tumor types observed
to increase in the 2-year gavage cancer study conducted by the National Toxicology Program
(1982a) (Osborne-Mendel rats and B6C3F, mice). The findings from this analysis are presented
hi Table 8-3. The ED01 were calculated based on Portier et al. (1984). Excess risks were then
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calculated from the EDOI using equation (1) in Section 8.2.2. All but one of the estimated ED01
values are above the lowest dose used in the experiment (approximately 1 ng/kg/day) and are
thus within the experimental range. The exception, liver cancer in female rats from the Kociba
study, is very near the lowest dose used in this study. Steady-state body burden calculations were
also used to derive doses for comparison across species (see Section 8.2). Absorption was
assumed to be 50% for the Kociba et al. (1978) study (feed experiment) and 100% (Rose et al.,
1976) for the NTP study (1982a) (gavage experiment). Also presented in Table 8-3 are the
shapes of the dose-response curves as determined by Portier et al. (1984).
The predominant shape of the dose-response curve in the experimental region is linear;
this does not imply that a nonlinear model such as the quadratic or cubic would not fit these data.
In fact, it is unlikely that in any one case, a linear model or a quadratic model could be rejected
statistically (Hoel and Portier, 1994). These studies had only three experimental dose groups;
hence these shape calculations are not based upon sufficient doses to guarantee a consistent
shape estimate; they should be viewed with caution. The body burdens at the ED01 values range
from a low value of 14 ng/kg based upon the linear model associated with liver tumors in female
rats, to as high as 1,190 ng/kg based upon a cubic model associated with thyroid follicular cell
adenomas in female rats.
8.3.3.2. Conclusions From Animal Cancer Dose-Response Modeling
The animal studies show an increase in cancer incidence in rats and mice at various sites.
The ED01 estimates of daily intake level obtained from an empirical linear model range from 0.8
to 43 ng/kg body weight/day depending on the tumor site, species, and sex of the animals
investigated. These are equivalent to steady-state body burdens of 14 to 1,190 ng/kg body
weight. By way of comparison, the ED01 estimate obtained from a linear mechanistic model of
liver tumor induction in female rats (Section 8.4.3.2) was 0.15 ng/kg body weight/day,
equivalent to a steady-state body burden of 2.7 ng/kg body weight (Portier and Kohn, 1996).
8.3.3.3. Knowledge Gaps in Animal Cancer Dose-Response Modeling
The dose-response data for cancer in animals following TCDD exposure are limited to
three exposure groups. Although nonlinear models could be applied to these data (Portier et al.,
1994), the estimates of the shape of the dose-response curve should be viewed with caution.
Studies with more dose groups and sufficient animals per dose group are needed for
distinguishing between different shapes of dose-response curves. Furthermore,
mechanism-based cancer modeling could be improved if physiological, biochemical, and tissue
response information were obtained from the same experiment.
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Hepatocellular carcinomas have been the main focus for much of the research on the
carcinogenicity of TCDD, although there has been increased tumor incidence in other organs.
With respect to extrapolation to humans, the investigation of lung and thyroid cancer should be
studied further. Animal cancer studies using other PCDDs, PCDFs, PCBs and complex mixtures
reflecting human exposure patterns have rarely been done and may add information to the
problem of complex human exposure.
8.3.4. Rodent Dose-Response Models: Noncancer Endpoints
8.3.4.1. Methodology
Risk assessments for noncancer endpoints traditionally have not used endpoint-specific
mathematical models. Instead they have relied on safety assessment involving determination of a
dose that is likely to be without risk, taking both data and model uncertainties into account.
Although many of the same biochemical effects involved in carcinogenesis are also involved in
many other toxicities, biologically based mathematical models for noncancer endpoints are not as
developed as are the cancer risk models. In the interim, we will use a simple empirical modeling
scheme to estimate effective doses and to discuss dose-response curve shape for the biological
and toxicological effects induced by TCDD. The models and the statistical details follow similar
analyses done by McGrath et al. (1995) and Murrell et al. (1998). In brief, two different models
were applied to the continuous data depending upon the number of dose groups used and the
overall quality of the data. First choice was to use a Hill model of the form
R(d) =
vd"
(4)
k"+d"
where R(d) is the response at dose d, and b, v, k, and n are model parameters to be estimated
from the data. The parameters each describe a different aspect of the dose-response curve: b is
the background response, v is the maximum attainable response, k is the dose yielding half of v,
and n is the Hill coefficient describing the curvature of the dose-response. As the shape of the
dose-response curve is critical for risk assessment, it is of interest to consider important
classifications based on n. When n is near or below 1, risk is predicted to be approximately
proportional to dose or climbing more rapidly than proportional. When n is much larger than 1
(n > 1.5), the dose-response is sigmoidal and has been described as appearing to have a threshold.
For these reasons, n will also be referred to as the shape parameter.
In the present exercise, n was not allowed to vary below 1, and thus the model as used
does not predict sublinearity. Estimates of n were restricted to be greater than 1 to avoid
instability. Estimates for the ED0] are sensitive to the slope of the dose-response curve evaluated
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at dose=0, and when n 1). The Weibull model as used in this analysis estimates
threshold-like behavior when k is large. In addition, k was not allowed to be less than 1 to avoid
instability in the analysis. The ED01 values from quantal data satisfy the excess risk relationship
described in equation (1) in Section 8.2.2 where R(°°) is equal to 1 for quantal endpoints.
The data sets examined in this exercise are found in the published literature. The studies
analyzed provided dose-response information on TCDD using at least three dose levels of TCDD
and a control. In addition, the mean and an estimate of the variance of the data had to be
presented in tabular form in the manuscript. Attempts to estimate the means and variances of
data presented in graphical forms proved unreliable, thus publications where the data were
presented only in graphs were not included in the analysis. Model fits, calculation of 1% ED01,
and 95% lower bound on the estimated ED01 were carried out using the U.S. Environmental
Protection Agency (EPA) Benchmark Dose Software (BMDS) version Lib (U.S. EPA, 1999).
In some cases, the BMDS software failed to locate a lower confidence bound on the ED01.
Qualitative assessment of the goodness of the model fit was determined as good (model curve
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included nearly all of the data point means), marginal (model curve was within one standard
deviation of the data point means), or poor (model fit was not within one standard deviation of
the means). There were 234 endpoints for which dose-response analyses could be made
(approximately 200 continuous endpoints and approximately 30 quantal effects), obtained from
more than 36 published manuscripts (see Appendix A). The number of data sets, categorized by
species, gender and study type, is shown in Table 8-4.
The analyses of the data are presented as summaries of the endpoint categories in Figure
8-1, Figure 8-2, Table 8-5, and Table 8-6 at the end of this section. The data are divided into
several categories on the basis of exposure regimen and endpoint. Exposure categories are
grouped as either single exposures or multiple exposures. For simplicity, effects were
categorized as biochemical, hepatic, immune, toxicity, tissue, retinol, or thyroid (Table 8-7).
Biochemical changes included alterations in mKNA, protein, or enzyme activities. The category
of hepatic changes included responses of hepatotoxicity, such as serum enzymes and histological
effects. Immune responses included alterations in lymphocyte phenotypes and functional
alterations such as altered responses to antigen challenge. Alterations in tissue and body weights
were classified as a tissue response. Developmental, reproductive, and tissue toxicities were
classified as toxic responses. Finally, there were limited studies on the effects of TCDD on
serum thyroid hormone concentrations and alterations in either serum or tissue retinoid
concentrations; these studies were categorized as either thyroid or retinol.
Comparison of the ED0] between studies is problematic for several reasons. The effective
dose is dependent upon the sensitivity of the endpoint examined and the dosing regimen
employed. For example, in studies examining the effects of TCDD following a single exposure,
the time after dosing when the determinations were made varied from days to weeks. For some
effects, the differences in the tune after the initial exposure probably influence the effective dose.
Similarly, in studies employing multiple doses, investigators used a variety of regimens including
daily exposure, weekly exposures, and loading/maintenance regimens. In addition, investigators
used a variety of exposure routes including dietary, oral gavage, subcutaneous, and
intraperitoneal. The different routes and vehicles (diet vs. oil solution) have different absorption
rates and percentage absorbed. In order to compare the multiple-dose studies using different
routes of exposure, the average daily dose was estimated for each study by calculating the total
dose administered to the animal over the course of the study and dividing by the length of the
study in days. In addition, for the multiple-dose studies, average steady-state body burden at the
ED01 was calculated using the equation in Section 8.2.2 and the percentage of dose adsorbed and
the half-lives for TCDD in Table 8-1.
In applying a consistent modeling approach across all endpoints, some uncertainty is
introduced for those data sets where this approach provides only a marginally adequate fit. In
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some cases, no trend was apparent below the highest dose examined, thus reducing the
confidence that can be placed in accurately estimating the dose associated with a change as small
as 1%. In other cases, it appeared that other models could provide a better fit to the data, with a
significantly different ED01. For example, sometimes the Hill model gave a dose-response curve
with sharp changes in slope, but a Weibull model could have provided a better fit to the data with
a smoother curve and a lower ED01. In addition, the ED01 and the 95% lower confidence interval
(LEDOI) were sometimes quite far apart (differing by more than tenfold), suggesting that little
confidence can be placed in some ED01 values as a precise index of toxicity. In such cases, it is
useful to look at the LED01 as a bound. Whenever the modeling results were problematic for
these or other reasons, we noted it and gave less emphasis to those results in our overall synthesis
of the data. In this way, the overall conclusions are based on the strongest results.
8.3.4.2. Multiple-Dose Studies
In the studies examining the effects of TCDD following multiple exposures, the range of
the ED01 values is highly variable within and across response categories (Figure 8.3.1). For the
multiple dose exposure studies, the ED01 values were modeled using the average daily dose from
each study. When examined by category, the median values for the ED01 for biochemical and
retinol responses are lower than the median ED01 for other types of response. Of the 101
endpoints examined from studies using multiple exposures, 11 have ED01 values less than 0.1
ng/kg/day. Seven of the 11 endpoints with an ED01 below 0.1 ng/kg/day are markers of immune
response. However, the ED01 for markers of immune function range over six orders of
magnitude, decreasing the confidence of any particular ED01 value for this response. In general
these ED0, values represent dose-response information from female rats and mice, with few
studies examining male rats and mice or other species. These knowledge gaps decrease our
confidence in making extrapolations between species and gender.
One measure of the degree of confidence of the ED0] estimate is the ratio of the ED0jto
the lowest dose used in the study from which it was derived (Table 8-5). A ratio of 1 or greater
indicates that the ED01 is within the doses examined. Ratios between 1 and 0.1 are within one
order of magnitude of the lowest dose tested and indicate that the ED01 may provide a realistic
value. Ratios less than 0.1 indicate that the estimate was more than an order of magnitude below
the lowest dose used in the study and should be viewed with caution. Forty-five of the 101
values had ratios of the EDOI/lowest-dose less than 1. However, of these 45 only 36 were less
than one order of magnitude below the lowest dose used in the study.
In general, an estimated shape parameter that is less than 1.5 indicates that the shape of
the dose-response curve tends to be linear at low doses, and those with shape parameters greater
than 1.5 tend to be threshold-like. Of the 101 endpoints for which an estimate was obtained, 43
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had shape parameters less than 1.5, indicating linear dose-response relationships (Table 8-6).
Approximately half of the biochemical and half of the tissue responses indicated a linear
dose-response relationship. The median shape parameter for the tissue responses is heavily
influenced by the consistently linear shapes for alterations in thymic weight (10 of 11
dose-response curves for thymic changes had shape parameters less than 1.5). In contrast, only
18% of the immune function responses were linear.
Although there is some consistency of shape within certain categories of these endpoints,
in general about half of the responses could be classed as either linear or nonlinear. These
observations do not strongly support linearity for TCDD dose-response, nor do they strongly
support the existence of thresholds within the observable range.
8.3.4.3. Single-Dose Studies: Adult Animals
In studies examining the effects of dioxin in adult rats and mice following a single
exposure, the median ED0] is above 10 ng/kg for all endpoints examined. Biochemical and
immune responses had the lowest median ED01 estimates, 180 and 65 ng/kg, respectively.
Hepatic and toxic responses gave median ED01s greater than 10,000 ng/kg. Once again there
was large variability in the ED01s for a given category; in general they varied approximately three
orders of magnitude within each category. The ED0] estimates were below the lowest dose tested
for 23 of the 75 endpoints examined. Of these 23 estimates, the ED01 was less than one order of
magnitude lower than the lowest dose tested for approximately half (10) of the values (Table 8-
5).
Following a single exposure to TCDD, 33 of the 77 endpoints examined (43%) had shape
parameters less than 1.5, indicating linear dose-response relationships (Table 8-6). There was no
consistent pattern in the shape of the dose-response relationships for the biochemical, immune,
and tissue response categories. In these categories both linear and threshold-like dose-response
relationships were observed. All endpoints in the toxicity category exhibited threshold-like
dose-response relationships.
8.3.4.4. Single-Dose Studies: Developmental Studies
Following a single exposure, a number of developmental effects have been examined.
These effects have been categorized as biochemical, tissue, or toxic. The majority of the effects
examined were considered tissue responses. The range of ED0, values was more than five orders
of magnitude, and the median values for all response categories were greater than 100 ng/kg,
with an overall median of 140 ng/kg (Figure 8.3.2). One of the more recent findings on the
effects of TCDD is its developmental reproductive effects in rats, hamsters, and mice (Mably et
al., 1992a-c; Gray et al., 1997; Theobald and Peterson, 1997). One striking species difference is
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that the ED01 values for the reproductive developmental effects in mice are 10 to 1,000 times
higher than those in rats. The ED01 values for developmental effects were within the dose range
tested in 26 out of 58 endpoints for which an estimate was obtained. Of the 32 estimates that
were below the experimental range, approximately half (17) were less than an order of magnitude
below the lowest dose tested (Table 8-5). The shape parameter for the developmental effects
was less than 1.5 for only 18 of the 60 endpoints analyzed (Table 8-6).
8.3.4.5. Summary of the Dose-Response Modeling for Noncancer Endpoints
The activation of the AhR by TCDD initiates a cascade of events resulting in alterations
in growth factors and their receptors, hormones and their receptors, and proteins involved in
numerous cellular functions such as cell cycle regulation and intermediary metabolism (see
Chapter 2 for a more detailed discussion of these processes). Many of these biochemical
changes, particularly the alterations in growth factors and their receptors, may mediate the toxic
effects of TCDD. The role of other biochemical changes, e.g., induction of aldehyde
dehydrogenase, is less certain. One can consider the biochemical and toxicological effects of
dioxins as a continuum, starting with biochemical changes leading to toxicological events.
Hence, understanding the shape of the dose-response relationship for the biochemical effects may
provide insight into the shape of the dose-response relationship for toxic responses, particularly
in the low-dose region.
Consistent with the hypothesis that the biochemical effects are precursors of the toxic
effects is that, in general, the biochemical responses tend to have lower ED0] estimates than other
types of endpoints examined. However, few of the biochemical changes examined have been
directly linked to toxic responses. For example, the induction of CYP1A proteins is perhaps the
best-characterized response to TCDD and related chemicals. Despite their known role as
modulators of intermediary metabolism for a number of classes of environmental chemicals in
both activation and elimination pathways, the direct relevance of these proteins to the toxic
effects of TCDD remains uncertain. Induction of CYP1A proteins has been proposed as a dose
surrogate for the carcinogenic effects of TCDD (Portier and Kohn, 1996). One of the best
examples of biochemical changes leading to toxicities is the TCDD-induced decreases in
circulating thyroid hormones. This is likely a result of TCDD-mediated induction in hepatic
glucuronosyltransferases (UGTs), which metabolize these hormones and increase their
elimination, van Birgelen et al. (1995a) determined total and free plasma thyroxine
concentrations and hepatic thyroxine glucuronidation (T4UGT) in rats exposed to TCDD for 90
days in the diet. The ED01 values for total plasma thyroxine, free plasma thyroxine, and T4UGT
are 33, 4.9, and 1.6 ng/kg/day. The increased sensitivity of T4UGT is consistent with the
mechanism by which the plasma concentrations of these hormones are decreased. In female
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Sprague-Dawley rats exposed biweekly to TCDD for 30 weeks, Sewall et al. (1995) examined
the effects of TCDD on UGT mRNA, serum total thyroxine, and serum TSH. All three
responses had shape parameters greater than 1.5 and the EDOI values were 0.37,1.3, and 26
ng/kg/day for UGT mRNA, total serum thyroxine, and serum TSH, respectively. Similar to the
data of van Birgelen, the induction of UGT is more sensitive than changes in total serum
thyroxine, which in turn is more sensitive than are changes in serum TSH. These data indicate
that simple biochemical responses have lower EDOI values than more complex phenomena such
as decreases in thyroxine and alterations hi the homeostasis of thyroid hormones.
One concern in the interpretation of the data is whether the study design can affect the
ED0, or the shape parameters. One example of this is the studies by Diliberto and co-workers.
Diliberto et al. (1995) examined both dose-response and time course for CYP1 Al-associated
hepatic ethoxyresorufin deethylase (EROD) activity at 7, 14,21, and 35 days after a single
exposure to TCDD. In these studies, the ED0] values and the shape parameters increased with
time after dosing. The increase most likely stems from the decreasing tissue concentrations of
TCDD and the subsequent decreases in enzyme induction from day 7 to day 35. The shape
parameter ranged from 1 at 7 days after dosing to 6.5 at the 35-day time point. The ED0i
increased from 27 ng/kg at 7 days after dosing to 740 ng/kg at the 35-day time point. These data
indicate that both the shape parameter and the ED01 are sensitive to the study design.
Comparisons of studies that determined EROD activity within 7 days of administration of TCDD
demonstrate considerable consistency. Four studies examined EROD induction in rats or mice
within 7 days of dosing and the EDOI values ranged from 16 to 84 ng/kg. The estimated shape
parameter is 1 for the Diliberto et al. (1995), Abraham et al. (1988), and Narasimhan et al. (1994)
studies and 1.8 for the van Birgelen et al. (1995a) study. It should be noted that two of these
studies are in mice and two are in rats, suggesting similar dose-response relationships for enzyme
induction between these species.
Another variation in study design that may affect dose-response modeling is dose
selection. The dose-response relationship for induction of hepatic EROD activity was modeled
for six studies (van Birgelen et al., 1995a,b; DeVito et al., 1994; Johnson et al., 1997; Schrenk et
al., 1994; Vogel et al., 1997). Only the data from DeVito et al. (1994) and Johnson et al. (1997)
had shape parameters greater than 1.5. The EDm values ranged from 0.4 to 3.2 ng/kg/day except
for the data of Vogel et al. (1997), which resulted in an ED01 more than 100-fold lower. Vogel et
al. (1997) used a loading/maintenance dosing regimen, and the doses used were 100 times lower
than those of the other studies. The much lower ED01 from this study may be a consequence of
the dose pattern and dose selection in this study compared to the other studies.
Another factor to consider is species and strain selection in the studies. The
developmental effects of TCDD have generated concern, particularly the developmental
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reproductive toxicities observed in rats and hamsters (Mably et al., 1992a,c; Gray et al., 1997).
These studies demonstrated decreases in epididymal sperm counts on postnatal day 63.
However, the shape parameters vary between 1 and 11 and the EDOJ values vary between 0.65
and 140 ng/kg. The studies used different strains of rats, and perhaps this may account for some
of the differences between the data sets. The decreases in the epididymal sperm counts were
greater in the Holtzman rat used by Mably et al. (1992a) when compared to the Long Evans rat
used by Gray et al. (1997) Overall, the study by Gray et al. (1997) demonstrated smaller effects
than the study by Mably et al. (1992a). Also, the data from Gray et al. (1997) demonstrate highly
nonlinear responses (shape parameters greater than 2 for all but 3 out of 32 responses examined).
In contrast, the effects observed in Mably et al. (1992a) were larger, the shape parameters
indicate a more linear dose-response, and the ED01 is almost two orders of magnitude lower than
those estimated from the data of Gray et al. (1997).
One of the apparent observations of this exercise is the limited number of studies
examined compared to the vast literature on the health effects of 2,3,7,8-TCDD. There are
thousands of research articles examining health effects of TCDD. Of these articles, less than 50
were analyzed. There are a variety of reasons why only a limited number of articles could be
included in this analysis. First, only studies in experimental animals were included, omitting
many articles on in vitro studies. Second, only studies providing dose-response data that
included a minimum of three dose levels and a control were included. Third, the data had to be
presented in tabular form with means, standard deviations or standard error, and the number of
samples for which the mean was calculated. It is likely that given the vast number of data sets
available, some were inadvertently excluded. However, most of the studies found in the
literature did not fit these criteria, either because of inadequate dose-response information or
graphical presentation. For some studies that provided adequate dose-response information but
presented the data in graphical format, the authors were asked to provide means and standard
deviations and kindly did so. One of the conclusions of this exercise is that when preparing data
for publication, authors conducting dose-response studies should consider the use of their data
and present it in such a way that it is usable in future independent analyses.
Care should be taken in interpreting these analyses. There tends to be a large variation in
both the shape parameter and the ED01 values for a given endpoint. Most of the studies examined
were designed to determine a no-observed-effect-level (NOEL) or lowest-observed-effect-level
(LOEL) and, as such, these data contain limited dose-response information. The limited
information contributes to the observed variation in the estimates of both the shape parameters
and the ED01 values. This should not be taken as a critique on the quality of the study designs. In
almost all instances, the authors of the studies used analysis of variance as a statistical tool and
the studies were designed for such an analysis. In contrast, the present exercise attempts to
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examine the dose-response relationships using nonlinear regression analysis as a statistical tool.
Because of the limited dose-response data available, particular caution should be used when
extrapolating to dose levels outside the experimental design. If this situation is to be improved
and uncertainties in data interpretation reduced, studies will need to be designed and data
produced that are more suitable for nonlinear regression. Second, and perhaps more
disappointing, was the frequency of inadequate reporting of the data. Many studies would
present a mean and some measure of variance without describing whether the variance was
presented as a standard deviation, a standard error of the mean, or some confidence interval.
These variables can be adjusted for use in modeling if the proper number of animals/group is
provided. However, often the number of animals/group was presented as a range.
Although ED01 values are intended as a common measure across studies and endpoints,
they must be interpreted in relation to their respective maximal responses. For example, if
enzyme induction varies over a considerably greater range in one strain than another (for
example, hepatic EROD induction in the studies by DeVito et al. [1994] compared to that
observed in the study of Vogel et al. [1997]), then their respective ED01 values will represent
different levels of induction. The biological significance of these responses may not be
commensurate with their respective ED01 values. In addition, comparisons across endpoints must
proceed cautiously. A 1% increase in response for decreased body weight may not necessarily be
comparable to a 1% excess effect on immune function or enzyme induction.
Several studies have demonstrated that control rats and mice have detectable amounts of
TCDD and related chemicals (Vanden Heuvel et al., 1994a; DeVito et al., 1998). The
concentrations of these chemicals are at or near the quantification limits. In the present analysis,
the background exposures of the control animals were not considered. The inclusion of
background exposure levels or tissue concentrations in the dose-response analysis may alter the
shape of the dose-response curves and is some cases may possibly increase the ED0] estimate
and/or the model estimate of the shape parameter. However, it is unlikely that any effect of the
estimates would substantially change the observed trends in the estimates or the main
conclusions of this dose-response chapter.
An important finding in this analysis is that the biochemical effects tend to have lower
ED0, values compared to more complex effects such as immunotoxicity or tissue weight loss.
This finding is consistent with the hypothesis that the biochemical responses are precursors to the
toxic responses of these chemicals. Another difference between the biochemical and
toxicological responses is that the biochemical responses tend to have lower shape parameters.
Thus, the dose-response relationships for the biochemical responses tend to be linear more often
than the toxicological responses. Because of the limited dose-response data available for many
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of these analyses, caution must be taken when making some of these generalizations. For
example, the decrease in thymus weight tends to have estimated shape parameters of 1.
8.4. MODE-OF-ACTION-BASED DOSE-RESPONSE MODELING
8.4.1. Introduction
Mode-of-action-based modeling for TCDD encompasses PBPK models for estimating
tissue dose and biochemical/tissue response models that describe the consequences of tissue
dose. The distinction between tissue dose and response is often maintained in developing
mechanism- or mode-of-action-based models. A number of PBPK models for TCDD have been
developed. These models have provided insights into key determinants of TCDD disposition in
TCDD-treated animals, such as diffusion-limited movement of TCDD between blood and tissue
and induction of hepatic binding. PBPK models may be extended to generate predictions for
biochemical consequences of the tissue dosimetry of TCDD. The molecular steps leading to
observed responses form a causal sequence that describes the mode of action by which pathology
is produced. Examples of carcinogenic modes of action include enhanced mutation by direct
DNA reactivity, increased cell proliferation related to toxicity or mitogenic stimulation, or
diminished apoptosis in a population of altered cells. The predictions of a PBPK model can be
used to describe parameters in the mathematical representation of this mode of action. The goal
of mode-of-action-based modeling is to express quantitatively the relationships between TCDD
exposure, TCDD tissue kinetics, and the biochemical alterations leading to effects on these
integrated responses. This section discusses models for dosimetry, biochemical, and tissue
responses, and how they ultimately lead to adverse effects of TCDD.
Risk assessments where mechanistic dosimetry models have been used without any
attempt to describe the mechanism of tissue response are a viable intermediate stage in the
development of mechanism-based risk assessments. This approach to risk assessment also
reflects the paucity of mechanistic models of tissue response, relative to models of tissue
dosimetry. The more ambitious modeling of the entire exposure-tissue response continuum
(Section 8.4.2) carries with it the greater requirement for mechanistic understanding of tissue
response. When our understanding of mechanisms of tissue dosimetry and response are
different, careful consideration should be given to the sources of uncertainty in the overall
modeling effort. The realization that dosimetry and response submodels can contribute
unequally to overall model uncertainty can help to guide the choices made in developing the final
risk model and the allocation of resources for additional research.
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8.4.2. Model Structures and Model Development
8.4.2.1. PBPKModels.
8.4.2.1.1. Issues pertaining to PBPK models. Tissue dosimetry encompasses the absorption of
an administered chemical and its distribution among tissues, metabolism, and elimination from
the body (ADME). TCDD dosimetry depends on physicochemical properties of TCDD (e.g.,
tissue permeation constants, partition coefficients, kinetic constants, and biochemical parameters)
and physiological parameters (e.g., organ volumes and blood flow rates). The mathematical
structure that describes the relationship between these factors and ADME constitutes a model for
the tissue dosimetry of dioxin. These models describe the pharmacokinetics of TCDD by a series
of mass-balance differential equations in which the state variables represent the concentration of
TCDD in anatomically distinct regions of the body. These tissue "compartments" are linked by a
physiologically realistic pattern of blood perfusion, called a PBPK model. Several research
documents discuss the development of PBPK models for general use (Gerlowski and Jain, 1998),
and use in risk assessment (Clewell and Anderson, 1985).
PBPK models have been validated in the observable response range for numerous
compounds in both animals and humans, making them useful for risk assessment, especially for
cross-species extrapolation. In addition, they aid hi extrapolation from one chemical to other
structurally related chemicals because many of the components of the model are the same or can
be deduced for related compounds. The tissue concentrations of several cellular proteins are
known to be modified by TCDD, making them useful as dose metrics. A model can be used to
predict the concentrations of these proteins as well. If one of these proteins is mechanistically
linked to a toxic endpoint, the protein could also serve as a dose metric of toxic effects.
The tune course of behavior in each compartment of a PBPK model is defined by an
equation containing terms for input and loss of chemical. The specific structure of a PBPK
model and the assumptions used to develop the model are encoded in the equations. A careful
evaluation of any PBPK model must involve the adequacy of its fit to the data, the relationship of
its structure to the underlying biology, and the mathematical details linking the two. Several
PBPK models have been developed for TCDD and related chemicals (see Chapter 1, Disposition
and Pharmacokinetics, for a brief overview). Models have also been developed for
polychlorinated biphenyls (Lutz et al, 1984; Matthews and Dedrick, 1984; Parham et al, 1997,
1998) and polychlorinated dibenzofurans in several species (King et al., 1983), including
humans.
There are four levels of complexity in PBPK models for the effects of TCDD. First is the
traditional PBPK model by Leung et al. (1988) with the added complexity of protein binding to
CYP1A2 in the liver. The next level of complexity are the models by Andersen et al. (1993) and
Wang et al. (1997) using diffusion-limited modeling and protein induction by interaction of DNA
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binding sites. The third level is represented by the model of Kohn et al. (1993) with extensive
hepatic biochemistry and the model for zonal induction of cytochromes P-450 (Andersen et al.,
1997b). Finally, there are the models that include coordination of responses in multiple organs
(Kohn et al., 1996) for hormonal interactions, and Roth et al. (1994) with its detailed description
of gastrointestinal uptake, lipoprotein transport, and mobilization of fat (Figure 8-3).
8.4.2.1.2. Initial attempts to include protein induction. Leung et al. (1988) developed a PBPK
model for TCDD disposition in mice, for Sprague-Dawley rats (Leung et al., 1990a) and for
2-iodo-3,7,8-trichlorodi-benzo-j?-dioxin in mice (Leung et al., 1990b). These initial models
considered tissue partitioning, protein binding in blood, specific binding of TCDD to inducible
hepatic proteins, binding of TCDD to the AhR, and activation of gene transcription by the
Ah-TCDD complex. Subsequent PBPK models have refined the representations of these
processes as more biological information became available.
This early PBPK model (Leung et al., 1990a) contained five flow-limited tissue
compartments, including blood, liver, fat, and slowly perfused and richly perfused tissues.
TCDD binding in blood was described by an effective equilibrium between the bound and free
TCDD given by a constant ratio. TCDD also binds to two liver proteins: one corresponding to
the high-affinity, low-capacity AhR and the other to a lower affinity, higher capacity microsomal
protein inducible by TCDD, now known to be CYP1A2. The predictions from this modeling
exercise prompted a series of experiments to examine the nature of these binding proteins in
mice (Poland et al., 1989a,b). In the PBPK model (Leung et al., 1990a), the concentration of the
AhR is held constant and the concentration of CYP1A2 is calculated using a Michaelis—Menten
equation for the instantaneous extent of induction as a function of hepatic TCDD concentration.
In various studies, TCDD has been administered by intravenous, intraperitoneal, or
subcutaneous injection; feeding; or by oral intubation (gavage). In the PBPK modeling
framework, intravenous injection can be represented by setting the initial amount in the blood
compartment equal to the injected dose. Oral intubation and subcutaneous injection were
modeled as first-order uptake from the site of administration, with TCDD appearing in the liver
blood after oral administration and in the mixed venous blood after subcutaneous injection.
Feeding was modeled (Leung et al., 1988, 1990a) as a constant input rate on days that TCDD was
included in the diet. With 2-iodo-3,7,8-trichlorodibenzo-p-dioxin, the estimated rate constant for
oral absorption was considerably larger in TCDD-induced than in naive animals. The
physiological basis of this change is unknown, but it may be a consequence of increased hepatic
lipid synthesis and elevated plasma lipid following TCDD treatment (Gorski and Rozman, 1987).
The descriptions of the routes of uptake are clearly not defined in specific physiological
terms, and this lack of detail represents a common limitation in all of the PBPK models for
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TCDD. These descriptions of the oral, subcutaneous, and skin routes are simply empirical
attempts to estimate an overall rate of uptake of TCDD into the PBPK model. This is one area in
which additional research could improve dose-response modeling for TCDD.
Partition coefficients for TCDD were estimated from measurements of tissue and blood
concentrations in exposed animals. Leung et al. (1990a) also modeled metabolic clearance as a
first-order process with the rate constant scaled inversely with (body weight)0-3. In the mouse
with the iodo-derivative, TCDD pretreatment at maximally inducible levels caused a threefold
increase in the rate of metabolism, probably through loss of iodine. However, Olson et al. (1994)
found that pretreatment of rats with 5 jag TCDD/kg body weight increased metabolism in isolated
hepatocytes only when at least 1 mM TCDD was present hi the medium. Induction of its own
metabolism by TCDD appears to be a minor high-dose effect.
Leung et al. (1990a) kept all physiological parameters (e.g., organ perfusion rates and
tissue volumes) constant over the lifetime of the-animal. Subsequent PBPK models have
included growth of the animals over time and changes in organ size due to growth and toxicity.
TCDD and TCDD analogues have dose- and time-dependent kinetics in both rodents (Kociba et
al., 1976,1978; Rose et al., 1976; Abraham et al., 1978; Poland et al., 1989b; Tritscher et al.,
1992) and humans (Carrier and Brodeur, 1991; Pirkle et al., 1989). As the exposure level
increases in single and short-duration exposures, the proportion of total dose found in the liver
increases. This initial model served as the basis of later models as new data were published on
dose and time dependence of TCDD tissue concentrations (Abraham et al., 1988 Tritscher et al.
1992).
In discussing the components that form the basis for a mechanistic model for TCDD, we
focus on aspects of the model that could lead to nonproportional response for low environmental
doses (nonlinear behavior). The model of Leung et al. (1990a) predicted slight nonlinearity
between administered dose and tissue concentration in the experimental dose range. In the
low-dose range, the model predicts a linear relationship between dose and concentration. The
authors argue, however, that tissue dose alone should not be used for risk assessment for TCDD
because of the large species specificity in the ability of TCDD to elicit some toxic responses.
They suggest instead that use of time-weighted receptor occupancy linked with a two-stage
model of carcinogenesis is a better approach to risk estimation. The time-weighted receptor
occupancy predictions derived from the Leung et al. (1990a) model are linear in the low-dose
region, reaching saturation in the range of high doses used to assess the toxicity of TCDD. This
discussion represented one of the early attempts to define a dose metric for the carcinogenic
action of TCDD.
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8.4.2.1.3. Refinements with DNA binding ofAh-TCDD complexes. Andersen et al. (1993a)
modified the model of Leung et al. (1990a) to include Hill kinetics in the induction of CYP1A1
and CYP1A2 and to treat tissue uptake of TCDD as diffusion limited instead of blood flow
limited as done by Leung et al. (1990a). Diffusion limitation was incorporated by replacing the
blood flow term in the expression for tissue uptake of TCDD by a permeability factor equal to
the diffusion coefficient times the cell membrane surface area accessible to the chemical.
Andersen et al. (1993a) assumed this quantity to be proportional to the tissue perfusion rate, with
a constant of proportionality less than 1. In the model used by Andersen et al. (1993a) each
tissue has two subcompartments, the tissue blood compartment and the tissue itself.
This revised model eliminated allometric scaling of the metabolic rate constant used in
the model of Leung et al. Instead, it treats TCDD as inducing its own metabolism, with a
maximal increase of 100%. The increase is a hyperbolic function similar to that for binding of
TCDD to the AhR. This induction led to an improved fit to observed liver and fat TCDD
concentrations. Subsequent research (Olson et al., 1994; McKinley et al., 1993) revealed no
induction of metabolism of TCDD suggesting that this is likely to be a minor high-dose effect.
Most of the physiological constants and many of the pharmacological and biochemical
constants used by Leung et al. (1990a) were modified for the Andersen et al. (1993a) model
because Wistar rats instead of Sprague-Dawley rats were used in the experiments they simulated.
The parameters in the model were optimized to reproduce tissue distribution and
CYP1A1-dependent enzyme activity in a study by Abraham et al. (1988) and liver and fat
concentrations in a study by Krowke et al. (1998) For the longer exposure regimens and
observation periods, changes in total body weight and the proportion of weight as fat
compartment volume were included via piecewise constant values (changes occurred at 840
hours and 1,340 hours).
Induction of CYP1 Al proteins in the model was modeled by including interaction
between the Ah-TCDD complex and presumed DNA binding sites. The concentrations of
CYP1A2 and CYP 1 Al were modeled as a function of hepatic AhR-TCDD concentration.
Although the revised model represented the kinetics with a Hill equation, the Hill exponent was
1, similar to the Michaelis-Menten model used by Portier et al. (1993) for the independent
induction of CYP1A2. The Hill exponent for CYP1A2 (2.3) introduced marked sigmoidicity in
the computed dose-response of this protein.
Andersen et al. (1993a) noted that the liver/fat concentration ratio changes with dose
because of an increase in the amount of microsomal TCDD-binding protein (CYP1A2) in the
liver. For high doses in chronic exposure studies, this introduces a nonlinearity into the
concentration of TCDD in the liver. In the low-dose region, because the Hill coefficients for
CYP1A2 concentration and for TCDD binding to the AhR are equal to 1, the liver TCDD
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concentration as a function of dose is still effectively linear. In the observable response range,
there is a slight nonlinearity in the concentration of TCDD in the liver as a function of dose under
chronic exposure (Andersen et al., 1993a). The dose-dependent changes in liver/fat ratio are
consistent with animal data and limited human data (Carrier and Brodeur, 1991), and are a
necessary part of the modeling for TCDD.
Andersen et al. (1993b) provided a simple comparison of the induction of CYP1A1 and
CYP1A2, the concentration of free TCDD in the liver, and the total concentration of TCDD in
the liver to tumor incidence (Kociba et al., 1976) and to the volume of altered hepatic foci (Pitot
et al., 1987). The computed cumulative hepatic concentrations of TCDD and induced proteins
were used as summary metrics of internal exposure. Tumor promotion correlated more closely
with predicted induction of CYP1 Al than with the other dose metrics. The choice of an
independent induction model for CYP1 Al and a Hill coefficient greater than 1 leads to nonlinear
low-dose behavior. These correlations were not based on any mechanistic considerations of the
role of induction of CYP1 Al in hepatocarcinogenesis.
8.4.2.1.4. Improving the physiological characteristics of the TCDD models. Kohn et al. (1993)
modeled the binding of TCDD to the AhR using explicit rate constants for association and
dissociation of ligand instead of dissociation equilibrium constants. However, large
unidirectional specific rates were used, leading to a predicted TCDD-AhR complex
concentration similar to that computed by Leung et al. (1990a) and Andersen et al. (1993a).
Other binding reactions in the model were handled similarly (e.g., TCDD binding to CYP1A2
and TCDD binding to blood protein). This approach avoids having to solve for the concentration
of TCDD in the liver using the mass conservation relationship described in Leung et al. (1990a)
as mass balance is automatically achieved. The physiology described in the Kohn et al. (1993)
model is dependent on the body weight of the animal. Body weighs as a function of dose and age
were recorded by Tritscher et al. (1992) and directly incorporated into the model by cubic spline
interpolation among the measured values. Tissue volumes and flows were calculated by
allometric formulas based on work by Delp et al. (1991). To allow the model to fit data at both
low and high doses (Tritscher et al., 1992), this model includes loss of TCDD from the liver by
lysis of dead cells, where the rate of cell death was assumed to increase as a hyperbolic function
of the cumulative amount of unbound hepatic TCDD. This assumption is based on the
observation of a dose-response for cytotoxicity in livers of TCDD-treated rats (Maronpot et al.,
1993) and is consistent with observed tissue burdens of TCDD. No information regarding the
rate of TCDD release from lysed cells is available; therefore, this feature of the Kohn et al.
(1993) model predicts a net contribution of TCDD clearance by TCDD-induced cell death.
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A further extension of this model, incorporating effects on thyroid hormones (Kohn et al.,
1996), included tissue blood compartments similar to those used by Andersen et al. (1993a).
Blood was distributed among these compartments and a compartment for the major blood
vessels, Instead of supplementing a generalized blood compartment with the tissue blood. The
GI tract was separated from the rapidly perfused tissues compartment to permit a more realistic
representation of uptake of TCDD and perfusion of the liver. The allometrically scaled
metabolic rate constant used in the Kohn et al. (1993) model was replaced by a Hill rate law, and
parameters were estimated to reproduce the kinetic data of Abraham et al. (1988) and the dose-
response data of Tritscher et al. (1992).
Transthyretin (also known as prealbumin) can bind hydroxylated PCDDs, (McKinney et
al., 1985) and single doses of TCDD can cause prolonged decrease in this protein (Albro et al.,
1978). A dose-dependent decrease was included in the model and the algebraic equation for
blood binding was replaced by a differential equation. The revised model, incorporating blood
binding, correctly predicted blood TCDD data not used in constructing the model. Ignoring
production of binding protein led to serious underestimation of the low-dose data, and ignoring
inhibition led to overestimation of the high-dose data. This revised model also differed from the
earlier version in its treatment of loss of TCDD from the liver consequent to cytotoxicity.
Instead of simply disappearing from the model, TCDD from lysed cells was assumed to pass via
the bile into the gut, where it was reabsorbed and redistributed to tissues. This model also
explicitly accounted for background exposures of TCDD equivalents in the feed, as observed by
Vanden Heuvel et al. (1994a).
The above models have been applied in developing dose metrics for biochemical and
tissue-response models. They do not necessarily include every aspect of the distribution of
TCDD within the mammalian organism. The following two efforts expand on issues related to
TCDD distribution. However, at this time they have not been included in the dose-response
models and are unlikely to dramatically change estimates of dose metrics.
8.4.2.1.5. Lipid metabolism and sequestration in blood. The above PBPK models empirically
represent sequestration of TCDD in blood without reference to the nature of the pools of TCDD
in the blood compartment. Animals exposed to high doses of TCDD and related compounds
exhibit alterations in lipid metabolism characterized by mobilization of fat stores and resulting in
wasting, hyperlipidemia, and fatty liver. Roth et al. (1993, 1994) constructed a PBPK model of
the distribution of TCDD in the rat over a 16-day period following an oral dose. The model did
not include tissue blood compartments but did consider diffusion limitation in uptake by
multiplying tissue perfusion rates by a fractional extraction, mathematically identical to the
formulations of Andersen et al. (1993a) and Kohn et al. (1996). A unique feature of this model
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was the division of the GI tract into five subcompartments—stomach, duodenum, jejunum,
cecum, and colon—with sequential passage of ingested material. The model also separates the
rapidly perfused tissues compartment into its constitutive organs and separates white and brown
adipose tissue because of their different perfusion rates and differences in ability to mobilize
lipid stores. The model included an earlier submodel of fatty acid metabolism in liver and
adipose tissues, triglyceride transport via lipoprotein particles in blood plasma, and uptake of
lipoprotein by liver and fat (Roth et al, 1994). Regulation of food consumption and lipolysis in
white adipose tissue were assumed to be regulated by a cytosolic receptor that,binds TCDD.
The model included the possibility for loss of body weight, muscle mass, and fat weight
and hypertrophy of the liver subsequent to TCDD administration. It matched data for the initial
increases and subsequent declines of TCDD in liver and brown and white fat. Fecal and urinary
excretion data also were reproduced. The model included induction of CYP1A2 binding sites for
TCDD. The measured concentration of TCDD in white adipose tissue shows a paradoxical
increase at 16 days postdosing despite the fact that TCDD was being cleared from the body. The
model of Rom et al. (1994) failed to reproduce this effect, but the concentration in the lipid
portion of the tissue did increase because the mass of lipid was decreasing in highly exposed
animals. Roth et al. suggested that barriers to uptake and efflux of TCDD may not be
symmetrical.
Roth et al. (1994) cited evidence that TCDD is absorbed from the gut, dissolved in dietary
fat, carried into the bloodstream by chylomicrons, and secreted into the gut lumen from the
intestinal mucosa. There does not appear to be a significant first-pass extraction of these
unprocessed lipoprotein particles by the liver. Several tissues (e.g., heart, spleen, and fat) have
high levels of receptors for such very-low-density lipoprotein vesicles. So TCDD transport may
be regulated by endocytosis of these particles and not be under equilibrium control, as has been
assumed in all other pharmacokinetic models. Such a process may reflect the mechanistic origin
of diffusion-limitation hi TCDD tissue uptake. Further research may be required to resolve this
point. Another feature of the Roth et al. (1994) model that suggests additional research is the
assumption that white adipose tissue contains a cytosolic TCDD receptor (adipose tissue does
express the AhR) which mediates effects on lipid metabolism.
8.4.2.1.6. Diffusion limitations in multiple tissues. Assessment of diffusion limitation in tissue
uptake has been hampered by a lack of data at short times after dosing with TCDD. Wang et al.
(1997) obtained time-course data for TCDD in blood, several tissues, and the remaining carcass
following a single oral dose. They fit an eight-compartment (blood, lung, liver, kidney, spleen,
fat, skin, carcass) PBPK model to these data, estimating the values of gut absorption rate, tissue
permeability, partition coefficients, AhR concentrations, and CYP1A2 induction parameters by
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an ad hoc method (no formal optimization). The terminal TCDD half-lives in liver and kidney
were assumed to reflect metabolism and were used to calculate an effective first-order rate
constant. Time courses in highly vascularized tissues (lung, spleen) could be fit with flow-
limited kinetics, but diffusion restriction was required for other tissues, especially kidney. The
model by Wang et al. was also used to predict induction of CYP1 Al and CYP1A2 protein in
liver and CYP1 Al and CYP1A2 enzyme activity in liver, kidney, lung, and skin (Santostefano et
al., 1998). This model has recently been shown to predict the TCDD tissue concentrations from
a study by Krowke and coworkers using a loading dose/maintenance dose exposure regimen
(Wang et al., 2000). However, it was not demonstrated that the model could reproduce responses
to chronic exposure to TCDD.
8.4.2.1.7. Modeling of dose-dependent tissue disposition in humans. Carrier et al. developed a
simple empirical model to account for dose-dependent hepatic sequestration of dibenzofurans
and other TCDD-like compounds (Carrier et al., 1995a,b). This description had two primary
parameters: a maximum proportion sequestration of body burden in the liver (Fmax) and a
half-saturation constant (Kd)(in units of ug TEQ/kg) for enhanced sequestration with increasing
dose. These two parameters were estimated by fitting the model to data on the dose-dependent
sequestration in the liver presumed to occur in the livers from human poisoning incidents in
Japan and China. The model was also used to derive similar empirical constants from the rat
data (Abraham et al., 1988). These two fitting parameters do not contain specific information
about the biology of TCDD and related compounds. A PBPK model for TCDD was used
recently to infer the relationship between specific biological factors and these two empirical
parameters (Evans and Anderson, 2000). With sensitivity analyses, the half saturation constant
(Kd) was found to be related to characteristics of the binding of TCDD to the AhR and the
AhR-TCDD complex binding to dioxin response elements on DNA. In contrast, the maximum
proportion in liver is determined by fatblood partition coefficients and binding parameters for
the interaction of CYP1A2 with TCDD. The composite parameters of Carrier's models
(1995a,b) have no obvious relationship to specific biological processes.
In principle, it is possible to convert a PBPK model of disposition of TCDD in a
laboratory rodent into one for a human by substituting human parameter values for rodent values.
(Andersen et al., 1997c). Although values for anatomical and physiological parameters are
available for humans, the biochemical parameters (e.g., TCDD metabolism, binding to the AhR
and CYP1A2, and induction of the various proteins cited above) are generally not available for
humans. Parameters for protein binding (Kd and basal Bmax) could be determined in vitro from
samples of human tissues obtained either postmortem or from surgical patients, but estimating
parameters for induction of proteins would require tissue samples from living individuals
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exposed to dioxin. Alternatives to measuring human parameter values include allometric scaling
of rodent values by the 2/3 or 3/4 power of body weight. This tactic is suspect, as species
differences in expression of proteins do not follow a simple pattern for all proteins.
8.4.2.2. Biochemical, Tissue, and Endocrine Response Models
The next step after the modeling of the disposition of TCDD within the body is the
modeling of effects of TCDD on biological responses that are plausibly linked with activation of
the AhR.
8.4.2.2.1. Generic receptor-mediated response models. Looking at one aspect of modeling of
TCDD's effects, Portier et al. (1993) examined the relationship between tissue concentration and
the response of three liver proteins by TCDD in intact female Sprague-Dawley rats. The effects
studied included the induction of two hepatic cytochrome P-450 isozymes, CYP1A1 and
CYP1A2, and the reduction in maximal binding of EOF to its receptor in the hepatic plasma
membrane.
Portier et al. (1993) modeled the rate-limiting step in the induction of CYP1 Al and
CYP1A2 following exposure to TCDD using a Hill equation. Hill equations are commonly used
for modeling ligand-receptor binding and enzymatic kinetics data. Consequently, these models
could be applied to other receptor-mediated effects and are not specific to TCDD and the AhR.
The Hill equation allows for both linear and nonlinear response below the maximal induction
range. A complete discussion of Hill kinetics and other models for ligand-receptor binding is
given by Boeynaems and Dumont (1980). Examples of the use of Hill kinetics for
ligand-receptor binding include the muscarinic acetylcholine receptors (Hulme et al., 1981),
nicotinic acetylcholine receptors, opiate receptors (Blume, 1981), the AhR (Gasiewicz and Rucci,
1984), estrogen receptors (Notides et al., 1985), and glucocorticoid receptors (Sunahara et al.,
1989). The Hill model can be thought of as a very general kinetic model that reduces to
hyperbolic kinetics when the Hill exponent is 1. Portier et al. (1993) also modeled the reduction
in maximal binding to the EOF receptor with Hill kinetics, assuming that TCDD reduces
expression of the receptor protein from the rate observed in control animals. For all EGFR,
CYP1A1, and CYP1A2, proteolysis was assumed to follow Michaelis-Menten kinetics. The
proposed models fit the data in the observable response range. The major purpose of this paper
by Portier et al. was to emphasize the importance of the mechanism of basal (i.e., uninduced)
expression on the curve shape of tissue concentration of protein vs. dose of TCDD. For each
protein, they considered two separate models of steady-state protein production.
In the first model, the additional expression of protein induced by TCDD is independent
of the basal-level expression. In their second model, basal expression of these proteins is
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mediated by a ligand of endogenous or dietary origin that competes with TCDD for binding sites
on the AhR, Using these simple models, Portier et al. (1993) see virtually no difference in
predicted protein concentrations between the independent and additive models in the observable
response range, even estimating almost equal Hill coefficients in the two models for all three
proteins. In the low-dose range where risk extrapolation would occur, the models differed
depending on the value of the Hill coefficient. An estimated Hill exponent exceeding 1 yielded a
concave upwards dose-response curve, especially for the independent model. This behavior
implies diminished increases in responses at very low doses followed by an accelerated response
as the dose increases. For CYP1A2, the Hill exponent was estimated to be about 0.5. When the
estimated Hill exponent is less than 1, the dose-response curve was convex upwards, indicating
greater than linear increases in response at low doses. Finally, for the EOF receptor, the Hill
exponent was approximately 1, in which case the two models are identical.
The additive model is expected to exhibit low-dose linearity because each additional
molecule of TCDD adds more ligand to the pool available for binding and, under subsaturating
conditions, proportionally increases the concentration of protein. Similar observations have been
made with regard to statistical (Hoel, 1980) and mechanistic (Portier, 1987) models for tumor
incidence. Thus, even though these two basic models show almost identical response in the
observable response region, their low-dose behavior is remarkably different. If either CYP1A1
or CYP1A2 levels had been used as dose surrogates for low-dose risk estimation, the choice of
the independent or additive model would yield differences of several orders of magnitude in the
risk estimates for humans. Using CYP1 Al as a dose surrogate, the independent model would
predict much lower risk estimates than the additive model. For CYP1A2, the opposite occurs.
For EGF receptor, there would be no difference.
8.4.2.2.2. Specific biochemical responses to TCDD. Kohn et al. (1993) have provided an
extensive model of the biochemistry of TCDD in the liver to explain TCDD-mediated alterations
ha hepatic proteins in the rat, specifically considering CYP1 Al, CYP1A2, and the Ah, EGF, and
estrogen receptors over a wide dose range. The model describes the distribution of TCDD to the
various tissues, accounting for both time and dose effects observed by other researchers. A
description of the PBPK portion of this model is described above. Earlier PBPK models
(Andersen et al., 1993a, Leung et al., 1990a) relied on several single-dose data sets (Rose et al.,
1976; Abraham et al., 1988) and were validated against dosimetry results from longer term
subchronic and chronic dosing regimens (Kociba et al., 1976; Krowke et al., 1989). These and
other studies (Tritscher et al., 1992; Sewall et al., 1993) were used to model the
pharmacokinetics and induction of gene products in female Sprague-Dawley rats (Kohn et al.,
1993). Among the data reported were concentrations of TCDD in blood and liver, concentrations
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of hepatic CYP1A1 and CYP1A2, and EOF receptor binding capacity in the hepatocyte plasma
membrane. The tissue dosimetry for the model (Kohn et al., 1993) was validated against single-
dose and chronic dosing regimen experimental data not used in estimation of model parameters.
In the biochemical effects portion of the model the AhR-TCDD complex upregulates four
proteins: CYP1A1, CYP1A2, the AhR, and an EGF-like peptide (treated nominally as
transforming growth factor-alpha, TGF-alpha). The induction of an EGF-like peptide is deduced
from observations on human keratinocytes (Choi et al., 1991; Gaido et al., 1992) and is
quantified on the basis of a presumed interaction with the EGF receptor, resulting in a
downregulation and internalization of the EGF receptor. However, TCDD-mediated induction of
TGF-alpha or of other EGF-like peptides has not been demonstrated in liver. For all four
proteins, synthesis is defined explicitly as a function of occupied AhR concentration.
Constitutive rates of expression for CYP1A2, AhR, and EGF receptor are substantial and were
assumed independent of the induced expression. The Hill coefficients for the induction of these
proteins were estimated to be 1.0, indicating low dose linearity in this response irrespective of the
mechanism of basal expression. Estimated ED01 values for TCDD-regulated responses predicted
from the dose-response model is shown in Table 8-8.
The model included a background of dioxin-like AhR agonists, which compete with
TCDD for binding to the receptor. Induction of CYP1 Al was assumed to be based on additive
induction because this enzyme is poorly expressed in the absence of an inducer and expression in
control animals is likely due to the background exposure. Again, the Hill exponent was
estimated to be 1, leading to low-dose linearity under either additive or independent assumptions.
This model predicts that the induction of all gene products appears to be a hyperbolic function of
dose without any apparent cooperativity. The discrepancy in the estimates of the Hill exponents
between this model and the other models discussed (Andersen et al., 1993a,b; Portier et al., 1993;
Kedderis et al., 1993) is probably related to the inclusion only in the Kohn et al. (1993) model of
induction of the AhR. The effects of TCDD on the AhR concentration are uncertain. In acute
studies, the AhR is decreased following TCDD exposure (Pollenz et al., 1998), whereas in
subchronic studies, there is some evidence that the AhR is increased (Sloop and Lucier, 1987).
Further studies are required to better understand the regulation of the AhR following TCDD
exposure.
The AhR-TCDD complex is assumed to downregulate the EGF receptor in the Kohn et
al. (1993) model. It was assumed that the estrogen receptor-estrogen complex synergistically
reacts with the AhR-TCDD complex to transcriptionally activate gene(s) that regulate synthesis
of an EGF-like peptide. This term was introduced to partially account for the observation of
reduced TCDD tumor-promoting potency in ovariectomized females as compared to intact
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female rats (Lucier et al., 1991). This mechanism of TCDD regulation of these proteins,
although supported by some data (Sunahara et al., 1989; Clark et al., 1991), is speculative.
Vanden Heuvel et al. (1994b) provided data on the production of CYP1A1 mRNA and
protein following a single oral dose of TCDD. These observations were used to extend the Kohn
et al. model and resulted in a model that predicted two critical DNA binding sites for the
liganded AhR with different affinities (Vanden Heuvel et al., 1994; Kohn et al., 1994). Both
sites had to be occupied in order to activate transcription. This rate equation led to a sigmoidal
dose-response curve for the message. Protein synthesis on the mRNA template was modeled by
a Hill equation. The optimal Hill exponent was less than 1 and the computed overall dose-
response was hyperbolic, as in the Kohn et al. model. This result suggests that the supralinear
response of protein to mRNA production compensates for the sublinear response of the message
to AhR-TCDD complex formation. It is possible that this reflects the greater sensitivity of the
RT-PCR method to detect CYP1 Al mRNA than measurement of CYP1 Al protein. Within this
context it is of note that there are more than two DREs within the human CYP1 Al promoter
region that may be occupied (Kress et al., 1998).
8.4.2.2.3. Tissue response models: zonal induction model. The mechanistic model of Kohn et
al. treats the TCDD-treated liver as a single homogeneous unit. With regard to the induction of
cytochromes P-450 in the liver, Tritscher et al. (1992) used antibody staining techniques,
showing that the induction of CYP1 Al and CYP1A2 by TCDD in the liver exhibits a
regiospecific pattern of induction characterized by increased areas of staining around the central
vein of the liver lobule. The size of the induced region in the centrilobular region increased with
increasing dose of TCDD. This sharp demarcation in observed induction within hepatocytes
could be due to an insensitivity in detection of low levels of CYP proteins in the cell using
immunohistochemical techniques: alternatively, it may indicate differences in the sensitivity of
hepatocytes to TCDD across the liver. In an attempt to model this regiospecific pattern of
induction, Andersen et al. assumed that the observed sharp demarcation in CYP1A expression
between induced and noninduced regions indicated that individual hepatocytes were either fully
induced or noninduced (Anderson et al., 1997a,b). In this model the liver lobular structure was
divided the into five concentric zones with a threefold difference between adjacent zones, in the
affinity of DREs for the liganded AhR. The model also further used Hill kinetics for induction,
with a Hill exponent of 4. The model reproduced the qualitative features of expanding zonal
induction and, with parameters selected to yield a fit to time-course data (Abraham et al., 1988)
and CYP1 Al mRNA data (Vanden Heuvel et al., 1994), produced a fit to P-450 data comparable
to that obtained with the homogeneous liver model of Kohn et al. (1993). The mRNA data were
fitted without proposing multiple DRE binding sites for transcriptional control of message.
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However, the low-dose extrapolated responses predicted by the regional induction model
exhibited greater low-dose sublinearity than a comparable homogeneous liver model. The model
predicted an 81-fold difference in AhR-TCDD binding between periportal and centrilobular
zones and utilized steep Hill kinetics; these two issues drive the low-dose nonlinearity of this
model and are important areas for further research.
8.4.2.2.4. Endocrine models: thyroid hormones. In addition to models of whole-tissue
responses such as that seen in the liver, attempts have also been made to model endocrine effects
that encompass changes that may occur in multiple tissues. This is demonstrated in the thyroid
hormone model of Kohn et al. (1994). TCDD induces thyroid tumors in male rats and female
mice at lower doses than those that induce liver tumors in female rats (NTP, 1982a). Sewall et al.
(1995) found increased circulating thyrotropin (TSH) and thyroid hypertrophy and hyperplasia in
TCDD-treated rats, suggesting that thyroid tumors may be a consequence of chronically elevated
serum TSH (Hill et al., 1989). Because this may be a sensitive endpoint for TCDD
carcinogenesis, the Kohn et al. (1993) model was extended (Kohn et al., 1996) to include effects
of TCDD on thyroid hormones.
The extended model added compartments for tissues involved in the production (pituitary
and thyroid glands) and storage (e.g., kidney, brown fat) of thyroid hormones as well as
equations for secretion and metabolism of the hormones. It reproduced the data used in the
original model, blood levels of thyroid hormones and TSH (Sewall et al., 1995), and mKNA
(vanden Heuvel et al., 1994b) for the thyroxine metabolizing enzyme UDP-glucuronosyl-
transferase-l*6 (UDPGT). It also reproduced experimental data for induction of this enzyme that
were not used hi the construction of the extended model. In the model, induction of UDPGT by
TCDD and subsequent endocrine changes in thyroid hormone homeostasis can lead to
chronically elevated serum TSH. This may be related to increased thyroid cancer risk. The
estimated dose-response relationships were hyperbolic in the experimental range, supporting a
linear dose-response at lower doses.
8.4.2.2.5. Dose-response behavior of biochemical/tissue dose-response models. The models of
Kohn et al. (1993,1996) are based on the concept that tissue-level responses are emergent
properties that arise from the accumulated molecular effects of exposure to TCDD. Thus, the
models were constructed in a bottom-up fashion starting from these more elementary steps, e.g.,
binding to the AhR, transcriptional activation, translation of mRNA, and the enzymatic functions
of the induced proteins. The calculated responses that can serve as dose metrics include altered
expression of CYP1A1, CYP1A2, and UDPGT. Because TCDD induces expression of the AhR,
lower computed doses are required to obtain the same responses as estimated by models that
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ignore this effect. The critical steps are binding of the liganded AhR to DREs and translation of
the mRNA into protein. The most important lesson of this modeling exercise is that lack of
significant sigmoidicity in the dose-response curves calculated for these proteins arises from
saturation of protein synthesis at low concentrations of mRNA, compensating for possible
sublinearity in transcription. Similar compensatory effects led to low-dose linearity in the more
complex responses of EOF receptor internalization and elevation of plasma TSH.
Any of the above responses can serve as indices of toxicity or pathology, and which is
selected for such use depends on the hypothesized origin of the endpoint. Use of CYP1A2 as a
marker for indirect DNA damage is based on the hypothesis that the catalytic properties of this
enzyme lead to the generation of free radicals or DNA-reactive quinones (Yager and Liehr,
1996). Use of the internalized EOF receptor as a marker for promotional effects in the liver is
based on the hypothesis that TCDD induces growth factors that are ligands of this receptor. Use
of TSH as a marker for promotional effects in the thyroid is based on the goitrogenic properties
of this hormone. Further experiments are required to determine if these postulated events are
causally related to the pathological responses. Nevertheless, if the computed responses are used
as dose metrics, the model indicates that linear extrapolation from the experimental dose range
can be used to estimate low-dose effects.
The main hepatic response motivating the regional induction model was the pattern of
staining within hepatic lobules in TCDD-treated rats (Tritscher et al., 1992). On the basis of
geometric considerations, hepatic lobular structure was described as a series of concentric lobular
regions with differing affinities of DNA binding sites for the Ah-TCDD complex (Andersen et
al., 1997a). A main underlying assumption was a linear correspondence between mRNA
concentrations and protein levels, modeled by an inducible rate of synthesis and a first-order
degradation. The rate of message production was modeled with Hill kinetics with respect to
receptor complex concentration. The successful parameterization required differences in binding
affinity between adjacent zones and very steep dependence on TCDD and Ah-receptor complex
concentration (i.e., the estimated Hill coefficients were large) in order to reproduce experimental
data. A single-compartment liver model was also examined. It could reproduce all data except
the heterogeneous distribution and low-dose mRNA levels. The major inference drawn from this
analysis was that induction should be considered on the level of the cell, not the gene. The
effects appear to be coordinate, cooperative expression of a battery of gene products and
emergence of new cellular characteristics. This behavior, if true, might be regarded as a
reversible differentiation of TCDD-transformed phenotype, rather than induction of single genes
in isolation. Overall linear behavior in the entire liver arises from composite responses of
individual cells with differing thresholds for induction. The sensitivity of cells in the
centrilobular region of the liver would determine the low-dose behaviors.
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In the present model the low-dose behavior of this small group of cells would be
distinctly nonlinear. The ED01 with this regional induction model was about 1.4 ng/kg/day
(Table 8-8). This value is close to the estimate of 0.34 for the induction of CYP1A2 estimated
by Kohn et al. More significant than the differences in ED01 values are the inferences drawn with
regard to the shape of the curve in the low-dose region by the two models. Specific studies on
regional induction and cellular level responses should be vigorously pursued to discriminate
between these two model structures. Regional induction of mRNA needs to be studied on a more
quantitative level and methods need to be developed for studying induction in primary
hepatocytes. Recent data in rats exposed to TCDD demonstrate that the hepatocytes in the
centrilobular region accumulate TCDD to a greater extent in the low-dose region and are more
responsive to TCDD than are the periportal hepatocytes (Santostefano et al., 1999).
8.4.3. Application of Models
The goal of biochemical response models is to link TCDD-regulated responses to adverse
effects associated with TCDD exposures. In principle, these models could be applied to a variety
of adverse responses. The focus of the application of these models has been to carcinogenic
endpoints. Much less attention has been given to the application of mathematical models to the
development of noncancer pathologies.
TCDD is a potent carcinogen in all animal species tested (see Chapter 6). TCDD is an
operational promoter, as defined in assay systems of skin and/or liver in mice and rats (Schrenk
et al., 1994; Maronpot et al., 1993; Clark et al., 1991; Pitot et al., 1980; van Birgelen et al., 1999;
Buchman et al., 1994) (see Chapter 6). Mathematical modeling can be a powerful tool for
understanding and combining information on complex biological phenomena such as
carcinogenesis. For the analysis of tumor promotion by TCDD, much of the focus on the use of
mathematical and mechanistic models has been on understanding the mechanism of
hepatocarcinogenesis induced by TCDD. Specifically, the focus has been on modeling the
development of putatively preneoplastic altered hepatocellular foci (AHF) that exhibit altered
expression of marker enzymes such as placental glutathione-s-transferase (POST), or
gamma-glutamyl transpeptidase (GOT). Mechanism-based modeling of carcinogenicity can be
accomplished by incorporating linkages between cell growth and mutation and the
biochemical/tissue responses of TCDD, within the context of the quantitative dose-response
models described above. In addition, analysis of changes in hepatocyte replication has been used
to estimate of parameter values for in some models.
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8.4.3.1. Modeling Preneoplastic Lesions
Within the framework of a two-stage model of carcinogenesis, these models treat AHFs
as an initiated phenotype produced by conversion of a normal cell by a mutational event. Models
for the numbers of normal and initiated cells also incorporate parameters related to the relative
birth rates and death rates of the respective cell populations. These growth and mutational
parameters may or may not be directly related to biological processes altered by TCDD. Three
research groups have evaluated growth and development of AHFs, using different mathematical
approaches, different assumptions of the phenotypic distribution of the AHFs, and different
linkages of biological processes to the model parameters.
8.4.3.1.1. Models with a single initiated phenotype. Portier et al. (1996) estimated the
parameters in the first half of a two-stage mathematical model of carcinogenesis from the
initiation-promotion data (Maronpot et al., 1993) using previously developed methods (Dewanji
et al., 1989). This analysis used daily average dose as the dose metric for examining dose
dependent effects of TCDD on model parameters. Maronpot et al. (1993) quantified the number
and size of liver AHF lesions expressing the placental form of glutathione-S-transferase (POST).
The modeling results indicate that TCDD stimulates the production of PGST-positive AHF
(which could indicate a mutational effect) and promotes the growth of POST AHF (as a result of
either increases in birth rate or decreases in the death rate). Data on cell replication indices and
liver weight could not explain the mutational effect of TCDD. Following upon the work of Kohn
et al. (1993), Portier et al. (1996) suggested this finding could be due to an increase in the
metabolism of estrogens to catechol estrogens, leading to subsequent increase in free oxygen
radicals and eventually to mutations. The analysis also indicated an interaction between DEN
and TCDD that results in dose-related formation of initiated cells throughout the study period.
Portier et al. (1996) also found that best-fitting curves (using maximum likelihood methods) for
the effect of TCDD on the mutation and birth rates reached saturation levels at doses below 3.5
,ng/kg/day.
As a validation exercise, Portier et al. (1987) used the same methods to analyze focal
lesion data from Pitot et al. The two studies utilized different initiation protocols. In the
Maronpot experiments, a necrogenic DEN dose (175 mg/kg) was used, whereas in the Pitot
experiments a non-necrogenic dose of DEN (30 mg/kg) was given 24 hours after partial
hepatectomy. These two initiation protocols lead to differences in background tumor rates and
differences in time course for tumor development following TCDD exposure.
In the Pitot experiment, three types of enzyme-altered AHF were quantified using the
marker enzymes gamma-glutamyltranspeptidase (GGT), canalicular adenosine triphosphatase
(ATP) and glucose-6-phosphatase (G6P). Portier et al. (1996) found that all four types of AHF
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from the two different studies produced similar qualitative results; TCDD had effects on both
mutation and birth rates. The effect of dose on the birth rates for both data sets produced similar
patterns, with an almost identical unexposed birthrate for all of the four lesion types, a maximal
increase over the background rate between 33% and 300%, saturation of the increased birthrate at
low doses, and a small increase in birthrate because of DEN initiation. The pattern of
dose-related changes in the mutation rate is slightly different in the ATP, GOT, and G6P AHF
than for the POST AHF, tending more toward linearity than the hyperbolic response seen for the
POST AHF. However, for all four lesions, the maximal induction rate tended to be the same.
Moolgavkar et al. (1996) analyzed data from Buchmann et al. (1994) on ATP AHF in
female Wistar rats exposed to 2,3,7,8-TCDD as well as 1,2,3,4,6,7,8-heptachlorodibenzo-
j>dioxin (HCDD). The initiation protocol was a non-necrogenic dose (10 mg/kg) for 5
consecutive days. In addition to the mathematical analysis developed by Dewanji et al. (1989),
Moolgavkar et al. (1996) used a modification that allowed for cellular replication focused on the
edge of the AHF. Although Moolgavkar et al. (1996) did not have information on multiple dose
groups, the results of their analysis for TCDD concur qualitatively with those of Portier et al.
(1996). In essence, they observed no effect on the birthrate of initiated cells, a significant
(sevenfold in noninitiated and twofold in initiated) effect of TCDD on the mutation, and a
prolonged effect of DEN following initiation (similar to the interaction effect observed by Portier
et al. [1996]). The observed lack of change in birthrates is similar to that of the nonsignificant
increase observed by Portier et al. (1996) for PGST+, GOT, and G6P foci, but smaller than that
for ATP foci in the Pitot et al. (1980) study. In the DEN-initiated groups, the associated
increases in the mutation rates were quantitatively similar to those observed for POST lesions in
the Portier et al. (1996) study (2.2-fold at 100 ng/kg/day in Moolgavkar et al. (1996), 2.5-fold at
125 ng/kg/day for POST), but much smaller than those observed for the ATP, GGT, and G6P
lesions from the Pitot et al. (1980) study (9.9-fold for ATP, 4.5-fold for GGT and 5.8-fold for
G6P). The observed increase in the mutation rate in noninitiated animals was much larger in the
Moolgavkar et al. (1996) analysis than that for the Portier et al. (1996) analysis. This study was
conducted at a single dose and the comparison is simply treated versus control.
8.4.3.1.2. Models with two initiatedphenotypes. Conolly and Andersen (1997) developed a
model for focal lesion growth based upon two types of initiated cells, applying the negative
selection mechanism for hepatic tumor promotion proposed by Jirtle et al. (1991a,b). In this
model, even though the two types of initiated cells express the same biochemical marker, they
respond differently to promotional stimulation in the liver. The model presumes that a
promotional stimulus to the liver is countered by mitomhibitory signals generated by the liver to
constrain proliferation. One set of mutated cells is sensitive to this mitoinhibition whereas the
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other set of mutated cells is insensitive and responds only to the promotional stimulus. The
result is that, under increasing doses of the promoter, one group of focal lesions is decreasing in
size, and hence number of cells, while the other group is increasing in size.
Conolly and Andersen's model is different from those of Portier et al. (1996) and
Moolgavkar et al. (1996) in that it can result in U-shaped dose-response curves for the total
number and mean size of observable focal lesions without using U-shaped parametric forms for
the mutation rates or the birthrates. Number and size of focal lesions were estimated using the
stochastic resampling methods outlined in Conolly and Kimbell (1994), with deterministic
growth replacing stochastic growth when colonies exceeded 1,000 cells. Twenty-five replicates
for each model output were compared to the data for the combination of all three focal lesion
types from the study by Pitot et al. (1980) to obtain parameter estimates for the birth and death
rates of the two types of mutated cells. This analysis used administered dose as the tissue dose
metric.
The two-cell model adequately fit the data with biologically reasonable parameter values.
An alternative model including an effect of TCDD on mutation rates was not considered.
Similarly, the earlier analyses of Portier and Moolgavkar did not consider two types of initiated
cells, so comparisons between models with one type of initiated cell versus two types of initiated
cells relating to the issue of the effect of TCDD on mutation rates cannot be made. This is an
area that could use additional research. The birthrates (combined for the two mutated clones in
the Conolly and Andersen model) for all three sets of models (Portier et al., 1996; Moolgavkar et
al., 1996; Conolly and Andersen, 1997) are comparable in the control groups but differ
substantially for the higher dose groups, with the two clone models having much larger rates.
This difference is partially due to the assumption in the Conolly and Andersen model that there is
no increase in mutation rate following initiation and partially due to the use of an increasing
death rate with exposure to TCDD. Portier et al. (1996) used a fixed death rate in their final
model and Moolgavkar et al. (1996) varied the death rate with the birth rate. Results from a
study of Stinchcombe et al. (1995) indicate a lack of significant effects of TCDD on cell
replication in POST foci, but remarkable suppression of apoptosis within PGST-positive AHF.
This study, however does not supply information on dose dependency of these parameters.
Given the lack of sufficient data, it is not possible to simultaneously estimate both the birth rates
and death rates for the initiated cell phenotypes.
8.4.3.1.3. Alternative dose metrics in promotion studies. In the above models, oral dose of
TCDD was essentially used as the dose metric. In contrast, Conolly and Andersen used the
fraction of the maximum possible induction of CYP1 Al and CYP1A2 calculated from the zonal
induction model (Andersen et al., 1997a) as a dose-surrogate for the effect of TCDD on the
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clonal expansion of both mutated cell types within the framework of a two-cell multistage model.
Andersen et al. (1997a) fit their multicompartment geometric model of hepatic zonation
(Andersen et al., 1997b) to data derived from several studies on the expression of CYP1A2 in
rats (Abraham et al., 1988; Tritscher et al., 1992; van den Heuvel et al., 1994b). The zonal
induction model is described previously in this review. The model was linked to the previous
PBPK model (Andersen et al., 1993 a) with modifications (Andersen et al., 1997b) to account for
the regional induction of CYP1A2, rather than to the original model which was based upon
uniform expression throughout the liver. Formal optimization methods were not used to obtain
model parameters; however, graphical comparisons of the model predictions to these data did not
appear to be obviously different from previous descriptions and provided adequate fits. The
dissociation constants for binding of the TCDD-AhR complex to dioxin-responsive elements for
CYP1A1 (0.6 to 2 nM for compartment 3) and CYP1A2 (0.08 to 1.0 nM for compartment 3)
were fit separately for each data set and varied by a factor of 3 from compartment to
compartment. This produced a model that fit the fraction of liver volume occupied by focal cells,
but failed to fit the number of foci per volume of liver as well as the original analysis. These
analyses used percent of liver expressing CYP1A2 as an indicator of the dose metric.
8.4.3.2. Estimation of Cancer Risks
Portier and Kohn (1996) combined the biochemical response model of Kohn et al. (1993)
with a single initiated phenotype two-stage model of carcinogenesis to estimate liver tumor
incidence in female Sprague-Dawley rats from the 2-year cancer bioassay of Kociba et al. (1978).
In the simplest of several models tested, the initial mutation rate to the initiated phenotype was
proportional to the instantaneous concentration of GYP 1A2 as predicted by the biochemical '
model of Kohn et al. The birthrate of mutated cells was a linear function of loss of EGFR. All
death rates were held constant, as was the second mutation rate from the initiated to the
malignant phenotype. This model adequately fit the tumor data, although it overestimated the
observed tumor response at the lowest dose hi the Kociba et al. (1978) study. The shape of the
dose-response curve was approximately linear and the estimated ED01 value for this model (1.3
ng/kg/day) is presented in Table 8-8. The corresponding body burden giving a 1% increased
effect was 2.7 ng/kg. The use of CYP1A2 as a dose metric for the first mutation rate is
consistent with its role as the major TCDD-inducible estradiol hydroxylase in the liver (Hayes et
al., 1996; Dannan et al., 1986) and with the hypothesized role of estrogen metabolites leading to
increased oxidative DNA damage and increased mutation (Yager and Liehr, 1996; Roy et al.,
1992; Cavalieri et al., 1997).
Even though the thyroid hormone model of Kohn et al. (1996) has not been strictly used
for modeling of thyroid neoplasia induced by TCDD, it is important to note that the hypothesis
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for induction of thyroid neoplasia consequent to growth stimulation by chronically elevated
serum TSH is highly plausible. In contrast there is weaker evidence in the liver that alteration in
CYP1A2 and EGFR are causally linked to carcinogenesis. Given that the alteration in thyroid
hormone homeostasis as a consequence of TCDD induction of UDPGT can be effectively
modeled provides an excellent opportunity to mechanistically link activation of gene expression
by TCDD with thyroid cancer risk.
8.4.4. Knowledge/Data Gaps
Knowledge gaps still exist with each of the models. All the PBPK models have
biological structure and encode hypotheses about the modulation of protein concentrations by
TCDD. However, each of them falls between curve fitting and mathematical representations of
known biology. Parameters in empirical equations representing overall production of the protein
gene products, for example, were estimated using dose-response data for protein concentrations
and enzyme activity. Although protein level is a direct consequence of gene expression, this
empirical approach constitutes curve fitting. In the cases of CYP1A1 and UDGPT induction,
information about both mRNA and protein levels was available, permitting a more realistic,
although still empirical, representation of the mechanism of induction. Similarly, equations for
metabolism of TCDD and thyroid hormones in the model of Kohn et al. (1996) and of lipids in
the model of Roth et al. (1994) are not based on detailed studies of the enzymatic kinetics but are
greatly simplified representations. Nonetheless, the structure of the physiological models was
specified by information on anatomy, physiology, and qualitative effects of TCDD. These PBPK
models reproduce protein concentrations in data sets that were not included in the construction of
the model and that were obtained from experimental designs different from those used to define
the model. This constitutes at least a partial mechanistic validation of these models.
Models for tissue response including lipid metabolism and hepatic lobular effects also
have aspects that need confirmation. The Roth et al. (1994) model has not been validated for
chronic exposures or low doses. Even though the Wang et al. (1997) model has examined
CYP1 Al and CYP1A2 induction, it has not been validated for chronic exposures. The regional
induction model (Andersen et al., 1997a,b) creates a hypothesis concerning-regional induction
that should be further studied. An alternative to altering the affinity of DREs to the liganded
AhR is a gradient in the receptor concentration across the liver acinus. The concentration of the
receptor in centrilobular hepatocytes was found to be more than 40 times that in periportal
hepatocytes (Lindros et al., 1997). The use of Hill kinetics to describe at least some of the
binding (or metabolic) reactions is a convenience to allow flexibility in estimating dose-response
relationships.
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The models for estimating values of the dose metrics for exposure or effects differ in
their mathematical representations of the same physiological processes while providing
comparable fits to the observed responses. The endocrine response model includes TCDD
induction of the AhR, binding to multiple DREs, and saturation kinetics for protein synthesis on
the mRNA template. This sequence of steps can potentially lead to nonlinear kinetics for the
overall responses, but the nonlinearities in the individual steps appeared to compensate for each
other, leading to approximately linear low-dose responses. The regional induction model
(Andersen et al., 1997a) collapses this sequence into a single overall process and uses Hill
kinetics to represent the potential overall nonlineariry. A high Hill exponent was required to
reproduce the sharp edge detected for the induced region of the liver, leading to sublinear
predicted responses below the experimentally accessible range of doses. Thus, emphasizing
different aspects of the underlying biology leads to different mathematical structures with
different predicted low-dose behavior. Which of these processes are most important in
producing the overall responses cannot be resolved by existing data.
The biochemical and tissue response models were linked to a two-stage cancer model
(Portier and Kohn, 1996). Although TCDD is not a mutagen in in vitro systems commonly used
to detect mutation through DNA damage, inferences drawn from biochemical data and
mechanistic modeling supported a secondary mechanism for TCDD-induced mutations (Portier
et al., 1996; Moolgavkar et al., 1996). Another approach, with secondary pathways leading to
mutations and two cellular phenotypes, also fit these data but does not require this secondary
effect on mutation rate (Andersen et al., 1997a,b; Conolly and Andersen, 1997). Even though
this secondary mechanism of mutation is still speculative, these studies present challenges to the
application of general models for cancer risk assessment based on direct chemical mutagenesis as
a fundamental mechanism for chemically induced or radiation-induced cancer and the notion of a
single cellular phenotype as a precursor for cancer.
8.4.5. Summary
The development of PBPK models describing the disposition of TCDD within
experimental animals has proceeded through multiple levels of refinement, with newer models
incorporating ever-increasing levels of biological complexity. The two most complete PBPK
models give similar predictions about TCDD tissue dose metrics. It is unlikely that additional
refinement of the current models will have a major impact on the model predictions within the
observable dose range. However, further work could better characterize the biological processes
involved in disposition.
Despite their availability, these PBPK models have been highly underutilized in aiding
empirical dose-response analyses for the effects of TCDD observed in laboratory studies.
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Differences in dosing regimens in experimental animals, such as exposure duration, route of
exposure, time after dosing to necropsy, use of maintenance-loading dose regimen, etc.,
complicate the use of a simple metric based on administered dose for comparative analyses
between studies (Section 8.3). The use of the current PBPK models could provide a more
scientifically credible description of a body burden dose metric and may reduce some of the
uncertainties introduced when converting a daily averaged dose ED0] to a body burden dose
metric.
Similarly, the application of these models to human dose-response data, while possible
has also not been pursued. The current level of detail in rodent PBPK modes for TCDD has not
been included in any current human PBPK model for TCDD. Human exposure assessment for
use in dose-response modeling utilizes either back-extrapolation based on a single measurement
of a tissue (plasma/serum) concentration or a dose metric based on an estimated external
exposure. Although extrapolation of the current generation of rodent PBPK models to humans
would have uncertainties, it is unlikely that predictions from such a model would be any less
uncertain that current methodologies used for estimating human body burdens.
With regard to the extension of PBPK models to biochemical response, tissue response,
and toxicological responses, the differences in interpretation of the mechanism of action of a
TCDD-dependent response lead to varying estimates of the dose-dependent behavior for similar
responses. In addition, the hypotheses and assumptions used in different models may restrict the
shape of the dose-response curves that are calculated and lead to differences in their low-dose
behaviors.
The use of specific biochemical/tissue responses as dose metrics for the evaluation of the
dose-response for toxicity are based upon hypotheses regarding specific linkages between these
responses and toxicity. A greater understanding of the mechanism of linkage of these dose
metrics to the toxicological endpoint of concern is required before an interpretation of the shape
of the dose-response curve or estimation of low-dose risk is credible.
In summary the state of the science for mechanism-based modeling has been greatly
improved by these newer PBPK models and incorporation of knowledge of the mode of action
of TCDD. These models may allow qualitative assessment of modes of action, i.e., low-dose
behavior; however, differences exist in the low-dose expectations of current models. Expanded
use of current PBPK models could reduce uncertainty in quantifying actual internal dose
following different dosing regimens.
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8.5. DATA GAPS
This chapter identified several important data and knowledge gaps. Information to fill
these gaps would substantially improve dose-response analysis and risk assessment. The most
substantial gaps are summarized below.
There are similarities and differences, both qualitative and quantitative, in responses to
TCDD between laboratory animals and humans. These are due to a variety of factors, including
disposition of TCDD, AhR properties and regulation, and tissue- and species-specific
biochemical responses and specific factors regulating these responses. A better understanding of
these factors could substantially improve dose-response analysis and risk assessment.
There are differences between AhR binding curves and dose-response curves for specific
toxic endpoints. This suggests that factors in addition to the AhR contribute to these toxic
endpoints. For complex endpoints, including frank toxicities, there are likely to be earlier
biochemical events, initiated by receptor binding, that lead ultimately to the toxic responses.
Detailed quantitative knowledge of this sequence of events would increase reliability in response
and species extrapolation, mechanistic modeling, and extrapolation to lower doses.
Tissue disposition of TCDD plays a critical role in the approach to risk assessment for
this chemical. Knowledge about the disposition of TCDD at or near the background exposures
experienced by the general population is limited. PBPK models can make predictions about
tissue disposition at these low levels of exposure, though these predictions tend to be below the
dose ranges for which the models have been validated. Lack of knowledge of disposition of low
doses is especially applicable to human exposures and exposures that may occur in the embryo at
critical time points. Furthermore., there is uncertainty about half-life in humans and about the
heterogeneity in this half-life among individuals. These factors add to the difficulty in
determining the proper dose metric for different endpoints and across different species. PBPK
modeling could help to address this problem if the existing models developed for laboratory
rodents were extrapolated to humans. Although there would be uncertainty associated with this
extrapolation, it would not necessarily be greater than, nor even as great as, the uncertainty
associated with the current approach.
In animals, more information is needed about background levels of exposure and how
they may affect dose-response analyses. This is especially true because greater emphasis is being
placed on low levels of exposure in animal experiments. Including background exposure data
may alter the shape of the dose-response curve and affect the estimate of the ED01.
Quantitative mechanism-of-action-based models can provide insights into the complex
interrelationships of the molecular and biochemical events that comprise a mechanism or mode
of action. However, the level of confidence in the models and their predictions should not be
greater than the level of confidence in the quality of the database and degree of scientific
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consensus about the mechanism or mode of action that the model describes. This is particularly
true when the model is to be used for risk assessment. It is possible to use alterations in the
concentrations of proteins known to be altered by TCDD as potential dose metrics. However,
more information is needed about the mechanistic linkages of these proteins to toxic endpoints to
improve estimations of shapes of dose-response curves and estimates of low-dose risks.
8.6. SUMMARY
Data available for several biochemical and toxicological effects of TCDD, and on the
mechanism of action of this chemical, indicate that there is good qualitative concordance
between responses in laboratory animals and humans. For example, human data on exposure and
cancer response appear to be qualitatively consistent with animal-based risk estimates derived
from carcinogenicity bioassays. These data would suggest that animal models are generally an
appropriate basis for estimating human responses. Nevertheless, there are clearly differences in
responses between animals and humans, and recognition of these is essential when using animal
data to estimate human risk. The level of confidence in any prediction of human risk depends on
the degree to'which the prediction is based on an accurate description of these interspecies
extrapolation factors.
Almost all data are consistent with the hypothesis that the binding of the TCDD to the
AhR is the first step in a series of biochemical, cellular, and tissue changes that ultimately lead to
toxic responses observed in both experimental animals and humans. As such, an analysis of
dose-response data and models should use, whenever possible, information on the quantitative
relationships between ligand (i.e., TCDD) concentration, receptor occupancy, and biological
response. However, it is clear that multiple dose-response relationships are possible when
considering ligand-receptor mediated events. For example, dose-response relationships for
relatively simple responses, such as enzyme induction, may not accurately predict dose-response
relationships for complex responses such as developmental effects and cancer. Cell-specific
factors may determine the quantitative relationship between receptor occupancy and the ultimate
response. Indeed, for TCDD there is much experimental data from studies using animal and
human tissues to indicate that this is the case.
One of the most difficult issues in risk assessment is the dose metric to use for
animal-to-human extrapolations. The most appropriate dose metric should reflect both the
magnitude and frequency of exposure, and should be clearly related to the toxic endpoint of
concern by a well-defined mechanism. However, considering the variety of endpoints in
different species, it is unlikely that a single dose metric will be adequate for interspecies
extrapolation for all of these endpoints. Furthermore, the use of different dose metrics with
respect to the same endpoint may lead to widely diverse conclusions. Nevertheless, it is possible
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to express dose in a form that allows for comparison of responses for selected endpoints and
species. This can be done by either choosing a given exposure and comparing responses or
choosing a particular response level and comparing the associated exposures. For particular
endpoints, and considering the large differences in half-lives for TCDD across multiple species,
it is best to compare the dose metric as body burden rather than daily intake. A useful and
common metric for comparison is the 1% effective dose or ED0], which is the exposure dose
resulting in 1% change in a particular endpoint. The possibility that existing PBPK models could
be used to a greater extent to compare tissue doses across experimental designs and between
species deserves further study.
TCDD has been classified as a known human carcinogen, and is a carcinogen in all
species and strains of laboratory animals tested. However, it is generally difficult to find human
data with sufficient information to model dose-response relationships. For those data that are
available, the uncertainties involved in the modeling of these data are considerable, and notably
include extrapolation of occupational exposure many years after it took place, and the type and
shape of the curve for the dose-response model used in the extrapolation. A linear model is often
used because the number of exposure groups for analysis is too small to support more complex
models. On the other hand, analysis of animal data suggests that many complex responses to
TCDD are nonlinear (Figures 8.3.1 and 8.3.2). Nevertheless, with these qualifications, it is
possible to apply simple empirical models to studies in which exposure data for TCDD are
available in human populations. An analysis of epidemiological studies of occupationally
exposed individuals suggests an effect of TCDD on all cancers, and on lung cancers in the adult
human male. The ED01 values based upon average excess body burden of TCDD ranged from 6
ng/kg to 161 ng/kg in humans. This compared well with the steady-state body burdens estimated
in animals, which ranged from 3 ng/kg to 1,190 ng/kg. For the effect of TCDD on lung cancers,
the only tumor site that increased in both rodents and humans, the human ED01 values ranged
from 36 ng/kg to 250 ng/kg, compared with the single estimate of 730 ng/kg in the rat.
At this point, sufficient data are not available to model noncancer endpoints in humans.
Many studies are available to estimate ED01 values for noncancer endpoints in animals.
However, there are a number of difficulties and uncertainties that should be considered when
comparing endpoints across species. Some of these include differences in sensitivity of
endpoints, times of exposure, exposure routes, species and strains, use of multiple or single
doses, and variability between studies even for the same response. The estimated ED01 values
may be influenced by experimental design, suggesting that caution should be used in comparing
values from different designs. In addition, caution should be used when comparing studies that
give ED0] estimates outside the experimental range. Furthermore, comparing values between
different categories of inducible responses may result in misleading estimates of a potential
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health risk. For example, the human health risk for a 1% change of body weight may not be
comparable to a 1% change in enzyme activity. Finally, background exposures are not often
considered in these calculations simply because they were not known. The latter consideration is
particularly important as the inclusion of these may alter the shape of the dose-response curve,
possibly increasing the shape parameter so that the responses would demonstrate more
threshold-like effects. Nevertheless, given these considerations several general trends were
observed. The lowest EDOI values tended to be for biochemical effects, followed by hepatic
responses, immune responses, and responses in tissue weight. An analysis of shape parameters
implies that many dose-response curves, for a variety of responses, were consistent with linearity
over the range of doses tested. This does not imply that the curves would be linear outside this
range of doses. The lower shape parameters, suggesting linearity, were for biochemical
responses, whereas the higher values for shape parameters, suggesting nonlinearity, were for
tissue responses. Overall, these data suggest that biochemical responses to TCDD are more likely
to be linear within the experimental dose range, while the more complex responses including
frank toxicity are more likely to assume a nonlinear shape. For cancer, the shapes were split
between linear (eight analyses) and nonlinear (five analyses).
The tissue weight changes seen for animals (using only data sets with good or moderate
empirical fits to the model) yielded a median EDOI of 510 ng/kg in the multidose studies (range
11 to 28,000 ng/kg) and a median ED01 of 160 ng/kg (range 0.0001 to 9,700 ng/kg) in the single-
dose studies. Toxicity endpoints from the single-dose studies resulted in a median value of 4,300
ng/kg (range 1.3 to 1,000,000 ng/kg). For tissue weight changes, 43% of the dose-response
curves exhibited linear response. In contrast, the toxicity endpoints from the single-dose studies
exhibited predominantly nonlinear responses (80%). All multidose studies demonstrated a
greater degree of linear response (41%) than did single-dose studies (37%), especially for tissue
weight changes and toxicity endpoints (50% linear for multidose versus 34% for single dose). In
general it is not possible to specify the differences between cancer and noncancer dose-response
as being due to differences in endpoint response or to differences in the length of dosing and
exposure. Also, a greater percentage of the noncancer ED01 values were below the experimental
dose range (42%) than was the case for the cancer endpoints (8% in animals and no
extrapolations in humans). However, many more noncancer data sets were examined compared
to the cancer endpoints.
Empirical models have advantages and disadvantages relative to mechanism-based
models. Empirical models provide a simple mathematical model that adequately describes the
pattern of response for a particular data set and can also provide the means for hypothesis testing
and interpolation between data points. In addition, empirical models can provide qualitative
insights into underlying mechanisms. However, the major disadvantage is their inability to
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quantitatively link data sets in a mechanistically meaningful manner. On the other hand,
comprehensive mechanism-based models can be powerful tools for understanding and combining
information on complex biological systems. Use of a truly mechanism-based approach can in
theory enable reliable and scientifically sound extrapolations to lower doses and between species.
However, any scientific uncertainty about the mechanisms that the models describe is inevitably
reflected in uncertainty about the predictions of the models.
PBPK models have been validated in the observable response range for numerous
compounds in both animals and humans. The development of PBPK models for disposition of
TCDD hi animals has proceeded through multiple levels of refinement, with newer models
showing increasing levels of complexity by incorporating data for disposition of TCDD and its
molecular actions with the AhR and other proteins, as well as numerous physiological
parameters. These have provided insights into key determinants of TCDD disposition in treated
animals. The most complete PBPK models give similar predictions about TCDD tissue dose
metrics. The PBPK models have been extended to generate predictions for early biochemical
consequences of tissue dosimetry of TCDD such as induction of CYP1A1. Nevertheless,
extension of these models to more complex responses is more uncertain at this time. Differences
in interpretation of the mechanism of action lead to varying estimates of dose-dependent
behavior for similar responses. The shape of the dose-response curves governing extrapolation
to low doses is determined by these hypotheses and assumptions. In the observable range around
1% excess response, the quantitative differences are relatively small. Below this response, the
different mechanisms can diverge rapidly. The use of predicted biochemical responses as a dose
metric for toxic responses is considered a potentially useful application of these models.
However, greater understanding of the linkages between these biochemical effects and toxic
responses is needed to reduce the potentially large uncertainty associated with these predictions.
8.7. CONCLUSIONS
Once an environmental agent has been deemed a health hazard, the two main questions to
be addressed in any dose-response assessment are: (1) What can be said about the shape of the
dose-response function in the observable range, and what does this imply about dose-response in
the range of environmental exposures? (2) What is a reasonable limit (critical dose or point of
departure) at the edge of the observable range, and what risk is associated with this exposure?
For the dose-response assessment of TCDD, these questions are complicated by the multiplicity
of responses observed and the complexity of the mechanisms known to impact upon those
responses. In the dose-response evaluation conducted for this chapter, we have attempted to use
the best available analytic procedures to provide insight into the answers to these questions. This
includes both the critical assessment of formal empirical dose-response analyses of the available
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data and, where appropriate, predictions of dose-response behavior using mechanism-based
models of TCDD.
Many different shapes of dose-response curves were seen in the observable range.
Although human data were available, the data were not adequate for addressing curvature of the
dose-response relationship. Consequently, the main conclusions on the shape of the
dose-response for TCDD are based on animal models.
Under simple empirical dose-response models, about half of the cancer endpoints
observed in animals were linear in the observable range and about half were not. Noncancer
endpoints had a greater degree of nonlinearity, with only 40% of the observed responses being
linear. Biochemical endpoints (more closely coupled to activation of the AhR) tended to exhibit
linear dose-response curves, whereas TCDD-inducible responses, which are likely more complex
and involve multigene interactions, exhibited more nonlinear behavior. Mechanism-based
modeling provided two different answers depending upon the approach used in the analysis and
the assumptions used in the approaches. The variability in the available data for
mechanism-based modeling did not allow us to clearly decide upon any one given model in favor
of another. For intermediate biochemical endpoints and preneoplastic lesions in the rat liver, we
saw model fits that strongly supported nonlinear dose-response shapes in the observable range.
This was based upon the assumptions of a nonlinear expression of proteins in the liver and upon
multiple types of focal lesions responding differently to the effects of TCDD. In contrast, using
an alternative model resulted in effectively linear dose-response (defined as response
proportional to dose in the low-exposure region, not necessarily the higher experimental doses)
for both endpoints and the proposition of a secondary effect of TCDD on increasing mutations
through changes in estrogen metabolism.
All humans tested contain detectable body burdens of TCDD and other dioxin-like
compounds that are likely to act through the same mode of action. This consideration, together
with the high percentage of observed linear responses, suggests that a proportional model should
be used when extrapolating beyond the range of the experimental data rather than using a
margin-of-exposure analysis. However, this decision would have to be based upon a policy
choice because this analysis dose not strongly support either choice.
Because we had human data for dose-response analysis and a strong desire to stay within
the range of responses estimated by these data, the risk chosen for determining a point of
departure was the 1% excess risk. Doses and exposures associated with this risk (the ED01) were
estimated from the available data using both mechanistic and empirical models. Comparisons
were made on the basis of body burdens (either averaged, steady-state, or administered dose) to
account for differences in half-life across the numerous species studied.
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In humans, restricting the analysis to linear models resulted in cancer ED01 values ranging
from 6 ng/kg to 161 ng/kg. This was similar to the estimates, from empirical modeling, from the
animal studies, which ranged from 14 ng/kg to 1,190 ng/kg (most estimates were in the range
from 14 to 500 ng/kg), and 2.7 ng/kg for the single-mechanism-based model.
Estimates for noncancer endpoints showed much greater variability, ranging over 10
orders of magnitude. In general, the noncancer endpoints displayed lower body burdens at the
ED01 for longer term exposures versus short-term exposures, and for simple biochemical
endpoints versus more complex endpoints such as tissue weight changes or toxicity. In addition,
the noncancer endpoints generally displayed higher estimated body burdens at the ED01 than the
cancer endpoints, with most estimates ranging from 100 ng/kg to 100,000 ng/kg. However, for
some endpoints the body burdens at the ED01 were below the range of the cancer endpoints. The
mechanism-based models for noncancer endpoints gave a lower range of body burdens at the
ED0] (0.17 to 105 ng/kg). Although most of these estimates were based upon a single model, the
estimate from the hepatic zonal induction model gave a body burden for the ED01 for CYP1A2
induction of 51 ng/kg and hence was within the same range.
These estimates, although highly variable, suggest that any choice of body burden, as a
point of departure, above 100 ng/kg would likely yield greater than 1% excess risk for some
endpoints hi humans. Also, choosing a point of departure below 1 ng/kg would in general be
supported only by analyses that gave estimates that were below the range of these data, and
would likely represent a risk of less than 1%. Any choice in the middle range of 1 ng/kg to 100
ng/kg would be supported by the analyses, although the data provide the greatest support in the
range of 10 ng/kg to 50 ng/kg.
This chapter has produced as extensive a summary of dose-response relationships as is
feasible at this time. The analyses and discussions synthesize a considerable breadth of data and
model types, drawing upon this information to highlight strengths and weaknesses in the
information base, gaps in our qualitative and quantitative understanding, and the uncertainties
inherent in making a decision concerning a point of departure for risk characterization. Even
though such an extensive evaluation may not be necessary for most environmental contaminants,
the concepts envisioned here can serve as a framework for evaluation in other settings. This
unique document hopefully marks the beginning of more objective, quantitative reviews of
information pertaining to risk decisions for environmental agents.
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Table 8-1. Estimated half-lives for species considered in the
analyses to follow and used for converting between daily
exposures and steady-state body burdens
Species
Half-life (days)
C57BL/6N mice
All other mouse strains
Golden Syrian hamster
Wistar rats
All other rat strains
Human
10
11
12
22
25
2,593
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Table 8-3. Doses yielding 1% excess risk (95% lower confidence bound) based upon
2-year animal carcinogenicity studies using simple multistage models
Tumor
Liver cancer in female rats (Kociba)
Squamous cell carcinoma of the tongue in male
rats (Kociba)
Squamous cell carcinoma of the nasal turbinates
or hard palate in male rats (Kociba)
Squamous cell carcinoma of the lung in female
rats (Kociba)
Squamous cell carcinoma of the nasal turbinates
or hard palate in female rats (Kociba)
Thyroid follicular cell adenoma in male rats
(NTP)
Thyroid follicular cell adenoma in female rats
(NTP)
Liver adenomas and carcinomas in female rats
(NTP)
Liver adenomas and carcinomas in male mice
(NTP)
Liver adenomas and carcinomas in female mice
(NTP)
Thyroid follicular cell adenomas and carcinomas
in female mice (NTP)
Subcutaneous tissue sarcomas in female mice
(NTP) ,
Leukemias and lymphomas in female mice
(NTP)
Shape
Linear
Linear
Cubic
Cubic
Linear
Linear
Cubic
Quadratic
Linear
Linear
Line'ar
Lin-Cubic
Linear
ED01
Intake for 1%
excess risk
(ng/kg/day)
0.77 (0.57)
14.1 (5.9)
41.4(1.2)
40.4 (2.7)
5.0 (2.0)
4.0(2.1)
33.0(3.1)
13.0 (1.7)
1.3 (0.86)
15.1 (7.8)
30.1 (14.0)
43.2(14.1)
10.0 (5.4)
Steady-state body
burden (ng/kg) at
ED01
14 (10)
254 (106)
746(22)
730 (48)
90 (36)
144 (76)
1,190(112)
469(61)
20.6 (13.6)
239 (124)
478 (222)
686 (224)
159(86)
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Table 8-4. Noncancer endpoints used for comparing ED0j values
Species
Mouse
Rat
Hamster
Total
Gender
Female
Male
Unknown
Female
Male
Female
Male
Multi-dose
26
0
—
59
16
0
0
101
Single-dose
Adult
24
36
—
10
4
0
1
75
Developmental
5
18
3
0
32
0
0
58
Total
55
54
3
69
52
0
1
234
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Table 8-5. Ratio of ED01/lowest dose, categorized by study type and endpoint type
Multi-dose
Category
Biochemical
Hepatic
Immune
Retinol
Thyroid
Tissue
Toxicity
Subtotals
Out of
range
22(18)
4(4)
7(5)
3(1)
3(3)
6(5)
—
45 (36)
In range
6
9
10
0
3
28
—
56
Single-adult
Out of range
3(2)
0
13 (5)
— .
—
7(3)
0
23 (10)
In range
14
13
3
—
—
9
13
52
Single-developmental
Out of range
0
—
—
—
—
26 (15)
6 (2)
32 (17)
In range
,3
—
—
—
—
22
1
26
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Table 8-6. Estimated shape parameters, categorized by study type and endpoint
type
Multi-dose
Category
Biochemical
Hepatic
Immune
Retinol
Thyroid
Tissue
Toxicity
Subtotals
Totals
Linear
15
3
3
3
2
17
—
43
Nonlinear
13
10
14
0
4
17
—
58
101
Single-adult
Linear
6
4
11
—
—
12
0
33
Nonlinear
11
9
6
—
—
5
13
44
77
Single-development
Linear Nonlinear
0 3
— —
— —
—
— —
14 36
4 3
18 42
60
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Table 8-7. Categorization of specific endpoints
Category
Biochemical
Hepatic
Immune
Retinol
Thyroid
Endpoint
CYPlAlmRNA
CYP1A1 (Protein)
CYP1A1 EROD in liver, lung, and
skin
CYP1A2 (Protein)
GYP1A2 ACOH
CYP1A2 mRNA
CYP1A2 MROD
CYP1B1 mRNA
EOF dissociation (Kd)
EGFR autophosphorylation
EGFR maximum binding
Serum 5'-nucleotidase
Serum alkaline phosphatase
Serum ALT
Serum BUN
Serum bilirubin (total, indirect,
direct)
Serum esterified cholesterol
Serum glucose
CD4+/CD8+
CD8+/CD4-
CD8-/CD4-
CD4+/CD8-
Cells/spleen(xlO-6)
Liver benzopyrene hydroxylase (CYP1A1 activity)
Liver cytochrome P-450 (total)
Renal retinol concentration
Renal RPH activity
Serum testosterone
Superoxide anion production by PLC
T4UGT
Total AhR binding
UGT mRNA
UGT1A1
Serum Not Esterified chloesterol
Serum S. Dehydrogenase
Serum SGPT
Serum TBA
Serum total cholesterol
Serum triglycerides
Immune footpad swelling (following SRBC)
Immune increment in ear thickness (following oxazalone)
PFC/106splenocytes
PFC/spleen(xlO-4)
Total thymic cells/mouse
Immune titer
Hepatic retinol
Thyroid-stimulating hormone
Thyroxine
Plasma retinol Hepatic retinyl-palmitate
Thyroxine free T4
Thyroxine total T4
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Table 8-7. Categorization of specific endpoints (continued)
Category
Tissue
<
Toxicity
Endpoint
Age at puberty
Body weight
Brain weight
Caput/corpus epid. sperm
numbers
Cauda epid. sperm numbers
Cauda epididymal weight
Coagulating glands
Daily sperm production
Dorsal prostate weight
DSP/g D day 120
Endometrial lesion diameter
Endometrial lesion weight
Cleft palate
Fertility index
Gestation period
Hydronephrosis
Litter size
Live birth index (%)
Epididymal sperm count
Epididymidis weight
Eye opening
Eye opening in F/M
Glans penis weight
Heart weight
Incisor eruption
Kidney weight
Liver weight
Ovarian weight
Ovulation (ova/rat)
Paired epididymal weight
Pituitary gland weight
Liver BDH
Liver fatty change
Liver HCC
Liver HCK
Number of copulatory plugs
Pinna detachment
Relative kidney weight
Relative liver weight
Relative spleen weight
Relative thymus weight
Seminal vesicle weight
Spleen atrophy
Spleen cellularity
Testes weight
Thymus atrophy
Thymus weight
Uterine horn weight
Uterus weight
Ventral prostate weight
Sperm morphology
Stomach edema
Testes MNGC
Testes SFEN
Testis descent
Total testis sperm numbers
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Table 8-8. Steady state ED0]l values calculated using mechanism-based dose-
response models of dioxin-regulated responses
Response
CYP1A1 (nmol/g)b
CYP1A2 (nmol/g)b
CYP1A2 (% liver induced)0
Internalized-EGFR (pmol/g)b
T4 (nM)b
UGT RNA pmol/g
UDPGT (nmol/g)b
TSHpMb
Liver cancerd
Response value
Control
(0 ng/kg/day)
0.0216
0.558
0
29.0
1.13
0.118
77.8
0.35
Maximum
(10 ng/kg/day)
6.09
7.17
2.09
3.96
14.1
0.416
179
1.00
ED01
(ng/kg/day)
0.0047
0.34
1.4
0.28
0.27
0.85
2.9
1.3
0.15
Body burden „,
(ng/kg)a
0.17
12.3
50.5
10.1
9.7
30.7
104.6
46.9
2.7
"Steady-state body burdens were calculated from the formula in Section 8.2.3. assuming 100% absorption, except for the
liver cancer model, which used 50% absorption.
bValues obtained using the extended thyroid hormone model.
cValues from the zonal induction model.
dMechanism-based cancer model.
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Figure 8-1. Distribution of ED01 and BB01 values in multidose studies by endpoint.
(a) ED01 values, (b) Body burden values at the ED0i. The distribution of individual values is
presented as box plots. The boxed region contains values within the 25th to the 75th percentiles of
the sample distribution, with the median value (50th percentile) shown as a line within the boxed
region. The error bars represent values within the 10th to the 90th percentiles. Values above the
9(T percentile and below the 10th percentile are shown as individual data points. Values are
categorized according to Table 8-7.
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(a)
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Figure 8-2. Distribution of ED01 values in single-dose studies by endpoint.
(a) Adult endpoints. (b) Developmental endpoints. The distribution of individual values is
presented as box plots. The boxed region contains values within the 25th to the 75th percentiles of
the_sample distribution, with the median value (50th percentile) shown as a line within the boxed
region. The error bars represent values within the 10th to the 90th percentiles. Values above the
90 percentile and below the 10th percentile are shown as individual data points. Values are
categorized according to Table 8-7.
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TCDD (food )
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Figure 8-3. Schematic representation of the linkage of current PBPK models and
biochemical/tissue response models for TCDD action.
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