United States
Environmental Protection
Agency
Office of Research and
Development
Washington. DC 20460
Chapter 8.
Dose-Response
Relationships
EPA/600/AP-92/001 h
August 1992
Workshop Review Draft
Review
Draft
(Do Not
Cite or
Quote)
Notice
This document is a preliminary draft. It has not been formally released by EPA and should not
at this stage be construed to represent Agency policy. It is being circulated for comment on
its technical accuracy and policy implications.
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DRAFT EPA/600/AP-92/001h
DO NOT QUOTE OR CITE August 1992
Workshop Review Draft
Chapter 8. Dose-Response Relationships
Health Assessment for
2,3,7,8-TetrachIorodibenzo-p-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.
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Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C.
Recycled/Recyclable
Printed on paper that contains
at least 50% recycled liber
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DISCLAIMER
This document is a draft for review purposes only and does not constitute Agency policy.
Mention of trade names or commercial products does not constitute endorsement or recommendation
for use.
Please note that this chapter is a preliminary draft and as such represents work
in progress. The chapter is intended to be the basis for review and discussion at
a peer-review workshop. It will be revised subsequent to the workshop as
suggestions and contributions from the scientific community are incorporated.
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CONTENTS
Tables v
Figures vii
List of Abbreviations viii
Authors and Contributors xiii
8. DOSE-RESPONSE MODELING FOR 2,3/7,8-TCDD 8-1
8.1. INTRODUCTION 8-1
8.1.1. Introduction to Modeling for TCDD 8-14
8.1.2. Dosimetric Modeling 8-23
8.2. TOXIC EFFECTS 8-40
8.2.1. Modeling Liver Tumor Response for TCDD 8-40
8.2.2. Tumor Incidence 8-41
8.2.3. Other Effects Mammary/Uterine/Anticancer Endpoints 8-62
8.2.4. NonCancer Endpoints (DeVito et al., 1992) 8-64
8.2.5. Neurological and Behavioral Toxicity 8-65
8.2.6. Teratological and Developmental 8-66
8.2.7. Immunotoxicity 8-69
8.2.8. Reproductive Toxicity 8-70
8.4. RELEVANCE OF ANIMAL DATA FOR ESTIMATING HUMAN RISKS 8-76
8.5. HUMAN MODELS 8-80
8.5.1. Introduction 8-80
8.5.2. Modeling Toxic Effects in the Liver 8-81
8.5.3. Lung Cancer and All Cancers Combined 8-83
8.6. CONCLUSIONS 8-102
8.7. KNOWLEDGE GAPS 8-103
8.8. REFERENCES 8-107
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CONTENTS (continued)
APPENDIX A: A MECHANISTIC MODEL OF EFFECTS OF DIOXIN ON GENE EXPRESSION
IN THE RAT LIVER
APPENDIX B: MODELLING RECEPTOR-MEDIATED PROCESSES WITH DIOXIN:
IMPLICATIONS FOR PHARMACOKINETICS AND RISK ASSESSMENT
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LIST OF TABLES
8-1 Examples of Levels of Information Available for Estimating Parameters
in Dose-Response Modeling 8-19
8-2 Design Considerations for Risk Assessment Purposes 8-22
8-3 Administered Dose, Tumor Response and Number at Risk of Hepatocellular
Neoplasms in Male Sprague-Dawley Rats From Carcinogenicity Experiments of
Kociba et al. (1978), Using the Pathology Review of Sauer 8-42
8-4 Fitting the Two-Stage (TS) Model to the Tumor Incidence Data of
Kociba et al. (1976) Using the Pathology of Sauer et al. (1991) 8-48
8-5 Net Parameter Estimates From the Two-Stage Model of Carcinogenesis 8-51
8-6 Results from Two-Stage Model for Hepatocarcinogenesis in Rats 8-54
8-7 Using Liver Foci Parameters to Fit Tumor Incidence 8-59
8-8 A Comparison of Dose-Surrogates With Parameters Estimates from the Two-Stage
Model of Carcinogenesis 8-60
8-9 Toxic Endpoints Data 8-74
8-10 Similiarities Between Laboratory Animals and Humans in Biological
Effects of TCDD 8-77
8-11 Rat and Human Comparison of Daily TCDD Intakes and Body and Liver
Concentration for Equitoxic Response 8-84
8-12 Measured serum TCDD Levels and Estimated Levels at Time of Last Occupational
Exposure to TCDD, Based on First-Order Elimination Kinetics and a Half Life
for Elimination of 7.1 Years 8-91
8-13 Estimate of Total Dose Based on Adjusted Median Concentration at Test Times,
Estimated Concentrations at Last Exposure, and Average Duration of Exposure, by
Study Cohort 8-96
8-14 Estimated Lifetime Average Daily Doses and Relative Risks by Individual Study
Cohort 8-97
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LIST OF TABLES (continued)
8-15 Calculation of Incremental Unit Cancer Risk Estimates and 95% Lower Limits for
Both the Additive and Relative Risk Models Based on the Lung Cancer Deaths
Response in the Fingerhut, Zober, and Manz Studies 8-100
8-16 Calculation of Incremental Unit Risk Estimates and 95% Lower Limits for Both the
Additive and Relative Risk Models Based on the Total Cancer Deaths Response in the
Fingerhut, Zober and Manz Studies 8-101
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LIST OF FIGURES
8-1 Dose/Response Graph Showing Proportional Relationship Between Receptor
Occupancy and Biological Response (Semilog Scale) 8-6
8-2 Dose/Response Graph Showing Proportional Relationship Between Receptor
Occupancy and Biological Response (Arithmetic Scale) 8-7
8-3 Multistage Carcinogenesis 8-9
8-4 The Linear Multistage Model of Carcinogenesis 8-11
8-5 Developing a Mechanistically-Based Mathematical Model 8-16
8-6 A Two-Stage Model of Carcinogenesis 8-44
8-7 Relative Risks of Lung Cancer and All Cancer Mortality in Three Recent Cohort
Studies of Workers Exposed to TCDD by Estimated Lifetime Average Daily Dose
Intake 8-99
8-8 Biologically Based Risk Assessment Approaches for Dioxin: Filling the Gaps 8-105
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LIST OF ABBREVIATIONS
ACTH Adrenocorticotrophic hormone
Ah Aryl hydrocarbon
AHH Aryl hydrocarbon hydroxylase
ALT L-alanine aminotransferase
AST L-asparate aminotransferase
BDD Brominated dibenzo-p-dioxin
BDF Brominated dibenzofuran
BCF Bioconcentration factor
BGG Bovine gamma globulin
bw Body weight
cAMP Cyclic 3,5-adenosine monophosphate
CDD Chlorinated dibenzo-p-dioxin
cDNA Complementary DNA
CDF Chlorinated dibenzofuran
CNS Central nervous system
CTL Cytotoxic T lymphocyte
DCDD 2,7-Dichlorodibenzo-p-dioxin
DHT 5a-Dihydrotestosterone
DMBA Dimethylbenzanthracene
DMSO Dimethyl sulfoxide
DNA Deoxyribonucleic acid
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LIST OF ABBREVIATIONS (cont.)
DRE
DTG
DTK
ECOD
EGF
EGFR
ER
EROD
EOF
FSH
GC-ECD
GC/MS
GOT
GnRH
GST
HVH
HAH
HCDD
HDL
HxCB
Dioxin-responsive enhancers
Delayed type hypersensitivity
Delayed-type hypersensitivity
Dose effective for 50% of recipients
7-Ethoxycoumarin-O-deethylase
Epidermal growth factor
Epidermal growth factor receptor
Estrogen receptor
7-Ethoxyresurofin 0-deethylase
Enzyme altered foci
Follicle-stimulating hormone
Gas chromatograph-electron capture detection
Gas chromatograph/mass spectrometer
Gamma glutamyl transpeptidase
Gonadotropin-releasing hormone
Glutathione-S-transferase
Graft versus host
Halogenated aromatic hydrocarbons
Hexachlorodibenzo-p-dioxin
High density lipoprotein
Hexachlorobiphenyl
IX
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LIST OF ABBREVIATIONS (cont.)
HpCDD
HpCDF
HPLC
HRGC/HRMS
HxCDD
HxCDF
ID*,
I-TEF
LH
LDL
LPL
LOAEL
LOEL
MCDF
MFO
mRNA
MNNG
NADP
NADPH
NK
Heptachlorinated dibenzo-p-dioxin
Heptachlorihated dibenzofuran
High performance liquid chromatography
High resolution gas chromatography/high resolution mass spectrometry
Hexachlorinated dibenzo-p-dioxin
Hexachlorinated dibenzofuran
International TCDD-toxic-equivalency
Dose lethal to 50% of recipients (and all other subscripter dose levels)
Luteinizing hormone
Low density liproprotein
Lipoprotein lipase activity
Lowest-observ able-adverse-effect level
Lowest-observed-effect level
6-Methyl-l ,3,8-trichlorodibenzofuran
Mixed function oxidase
Messenger RNA
W-methyl-/V-nitrosoguanidine
Nicotinamide adenine dinucleotide phosphate
Nicotinamide adenine dinucleotide phosphate (reduced form)
Natural killer
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LIST OF ABBREVIATIONS (cont.)
NOAEL
NOEL
OCDD
OCDF
PAH
PB-Pk
PCB
OVX
PEL
PCQ
PeCDD
PeCDF
PEPCK
PGT
PHA
PWM
ppm
ppq
ppt
RNA
SAR
No-observable-adverse-effect level
No-observed-effect level
Octachlorodibenzo-p-dioxin
Octachlorodibenzofuran
Polyaromatic hydrocarbon
Physiologically based pharmacokinetic
Polychlorinated biphenyl
Ovariectomized
Peripheral blood lymphocytes
Quaterphenyl
Pentachlorinated dibenzo-p-dioxin
Pentachlorinated dibenzo-p-dioxin
Phosphopenol pyruvate carboxykinase
Placental glutathione transferase
Phytohemagglutinin
Pokeweed mitogen
Parts per million
Parts per trillion
Ribonucleic acid
Structure-activity relationships
XI
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LIST OF ABBREVIATIONS (cont.)
SCOT
SGPT
SRBC
t*
TCAOB
TCB
TCDD
TEF
TGF
tPA
TNF
TNP-LPS
TSH
TTR
UDPGT
URO-D
VLDL
v/v
w/w
Serum glutamic oxaloacetic transaminase
Serum glutamic pyruvic transaminase
Sheep erythrocytes (red blood cells)
Half-time
Tetrachloroazoxybenzene
Tetrachlorobiphenyl
Tetrachlorodibenzo-p-dioxin
Toxic equivalency factors
Thyroid growth factor
Tissue plasminogen activator
Tumor necrosis factor
lipopolysaccharide
Thyroid stimulating hormone
Transthyretrin
UDP-glucuronosyltransferases
Uroporphyrinogen decarboxylase
Very low density lipoprotein
Volume per volume
Weight by weight
xn
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AUTHORS AND CONTRIBUTORS
The Office of Health and Environmental Assessment (OHEA) within the Office of Research
and Development was responsible for the preparation of this chapter. The chapter was prepared
through Syracuse Research Corporation under EPA Contract No. 68-CO-0043, Task 20, with Carol
Haynes, Environmental Criteria and Assessment Office in Cincinnati, OH, serving as Project Officer.
During the preparation of this chapter, EPA staff scientists provided reviews of the drafts.
AUTHORS
This chapter was prepared by the Dioxin Dose-Response Modeling Workgroup.
The Workgroup is co-chaired by M.A. Gallo (Environmental and Occupational Health Sciences
Institute [EOHSI], Piscataway, NJ) and G.W. Lucier (National Institute of Environmental Health
Sciences [NBEHS], NC). Other members are: M. Andersen (Duke University, formerly of Chemical
Industry Institute of Toxicology [CIIT], NC); S. Bayard and P. White (U.S. EPA, Washington, DC),
K. Cooper, P. Georgopolous, and L. McGrath (EOHSI, Piscataway, NJ); E. Silbergeld (University of
Maryland, Baltimore); M. DeVito (U.S. EPA, NC); L. Kedderis (CEM, University of North Carolina-
Chapel Hill); J. Mills (CIIT, NC); and C. Portier (NIEHS, NC).
The two Appendices were not prepared by the Workgroup but are included with the gracious
permission from the authors.
EPA CHAPTER MANAGER
Steven P. Bayard
Office of Health and Environmental Assessment
Washington, DC
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8. DOSE-RESPONSE MODELING FOR 2,3,7,8-TCDD
8.1. INTRODUCTION
Most of the information presented in the Introduction is found in more
extensive detail in the other background chapters. We feel that it is useful to
summarize the salient features of those papers which impact on the development
of dose response models so that readers of this chapter will be able to evaluate
the scientific foundation on which our dose response models are based.
2,3,7,8-TCDD is the most potent form of a broad family of xenobiotics that
bind to an intracellular protein known as the Ah receptor. Other members of this
family include halogenated hydrocarbons such as the PCBs, naphthalenes and
dibenzofurans, as well as nonhalogenated species such as 3-methylcholanthrene and
3-naphthaflavone. The biologic properties of dioxins have been investigated
extensively in over 5000 publications and abstracts since the identification of
TCDD as a chloracnegen (Kimming and Schulz, 1957). Much of the biological
activity of TCDD follows the rank order binding affinity of the congeners and
analogs to the Ah receptor (AhR). This rank order holds for toxic responses such
as acute toxicity and teratogenicity, and concentration of several hepatic
proteins including the up regulation of P-450IA1 and IA2, and the modulation of
the estrogen receptor and EGF receptor. The carcinogenicity of TCDD has been
shown in several strains of laboratory mice and rats, and the tumor sites include
liver, thyroid and the respiratory tract, as well as others. However, TCDD does
not interact directly with the DNA to cause mutations. The study most utilized
for the cancer risk assessment of TCDD is that of Kociba et al. (1978). These
authors reported an increase in hepatocellular carcinomas and hepatomas, along
with decreases in several endocrine tumors in female rats receiving TCDD at the
level of 1 ng/kg/day. Male rats were remarkably less susceptible to TCDD action
in these studies. However, there is no striking sex specifity in the
hepatocarcinogenic actions of TCDD in mice.
The overall hypothesis of TCDD action, put forth by several groups, has been
proposed for the effects of TCDD on the transcriptional activities of the
cytochrome P-450IA1 gene. The biological basis for this approach is outlined in
the chapter by Whitlock. Although substantial gaps in our knowledge remain, it
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is accepted by most researchers that all cellular responses to TCDD follow upon
the interaction between TCDD and an intracellular macromolecule, the so called
Ah or Dioxin receptor (AhR). The binding of TCDD to AhR is similar, although not
necessarily identical, to the interaction of many steroid hormones with their
intracellular receptors, as pointed out by Gustafsson in a series of articles
drawing comparisons between the AhR and the glucocorticoid receptor (Poellinger
et al., 1986a, 1987; Cuthill et al., 1988). DeVito et al. (1991) have also drawn
the analogy of the action of TCDD and estradiol at their respective receptor
protein. Each AhR appears to bind one molecule of TCDD, and at low
concentrations of ligand (i.e., when [ligand]«[receptor]), the binding of TCDD
to AhR is linearly related to [TCDD]. However, the presence of TCDD in cells
induces an increase in the cytosolic concentration of AhR, possibly by increasing
receptor synthesis, displacement of an endogenous ligand from the receptor or by
movement of "spare" receptors from the nucleus. The increase in available
receptors could amplify the signal associated with receptor binding by increasing
opportunities for TCDD to bind to AhR.
The binding of TCDD to AhR is reversible. However, subsequent events seem
to reduce the likelihood of dissociation of the ligand:receptor complex. One
such event that has been recently studied is the association of the
ligand:receptor complex with another macromolecule, the so called ARNT (AhR
nuclear transport) protein (Hoffman et al., 1991). There may be a family of ARNT
proteins that differ by cell types which could account, in part, for the
diversity of actions of TCDD in different tissues. The association of ARNT with
the ligand bound receptor induces some physical changes in the complex, which
tends to reduce dissociation of the ligand and favors the movement of the complex
into the nucleus. Overall, the relationship between TCDD concentration and
nuclear AhR-TCDD concentration appears to be linear, indicating that at low
ligand concentrations, ARNT is not a rate limiting factor. In the nucleus, the
AhR-ARNT-TCDD complex (activated TCDD complex) associates with specific elements
in the genome called the xenobiotic (or dioxin) responsive elements (XREs or
DREs). Some of these elements have now been sequenced and identified upstream
of promoter elements in several dioxin responsive genes (Sutter et al., 1992;
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Denison, 19??). The association of the activated TCDD complex with the XRE is
also reversible, but little is known of its kinetic properties (Gasiewicz et al.,
1991). This XRE binding in turn evokes the production (or perhaps the
suppression) of several mRNA species. The structure and amino acid sequence of
the AhR protein was reported by Poland and co-workers (Burbach et al., 1992; Ema
et al., 1992). Both the XRE(s) and the structure of the AhR are analogous to the
steroid receptors and the respective genomic response elements. This similarity
is important in regard to biological models of TCDD action and risk assessment.
The steroid hormones and their receptors belong to a multigene family that
includes the thyroid hormone receptors, oncogene products, glucocorticoids,
mineralcorticoids, vitamin D, retinoids, androgens, estrogens and progestins
(Evans et al., 1988). Biologically, these are all multipotent agents, that
induce a range of cellular responses in different organs, many at extremely low
concentrations. They share a nuclear location for the transduction of
ligand:receptor action, and their common mechanism of action is the regulation
of gene expression (Jensen, 1991??). Within the family of known receptors from
these agents, there is considerable sequence homology and a common basic
structure, consisting of a ligand-binding domain and a DNA-binding domain. The
biological activity of these receptors is varyingly regulated by metals and by
phosphorylation state. Some — but not all— hormone receptors may interact with
chaperone-type proteins, subsequent to ligand binding, which transduce
conformational changes and other events critical to nuclear translocation and DNA
binding. The Ah receptor nuclear translocator protein (ARNT) functions in this
fashion (Hoffman et al., 1991). Other receptors are associated with heat shock
proteins that must be shed to transform the liganded receptor into a DNA-binding
form, and the DNA-binding domain of some receptors contains zinc finger loops.
The steroid hormone receptors regulate gene transcription through specific
DNA sequences near the regulated gene. But the situation is even more complex
because of interactions between the liganded receptor and nuclear proteins that
function as transcription factors by binding to other DNA sites upstream of the
regulated gene. These transcription factors may regulate the binding affinity
of the steroid hormone receptor itself to DNA (Muller et al., 1991). As
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mentioned above, a consensus binding site has been proposed for the Ah receptor
by Denison et al. (1988). A final level of complexity is introduced by the
interactions among steroid hormone receptors, at the genetic level, and by the
effects of hormone upon the number, conformation, and localization of receptors.
Down- and up-regulation of receptor gene transcription and receptor synthesis,
may also be involved in cell-level modulation of steroid hormone action. As
pointed out by Muller and Renkowitz (1991), every step in the signal transduction
pathway of these hormones, from receptor gene transcription to
ligand:receptor:DNA action, is likely to be inter- and independently regulated.
Attempts to model the steps involved in signal transduction have examined
events step-by-step as well as the overall set of reactions from entrance of
hormone to cellular response. Of interest to us is the information that may be
available concerning the overall dose:response relationship from steroid
hormones. The highly complex cascade of biological events that intervenes from
hormone entrance to cell response may modulate hormone action in the following
ways: it may amplify cell response, in the way that second messengers for
membrane-associated receptors (such as neurotransmitter receptors) appear to
amplify molecular signalling; it may transduce response in a manner proportional
to molecular signalling; it may transduce response in a manner proportional to
concentration of hormone (that is, linearly); or it may introduce dampening into
the response network. Amplification of signal transduction implies that at some
stage in a multistep process, more that one event is triggered as a consequence
of one preceding event. Dampening implies that at some stage, more than one
event must occur before the next event is triggered. Straight transduction
implies that the relationship of event to event, for all steps, is invariant.
In considering these possible dose:response relationships, it may be
important to distinguish among endogenous and exogenous ligands for the same
steroid hormone receptor, particularly if the two types of ligands differ in
rates of turnover (degradability) or affinity for the receptor. We are hampered
in our inferences for the dioxins because the endogenous ligand(s), if present,
has not yet been identified, and thus, we are not certain if TCDD is more or less
stable than this ligand, or if its affinity is higher or lower than an endogenous
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ligand. With respect to stability, it is unlikely that an endogenous ligand
would be as stable as TCDD. Most endogenous ligands for steroid hormone and
other receptors are rapidly cleared, either by compartmentation (as with
neurotransmitter reuptake processess) or by enzymatic degradation, as with
peptides. With respect to kinetic* of bidding of TCDD, its affinity for the
* I
receptor is extremely high, in the 10'" radge. If the affinity for the natural
ligand is even higher, then it is likely that the overall relationships between
natural ligand and receptor are even stronger than those we may explicate for
TCDD; if it is lower, then it would be an unusual member of the steroid hormone
family. Of course, differences in affinity, if these exist, may not influence
the overall kinetics of the dose:response relationship as much as differences in
the number of events required to trigger the reaction from step to step.
Evaluation of dose-response relationships for receptor-mediated events
require information on the quantitative relationships between ligand
concentration, receptor occupancy and biologic response. Roth and Grunfeld in
The Textbook of Endocrinology (1985) state:
"At very low concentrations of hormone ([H]«Kd), receptor
occupancy occurs but maybe 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 concentration;
at receptor occupancy (occupied receptors/total receptors, or
[HR]/[Ro]), below 10%, [HR] is linearly related to [H],
whereas at occupancies of 10 to 90%, [HR] is linear with
log[H]-a given increase in [H] is more effective in generating
HR at the lowest part of the curve than at the middle."
Figure 8-1 illustrates a situation where there is a proportional relationship
between receptor occupancy and biological response. In this situation occupancy
of one receptor would produce a response although it would be unlikely that this
response could be detected. It :'.s important "to note that the data in Figure 8-1
are plotted on a semilog scale. If the same data are plotted arithmetically
(Figure 8-2), then the shape of the dose-response curve readily conveys the
linear relationship between receptor occupancy and biological response at lower
concentrations and saturation at higher concentrations.
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0
FIGURE 8-2
Dose/Response Graph Showing Proportional Relationship Between Receptor
Occupancy and Biological Response (Arithmetic Scale)
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Such a simple proportional relationship is not adequate to explain the
diverse biological responses caused 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. These differences are due to tissue
and cell specific factors that modulate the qualitative relationship between
receptor occupancy and response. Therefore, it would be expected that there
would be markedly different dose-response relationships for different effects of
TCDD. Coordinated biological responses, such as TCDD-mediated increases in cell
proliferation, likely involve other hormone systems, which means that the dose-
response relationships for relatively simple responses (i.e., CYPIA1 induction)
may not accurately predict dose-response relationships for cancer. As we gain
more understanding of the entire sequence of events responsible for TCDD-mediated
toxic effects, we will enhance our ability to more accurately predict dose-
response relationships. The mechanism(s) responsible for qualitative and
quantitative diversity in receptor-mediated responses will be discussed in more
detail in Section V "Knowledge Gaps."
Dose-response relationships for TCDD's toxic effects have been established
for several endpoints in intact and surgically altered animals. In vitro
experiments have been used to determine critical concentrations and structural
relationships for TCDD effects at the cellular and molecular level. In the vast
majority of these studies the role of the AhR-ligand interaction has been
essential but not necessarily sufficient to evoke a detectable biological
response. It must also be kept in mind that not all responses will necessarily
require binding of the Ah receptor ligand complex to responsive elements on DNA.
Cancer is a multistage disease using a model composed of four or five
operationally defined processes: initiation, fixation, promotion and progression
(Figure 8-3). In experimental systems, initiation is generally thought to be a
DNA damaging or altering event, and fixation is the immortalization of the
mutation in clonally expanded progeny. Promotion is the enhancement, via
modifications in growth kinetics, of the initiated cell population by an
endogenous and/or exogenous factors. Progression refers to the growth of the
tumor to an end stage. Tumor promotion in experimental animals is a well
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established paradigm that has demonstrated carcinogenicity in many tissues
including liver, breast, bladder and skin. Dioxin is a promoter in skin and
liver of nice and rats, respectively. However, TCDD promoted, DEN-initiated
liver tumors or foci are suppressed in ovariectomized rats. Hence, promotion of
DEN initiated hepatocytes by TCDD in rat* is dependent on ovarian factors or the
receptors for these factors. Articles by Pitot et al. (1987) and Lucier et al.
(1991) present data on liver tumor promotion by TCDD.
The general approach of the U.S. EPA to regulation of carcinogens is to use
the the Armitage-Doll model of carcinogenesis [Linearized Multistage, IMS]
(Figure 8-4).
In this model, the movement of cells from one stage to the other are assumed
to be due to a sequence of mutations similar to the step of initiation/fixation
discussed above.
As with any mathematical model, specific forms must be chosen for the rate
constants which define the process.
The EPA formulation of this model assumes the mutation rates [u,(d,t)] are
a linear function of dose and are constant over time. These assumptions result
in a tumor incidence rate which is a polynomial function of dose. In the low
dose region, the upper bound on risk is dominated by the linear term in the
polynomial (qj*). EPA generally uses a upper confidence limit on the linear term
of this formulation of the multistage model for cancer risk assessment. However,
this choice has not been predicated solely on the correctness of a K-mutation
process of cancer development. The linearized mathematical properties of the
multistage model can be appropriate for a larger class of mechanisms: Dose
response behavior, which is linear at low dose, may have upward curvature in the
intermediate range and shows downward curvature or saturation of response at high
dose. In particular, arguments that a compound's action is additive to
background biological processes lead to a linear response at low dose under
rather general conditions (Crump, 1976??). Therefore, for practical modeling
purposes, it is important to address whether biological knowledge about the
action of a carcinogen can fit the general dose-response shape predicted by the
linearized multistage model. Cross species extrapolation is carried out using
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dose expressed as daily exposure per unit surface area, where an allometric
scaling factor is applied to body weight to estimate the surface area. The
potency of a carcinogen is related to the slope of the dose-response curve (Q*).
For other toxicological endpoints such as terata, organ toxicity, acute
toxicity, etc., a threshold has been assumed as a natter of policy. For these
endpoints, safety factors or uncertainty factors have been used to estimate no-
effect exposure levels (NAS/NRC, 1977). This threshold approach is used by the
World Health Organization to set ADIs for direct and indirect food additives.
EPA now uses the term reference dose. As discussed above, EPA policy assumes the
dose-response curve for excess carcinogenic risk to be linear through dose zero.
Several mechanisms could generally lead to this form of response including direct
mutational activity of the chemical agent and/or additivity to background rate
of tumor formation (Portier, 1976??). Since TCDD does not bind covalently to DNA
and must exert its effects through receptor action, this default position must
be carefully reexamined.
Since TCDD action is clearly receptor mediated, it is reasonable to attempt
to model the receptor kinetics using equations that have been used for steroid
receptor mediated responses. Recent rodent experiments suggest the induction of
cytochrome P-450IA1 is linear through zero using long term exposure over a wide
dose range (Taitscher et al., 1992; DeVito et al., in press). Exposure data in
humans indicates that the general population has a body burden of 5-10 ppt (lipid
adjusted). In occupational epidemiology, human disease associated with TCDD
exposure has not been detectable until fat levels are one to two orders of
magnitude higher than the general population (100-1000 ppt). Thus, in addition
to other Uganda, one must also be concerned with the present level of TCDD in
the general population and the long environmental and biological half-life of
this compound.
The current effort to reevaluate the risk of exposure to dioxins is being
termed a Biological Basis for Risk Assessment. The underlying premise is that
this is a special case for a nonmutagenic, receptor mediated carcinogen. The
goal of this reassessment is to consider more mechanistically-based models which
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are sufficiently credible to the scientific community. Such approaches can be
recommended for practical risk assessment needs.
There are several models under consideration at the present time ranging
from very simple to complex. What has become obvious over the past year is 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 Ah receptor, are generally response-independent. The response-dependent
events are species, gender, organ, tissue and perhaps cell specific. If the
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 than dose of subsequent actions as long as the dose-response
curves for these subsequent actions are parallel to the receptor binding curves.
In general, the data to date indicate receptor mediation or receptor coordination
does indeed exist for most if not all low dose actions of TCDD. Since the AhR
has been detected in virtually all cells but all cells do not respond to TCDD,
there must be other factors that are necessary for TCDD action. The roles of the
other factors must be elucidated before there is a complete understanding of TCDD
action. However, a relatively complete model can be developed for specific
endpoints by using available data and reasonable assumptions.
Several important factors have been generally accepted. One, TCDD is a
member of a class of xenobiotics (and probably natural products) that is
nonmutagenic, binds to a cellular receptor and alters cell growth and
development. Two, a significant amount of information is available for
estimating risks from exposure to this compound and the default position of
directly applying the linearized multistage model (LMS) as a function of dose
needs to be reevaluated. Three, the biology of receptor mediation should be
included in any modeling exercise for TCDD utilizing existing mathematical
developments. This is not to say that uncertainties do not remain. However, the
goal of the modeling is to use as much data as possible to reduce these
uncertainties, and to identify the areas where data gaps exist.
One difficulty with a novel, albeit biologically based, approach is that it
is replacing paradigms (Safety Factors and LMS Models) upon which the U.S.
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government's risk assessments have been based. There is no a priori reason to
believe that a more biologically-based model will be more or less conservative
than IMS. However, basing the modeling on a mechanistic understanding of the
biochemistry of TCDD-induced toxicity should increase our confidence in the
resultant risk estimates. As previously stated by Greenlee and collaborators:
"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 dioxin and target tissues. Biologically-based
strategies use knowledge of the mechanistic events in the various
steps in the scheme for risk assessment. Interspecies extrapolation
strategies would be conducted based on how these mechanistic steps
vary from species to species. There are numerous steps that can be
examined mechanistically, and fairly ambitious programs have been
proposed to examine the mechanistic details of many or most of these
individual steps. More focused risk assessment approaches are also
being proposed based on examination of individual steps believed to be
critical in establishing the overall shape of the dose-response curve
for the induction of tumors (or other toxic endpoints) by dioxin."
(Greenlee et al., 1991)
This chapter presents the current thinking on TCDD mechanistic action. It
examines several endpoints and focuses on dose response models for cancer in
laboratory animals and man. It also evaluates for use of biomarkers of TCDD
action as surrogates for modeling receptor mediated events. In addition the
chapter presents a section on "Knowledge Gaps." Critical examination of this
section leads to new experiments which will add to the already impressive
database on TCDD action. Addition of key molecular, cellular and tissue specific
information to the current data base will be important to establish a new risk
paradigm based on biological mechanism of action of TCDD and perhaps other
receptor-mediated nonmutagenic toxicants. The chapter reviews some of the
critical data on noncancer endpoints but does not attempt to model them. These
endpoints are clearly important when considering the public health risk of
dioxin. However, the lack of key molecular information of action and molecular
dosimetry limits mathematical modeling of noncancer endpoints at this time.
8.1.1. Introduction to Modeling for TCDD. Mathematical modeling can be a
powerful tool for understanding and combining information on complex biological
phenomenon. The development and use of mathematical models is illustrated by
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Figure 8-5. In the development of a mechanistically-based mathematical model,
the beginning point is generally a series of experiments aimed at studying a
xenobiotic agent. The experimental results (data) can indicate a mechanism which
leads to the creation of a mathematical model. The model is used to make
inferences which are then validated against the existing knowledge base for the
effect under study and the xenobiotic agent. This can then lead to new
experiments and further laps through this model development loop. Each time
through the loop the model either gains additional validation by predicting the
new experimental results or it is modified to encompass the new results without
sacrificing the prediction of previous results. In either case, subsequent loops
through the model generally increase our confidence in the model (although it may
be difficult or impossible to quantify this confidence).
There is no one model development loop for any given compound or effect.
Instead, there are always numerous exercises which lead to the development of a
mechanistic model. In modeling the effects from exposure to TCDD, there are many
smaller model development circles which make up the larger overall model. For
example, a mechanistic approach to TCDD-induced carcinogenicity must include
models of exposure, tissue distribution, tissue diffusion, cellular biochemistry,
cellular action, tumor incidence and cancer mortality. At each stage and for
each model, data must be collected and understood in order for the model to be
valid and acceptable as a tool for understanding the observed effects and for
predicting the effects of TCDD outside of the range of experimental findings.
Confidence in any one model is not only dependent upon the information
available for that compound, but is also supported by the information available
on other systems which act similarly and for which models have already been
developed. In the case of TCDD, the modeling of effects will be greatly enhanced
by existing information on the receptor-based systems, general work in
physiologically-based pharmacokinetics models and in tumor incidence modeling.
Risk assessment, however, is another issue. The use of mechanistically-
based modeling in extrapolating to exposure patterns and exposure doses outside
the range of the data is in its infancy. Even though there may be high
confidence in the ability of the model to predict experimental results, there
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Experimental
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Developing a Mechanistically-Based Mathematical Model
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could be low confidence in the ability of the model to predict outside the range
of data. Risk assessment inherently demands a careful scrutiny of the behavior
of a mechanistic model under a variety of exposure scenarios, scrutiny which has
not generally been applied to the use of mechanistic models in science.
Confidence can only be gained by numerous loops through this model development
process for the express purposes of risk assessment. Only then will the process
be acceptable as a tool for risk extrapolation. It is important to note that
mechanistic modeling has a role to aid in explaining and understanding
experimental results which is devoid of its proposed use for risk assessment and
that our confidence in the methods used in mechanistic modeling will differ upon
the history of it's use. For historical perspective, it is important to
recognize that this is not the first loop through the cycle of mechanistic
modeling for carcinogenesis. Early exercises based upon tolerance distributions
used the then current understanding of carcinogenesis to develop statistical
models which could be used for risk estimation. Later use of the linearized-
multistage model was also based on an understanding of the carcinogenic
mechanism. This exercise also benefits from recent attempts to use
physiologically-based pharmacokinetic (PB-PK) models in risk estimation. Thus,
this exercise is a logical next step in the continuum of mechanistically-based
modeling for risk assessment purposes.
In any realistic and practical modeling exercise, the major component of the
model revolve around the estimation of parameters in the model utilizing
statistical tools. These tools range from very simple techniques such as
estimating a mean to extremely complicated approaches such as estimation via
maximizing a statistical likelihood. The estimation of parameters is not done
in a vacuum, but is intimately tied to the data available to characterize the
model. The way in which model parameters are estimated and the data used to
estimate those model parameters are the major components in determining the
reliability and trust we will place in any mathematical model. Fundamentally,
sufficient biological data need to be available to convincingly show that the
model correctly represents in vivo processes and that the processes modeled are
the ones that lead to toxic events.
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In modeling biological phenomenon, the data can be divided into 4 broad
categories as shown in Table 8-1. At the top are effects on the whole animal.
Examples of data included in this category would be data on survival of the
organism, its ability to reproduce and it's ability to properly function (e.g.
behavioral data). The levels of data then get increasingly specific going from
whole organism to tissue/organ system responses to cellular responses down to
biochemical responses in the cell. Other groupings are possible and even more
detailed categories (e.g. molecular biochemistry); the point is that the data
range from data at the bottom of Table 8-1 which is extremely mechanistic and
deals with the interactions between molecules, to the data at the top of Table
8-1, which is effectively counting bodies. All of this information is useful and
should be incorporated into a mathematical model aimed at understanding some
biological response.
Mathematical models which incorporate parameters which are mechanistic in
nature do not automatically constitute "mechanistic models." The available data
for characterizing the model and the method by which this data is incorporated
into the model are important in determining if a model is truly "mechanistic" and
soundly based on the biology or is instead, simply a curve fit to data.
There are two basic ways in which biological effects can be estimated. The
first and most common approach is a "top-down" approach. In the "top-down"
approach, data on the effect of interest (e.g., carcinogenicity) is modeled
directly by applying statistical tools to link the observed data (e.g., tumor
incidence data from a carcinogenicity experiment) to a model (e.g., the
multistage model of carcinogenesis). This approach is extremely powerful in it's
ability to describe the observed results and to generate hypotheses about model
parameters and the potential effects of changes in these parameters. Where this
modeling approach begins to lack credibility is in the ability to predict
responses outside the range of the data currently being evaluated. Even when the
model being applied to the data is mechanistic in the sense that the model
parameters are tied to some mechanism for the toxic effect (e.g., mutation rates
and molecular effects), without direct evidence concerning the value for this
parameter or even evidence supporting the particular structure of the model, one
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TABLE 8-1
Examples of Levels of Information Available for
Estimating Parameters In Dose-Response Modeling
Organism
Tissue
Cell
Biochemical
Morbidity
Mortality
Fertility
Hyperplasia
Hypertrophy
Carcinogenicity
Chemical Distribution/Disposition
Improper Development/Function
Mitosis
Cell Death
Gene Expression
Protein Levels
Receptor Binding
Adduct Formation
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is basically left with a curve fit to the data. The historical applications of
the "linearized" multistage model in risk assessment has been in this fashion.
We view true mechanistic modeling in a different fashion. In this case, the
model structure and the parameters in the model are derived in a "bottom-up"
fashion. The mechanistic parameters in the model are estimated directly from
mechanistic data rather than from effects data or data one level higher in the
hierarchy of data illustrated in Table 8-1. The goal of true mechanistic
modeling is to explain all or most known results relating to the process under
study in a way which is reasonable in it' s biology and soundly rooted in the data
at hand. In this case, biological confidence in predictions from the model would
be much higher than that from the "curve fitting" approach.
In practice, it is generally impossible to completely divorce mechanistic
modeling from curve fitting. At some point in the modeling process, gaps must
be filled relating the modeled, mechanistic effects to the observed toxic
effects. It is generally at this point that some amount of curve-fitting is
necessary to calibrate the mechanistic response to the toxic effect. Although
not technically mechanistic modeling, this combined approach is preferred to
simple curve fitting when inferences outside of the range of the toxic effects
data is desired.
This is not intended to infer that with mechanistic modeling, we can get a
precise estimate of risk of a toxic effect outside the range of the data or even
a more precise estimate of risk. Without data, the statistical issue of the
accuracy of a prediction cannot be easily addressed. Thus, while there may be
a greater deal of 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 given the practical constraints on the
experimental protocols. In general, effects can be demonstrated at lower
exposures for data at the bottom of Table 8-1 compared to the data at the top.
If this is the case, there may be both increased biological confidence in
extrapolated results and increased statistical accuracy. This is also not
intended to infer that models derived through curved fitting should always be
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given little weight. Many of the advances in biology and science are due to the
application (either formally or informally) of a model to data at a higher level
in order to generate hypotheses about effects at lower levels. The major
difference between the application of a "curve-fit" model in basic biology and
that in risk estimation is that in basic biology one is creating hypotheses which
at some point will be tested. In risk estimation, it is unlikely that one will
ever be able to validate extrapolated risk estimates.
Many side issues are also related to the frequency of use of this model
development loop in trying to understand a biological mechanism. One of
considerable importance is experimental design. For mechanistic modeling aimed
at risk assessment, we are just beginning to understand the types of experiments
which may benefit the risk estimation process. Because of this, now is the
perfect time to consider the types of designs which are best suited to addressing
problems which are specific to risk assessment. In general design situations one
would have a mechanism in mind, qualitatively describe that mechanism and form
the structure of a mechanistic model, make educated guesses about the parameters
of this model, then use the quantitative model to locate designs which are
optimal at characterizing the mechanism. For the purpose of risk estimation, the
basic outline also holds. There are also some simple design rules which would
aid the extrapolation of results to doses outside the observed response range and
to humans from animals. A few of these design points are listed in Table 8-2.
TCDD has been chosen as a prototype for exploring and examining the ability
of mechanistic modeling to improve the accuracy of quantitative risk assessment.
In essence, the data base for a mechanistic modeling approach to TCDD is very
extensive and contains a considerable amount of information on low-dose behavior.
In addition, there is good human data which is supported by the experimental
evidence in animals. On the other hand, some aspects of the mechanism by which
TCDD induces it's effects, such as binding to the Ah receptor have not been
modeled extensively and, thus, even for scientific purposes are in only their
first few loops through the model development cycle shown in Figure 8-5. In this
case, several competing mechanistic theories will agree with the existing data
adding to the uncertainty in any projected risk estimates. This outcome is
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TABLE 8-2
Design Considerations for Risk Assessment Purposes
Aspects of Risk Estimation
Mechanism
Species Extrapolation
Other
Design Points to Consider
Multiple Times of Observation
Multiple Doses
Multiple Ages at Exposure
Pharmacokinetics
Multiple Species
Both Sexes
Tissue Concentrations (including blood)
System Clearance
Essential interactions with endocrine systems
Record data on the level of individual animals
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inevitable for a novel mechanism and for the application of the technology of
mechanistic modeling to a new area. To reiterate an earlier point, mechanistic
modeling has a role to aid in explaining and understanding experimental results
which is devoid of it's proposed use for risk assessment and our confidence in
the methods used in mechanistic modeling will differ depending upon the history
of it's use. As we know more about the limitations of current data and current
methods for the application of mechanistic models to risk estimation, we can
improve experimental designs and significantly improve the process. Since TCDD
is an early attempt along these lines, we must be cautious in coming to a
judgement concerning the overall utility of mechanistic modeling as an important
tool for risk assessment based upon this one case study.
8.1.2. Dosimetric Modeling. Dose Delivery and Tissue Modeling and Biochemical
Modeling (See Appendix for complete manuscripts by Kohn et al., 1992 and Andersen
et al., 1992)
In the NAS report, Risk Assessment in the Federal Government: Managing the
Process (NRC, 1983), "dose-response assessment" referred to the process of
estimating the expected incidence of response for various exposure levels in
animals and humans. Tissue response is not always directly related to exposure.
This can be due to saturation and activation of metabolic pathways. (Hoel et al.,
19??), influence of competing pathways having different efficiencies of action
for the parent compound and/or its key metabolites and factors such as
cytotoxicity, mitogenesis or endocrine influences which can radically modify the
homeostatic properties of the tissue. These complex interactions can result in
markedly nonlinear dose-response; nonlinearities which could lead to risk
estimates which may be greater or less than the risk derived from a linear model.
Because of the potential for nonlinearities, it is essential to distinguish
between exposure level and dose to critical tissue when modeling risks from
exposure to xenobiotics. It is also essential that we understand the
quantitative relationship between target tissue dose and changes in gene
expression. This is especially true when extrapolating to low doses and
extrapolating across species.
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For dioxin, the abundance of data on many levels allows one to create a
collection of models which include the determination of the quantitative
relationship between dioxin exposure and tissue concentration, tissue
concentration and cellular action, cellular action and tissue response and
finally tissue response and host survival (Andersen and Greenlee, 1991; Portier
et al., 1984). This portion of the reevaluation of dioxin risks entails the
description and development of mechanistically based mathematical models of the
effects of TCDD. This includes a discussion of the extrapolation of tissue
dosimetry and response from high dose exposures to those expected at much lower
exposure and the extrapolation of dose-response models from test animals to
people. These extrapolations will be based upon empirical relationships used to
derive explicit, though incomplete, biologically-based mechanistic models of the
events involved in the toxic action of dioxin.
Biological modeling is the process of developing mathematical descriptions
of the interrelationships among the mechanistic determinants of those toxic
events resulting from exposure to TCDD. Research with dioxin has focused on
biological responses at the levels of organization shown in Table 8-1 (i.e.,
biochemical, cellular, tissue and organism). At each level of organization, we
focus on the mechanisms responsible for these observations. Mechanism refers to
the critical biological factors that regulate occurrence, incidence and severity
of a particular factor and the nature of the interrelationships among these
factors. The details of the mechanisms of interaction differ markedly for the
various levels of biological organization with specific determinants of the
behavior at each level driving the creation on an appropriate quantitative model.
For dioxin, the mechanisms of three processes are of primary interest:
(1) the dosimetry of dioxin throughout the body and specifically to target
tissues; (2) the molecular interactions between dioxin and tissues, emphasizing
the activation of gene transcription and increases in cellular protein
concentrations of specific, growth regulatory gene products and specific
cytochromes; and (3) the progressive tissue level alterations resulting from
these interactions which lead, eventually, to toxicity. The modeling process
involves identification of the mechanistic determination of the dose-response
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continuum through experimentation and the encoding of these processes in
mathematical equations. The extent to which model predictions coincide with
experimental results is a test of the validity of the model structure. After
validation, the model can be used for risk assessment. In addition to their use
in risk assessment, these models have importance for aiding in the design of
future research, both in terms of a basic understanding of dioxin toxicity and
further risk estimation.
The following sections discuss the mechanistic biological modeling for
dioxin with regard to dosimetry, induction of gene transcription and tissue
response, especially those associated with hepatic carcinogenesis. This modeling
effort follows a natural progression related to the kind of information available
at the time at which the model was developed. We will begin with a review of
tissue concentration followed by modulation of protein concentrations and tissue
response.
Tissue dosimetry encompasses the absorption, distribution, metabolism and
elimination of dioxin from tissues within the body. The determinants of dioxin
dosimetry in the body include physicochemical properties (e.g., diffusion
constants, partitioning constants, kinetic constants and biochemical parameters
for metabolism) and physiological parameters (e.g., organ flows and volumes).
The mathematical structure which describes the interconnections among these
determinants constitutes a mathematical model for the tissue dosimetry of dioxin.
The model we will use in the context of this reassessment is a PB-PK model.
These types of models describe the pharmacokinetics of dioxin with a series of
mass-balance differential equations. These models have been validated in the
observable response range for numerous compounds in both animals and humans
making them extremely useful for risk assessment; especially for cross-species
extrapolation. In addition, they aid in extrapolation from one chemical to other
structurally related chemicals since many of the components of the model can be
deduced for structurally related compounds. The development of PB-Pk models for
general use is discussed in Galowski and Jain (1983) and for use in risk
assessment by Clewell and Andersen (1985).
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In brief, a PB-Pk model consists of a series of compartments that are based
on the anatomy and physiology of the test animals; hence, the term PB-PK (ref.
original use of term). The time course of behavior in each compartment is
defined by an equation containing terms for input and loss of chemical. For
example, if Ct represents the concentration of compound in a tissue (£) and CB
the concentration of compound in blood (B), one of the simplest relationships one
might use is:
dC,
- rM CB ~ Iw ct ~ ** ct (equation!)
where dct/dt represents the change in the concentration in the tissue over time
(t), rjj/ is the rate (per unit concentration) of the movement of the compound
from blood to tissue, r^g the rate from tissue to blood and rm the rate of
metabolism in the tissue. Equations of this form will be used in mass balance
modeling of the pharmacokinetic processing of TCDD.
Several PB-Pk models have been developed for dioxin and related chemicals
(see Chapter 1 for a brief overview). PCBs have been extensively studied (Lutz
et al., 1977, 1984; Mathews and Dedrick, 1984). King et al. (1983) modeled the
kinetics of 2,3 7,8-TCDF in several species and Kissel and Rambarge (1988)
proposed a human PB-Pk model.
The development of PB-Pk models for TCDD began with work by Leung et al.
(1988) in mice. This model was extended to Sprague-Dawley rats by Leung et al.
(1990a) and to 2-iodo-3,7,8-trichlorodibenzo-p-dioxin in mice (Leung et al.,
1990b). Since many of the regulatory standards for dioxin have been based upon
a finding of hepatocarcinogenicity in female Sprague-Dawley rats, we will focus
on the model by Leung et al. (1990a) in this species.
The Leung et al. (1990a) PB-Pk model contains five tissue compartments
including blood, liver, fat, slowly perfused tissue and richly perfused tissue.
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This early model is blood flow limited, a condition which is appropriate when
membrane diffusion is much more rapid than blood flow to the tissue. Thus, in
this PB-Pk model, the tissue/tissue blood compartments are lumped together as a
single compartment in which the effluent venous blood concentration of TCDD is
equilibrated with the tissue concentration. The model includes a TCDD binding
component in blood described by a linear process with an effective equilibrium
between the bound and free TCDD given by a binding constant, K^. They also
include binding of TCDD to two classes of protein in the liver: one
corresponding to the high affinity, low capacity Ah receptor and the other to a
lower affinity, higher capacity microsomal protein (CYP1A2) which is inducible
by TCDD. In the formulation of this PB-Pk model by Leung et al. (1990a) both
types of binding proteins are explicitly defined using an instantaneous
correspondence between occupancy and induction using separate binding capacities
and dissociation constants for each protein. These binding reactions are modeled
via Michaelis-Menten equations.
In the Leung et al. (1990a) model, the tissue storage capacity depends upon
the partition coefficients (assumed to be linear with concentration) and the
specific protein binding. Dioxin is very lipophilic and is found in higher
concentrations in liver than would be expected based on partition coefficients.
The specific binding of dioxin to a liver protein used by Leung et al. (19??) is
an improvement over earlier models for these lipophilic compounds.
In various studies, dioxin has been administered by intravenous
administration, intraperitoneal injection, oral feeding or intubation (gavage)
or by subcutaneous injection. In the PB-Pk modeling framework, intravenous
injection can be described by starting the integration with an initial mass equal
to the dose in the blood compartment. Oral intubation and sc injection can be
modeled as if they adhere to first-order uptake kinetics with dioxin appearing
in the liver blood after oral administration and in the mixed venous blood after
sc injection. Feeding was modeled by Leung et al. as a zero-order input on days
that dioxin was included in the diet. These descriptions of the routes of uptake
are clearly not defined in specific physiological terms; they are empirical
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attempts to estimate an overall rate of uptake of TCDD into the PB-Pk model.
This is one area in which additional research could improve dose-response
modeling for TCDD. Efforts to provide more biological details concerning the
physiological basis of absorption across these various membranes, including
intact skin, would prove valuable for exposure assessments with dioxin. With the
iodinated analog, 2-iodo,3,7,8-trichlorodibenzo-p-dioxin, the estimated rate
constant for oral absorption was considerably larger in induced (0.15/hour) than
in naive animals (0.4/hour). The physiological basis of this change is unknown.
With many volatile organic chemicals there are convenient in vitro methods
for estimating partition coefficients (Dato and Nakajima, 1978; Gargas et al.,
1989). For TCDD and other highly lipophilic, essentially nonvolatile compounds,
there are no reliable in vitro methods and these constants have to be estimated
from measurements of tissue and blood concentrations in exposed animals. This
leads to a difficulty in differentiating between specific tissue binding and the
partitioning to the tissue. Leung et al. (1990a) overcame this problem by
assuming binding occurred only in the liver and that the liver partition
coefficient was the same as the kidney. This permitted estimation of the
relative binding capacities and affinities of specific hepatic proteins. 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).
Metabolic clearance was modeled as a first-order process. In the mouse with
the iodo-derivative, dioxin pretreatment at maximally inducible levels caused a
3-fold increase in the rate of metabolism. There is no evidence to suggest an
increase of metabolism in the rat for TCDD; however, there is data supporting
small increases in metabolic clearance at high doses for the tetrabrominated
analog (Kedderis et al., 1991). The identity of the enzymes responsible for TCDD
metabolism are as yet unknown.
Finally, Leung et al. (19??) kept all physiological parameters (flow rates,
tissue weights, etc.) constant over the lifetime of the animal.
Dioxin and dioxin analogs have dose- and time-dependent kinetics in both
rodents (Kociba et al., 1976; Poland et al., 1989a; Abraham et al., 1988; Rose
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et al., 1976; Tritscher et al., 1992) and humans (Wolf, 19???; Carrier, 1991).
For single- and short-duration exposures, as the exposure level increases, the
proportion of total dose found in the liver increases. For chronic exposures,
there appears to be a linear relationship between dose and tissue concentration
in the gavage study of Tritscher et al. (1992), but this may be simply an
inability to observe nonlinearity in liver in intermediate dose ranges. The
Leung et al. (1990a) model adequately predicts the tissue concentrations observed
by Rose et al. (1976), but did considerably worse at predicting the results
observed by Kociba et al. (1976) underpredicting concentrations at the lowest
dose by a factor of 3.2 and overpredicting concentrations at the highest dose by
a factor of 2. The data of Abraham et al. (1988) and Tritscher et al. (1992)
were not available at the time this model was developed, but at least for the
data of Tritscher et al. (1992), this model has been shown to overpredict the
tissue concentrations (Kohn et al., 1992).
As mentioned earlier, the default position of the EPA in estimating risks
from exposure to xenobiotics involves the use of a model which produces risk
which is proportionate to dose for low doses (low-dose linearity). Thus, in
discussing the models and submodels which form a basis for a mechanistic model
for TCDD, we will focus on aspects of the model which could lead to
nonproportional response for low environmental doses. The model of Leung et al.
(19??) predicts 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. [They argue,
however, that tissue dose alone should not be used for risk assessment for TCDD
due to the large species specificity in the ability of TCDD to elicit toxicity
(Andersen, 1987)]. They instead suggest that use of time-weighted receptor
occupancy linked with a two-stage model of carcinogenesis as 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.
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Application of this proposed effective dose with the two-stage model of
carcinogenesis is presented later in this chapter.
Looking at one small aspect of modeling TCDD's effects, Portier et al.
(1992) examined the relationship between tissue concentration and the modulation
of the three liver proteins by TCDD in intact female Sprague-Dawley rats. The
proteins studied included the induction of two hepatic cytochrome P-450 isozymes,
CYP1A1 and CYF1A2, and the reduction in maximal binding to the EGF receptor in
the hepatic plasma membrane. The modulation of these proteins is believed to be
mediated through TCDD binding to the Ah receptor. Then, as described in earlier
chapters, through a series of alterations in the receptor-dioxin complex,
transport to the nucleus, binding to transcriptionally active recognition sites
on DMA, activation of gene transcription and alterations in gene mRNA products,
CYP1A1 and CYP1A2 are induced. Reduction in maximal binding to the EGF receptor
requires additional protein interactions.
General empirical models have been developed for the regulation of gene
expression (Hargrove et al., 1990). This modeling approach includes mRNA
production by a zero-order process and first-order degradation. Activation
alters one or both of these rates. The production of protein is assumed to be
directly related to mRNA concentration. A more specific pharmacodynamic model
has been described to account for the induction of TAT activity by the
corticosteroid prednisolone (Nichols et al., 1989). In this induction model, the
input prednisolone concentration is specified by the measured time course of
prednisolone in plasma. Prednisolone binding to receptor is specified by
association and dissociation rate constants. The prednisolone receptor binds DNA
with a specified association rate constant and the bound receptor recycles to
cytosol with a transport time, T (effective compartment transport times are
included to account for delays between interaction with DNA and the appearance
of TAT activity). A power function can describe a nonlinear relationship between
the concentration of prednisolone receptor and the production rate of protein.
The actions of prednisolone and maintenance of its tissue concentration are much
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more short-lived than those of dioxin and the modeling period of interest is only
on the order of several hours to a day instead of days, weeks or months as with
dioxin.
The important relationships presented here are the association of dioxin
with the Ah receptor and the association of the dioxin receptor complex with DNA.
As described above, Leung et al. (1988) modelled the induction of CYP1A2 as due
to a basal amount of protein plus an additional amount of protein resulting from
binding of TCDD to the Ah receptor. The extent of induction was calculated as
instantaneously related to percent occupancy of the Ah receptor via a Michaelis-
Menten type relationship. Changes in CYPIA1 and EGF receptor binding were not
modeled by Leung et al. (1990a).
Portier et al. (1992) modeled the rate limiting step in the induction of
CYP1A1 and CYP1A2 following exposure to TCDD using a Hill equation. Hill
equations are commonly used for modeling ligand-receptor binding data. This
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
(Colquhoun, 1979??), opiate receptors (Blume, 1981)?? and the Ah receptor
(Gasewicz, 1984). As a direct comparison to what was done by Leung et al.
(1990a), it is interesting to note that the Hill model can be thought of as a
very general kinetic model which includes standard Michaelis-Menten kinetics when
the Hill exponent is 1. Portier et al. (1992) modeled the reduction in maximal
binding to the EGF receptor also as following Hill kinetics, but with TCDD
reducing the binding from the maximum level when no TCDD is present. For all
three proteins, proteolysis was assumed to follow Michaelis-Menten kinetics. The
proposed models fit the data in the observable response range.
The major purpose of the paper by Portier et al. (1992) was to emphasize the
importance of endogenous protein expression on the shape of the tissue
concentration/response curve. For each protein, they considered two separate
models. In the first, the additional expression of protein induced by TCDD is
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independent of the basal level expression. Such a mechanism is similar to that
used by Leung et al. Under this model protein expression is given by the
equation:
where P is the concentration of protein in the liver, Bp is the basal rate of
production of protein, Vm is the maximal level of induction of protein by TCDD,
Kd is the apparent dissociation constant for binding (in the rate-limiting step),
C is the concentration of TCDD in the tissue, V_ is the maximal rate of
proteolysis, K_ is the proteolysis rate constant and n is the Hill exponent.
When the Hill exponent is estimated to be an integer, the estimate of n can be
interpreted as corresponding to the effective number of binding sites which must
be occupied for the effect of the binding reaction to be expressed. When the
Hill exponent is not an integer, no real molecular meaning can be attributed to
the equation and the model becomes phenomenological (Boeynaems and Dumont,
1980??).
The second model they considered was one in which the basal expression of
these proteins was due to an endogenous ligand which competed with TCDD for
binding sites. This leads to equations of the form:
8P _ .„,-.-, ,D. (equation!)
where E refers to the concentration of the endogenous ligand in units of TCDD
binding-affinity equivalents. Under steady-state conditions, equations (2) and
(3) are simplified (Portier et al., 1992).
Using these simpler formulas, they see virtually no difference between the
independent and additive models in the observable response range, even to the
point of getting almost equal Hill coefficients in the two models for all three
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proteins. In the low-dose range where risk extrapolation would occur, the models
differed depending upon the value of the Hill coefficient.
In all cases, the additive model resulted in low-dose linearity. This is
expected, since, under the additive model, each additional molecule of TCDD adds
more ligand to the pool available for binding and thus increases the
concentration of protein. Similar observations have been made with regards to
statistical (Hoel, 19??) and mechanistic (Portier, 1986??) models for tumor
incidence. For CYP1A1, the Hill exponent was estimated to be "2. When the Hill
exponent is >1, the independent model yields a nonlinear dose-response which is
concave (threshold looking). For CYP1A2, the Hill exponent was estimated to be
-0.5. When the Hill exponent is estimated to be <1, dose-response is again
nonlinear, but in this case it is convex, indicating greater than linear
increases in response for low doses. Finally, for the EGF receptor, the Hill
exponent was approximately 1 in which case the two models are identical.
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 make a
difference of several orders of magnitude in the risk estimates for humans.
Using CYP1A1 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. We do not propose to directly employ
the models of Portier et al. (1992) for risk estimation.
Andersen et al. (1992) modified the model of Leung et al., (19??) to include
Hill kinetics in the induction of CYP1A1 and CYP1A2 and to use a diffusion
limited approach to the development of a PB-Pk model as compared to the blood
flow limited approach used by Leung et al. Diffusion' limited modeling is
preferred when diffusion into a tissue is less rapid than blood flow to a tissue.
In the model used by Andersen et al. (1992) each tissue has two subcompartments,
the tissue blood compartment and the tissue itself. Free TCDD flows into the
tissue blood compartment and, from there, diffuses into the tissue. There is no
direct relationship between effluent venous concentrations and tissue
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concentration in this diffusion limited model. For TCDD, the diffusion limited
approach is preferred due to the compounds potentially slow diffusion into the
liver from blood (Kohn et al., 1992).
Binding of TCDD to the Ah receptor was modeled in a fashion identical to
that used by Leung et al. (1990a) The concentration of CYP1A2 was modeled as
before using a steady-state model, in this case, with Hill kinetics instead of
a Michaelis-Menten model. The resulting equation is identical to that used by
Portier et al. (1992) for the independent induction of CYP1A2 except that they
related this to the concentration of Ah-receptor/TCDD complex rather than the
concentration of TCDD in the liver. Since they assume binding of TCDD to the Ah-
receptor follows Hill kinetics with a Hill coefficient of 1 (Michaelis-Menten
kinetics), the model of Andersen et al. (1992) approaches the independent
induction model of Portier et al. (19??) for low doses.
The induction of CYP1A1 was modeled as a time dependent process as in
equation (2), again utilizing TCDD bound to the Ah receptor rather than tissue
concentration of TCDD. Most of the physiological constant, and many of the
pharmacological and biochemical constants used in the Leung et al. (1990a) model
were changed for the Andersen et al. (1992) model to correspond to Wistar rats.
The parameters in the model were optimized to reproduce tissue distribution and
CYPlAl-dependent enzyme activity in a study by Abraham et al. (1988), and liver
and fat concentrations in a study by Krowke et al. (1989). 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 1340 hours).
Andersen et al. (19??) noted that the liver/fat concentration ratio changes
as dose changes due to an increase in the amount of binding protein 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
coefficient for CYP1A2 and for the Ah receptor are equal to 1, the liver
concentration as a function of dose is still effectively linear (i.e., small
doses of TCDD will bind • to the Ah receptor increasing the amount of Ah-
receptor/TCDD complex which then induces additional production of CYP1A2 which
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can bind to free dioxin). 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., 1992). This nonlinearity in the dose
region of 1-100 ng/kg/day does not agree with the findings of Kociba et al.
(1976) and Tritscher et al. (1992) for chronic exposure in Sprague-Dawley rats.
The plateau in total liver concentration predicted by the model of Andersen et
al. (10??) does occur in the data of Kociba et al. (19??) and Tritscher et al.
(19??) but in the range of 100 ng/kg/day rather than the 10,000 ng/kg/day
predicted by Andersen et al. However, the changes in liver/fat ratio observed
by Andersen et al. (19??) and supported by human evidence (Carrier, 1991) are a
necessary part of the modeling for TCDD.
Finally, with regards to risk estimation, Andersen et al. (19??) compared
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 the volume of altered hepatic foci (Pitot et al., 1987). Using
a biweekly dosing regimen assuming gavage ??? exposure for ??? weeks, they
integrated the concentration of TCDD in the liver over time and the concentration
of induced protein over time to get summary measures of internal exposure. They
concluded that tumor promotion correlated more closely with predicted induction
of CYP1A1 than the other integrated quantities. No formal measure was used to
support this observation. It has been shown for the tumor incidence data that
it is difficult to determine nonlinearity or linearity although it is not
inconsistent with a linear response in the low dose region (Portier et al.,
1984). In general, one cannot expect a linear correlation between appropriate
dose measures and toxic response. In addition, the choice of an independent
induction model for CYP1A1 and a Hill coefficient >1 leads to nonlinear low-dose
behavior. If the promotional effects of TCDD follow a similar mechanism, the
risk from exposure at low doses will be negligible. For risk assessment, it is
important to know if an additive model also fits this data and agrees with the
promotional effects of TCDD since such a model will have different low-dose
behavior than the independent model.
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Kohn et al. (1992) expanded upon the model of Leung et al. (19??) to include
Hill kinetics, a restricted flow-limited PB-Pk formulation and an extensive model
of the biochemistry of TCDD in the liver. The goal of the model was to explain
TCDD-mediated alterations in hepatic proteins in the rat, specifically
considering CYP1A1, CYP1A2, Ah receptor, EGF receptor and estrogen receptor over
a wide dose range. In addition, the model describes the distribution of TCDD to
the various tissues, accounting for both time and dose effects observed by other
researchers. The PB-Pk models developed by Leung et al. (19??) and Andersen et
al. (19??) 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 regiments (Kociba et al., 1976, 1978; Krowke et
al., 1989). More recent studies in which female Sprague-Dawley rats received
TCDD (Tritscher et al., 1992; Sewall et al., 1992) were used by Kohn et al.
(1992) to model the pharmacokinetics and induction of gene products in this sex
and species. Among the data reported by Tritscher et al. (1992) and Sewall et
al. (1992) were concentrations of TCDD in blood and liver, concentrations of
hepatic CYP1A1 and CYP1A2 and EGF receptor on the hepatocyte plasma membrane.
Kohn et al. (1992) refer to their model as the Kohn model. The tissue dosimetry
for the Kohn model were validated against the single dose and chronic dosing
regimen experiments employed by Leung et al. (1990a) and Andersen et al. (1992).
In the biochemical effects portion of the Kohn model, the binding of TCDD
to the Ah receptor is modeled using explicit rate constants instead of
dissociation equilibrium constants (equation 2 with B = 0). However, larger
dissociation rates (kd, k;) were used leading to a formulation of the amount of
TCDD-Ah-receptor complex similar to that used by Leung et al. (19??) and Andersen
et al. (1992) Many of the other binding reactions in the model were handled
similarly (e.g., TCDD binding to CYP1A2 and TCDD bound to blood). This is simply
a numerical trick to avoid the necessity of solving for the concentration of TCDD
in the liver using the mass conservation relationship described in Leung et al.
(1990a).
The physiology described in the Kohn model is dependent upon the body weight
of the animal. Body weight changes as a function of dose and age were recorded
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by Tritscher et al. (19??) and directly incorporated into the model via a smooth
function. Tissue volumes and flows were calculated as a allometric formula based
upon recent work by Delp et al. (1991). To allow the model to fit the data of
both Rose et al. (1976) and Tritscher (1992), the Kohn model includes loss of
TCDD from the liver by lysis of dead cells where the rate of cell death (and the
resulting lysis) was assumed to increase as a hyperbolic function of the
cumulative exposure in the liver to unbound TCDD. No information regarding the
rate of TCDD from lysed cells is available, therefore, this feature of the Kohn
model represents a net contribution of TCDD clearance by TCDD-induced cell death.
In the biochemical effects portion of the Kohn model, the Ah-receptor/TCDD
complex up-regulates four proteins; CYP1A1, CYP1A2, the Ah receptor and
transforming growth factor-a (see Fig. 1). For all four proteins, synthesis and
degradation rates are defined explicitly. Changes in CYP1A1, CYP1A2 and the Ah
receptor are compared to data on these concentrations. The induction of TGF-a
is deduced from observations on human keratinocytes (Choi et al., 1991; Gaido et
al., 1992) and is quantified based on an assumed interaction with the EGF
receptor. However, TCDD-mediated induction of TGFa has not been clearly
demonstrated in liver (see Appendix). Constitutive rates of expression for
CYP1A2, Ah receptor and EGF receptor are assumed independent (equation 2) at the
induced expression. This has no effect on low-dose rate extrapolation since the
Hill coefficients for the induction of these proteins by the Ah-receptor/TCDD
complex were estimated to be 1. Induction of CYP1A1 was assumed to be based upon
additive induction (equation 3), but again the Hill exponent was estimated to be
1 leading to low-dose linearity under either model (2 or 3). Thus, the Kohn
model found that the induction of all gene products appear to be hyperbolic
functions of dose without any apparent cooperativity (i.e., the value of the Hill
exponent, n, in equation 2, is estimated to be 1). The discrepancy in the
estimates of the Hill exponents between this model and the other models discussed
(Portier et al., 1992; Andersen et al., 1992; Kedderis et al., 1992) is probably
related to the inclusion of induction of the Ah receptor in the Kohn model.
In the Kohn model, the Ah-receptor/TCDD complex down-regulates the estrogen
receptor. It is assumed that the estrogen receptor-estrogen complex
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synergistically reacts with the Ah-receptor/TCDD complex to transcriptionally
activate TGF-a. This synergy was introduced to partially account for the
observation of reduced TCDD promoting potency in males and ovariectomized females
as compared to female rats (Lucier et al., 1991). This mechanism, although
supported by some data (Clark et al., 1991; Sunahara et al., 1989) is speculative
(Kohn et al. (19??) (see Appendix).
There are basically three levels of complexity of Pb-Pk models for the
effects of TCDD. First is the traditional PB-Pk model by Leung et al. (1988)
with the added complexity of protein binding in the liver. The next level of
complexity is the model by Andersen et al. (1992) using diffusion limited
modeling and more detailed modulation of liver proteins. Finally, there is the
model of Kohn et al. (1992) with extensive liver biochemistry. All three models
have biological structure and encode hypotheses about the modulation of liver
proteins by TCDD. However, for gene expression, all three models fall in between
curve fitting and mechanistic modeling. In their derivation, the parameters were
estimated using dose-time-response data for protein concentrations and enzyme
activity which are a direct consequence of gene expression. This constitutes
curve fitting at this level. However, the structure of the models is derived
from qualitative information on the effects of TCDD, the PB-Pk model and even the
biochemical model of Kohn et al. (1992), for protein concentrations using data
sets which were not included in the original derivation of the model and which
were derived from designs other than those used to characterize the original
model. This constitutes a mechanistic validation of the original models and
places these exercises in the realm of partial curve fitting and partial
mechanistic modeling.
In terms of low-dose risk estimation, all three models have limitations.
The Leung et al. (1988) model fails to reproduce the tissue concentration data
from Kociba et al. (1976) and Tritscher et al. (1992). This is probably due to
the high concentration of liver binding protein (CYP1A2) predicted by this model.
The Andersen et al. (1992) and Kohn et al. (1992) models use Hill kinetics
to describe at least some of the binding reactions. Hill's equations are based
upon a molecular scheme of interaction in which it is assumed that there are n
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binding sites for the ligand and that the reaction is stable in only two states:
either completely unoccupied or fully occupied. This implies that the
association process is a (n+1) - molecular reaction. In the application of this
model to experimental concentration curves, very little molecular meaning can be
attributed to the resulting model due to the flexibility of this function with
regards to dose-response shape. In addition, there is no unequivocal
relationship between an estimated value of n and the existence of molecular
interactions between binding sites. For example, two binding sites may exist,
but binding to one site produces a small effect, while binding to both sites
produces a much greater effect. This would lead to a noninteger value for n when
curve fitting. Considering the importance of the Hill coefficient in terms of
low-dose extrapolation (Portier et al., 1992) and considering its limitations in
terms of biological understanding of the sequence of molecular events involved
in induction (Andersen et al., 1992), caution must be used when extrapolating to
tissue dose regions outside of those examined directly in the experiment. It is
thus appropriate to conclude that independence of the action of TCDD would
necessarily imply a nonlinear response at low dose.
Some of the mechanistic assumptions in these models are speculative. Many
of the binding and induction equations related to the Ah-receptor/TCDD complex
are encoded in equations, but their exact nature and level of control at the
molecular level are unknown. This is true of CYP1A1, CYP1A2, the Ah-receptor,
the estrogen receptor and TGF-a. Also, the reduction in EGF receptor by
internalization described in the model by Kohn et al. (1992) represents just one
mechanism for its depletion. It is also possible that the synthesis or
degradation of this protein may be under direct control of the Ah receptor,
although TCDD does not alter mRNA levels in either human keratinocytes (Osborne
et al., 1988) or mouse liver (Lin et al., 1991) and EGF receptor does seem to
move from the plasma membrane to the cell interior following TCDD exposure in
female rats (Sewall et al., 1992).
It should be noted that the mechanistic models have the advantage of
suggesting experimental strategies for pursuing the hypothesis of action of
TCDD. These models propose specific mechanisms, which can be tested in the
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laboratory as a means of validation of this model. For the purposes of risk
estimation, one must be careful to recognize that these models do not necessarily
impart added confidence in low-dose risk estimates because many molecular/
biological events that lead to toxic responses are not known. However, the
mechanistic assertions put forward by the model can be discussed in scientific
peer review and, if found valid, can provide additional confidence.
8.2. TOXIC EFFECTS
8.2.1. Modeling Liver Tumor Response for TCDD
[This section has not been reviewed by all members of the Dose Response
Committee, so it does not represent a consensus section at this time. There
was considerable discussion regarding the appropriateness of applying dose
response models for risk assessment in this chapter. The decision was made
that we would include progress on the development of biologically-based
models using liver cancer in rats as the example. This approach is
obviously fraught with uncertain assumptions. Nevertheless, we feel that
this information will be of considerable use to the peer review panel in
their attempts to generate a risk characterization for dioxin based on an
evaluation of dose response relationships.]
Long term carcinogenicity studies in rodents have shown TCDD to be a potent
carcinogen (Huff, 1991). The most seriously affected organ has been the liver in
female rodents. The lack of any detectable DNA binding and the failure of TCDD
to produce positive findings in a battery of short term tests for genotoxicity
compels one to conclude that TCDD does not possess direct genotoxic activity.
It can only be concluded that the mechanism of action of TCDD is by secondary
effects. One possible mechanism of hepatocarcinogenicity from exposure to TCDD
is via the promotion of previously incurred genetic damage. This has been
illustrated in several initation-promotion studies in rat livers (Pitot, 1987;
Clark, 1991a). There is definitely a role of estrogen in the promotional effects
of TCDD in rat liver since TCDD enhances hepatocyte proliferation and stimulates
the development of enzyme-altered hyperplastic foci in intact female rats but not
in ovariectomized rats (Lucier, 1991). The conclusion resulting from these
experiments are not definitive, but it has been suggested that the type of
promotional effect resulting from TCDD exposure is receptor-mediated mitogenesis.
There is also the possibility that additional CYPIA2 mediated metabolism could
lead to an increase in the activation of estrogens leading to cell damage (e.g.,
via free oxygen radicals).
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This section will discuss modeling the tumor response observed in rodents
and comparing that modeled response to some of the biomarkers of exposure
discussed in the section on the biochemistry of TCDD. This approach will deviate
from the pure mechanistic modelling outline discussed in the introduction. Due
to limitations in the data available for characterizing the models we will
employ, some of the parameters used in this modeling exercise had to be obtained
directly from the tumor incidence data. These parameters are then compared to
the relative changes we would have expected using the biomarkers of effect and
exposure which seem reasonable for this endpoint. Thus, this exercise falls in
between curve fitting and pure mechanistic modeling. At the end of this section,
we will discuss the data needed to move this approach into a full mechanistic
development and discuss tumor responses in other sexes and species at other
sites.
The carcinogenicity data we will use are from a 2-year feeding study in male
and female Sprague-Dawley rats (Kociba et al., 1978). For female rats, the study
used 86 animals in the control group and 50 animals per group in the three
treated groups given doses of 1, 10, 100 ng/kg/day. The original pathology of
the study recorded significant, dose-related increases in tumor incidence in the
lung, nasal turbinates, hard palate and liver. The original liver pathology has
been reviewed several times, most recently by a group convened by the Maine
Scientific Advisory Panel (PWG, 1990). The data we will concentrate on is this
analysis is the incidence of liver adenomas and carcinomas (combined) based upon
the most recent pathology review. A summary of these data are presented in
Table 8-3.
There was a substantial reduction in survival in all experimental groups
(including controls) during the course of the study. Other studies have shown
that correcting for this drop can result in as much as a 2-fold change in the
low-dose risk estimates ( ). A simple correction for survival differences ( )
was applied to these data to present the risk summaries given in Table 8-3. In
the analysis which follows, a more rigorous statistical approach was employed.
8.2.2. Tumor Incidence. In recent years, there has been a resurgence in
interest in refining the mechanistic representation of mathematical models of
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TABLE 8-3
Administered Dose, Tumor Response and Number at Risk of Hepatocellular
Neoplasms in Male Sprague-Dawley Rats From Carcinogenicity Experiments of
Kociba et. al, 1978, Using the Pathology Review of Sauer (PWG, 1990)
Original Dose (ng/kg/day)
Number with Neoplasm
Number on Study
Survival-adjusted number at risk8
Lifetime Tumor Risk
0.0
2
86
57
0.035
1
1
50
34
0.029
10
9
50
27
0.333
100
18
50
31
0.581
aUsing the "poly-3" survival adjustment suggested by Portier and Bailer
(19??)
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carcinogenesis. With few exceptions, the mathematic modeling of carcinogenesis
at the cellular level has concentrated on the use of the multistage model.
Theoretical discussions on these models began in the mid-20th century (Arley and
Iverson, 1952; Fisher and Holloman, 1951; Nordling, 1953). The first practical
application of models from this class was done by Armitage and Doll (1954). One
major failure of the Armitage-Doll model is a lack of growth kinetics of the cell
populations {Armitage and Doll, 1957; Neyman and Scott, 1967; Moolgavkar and
Venzon, 1979). Several researchers proposed a second model, deemed the two-stage
model which is illustrated in Figure 8-6.
The two-stage models assumes that carcinogenesis is the result of two
separate mutations, the first resulting in an intermediate cell population and
the second resulting in malignancy. Cells in the normal and intermediate
populations are allowed to expand in number via replication or reduce in number
due to death or differentiation. There is considerable confusion as to the
underlying mathematics in this model since several groups have proposed the same
model but used different mathematical developments to predict tumor incidence
from this model (Armitage and Doll, 1957; Neyman and Scott, 1967; Moolgavkar and
Venzon, 1979; Greenfield et al., 1984??). In the application of the two-stage
model which follows, the mathematical development of this model by Moolgavkar and
Venzon (1979) and subsequent development of this model, will be used.
The two-stage model (Figure 8-6) has six basic rates which must be
estimated. These are:
1. PN = birth rate for cells in the normal state.
2. 6N = death/differentiation rate for cells in the normal state.
3. JJN_J = rate at which mutations occur adding cells to the
intermediate state.
4. Pj = birth rate for cells in the intermediate state.
5. 6j = death rate for cells in the intermediate state.
6* ^I-M s rate at which mutations occur adding cells to the malignant
state.
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ft.
. A
a
C*H*
•* cuft
FIGURE 8-6
A Two-Stage Model of CarcinogenesiB
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To apply this model to dioxin, or any other chemical carcinogen, requires
estimates of these rates as they change over dose and time. A mechanistic
approach to this would be to incorporate some of the relative changes in proteins
seen in the Kohn et al. model directly into the two stage model as rate changes
in these parameters. Considering the complexity and novelty of this approach,
it is left as a research topic. Instead, we will apply this model directly to
tumor incidence data, focal lesion data and cell labeling data comparing the
resulting parameter estimates to predicted dose-surrogates from the models of
Leung et al. (1988), Andersen et al. (1992) and Kohn et al. (1992).
This is not the first application of TCDD data to the two-stage model. An
application of this model to TCDD was presented by Thorslund (1987). Thorslund
treated the effects of TCDD as a direct promoter having an effect only on the
birth rate of intermediate cells (fJj) in the two-stage model. The number of
normal cells were assumed constant (this is equivalent to setting PN = N ~ ^
in the model in Figure 8-6). Two parametric models of the change in Pj as a
function of dose were used, one model having a single parameter (a first-order
kinetic or exponential model) and the second based upon two parameters (a log-
logistic model). The parameters in the exponential two-stage model were
estimated from the tumor incidence data of Kociba et al. (1978) and validated by
goodness-of-fit, cell labeling data and species/sex/strain extrapolations. The
slope parameter in the log-logistic two-stage model chosen to be 1, 2 or 3 based
upon slopes observed in other biological systems. The remaining parameters in
this model were estimated from the Kociba et al. (1978) data.
The liver tumor response from the Kociba et al. (1978) study are given in
Table 8-3 using the most recent pathology review of the liver sections (PWG,
1991). Shown are the number of animals with tumor (row 2), number of animals
placed on study (row 3), a survival-adjusted (Portier and Bailer, 19??) number
of animals at risk (row 4), and the survival-adjusted lifetime tumor probability
(row 5 which equals the entry in row 2 divided by the entry in row 4).
The data is more complex than appears in Table 8-3 since death times were
recorded. Since there are no interim sacrifices which would allow for an
estimation of tumor-induced mortality (Portier, 1986), the two-stage model was
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fit to this data using the assumption of incidental tumors (Hoel and Walbury,
1972??; Dinse and Lagakos, 19??). Since liver tumors induce little, if any,
mortality in male and female Fischer 344 rats and B6C3F mice (Portier et al.,
1986; Portier and Bailer, 1989), this assumption seems to be warranted.
There are a variety of mathematical formulations which could be used to
derive a tumor incidence rate under the two-stage model. As has been assumed by
other authors ( ), we assume:
1. all cells act independently of all other cells;
2. the rates in the two-stage model (Figure 8-6) are constant over the
life span of the animal; and
3. the tumor incidence rate corresponds to the rate of appearance of
the first malignant cells.
All three of these assumptions are likely to be violated in the case of dioxin.
In most tissues, there is a homeostatic feedback system to control the number of
cells in the tissue. No such system can be assumed here since it results in a
mathematic formulation which is intractable. For the large pool of normal cells
in the liver, this is unlikely to have an effect, but for the small number of
intermediate cells (at least for early times) this could have a small effect on
tumor incidence. This issue cannot be resolved without further research.
Assumption (2) is clearly violated based upon the behavior of the PB-Pk models
presented earlier. Time-dependent changes in tumor incidence could be
incorporated into the modeling and will be at a later time. Finally, the
kinetics of cell growth for malignant clones has been studied (Moolgavkar and
Lubeck, 1992) and the assumption (3) was found to have a moderate impact on the
tumor incidence rates. This also will be investigated at a later time.
The exact tumor incidence rate was used to avoid potential bias from the
routinely used approximation (Kopp and Portier, 1989). Thorslund (1987) used
this approximation in his analysis. The methods outlined by Portier and Kopp-
Schneider (1991) employing the Kolmogorov backwards equations were used to derive
the exact tumor incidence rate.
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Under these assumptions, when fitting the two-stage model to the Kociba et
al. (1976) data, there is the potential to estimate as many as 24 parameters
(four dose groups, each with its own two-stage model having six parameters). The
data given in this study make it impractical to estimate this many parameters
with any degree of accuracy (Kopp-Schneider and Portier, 1992). To reduce the
number of parameters in the model, P^ and 6^ were assumed to be known without
error. Assuming that, on average and over finite time, the population of normal
cells is effectively constant, it is reasonable to assume that PN = 6N. This
assumption may not hold true for TCDD since the labeling index for normal cells
seems to increase with increasing exposure to TCDD (Lucier et al. (19??).
However, to illustrate the use of the two-stage model, this assumption will be
employed. Labeling data ( ) suggest that normal hepatocytes undergo mitosis
at an average rate of one mitotic event per 300 days (a 2% labeling index for a
6 day labeling experiment) suggesting that (5N = 3.333xlO~3. This value was used
in the analysis which follows. Other values for PN = 6N was specified and
assuming changes in P^ do not imply changes in p^-i (Portier and Kopp-Schneider,
1991). The number of normal hepatocytes was assumed to be 6xl08 for the female
rat.
This leaves 16 parameters to be estimated from the tumor incidence data.
Table 8-4 shows one set of parameter estimates resulting from this fitting
exercise (labeled "Unrestricted" under the model heading). Multiple attempts at
fitting this model to the tumor incidence data indicated that the model was not
identifiable; this means that there are an infinite number of parameter estimates
which give effectively the same answer for this data. The main problem seems to
be estimating pN.j and ^.M simultaneously (for example, in the high-dose group,
any model with ^N.j • pj.M eg^ial to 3.4 • 10"15, seemed to yield equivalent fits)
and with fitting P] and 6j separately (no obvious relationship was observed). The
main reason for including the "Unrestricted" model in Table 8-4 is that this
model represents the best fit we can possibly achieve and the likelihood (Column
6) represents a best possible measure of goodness-of-fit. We will compare the
likelihood for other models with this target likelihood.
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•c*
03
O
00
TABLE 8-4
Fitting the Two-Stage (TS) Model to the Tumor Incidence Data of Kociba et al. (1976) Using the Pathology of Sauer et al. (1991)
Model
Unrestricted
Unrestricted fifg.|
Unrestricted MI-M
Unrestricted B(
Unrestricted 6,
(B| in low-dose group
set to zero)
Dose
(ng/kg/day)
0
1
10
100
0
1
10
100
0
1
10
100
0
1
10
100
0
1
10
100
Parameters
"N-l
I.HxIO-10
9.48x10~10
9.85x10'10
3.33x10'5
2.55x10'8
1.37x10'8
1.36x10"7
5.80x10"7
1/31x10'5
-
-
-
2.56x10'7
-
-
-
2.56x10'7
_
-
-
Bl
6.90x10~2
4.75x10'2
5.27x10'2
2.01x10'2
1.75x10'2
-
-
-
2.86x10'2
-
-
-
1.24x10'2
5. 82x1 0'3
2.15x10"2
2.46x10'2
6.70x10"3
0.0
1.59x10'2
1.90x10"2
«l
3.Kx10'2
1.58x10'2
2.44x10'2
1.61x10"2
1.06x10'2
-
-
-
2.16x10'2
-
-
-
1.79x10'2
-
-
-
1.23x10'2
—
-
-
''l-M
1.20x10"12
9/2-x!-'15
1.12x10'12
1.03x10'10
1.31x10"9
-
-
-
2.46x10'12
1.37x10'12
1. 53x10"' '
5.81x10'11
3. 02x1 0'9
3. 02x1 0'9
-
-
-
Likelihood
-58.61
-64.96
64.96
-67.57
67.57
vO
to
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The first restricted model to be considered is a model in which the effect
of TCDD is only on the mutation rate from normal cells to initiated cells in the
two-stage model. This model is fit by forcing Pj, 6j and p/j_M to be constant
across all dose groups and allowing pN.j to vary freely as dose changes. The
parameter estimates for this model are given in Table 8-4 as model (2). There
are nine less parameters in this model than in the unrestricted model. Comparing
the two likelihoods, it is evident that this model fits the data significantly
worse than does the unrestricted model (X29 = 12.71, p<0.01, as a technical note,
since there is a problem with identifiability, the degrees-of-freedom, 9, for
this X2 random variable is inflated which would inflate the p-value and the
significance of the result would remain).
The problem with identifiability does not abate by restricting the
parameters in the model. This is illustrated by the next model (3) in Table 8-4.
In this model, it is assumed the effect of TCDD is restricted to the rate of
mutation from the initiated state to the malignant state. In this model, P], 6j
and pN_j are held constant over all groups and /JJ.M changes as dose changes.
Three points indicate the problem of nonidentiflability with this model. The
first is that the likelihood for this model (3) and the model changing pN.j
(2) are identical. This indicates that the two models explain the same amount
of noise in the data. The second point is that, even thought the magnitude of
the birth rate Pj has changed, the difference Pj - 8j has not (~0.7xlO~2). Further
modeling with this data indicates this to be true over a wide range of Pj values.
The third point is that, for each dose group, /JN.J • A/J_M is the same in the two
models. Again, use of different fixed values for pN.j in model (3) and /JJ_M in
model (2) support this result.
The net result is that, unlike the results seen by Moolgavkar and Lubeck
(1992) when fitting colon cancer data to the two-stage model, the magnitude of
the mutation rate pN_j relative to pj.M cannot be addressed for the TCDD data.
However, the relative change in the product of these two mutation rates as a
function of dose can be studied. This will be done later in this document.
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A second restricted model is to consider that dioxin's only effect is on the
birth rate of initiated cells (Pi). This is in fact the modeling approach used
by Thorslund (1987). This model assumes that A/N.J* 5j and pj.M do not change as
a function of exposure to dioxin. This model is given as model (4) in Table 8-4.
This model is also inconsistent with the data (likelihood = 67.57) and provides
a fit which worse than the mutational effect model given by (2). [Technical
note: it is difficult to directly compare thee two models since they constitute
nonnested models (Becker and Wahrendorf,19??) ]. This implies that the effect of
TCDD on tumor incidence in these rodents is more likely due to a mutational
effect than a mitogenic effect on initiated cells. Caution must be taken when
interpreting this result. Firstly, no statistical confidence can be placed on
this statement so the observed difference in the two models may solely be due to
random change. Secondly, this statement is only justified within the restricted
context of this two-stage model of carcinogenesis. If any assumptions of the
model are incorrect (independent cell action, constant rates, two stages, etc.),
the interpretation based upon this model could be biased.
The problem with identifiable parameters also remains with this formulation
of the model. This is illustrated by the fifth model in Table 8-4. In this
model, the birth rate in the lowest dose group was fixed to be zero. The
resulting likelihood and estimates of p^-i and PJ.M remained the same. The
estimated birth rates (P| and death rate 6j) changed, but the difference between
the two in the various dose groups did not change. Repeated applications of this
formulation of the model confirmed this problem of nonidentifiability in this
case.
With this problem of identifiability, there are basically only two
parameters to be estimated for the two stage model for each dose group. These
are the mutation parameter, given by fi - n^_\ ' A'j-M' anc* *-ne proliferation
parameter, given by II = Pj - 6j. Table 8-5 gives the estimates of these
parameters for the simple models considered here. The first model corresponds
to the case where we allow both the mutation parameter and the proliferation
parameter to change as a function of dose (model (1) in Table 8-4). For this
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CD
1
in
M
TABLE 8-5
Net Parameter Estimates From the Two-Stage Model of Carcinogenesis
Two- Stage Parameters Changed
Both
Both
(proliferation rate <0.02)
Nutation rates
Proliferation rates
Doses
Control
1.37x10'22
0.0376
2.80x10"20
0.02
3.34x10'17
-0.0055
1 ng/kg/day
0.077x10'22
0.0317
1.91x10'20
0.0196
1.79x10"17
-0.0121
10 ng/kg/day
8.86x10'22
0.0283
18.79x10'20
0.0196
17.8x10'17
0.00362
100 ng/kg/day
3.43x10'15
0.0040
3.58x10'15
0.00198
76.0x10'17
0.00669
Other Parameters
proliferation rate = 6.90x10
mutation rate = 7.73x10"16
DRAFT — DO NOT
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8
O
M
H
c
o>
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model, as dose increases, the mutation parameter drops to one-half the control
value for the 1 ng/kg/day dose group and increases substantially in the remaining
two dose groups (6.5-fold for 10 ng/kg/day and 25xl06-fold for 100 ng/kg/day).
The hepatocyte replication parameter drops as a function of dose. In the 100
ng/kg/day dose group, the replication parameter is small relative to control
(10-fold smaller) to adjust for the much larger mutation parameter. The reason
for this particular pattern is clear if one studies the tumor incidence data from
the Kociba et al. (19??) study. Over time, the tumor incidence in the highest
dose group is larger than the others (the mutation parameter and the replication
parameter combine to control the magnitude of the response) but climbs less
steeply with time (this is controlled by the replication parameter).
The replication rates in the control group and the two lowest dose groups
are very high. Assuming the death rate is zero, this would correspond to a
labeling index of 40.9% for a 7-day labeling experiment in the control animals.
If the death rate is >0, the labeling index would be even larger. Also, if one
cell entered the initiated state on day 1, by 730 days (2 years), one would
expect a clone of size 8xlOu, "140 times the size of a normal rat liver. To
control for this problem, the same model was fit to the data with the replication
parameter constrained to be less than 0.02 (a first day clone would be expected
to have size of 2xl06 by study end). This resulted in the same pattern of
mutation parameters but with a mutation parameter two orders of magnitude larger
in the control and two lowest dose groups. For all three groups, the replication
parameter was estimated to be at the boundary (0.02). The parameters for the
high dose group did not change.
The model in which the replication parameter is constant across dose groups
and the mutation parameter changes for each new dose is given in row 3 ("Mutation
Rate") of Table 8-5. The pattern in this case is very clear and matches the
observed cumulative tumor incidence will with the mutation rate dropping in the
1 ng/kg/day dose group then rising in the remaining groups. This observed drop
is not statistically significant from the control mutation parameter (likelihood
ratio test, p>0.05). The constant replication rate (6.9xlO'3) is reasonable and
is not likely to produce unrealistically large clones of initiated cells. As
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mentioned before, this model provided a significantly worse fit to the data than
did the unrestricted model.
Finally, the estimated parameters for the model in which the replication
parameter changed with dose and the mutation parameter was constant over all
doses is given by the last row ("Replication Rates") in Table 8-5. The change
in replication rates mirrors the tumor response in the same manner as was seen
for the mutation rates. The replication rates are of reasonable size and should
not produce impossibly large clones. As mentioned earlier, this model does not
fit the data as well as the unrestricted model or as well as the model with fixed
replication rate over dose and varying mutation rate over dose.
There is other evidence which can be used to examine the adequacy of this
model for tumor incidence from exposure to TCDD. Lucier and his colleagues
recently conducted an initiation/promotion study in female Sprague-Dawley rats
(Tritscher et al., 1992; Kohn et al., 1992; Portier et al., 1992; Sewall et al.,
1992; Maronpot et al., 1992). In this study, they measured number and size of
preneoplastic foci in liver sections. It has been suggested that the cells in
these lesions correspond to the initiated cells in the two-stage model of
carcinogenesis. (reference). If this is true, it is possible to apply the
methods of DeWanji et al. (1990) to this data to analyze the growth
characteristics of these cells (Moolgavkar et al., 1991). However, as before,
we run into the problem of nonidentifiability. Since the data in these studies
were collected at only one time point, it may not be possible to estimate all of
the parameters in the first half of the two-stage model (i.e. MN-!' PI and 6I> and
get a unique solution. For each dose group, one of these parameters should come
from outside information.
Maronpot et al. (1992) measured the rate of cell proliferation in these
rodents (Tritscher et al., 1992) using immunocytochemical detection of cells that
had undergone replicative DNA synthesis. The average labeling index by dose
group is given in Table 8-6 for the uninitiated animals (saline controls) in this
study. It is seen there is a slight drop in mean labeling index (p>0.05) from
control to the group given 3.5 ng/kg/day. The labeling index then increases with
increasing dose. Under the assumption of a linear birth death process, it is
8-53 08/27/92
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00
I
U1
TABLE 8-6
Results from Two-Stage Model for Hepatocarcinogenesis in Rats*
Model
Labeling index data and
calculations
Using labeling index
(Model A)
Allowing all parameters to vary
(Model B)
Constant ratio (f1/B1 estimated
.31X10 •)
Parameter
labeling index
birth rates
relative change
(X)
scaled birth
rates
mutation rate
birth rate
ratio ((5,/B,)
birth rate
ratio (£i/Bj)
mutation rate
hepatocytes
initiated
birth rate
mutation rate
Control
3.41
2.75x1(T3
0
1.67x10-13
7.505x10"13
1.23x10'2
0.01
2.60x10"2
7.74x10'12
1.507x10'12
2. OX
2.60x10'2
1.49x10'12
TCDD Dose Administered
3.5 ng/kg/day
3.22
2.34x10'3
-14.9
1.42x10'2
13.98x10"13
1.41x10'2
0.01
3. 36x1 0'2
5.28x10'2
1.087x10'12
4.34X
3. 20x1 0'2
1.035x10'12
10.7 ng/kg/day
4.87
3.56x10'3
29.5
2.16x10'2
8.007x10'13
2.14x10"2
0.01
5. 02x1 0'2
3.75X10"1
0.881x10'12
3.32X
3.34x10'2
0.59x10'12
35.7 ng/kg/day
5.31
3.90x10'3
41.8
2.34x10'2
25.20x10'13
2.32x10'2
0.01
3.47x10"2
1.79x10'2
1.896x10'12
12.84X
3.41x10'2
1.875x10-12
1 25 ng/kg/day
7.09
5.26x10'3
91.3
3.19x10'2
9.912x10'13
2.30x10'2
0.01
3. 33x1 0'2
LOOxlO"5
1.572x10'12
8.72X
3.32x1Q-2
1.579x10'12
I
O
O
z
o
O
G
O
H
M
Source: Lucier et al.f 1991; Tritscher et al., 1991; Maronpot et al., 1992
O
co
10
\o
K)
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possible to convert these labeling indices into estimated birth rates for these
cells using the formula:
Where f) is the birth rate, t is the number of days over which labeling was done
and LI is the labeling index (Moolgavkar and Lubeck, 1992). This conversion is
given in row 2 of Table 8-6. These rates are in nonfocal hepatocytes (normal
cells) and correspond to an average of one to two births per year per hepatocyte.
TCDD seems to double the control birth rate for a dose of 125 ng/kg/day (a
relative change of almost 100% — row 3 of Table 8-6).
Maronpot et al. (1992) also calculated the labeling index in focal
hepatocytes in the high-dose group from this study. The mean LI for animals in
the noninitiated group exposed to 125 ng/kg/day of TCDD was 36% which corresponds
to a birth rate of 0.0319. The ratio of birth rate in nonfocal cells to birth
rate in focal cells is 0.0319/0.00526 = 6.06. Assuming this ratio is constant
over all dose groups, we can rescale the birth rates in the remaining dose groups
to correspond to birth rates for focal cells resulting in the rates show as row
4 in Table 8-6.
Using the same method as that used by Moolgavkar et al. (1991), given these
birth rates, it is possible to estimate pN.j and 6j for the focal lesion data of
Lucier et al. (1991). The resulting parameter estimates are given as rows 5 (pN_
j) and 6 (6(/Pj) in Table 8-6. In all cases, the best estimate of the death rate
was 6j = 0.01, an arbitrary lower bound chosen by curve-fitting. This indicates
that these birth rates are far too small for the sizes of the lesions observed
since death rates of this magnitude would result in almost no loss of initiated
cells. (Technical note: the results do not change markedly if the death rate
is allowed to be estimated as zero). The estimated mutation rates are also
extremely small and have no set pattern to them. Finally, there is considerable
lack-of-fit with these parameters of the model to the focal lesion data
themselves.
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To get a handle on how large the labeling index would have to be to result
in a reasonably good fit to the data, the model was again fit to the liver foci
data, starting with the solution given in rows 3 and 4 of Table 8-6 and allowing
|J| to roam freely and the ratio 6j/Pj to be >10"5 (rather than 10~6). The resulting
parameter estimates are given in rows 5, 6 and 7 of Table 8-6.
The first point to note in this table is that the estimated birth rate in
the high dose group (0.0333) is approximately the same as that estimated from the
labeling data. This provides some mechanistic support for this particular
parameterization of the model. We also see that the death rate of initiated
cells (6j - ratio • |Jj) is near zero in all groups except for the middle dose
group. The birth rate is virtually identical in the 3.5, 35.7 and 125 ng/kg/day
dose groups corresponding to a labeling index in 7 days of 37.5%. The control
group has a birth rate for initiated cells of 0.026 which corresponds to an LI
of "30.5% and the dose group receiving 10.7 ng/kg/day have a birth rate of 0.050
corresponding to an Li of roughly 50%. These values suggest that TCDD may have
a small effect on the growth characteristics of initiated cells, but the effect
is not as large as might have been expected. There is very little change in the
mutation rate as a function of dose level. These small changes do, however,
result in changes in the expected number of cells in the initiated state; this
is shown in row 11 of Table 8-6. This is calculated as follows: assuming that
liver cells have a radius of 0.012 mm, the volume of a single liver cell is 4/3
n (.012)3 = 1.5079xlO'3 mm3. Inverting this gives a total of 663 cells/mm3. The
expected number of initiated cells per mm3. The expected number of initiated
cells per mm3 is given by the formula:
where t * 229 days (the length of the experiment). The percentage is simply
E[X,]/663 • 100%.
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The differences observed in the percentage of initiated hepatocytes can be
easily explained by examining Pj and A
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As one final exercise with the use of these foci data for risk assessment,
we applied the parameters estimated from the liver foci data to the tumor
incidence data of Kociba et al. (1978), and only estimated the remaining
parameter in the model, PJ_N- Interpolated values for ^N-1' Pi anc* 6I na<* to be
derived. These are given in Table 8-7.
This resulted estimates of pj_M yielded a model which was not significantly
different from the best fitting model (p>0.10). This mutation parameter in the
second stage shows a dramatic change as a function of dose indicating a strong
mutational effect for TCDD in the second stage.
It is possible to compare the parameters estimated for the two-stage model
to predictions from the PB-Pk models to try to locate a reasonable mechanistic
link between the two classes of models, to aid in species extrapolation and to
help guide us in choosing the most appropriate curvature for low-dose
extrapolation. The two-stage model parameters which were estimated from the
tumor incidence data are compared to 14 dose surrogates from the 3 main Pb-Pk
models reviewed earlier. Leung et al. (19??) suggested using occupancy of the
cytosolic (Ah) receptor, averaged over the length of the study. They also
suggested using average binding to the microsomal proteins. These predictions
are shown in Table 8-8 for the study of Kociba et al. (1978), along with the
predicted final concentration of TCDD in the liver of these animals. The
correlation of these parameters with the two-stage model parameters in rows 2
("Both"), 3 ("Mut. Rates") and 4 ("Prol. Rates") is shown in the last 3 columns
of Table 8-8. It is clear that these 3 surrogates correlate well with all but.
the two-stage model in which the effects of TCDD were treated as pure promotional
effects. The high correlations and low p-values for these correlations is driven
by the order of magnitude differences in the parameters and the dose surrogates.
(Caution should be used in judging these correlations to carry any weight of
scientific evidence.)
Andersen et al. (1992) suggested four dose surrogates to be used in any risk
assessment for TCDD. These are based upon free TCDD in the liver, total TCDD in
the liver, amount of induced CYP1A1 and amount of induced CYP1A2. As for Leung
et al. (19??), these are integrated over the lifespan of the animal (in this
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TABLE 8-7
Using Liver Foci Parameters to Fit Tumor Incidence
Dose
Control
1 ng/kg/day
10 ng/kg/day
100 ng/kg/day
^N-I
1.491X10'12
1.361xlO'12
0.591xlO'12
1.579X10'12
Pi
2.60xlO-2
2.77x10-2
3.34xlO-2
3.32x10-2
«I
3.95xlO'4
4.21xlO'4
B.OSxlO'4
B.OBxlO'4
VW
(estimated)
1.26X10'9
1.94xlO-10
2.56xlO"4
4.05xlO'3
Like - 66.982
12
16.74
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TABLE 8-8
A Comparison of Dose-Surrogates With Parameters Estimates from the Two-Stage Model of Carcinogenesis
Dose Surrogate
Dose
Control
1 ng/kg/day
leungg et al. (1988)
Concentration6
Ah- receptor
occupancy
Microsomal
binding*
0.0
0
25
0.3
2
29
10 ng/kg/day
100 ng/kg/day
Correlation"
Bothb
Hut.
Rates
Prol.
Rates
4.4
17
132
69.4
61
132
1.00"1"1"
-1.00+ +
0.96+
-0.96+
0.96 +
-0.96"1"
0.99+
1.00* +
1.00+ +
0.70
0.81
0.81
Andersen et al. (1992)
Free TCDD in
liver'
Total TCDD in
liver0
Induced CYP1A1h
Induced CYP1A21
0
0
0
0
6.4
590
3900
57,000
53
11,000
270,000
210,000
Kohn et al. (1992)
Free TCDD in
liver1
Ah -receptor/
TCDD complex
CYP1A2/TCDD
complex
TGF-am
Internalized
EGFR"
Total CYP1A10
Total CYP1A2"
0
0
0
0
0
190
4517
0.1237
1.0202
1.6857
0.005899
0.4829
2198
5999
0.7663
8.8617
27.3142
0.05408
3.7213
14,213
15,747
420
120,000
200,000
320,000
0.99+ +
-0.99+ +
1.00+ +
-1.00+ +
0.99* +
-0.99 +
0.79
-0.79
1.00+ +
1.00+ +
1.00+ +
0.89
0.73
0.72
0.74
0.87
5.0522
49.352
464.85
0.3788
13.668
42,196
44,579
0.99+
-0.99 +
0.99 +
-0.98"1"
1.00+ +
-1.00 + +
0.99+ +
-0.98 +
0.97+
-0.96
0.95
-0.95
0.96+
-0.96+
1.00* +
1.00+ +
0.99+
1.00+ +
1.00+ +
0.99+ +
1.00+ +
0.74
0.76
0.69
0.74
0.83
0.83
0.81
"Pearson correlation coefficient -+ indicates statistical significance at the 0.05 level, +«• indicates
significance at the 0.01 level (caution should be used in applying the p values too literally due to
the small smaple sizes involved
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case, total integrated rather than average, but this will not affect the
correlations). As for Leung et al. (19??), the correlation with all two-stage
model formulations/parameters is significantly different from zero for three of
the surrogate doses (excluding CYP1A2) and all but the promotion-only TCDD model.
The correlation of induced CYP1A2 with the two-stage model parameters is not
significantly different from zero for all two-stage models. Note that the highly
nonlinear CYP1A1 curve correlates well with the first two two-stage models. If
this were used as a dose surrogate for TCDD toxicity [in the independent
framework chosen by Andersen et al. (19??)], the resulting low-dose risks would
be substantially smaller than those of any other dose surrogate. Induced CYP1A2
fails to correlate due to the strong saturation seen at the two highest doses;
an effect not observed in the two-stage model fits.
Kohn et al. (1992) did not suggest specific dose surrogates, but did suggest
mechanisms that naturally lead to the choice of certain surrogates. Seven such
surrogates are given in Table 8-8: free liver TCDD, Ah-receptor bound TCDD,
CYPlA2-bound TCDD, integrated TGF-a, internalized EGF receptor, integrated CYP1A1
and integrated CYP1A2. With the exception of integrated CYP1A1, all dose
surrogates correlated as well with the two-stage parameters as the dose
surrogates from the other two PB-Pk models. None of the surrogates resulted in
correlations with the promotion model, which were significantly >0. The
internalized EGF receptor correlates well with the promotion model, but not
significantly better than 0 and not better than induced CYP1A2 in the Andersen
et al. (19??) model. Total CYP1A2, which Kohn et al. (19??) suggest, could be
tied to secondary mutagenic effects of TCDD correlates well with the mutation
model. All of the dose surrogates predicted by the Kohn et al. (19??) model have
positive slope at dose 0 and would behave, in the low-dose region, as a linear
model. Risk estimates utilizing these dose surrogates and the two-stage model
would likely result in a <10-fold change in risk over what would be estimated by
applying a one-stage model to the Kociba et al. (19??) data.
It should be possible to correlate these same dose surrogates with the model
parameters arising from the liver foci analysis. However, none of the authors
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calculated dose surrogates for this design situation. This can be done at a
later time.
One final point on dose surrogates. The choice of which measure of exposure
you decide to correlate with which measure of effect is a somewhat arbitrary
decision. The choices above were mostly chosen because they have traditionally
been used in this context. However, there is no pressing mechanistic reason why
these particular choices are best. The major two variables in this decision are
which endpoints(s) and how to consider time factors. For example, we could also
have considered induced amount of Ah receptor as a dose surrogate; there is no
a priori reason to exclude it. It is also possible to integrate over shorter
periods of time. This is not really mechanistically justifiable for TCDD, but
other compounds, which show a short mitogenic effect or get rapidly metabolized
into toxic compounds of brief duration, shorter integration periods would be more
appropriate. Thus, some thought should be given to a choice of dose surrogate
based upon mechanistic considerations. The paper by Kohn et al. (19??)
discussing their mechanistic model can be used to provide considerable direction
on this topic (see Appendix).
8.2.3. Other Effects Mammary/Uterine/Anticancer Endpoints. There are several
lines of evidence that suggest that TCDD is anticarcinogenic in some organs of
experimental animals. The most compelling data are from the Kociba bioassay
(Kociba et al., 1978), which reported a dose-dependent decrease in several
endocrine tumors in females. The most remarkable changes occured in the
incidence if carcinoma of the breast and uterus and all tumors of the pituitary.
The decrease in these tumors coincided with the increase in hepatic tumors in the
same animals. Tumor inhibition has also been reported in skin initiation-
promotion experiments if TCDD administration precedes the initiator. This TCDD
induced inhibition occurs even if the complete initiator-promoter protocol is
carried out following the TCDD administration (DeGiovanni et al., 1977; Marks et
al., 1981).
Mechanistic explanations for these observations are incomplete. Several
laboratories have attempted to elucidate the molecular events leading to the
decrease in tumors particularly in the breast and uterus. Three lines of
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evidence are under great scrutiny at this time including (1) alteration of the
estrogen receptor, (2) alteraion of estrogen metabolism, and (3) alteration of
binding of estrogen and estrogen receptor at the DNA level.
Down regulation of the estrogen receptor has been reported by laboratories
of Gallo (Gallo et al., 1986; DeVito et al., 1990) and Safe (Romkes and Safe,
1987; Astroff and Safe, 1988, 1990) and Gierthy et al. (1987). This down
regulation occurs in a rank order relationship with the affinity of TCDD
structural anaolgs to bind to the Ah receptor (Harris et al., 1990), and
parenthetically with the ability of the compounds to induce cytochrome P-450IA1.
There is differential regulation of the ER by TCDD in uterus and liver (liver
being more sensitive) and the regulation of the uterine ER disappears in the
adult animal (DeVito et al., 1992). Collectively, these observations suggest
that there are at least two mechanisms involved in ER regulation by TCDD,
Gierthy has supported the argument that, in MCF-7 cells, the alteration of
estrogen metabolism following the induction of cytochromes P-450 is the
explanation for the decreased activity of the ER. Safe has reported that the
ED50 for decreased ER in MCF-7 mice cells is several orders of magnitude below
the ED50 for induction of EROD in these same cells (Zacharewski et al., 1991).
Gierthy's position is supported by Bradlow who suggests that
Indolo(3,2B)carbazole, the active inducer of cytochrome P-450IA1 in cruciferae,
decreases breast cancer in rodents by increasing the metabolism of estradiol
(Bradlow et al. 19??) In vivo studies suggest that the uterine response is more
responsive to changes in circulating estradiol (which may or may not hold up for
breast tissue) but the hepatic ER response to TCDD is independent of circulating
estradiol levels (DeVito et al., 1992). Another possibility is that TCDD induces
enzymes that qualitatively change in situ estrogen metabolism and that these
novel metabolites (or families of metabolites) alter estrogen action. Recent
evidence suggests the presence of a 15 alpha metabolite of estradiol in liver
microsomes, from TCDD treated females, incubated with estradiol (Gallo and Conney
19unpublished). This observation is similar to that of Gierthy and Lincoln
(1990) in MCF-7 cells.
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Another possible mechanism of the decrease in breast cancer is that the
binding of the activated AhR complex to the Ah locus locus on the DNA is in the
upstream region of the ER gene or is in the vicinity of the binding region of the
activated ER. Several lines of evidence also support this hypothesis since these
is ample evidence that other steroid receptor complexes modify the DNA binding
of different members of the steroid receptor super family (Gustafsson et al.,
1987; Cutliff et al., 1987).
The last area to be discussed here is the role of the pituitary in the
regulation of the endocrine system and in particular the relationships between
pituitary-hypothalmus and the estrogen receptors. The ER in liver and uterus is
controlled by the pituitary (Lucier et al., 1972) and growth restores the
albation of hypophysectomy. Hypophysectomy has little effect on TCDD induction
of P-450. but TCDD does decrease ER in growth hormone restored animals (DeVito
et al., 1991). Interestingly, Peterson et al. (19??) has made similar
observations for regulation of testosterone and testicular function by TCDD.
The importance of this subsection is at least 2-fold. The regulation of the
estrogen receptor is one of the most sensitive non-P-450 markers of TCDD exposure
in females and this regulation coincides in rank-order fashion with decreases in
mammary, uterine and pituitary tumors in TCDD treated female Sprague-Dawley rats.
Second, the removal of the ovaries (the primary source of estradiol) inhibits the
formation of hepatic tumors by TCDD (Lucier et al., 1991) without altering the
ability of TCDD to induce cytochrome P-450IA1 (DeVito et al., 1992).
The modeling of the hepatic tumors clearly shows a biological relationship
between the absence of ovaries (which decreases both estrogens and estrogen
receptors) and the absence of the hepatic tumors in rats.
8.2.4. NonCancer Endpoints (DeVito et al. 1992). Previous risk assessments
have focused primarily on cancer as the most important and sensitive endpoint.
This assumption has recently been questioned. For example, lead is carcinogenic
in experimental paradigms yet it is neurotoxicity which drives the risk
assessment. Past risk assessments of TCDD and its congeners have also focused
on cancer as the primary toxic endpoint, although it produces adverse effects in
a wide variety of tissues and cells. It is possible that the immunological,
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reproductive or developmental toxicities of TCDD maybe just as sensitive and
important in the risk assessment process. For noncancer endpoints, health
assessments have used the safety factor method to estimate risk. Biologically-
based mathematical models for noncancer endpoints have not been extensively
utilized and are not as developed as are cancer risk models. The development of
biologically-based models requires that the responses are well characterized,
tissue doses have been established, and sufficient data is available to propose
a mechanistic model. Many of the toxic effects of TCDD are well characterized
with respect to the dose-response relationship, time-course relationships,
species differences and the magnitude for the effects. These type of data can
be applied to a variety of curve-fitting models which can be used to describe the
responses quantitatively. The development of biologically-based mechanistic
models for noncancer endpoints requires more extensive data sets on the putative
mechanisms through which TCDD produces its toxic effects. However, it is
important to note that many of the same molecular events involved in TCDD-
mediated cancer may also be involved in the production of non-cancer endpoints
such as TGFa, EGF receptor and estrogen receptor. Therefore, as we learn more
about the mechanisms of TCDD-mediated, noncancer effects we may be able to
readily apply cancer mechanistic models to other toxic effects.
8.2.5. Neurological and Behavioral Toxicity. TCDD affects differentiation,
proliferation and programmed cell death. All of these phenomena are involved in
the developing nervous system thus making the developing nervous system a
potential target for the toxic effects of TCDD. There are only a few studies
which have tested the potential neurological and behavioral toxicity of TCDD or
other Ah receptor agonists. In primates, prenatal exposure to TCDD produces
specific deficits in object learning while not effecting other behavioral tasks
such as spatial learning, reflex learning, locomotor activity or fine motor
control (Schantz and Bowman, 1989). In rats, prenatal exposure produces a
feminization of male sexual behavior but does not alter performance on working
or reference memory tasks nor does it affect field locomotor activity (Mably et
al., 1992). The potential neurotoxic and behavioral effects of other Ah agonist
have also been examined. Prenatal exposure to 3,4,3',4'-tetrachlorobiphenyl
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produces permanent alterations in striatal dopaminergic synapses in mice (Agarwal
et al., 1981; Chou et al., 1979; Tilson et al., 1979). Several studies have
demonstrated that prenatal exposure to PCB mixtures produce & variety of
neurological effects in monkeys, rats, mice as well as humans (Tilson et al.,
1990).
The neurological and behavioral effects of TCDD and other Ah receptor
agonists are not well characterized. The evidence available indicates that TCDD
and its congeners can produce behavioral effects in several species and are
potentially neurotoxic in humans. Many of the PCBs can decrease dopamine stores
in the central nervous system (Seegal et al., 1990). However, structure activity
relationships indicate an Ah receptor independent mechanism is involved in the
decreased dopamine stores (Shain et al., 1991), and it is unclear if these
studies are applicable to TCDD and other Ah receptor agonists. The mechanism of
the developmental and behavioral toxicities of TCDD, and its congeners are
undetermined. Future studies on the behavioral deficits produced by prenatal
exposure to TCDD are warranted to better characterize these effects.
8.2.6, Teratological and Developmental
8.2.6.1. CLEFT PALATE — Dioxins produce structural, malformations and
developmental toxicity in several species. Considerable information is becoming
available on mechanisms of cleft palate formation and it may be possible to
construct mechanistic models for this effect. In mice, increases in the
incidence of cleft palate is a well characterized phenomenon (Birnbaum et al.,
1987a,b, 1991). The doses of TCDD required to produce cleft palate in mice are
well below doses that produce maternal toxicity or fetal mortality. In the
normal developing palate, the peridermal medial epithelial cells cease to express
EGF receptor, decrease cell proliferation and eventually undergo programmed cell
death while the basal cells differentiate into mesenchyme, allowing the left and
right palatal shelves to fuse. Temporal changes in the expression of EGF, TGF-or,
TGF-P 1 and TGF-P 2 are critical for the fusion of the palate. Experimental
evidence indicates that changes in the expression of these factors, induced by
TCDD, results in cleft palate formation. The medial epithelial cells of cultured
mouse embryonic palates exposed to TCDD, express EGF receptor, incorporate [3H]
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thymidine and differentiate into a stratified squamous oral-like epithelium in
a dose-dependent manner (Abbott and Birnbaum, 1989). Changes in medial
epithelial cell differentiation are associated with increased EGF receptor,
TGF-p 1 and TGF-f) 2 and decreased TGF-a levels (Abbott and Birnbaum, 1992).
The use of cultured embryo palates (Abbott and Birnbaum, 1990a, 1991; Abbott
et al., 1989) has (1) led to a greater understanding of the mechanism of TCDD
induced cleft palate, and (2) enabled researchers to compare TCDD induced
biochemical changes in the palate tissue of several species. In vitro
observations found that the human and rat palates are sensitive to cleft palate
formation through the same mechanism seen in mice; changes in growth factors
(i.e., EGF and TGFs) that are involved in the mechanism of altering programmed
cell death in the medial epithelial cells of the palate. The response to TCDD
in mouse palate cultures was '100-1000 times more sensitive that the response in
human or rat palate cultures.
The available data provide substantial information to develop a qualitative
model through which TCDD induces cleft palate. The induction of cleft palate in
mice by TCDD is mediated through the Ah receptor. TCDD binds to the Ah receptor
in the medial epithelial cells, and the activation of the Ah receptor initiates
a cascade of events which increases TGF-fl 1 mRNA and protein, increases in
TGF-p 2 and EGF receptor protein levels and decreases TGF-a protein levels
(Abbott and Birnbaum, 1992). These changes alter the normal signalling pathways
in the medial epithelial cells. In control animals, the interaction between
these signalling pathways results in the programmed cell death of the peridermal
medial epithelial cells. The alterations in growth factor regulation by TCDD
results in continued proliferation of the peridermal medial epithelial cells and
the redifferentiation of the basal epithelial cells to stratified squamous oral-
like epithelial cells which subsequentially prevents the fusion of the palate
(Abbott and Birnbaum ,1989).
This preliminary model for the induction of cleft palate by TCDD requires
the better characterization of several steps. Although, structure activity
relationships indicate that the Ah receptor is involved, there is no direct
evidence that the Ah receptor is present in the medial epithelial cells of the
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developing palate. Cytosolic fractions of embryonic palatal shelves do contain
an Ah receptor but which cells are expressing the Ah receptor are undetermined
(Denker and Pratt, 1987). It is presently unknown if the increases in TGF-jJ 1
mRNA is mediated by the interaction of the Ah receptor with a ORE directly
activating transcription of the TGF-P 1 gene or if the increases in TGF-jJ 1 mRNA
is due to the initiation of a cascade of cytosolic-plasma membrane of events by
the Ah receptor. Further research is needed into the interaction of the growth
factors and their specific roles in palate formation. Much of the data used to
formulate this model are from studies using cultured palates. The development
of quantitative models would require dose-response data for the in vivo
alterations of these growth factors by TCDD which is unavailable at this time.
Cleft palate in rats (Schwetz et al., 1973; Couture et al., 1989) and
hamsters (Olson et al., 1990) is induced only at doses that result in significant
maternal toxicity and fetal mortality, and maximal induction of cleft palate is
between 10% and 20% while in the mouse, cleft palate can reach 100% incidence
before any fetal mortality or maternal toxicity is demonstrated. These data
indicate that the mouse is extremely sensitive to this response. In vitro
studies indicate that humans may be much less sensitive than mice to TCDD-
mediated increases in cleft palate, so it is plausible that in humans cleft
palate may only occur after high exposures.
8.2.6.2. HYDRONEPHROSIS — In mice, hydronephrosis is also produced by
TCDD following prenatal exposure at doses that do not produce fetal mortality
(Couture-Haws et al., 1991). Postnatal exposure prior to day 4 can also produce
hydronephrosis in mice (Couture et al., 1989). The hydronephrosis induced by
TCDD is due to occlusion of the ureter by epithelial cells (Abbott and Birnbaum,
1990). Increased proliferation of the epithelial cells by TCDD is associated
with increased EGF receptor. Hydronephrosis has not been reported in any other
species at doses that do not result in significant fetal mortality (Birnbaum et
al., 1991).
Mice are the only species in which TCDD produces frank terata at doses that
are not fetotoxic. At present, there is no evidence that indicates humans are
as sensitive as mice to these effects. The only available data comparing the
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sensitivity of fetal tissue demonstrates that human and rat fetal tissue are
equally sensitive to the effects of TCDD (Abbott and Birnbaum, 1991; Birnbaum,
1991). These data suggest that sublethal exposure to TCDD may not result in
frank terata.
8.2.6.3. THYMZC ATROPHY — Thymic atrophy has been reported in rats,
guinea pigs and hamsters, and there has been some suggestion that there are
thymic effects in humans. In all mammalian species tested, thymic atrophy is one
of the most sensitive clinical indicators of toxicity. Experimental evidence
indicates that TCDD induced thymic atrophy segregates with the Ah locus. In
human risk assessment TCDD induced thymic atrophy is relevant during fetal
development. In animal studies prenatal exposure to TCDD produces thymic atrophy
in all species tested and occurred at doses well below those that cause maternal
or fetal toxicity (Birnbaum, 1991). Thymic atrophy occurs at similar doses in
rats, guinea pigs and hamsters exposed prenatally despite a 5000-fold difference
in the LD50 in the adult animals (Olson et al., 1990). The sensitivity and
interspecies consistency of this response indicates that prenatal exposure to
TCDD may result in thymic atrophy in humans. The mechanism of thymic atrophy has
not been elucidated sufficiently to incorporate into a biologically based
mechanistic model. Current research focuses on the TCDD induced alteration in
thymocyte development, and their role in immunotoxicity.
8.2.7. Immunotoxicity. Dioxins are immunotoxic in several species. However,
much of the data available on the immunotoxic effects of TCDD are
phenomenological and do not provide adequate information to propose a mechanism
(Holsapple et al., 1991; Kerkvliet, this document). Decreased T-cell mediated
immunity is associated with thymic atrophy which may be due to alterations in the
thymic epithelial cells (Greenlee et al., 1985). These cells provide a
microenvironment which supports the maturation and differentiation of the T-
cells. Dioxin exposure results in the formation of epithelial cell aggregates
which may alter the microenvironment in the thymus and inhibit the maturation of
T-cells (Vos et al., 1991). However, the mechanism by which this occurs is
unclear. While there is ample evidence indicating that these effects are
mediated through the Ah receptor, steps beyond the binding of TCDD to the
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receptor are unknown. Furthermore, there is evidence that the effects are not
solely mediated through changes in the thymic epithelial cells but may involve
direct effects on the T-cells (McKonkey et al., 1988) or prothymocytes (Fine et
al., 1990). Future studies are required to identify the target tissue involved
in the TCDD-mediated thymic atrophy and its relationship to decreases in T-cell
mediated immunity. Similarly, the available data on the effects of TCDD on B-
lymphocytes are primarily phenomenological. The most consistent effect is the
decrease in plaque forming cells in mice in response to sheep red blood cells
which have been documented by several laboratories. The ED50 of this response
ranges from 0.7 to 1.2 pg/kg in female B6C3F/1 mice (Kerkvliet, this document).
Inhibition of B cell maturation is thought to be responsible for the suppressed
antibody response (Tucker et al., 1986; Luster et al., 1988). Alterations in
protein tyrosine phosphorylation have been correlated with inhibition of antibody
synthesis in vitro (*Clark et al., 1991a). Whether there is a direct cause and
effect relationship between changes in phosphorylation and inhibition of antibody
synthesis remains to be demonstrated. The suppression of the antibody response
appears to be Ah receptor mediated. The lack of information for the mechanism
of immunosuppression by TCDD does not allow for mechanistic mathematical
modelling of this response.
There are other difficulties in mechanistic modeling of immunosuppressive
responses. The quantitative and qualitative relationship between immune function
changes and end stage diseases is unclear (Luster et al., 1992). Without
increased understanding of these relationships quantitative extrapolation between
species remains difficult.
8.2.8. Reproductive Toxicity
8.2.8.1. FEMALE REPRODUCTIVE TOXICITY — Several studies have demonstrated
that TCDD affects female reproductive function in mice, rats and monkeys. TCDD
at low doses reduces fertility, litter size and uterine weights in some species.
TCDD alters menstrual or estrus cycling in monkeys, mice, and rats. These data
indicate that TCDD has antiestrogenic effects which may results in decreased
reproductive functioning.
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The antiestrogenic actions of TCDD could be mediated by either changes in
circulating estradiol, qualitative changes in estrogen metabolism or through
decreases in estrogen receptors. In mice, TCDD does not alter serum estradiol
levels and the antiestrogenic actions of TCDD are associated with decreases in
uterine and hepatic estrogen receptors (DeVito et al., 1992). Similarly, TCDD
decreases rat hepatic and uterine estrogen receptor (Romkes et al., 1987) but
does not affect serum estradiol levels (Shiverick and Muther, 1982). Structure-
activity studies suggest that the Ah receptor mediates the down-regulation of the
estrogen receptor. The estrogen receptor is down-regulated by TCDD in several
breast cancer cell lines (Safe et al., 1992). TCDD also decreases estrogen
receptors in Hepa Iclc7 cells but not in mutant cell types that do not express
a high affinity form of the Ah receptor nor in cells which do not accumulate
activated Ah receptors in their nucleus (Zacharewski et al., 1991). These
studies provide further evidence that the Ah receptor is directly involved in the
down-regulation of the estrogen receptor.
One possible mechanism for the antiestrogenic actions of TCDD is that TCDD
binds to the Ah receptor in the target tissue and through a cascade of events
decreases the amount of estrogen receptor in the cell, thus inhibiting the
actions of estrogens. The down-regulation of the estrogen receptor by TCDD can
be mediated either by decreased transcription of the estrogen receptor gene or
through nontranscriptional mechanisms. At present, it is unclear how TCDD down-
regulates the estrogen receptor other than it is mediated through the Ah
receptor.
An alternative mechanism by which TCDD inhibits estrogenic actions is
through increases in estradiol metabolism. Following TCDD exposure, estradiol
metabolism is increased 100-fold in MCF-7 cells (Spink et al., 1990). Microsomal
hydroxylation of estradiol is increased 2- to 4-fold in rats treated with TCDD
(Graham et al., 1988). The role of estrogen metabolism in the antiestrogenic
actions of TCDD remains to be determined. While there is more evidence
supporting the role for the down-regulation of the estrogen receptor mediating
the antiestrogenic actions, further studies are required to determine the extent
of estradiol metabolism in vivo following TCDD treatment.
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8.2.8.2. MALE REPRODUCTIVE TOIICITY — When administered to adult rats,
TCDD decreases testis and accessory sex organ weights, decreases spermatogenesis
and reduced fertility (Moore et al., 1985; Moore and Peterson, 1988; Bookstaff
et al., 1990a). These effects are associated with decreases in plasma
testosterone (Moore et al., 1985). The decreases in circulating androgens are
due to decreased testicular responsiveness to luteinizing hormone and increased
pituitary responsiveness to feedback inhibition by androgens (Moore et al., 1989,
1991; Bookstaff et al., 1990a,b; Kleeman et al., 1990). While the antiandrogenic
effects occur within 24 hours, the doses required to produce these effects are
overtly toxic and decrease food intake and body weight. The high doses needed
demonstrate that the antiandrogenic effects are not very sensitive effects.
In contrast to the adults, the developing male reproductive system is very
sensitive to the effects of TCDD. In rats, prenatal exposure to TCDD results in
decreases in sex organ weight, impairs spermatogenesis and luteinizing hormone
secretion, and demasculinizes and feminizes sexual behavior (Mably et al., 1991).
Maternal doses as low as 0.064 pg/kg can produce these effects indicating that
the developing male reproductive system is one of the most sensitive endpoints
for the toxic effects of TCDD. The alterations in male reproductive development
are associated with decreases in testosterone levels. It is also possible that
these effects are due in part to alterations in tissue sensitivity to
testosterone (Mably et al., 1991). The exact mechanism of these effects is
unknown and the precise target tissue remains undetermined.
In summary, there is ample evidence that noncancer endpoints are extremely
sensitive to the toxic effects of TCDD. However, for many of these effects the
mechanism by which they occur are unknown and in some cases the target tissue
remains undetermined. Furthermore, few if any of the molecular events beyond
ligand binding to the Ah receptor are understood. The only information we have
to develop mechanistic models is dose-response relationships. The available data
do not provide enough information to develop biologically based mechanistic
models for noncancer endpoints. Future studies which better characterize target
tissues and the molecular mechanisms underlying these events are indicated.
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management decisions where research cannot yet be used to fill the knowledge gaps
in the comparison.
There are two basic problems with the comparison of toxic potency across
endpoints. There are (1) comparison of measures of mortality (or serious threats
to mortality) to measures of morbidity or to measures of biochemical
modifications; and (2) statistical issues concerning the power to detect effects
and the inclusion of background responses. We will discuss each of these in
detail using TCDD as an example of the problems involved in this undertaking.
We will consider these issues in reverse order starting with the statistical
problem first. Statements such as the one above sometimes reflect a statistical
artifact due to the nature of the biological assay being analyzed. To illustrate
this point, consider the data given in Table 8-9. This table considers four
different responses to TCDD; liver cancer in female mice and rats, immunological
changes in male mice, changes in protein concentrations in female rats and rates
of terata in mice. One practical way to address potency across endpoints is to
rely on significant difference from control for each treatment group. On the
basis of a statistical test (footnote), it is clear that the most sensitive
endpoint is an effect on the immune system where a response was detected at a
dose of 0.644 ng/kg.
Secondly, the endpoint being measured (plague forming cell response) has a
greater numerical value that does other responses (such as tumor incidence) with
a small variability allowing for much greater statistical power in obtaining a
response. Also, the lowest dose for induction of CYPIA1 shows a significant
effect on immune responses; it is impossible to know if lower doses might not
have been significant also. Thus the location of a NOEL is dependent upon the
statistical properties of the endpoint being studied and the sensitivity of a
particular response.
A second approach would be to consider the relative change in response over
background in the TCDD treated groups. For example, the drop in PFC response
fron control to low dose is 6% or "0.06 relative units of response per 0.322
units (pg/kg/day) increase in dose, or a slope of 0.0186. This is "3 times
greater than the relative increase in CYPIA1 (slope of 0.068) and about one-tenth
the relative increase in liver tumors in female mice (NTP, 1982). Even more
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TABLE 8-9
Toxic Endpoints Data
Endpoint
Liver cancer in female mice
Liver cancer in female rats
Plaque- forming cells pwe 10 viable
spleen cells
Concentration of cytochrome P4501A1
in microsomal protein form
hepatocytes (pmol/mg)
Concentration of cytochrome P450IA2
in microsomal protein form
hepatocytes (pmol/mg)
Cleft palate in mice fetus
Dose"
0.0
1.4
7.1
71.0
0.000
1.0
10.0
100.0
0.00
0.322
0.644
1.191
3.091
0.0
3.5
10.7
35.7
125.0
0.0
3.5
10.7
35.7
125.0
0.0
6000.0
9000.0
12000.0
15000.9
Response
0.041C (3/73)d
0.120 (6/50)
0.125 (6/48)
0.234 (11/47)'
0.051° (3/58. 4)h
0.029 (1/34.0)
0.272 (9/33. 1>*
0.607 (19/31/3)*
777±88
731±190
438i96*
118±46*
65±15*
12.9±11.3
56.4±26.7*
111.5±30.3'
181.4±18.4e
293.3*17.1*
63.5*38.4
88.3*23.0
161.0155.78
193.1*60.2*
297.4*88.3*
0.000 (0/159)
0.019 (2/107)
0.213 (26/122)*
0.505 (50/103)*
0.777 (84/108*
Reference
NTP (1982b)
Kociba et al., 1978f
Davis and Safe, 1988'
Tritscher et al., 1992
Tritscher et al., 1992
Birnbaum et al., 1989
•in ng/kg/day
Gavage dosing
cProbablility of getting a tumor prior to the end of the study
^Number with tumor/number examined for the tumor
'Significantly different form control response (p<0.05)
fTCOD in diet
"Surviav-adjusted using poly-3 adjustment of Portier and Bailer (1989)
^Number with tumor/poly-3 adjusted number at risk of tumor
'Single intraperitoneal injection
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dramatic is the infinite change in response from control (0.00) to low dose
(0.019) for cleft palate in the Birnbaum et al. (1989) study. Thus, using this
measure of relative potency, cleft palate is the most sensitve endpoint with
liver cancer second and the immunological response third. Thus, a second
practical measure of potency, changes relative to background, paints a very
different picture than does the use of statistical p-values and is sensitive to
zero response in the control population. In addition, you have to scale between
relative drops and relative increases; the one bounded by 0 and the other
unbounded (although for practical purposed, this is easily dealt with). Finally,
one could also use absolute (rather than relative change over background), but
then you would be faced with the problem of determining how a change of one unit
in PFC response relates to a change of one unit in CYPIA1 response.
The only (eventually) practical manner in which a relative comparison of
potency can be made is in terms of mortality. In this way, all responses are in
the same units and have a common control response in terms of the background
mortality in the population. However, this approach is currently infeasible.
There are both technical and practical considerations which must be ironed out
before an approach of this type can be applied. On the technical side, there are
issues concerning life expectancy versus incidence of death. For example,
suppose TCDD increased resorptions by 5% at some chosen dose. This would
represent a 5% increase in mortality for potential fetuses and an overall loss
of 5% of the total available number of animal lifetimes (resulting in a 5% drop
in life expectancy). Suppose also that this same dose of TCDD increased
mortality from cancer so that by the end of the study, an additional 20% of the
animals have died. Suppose also that this increase in mortality is late in life
so that the overall drop in life expectancy is only 5%, Thus, on one scale (life
expectancy), the two endpoints, resorptions and cancer, produce the same results,
wheras on another scale, mortality by age, they are different. This is
illustrated in Rogan et al. (1990??). It is unclear which measure is most
appropriate for ranking the observed effects of TCDD. On the more practical,
biological side of the issue, one must relate increases in tumor incidence to
changes in mortality , modifications in immune response and/or protein
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concentrations to changes in mortality, etc. The information needed to model
these relationships is currently unavailable.
This is not to say that statements concerning the relative importance of
certain endpoints for toxicity from exposure to TCDD can not be addressed.
However, caution must be used in this endeavor and one must be careful to explain
the methods by which the relative potencies were established. When this is done,
the gaps in our knowledge become obvious and future research can be better
directed to fill those gaps.
8.4. RELEVANCE OF ANIMAL DATA FOR ESTIMATING HUMAN RISKS
The reliability of using animal data to estimate human risks has been
questioned and this issue is especially important for TCDD. We know there are
unusually wide species differences in acute toxic responses to TCDD, but we do
not know if such wide differences exist for carcinogenic and other toxic effects.
However, we do not know that the rank order of species differences in acute
effects does not predict the rank order for all other toxic effects. For
example, mice appear to be considerably more sensitive than rats to the
teratogenic and immunotoxic effects of TCDD, but we do not know if dose-response
relationships for immunotoxic effects in humans resemble those for rats, mice or
neither.
Although the human data is limited, it does appear that animal models are,
in general, appropriate for estimating human risks, keeping in mind that for some
responses, where wide species differences exist, the relative placement of human
responses may not be possible at this time. However, humans contain a fully
functional Ah receptor (Okey, ???; Manchester, ???; Cook and Greenlee, ???) and
many of the biochemical effects produced by TCDD in animals also occur in humans.
Data on effects of TCDD and its analogs in humans is based on in vitro (i.e., in
culture) as well as epidemiological studies. A comparison of the effects of CDDs
and CDFs on laboratory animals versus humans is given in Table 8-10. In vitro
systems such as keratinocytes or thymocytes in culture have clearly shown that
human cells possess Ah receptors, and they respond similarly to cells derived
from rodents. Several reports in the literature suggest that exposure of humans
to dioxin and related compounds may be associated with cancer at many different
sites including malignant lymphomas, soft tissue sarcomas, thyroid tumors and
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TABLE 8-10
Similarities Between Laboratory Animals and Humans in Biological
Effects of TCDD*
Effect
Laboratory
Animals
Human or
Human Cells
In vitro
Presence of Ah receptor
Enzyme induction
Altered pattern of growth and
dif ferentation
Immunosuppression
Choracnogenic response
+
+
+
+
+
•f
+
+
+
In vivo
Presence of Ah receptor
Enzyme induction
Altered lipid metabolism
Immune effects
Cancer
Reproductive effects
Teratogenic effects
Altered epithelia cell
differentiation
Tumor promotion
+
+
+
+
+
+
+
+
+
+
+
+
+/-
+
+/-
+/-
?
?
*Source: Silbergeld and Gasiewicz, 1989
The + indicates a clear association while +/- indicates conflicting or
unclear associations; the ? indicates that nothing is known on the
effects of TCDD on the system.
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lung tumors (Fingerhut et al.f 1991; Manz et al., 1991; Zobel et al., 1991;
Saracci et al., 1991; Poole, this document). Recently three large cohort studies
performed by IARC and NIOSH have been completed (Boehringer,???}. All three
studies included individuals who were suspected of exposure to dioxin as a result
of occupational exposure. An increase in thyroid tumors was noted in the IARC
registry. Increased risk of all cancers was observed in the NIOSH registry as
well as increased risk of respiratory tract and lung cancer (Fingerhuut et al.,
1991; Manz et al., 1991). Mortality from several cancers in the Seveso, Italy,
area included biliary cancer has been reported (Bertazzi et al., 1989). Although
several earlier studies showed a lack of liver tumors in humans, the majority of
cohorts were male. Based on data obtained in rats, liver tumor formation by TCDD
is partially dependent on ovarian hormones since liver tumor incidence in dioxin-
exposed rats is markedly decreased in ovariectomized rats (Lucier et al., 1991).
However, TCDD promotion of lung tumors occurs in ovariectomized but not intact
rats (*Clark et al., 1991b). These results emphasize the importance of TCDD
endocrine interactions in the observed site specificity of cancer. The emerging
realization from the human cancer studies is that TCDD is a multisite carcinogen,
which is not unexpected if we assume that TCDD is acting like a potent and
persistent hormone agonist/antagonist. Likewise, TCDD is a multisite carcinogen
in animals (Lucier, this document).
Several non-carcinogenic effects of CDDs and CDFs show good concordance
between laboratory species and humans as well. For example, in laboratory
animals, TCDD causes altered intermediary metabolism manifested by changes in
lipid and glucose levels. In alliance with these results, workers exposed to
TCDD 7-8 years previously during the manufacture of trichlorophenol showed
elevated total serum triacylglycerides and cholesterol with decreased HDL
(Walker, 1979). Recently, the results of a statistical analysis of serum dioxin
analysis and health effects in Air Force personnel following exposure to Agent
Orange was reported (Wolfe et al., 1991). Significant associations between serum
dioxin levels and several lipid-related variables were found (percent body fat,
cholesterol, triacylglycerols and HDL). Another interesting result of these
studies was a positive relationship between dioxin exposure and diabetes, to our
knowledge the first report of such an association.
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The human to experimental animal comparison is confounded by at least two
factors: (1) For every toxic effect 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 teratogenicity, 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 CDDs and CDFs. Also, the results
of many epidemiological studies are hampered by small sample size and in many
cases the actual amounts of dioxin and related compounds in the human tissues
were not examined. However, based on the available information, it appears that
humans are sensitive to several of the toxic effects of CDDs and CDFs and that
there is good agreement with the effects observed in laboratory species.
There is also relatively good concordance in the biochemical/molecular
effects of TCDD between laboratory animals and humans. Placentas from Taiwanese
women exposed to rice oil contaminated with CDFs have markedly elevated levels
of CYPIA1 (Lucier et al., 1987; Wong et al., 1986). Comparison of these data
with induction data in rat liver suggest that humans are at least as sensitive
as rats to enzyme inductive actions of TCDD and its structural analogs (Lucier,
1991). Consistent with this contention, the in vitro
EC50 for TCDD-mediated induction of CYPIAl-dependent enzyme activities is "1.5
nM when using either rodent or human lymphocytes (Clark et al., 1992??).
However, binding of TCDD to the Ah receptor occurs with a higher affinity in rat
cellular preparations compared to humans (Lorenzen and Okey, 1991; Okey, 1989).
This difference may be related to the greater lability of the human receptor
during tissue preparation and cell fractionation procedures (Manchester et al.,
1987). In any event, it does appear that humans contain a fully functional Ah
receptor (Cook and Greenlee, 1989) as evidenced by significant CYPIA1 induction
in tissues from exposed humans and 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 stuctural analogs produce the same effects on the
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EGF receptor in human cells and tissues as observed in experimental animals.
First, incubation of human keratinocytes with TCDD decreases plasma membrane EGF
receptor and this effect is associated with increased synthesis of TGF-a (Choi
et al., 1991; Hudson et al., 1985). Second, placenta from humans exposed to rice
oil contanimated with polychlorinated dibenzofurans, exhibit markedly reduced EGF
stimulted autophophorylation of the EGF receptor and this effect occurred with
similar sensitivity as observed in rats (Lucier, 1991; Sunahara et al., 1987).
The magnitude of the effect on autophosphorylation was positively correlated with
decreased birth weight of the offspring.
In summary, animal models are 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 changes to toxic responses. It is
also important to note that the mechanism of carcinogenesis (sequence of
molecular events) may be quite different at different sites. For example, the
mechanism responsible for TCDD-mediated lung cancer appears to be different that
that responsible for liver cancer.
8.5. HUMAN MODELS
8.5.1. Introduction. Unlike animal data where a recent studies have allowed
modeling for dosimetry, induced proteins, cell proliferation, and toxic effects,
human data are very sparse. With regard to toxic effects, Chapter 7 presents
recent evidence suggesting TCDD's effects on human reproduction, neurotoxicity,
diabetes and cancer. From a modeling viewpoint, male reproduction, diabetes, and
thyroid cancer appear to be good candidates for modeling from a "bottom-up"
mechanistic approach, since TCDD's effects on male serum testosterone levels, the
insulin receptor and thyroxin have been documented. These, however, remain
efforts for the future. The focus of this section will be on cancer, and
specifically liver cancer, respiratory cancer and all cancers combined. There
are two reasons for this. First, the emphasis on liver cancer in EPA's history
of TCDD regulation demands it, and it is a logical sequence to Section 8.3.
Second, the recent epidemiology evidence for respiratory cancer, soft tissue
sarcoma and all cancers combined suggests that dioxin is a human carcinogen.
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Modeling for these cancers in humans, however, requires, and receives
different approaches than has been presented in earlier sections of this chapter.
The sparsity of TCDD dose-response data for enzyme induction in humans and
especially in the human liver and lung basically limit analysis to dose-cancer
response. For liver cancer, (Section IV), the female rat dose-response is used
since there is very little evidence of TCDD induced liver cancer in humans. The
approach used is one involving interspecies toxic equivalent liver doses, with
extrapolation from a rat to human NOAEL. The modeling approach used for the
human epidemiology data for lung cancer and all cancers combined, Section 8.?.?,
involves estimating human intake dose associated with cancer response and curve
fitting both additive and multiplicative risk models to the data.
8.5.2. Modeling Toxic Effects in the Liver. One of the intrinsic features of
PB-PK descriptions is that interspecies extrapolations can be attempted with
reasonable confidence in the result, assuming that relevant changes are made in
the configuration of the model to allow for changes in physiology, metabolism and
protein binding. The limiting factor with any modeling description is however,
the availability of relevant data-sets. This is particularly true when
attempting to model the pharmacokinetics of dioxins and dioxin-like chemicals in
people. The use of a physiological pharmacokinetics description to analyze human
TCDD exposure data was first attempted by Kissel and Rombarge (1988). In this
work the authors used a fugacity approach to examine the elimination of TCDD from
humans using data derived from estimates of background exposure and tissue
levels, half-lives from Ranch-Hand veterans and a self-exposure experiment by
Poiger (Poiger and Schlatter, 1986).
This fugacity-based model attributes the distribution of TCDD as a simple
partitioning process with expected dose-independent linear kinetics. In both
rats and mice, as noted earlier, a disproportionate amount of TCDD is found in
the liver with increasing dose. This phenomenon cannot be described by simple
solubility/partitioning alone. The dose-dependent liver to fat concentration
ratio is a good indicator of this trend. In fact, analysis of data from people
exposed to CDFs, congeners of TCDD, from consumption of contaminated oil show a
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higher liver:fat ratio with increasing body burden (Carrier and Brodeur, 1991).
More complex models need to be constructed to account for the nonlinearities in
dioxin disposition.
Carrier and Brodeur (1991) constructed a toxicokinetic model for HPAH in
humans. This model is not a classical PB-PK model. The analysis begins with the
observation that the tissue distribution of dioxin-like HPAHs in people and
animals is body-burden dependent. As the body burden increases (Cbody), the
proportion of that body burden associated with the liver (Fh) increases towards
a maximum value (Fmax). This has been previous described for rats (Abraham et
al., 1988). The data from the Kociba et al. (1976) in rats, monkeys (McNulty et
al., 1982) and from people, using toxic equivalency factor conversions (Kuroki
and Masuda, 1978) was examined using an empirical, Michaelis-Menton type
saturable binding isotherm:
Liver fraction (Fh) = Fmax Cbody / (Kd + Cbody)
Considering the body burden as a surrogate for liver concentration, this equation
can be loosely interpreted as the induction of binding species in the liver as
dose increases. Indeed, analyses showed that the Kd was very similar for people
and experimental animals, possibly indicative of similar protein induction
dynamics in various species. With different dioxin-like isomers Fmax and Kd
vary, this can be thought of as changes due to different binding affinities.
This empirical model fits the observed data in various species, however it
is a fitting exercise and not an examination of underlying biology. The model
is not physiologically based. It in effect examines the steady state condition
of a two-compartment model, consisting of the liver and "the rest of the body."
The terms Cbody and Fmax are difficult to interpret in biological terms. Cbody
represents a body burden of chemical rather than a tissue concentration and the
term for maximum liver concentration Fmax is derived empirically.
In its present form the model assumes that at very low doses the hepatic
fraction is zero. This is unlikely due to the partitioning of dioxin into the
liver and the low dose binding characteristics of the Ah receptor and CYPIA2 in
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the liver. Also in this description the metabolism is modeled as saturable at
maximal induction of liver TCDD sequestration. Given the experimental data in
many species, (Olson, this document), this seems unlikely. The analysis of
human dioxin kinetics does show that the dose-response curves for and induction
of hepatic binding species for dioxin in rodents and people appear to be very
similar. If you assume that humans and rodents are also similarly sensitive in
the toxic responses to TCDD, based on liver concentrations, some simple exposure
calculations can be made. Interspecies comparisons of the daily intake of TCDD
needed to reach equitoxic concentrations in the liver have been estimated by
Carrier (1991). These are presented in Table 8-11. It is important to note that
a number of assumptions are made to make these comparisons and considerable
uncertainty exists.
The integration of the toxicokinetic description put forward by Carrier, and
the accompanying data-sets, with the more physiological approaches described for
rats by Andersen et al. (1992) and Kohn et al. (1992), will provide an
opportunity to further investigate the determinants of disposition of TCDD in
humans.
The prediction for Carrier's model of 40 pg/kg/day for a no effect dose is
"7000 times as high as that currently used by EPA but only 6-7 times higher than
that of other western countries (Kociba, 1991). However, estimates derived below
based on human studies showing increased mmortality from lung cancer and all
cancers combined suggest that in humans other organs may be for more sensitive
than the liver.
8.5.3. Lung Cancer and All Cancers Combined. Data from four recent
epidemiology retrospective cohort studies provide evidence of human
carcinogenicity of dioxin. All showed increased mortality from respiratory
cancer, the two largest (Saracci et al., 1991; Fingerhut et al., 1991) showed
increases in mortality from soft tissue sarcoma, and three showed increased
mortality from all cancers combined. The largest study with 18,000 total workers
(Saracci, 1991) showed no increase in overall cancer mortality, but those authors
have not presented the data allowing for a latent period. In the Fingerhut et
al. (19??) study the results showed significance only for the high-exposure, long
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TABLE 8-11
Rat and Human Comparison of Daily TCDD Intakes and Body and Liver
Concentration for Equitoxic Response
Parameter
Daily Intake
(TCDD eq)
Total body concentration
(TCDD eq/body)
Liver concentration
(TCDD eq/kg)
Low Intake
Rat
1 ng/kg
61 ng/kg
540
ng/kg
Human
40.7 pg/kg
70 ng/kg
540 ng/kg
High Intake
Rat
100 ng/kg
1.45 pg/kg
24 pg/kg2
Human
1 ng/kg
1.2 pg/kg
24 pg/kg2
'Source: Carrier, 1991
bRat NOAEL (Kociba et al.f 1978)
cMarked liver toxicity; tumors in rats
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latent period subcohort. Furthermore, the Saracci et al. (197?) study, unlike
the other three, provides no way to quantitatively estimate TCDD exposure to
their cohort. Based on this lack of information, this modeling exercise will be
restricted to the other three cohorts.
Kuratsune (1988) reported increased lung cancer in male victims (SMR=3.3,
based on eight cases) of the Yusho PCB and CDF contamination rice poisonings.
While there are serum measurements and 37 TEF estimates available for this
cohort, there was no actual TCDD in the contaminants. Thus, this cohort will
also not be used in the modeling effort here.
The largest of the three studies used here is the Fingerhut et al. (1991)
study of >5000 U.S. workers from 12 plants producing chemicals contaminated with
TCDD. Of 1520 workers exposed to TCDD-contaminated processes for at least 1 year
with a 20+ year latency, mortality was significantly increased for both
respiratory cancer [SMR=142; 95% C.I. 103-192] and for all cancers combined
(SMR=146; 95% C.I. 121-176). A similar-sized cohort with less than 1 year
exposure with a 20+ year latency showed no increase in either all cancers or
respiratory cancers. Manz et al. (1991), in a smaller cohort of 1200 men in an
herbicide manufacturing plant in Hamburg, Germany, also found statistically
significant increases in deaths from lung cancer (SMR=167; 95% C.I. 109-244) and
all cancers combined (SMR=139; 95% C.I. 110-175). Cancer mortality increased
both among groups with increased duration of exposure and among groups with
suspected highest levels of exposure. In the smallest of these recent studies,
Zober et al. (19??) studied three subcohorts totaling 250 workers exposed to
dioxin during an industrial accident in 1953. Of the 127 who developed either
chloracne or erythema, and who were considered among the most highly exposed, for
those with a 20+ year latent period, mortality from all cancers (SMR=201; 95%
C.I. 122-315) and from lung cancer (SMR=252; 95% C.I. 99-530) were both
statistically increased. Furthermore, the increase in total cancer deaths in all
these studies does not appear to be due totally to the increase in respiratory
deaths. The SMR's for all cancer deaths not including lung cancer remain
statistically significant in all three studies. Further details of these studies
are presented in Chapter 7.
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These findings are supported by recent animal evidence from Lucier et al.
(1991) who found lung tumors in ovariectomized but not intact female Sprague-
Dawley rats following administration of TCDD. Other animal data support the
tumor-promoting ability of dioxin in the liver and skin (Pitot et al., 1980).
Based on the evidence from these three studies, a quantitative analysis of
dioxin's cancer potency will be modeled from these data. All three of the
epidemiology studies attempted to verify dioxin levels in samples of their
working cohorts, although in all cases the subjects were tested decades after
exposure ended. Thus, with the limited information available, assumptions must
be made about both the representativeness of these sampled subjects and the dose-
response model used to estimate risk. The details are presented below.
8.5.3.1. DOSE-RESPONSE MODELS — The following analysis provides maximum
likelihood and 95% lower confidence limits of incremental cancer risk based on
the cancer death response in the lung and all cancers combined in the three
recent cohort studies (Fingerhut et al., 1991; Zober et al., 1990; Manz et al.,
1991). Both additive and relative risk models are used. This type of analysis
has been used previously with epidemiologic studies in several EPA health
assessments (e.g., methylene chloride, nickel and cadmium). For this report the
analyses will be done both separately for each study and for all studies
combined. A description of the models follows.
8.5.3.1.1. Excess or Additive Risk Model. This model follows the
assumption that the excess cause-age-specific death rate at age t due to dioxin
exposure, h,(t), is increased in an additive way by an amount proportional to the
cumulative exposure up to that age. In mathematical terms, this is
hi(t) = BXt
where \ is the cumulative exposure up to age t, and B, the parameter to be
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estimated, is the proportional increase. The total cause-age-specific rate h(t)
is then additive to the background cause-age-specific rate h0(t) as follows:
h(t) = ho(t) + h,(t)
For a group exposed at a cumulative level X:, the sum of the h(t) for all
individuals in the group yields
Nj
E
or, using summary notation,
EOJ
where Ej is the total number of expected cancer deaths in the observation period
from the group exposed to cumulative exposure X:. E0: is the expected number of
cancer deaths due to background causes (lifetable "expected" rates); W: is the
number of person-years of observation for the jth exposure group; and the
parameter B represents the slope of the dose-response model. To estimate B, the
observed number of cause-specific deaths in group j, Oj, is assumed to be
distributed as a Poisson random variable with expected value Ej. The parameter
estimate, b, can be tested for being significantly >0. A statistically
significant result is evidence of an additional cancer effect due to cumulative
dioxin exposure.
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Under the above assumptions, the solution by maximum likelihood proceeds as
follows: the likelihood equation is
N
L -
where N = the number of separate exposure groups. The maximum likelihood
estimate (MLE) of the parameter B is obtained by taking the first derivative of
the log likelihood equation, setting it equal to 0 and solving for b:
The asymptotic variance for the parameter estimate b is
£ XW / {E0j
where b is the MLE. This variance can then be used to obtain approximate 95%
upper and lower bounds for B. Lifetime incremental cancer risk estimates for
continuous exposure are estimated by multiplying b by 70 if X is in units of
lifetime continuous exposure, i.e. lifetime average daily dose (LADD).
8.5.3.1.2. Multiplicative or Relative Risk Model. This model follows the
assumption that the background cause-age-specific rate at any age t is increased
in a multiplicative way by an amount proportional to the cumulative dose up to
that age. In mathematical terms this is
h(t) - ho(t)(l + BXt)
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As above, summing over the observed and expected experience yields, for each
exposure group,
- 1 +
Again, to estimate B, the observed number of cause-specific deaths/ O:, assumed
to be a Poieson random variable, is substituted for E:. Following the same
procedure as above, the MLE, b, is the solution to
-E0jXj + (E0jXj / (1 + bXj» = 0
with asymptotic variance
N
£ (EbjXp/d ^bXj)]-1
If X: is in units of LADD, then lifetime incremental risk estimates per unit
under this model are obtained by multiplying b by the background lifetime cause-
specific risk of death, P0. The values P0 are derived using life table methods
for competing risks and 1973-1977 U.S. death rates. For lung and all cancers
combined these are 0.038 and 0.185, respectively.
8.5.3.2. EXPOSURE AND DOSE ESTIMATES — Exposure estimates are derived
from serum dioxin levels in workers sampled long after exposure ended and
extrapolated backward using a first-order model for elimination (EPA, 1992) with
a biological half-life of 7.1 years (Pirkle et al., 1989). In humans, dioxin
deposits primarily in adipose tissues at normal exposure levels, although body
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deposition dose-dependency has been shown in animal studies (see Chapter 2 and
preceding sections). In those rat studies, liver/fat concentration ratios
increase with increasing dose, due to TCDD induction of the binding protein
P4501A2 in the rat liver. However, while there is evidence that dioxin causes
some human liver toxicity (Di Domenico and Zopponi, 1986) and that the dioxin-
like compounds PCBs and dibenzofurans can also cause liver cancer in humans
(Kuratsune, 1988) (see Chapter 7), there is no evidence that TCDD induces cancer
in human liver. Thus, while Table 8-11 presents rat-to-human liver concentration
toxic equivalents for various rat exposures, this section uses only human data.
In humans, all estimates suggest that adipose tissue is the major storage
compartment. Schlatter (1991) estimated a liver/fat concentration ratio of about
1/6. With a body adipose tissue fat weight of 15% to 20% and a liver weight of
2.5%, over 90% of stored dioxin will be in adipose tissue.
Direct initial exposure to the lung in these studies is also difficult to
estimate. Both inhalation and skin absorption are the equally likely routes of
initial exposure, but the exposure scenario cannot be distinguished. Di Domenico
and Zapponi (1986) estimated that "50-90% of TCDD exposure to the Seveso
residents following the 1976 accident occurred via the dermal route but they
assumed 100% dermal and inhalation absorption. A more likely 1-10% dermal and
75% inhalation absorption estimate (U.S. EPA, 1985) would project that the
inhalation route provided the major TCDD exposure.
The data on body concentration levels in the three studies are presented in
Table 8-12. Fingerhut et al. (1991) measured serum levels adjusted for lipids
in a sample of 253 of the workers from 2 of the 12 plants approximately 21 years
after last known exposure. They found a highly statistically significant
correlation (r-0.72; p<0.0001) between the logarithm of number of years of
exposure to processes involving TCDD contamination and the logarithm of TCDD
serum levels. Based on this correlation, they divided the sample into a high-
exposure group (those exposed more than 1 year) and a low-exposure group, which
was exposed <1 year. The mean TCDD level of the low-exposure group was 69 ppt,
8-90 08/27/92
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00
Table 8-12
Measured serum TCDD levels and estimated levels at time of last occupational exposure to TCDD, based on first-order elimination kinetics and a
half life for elimination of 7.1 years
Study
Fingerhut et el., 1991
Zober et al., 1991
Sample
Surviving cohort of workers from Plants 1 and 2 tested
approximately 21 years after last occupational
exposure.
Exposed >1 year, all
Exposed >1 year, >20-year latency
Exposed <1 year, all
Exposed <1 year, >20-year latency
Sample of survivors tested 32 years after the accident.
High exposure scenario
Medium exposure scenario
Low exposure scenario
Either Chlorecne or erythema
Neither Chloracne or erythema
Sample
size
253
119
95
134
81
28
10
7
11
16
12
Concentration at test time
(PPt)
Including
Background
mean 418
median 231
mean 462
mean 69
median 24
mean 78
mean 60
median 24.5
mean 25
median 9.5
mean 25
median 8.4
mean 50
median 15
mean 25
median 5.8
Adjusted for
background
226
226°
19
19°
19.5
4.5
4.4
10
0.8
Estimated
concentration at
time of last
exposure adjusted
for background
Range 2,000-32,000°
1770
150
450
105
100
230
20
o
o>
N>
-J
VO
K)
"Background adjustment by subtracting 5 ppt from median value
Estimation based on adjusted medians, since serum levels are not normally distributed
Estimated based on median for all latency periods
"Calculated by Fingerhut et al. (1991) not adjusted for background
-------
Study
Manz et a I., 1991
Schecter et al., 1989
Schecter, 1991
TABLE 8-12 (cont.)
Sample
sample of nonselected members of cohort tested
approximately 31 years after 1954 TCP production
stopped
high exposure scenario
medium and low exposure scenario
sample of U.S. veterans of Operation Ranch Hand
sample of U.S. general population
Sample
size
48
37
11
10
100
Concentration
Including
Background
mean 296
median 137
mean 83
median 60
mean 22
median 17
Range 7-55
mean 5.2
Adjusted for
Background
123
55
12
0
Estimated Median
Concentration at Time
of Last Exposure
Adjusted for Background
Assumes no TCDO exposure after 1954
"Assumes no TCOO exposure after 1969
Continuous background exposure only, no adjustment
O
M
H
o
00
M>
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while that of the high exposure group was 418 ppt. Among the 176 workers last
exposed >20 years before, "those with under one year of exposure (N=81) had a
mean level of 78 ppt, and those with over one year of exposure (N=95) had a mean
level of 462 ppt.
For the Zober et al. (19??) study, the serum level data are based on a
nonrandom sample of 28 survivors tested 32 years after the 1953 accident. These
subjects were then classified into three groups by scenario of high (Cl), medium
(C2), or low (C3) chance of TCDD exposure, with the mean (median) levels of 60
(24.5), 25 (9.5), and 25 (8) ppt, respectively. An alternative breakdown by the
16 sample subjects who exhibited either chloracne or erythema versus those 12 who
did not yields estimates of 50 (15) and 25 (5.8) ppt.
For the Manz et al. (19??) study, serum levels were measured in 48
unselected members of the cohort who had what the authors believed to be similar
exposures to TCDD as the cohort. Workers were classified into three groups,
having either high-, medium- or low-exposure opportunities, and interviews with
these 48 members led to a division of 37 into the high group (mean 296 ppt,
median 137 ppt) and 11 into the medium and low-exposure groups combined (mean 83
ppt, median 60 ppt).
Also included in Table 8-12 are measured serum levels of U.S. veterans of
Operation Ranch Hand and a sample of 100 U.S. men from the general population.
The mean U.S. estimate of 5 ppt is identical to that reported from four controls
in the German population (Schecter et al., 1988). The Fingerhut et al. (19??)
referent controls had a mean level of 7 ppt.
Table 8-12 also presents estimates of median TCDD concentrations at time of
last exposure for the median levels of various cohorts, based on first-order
elimination kinetics, assuming a 7.1 year half-life. In order to be consistent
with the requirements of the model, background levels of 5 ppt are subtracted
from each median before back extrapolation. The formula used is:
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where
Ct = concentration at time of measurement
C0 = estimated concentration at time of last exposure
ke = elimination constant (per year)
t = years since last exposure
In order to back extrapolate from C0 to intake dose, the conversion formula
used is (C. Jenkins, Personal communication)
TO; = D't = C0'f-ket/( (a-e'ket) (RA) )
where
D = dose per body weight per year
f = fraction of adipose tissue = 0.20
t = years of exposure in period i
TDj = total dose during period i
RA = absorption fraction, assumed to be 0.5, based on 75% absorption via
inhalation and significantly less through dermal exposure
Then lifetime average daily dose (LADD) is
LADD = TD/(70 years x 365 days/year)
where
TD = Total Dose = £ TD;
An example calculation for the high exposure group of Fingerhut et al.
(19??) (average exposure = 6.8 years) yields
TD = (1770 ppt) (0.20) (0.098) . 91Q /k
l-e-(0.098)(6.8)
For body dose after external exposure to TCDD ends, the long elimination
half-life of 7.1 years leads to continuous exposure. Furthermore, it is
reasonable to assume that continued dioxin in the body will continue to elicit
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biologic effects. Thus, for the Fingerhut et al. (19??) study high exposure
group, the median adipose tissue concentration for the 21-year post-exposure
period is estimated as:
and average equivalent intake dose per year is:
- =300ng/kg-year
Then total body equivalent intake during the post exposure period is
, 300ng/kg
and the total dioxin dose intake is estimated as
TD = 970 + 6,300 - 7,270 ng/kg
On a per day intake basis, this LADD is (eq 8.4.3-2) 0.284 ng/kg-day. Estimates
for the other subcohorts are also presented in Table 8-13.
This conversion from serum ppt to dose essentially uses the body
concentration x time dose metric to account for the long duration time of dioxin
in the human body. Estimation of risks could also be made on the basis of
estimated dose during the exposure period only, but this would probably
substantially overstate the risks.
8.5.3.3. CALCULATION OP RISK ESTIMATES — Table 8-14 presents the LADD '8
from Table 8-13, the estimated relative risks, and sample size information for
the various subcohorts. Whenever the data could be found, the subcohorts with
at least 20 year latency are presented, in order to coincide as closely as
8-95 08/27/92
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TABLE 8-13
Estimate of Total Dose based on Adjusted Median Concentration at Test Times, Estimated Concentrations
at Last Exposure, and Average Duration of Exposure, by Study Cohort
Study
Fingerhut et al.,
1991
Zober et al.,
1990
Manz et al., 1991
Cohort
exposed >1 year, all
exposed >1 year, 20
year latency
exposed <1 year, all
exposed <1 year, >
20 year latency
high exposure
scenario
medium exposure
scenario
low exposure
scenario
either chloracne or
erythema
neither chloracne
nor erythema
high exposure
scenario
medium and low
exposure scenario
Adjusted"
Median Co
1770
150
450
105
100
230
20
2750
1150
Average
Exposure
Furation
(years)
6.8
6.8
0.3
0.3
0.1C
0.1
0.1
0.1
0.1
2d
2
Dose During
Exposure
Period'
(ng/kg)
970
60
180
40
40
90
8.0
1210
510
Number Years
Post -exposure
to End of
Follow-up
21
21
21
21
32
32
32
32
32
31
31
Dose During
Post -exposure
Period
(ng/kg)a
6300
530
1760
410
390
900
80
10,350
4330
Total
Dose
(ng/kg)
7270
680
1940
450
430
990
88
11560
4840
Lifetime6
Average
Daily Dose
(ng/kg/day)
0.284
0.026
0.076
0.018
0.017
0.039
0.003
0.452
0.189
o
o
I
M
§
o
M
W
00
I
«o
o
00
10
-J
to
to
"Equation 8.4.3-1
Equation 8.4.3-2
°Most exposure assumed due to accident
Estimated based on described TCP production period
'From Table 6
-------
TABLE 14
Estimated Lifetime Average Daily Doses and Relative Risks by Individual Study Cohort
Study
Fingerhut et al.
(1991)
Zober et at.
(1990)
Manz et al.
(1991)
Cohort
exposed >1 year all
exposed 1 year, > 20 year
latency
exposed <1 year, S 20 year
latency
(>20-year latency)
high-exposure scenario
medium exposure scenario
low exposure scenario
either ch I or acne or erythema
neither chloracne nor
erythema
(>20-year latency)
high -exposure exposure
scenario
medium and low exposure
scenario
LADD
(ng/kg day)
0.284
0.026
0.076
0.016
0.017
0.039
0.003
0.452
0.189
Cohort Size
Ni
5172
1520
1516
57
74
81
109
103
96"
200"
Person
Years
116,748
15,136
12,299
673
524
676
1199
674
No Data
No Data
Respi ratory
Cancer Deaths
DBS. EXP. REL. RISK
96 84.5 1.13
43 30.2 1.42
19 18.4 1.03
3 1.25 2.52
2 1.03 1.94
0 0.97 0
5 2.09 2.39
0 1.16 0
N = 1148
26 15.6 1.67
All Cancer Deaths
DBS. Exp. Rel. Risk
265 229.9 1.15
114 78.0 1.46
48 46.8 1.02
7 4.20 1.67
8 3.37 2.38
1 3.39 0.29
14 6.96 2.01
2 4.00 0.50
(entry before 1955)
16 5.72 2.77
27 17.3 1.56
H
I
O
O
1
s
w
o
M
H
M
00
I
to
-J
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"Entry before 1955
Assumed because of entry before 1955; actual data unavailable
VO
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DRAFT—DO NOT QUOTE OR CITE
possible with the Fingerhut et al. (19??) cohort. For the Manz et al. (19??)
study no data on person-years at risk are available, so only the relative risk
model could be used to estimate risk. For the other studies, both models could
be used. The data are shown in Figure 8-7 and indicate trends with increasing
LADD's for all three studies and for both respiratory cancers and all cancers
combined.
Calculations of the incremental unit risk estimates for lung cancer and all
cancers combined are presented in Tables 8-15 and 8-16, respectively, for each
of the three cohorts separately and all cohorts combined, for both the additive
and multiplicative risk models. The results show statistically significant
estimates of the slope parameter for the Fingerhut (19??) and the Manz (19??)
study and for all studies combined. While the slope estimates for both the Manz
(19??) and Zober (19??) studies are greater than those for the Fingerhut study,
the Fingerhut (19??) data provides the bulk of the weight. Thus, the estimates
from the combined studies are closer to those based on the Fingerhut (19??) study
alone than to the others.
Also shown in Tables 8-15 and 8-16 are estimates of the lifetime incremental
cancer risk for 1 pk/kg-day LADD intake. These are derived by substituting the
MLE estimates of B back into the age-specific hazard rates and deriving lifetime
incremental risk estimates based on lifetable probabilities with competing risks
(for practical purposes the procedure described in the Table footnotes produces
the same results). For lung cancer these unit risk estimates range from 3xlO"5
to 2xlO"3 (pg/kg-day)"1, with the estimates for all studies combined between 6xlO"5
and IxlO"4 (pg/kg-day)"1. For all cancers combined the range of MLE estimates is
between 3xlO"4 and l.OxlO"2 (pg/kg-day)"1 with the estimates based on all studies
combined between 3xlO"4 and 5xlO"4 (pg/kg-day)"1. These estimates from all studies
combined (both lung cancer and total cancers) range from 6xlO"5 to 6xlO"4
(pg/kgday)"1. They border the upper-limit estimate of l.exlO"4 (pg/kg-day)"1
previously derived by EPA (U.S. EPA, 1985) based on the total cancer response in
the female Sprague-Dawley rat in the Kociba et al. (1978) lifetime feeding study,
and the IMS. Using the same Kociba (19??) study and IMS model but with the liver
histopathology readings from a recent reanalysis (Sauer and Goodman, 1992) and
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Q
FIGURE 8-7
Relative risks of lung cancer and all cancer mortality in three recent
cohort studies of workers exposed to TCDD by estimated lifetime average daily
dose intake.
Source: LADD, 197?
8-99
08/27/92
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TABLE 15
Calculation of Incremental Unit Cancer Risk Estimates and 95X Lower Limits for both the Additive and Relative
Risk Models based on the Lung Cancer Deaths Response in the Fingerhut, Zober and Manz Studies
Study
Fingerhut et al.,
1991
Zober et a I.,
1990
Manz et al.,
1991C
All studies
combined
Model Used
additive
multiplicative
additive
H-M-L Scenario
Chloracne or
not
multiplicative
H-M-L Scenario
Chloracne or not
multiplicative
additive
multiplicative
Parameter
Estimates
b8
2.96x10"3
1.491
2.78x10'2
5.78x10'2
16.20
33.20
2.01
3.09x10'3
1.541
Asymptotic
Variance
Estimates
2.30x10'6
0.580
8.69x10"4
2.18x10'3
269.2
717.0
1.14
2.32x10'6
0.586
p- Value for
Slope
Estimate*
0.02
0.02
0.17
0.11
0.16
0.11
0.03
0.02
0.02
Lifetime Incremental Cancer Risk per 1 pg/kg-day
LADD intake
95X
Lower Maximum
Limit
0
0
0
0
0
0
0
3.7x10-°
1.5x10-e
Likelihood Estimate
l.lxlO'4
5.7x10'5
I.OxlO'3
2.0x10'3
6.1x10'4
1.3x10'3
7.6x10'5
1.1x104
5.9x10'6
oo
i
o
o
o
O
o
G
O
H
M
8
n
o
CD
M
-J
"Estimates for additive risk model gives in (ng/kg-day)"1 . Estimates for multiplicative risk model given in (ng/kg-day)'1/h0(t) where h0(t)
background age-specific hazard rate
for additive risk model approximated by multiplying by 35 since risk before age 35 is close to zero. MLE for multiplicative risk model
approximated by multiplying P0=0.038
^ata set in Table 8.4.3-3 with estimated LADD = 0.3 ng/kg - day
Fingerhut and Zober (chloracne) cohorts only
80ne sided
-------
TABLE 16
Calculation of Incremental Unit Risk Estimates and 95% Lower Limits for Both the Additive and Relative Risk Models
Based on the Total Cancer Deaths Response in the Fingerhut, Zober and Hanz Studies
Study
Fingerhut et al., 1991
Zober et al., 1990
Manz et a I.. 1991
All studies combined
Model Used
additive
multiplicative
additive
H-M-L scenario
chloracne or not
multiplicative
H-M-L scenario
chloracne or
not
multiplicative
additive0
multiplicative**
Parameter
Estimates
ba
8.32x1CT3
1.62
5.80x10'*
1.44X10'1
10.11
24.70
3.53
8. 57x1 0'3
2.07
Asymptotic
Variance Estimates
6.2x10'6
0.230
2.4x10~3
6.2x10'3
64.6
184.3
1.22
6.1x10'6
0.198
p-Value
for Slope
Estimate"
0.0004
0.0004
0.12
0.03
0.11
0.03
0.001
<0.001
<0.0001
Lifetime Incremental Cancer Risk
per 1 pg/kg-day LADD intake
95%
Lower Maximum Limit
2.4x10'4
1.3x10'4
0
0
0
0
5.6x10'4
2.6x10'4
2.2x10'4
Likelihood
Estimate
5.8x10'4
3.0x10'4
4.0x10"4
1.0x10'2
1.9x10'3
4.6x10'3
6.5x10'4
6.0x10'4
3.4x10'4
oo
i
H
a
o
z
o
I
n
o
M
H
"Estimates for additive risk model given in (ng/kg-day"1); estimates for multiplicative risk model given in (ng/kg-day)"1/h0(t) where h0(t) =
background age specific hazard rate
We for additive risk model approximated by multiplying b by 70; MLE for multiplicative risk model approximated by multiplying by P0=0.185
cFingerhut and Zober (chloracne) cohorts only
dFingerhut, Zober (chloracne) and Manz
"One sided
o
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original Kociba readings for the other tumor sites, the upper-limit estimate is
O.SxlO"4.
8.5.3.4. LOW-DOSE DEVIATION FROM LINEARITY — Based on the LADD's and the
relative risk estimates presented in Table 8-14 some idea of the degree of
nonlinearity in the dose response for these cancers can be derived. For the
Fingerhut et al. (19??) cohort the ratio of 10.9 for high to low LADD's
(0.284/0.026) corresponds to a ratio of increased risk of 14 (0.42/0.03) for
respiratory cancer and 23 (0.46/0.02) for all cancer mortality combined. For the
Manz et al. (19??) cohort, the comparisons are also consistent with linearity or
Blight sublinearity; the LADD ratio of 2.4 (0.452/0.189) corresponds to an
increased risk ratio of 3.2 (1.77/0.56). The Zober et al. (19??) chloracne
versus no chloracne cohort also suggests some low dose sublinearity, but no
quantitative comparisons can be derived, since the relative risk estimates for
the low-dose group are <1. For the Saracci et al. (19??) cohort, no direct
comparison can be made, either, except to note that the relative risk for lung
cancer for the low-exposure group was actually higher than that for the high-
exposure group. Thus, for three of the cohorts there is some suggestion of
sublinearity in dose response, but for the largest cohort the suggestion is the
opposite.
Estimates of LADD's of TCDD intake in the general U.S. population range from
0.3-1.0 pg/kg-day (U.S. EPA, 1992). The LADD's estimated in these studies range
from 3-452 pg/kg-day above background with increased risks seen >20 pg/kg-day.
The LADD estimates, themselves are just too imprecise for more definitive
statements.
8.6. CONCLUSIONS
Epidemiology studies suggest that the lung in the human male is a much more
sensitive target organ for TCDD than is the liver, and that the human is a
sensitive species for cancer response. The studies also show increases for all
cancers combined. Estimates derived from the data suggest that a range of
estimated unit risk for lung cancer is 6xlO"5 to IxlO"4 (pg/kg-day)"1. For all
cancers combined the range of unit risk is 3xlO"4 to 6x10"* (pg/kg-day)"'.
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Estimates based on mortality from all cancers suggest that humans are also
sensitive to an overall carcinogenic effects of TCDD.
8.7. KNOWLEDGE GAPS
Considerable information is now available on the mechanisms of action
responsible for TCDD's effects in experimental animals and humans and important
new information is now being generated. These data are, of course, essential to
the development of reliable biologically-based models for the estimation of human
risks as a consequence of exposure to TCDD and its structural analogs such as the
CDFs and co-planar PCBs. Uncertainty in such models reflect incomplete knowledge
of mechanisms and inadequacies in exposure/tissue dose relationships. In the
process of developing and evaluating biologically-based models we can identify
those knowledge gaps which create uncertainty. The idea that interactions of
TCDD with the Ah receptor is an essential first step in most, if not all, of
dioxin's effects has been considered as a reasonable assumption for over a
decade. The recent Banbury Conference on dioxin formalized this as a general
consensus among dioxin researchers. The development of models which accurately
predict risks also require tissue and cell dosimetry data in experimental animals
and humans. This kind of dosimetry information is available for blood, liver and
adipose tissue, but dosimetry data in other target tissues such as the lung,
skin, pituitary and reproductive tract is not available or incomplete. It would
be especially relevant to the development of biologically-based dose response
models to have dosimetry data (relationship between exposure, dose and cell
specific dose) in target cells when the target cell is known. For example, the
lung is comprised of numerous cell types but the identity of the target cell(s)
for TCDD-mediated lung cancer are not known nor is there much data on dose-
response relationships for concentrations of TCDD in whole lung or discrete cell
types. Since the vast majority of dioxin is found in the liver and adipose
tissue under chronic exposure-steady state conditions and the lung is clearly a
target organ for biochemical and toxic effects, it would seem that the lung and
perhaps other organs require far less tissue/cell levels of dioxin to exhibit
toxic effects than in liver.
One of the most confounding yet important knowledge gaps in the development
of mechanistic models is the evaluation of the adverse health consequences, if
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any, of current background exposure to the CDDs and CDFa, which is estimated at
1-3 pg TCDD equivalents/kg/day. More accurate information on the potency of
dioxin like PCBs is also an essential component in evaluating the health impact
of background exposure to chemicals which bind the Ah receptor.
Many of the molecular events that follow binding of TCDO to the Ah receptor
are now known for transcriptional activation of the CYPIA1 gene. However, there
is little information on the characterization of analogous events for dioxin's
many other effects on gene expression such as Ah receptor-mediated alterations
in the EGF or estrogen receptor. Most of the mechanistic or dose-response
information on dioxin's effects has been generated on changes in gene expression
of single genes such as CYPIA1 induction. There is only limited information on
the complex interaction of biochemical, molecular and biological events that are
necessary to produce a frank toxic effect such as cancer, developmental defects,
reproductive effect or neurological effects. Figure 8-8 from Greenlee et al.
(1992) summarizes the series of interconnected steps within the three major
components of receptor-mediated events (recognition, transduction and response).
Although this scheme is simplified (i.e., each step may be comprised of several
events) it does provide a framework for identifying knowledge gaps that create
uncertainty. Clearly interactions with other endocrine systems are involved in
some effects and our ability to construct accurate dose-response models for non-
cancer endpoints would be enhanced if we had a better understanding of
TCDD/endocrine interactions.
One of the more active areas of research on hormone action is directed at
identifying the cell specific factors which produce diversity of responses for
receptor-mediated responses, that is, how does a single receptor and a single
ligand produce the wide spectrum of cell specific responses characteristic of
exposure to a given hormone. Since TCDD is acting like a potent and persistent
hormone agonist/antagonist, the mechanisms responsible for qualitative and
quantitative differences in dose-response relationships for Ah receptor-mediated
events might be similar to those mechanisms identified for steroid hormones.
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Dioxin Exposure
4
Free Dioxin in Tissues
4
Dioxin Binding to the [RECOGNITION]
Ah receptor in Tissue
4
Ah receptor - Dioxin
Comples Binding with [TRANSDUCTION]
DNA
4.
Gene Regulation
i
n-RNA Regulation
4.
Protein synthesis
4
Biochemical Alterations[RESPONSE)
4
Early Cellular Responses
(cell growth stimulation)
4
Late (Irreversible) Tissue
Response (cancer, terata)
FIGURE 8-8
Biologically Based Risk Assessment Approaches for Dioxin:
Filling the Gaps
Source: Greenlee et al. 1992
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Fuller (1991) has summarized some of the mechanisms responsible for generating
diversity and these are listed below:
ligand< agonist, antagonist
target tissue< receptor gene expression
activating or inactivating enzymes
binding proteins (extra of intracellular)
receptor< cytoplasmic versus nuclear
isoforms-differential
splicing
gene duplication
dimers< hetero or homodimers
DNA binding factors
nuclear factors< antagonist isoforms
squelching
response elements< consensus versus nonconsensus
number of copies
position
proximity of other response elements
transactivation< gene specific factors
cell specific factors
In addition to the above considerations, there is considerable speculation
regarding the normal cellular functions of the Ah receptor and the identity of
any endogenous ligands for the Ah receptor. If sound scientific information were
available on the normal functions of the receptor, especially if those functions
involve regulation of cell proliferation and differentiation, it would greatly
enhance our ability to predict the health consequences of low level dioxin
exposure. It would also help considerably in the selection of appropriate animal
models for estimating dioxin risks.
Interindividual variation in human responses to TCDD and its structural
analogs is one of the most difficult issues to accommodate in the development of
biologically-based dose-response models. We know from epidemiology studies that
some individuals develop chloracne from a given exposure to dioxin whereas other
individuals exposed to the same amount of dioxin do not develop chloracne. The
mechanisms responsible for sensitivity or resistance to the chloracnegenic
actions of dioxin are not known nor is there any information on the relationship
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of chloracne to other toxic effects. For example, are individuals who are
susceptible to chloracne also susceptible to the carcinogenic actions of dioxin.
Likewise, there are considerable differences in the magnitude of enzyme induction
when human cells are cultured with dioxin. We need to understand the molecular
mechanisms responsible for these differences and whether high inducers are more
or less susceptible to the toxic effects of dioxin and its structural analogs.
These kinds of data would allow the development of epidemiologic and laboratory
approaches for evaluating health consequences in both sensitive or resistant
populations.
In summary, we have gained considerable and valuable insights regarding
mechanism of dioxin and dose-response relationships for dioxin effects. These
data are not yet complete but are appropriate for the development of preliminary
biologically-based models that may eventually be useful for estimating dioxin's
risks to humans. When sufficiently developed these models should provide
increased confidence and decreased uncertainty than are present with the current
default approaches (LMS or safety factor). They should also accommodate new
scientific information from research directed at filling knowledge gaps to
further reduce uncertainty. Based on the model structures presented in this
chapter it should be possible to design specific experiments to fill key
knowledge gaps.
Specific experiments should be developed as part of the peer-review process
to fill knowledge gaps that limit application of dose-response models.
8.8. REFERENCES
Abbott, B.D., M.W. Harris and L.S. Birnbaum. 1992. Comparisons of the effects
of TCDD and hydrocortisone on growth factor expression provide insight into their
interaction in the embryonic mouse palate. Teratology. 45: 35-53.
Abbott, B.D. and L.S. Birnbaum, L.S. 1991. TCDD exposure of human embryonic
palatal shelves in organ culture alters the differentiation of medial epithelial
cells. Teratology. 43: 119-132.
8-107 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Abbott, B.D. and Birnbaum, L.S. 1990. Effects of TCDD on embryonic ureteric
epithelial EOF receptor expression and cell proliferation. Teratology. 41:
71-84.
Abbott, B.D. and L.S. Birnbaum. 1990a. Rat embryonic palatal shelves respond
to TCDD in organ culture. Toxicol. Appl. Pharmacol. 103: 441-451.
Abbott, B.D., J.J. Diliberto and L.S. Birnbaum. 1989. 2,3,7,8-tetrachlor-
dibenzo-p-dioxin alters embryonic palatal medial epithelial cell differentiation
in vitro. Toxicol. Appl. Pharmacol. 100: 119-131.
Abbott, B.D. and Birnbaum, L.S. 1989. TCDD alters medial epithelial cell
differentiation during palatogenesis. Toxicol. Appl. Pharmacol. 99: 276-288.
Abraham, K., R. Krowke and D. Neubert, D. 1988. Pharmacokinetics and biological
activity of 2,3,7,8-tetrachlordibenzo-p-dioxin: 1. Dose-dependent tissue
distribution and induction of hepatic ethoxyresorufin O-deethylase in rats
following a single injection. Arch. Toxicol. 62, 359-368.
Adair, G.S. 1925. JBC 63, 529.
Agrawal, A.K., H.A. Tilson andS.C. Bondy. 1981. 3,4,3',4'-Tetrachlorobiphenyl
given to mice prenatally produces long-term decreases in striatal dopamine and
receptor binding sites in the caudate nucleus. Toxicol. Lett. 7: 417-424.
Andersen, M.E., H.J. Clewell, III, M.L. Gargas, F.A. Smith and R.H. Reitz. 1987.
Physiologically based pharmacokinetics and risk assessment process for methylene
chloride. Toxicol. Appl. Pharmacol. 87: 185-205.
Andersen, M.E., K. Krishnan, R.B. Conolly and R.O. McClellan. 1992. Mechanistic
toxicology and biologically-based modeling: Partners for improving quantitative
risk assessments. CUT Activities. 12: 1-7.
8-108 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Andersen, M.E. and W.F. Greenlee. 1991. Biological determinants of
2,3,7,8-TCDD pharmacokinetics and their relation to a biologically-based risk
assessment. In; Banbury Report 35: Biological Basis for Risk Assessment of
Dioxins and Related Compounds, M. Gallo, R. Scheuplein and K. Van der Heijden,
Ed. Cold Spring Harbor Laboratory Press, p. 291-303.
Andersen, M., J. Mills, L. Kedderis, D. Neubert and W. Greenlee, W. 1992.
Modeling receptor-mediated protein induction by dioxin: Implications for
pharmacokinetics and risk assessment. Submitted to J. Risk Analysis.
Andersen, M.E., J.J. Mills, M.L. Gargas et al. 1992. Modeling receptor-mediated
processes with dioxin: implications for pharmacokinetics and risk assessment. J.
Risk Analysis. (In review)
Ailey and Anesen, 1952
Armitage, P. and R. Doll. 1954. The Age distribution of cancer and a multistage
theory of cancer. Br. J. Cancer. 8: 1-12.
Armitage, P. and R. Doll. 1957. A two-stage theory of carcinogenesis in
relation to the age distribution of human cancer. Br. J. Cancer: 11: 161-169.
Astroff, B. and S. Safe. 1988. Comparative antiestrogenic activities of
2,3,7,8-Tetrachlorodibenzo-p-dioxin and 6-methyl-l,3,8-trichlorodibenzofuran in
the female rat. Toxicol. Appl. Phramacol. 95: 435.
Bertazi, P.A., C. Zocchetti, A.C. Pesatori, S. Guercilena and L. Radice. 1989.
Ten-year mortality study of population involved in the Seveso incident in 1976.
Am. J. Epidemiol. 6: 507.
8-109 OB/21/92
-------
DRAFT—DO NOT QUOTE OR CITE
Birnbaum, L.S. 1991. Developmental toxicity of TCDD and related compounds:
Species sensitivities and differences. In; Banbury Report 35: Biological Basis
for Risk Assessment of Dioxins and Related Compounds, M. Gallo, R. Scheuplein and
K. Van der Heijden, Ed. Cold Spring Harbor Laboratory Press, p. 51-67.
Birnbaum, L.S., H.W. Harris, E.R. Barnhart and R.E. Morrissey. 1987a.
Teratogenicity of three polychlorinated dibenzofurans in C57BL/6N mice. Toxicol.
Appl. Pharmacol. 90: 206-216.
Birnbaum, L.S., M.W. Harris, D.D. Crawford and R.E. Morrissey. 1987b.
Teratogenicity of three polychlorinated dibenzofurans in combination in C57BL/6N
mice. Toxicol. Appl. Pharmacol. 91: 246-255.
Birnbaum, L.S., R.E. Morrissey and M.W. Harris. 1991. Teratogenicity of
2,3,7,8-tetrabromodibenzo-p-dioxin and three polybrominated dibenzofurans in
C57BL/6N mice. Toxicol. Appl. Pharmacol. 107: 141-151.
Boehringer
Boeymans and Dumont 1980)
Bookstaff, R.C., R.W. Moore and R.E. Peterson. 1990a. 2,3,7,8-Tetrachloro-
dibenzo-p-dioxin increases the potency of androgens and estrogens as feedback.
inhibitors of luteinizing hormone secretion in male rats. Toxicol. Appl.
Pharmacol. 104: 212-221.
Bookstaff, R.C., F. Kamel, R.W. Moore, D.L. Bjerke and R.E. Petersen. 1990b.
Altered regulation of pituitary gonadotropin (GnRH) receptor number and pituitary
responsiveness to GnRH in 2,3,7,8-TCDD-treated male rats. Toxicol. Appl.
Pharmacol. 105: 78.
Bradlow
8-110 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Burbach, K.M., A. Poland and C.A. Bradfield. 1992. Cloning the Ah receptor
CDNA. Toxicologist. 12: 194.
Carrier, G. and J. Brodeur. 1991. Non-linear toxicokinetic behavior of TCDD-
like halogenated polycyclic aromatic hydrocarbons (H-PAH) in various species.
Toxicologist. 11: 895.
Carrier, G. 1991. Response de 1'organisme human aux BPC, dioxines et furannes
et analyse des risques toxiques. Le Passeur Press, Canada. (Fre.)
Choi, E.J., D.G. Toxcano, J.A. Ryan, N. Riedel and W.A. Toscano. 1991. Dioxin
induces transforming growth factor-alpha in human keratinocytes. J. Biol. Chem.
266: 9591-9597.
Chou, S.M., T. Miike, W.M. Payne and G.J. Davis. 1979. Neuropathology of
"spinning syndrome" induced by prenatal intoxication with a PCB in mice. Ann.
NY Acad. Sci. 320: 373-395.
Clark, G.C., J.A. Blank, D.R. Germolec and M.I. Luster. 1991. 2,3,7,9-Tetra-
chlorodibenzo-p-dioxin stimulation of tyrosine phosphorylation in B lymphocytes:
Potential role in immunosuppression. Mol. Phamacol. 39: 495-501.
Clark, G., A. Tritscher, R. Haronpot, J. Foley and G. Lucier. 1991. Tumor
promotion by TCDD in female rats. In; Banbury Report 35: Biological Basis for
Risk Assessment of Dioxin and Related Compounds, M. Gallo, R. Scheuplein and K.
Van der Heijden, Ed. Cold Spring Harbor Press.
Clark, 1992
Clewell, H.J. and M.E. Andersen. 1985. Risk assessment extrapolations and
physiological modeling. Toxicol. Ind. Health. 1: 111-131.
Colquhon 1979)
8-111 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Conolly, R.B. and M.E. Andersen. 1992. An approach to mechanisms-based risk
assessment: Formaldehyde. Environ. Health Perspect. (In press)
Conolly, R.B. and M.E. Andersen. 1991. Biologically-based pharmacodynamic
models: Tools for toxicological research and risk assessment. Ann. Rev.
Pharmacol. Toxicol. 31: 503-523.
Cook, J.C. and W.F. Greenlee. 1989. Characterization of a specific binding
protein for 2,3,7,8-tetrachlorodibenzo-p-dioxin in human epithelial cells. Mol.
Pharm. 35: 713-719.
Couture, L.A., M.W. Harris and L.S Birnbaum. 1989. Developmental toxicity of
2,3,4,7,8-pentachlorodibenzofuram in the Fischer 344 rat. Fundam. Appl. Toxicol.
12: 358-366.
Couture-Haws, L.A., M.W. Harris, A.C. Lockhart and L.S. Birnbaum. 1991.
Evaluation of the persistence of hydronephrosis induced in mice following in
utero and/or lactational exposure to tetrachlorodibenzo-p-dioxin (TCDD).
Toxicol. Appl. Pharmacol. 107: 402-413.
Cuthill, S., A. Wilhelmsson, G.G.F. Mason, M. Gillner, L. Poellinger, J.A.
Gustafsson. 1988. The dioxin receptor: A comparison with the glucocorticoid
receptor. J. Steroid Biochem. 30: 277-280.
Pato and Nakajima 1978)
Davis, D. and S. Safe. 1988. Immunosuppressive activities of polychlorinated
dibenzofuran congeners: Quantitative structure-activity relationships and
interactive effects. Toxicol. Appl. Pharmacol. 94: 141-149.
Delp, M.D., R.O. Manning, J.V. Bruckner and R.B. Armstrong. 1991. Distribution
of cardiac output during diurnal changes of activity in rats. Am. J. Physiol.
261(Heart Circ. Physiol., 30): H1487-H1493.
8-112 OB/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Dencker, L. and R.M. Pratt. 1981. Association between the presence of the Ah
receptor in embryonic murine tissues and sensitivity to TCDD-induced cleft
palate. Teratogen. Carcinogen. Mutagen. 1: 399-406.
Denison, M.S., J.M. Fischer and J.P. Whitlock. 1988. The DMA recognition site
for the dioxin-Ah receptor complex: Nucleotide sequence and functional analysis.
J. Biol. Chem. 263: 17221.
DeVito, M.J., T. Thomas, E. Martin, T. Umbreit and M.A. Gallo. 1992.
Antiestrogenic action of 2,3,7,8-Tetrachlorodibenzo-p-dioxin: Tissue specific
regulation of estrogen receptor in CD1 mice. Toxicol. Appl. Pharmacol. 113:
284-292.
DeVito, M.J., T. Umbreit, T. Thomas and M.A. Gallo. 1991. An analogy between
the actions of the Ah receptor and the estrogen receptor for use in the
biological basis for risk assessment of dioxin. In; Banbury Report 35:
Biological Basis for Risk Assessment of Dioxins and Related Compounds, M. Gallo,
R. Scheuplein and K. Van der Heijden, Ed. Cold Spring Harbor Press, p. 427-440.
DeVito, M.J., T. Thomas, T.H. Umbreit and M.A. Gallo. 1990. Antiestrogenicity
of TCDD involves the downregulation of the estrogen receptor mRNA and protein.
Toxicologist. 10: 981.
Di Domenico, A. and A. Zapponi. 1986. 2,3,7,8-Tetrachlorodibenzo-p-dioxin
(TCDD) in the environment: Human health risk estimation and its application in
the Seveso case as an example. Reg. Toxicol.Pharmacol. 6: 248-260.
DiGiovanni, J., A. Viaje, D.L.Berry, T.J. Slaga and M.R. Junchau. 1977. Tumor-
Initiating ability of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and Arochlor
1254 in the two-stage system of mouse skin carcinogenesis. Bull. Env. Contain.
Toxicol. 18: 552-557.
Ema 1992
8-113 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Evans, R.M. 1988. The steroid and thyroid hormone superfamily. Science.
p. 240, 889.
Fine, J.S., A.E. Silverstone and T.A. Gasiewicz. 1991. Impairment of
prothymocyte activity by 2,3,7,8-tetrachlorodibenzo-p-dioxin. J. Immunol. 144:
1169-1178.
Fisher and Holloman
Fuller, P. 1991. The steroid receptor superfamily: mechanisms of diversity.
FASEB. 5: 3092-3099.
Gaido, K.W., S.C. Maness, L.S. Leonard and W.F. Greenlee. 1992. TCDD-dependent
regulation of transforming growth factors-a and TGF-b2 expression in a human
keratinocyte cell line involves both transcriptional and post-transcriptional
control. J. BIol. Chem. (In press)
Gallo, M.A., E.J. Hesse, G.J. MacDonald and T.H. Umbreit. 1986. Interactive
effects of estradiol and 2,3,7,8-tetrachlorodibenzo-p-dioxin on hepatic
cytochrome P450 and mouse uterus. Toxicol. Lett. 32: 123.
Gargas, M.L., R.J. Burgess, D.E. Voisard, G.H. Cason and M.E. Andersen. 1989.
Partition coefficients of low molecular weight volatile chemicals in various
liquids and tissues. Toxicol. Appl. Pharmacol. 98: 87-99.
Gasiewicz, ARNT
Gasiewicz, T.A., C.J. Elferink and E.G. Henry. 1991. Characterization of
multiple forms of the Ah receptor: Recognition of a dioxin-response enhancer
involves heteromer formation. Biochemistry. 30: 2909.
8-114 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Gasiewicz, T.A. and G. Rucci. 1984. Cytosolic receptor for 2,3,7,8-tetrachloro-
dibenzo-p-dioxin. Evidence for a homologous nature among various mammalian
species. Mol. Pharm. 26: 90.
Gerlowaki, L.E. and R.K. Jain. 1983. Physiologically based pharmacokinetic
modeling: Principles and applications. J. Pharm. Sci. 72: 1103-1126.
Gierthy, J.F., D.W. Lincoln, M.B. Gillespie et al. 1987. Suppression of
estrogen-regulated extracellular plasminogen activator activity of MCF-7 cells
by 2,3,7,8-tetrachlorodibenzo-p-dioxin. Cancer Res. 47: 6198.
Goodman, D. and R.M. Sauer. 1992. Hepatotoxicity and carcinogenicity in female
Sprague-Dawley rats treated with 2,3,7,8-TCDD: A pathology working group
reevaluation. Reg. Tox. and Pharmacology. 15: 245-253.
Graham, M.J., G.W. Lucier, P. Linko, R.R. Paronpot and J.A. Goldstein. 1988.
Increases in cytochrome P-450 mediated 17 beta-estradiol 2-hydroxylase activity
in rat liver microsomes after both acute administration and subchronic
administration of 2,3,7,8-tetrachlorodibenzo-p-dioxin in a two-stage
hepatocarcinogenesis model. Carcinogenesis. 9: 1935-1941.
Greenfield
Greenlee, W.F., K.M. Kold, R.D. Irons and R. Osborne. 1985. Evidence for direct
action of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on thymic epithelium.
Toxicol. Appl. Pharmacol. 79: 112-121.
Greenlee, H.F., M.E. Andersen and G.W. Lucier. 1991. A perspective on
biologically-based approaches to dioxin risk assessment. Risk Analysis. 11(4):
565-568.
Guengerich, F.P. 1989. Characterization of human microsomal cytochrome P-450
enzymes. Ann. Rev. Pharmacol. Toxicol. 29: 251-264.
8-115 OB/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Gustafsson. K.A. et al. 1987. Biochemistry, molecular biology and physiology
of the glucocorticoid receptor. Em\ndor. Rev. 8: 185-234.
Fingerhut, M.A., W.E. Halpern, D.A. Marlow et al. 1991. Cancer mortality in
workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. N. Eng. J. Hed. 324:
212.
Hargrove, J.L., M.G. Hulsey, F.H. Schmidt and E.G. Beale. 1990. A computer
program for modeling the kinetics of gene expression. Biotechniques. 8:
654-659.
Harris, M., T. Zacharewski and S. Safe. 1990. Effects of 2,3,7,8-tetrachloro-
dibenzo-p-dioxin and related compounds on the occupied nuclear estrogen receptor
in MCF-7 human breast cancer cells. Cancer Res. 50: 3579.
Hill, A.V. 1910) J. Physiol. 40, 190.
Hoel 19—)
Hoel, D.G., Baseman, J. , Hogan, M.D., Huff, J. and McConnell, E.E. 1988) The
Impact of Toxicity on Carcinogenicity Studies: Implications for Risk Assessment.
Carcinogenesis 9, 2045-2052.
Hoffman, B.C., H. Reyes, F.F. Chu, F. Sander et al. 1991. Cloning of a factor
required for activity of the Ah (dioxin) receptor. Science. 252: 954-958.
Holsapple, M.P., D.L. Morris, S.C. Wood and N.K. Snyder. 1991. 2,3,7,8-Tetra-
chlorodibenzo-p-dioxin-induced changes in inununocompetence: Possible mechanisms.
Ann. Rev. Pharmacol. Toxicol. 31: 73-100.
Holsapple, 1990
Holsapple, 1992
8-116 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Hudson, L.G., W.A. Toscano, W.F. Greenlee. 1985. Regulation of epidermal growth
factor binding in human keratinocyte cell line by 2,3,7,8-tetrachlorodibenzo-
p-dioxin. Toxicol. Appl. Pharm. 77: 251-259.
Hulme 1991)
Jensen, E.V. 1991. Overview of the nuclear receptor family. In: Nuclear Hormone
Receptors Molecular Mechanisms, Cellular Functions and Clinical Abnormalities,
M. Parker, Ed. Academic Press, New York. p. 1-10.
Kedderis, L., J. Dilberto, P. Linko, J. Goldstein and L. Birnbaum. 1991.
Disposition of 2,3,7,8-tetrabromodibenzo-p-dioxin and 2,3,7,8-tetrachlorodibenzo-
p-dioxin in the rat: Biliary excretion and induction of cytochromes CYPIA1 and
CYPIA2. Toxicol. Appl. Pharmacol. Ill: 163-172.
Kedderis, L.B., J.J. Mills, M.E. Andersen and L.S. Birnbaum. 1992. A
Physiologically-based pharmacokineticmodel of 2,3,7,8-tetrabromodibenzo-p-dioxin
(TBDD) in the rat: Tisssue distribution and CYP1A induction. Toxicol. Appl.
Pharmacol. (In review)
Kimming, J. and K. Schultz. 1957. Chlorinated aromatic cyclic others as the
cause of chloracne. Naturwissenschaften. 44: 337.
King, F.G., R.L. Dedrick, J.M. Collins, H.B. Matthews and L.S. Birnbaum. 1983.
Physiological model for the pharmacokinetics of 2,3,7,8-tetrachlorodibenzofuran
in several species. Toxicol. Appl. Pharmacol. 67: 390-400.
Kissel, J.C. and G.M. Robarge. 1988. Assessing the elimination of 2,3,7,8-TCDD
from humans with a physiologically based pharmacokinetic model. Chemosphere. 17:
2017-2027.
8-117 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Kleeman, J.M., R.W. Moore and R.E. Peterson. 1990. Inhibition of testicular
steroidogenesis in 2,3,7,8-tetrachlorodibenzo-p-dioxin-treated rats: Evidence
that the key lesion occurs prior to or during pregnenolone formation. Toxicol.
Appl. Pharmacol. 106: 112-125.
Kociba, R.J., P.A. Keeler, C.N. Park and P.J. Gehring. 1976. 2,3,7,8-Tetra-
chlorodibenzo-p-dioxin (TCDD): Results of a 13-week oral toxicity study in rats.
Toxicol. Appl. Pharmacol. 35: 553-574.
Kociba, R. 1991. Rodent bioassays for assessing chrobasis for nic toxicity and
caricogenic potential of TCDD. In; Banbury Report 35: Biological Basis for Risk
Assessments of Dioxins and Related Compounds, M. Gallo, R. Scheuplein and K. Van
der Heijden, Ed. Cold Spring Harbor Laboratory Press.
Kociba, R.J., D.G. Keyes, J.E. Beyer et al. 1978. Results of a two-year chronic
toxicity and oncogenicity study of 2,3,7,8-tetrachlorodibenzo-p-dioxin in rats.
Toxicol. Appl. Pharmacol. 46: 279-303.
Kohn, M.C., G.W. Lucier, G.C. Clark, C. Sewall, A.M. Tritscher and C.J. Portier.
1992. A mechanistic model of effects of dioxin on gene expression in the rat
liver. Toxicol. Appl. Pharmacol. (In review)
Kopp, J. and C. Portier. 1989. A note on approachng the cumulative distribution
functions of the time to tumor onset in multistage models. Biometrics. 45:
1259-1264.
Krowke, R., I. Chahoud, I. Baumann-Wilschke and D. Neubert. 1989. Pharmaco-
kinetics and biological activity of 2.3,7,8-tetrachlorodibenzo-p-dioxin: 2.
Pharmacokinetics in rats using a loading-dose/maintenance dose regimen with high
doses. Arch. Toxicol. 63: 356-360.
8-118 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Kuratsune, M., M. Ikedo, Y. Nakamura and T. Hirohata. 1988. A cohort study in
mortality of "Yusho patients: A preliminary report in unusual occurrences as
clues to cancer etiology. Japan Sci. Soc. Press.
Kuroki, H. and W. Masuda, Y. 1978. Determination of polychlorinated
dibenzofuran isomers retained in patients with Yusho. Chemosphere. 7: 771.
Leung, H.W., R.H. Ku, D.J. Paustenbach and M.E. Andersen. 1988. A
Physiologically based pharmacokinetic model for 2,3,7,8-tetrachlorodibenzo-
p-dioxin in C57BL/6J and DBA/2J mice. Toxicol. Lett. 42: 15-28.
Leung, H.W., A.P. Poland, D.J. Paustenbach and M.E. Andersen. 1990b. Dose-
dependent pharmacokinetics of [125I]-2-lodo-3,7,8-trichlorodibenzo-p-dioxin in
mice: Analysis with a physiological modeling approach. Toxicol. Appl. Pharmacol.
103: 411-419.
Leung, H.W., D.J. Paustenbach, F.J. Murray and M.E. Andersen. 1990a. A
physiologically pharmacokinetic description of the tissue distribution and
enzyme inducing properties of 2,3,7,8-tetrachlorodibenzo-p-dioxin in the rat.
Toxicol. Appl. Pharmacol. 103: 399-410.
Leung, H.W. 1991. Development and utilization of physiologically based
pharmacokinetic models for toxicological applications. J. Toxicol. Environ.
Health. 32: 247-267.
Lin, F.H., G. Clark, L.S. Birnbaum, G.W. Lucier and J.A. Goldstein. 1991.
Influence of the Ah locus on the effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin
on the hepatic epidermal growth factor receptor. Mol. Pharmacol. 39: 307-313.
Lindberg, R.L.P. and M. Negishi. 1989. Alteration of mouse cytochrome P-450
substrate sopecificity by mutation of a single amino-acid residue. Nature. 339:
632-634.
8-119 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Lorenzen. A., and Okey, A.B. 1991TAP or Chemosphere
Lucier, 1991a? or may be the same enzyme induction vs. humans
Lucier, G.W., A. Tritscher, T. Goldsworthy et. al. 1991. Ovarian hormones
enhance 2,3,7,8-TCDD mediated increases in cell proliferation and preneoplastic
foci in a two-step model for rat hepatocarcinogenesis. Cancer Res. 51: 1391.
Lucier, 1991 EGF rec.
Lucier, G.W., K.G. Nelson, R.B. Everson et al. 1987. Placental markers of human
exposure to polychlorinated biphenyls and polychlorinated dibenzofurans.
Environ. Health Perspect. 6: 79.
Luster, M.I., D.R. Gremolec, G. Clark, G. Wiegand and G.J. Rosenthal. 1988.
Selective effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin and corticosteroid on
in vitro lymphocyte maturation. J. Immunol. 140: 928-936.
Luster, M. I., C. Portier, D.G. Pait et al. 1992. Risk assessment in
immunotoxicology: I. Sensitivity and predictability of immune tests. Fund. Appl.
Toxicol. 18: 200-210.
Lutz, R.J., R.L. Dedrick, H.B. Matthews, T.E. Edling and M.W. Anderson. 1977.
A prelimnary pharmacokinetic model for several chlorinated biphenyls in the rat.
Drug Metabol. Disp. 5: 386-396.
Lutz, R.J., R.L. Dedrick, D. Tuey, I.G. Sipes, M.W. Anderson and H.B. Matthews.
1984. Comparison of the pharmacokinetics of several polychlorinated biphenyls
in mouse, rat, dog and monkey by means of a physiological pharmacokinetic model.
Drug Metabol. Disp. 12: 527-535.
8-120 08/27/92
-------
DAAFT—DO NOT QUOTE OR CITE
Ma, X., N. Mufti and J.G. Babish, 1992. Protein tyrosine phosphorylation as an
indicator of 2,3,7,8-tetrachlorodibenzo-p-dioxin exposure in vivo and in vitro.
Toxicologist. 12: 81.
Mably, T.A., R.W. Moore and R.E. Peterson. 1992a. In utero and lactational
exposure of male rats to 2,3,7,8-tetrachlorodibenzo-p-dioxin: 1. Effects on
androgenic statis. Toxicol. Appl. Pharmacol. 114: 97-107.
Mably, T.A., R.W. Moore, R.W. Goy and R.E. Peterson. 1992a. In utero and
lactational exposure of male rats to 2,3,7,8-tetrachlorodibenzo-p-dioxin:
2. Effects on sexual behavior and the regulation of luteinizing hormone secretion
in adulthood. Toxicol. Appl. Pharmacol. 114: 108-117.
Mably, T.A., D.L. Bjerke, R.W. Moore, A. Gendron-Fitxpatrick and R.E. Peterson.
1992a. In utero and lactational exposure of male rats to 2,3,7,8-tetrachloro-
dibenzo-p-dioxin: 3. Effects on spermatogenesis and reproductive capability.
Toxicol. Appl. Pharmacol. 114: 118-126.
Manchester, O.K., S.K. Gordon, C.L. Golas, E.A. Roberts and A.B. Okey. 1987.
Cancer Res. 47: 4861-4868.
Manz, A., J. Barger, J.H. Dwyer, et al. 1991. Cancer mortality among workers
in chemical plant contaminated with dioxin. The Lancet. 338(8773): 959-964.
Maronpot 1992)
Marks, T.A., G.L. Kimmel and R.E. Staples. 1981. Toxicol. Appl. Pharmacol. 61:
269-276.
Matthews, H.B. and R.L. Dedrick. 1984. Pharmacokinetics of PCBs. Ann. Rev.
Pharmacol. Toxicol. 24: 85-103.
8-121 08/27/92
-------
DRAFT--DO NOT QUOTE OR CITE
McKonkey, D.J., P. Kartell, S.K. Dudy, H. Hakansson and S. Orrenius. 1988.
2,3,7,8-Tetrachlorodibenzo-p-dioxin kills immature thymocytes by Ca++ mediated
endonuclease activation. Science. 242: 256-259.
McNulty, W, I. Pomerantz and T. Farrel. 1985. Toxicity and fetotoxicity of
TCDD, TCDF and PCB isomers in rhesus macaques (Hacaca mulatta. Environ. Health
Perspect. 60: 77.
McNulty, H.P., K.A. Nielsen-Smith, J.O. Lay, et al. 1982. Persistence of TCDD
in monkey adipose tissue. Food Chem. Toxicol. 20,:985-987.
Metzler 1984)
Moolgavkar, S.H. and A.G. Knudson, Jr. 1981. Mutation and cancer: A model for
human carcinogenesis. J. Natl. Cancer Inst. 66: 1037-1052.
Moolgavkar, S.H. and D.J. Venzon. 1979. Two-event model for carcinogenesis:
Incidence curves for childhood and adult tumors. Math. Biosci. 47: 55-77.
Moolgavkar, S.H. 1992. Multistage carcinogenesis: Population-based model for
colon cancer. J. Natl. Cancer Inst. 84: 610-617.
Moolgavkar, S.H. and Lubeck, 1992
Moore, R.W., C.R Jefcoate and R.E Peterson. 1991. 2,3,7,8-Tetrachlorodibenzo-
p-dioxin inhibits steroidogenesis in the rat testis by inhibiting mobilization
of cholesterol to cytochrome P450SCC. Toxicol. Appl. Pharmacol. 109: 85-97.
Moore, R.W., J.A. Parsons, R.C. Bookstaff and R.E Peterson. 1989. Plasma
concentrations of pituitary hormones in 2,3,7,8-tetrachlorodibenzo-p-dioxin-
treated male rats. J. Biochem. Toxicol. 4: 165-172.
8-122 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Moore, R.W., C.L. Potter, H.M. Theobald, J.A. Robinson and R.E. Peterson. 1985.
Androgenic deficiency in male rats treated with 2,3,7,8-tetrachlorodibenzo-
p-dioxin. Toxicol. Appl. Pharmacol. 79: 99-111.
Moore, R.W. and R.E. Peterson. 1988. Androgen catabolism and excretion of
2,3,7,8-tetrachlorodibenzo-p-dioxin-treated rats. Biochem. Pharmacol. 37: 560.
Muller, M., C. Baniahmad, C. Kaltschmidt, R. Schule and R. Renkawitz. 1991. No
title. In; Nuclear Hormone Receptors Molecular Mechanisms, Cellular Functions
and Clinical Abnormalities, M. Parker, Ed. Academic Press, New York.
p. 156-174.
NAS/NRC, 1977noel
NRC (National Research Council). 1983. Risk assessment in the federal
government: Managing the process. National Academy Press, Washington, DC.
NTP (National Toxicology Program). 1982. Carcinogenicity Bioassay of 2,3,7,8-
Tetrachlorodibenzo-p-dioxin in Osborne-Mendel Rats and B6C3Fl-mice (gavage study.
NTP Tech. Rep. Ser. no. 209. Research Triangle Park, North Carolina.
Nebert, et al. 1989. The P450 superfamily: Updated listing of all genes and
recommended nomenclature for the chromosome loci. DNA. 8: 1-13.
Nebert, 1990 env health perspecitve
Neyman. J. and E. Scott. 1967. Statistical aspects of the problem of
carcinogenesis. In: Proceedings of the 5th Berkely Symposium on Mathematical
Statistics and Probability. University of California Press, Berkeley, CA.
p. 745-776.
8-123 08/27/92
-------
DRAFT—DO NOT QUOTE OR 7ITE
Nichols, A.I., F.D. Boudinot and W.J. Jusko. 1989. Second generation model for
prednisolone pharmacodynamics in the rat. J. Pharmacokin. Biopharm. 17:
209-227.
Nordling
Okey, 1989 TCDD binding
Olson, J.R., B.P. McGarrigle, D.A. Tonucci, A. Scheter and H. Eichelberger.
1990. Developmental toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin in the rat
and hamster. Chemosphere. 20: 1117-1123.
Osborne, R., J.C. Cook, K.M. Dold, L. Ross, K. Gaido and W.F. Greenlee. 1988.
TCDD receptor: Mechanisms of altered growth regulation in normal and transformed
human keratinocytes. In; Tumor Promoters: Biological Approaches for Mechanistic
Studies and Assay Systems, R. Langenbach, Ed. Raven Press, New York.
p. 407-416.
Pathology Working Group. 1990. Hepatotoxicity in Female Sprague-Dawley Rats
Treated with 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD). Prepared by R.M. Sauer,
PWG Chairperson and D.Goodman, PATHCO, Inc., submitted to R.A. Michaels,
Chairperson Maine Scientific Advisory Panel, April 27, 1990.
Pirkle, J.L., W.H. Wolfe, D.G. Patterson, L.L. Needhant, et al. 1989. Estimates
of the half-life of 2,3,7,8-TCDD in Vietnam veteran of Operation Ranch Hand. J.
Toxicol. Environ. Health. 27: 165.
Pitot, H.C., T.L. Goldsworth, H.A. Campbell and A. Poland. 1980. Quantitative
evaluation of the promotion by TCDD of hepatogenesis and diethylnitrosamine.
Cancer Res. 40: 3616.
8-124 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Poellinger, L., A. Wilhelmsson, S. Cuthill et al. 1987. Structure and function
of the dioxin receptor: A DNA-binding protein similar to steriod hormone
receptors. Chemosphere. 16: 1681-1686.
Poellinger, L., A. Wilhelmsson, J. Lund and J.A. Gustafsson. 1986. Biochemical
characterization of the rat liver receptor for 2,3,7,8-TCDD: A comparison to the
rat liver glucocorticoid receptor. ??????
Poiger, H. and C. Schlatter. 1986. Pharmacokinetiks of 2,3,7,8-TCDD in man.
Chemosphere. 15: 1489.
Poland, A., P. Teitelbaum and E. Glover, E. 1989a. [1251J2-Iodo-3,7,8-tri-
chlorodibenzo-p-dioxin-binding species in mouse liver induced by agonists for the
Ah receptor: Characterization and localization. Mol. Pharmacol. 36: 113-120.
Poland, A., P. Teitelbaum, E. Glover and A. Kende. 1989b. Stimulation of in
vivo hepatic uptake and in vitro hepatic binding of [1125]-2-3,7,8-trichloro-
dibenzo-p-dioxin by the administration of agonists for the Ah receptor. Mol.
Pharmacol. 36: 121-127.
Poland, A. and J.C. Knutson. 1982. 2,3,7,8-Tetrachlorodibenzo-p-dioxin and
related halogenated aromatic hydrocarbons: Examination of the mechanism of
toxicity. Ann. Rev. Pharmacol. Toxicol. 22: 517.
Portier, C., A. Tritscher, M. Kohn et al. 1992. Ligand/receptor binding for
2,3,7,8-TCDD: Implications for risk assessment. Fund. Appl. Toxicol. (In
review)
Portier, 1976
Portier 1986)
Portier and Bailer,
8-125 OB/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Portier, C., D. Hoel and J. Van Ryzin. 1984. Statistical analysis of the
carcinogenesis bioassay data relating to the risks from Exposure to 2,3,7,8-
tetrachlorodibenzo-p-dioxin. In; Public Health Risks of the Dioxins,
W. Lowrance, Ed. W. Kaufmann, Los Altos, p. 99-120.
Portier, C. and A. Knopp-Schneider. 1991. A multistage model of carcinogenesis
incorporating DMA damage and repair. Risk Analysis. 11: 535-543.
Quattrochi, L.C. and R.H. Tukey. 1989. The human CypIA2 gene contains
regulatory elements responsive to 3-methlycholanthrene. Mol. Pharmacol. 36:
66-71.
Rogan, 1990
Romkes, M., J. Piskorska-Pliszczynska and S. Safe. 1987. Effects of 2,3,7,8-
tetrachlorodibenzo-p-dioxin on hepatic and uterine estrogen receptor levels in
rats. Toxicol. Appl. Pharmacol. 92: 368-380.
Rose, J.Q., J.C. Ramsey, T.H. Wenzler, R.A. Hummel and P.J. Gehring. 1976. The
fate of 2,3,7,8-tetrachlorodibenzo-p-dioxin following single and repeated oral
doses to the rat. Toxicol. Appl. Pharmacol. 336: 209-226.
Roth, J. and C. Grunfield. 1985. Mechanism of action of peptide hormones and
catecholamines. In; Textbook of Endocrinology, 7th ed, J. Wilson and D. Foster,
Ed. W.B. Saunders, Philadelphia, p. 114.
Safe, S., B. Astroff, M. Harris, Zacho. 1992. dibenzp-p-dioxin (TCDD).
Neurotoxicol. Teratol. 11: 13-19.
Saracci, R., M. Kogevinos et al. 1991. Cancer mortality in workers exposed to
chlorophenxy herbicides and chlorophenols. Lancet. 338(8774): 1027-1032
Schantz and Bavrian
8-126 OB/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Schecter, A. and J.J. Ryan. 1988. Polychlorinated dibenzo-para-dioxin and
dibenzofuran levels in human adipose tissues from workers 32 years after
occupational exposure to 2,3,7,8-TCDD. Chemosphere. 17: 915.
Schlatter, C., 1991. Data on kinetics of PCDDa and PCDFs as a prerequisite
for human risk assessment. In; Banbury Report 35: Biological Basis for Risk
Assessments of Dioxins and Related Compounds, M. Gallo, R. Scheuplein and K. Van
der Heijden, Ed. Cold Spring Harbor Laboratory Press.
Schwetz, B.A. 1973. Toxicology of chlorinated dibenzo-p-dioxins. Env. Health
Perspect. 5: 87-89.
Schlatter, 1991 liver/fat cone.
Seegal, R.F., B. Bush and W. Shain. 1990. Lightly chlorinated ortho-substituted
PCB congeners decrease dopamine in nonhuman primate brain and in tissue culture.
Toxicol. Appl. Pharmacol. 106: 136-144.
Sesardic, D. A.R. Boobis, R.J. Edwards and D.S. Davies. 1988. A form of
cytochrome P450 in man, orthologous to form d in the rat, catalyses the
O-deethylation of phenacetin and is inducible by cigarette smoking. Br. J. Clin.
Pharmacol. 26: 363-372.
Sewall. 1992
Shain, W., B. Bush and R. Seegal. 1991. Neurotoxicity of polychlorinated
biphenyls: Structure-activity relationship of individual congeners. ?????
Shiverick and Muther, 1982
Sloop, T.C. and G.W. Lucier. 1987. Dose-dependent elevation of Ah receptor
binding by TCDD in rat liver. Toxicol. Appl. Pharmacol. 88, 329-337.
8-127 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Silbergeld, E.K. and T.A. Gasiewicz. 1989. Dioxins and the Ah receptor. Am.
J. Ind. Med. 16: 455.
Spink, D.C., D.W. Lincoln II, H.W. Dickerman and J.F. Gierthy. 1990. 2,3,7,8-
Tetrachlorodibenzo-p-dioxin causes an extensive alteration of 17-beta-estradiol
metabolism in MCF-7 breast tumor cells. Proc. Natl. Acad. Sci. 87: 6917-6921.
Sloop, T.C. and G.W. Lucier. 1987. Dose-dependent elevation of ah receptor
binding by TCDD in rat liver. Toxicol. Appl. Pharm. 88: 329.
Sunuhura, 1987
Sunahara 1989)
Sutter, T.R. M.E. Andersen, J.C. Gorton, K. Gaido, K. Guzman and W.F. Greenlee.
1991. Development of a molecular basis for dioxin risk assessment in humans.
In: Banbury Report 35: Biological Basis for Risk Assessments of Dioxins and
Related Compounds, M. Gallo, R. Scheuplein and K. Van der Heijden, Ed. Cold
Spring Harbor Laboratory Press, p. 427-440.
Thorslund, T. 1987. Quantitative dose-response model for tumor-promoting
activity of TCDD. Appendix A: A cancer risk-specific dose estimate for
2,3,7,8-TCDD. EPA/600/6-88/007Ab.
Tilson, H.A., G.J. Davis, J.A. McLachlan and G.W. Lucier. 1979. The effects of
polychlorinated biphenyls given prenatally on the neurobehavioral development of
mice. Environ. Res. 18: 466-474.
Tilson, H.A, J.L. Jacobson and W.J. Rogan. 1990. Polychlorinated biphenyls and
the developing nervous system: Cross species comparisons. Neurotoxicol. Teratol.
12: 239-348.
8-128 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Tritscher, A.M., J.A. Goldstein, C.J. Portier, Z. McCoy and G. Clark. 1992.
Dose-response relationships for chronic exposure to 2,3,7,8-tetrachlorodibenzo-p-
dioxin in a rat tumor promotion model: Quantification and immunolocatization of
CYPIal and CYPIA2 in the liver. Cancer Res. 52: 3426-3442.
Tucker, A.N., S.J. Vore and M.I. Luster. 1986. Suppression of B-cell
differentiation by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD. Mol. Pharmacol.
29: 372-381.
U.S. EPA 1985. Health Assessment Document for PCDDs. Prepared by the Office
of Health and Environmental Assessment, Environmental Criteria and Assessment
Office, Cincinnati, OH for the Office of Emergency and Remedial Response,
Washington, DC. EPA-600/81-003.
U.S. EPA 1992. Estimating Exposure and Dioxin-Like Compounds. Workshop teview
Draft. Office of Health and Environmental Assessment, Washington, D.C.
EPA/600/6-88/005B.
Vos, J.G., H. Van Loveren and H.J. Schuurman. 1991. Immunotoxicity of dioxin:
Immune function and host resistance in laboratory animals and humans. In;
Banbury Report 35: Biological Basis for Risk Assessments of Dioxins and Related
Compounds, M. Gallo, R. Scheuplein and K. Van der Heijden, Ed. Cold Spring
Harbor Laboratory Press, p. 79-93.
Walker, 1979
Wolfe, 1991
Wong, 1986
Whitlock, J.P. 1987. The regulation of gene expression by 2,3,7,8-Tetrachloro-
dibenzo-p-dioxin. Pharmacol. Rev. 39: 147.
8-129 08/27/92
-------
DRAFT—DO NOT QUOTE OR CITE
Wrighton S.A. 1990. Human cytochromes P450 responsible for hepatic drug
metabolism: New horizons in molecular toxicology. Lilly Research Laboratories
Symposium, May 21-22, 1990, Indianapolis, IN. p. 80-86.
Wrighton, 1989 non-specific binding protein
Zacharewski, T.M., M. Harris and S. Safe. 1991. Evidence for the mechanism of
action of 2,3,7,8-tetrachlorodibenzo-p-dioxin-roediated decrease of nuclear
estrogen receptor levels in wild-type and mutant Hepa Iclc? cells. Biochem.
Pharmacol. 41: 1931-1939.
Zober, H., P. Messerer and P. Huber. 1990. Thirty-four year follow-up of BASF
employees exposed to 2,3,7,8-TCDD after the 1953 accident. Int. Arch. Occup.
Environ. Health. 62: 139.
8-130 08/28/92
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DRAFT-DO NOT QUOTE OR CITE
APPENDIX A
A Mechanistic Model of Effects of Dioxin
on Gene Expression in the Rat Liver
Michael C. Kohn, George W. Lucier, George C. Clark,
Charles Sewall, Angelika M. Tritscher, and Christopher J. Portier
Division of Biometry and Risk Assessment
National Institute of Environmental Health Sciences
P.O. Box 12233
Research Triangle Park, NC 27709
Correspondence to:
Michael C. Kohn
Statistics and Biomathematics Branch
Division of Biometry and Risk Assessment
National Institute of Environmental Health Sciences
P.O. Box 12233
Research Triangle Park, NC 27709
(919) 541-4929
Running title: Model of Effects of Dioxin on Hepatic Gene Expression
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Abstract
A Mechanistic Model of Effects of Dioxin on Gene Expression in the Rat Liver. KOHN, M.C.,
LucffiR, G.W., CLARK, G.C., SEWALL, C, TRITSCHER, A.M., AND PORTIER, C.J. (199J Toxicol.
Appl. Pharmacol. , ___-__. Improved methods for estimating the shape of the response
curve for effects of exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) are needed in order
to evaluate possible adverse health effects of TCDD. A mathematical model has been constructed
to describe TCDD-mediated alterations in hepatic proteins in the rat. In this model it was assumed
that TCDD mediates increases in the liver concentration of transforming growth factor-alpha
(TGF-a) by a mechanism which requires the aryl hydrocarbon (Ah) receptor. TGF-cc subse-
quently binds to the epidermal growth factor (EGF) receptor, a process which is known to cause
internalization of this receptor in hepatocytes. This action is thought to be an early event in the
generation of a mitogenic signal. Because TCDD decreases binding of EGF in the livers of intact
female rats but not in ovariectomized rats, this effect was further assumed to be dependent on
estrogen action. The model postulates Ah receptor-dependent effects on the concentration of cyto-
chrome P450 1A2 (CYP1A2), which is involved in the metabolism of estradiol, and on the con-
centration of the estrogen receptor. The model also incorporates information on induction of
cytochrome P450 1A1 (CYPlAl) by TCDD. The biochemical response curves for all these pro-
teins were hyperbolic (Hill exponents in the equations for their expression were found to be 1),
indicating a proportional relationship between target tissue dose and protein concentration at low
administered doses of TCDD. The model successfully reproduced the observed tissue distribution
of TCDD, the concentrations of CYPlAl and CYP1A2, and the effects of TCDD on the Ah,
estrogen, and EGF receptors over a wide dose range.
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Introduction
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a potent carcinogen which is associated with
increased incidence of liver tumors in female rats but not in male rats (Kociba et al., 1978;
National Toxicology Program, 1982; Clark et al., 1991). Similarly, TCDD enhances hepatocyte
proliferation and stimulates development of enzyme-altered hyperplastic foci in intact female rats
but not in ovariectomized rats (Lucier et al., 1991), suggesting that estrogens play a major role in
TCDD-mediatedhepatocarcinogenesis.
Biochemical signals regulating cellular proliferation are mediated in many tissues, including
the liver, by the epidermal growth factor (EGF) receptor (Schlessinger et al., 1983). The EGF
receptor is a member of a family of plasma membrane receptors that, on binding of ligand, trans-
duce signals by tyrosine kinase activity and by internalization of the ligated receptor (Hunter and
Cooper, 1985). Phosphorylation of various proteins by this tyrosine kinase leads to alterations in
cellular regulation and mitotic activity.
Livers of TCDD-treated rats show a dose-dependent decline in the maximal binding of EGF
(Lucier, 1991) although TCDD has no effect on the amount of mRNA for the EGF receptor in
keratinocytes (Osborne et al., 1988) or mouse liver (Lin et al., 1991a). Antibodies raised against
the EGF receptor stain the plasma membranes of hepatocytes in control rats, but in TCDD-treated
rats there is an apparent redistribution of the receptor into the cytosol (C. Sewall and A.M.
Tritscher, unpublished results). This is consistent with the notion that the loss of EGF binding
capacity in liver plasma membranes is due to internalization of the ligated receptor.
The liver does not produce EGF, but there is evidence (Mead and Fausto, 1989) that it does
produce transforming growth factor-alpha (TGF-a), another ligand of the EGF receptor. TCDD
induces expression of TGF-a in keratinocytes (Choi et al., 1991), suggesting that TCDD may also
induce TGF-a in the liver. Increased production of this peptide would subsequently stimulate
EGF receptor-mediated events in that organ.
In order to obtain a quantitative relationship between exposure to TCDD and consequent alter-
ations in the properties of the EGF receptor, we have constructed a mathematical model of the tis-
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sue distribution of TCDD in the rat and its effect on the concentrations of several important liver
proteins. The model includes equations for the aryl hydrocarbon (Ah) receptor-dependent induc-
tion of cytochrome P450 isozymes 1A1 (CYP1A1) and 1A2 (CYP1A2) and of the Ah receptor
itself. The complex between this receptor and TCDD (Ah-TCDD) is treated as inducing expres-
sion of TGF-a. It is also assumed that estrogen action is required for TCDD-mediated induction
of TGF-a. TGF-a is modeled as released into the liver interstitium. From there it binds to the
EGF receptor, causing its internalization. The model calculates the distribution of the receptor
between the plasma membrane and cytosol.
Because estrogens appear to be required for TCDD-mediated effects on the EGF receptor,
production of the estrogen receptor, CYP1A2-catalyzed formation of catechol estrogens, and
deactivation of estrogens by glucuronidation were included in the model. In this effort, data on the
stimulation of estrogen receptor synthesis by estradiol and the inhibition of estrogen receptor syn-
thesis by TCDD were incorporated.
The model's predictions were compared to the data of Tritscher et al. (1992) and Sewall et al.
(1992). Their experiments were a two-stage protocol in which female Sprague-Dawley rats were
injected with an initiating dose of diethylnitrosamine (DEN). After 14 days the rats were sub-
jected to biweekly gavage with TCDD in corn oil at doses equivalent to 3.5-125 ng/kg/day for 30
weeks. The rats were killed one week following the last dose, and their livers were assayed for
TCDD, CYP1 Al and CYP1A2, and plasma membrane binding of EGF. All such measurements
were performed using samples from the same livers.
This model was used to predict tissue concentrations of TCDD and concentrations of induced
proteins following administration of TCDD. A model which reproduces the dose-response rela-
tionships of experimental data and is consistent with the biochemical and physiological processes
known to occur in rats exposed to TCDD might permit extrapolation of responses beyond the
range obtained from experimental data and lead to scientifically sound approaches for estimating
risks of adverse health effects of exposure to TCDD.
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Methods
The present model, hereafter referred to as the NDEHS model (see Appendices 1 and 2 for a
complete description), was adapted from the physiologically based pharmacokinetic model of Le-
ung, Puastenbach, Murray, and Andersen (1990), hereafter referred to as the LPMA model. To fit
the data of Tritscher et al. (1992) and Sewall et al. (1992), the LPMA model has been modified as
described below. A flowchart of the resulting model is given in Figure 1.
TCDD in the gut compartment is periodically increased by orally administered TCDD accord-
ing to the experimental schedule (Tritscher et al., 1992). TCDD is extracted from the gut into the
blood compartment, where some of it binds to unspecified serum protein. From the blood it is dis-
tributed to the tissue compartments.
TCDD in the liver is distributed between "metabolically available" and protein-bound pools.
The latter category includes binding to CYP1A2 and to the Ah receptor, which is presumed to
mediate all of the tissue's responses to TCDD. Unbound TCDD is converted to metabolites which
are either transferred to the gut via the bile or released into the blood. The metabolites are treated
as partitioning between the liver and blood in the same manner as TCDD. Gut metabolites ulti-
mately appear in the feces, and blood metabolites appear in the urine. TCDD is also cleared from
the liver by lysis of dead cells consequent to prolonged exposure.
Equations for the pharmacodynamic effects of TCDD described above were also included in
the NIEHS model. Proteins whose synthesis is represented in the model are removed by proteolysis
or, in the case of the EGF receptor, by endocytosis.
In many cases quantities that were reported in a variety of units had to be converted to those
units (nmole of material, volume in liters, time in days) used in the model. These calculations
were performed using conversion factors of 100 mg cytosolic protein/g liver (Poland and Knut-
son, 1982), 18 mg microsomal protein/g liver (based on average yield of membrane protein;
Tritscher et al., 1992), and 4.2 mg plasma membrane protein/g liver (based on average yield of
membrane protein; Sewall et al., 1992). Rates obtained from in vitro measurements conducted at
temperatures other than body temperature were adjusted to 37°C using a doubling of the rate for
-------
every 10°C increase in temperature, a factor which is typical of enzymatic reactions.
The animals used in these experiments (Tritscher et al., 1992) grew, on average, from 220 g to
390 g during the 31 weeks of the investigation. To account for the dilution of material with growth
the parameters in the model were expressed per liter in the appropriate compartment. Because of
the increase in compartment volume, a steady state concentration of a protein whose synthesis is
represented in the model actually corresponds to an increase in the tissue content of the protein
with time.
TCDD Distribution and Clearance
The LPMA model is said to be flow-limited; the rate of uptake by tissues is limited by the rate
of delivery via the blood. In such a model, material is transferred from the arterial blood to various
tissues at a rate given by
BloodContent x Flow
BloodVolume
where Flow represents the rate of blood flow through the particular tissue. Transfer of material
from the tissue to the venous blood is given by
TissueContent x Flow
TissueVolume x PartitionCoeff
where the partition coefficient is the ratio of tissue to blood concentrations at equilibrium. The
LPMA model includes compartments for gut, blood, fat, muscle (plus skin), liver, and for other vis-
cera. The cell membranes of these tissues constitute a barrier to transport of TCDD. In the NIEHS
model the rates of transport given by the above expressions were multiplied by a "diffusion coef-
ficient" to account for the membrane permeability. Different compartments were assigned differ-
ent coefficient values as required to match the time course data of Abraham et al. (1988). For these
calculations the dose was absorbed directly into the blood from the site of injection with a scaled
rate constant of 0.37 kg0 75/day, bypassing the gut compartment.
The observed blood levels of TCDD (A.M. Tritscher, unpublished results) were an order of
-------
magnitude smaller than those predicted by the LPMA model. The LPMA model includes binding
of TCDD to unspecified blood lipoproteins and uses a factor of 2.5 for the ratio of protein-bound
to free TCDD in the blood. This constant was replaced in the NIEHS model by a reverse hyper-
bolic function (Appendix 1) for the maximal amount of TCDD-binding blood protein. Although
the form of this equation suggests that TCDD decreases the hepatic production of blood lipopro-
tein, the equation is intended as just an empirical relationship.
TCDD has been found to bind to CYP1A2 in rat liver (Voorman and Aust, 1989) and P450d in
the mouse liver (Poland et al., 1989) with an apparent Kd of 30 nM. The estradiol 2-hydroxylase
activity of this cytochrome is inhibited as a consequence of this binding (Voorman and Aust,
1987). As Poland et al. (1989) concluded that TCDD congeners bind at or near the active site on
this enzyme, such binding is treated as competitive against estradiol in the NIEHS model. It was
further assumed that TCDD only binds to the CYP1A2 that is not complexed with NADPH:cyto-
chrome P450 reductase.
The appearance of unabsorbed TCDD in feces and TCDD metabolite(s) in both feces and
urine are included in the NIEHS model. Birnbaum et al. (1980) administered a single oral dose of
14C-labelled 2,3,7,8-tetrachlorodibenzofuran (TCDF) to rats. They calculated rate constants of
0.381 day1 for the appearance of metabolites in the feces and 0.536 day"1 for the appearance of
metabolites in the urine. However, the calculated rate constant for clearance of radiolabel from the
blood during the first 3 hours in their experiments (32 day1) was an order of magnitude higher
than after 3 hours (0.6 day1). Because excretion of metabolites of TCDF is limited by their rates
of formation, the rate constants for clearance at late time most likely reflect this limitation. This
notion is supported by nearly identical calculated rate constants for clearance of radiolabel from
liver and from blood after 3 hours (Birnbaum et al., 1980). The higher rate constant for clearance
at early time more likely reflects the rate of uptake of labelled material from the blood by the var-
ious tissues, which therefore cannot be rate limiting for clearance. The rates of transport of
TCDF's and TCDD's metabolites across cell membranes should be similar. This leads to the con-
clusion that the specific rates of transport of TCDD's metabolites from liver to bile and from
-------
blood to urine must be larger than the rate constants for net clearance for times greater than 3
hours. Therefore, the above rate constants were increased by a factor of 10 for this model to
ensure that clearance of metabolites is not limited by transport.
Rose et al. (1976) found as much TCDD in the livers of rats after 7 weeks of chronic dosing as
Tritscher et al. (1992) found after 31 weeks at comparable doses. A preliminary version of the
NIEHS model indicated that parameter values which enabled the model to reproduce the results
of Tritscher et al. (1992) would uniformly underestimate the results of Rose et al. (1976) by about
50%. The preliminary model included a first-order rate constant for metabolism of TCDD whose
value could not be altered to fit the data of Rose et al. (1976) without destroying the fit to the data
of Tritscher et al. (1992). Therefore, an additional mechanism for clearance from the liver was
necessary to permit fitting both data sets simultaneously.
The cellular proliferation rate in the livers of rats exposed to biweekly doses of 1.4 jig/kg of
TCDD for 30 weeks is 7.3% of the liver cells/week (Lucier et al., 1991). At this rate, one would
expect a 226% increase in the weight of the liver over the course of the experiment. Only a 90%
increase was observed (Lucier et al., 1991; A.M. Tritscher, unpublished results), suggesting that
toxic effects of cumulative exposure to TCDD result in cell death. The NIEHS model includes
loss of TCDD from the liver by lysis of dead cells. The specific rate of clearance by cell lysis was
assumed to increase as a hyperbolic function of the cumulative exposure to unbound liver TCDD
(Appendix 1). No information regarding the fate of TCDD from lysed cells is available. There-
fore, this feature of the model merely represents the net contribution to clearance by cell turnover.
TCDD-induced Changes in Gene Expression
The complex formed by binding of TCDD to the Ah receptor has been clearly implicated in
induction of CYP1A1 (Fisher et al., 1990). The ligated Ah receptor modulates expression of
CYP1A1 by forming a ternary complex with at least one other transcription factor (Hoffman et
al., 1991), and this complex then binds to enhancer sequences for the CYP1 Al gene. This mecha-
nism was used as a prototype for all Ah receptor-dependent protein synthesis in the model. There
are no data for the concentrations of the additional transcription factors required for expression
-------
nor for the competition among various receptors for binding to them. Therefore, the NIEHS
model combines binding of additional transcription factors to the Ah-TCDD complex into the
equations for the rate of Ah-dependent induction by treating induction of this protein as proceed-
ing with saturation kinetics subsequent to binding of the Ah-TCDD complex itself to the appro-
priate enhancer sequences. That is, the Ah-TCDD complex was treated as though it were the
"substrate" of the rate-limiting reaction in expression.
TCDD increases the liver concentration of the Ah receptor itself (Poland and Knutson, 1982;
Sloop and Lucier, 1987). In the NIEHS model, increased synthesis of the Ah receptor is postu-
lated to occur by an Ah-dependent mechanism similar to that outlined above. Bradfield et al.
(1988) observed the Ah receptor's Kd for 2-iodo-7,8-dibromodibenzo-;?-dioxin to decline from
0.16 nM to 0.012 nM on 32-fold dilution of the Ah receptor protein. Because this protein is
induced by TCDD, the Kd in the intracellular milieu should be closer to the value at the higher
concentration. Therefore, the model uses the comparable value of 0.27 nM reported by Poland
and Knutson (1982) for the Kd of TCDD.
A rate law as described above treats the synthesis of gene product as occurring by a single
event. The appearance of gene product is actually the result of many steps and requires some time
before measurable amounts of protein are synthesized. The data of Sloop and Lucier (1987) show
that 6 hours elapse between administration of TCDD and increased production of Ah receptor
protein. To account for such a delay, the concentration of the Ah-TCDD complex at time t - 6
hours is substituted into the rate law to compute the rate of synthesis of protein at time t. The same
approach was used for binding of all receptor complexes in all gene expression processes in the
model. The model's predictions were observed to be fairly insensitive to reasonable numerical
values of this delay.
Fisher et al. (1990) found four Ah-responsive enhancer sites associated with the CYP1A1
structural gene. This finding raises the possibility of cooperative binding of Ah-TCDD complexes
to these binding sites and, by analogy, to the other Ah-TCDD binding sites postulated in the
model. To test for such cooperativity, Hill exponents were included in the rate equations for pro-
-------
tein synthesis and those values which would enable the model to fit experimental observations
were identified.
Proteins in the model were treated as being removed from the system with first-order kinetics.
The rate constant for the proteolytic degradation of all proteins whose synthesis is represented in
this model was determined from the data of Lucier et al. (1972).
Effects of TCDD on Epidermal Growth Factor Receptor
Choi et al. (1991) found TCDD to induce expression of TGF-a by an Ah-dependent mecha-
nism in keratinocyte cultures. The TGF-a produced was exported to the extracellular fluid. A sim-
ilar mechanism has been included in the NIEHS model, representing the product peptide as
appearing in the interstitial space of the liver.
Intact female rats subjected to 30 weeks of oral dosing with 125 ng TCDD/kg body weight/
day show a 65% reduction in the maximal binding capacity (B^ of the EGF receptor in liver
plasma membranes (Sewall et al., 1992) at a liver TCDD content of 0.063 nmole/g (Tritscher et
al., 1992). Ovariectomized rats exhibit only a 19% reduction of EGF Emcai at a liver TCDD content
of 0.106 nmole/g after 30 weeks of oral dosing at 100 ng TCDD/kg/day (Clark et al., 1991). Suna-
hara et al. (1989) observed a 56% reduction in the Bma}i in female rats ten days following gavage
with 10 ng TCDD/kg. Madhukar et al. (1984) observed a 45% decline in the B^ of the EGF
receptor of male rats ten days after'a single intraperitoneal injection of 10 fig TCDD/kg. The
NIEHS model predicts a liver TCDD content of 0.217 nmole/g for females and 0.223 nmole/g for
males at the end of the ten day period. The responses of males, ovariectomized females, and intact
females may indicate different sensitivities for transcriptional activation of the TGF-a gene by the
Ah-TCDD complex as a consequence of differences in the amount of hepatic estrogen (or some
other ovarian hormone). Intact females, having much more circulating estrogen than either males
or ovariectomized females, should be the most sensitive.
Both the Ah-TCDD and estrogen receptor-estrogen (ER-E) complexes may be acting as tran-
scriptional activators of the TGF-a gene, but a synergistic interaction between them appears to be
necessary to fully activate transcription. In the NIEHS model, binding of the ER-E complex is
10
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treated as activating the TGF-a gene for subsequent binding of the Ah-TCDD complex. However,
binding of this complex to the TGF-a gene has not been proven, and other mechanisms are possi-
ble.
Estrogen metabolism and the production of the estrogen receptor are included in the NIEHS
model to account for the effects of estrogen on alterations in the EGF receptor. The kinetic con-
stants for estradiol 2-hydroxylase activity (Appendix 2) would predict an increase in enzymatic
activity comparable to the nine-fold rise in CYP1A2 resulting from exposure to TCDD. Only a
three-fold increase in activity was actually observed (Graham et al., 1988) in female rats. The
smaller increase in activity was attributed to rate limitation by the availability of NADPH:cyto-
chrome P450 reductase.
This reductase forms a complex with cytochrome P450 (Coon et al, 1977). The complex binds
molecular oxygen and is reduced by NADPH to eliminate water, leaving a single oxygen atom to
combine with substrate (Estabrook and Werringloer, 1977). As all P450 isozymes compete for
binding to reductase, there might not be sufficient reductase to bind to all the increased CYPl A2.
Indeed, there is evidence (Ullrich and Duppel, 1975) that P450 reductase is limiting for other
mono-oxygenase reactions that are not induced. Therefore, binding of the reductase to both
CYPl Al and CYP1A2 were included in the NIEHS model. Estradiol and reductase were allowed
to bind to CYP1A2 in random order, and oxygen and NADPH were treated as saturating.
The catechol estrogens produced by the activity of CYPl A2 bind to the estrogen receptor an
order of magnitude more weakly than does estradiol (Li et al., 1985). Such binding is included in
the NIEHS model, and the resulting complex was allowed to function identically to the ER-estra-
diol complex. This means that the ER-estradiol and ER-catechol estrogen complexes produce the
same effect on gene expression, but that ten tunes as much catechol estrogen as estradiol is
required to achieve the same quantitative response. Therefore, the symbol ER-E is used in this
report to represent complexes between the estrogen receptor and either estradiol or catechol estro-
gen.
The amount of estrogen receptor in the livers of ovariectomized or immature female rats is
11
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increased by added estradiol (Romkes et al., 1987). One interpretation of this observation is that
there is synthesis of additional receptor protein consequent to binding of the ER-E complex to the
estrogen receptor gene. Female rats treated with 100 ng TCDD/kg/day exhibit a 55% reduction in
the estrogen receptor (Clark et al., 1991). A 40% reduction in estrogen receptor levels was
observed in the livers of mice exposed to a single 100 ng/kg dose of TCDD (Lin et al., 1991b).
"Ah-unresponsive" mice having defective or insufficient Ah receptors require higher TCDD expo-
sures than do normal mice to achieve a given reduction in estradiol binding capacity (Lin et al.,
1991b). These observations may be interpreted as indicating that induction of estrogen receptor is
inhibited by binding of the Ah-TCDD complex at another DNA binding site. This mechanism is
represented in the NIEHS model as noncompetitive inhibition of receptor synthesis by the Ah-
TCDD complex (Appendix 1).
The NIEHS model was implemented in the SCoP simulation program (Kootsey et al., 1986;
Kohn et al., 1992). Values for parameters that could not be obtained from the literature were
adjusted to make the model reproduce the observations of Tntscher et al. (1992) and Sewall et al.
(1992). Where experimental data were available, parameters were estimated using the "praxis"
algorithm (Brent, 1973) in the SCoPfit program (part of the SCoP package).
The fully assembled model was composed of 33 first-order ordinary differential equations.
There were 77 constants in these equations, of which 15 were freely adjustable parameters. The
remaining 62 constants were either obtained from the literature, constrained by experimental data,
or were mathematically determined by the law of conservation of mass. The model's equations
are listed in Appendix 1, and all parameter values (estimated or from the literature) are given in
Appendix 2. The experiments of Tntscher et al. (1992) and Sewall et al. (1992) were simulated by
integration of the differential equations to 217 days of biweekly exposure to TCDD using a C lan-
guage translation of the LSODA Gear integration package from Lawrence Livermore Laboratory.
Results
The NIEHS model predicts that 92.5% of the ingested TCDD is absorbed into the bloodstream
and 7.5% appears unchanged in the feces. As some of the TCDD cleared from the liver by cell lysis
12
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may be returned to the gut (the model does not treat the fate of this material), this result may be an
underestimation of the amount of TCDD in the feces. Rose et al. (1976) report an average value of
90.3% of [14C]-TCDD extracted from the guts of female rats. The model predicts that 92.2% of the
metabolite appears in the feces and 7.8% appears in the urine at a dose of 125 ng/kg/day. The dose
of TCDD did not have a significant effect on these predictions. About 2% of the cumulative dose
is computed to appear as urinary metabolites. Urinary radioactivity at doses comparable to those
of Tritscher et al. (1992) was often undetectable in the chronic dosing experiments of Rose et al.
(1976).
The computed time courses for TCDD in the liver and fat for biweekly oral doses of 125 ng/kg
body weight/day (Tritscher et al., 1992) and for a single subcutaneous injection of 300 ng/kg
(Abraham et al., 1988) are given in Figures 2 and 3, respectively. From the fit to the data of Abra-
ham et al. (1988) the NIEHS model predicts an initial half time of 11.8 days for clearance of TCDD
from the liver and an overall liver half time of 13.5 days. Abraham et al. (1988) report an initial
half time of 11.5 days and an overall half time of 13.6 days in the liver. The model predicts a half
time of 22.3 days in the fat; Abraham et al. (1988) report a half time of 24.5 days in fat.
The predicted dose dependency of TCDD concentration in the blood, liver, and fat after 217
days of exposure (biweekly oral doses of TCDD for 31 weeks) is given in Table 1. The NIEHS
model predicts a linear relationship between administered dose and the concentration in the liver
at doses between 3.5 and 125 ng/kg/day (Figure 4) in agreement with the experimental observa-
tions (Tritscher et al., 1992). Table 2 gives the model's predictions for repeated doses, and Table 3
gives the model's predictions for single doses.
The Hill exponents in the equations for the rates of induction of CYP1 Al and CYP1A2 were
estimated from the data of Tritscher et al. (1992). Hill exponents of 1.0 for induction of CYP1A1
(Figure 5) and CYP1A2 (Figure 6) reproduced the observed responses. Because binding of TCDD
to the Ah receptor is the initial step in the induction of cytochrome P450 isozymes, the fractional
occupancy of the receptor will have a strong effect on the predictions of the model. As fractional
occupancy is dramatically affected by cooperativity in ligand binding, estimation of the Hill expo-
13
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nent for this binding was included in the course of estimating the gene induction parameters for
CYP1A1 and CYP1A2. The optimal value of the exponent was 0.96, indicating a lack of cooper-
ativity (i.e. hyperbolic binding). This value agrees with the estimate of 1.0 obtained by Gasewicz
(1984) and by Bradfield et al. (1988).
Table 4 compares the NIEHS model's predictions to the observed terminal concentrations of
the Ah receptor and of the induced P450 isozymes. The model predicts that the fractional occu-
pancy of the Ah receptor by TCDD (not included in Table 4) rises from 13.4% at a dose of 3.5 ng
TCDD /kg/day to 69.3% at 125 ng/kg/day. The liver concentration of CYP1A1 is 0.02 nmole/g in
the absence of administered TCDD (Tritscher et al., 1992), consistent with (but not proof of) the
notion of an endogenous ligand or a ligand of dietary origin for the Ah receptor. A liver concen-
tration of 1.5 pM (in TCDD equivalent units) of this ligand reproduces the observed basal level of
CYP1A1. Needham et al. (1991) detected a background level of TCDD of 2 pM in human livers.
Table 5 gives the predicted concentration of interstitial TGF-cc after 217 days of exposure to
TCDD and the consequent distribution of the EOF receptor between the plasma membrane and
cytosol. Figure 7 gives the decline in EGF binding capacity of the plasma membranes with the
dose of TCDD and corresponds to that fraction of the receptor which had not been internalized
consequent to binding of TGF-a. Figure 8 relates the predicted degree of internalization of the
EGF receptor to the TGF-a concentration. The points on this curve corresponding to the predicted
responses for the four dose groups of Sewall et al. (1992) are also shown. Note that the TGF-a
concentration is given in pmole/g liver; multiplying by 10 gives the concentration in the intersti-
tial fluid in nM. These interstitial concentrations are comparable to the concentrations produced
by TCDD in the extracellular fluid of keratinocyte cultures exposed to TCDD (Choi et al., 1991).
These results were obtained with a model which required prior binding of the ER-E complex to
activate binding of the Ah-TCDD complex to the TGF-a gene. An alternative model which postu-
lates random-order binding of the ER-E and Ah-TCDD complexes produced quantitatively simi-
lar results.
The NIEHS model predicts that ten days after administration of a single dose of 1 \ig TCDD/
14
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kg there should be a 20.1 % decrease in the Bmax of the plasma membrane EGF receptor in female
rat livers (14% observed; Sunahara et al. 1989), but only a 9.6% decrease in male rat livers (10%
observed on average; Madhukar et al., 1984). This computed two-fold greater responsiveness of
the female over the male persisted over all doses in the range of 0.1 Hg/kg to 10 |ig/kg.
As estrogen plays a major role in regulating TCDD's effects on the EGF receptor, estrogen ac-
tion was examined in the model. Chronic exposure to TCDD was found to reduce the concentration
of the hepatic estrogen receptor in DEN-initiated female rats from 5.1 pmole/g to 2.3 pmole/g
(Clark et al., 1991). The model reproduces this decrease in the estrogen receptor level (Table 6). It
also predicts that the reduction is dependent on the dose of TCDD with a curve shape similar to
that obtained for mouse liver by Lin et al. (1991b).
The calculated liver concentrations of estradiol and catechol estrogen and the calculated rate
of the estradiol 2-hydroxylase activity of CYP1A2 at various doses are given in Table 6. At a dose
of 125 ng TCDD/kg/day the calculated rate of the estradiol 2-hydroxylase is 3.1-fold greater than
in the absence of administered TCDD whereas CYP1A2 is computed to increase 8.9-fold over its
basal level. The NIEHS model predicts that only 44.9% of the CYP1A2 is complexed with reduc-
tase in the absence of administered TCDD. This percentage decreases slightly as more enzyme is
induced; at a dose of 125 ng/kg/day, 39.8% of CYP1A2 is calculated to be complexed with reduc-
tase. The lack of stoichiometric amounts of bound reductase is consistent with the conclusion of
Graham et al. (1988) that P450 reductase is limiting for the hydroxylation of estradiol. However,
at a dose of 125 ng/kg/day, the estradiol concentration in the liver is computed to fall to 39% of its
level in the absence of administered TCDD. As the computed estradiol concentration is below its
Km for this enzyme, the enzymatic rate is probably limited more by depletion of substrate than by
availability of reductase.
Discussion
The purpose of this modeling effort was to suggest plausible biochemical mechanisms which
may explain the observed tissue effects of TCDD in female rats. Internalization of the EGF recep-
tor in response to induction of TGF-oc is a possible origin for the mitogenic signal important to car-
15
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cinogenesis. The NIEHS model's prediction of TGF-oc concentrations that are comparable to those
observed in cell cultures is consistent with this mechanism, and the model's success in reproducing
observed responses to TCDD supports the proposed mechanism. In addition, the production of a
considerable quantity of catechol estrogens may play a role in carcinogenesis (Li et al., 1985) either
by covalent binding to protein or DNA (Tsibris and McGuire, 1977) or by generation of active ox-
ygen species (Cerutti, 1978). The proliferative effect of EOF receptor-mediated events could also
provide genetically altered cells with a selective growth advantage. This mechanism is consistent
with the general agreement that TCDD is a tumor promoter, but not an initiator, in two-stage mod-
els for hepatocarcinogenesis (Kociba et al., 1978; Pitot and Sirica, 1980; Lucier et al., 1991) and a
complete carcinogen in the two-year chronic animal bioassay (Huff et al., 1991).
EGF Receptor and Cellular Proliferation
Figure 7 shows the predicted and observed responses of the maximal binding of EGF to liver
plasma membranes vs. dose of TCDD. Although the liver TCDD concentration is linear in the
administered dose over the range 3.5-125 ng/kg/day, the response of the EGF receptor resembles
a hyperbolic curve. As this response may be involved in the mechanism of tumorigenesis in
TCDD-treated rats, it would be expected that it would correlate with tumor incidence better than
does tissue dose. If this is true, linear extrapolation of effects at high doses to low doses by a line
that passes through the background incidence would underestimate low-dose effects. However,
such hyperbolic curves are approximately linear in the low dose range, indicating that linear
extrapolation from low doses to extremely low doses should still be valid.
Because the interstitial space occupies about 10% of the liver volume, the accumulated TGF-
a is ten times more concentrated in the interstitium than it would be in the cytosol. This concen-
tration effect has significant consequences for the predicted reduction in the B^ of the EGF
receptor. If the TGF-cc were actually produced in some other tissue, it would have to be trans-
ported to the liver interstitium via the blood. This would greatly dilute the concentration of the
peptide and require synthesis of 15 times as much material to result in the observed decrease in
EGF-binding capacity of the plasma membrane.
16
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Although production of TGF-a by the liver was modeled as being induced by the Ah-TCDD
complex, there is evidence (Gaido et al, 1992) for posttranscriptional regulation as well. TCDD
may act to "stabilize" the mRNA for that peptide. If information regarding the mechanism of such
an effect were available, it could readily be incorporated into this model. As it is, the model
merely states that TCDD stimulates the rate-limiting step in the net synthesis of hepatic TGF-a.
Under normal conditions, one of the functions of TGF-a is as a signal for regenerative hyper-
plasia of injured tissue (Burgess, 1989; Mead and Fausto, 1989). The substrates for the internal-
ized EGF receptor's tyrosine kinase may be involved in modulating this process, and their
enzymatic activities may be regulated by phosphorylation. According to the NIEHS model,
TCDD causes inappropriate stimulation of this process. Cells which had previously sustained
nonlethal DNA damage could be stimulated to reproduce before the damage could be repaired.
With the damage fixed in the genome, clonal expansion of the transformed cells could provide a
necessary step in tumor development.
Different populations of cells may differ in the sensitivity of their proliferative responses to
TCDD. The small population of polyploid cells in the livers of rats treated with DEN and ethi-
nylestradiol are especially rich in EGF receptors (Vickers and Lucier, 1991). It is possible that
these cells would show a correspondingly larger concentration of internalized EGF receptors in
response to induction of TGF-a and, hence, a stronger mitogenic signal. If the population of poly-
ploid cells includes preneoplastic cells, proliferation of these previously transformed cells may
represent the most important effect of TCDD in hepatocarcinogenesis.
The NIEHS model predicts that after 31 weeks of treatment, as much EGF receptor is inter-
nalized in the male at a dose of 125 ng TCDD/kg/day as is internalized in the female at a dose of
15 ng/kg/day. This behavior is consistent with the observed lower sensitivity of the male
(Madhukar et al., 1984). It arises because serum estradiol in the male is about 10% of that in the
female (D. Schomberg, Duke University, personal communication), resulting in concentrations of
the ER-E complex which only slightly stimulates production of TGF-a. Because the male rat is
predicted to produce far less TGF-a than the female, the rate of cellular proliferation in the male
17
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at low doses of TCDD should be indistinguishable from that at zero dose. This prediction of the
model could explain the absence of excess liver tumors in male rats exposed to TCDD (Kociba et
al., 1978; National Toxicology Program, 1982).
Cytochrome P450 and Estrogen Metabolism
As estrogen is necessary for TCDD-mediated internalization of the EGF receptor, induction of
CYP1A2 may also be an important index of cancer risk because this cytochrome catalyzes oxida-
tion of estradiol to catechol estrogen. The predicted response curve for this indicator (Figure 6) is
hyperbolic. This behavior is attributable to the Hill coefficient of 1 determined for induction of this
protein.
The finding of at least four enhancer sequences for induction of CYP1 Al (Fisher et al., 1990)
suggests the possibility that several enhancer sites must be occupied to enable transcriptional acti-
vation of the CYP1A1 gene. Such a mechanism could lead to sigmoidal response curves for the
induction of this cytochrome. The Hill exponent of 1, which gave the best fit to the data of Tritscher
et al. (1992), yields a hyperbolic response curve (Figure 5). Because many factors other than co-
operative interactions among binding sites (e.g. heterogeneous uptake of TCDD in the liver and
recycling of TCDD among Ah receptor molecules) could influence the shape of the response curve,
the value of the Hill exponent is not necessarily related to the number of binding sites for the Ah-
TCDD complex.
Using steady-state kinetics and a simpler model, Portier et al. (1992) estimated Hill exponents
of 1.86 for CYP1A1 induction and 0.534 for CYP1A2 induction. A possible origin of the differ-
ence in Hill exponents is the steady state model's neglect of the increase in the concentration of
the Ah receptor with increasing dose of TCDD. The maximal slope of the curve for induction rate
vs. inducer concentration is steeper for cooperative kinetics (Hill exponents not equal to 1) than
for hyperbolic kinetics. The increase in the concentration of the Ah receptor with dose can mimic
the steeper rise of rate of synthesis with dose exhibited by cooperative kinetics.
Portier et al. (1992) showed the importance of the mechanism by which putative alternative
ligands bind to the Ah receptor and/or modulate protein synthesis. In the NIEHS model, constitu-
18
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tive expression of all proteins except CYP1A1 was assumed to be independent of the Ah receptor.
Background CYP1A1 levels were treated as being produced by an additional Ah receptor-depen-
dent mechanism. An "independent" mechanism accentuates any sigmoidicity in the response
curve. An "additive" mechanism minimizes such sigmoidicity and leads to linearity in the low-
dose response. Such an effect may be involved in producing the hyperbolic response of CYP1 Al
(Figure 5). It is also possible that CYP1 Al is constitutively expressed at a low rate by a mechanism
that doesn't involve the Ah receptor. Additional experiments on the mechanism of expression of
CYP1A1 in the absence of administered TCDD could resolve this issue.
As catechol estrogens bind more weakly to the estrogen receptor than does estradiol, it is con-
ceivable that their production from estradiol by the estradiol 2-hydroxylase activity of CYP1A2
could result in reduced ligation of the estrogen receptor. Because the ER-E complex induces syn-
thesis of the estrogen receptor protein, a reduced rate of synthesis of the receptor could result
from the enzymatic activity of CYP1A2. An alternative model, in which the ER-E complex was a
transcriptional activator of the estrogen receptor gene, but the Ah-TCDD complex was not a tran-
scriptional inhibitor of the gene, predicted only an 18.9% decrease in the estrogen receptor con-
centration. As a 55% reduction in the estrogen receptor concentration was observed (Clark et al.,
1991), an Ah receptor-mediated effect of TCDD on expression of the estrogen receptor such as
that incorporated into the NIEHS model is likely. This result is consistent with the observation
(Lin et al., 1991b) of only a 10% decrease in receptor level in Ah-deficient mice.
Serum estradiol in the female rat oscillates from 15 to 120 pg/mL over a five-day estrus cycle.
Including such oscillations in the NIEHS model could make as much as a 12% difference in the
model's predictions, depending on where in the cycle the simulation was begun. The serum levels,
50-60 pg/mL, reported by Shiverick and Muther (1983) are close to the average level over the en-
tire cycle, and the experimental data are the average results for nine individuals. Therefore, use in
the model of Shiverick and Muther's value for the circulating estradiol concentration is justified.
TCDD Tissue Dose
The LPMA model predicts a liver TCDD content 2.4-fold higher than observed at a dose of
125 ng/kg/day (Tritscher et al., 1992). The LPMA model assumes that TCDD binds to total
19
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CYP1A2 with a Kd of 7 nM, and it predicts a liver CYP1A2 concentration 60% higher than
observed at a dose of 125 ng/kg/day (Tritscher et al., 1992). The resulting large calculated amount
of bound TCDD is most likely responsible for the overprediction of liver TCDD.
Although the NIEHS model accurately reproduces the observed CYP1A2 concentration,
much less binding had to be assumed in order to reproduce the experimental data. An alternative
model in which TCDD is bound nonspecifically to an unspecified protein with Kd between 5 and 8
nM required only about 0.2 nmole/g liver of that protein to reproduce the observed liver TCDD
content. Setting the binding protein concentration to values comparable to that of CYP1A2 neces-
sitated a higher rate constant for clearance of TCDD from the liver in order to match the experi-
mental data and resulted in a half time in the liver of only 7 days, half the experimentally observed
value.
Livers of TCDD-treated rats often show an increase in lipid content. From the data of
Tritscher et al. (1992) the relationship
(fraction lipid) = 0.01865 x (nmole total liver TCDD) + 0.1443 x (kg body weight)
was obtained. Allowing the TCDD to partition between the aqueous phase of the liver cytosol and
lipid droplets with partition coefficients between 140 and 350 did not sequester enough TCDD to
match the data without also requiring induction of TCDD-binding protein.
The dose-response relationships illustrated by the NIEHS model are important for understand-
ing the risks of adverse health effects following exposure to TCDD. Sigmoidicity in the response
requires a higher concentration to produce a given response at low dose than does hyperbolic re-
sponse exhibiting the same concentration for half-maximal effect. Such behavior can have dramat-
ic consequences for the estimated minimum exposure to TCDD that should produce an
unacceptable risk of adverse health effects (Portieret al., 1992). All of the amounts of protein cal-
culated in this model show hyperbolic dependence on dose. The fact that the model presented here
does not predict sigmoidicity in the observed responses indicates that the response is approximate-
ly linear at very low doses. The model's success in reproducing these responses in a biochemically
realistic way suggests that it should also be useful in the analysis of more complex dose-response
relationships, such as for cellular proliferation rates and tumor incidence.
20
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Appendix 1
Model Equations
Compartment and Flow Characteristics
CardiacOutput = 404 x BodyWeight0-75 (Delp et al., 1991)
StartWeight = 0.237 kg (Tritscher et al., 1992)
BodyWeight = StartWeight + 0.219674 x e-0-002859***6 x time / (116.345 + time)
(BodyWeight was estimated from the data o/Tritscher et al. (1992) as a Junction of dose
and time by nonlinear regression)
BloodVolume = 0.054 x BodyWeight (Delp et al., 1991)
FatVolume = 0.08 x BodyWeight (Average of literature values: Leung et al.,
1990; Delp etal., 1991)
MuscleVolume = 0.59 x BodyWeight (Delp et al., 1991)
(This compartment also includes skin and other slowly perfused tissues)
VisceraVolume = 0.083 x BodyWeight (Delp etal., 1991)
(This compartment includes all richly perfused tissues)
LiverVolume = BodyWeight x (0.03324 + 0.01927 x dose / (88.91 + dose))
(Liver weight was estimated from the data ofTritscher et al. (1992) as a function of dose
by nonlinear regression)
LiverlnterstitialVolume = frac_interstitium x LiverVolume
LiverFlow = 0.072 x CardiacOutput (Delp etal., 1991)
FatFlow = 0.07 x CardiacOutput (Delp et al., 1991)
MuscleFlow = 0.36 x CardiacOutput (Delp et al., 1991)
VisceraFlow = 0.40 x CardiacOutput (Delp et al., 1991)
Distribution of TCDD
GutTCDD = GutTCDD + dose(f) x BodyWeight 7322
(Doses at time t—in ng/kg—were introduced into the gut compartment according to the
experimental schedule; dose(f) = 0 if no dose is scheduled at time t)
GutTCDD' = - k_absorption x GutTCDD / BodyWeight0 75- k_feces x
GutTCDD
FecesTCDD' = k_feces x GutTCDD
21
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cum_exposure
BloodProtein
BloodTCDD'
BloodBoundTCDD'
MuscleTCDD'
FatTCDD'
VisceraTCDD'
LiverTCDD'
LiverTCDD
MaxBloodProtein x BloodVolume x Ki_BloodProtein /
(Ki_BloodProtein + LiverTCDD / LiverVolume)
k_absorption x GutTCDD / BodyWeight0 75 - BloodTCDD x
MuscleDiffusion x MuscleFlow / BloodVolume - BloodTCDD x
FatDiffusion x FatFlow / BloodVolume - BloodTCDD x
VisceraDiffusion x VisceraFlow / BloodVolume - BloodTCDD x
LiverDiffusion x LiverFlow / BloodVolume + MuscleTCDD x
MuscleDiffusion x MuscleFlow / (MuscleVolume x
MusclePartition) + FatTCDD x FatDiffusion x FatFlow /
(FatVolume x FatPartition) + VisceraTCDD x VisceraDiffusion x
VisceraFlow / (VisceraVolume x VisceraPartition) + LiverTCDD
x LiverDiffusion x LiverFlow / (LiverVolume x LiverPartition) -
k_binding x BloodTCDD x BloodProtein + k_binding x
K_BloodProtein x LiverVolume x BloodBoundTCDD
k_binding x BloodTCDD x BloodProtein - k_binding x
K_BloodProtein x LiverVolume x BloodBoundTCDD
BloodTCDD x MuscleDiffusion x MuscleFlow / BloodVolume -
MuscleTCDD x MuscleDiffusion x MuscleFlow /
(MuscleVolume x MusclePartition)
BloodTCDD x FatDiffusion x FatFlow / BloodVolume -
FatTCDD x FatDiffusion x FatFlow / (FatVolume x FatPartition)
BloodTCDD x VisceraDiffusion x VisceraFlow / BloodVolume -
VisceraTCDD x VisceraDiffusion x VisceraFlow /
(VisceraVolume x VisceraPartition)
BloodTCDD x LiverDiffusion x LiverFlow / BloodVolume -
LiverTCDD x LiverDiffusion x LiverFlow / (LiverVolume x
LiverPartition) - k_metabolism x LiverTCDD - k_binding x
LiverTCDD x LiverCYPl A2 + k_binding x K_TCDD x
LiverVolume x LiverCYPl A2_TCDD - k_binding x LiverTCDD
x LiverAhReceptor + k_binding x K_AhR x LiverVolume x
LiverAhRJTCDD + k_proteolysis x (LiverAhRJTCDD +
LiverCYPl A2JTCDD) - k_lysis x cum_exposure/ (crit_exposure
+ cum_exposure) x LiverTCDD
22
-------
LiverCYPlA2_TCDD' = k_bindingxLiverTCDDxLiverCYPlA2-k_bindingx
K_TCDD x LiverVolume x LiverCYPl A2_TCDD
TCDD Metabolite Clearance
LiverMetabolite'
BloodMetabolite'
UrineMetabolite'
GutMetabolite'
FecesMetabolite'
Ah Receptor and P450 Induction
= k_metabolism x LiverTCDD - k_bile x LiverMetabolite +
BloodMetabolite x LiverDiffusion x LiverFlow / BloodVolume -
LiverMetabolite x LiverDiffusion x LiverFlow / (LiverVolume x
LiverPartition)
= - BloodMetabolite x LiverDiffusion x LiverFlow / BloodVolume
+ LiverMetabolite x LiverDiffusion x LiverFlow / (LiverVolume
x LiverPartition) - k-urine x BloodMetabolite
= k_urine x BloodMetabolite
= k_bile x LiverMetabolite - k_feces x GutMetabolite
= k feces x GutMetabolite
LiverAhR TCDD'
LiverAhR Induced
LiverAhReceptor'
LiverCYPlAl'
= k_binding x LiverTCDD x LiverAhReceptor - k_binding x
K_AhR x LiverVolume x LiverAhR_TCDD - k_proteolysis x
LiverAhR_TCDD
= k_binding x LiverVolume x Natural_Inducer x LiverAhReceptor
- k_binding x K_AhR x LiverVolume x LiverAhR_Inducer -
k_proteolysis x LiverAhR_Inducer
= V_AnRinduction x LiverVolume / (1 +(K_AhRinduction x
LiverVolume) / LiverAhR_TCDD(/ - It)) + AhRexpression x
LiverVolume - k_binding x LiverTCDD x LiverAhReceptor +
k_binding x K_AhR x LiverVolume x LiverAhRJTCDD -
k_binding x LiverVolume x Natural_Inducer x LiverAhReceptor
+ k_binding x K_AhR x LiverVolume x LiverAhR_Inducer -
k_proteolysis x LiverAhReceptor
= V_CYPlAlinduction x LiverVolume / (1 +
(K_CYP1 Alinduction x LiverVolume) / (LiverAhR_TCDD(r - It)
+ LiverAhR_Inducer(f - It))) - k_proteolysis x LiverCYPlAl -
k_binding x LiverCYPlAl x LiverP450Reductase + k_binding x
K_Reductase x LiverVolume x LiverCYPl A1_R
23
-------
LiverP450Reductase' = P450Red_expression x LiverVolume - k_proteolysis x
LiverP450Reductase - k_binding x LiverCYPl Al x
LiverP450Reductase + k_binding x K_Reductase x LiverVolume
x LiverCYPlA1_R - k_binding x LiverCYPlA2 x
LiverP450Reductase + k_binding x K_Reductase x LiverVolume
x LiverCYPl A2_R - k_binding x LiverCYPl A2_E2 x
LiverP450Reductase + k_binding x K_Reductase x LiverVolume
x LiverCYPl A2_R_E2
LiverCYPl A 1_R' = k_binding x LiverCYPl Al x LiverP450Reductase - k_binding x
K_Reductase x LiverVolume x LiverCYPlAl_R
Estradiol Metabolism
LiverE2'
LiverCYPlA2'
LiverCYPlA2 R'
LiverCYPlA2_E2'
ConcBloodE2 x LiverFlow - LiverE2 x LiverFlow /
(LiverVolume x LiverPartition) - k_binding x LiverCYPl A2 x
LiverE2 + k_binding x K_E2 x LiverVolume x LiverCYPl A2_E2
- k_binding x LiverCYPl A2_R x LiverE2 + k_binding x K_E2 x
LiverVolume x LiverCYPl A2_R_E2 + k_proteolysis x
LiverER_E2 - k_binding x LiverEReceptor x LiverE2 +
k_binding x K_ER_E2 x LiverVolume x LiverER_E2
V_CYPlAlinduction x LiverVolume / (I +
(K_CYP1 A2induction x LiverVolume) / LiverAhR_TCDD(r - It)
+ CYPlA2expression x LiverVolume - k_proteolysis x
(LiverCYPlA2 + LiverCYPlA2_E2) - kjnnding x
LiverCYPl A2 x LiverP450Reductase + k_binding x
K_Reductase x LiverVolume x LiverCYPlA2_R - k_binding x
LiverCYPl A2 x LiverE2 + k_binding x K_E2 x LiverVolume x
LiverCYPlA2_E2
k_binding x LiverCYPl A2 x LiverP450Reductase - k_binding x
K_Reductase x LiverVolume x LiverCYPl A2_R + V_E2H x
LiverCYPlA2_R_E2
k_binding x LiverCYPl A2 x LiverE2 - k_binding x K_E2 x
LiverVolume x LiverCYPl A2_E2 - k_binding x
LiverCYPlA2_E2 x LiverP450Reductase + k_binding x
K_Reductase x LiverVolume x LiverCYPl A2_R_E2
24
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LiverE2OH'
LiverEReceptor'
LiverCYPlA2_R_E2' = k_binding x LiveiCYPl A2_E2 x LiverP450Reductase -
k_binding x K_Reductase x LiverVolume x LiverCYPl A2_R_E2
+ kjnnding x LiverCYPl A2_R x LiverE2 - k_binding x K_E2 x
LiverVolume x LiverCYPl A2_R_E2 - V_E2H x
LiverCYPlA2_R_E2
= V_E2H x LiverCYPlA2_R_E2 - k_binding x LiverEReceptor x
LiverE2OH + kjnnding x K_ER_E2OH x LiverVolume x
LiverER_E2OH - k_conjugation x LiverE2OH
= V_ERinduction x LiverVolume / ((1 + K_ERinduction x
LiverVolume / (LiverER_E2(r - It) + LiverER_E2OH(f - It))) x (1
+ LiverAhR_TCDD(r - It) / (Ki_ERinduction x LiverVolume))) +
ERexpression x LiverVolume - k_proteolysis x LiverEReceptor -
k_binding x LiverEReceptor x LiverE2 + k_binding x K_ER_E2
x LiverVolume x LiverER_E2 - k_binding x LiverEReceptor x
LiverE2OH + k_binding x K_ER_E2OH x LiverVolume x
LiverER_E2OH
= - k_binding x LiverEReceptor x LiverE2 + k_binding x K_ER_E2
x LiverVolume x LiverER_E2 - k_proteolysis x LiverER_E2
= - k_binding x LiverEReceptor x LiverE2OH+ k_binding x
K_ER_E2OH x LiverVolume x LiverER_E2OH - k_proteolysis x
LiverER E2OH
LiverER_E2'
LiverER E2OH'
EGF Receptor and TGF-a
LiverEGFReceptor'
LiverTGF
LiverlnternalEGFR'
EGFRexpression x LiverVolume - k_endocytosis x
LiverEGFReceptor
V_TGFinduction x LiverVolume / (1 + K_TGFinduction x
LiverVolume / (LiverAhR_TCDD(f - It) x (1 + Ka_TGFinduction
x LiverVolume / (LiverER_E2(r - It) + LiverER_E2OH(f - It))))) -
k_proteolysis x LiverTGF - k_binding x LiverEGFReceptor x
LiverTGF + k_binding x K_TGF x LiverVolume x
LiverlnternalEGFR
k_binding x LiverEGFReceptor x LiverTGF - k_binding x
K_TGF x LiverVolume x LiverlnternalEGFR - k_endocytosis x
LiverlnternalEGFR
25
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Initial Conditions
LiverAhReceptor
LiverAhR_Inducer
LiverCYPlAl
LiverCYPlAl_R
LiverCYPlA2
LiverCYPlA2_R
LiverP450Reductase
LiverE2
LiverEReceptor
LiverER_E2
LiverER_E2OH
LiverEGFReceptor
= 0.017806 nmole (Sloop and Lucier, 1987)
= 0.000674 nmoleb
= 0.063174 nmole (Tritscher et al., 1992)
= 0.094902 nmoleb
= 2.4199 nmole (Tritscher et al., 1992)
= 1.9799 nmole8
= 0.602536 nmole (Miwa et al., 1978)
= 0.00123 nmoleb
= 0.01495 nmole (Clark et al., 1991)
= 0.02325 nmolea
= 0.0066792 nmole3
= 0.022563 nmole (Sewall et al., 1992)
(All other state variables start at zero.)
a.. Determined from conservation of mass
b. Assumed at equilibrium with blood
26
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Appendix 2
Physiological parameters
frac_interstitium =0.1
It = 0.25 day (Sloop and Lucier, 1987)
(delay between binding ofligated receptor to DNA and appearance of induced proteins in
the liver cell)
ConcBloodE2 = 0.185nmole/L(ShiverickandMuther, 1983)
crit_exposure = 0.3 nmole
Production of blood proteins8
MaxBloodProtein = 5 nmole/L
Ki_BloodProtein = 0.1 nM
K_BloodProtein = 0.4 nM
Receptor binding constants
K_AhR = 0.27 nM (Apparent Kd in biological materials: Poland and
Knutson, 1982)
K_ER_E2 = 0.13nM(Vickersetal., 1987)
K_ER_E2OH = 1.3 nM (Li et al., 1985)
K_TGF = 0.28 nM (Hudson et al., 1985)
KJTCDD = 30 nM (Poland et al., 1989; Voorman and Aust, 1989)
Constitutive expression ratesb
AhRexpression = 1.4553 nmole/L/day
ERexpression = 0.67273 nmole/L/day
CYPlA2expression = 213.4 nmole/L/day
P450Reductase_expression = 47.45 nmole/L/day
EGFRexpression = 0.69376 nmole/L/day
Endogenous ligand of Ah receptor
Naturaljnducer = 0.0015 nmole/La
(assumed same Kd as for TCDD)
Gene induction parameters
V_AhRinduction = 7.2 nmole/L/day (Lower bound of 4 nmole/L/day: Sloop and
Lucier, 1987)
K_AhRinduction = 4 nM (Sloop and Lucier, 1987)
27
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V_CYPlAlinduction
K_CYPlAlinduction
V_CYPlA2induction
K_CYPlA2induction
V_ERinduction
K_ERinduction
Ki_ERinduction
V_TGFinduction
KJTGFinduction
Ka_TGFinduction
Metabolic parameters
V_E2H
K_E2
K_Reductase
Rate constants
k_absorption
k_binding
= 3319nmole/L/daya
= 4.279 nMa
= 4197nmole/L/daya
= 7.458 nMa
= 3.2488 nmole/L/day (Romkes et al., 1987)c
= 0.35 nMc
= 3.1 nMc
= 1.5nmole/L/dayd
= 9nMd
= 0.75 nMd
= 8496 day1 (Graham et al., 1988)
= 9400 nM (Voorman and Aust, 1987)
= 83.5 nM (Miwa et al., 1987)
= 4.8 kg°-75/day (Leung et al., 1990)
= 105 nmole^day1
(value selected to ensure that binding reactions are always close to equilibrium)
k_proteolysis
k_endocytosis
k_metabolism
k_urine
k_bile
k_feces
k_conjugation
k_lysis
= 0.693 day1 (Lucier et al., 1972)
= 0.271 dayld
= 2.75 day1
= 5.36 day1 (Birnbaum et al., 1980)
= 3.81 day1 (Birnbaum et al., 1980)
= 1.152 day1 (Lutz et al., 1977)
= 56.693 day1 (Lucier and McDaniel, 1977)
= 9.5 day1
Partition and diffusion coefficients
FatPartition
MusclePartition
VisceraPartition
LiverPartition
FatDiffusion
MuscleDiffusion
VisceraDiffusion
LiverDiffusion
= 350 (Leung et al., 1990)
= 40 (Leung et al., 1990)
= 20 (Leung et al., 1990)
= 20 (Leung et al., 1990)
= O.le
= 0.5e
= 0.5e
= 0.8e
28
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a. Determined from the data of Tritscher et al. (1992)
b. Determined from conservation of mass
c. Determined from the data of Clark et al. (1991)
d. Determined from the data of Sewall et al. (1992)
e. Selected to fit the data of Abraham et al. (1988)
29
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References
Abraham, K., Krowke, R., and Neubert, D. (1988). Pharmacokinetics and biological activity of
2,3,7,8-tetrachlorodibenzo-p-dioxin. Arch. Toxicol. 62, 359-368.
Birnbaum, L.S., Decad, G.M., and Matthews, H.B. (1980). Disposition and excretion of 2,3,7,8-
tetrachlorodibenzofuran in the rat. Toxicol. Appl. Pharmacol. 55, 342-352.
Bradfield, C.A., Kende, A.S., and Poland, A. (1988). Kinetic and equilibrium studies of Ah recep-
tor-ligand binding: use of [125I]2-iodo-7,8-dibromodibenzo-p-dioxin. Mol. Pharmacol. 34,
229-237.
Brent, R.P. (1973). Algorithms for Minimization without Derivatives. Prentice-Hall, Englewood
Cliffs, NJ.
Burgess, A.W. (1989). Epidermal growth factor and transforming growth factor a. British Med.
Bulletin 45, 401^24.
Cerutti, P. (1978). Repairable damage in DNA. In: DNA Repair Mechanisms (P. Hanawalt, E.
Friedberg, and C. Foe, eds), pp. 1-14. Academic Press, New York.
Choi, E.J., Toscano, D.G., Ryan, J.A., Riedel, N. Toscano, W.A. (1991). Dioxin induces trans-
forming growth factor-a in human keratinocytes. J. Biol. Chem. 266,9591-9597.
Clark, G., Tritscher, A., Maronpot, R., Foley, J., and Lucier, G. (1991). Tumor Promotion by
TCDD in Female Rats. In Banbury Report 35: Biological Basis for Risk Assessment ofDiox-
ins and Related Compounds, pp. 389-404. Cold Spring Harbor Press.
Coon, M.J., Vermillion, J.L., Vatsis, K.P., French, J.S., Dean, W.L., and Haugen, D.A. (1977).
Biochemical studies on drug metabolism: isolation of multiple forms of liver microsomal
cytochrome P450. In: Drug Metabolism Concepts (D.M. Jerina, ed). ACS Symposium Series,
Vol. 44, pp. 46-71. American Chemical Society, Washington.
Delp, M.D, Manning, R.O., Bruckner, J.V., and Armstrong, R.B. (1991). Distribution of cardiac
output during diurnal changes of activity in rats. Am. J. Physiol. 261, H1487-H1493.
Estabrook, R.W., and Werringloer, J. (1977). Cytochrome P450—its role in oxygen activation for
drug metabolism. In: Drug Metabolism Concepts (D.M. Jerina, ed). ACS Symposium Series,
30
-------
Vol. 44, pp. 1-26. American Chemical Society, Washington.
Fisher, J.M., Wu, L., Denison, M.S., and Whitlock, J.P. (1990). Organization and function of a
dioxin-responsive enhancer. J. Biol. Chem. 265, 9676-9681.
Gaido, K.W., Maness, S.C., Leonard, L.S., and Greenlee, W.F. (1992). TCDD-dependent regula-
tion of transforming growth factors-a and TGF-|52 expression in a humnan keratinocyte cell
line involves both transcriptional and posttranscriptional control. J. Biol. Chem., submitted.
Gasewicz, T.A. (1984). Evidence for a homologous nature of Ah receptors among various
mammmalian species. In Biological Mechanisms ofDioxin Action (A. Poland and R.D. Kim-
brough, eds), pp. 161-176. Cold Spring Harbor Press.
Graham, M.J., Lucier, G.W., Linko, P., Maronpot, R.R., and Goldstein. J.A. (1988). Increases in
cytochrome P450 mediated 17|3-estradiol 2-hydroxylase activity in rat liver microsomes after
both acute administration and subchronic administration of 2,3,7,8-tetrachlorodibenzo-p-
dioxin in a two-stage hepatocarcinogenesis model. Carcinogenesis 9,1935-1941.
Hoffman, B.C., Reyes, H., Chu, F.F., Sander, F., Conley, L.H., Brooks, B.A., and Hankinson, O.
(1991). Cloning of a factor required for activity of the Ah (dioxin) receptor. Science, 252,
954-958.
Hudson, L.G., Toscano, W.A., and Greenlee, W.F. (1985). Regulation of epidermal growth factor
binding in a human keatinocyte cell line by 2,3,7,8-tetrachlorodibenzo-p-dioxin. Toxicol.
Appl. Pharmacol. 77, 251-259.
Huff, J..E., Salmon, A.G., Hooper, N.K., and Zeise, L. (1991). Long-term carcinogenesis studies
on 2,3,7,8-tetrachlorodibenzo-p-dioxin and heachlorodibenzo-/?-dioxins. Cell Biol. Toxicol. 7,
67-94.
Hunter, T., and Cooper, J.A. (1985). Protein-tyrosine kinases. Ann. Rev. Biochem. 54, 897-930.
Kociba, R.J., Keyes, D.G., BeyerJ.E., Carreon, R.M., Wade, C.E., Dittenber, D.A., Kalnins, R.P.,
Frauson, L.E., Park, C.N., Barnard, S.D., Hummel, R.A. and Humiston, C.G. (1978). Results
of a two-year chronic toxicity and oncogenicity study of 2,3,7,8-tetrachlorodibenzo-/?-dioxin
in rats. Toxicol. Appl. Pharmacol. 46,279-303.
31
-------
Kohn, M.C., Hines, M.L., Kootsey, J.M., and Feezor, M.D. (1992). A block organized model
builder. Adv. Mathem. Computers Med., in press.
Kootsey, J.M., Kohn, M.C., Feezor, M.D., Mitchell, G.R., and Fletcher, P.R. (1986). SCoP: an
interactive simulation control program for micro- and minicomputers. Bull. Math. Biol. 48,
427-441.
Leung, H.-W., Paustenbach, D.J., Murray, F.J., and Andersen, M.E. (1990). A physiological phar-
macokinetic description of the tissue distribution and enzyme-inducing properties of 2,3,7,8-
tetrachlorodibenzo-p-dioxin in the rat. Toxicol. Appl. Pharmacol. 103, 399-410.
Li, S.A., Klicka, J.K., and Li, J.J. (1985). Estrogen 2- and 4-hydroxylase activity, catechol estro-
gen formation, and implications for estrogen carcinogenesis in the hamster kidney. Cancer
Res.,45, 181-185.
Lin, F.H., Clark, G., Birnbaum, L.S., Lucier, G.W., and Goldstein, J.A. (1991a). Influence of the
Ah locus on the effects of 2,3,7,8-tetrachlorodibenzo-/?-dioxin on the hepatic epidermal
growth factor receptor. Mol. Pharmacol. 39, 307-313.
Lin, F.H., Stohs, S.J., Birnbaum, L.S., Clark, G., Lucier, G.W., and Goldstein, J.A. (1991b). The
effects of 2,3,7,8-tetrachlorodibenzo-/?-dioxin (TCDD) on the hepatic estrogen and glucocor-
ticoid receptors in congenic strains of Ah responsive and Ah nonresponsive C57BL/6J mice.
Toxicol. Appl. Pharmacol. 108, 129-139.
Lucier, G.W. (1991). Humans are a sensitive species to some of the biochemical effects of struc-
tural analogs of dioxin. Environ. Toxicol. 10, 727-735.
Lucier, G.W., Klein, R., Matthews, H.B., and McDaniel, O.S. (1972). Increased degradation of rat
liver co-binding pigment by methylmercury hydroxide. Life Sci. Part II11, 579-604.
Lucier, G.W., and McDaniel, O.S. (1977). Steroid and non-steroid UDP glucuronyltransferase:
glucuronidation of synthetic estrogens as steroid. J. Steroid Biochem. 8, 867-872.
Lucier, G.W., Tritscher, A., Goldsworth, T., Foley, J., Clark, G., Goldstein, J., and Maronpot, R.
(1991). Ovarian hormones enchance 2,3,7,8-tetrachlorodibenzo-p-dioxin-mediated increases
in cell proliferation and pre.neoplastic foci in a two-stage model for rat hepatocarcinogenesis.
32
-------
Cancer Res. 51, 1391-1397.
Lutz, R.J., Dedrick, R.L., Matthews, H.B., Eling, T.E., and Anderson, M.W. (1977). A prelimi-
nary pharmacokinetic model for several chlorinated biphenyls in the rat. Drug Metab. Disp. 5,
386-396.
Madhukar, B.V, Brewster, D.W., and Matsumura, F. (1984). Effects of in v/vo-administered
2,3,7,8-tetrachlorodibenzo-p-dioxin on receptor binding of epidermal growth factor in the
hepatic plasma membrane of rat, guinea pig, mouse, and hamster. Proc. Natl. Acad. Sci. 81,
7407-7411.
Mead, I.E., and Fausto, N. (1989). Transforming growth factor-cc may be a physiological regula-
tor of liver regeneration by means of an autocrine mechanism. Proc. Natl. Acad. Sci. USA 86,
1558-1562.
Miwa, G.T., West, S.B., and Lu, A.Y.H. (1978). Studies on the rate-limiting enzyme component in
the microsomal monooxygenase system. J. Biol. Chem. 253,1921-1929.
National Toxicology Program (1982). Carcinogenesis bioassay of 2,3,7,8-tetrachlorodibenzo-p-
dioxin in Osborne-Mendel rats and B6C3F1 mice. National Toxicology Program Technical
Report Number 209, Research Triangle Park, North Carolina 27709.
Needham, L.L., Patterson, D.G., and Houk, V.N. (1991). Levels of TCDD in selected human pop-
ulations and their relevance to human risk assessment. In Banbury Report 35: Biological Basis
for Risk Assessment ofDioxins and Related Compounds, pp. 229-257. Cold Spring Harbor
Press.
Osborne, R., Cook, J.C., Dold, K.M., Ross, L., Gaido, K., and Greenlee, W.F. (1988). TCDD
receptor: mechanisms of altered growth regulation in normal and transformed human kerati-
nocytes. In Tumor Promoters. Biological Approaches for Mechanistic Studies and Assay Sys-
tems (R. Langenbach, ed.), pp. 407-416. Raven Press, New York.
Pitot, H.C. and Sirica, A.E. (1980). The stages of initiation and promotion in hepatocarcinogene-
sis. Biochim. Biophys. Acta 605,191-215.
Poland, A., and Knutson, J.C. (1982). 2,3,7,8-Tetrachlorodibenzo-p-dioxin and related haloge-
33
-------
nated aromatic hydrocarbons: examination of the mechanism of toxicity. Ann. Rev. Pharma-
col. Toxicol. 22, 517-554.
Poland, A., Teitelbaum, P., and Glover, E. (1989). [125I]2-Iodo-3,7,8-trichlorodibenzo-p-dioxin-
binding species in mouse liver induced by agonists for the Ah receptro: characterization and
Identification. Mol. Pharmacol 36,113-120.
Portier, C, Tritscher, A., Kohn, M, Sewall, C., Clark, G., Edler, L., Hoel, D., and Lucier, G.
(1992). Fund. Appl. Toxicol., in press.
Romkes, M., Piskorska-Pliszczynska, J., and Safe, S. (1987). Effects of 2,3,7,8-tetrachloro-
dibenzo-/?-dioxin on hepatic and uterine estrogen receptor levels in rats. Toxicol. AppL Phar-
macol. 87, 306-314.
Rose, J.Q., Ramsey, J.C., Wentzler, T.H., Hummel, R.A., and Gehring, P.J. (1976). The fate of
2,3,7,8-tetrachlorodibenzo-p-dioxin following single and repeated oral doses to the rat. Toxi-
col. Appl. Pharmacol. 36, 209-226.
Schlessinger, J., Schreiber, A.B., Levi, A., Lax, I., Liberman, T., and Yarden, Y. (1983). Regula-
tion of cell proliferation by epidermal growth factor. CRC Crit. Rev. Biochem. 14, 93-111.
Sewall, C., Lucier, G., Tritscher, A., and Clark, G. (1992) Dose-response relationships for TCDD-
mediated changes in hepatic EGF receptor in an initiation-promotion model for hepatocar-
cinogenesis in female rats. Cancer Res., submitted.
Shiverick, K.T., and Muther, T.F. (1983). 2,3,7,8-Tetrachlorodibenzo-/?-dioxin (TCDD) effects on
hepatic microsomal steroid metabolism and serum estradiol of pregnant rats. Biochem. Phar-
macol. 32, 991-995.
Sloop, T.C., and Lucier, G.W. (1987). Dose-dependent elevation of Ah receptor binding by TCDD
in rat liver. Toxicol. Appl. Pharmacol. 88, 329-337.
Sunahara, G.I., Lucier, G.W., McCoy, Z., Bresnick, E.H., Sanchez, E.R., and Nelson, K.G.
(1989). Characterization of 2,3,7,8-tetrachlorodibenzo-p-dioxin-mediated decreases in dex-
amethasone binding to rat hepatic cytosolic glucocorticoid receptor. Mol. Pharmacol. 36,
239-247.
34
-------
Tritscher, A.M., Goldstein, J.A., Portier, C.J., McCoy, Z., Clark, G.C., and Lucier, G.W. (1992).
Dose-response relationships for chronic exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxir. in a
rat tumor promotion model: quantification and immunolocalization of CYP1A1 and CYP1A2
in the liver. Cancer Research, 52, 3436-3442.
Tsibris, J.C.M., and McGuire, P. (1977). Microsomal activation of estrogens and binding to rat
liver DNA in vivo by selective induction of microsomal and nuclear aryl hydrocarbon hydrox-
ylase activity. Cancer Res., 38,4640-4644.
Ullrich, V., and Duppel, W. Iron- and copper-containing mono-oxygenases. In The Enzymes, 3rd
ed. (P.D. Boyer, ed.). Vol. 12, pp. 253-297. Academic Press, New York.
Vickers, A.E.M., and Lucier, G.W. (1991). Estrogen receptor, epidermal growth factor receptor
and cellular ploidy in elutriated subpopulations of hepatocytes during liver tumor promotion
by 17oc-ethinylestradiol in rats. Carcinogenesis 12, 391-399.
Vickers, A.E.M., Nelson, K., McCoy, Z., and Lucier, G.W. (1989). Changes in estrogen receptor,
DNA ploidy, and estrogen metabolism in rat hepatocytyes during a two-stage model for hepa-
tocarcinogenesis using 17a-ethinylestradiol as the promoting agent. Cancer Res. 49, 6512-
6520.
Voorman, R., and Aust, S.D. (1987). Specific binding of polyhalogenated aromatic hydrocarbon
inducers of cytochrome P450A to the cytochrome and inhibition of its estradiol 2-hydroxylase
activity. Toxicol. Appl. Pharmacol. 90,69-78
Voorman, R., and Aust, S.D. (1989). TCDD (2,3,7,8-tetrachlorodibenzo-/?-dioxin) is a tight bind-
ing inhibitor of cytochrome P-450d. J. Biochem. Pharmacol. 4,105-109.
35
-------
Table 1: Computed TCDD distribution
Dose,
ng/kg/day
0.01
0.1
0.3
1.0
3.5
7.0
10.7
15.0
20.0
25.0
30.0
35.7
45.0
55.0
65.0
75.0
85.0
100.0
115.0
125.0
Blood, nMa
0.0000967
0.000925
0.00254
0.00672
0.0150
(0.0127-0.0174)
0.0218
0.0269
(0.0143-0.0251)
0.0315
0.0361
0.0400
0.0435
0.0471
(0.0323-0.103)
0.0523
0.0573
0.0618
0.0661
0.0700
0.0754
0.0806
0.0839
(0.0401-0.134)
Liver, nmole/g
0.00000394
0.0000395
0.000118
0.000389
0.00142
(0.000652-0.00267)
0.00313
0.00515
(0.00435-0.00683)
0.00754
0.0105
0.0134
0.0165
0.0199
(0.0155-0.0311)
0.0252
0.0307
0.0360
0.0417
0.0468
0.0543
0.0618
0.0673
(0.0385-0.134)
Fat,
nmole/g
0.00000432
0.0000421
0.000120
0.000350
0.000954
0.00162
0.00223
0.00289
0.00361
0.00431
0.00496
0.00568
0.00683
0.00804
0.00925
0.0104
0.0115
0.0133
0.0149
0.0160
a. A.M. Tritscher, unpublished results
b. Range of observed values (Tritscher et al., 1992) given in parentheses
36
-------
Table 2: Fit of model to observed TCDD concentrations following repeated dosesa
10
Dose, ng/kg
100
dosing 5 days/week for 3 weeks
Liver
Fat
0.00235
(0.00217-0.00280)
0.00108
(0.000932)
dosing 5 days/week for 7 -weeks
Liver
Fat
0.00408
(0.00342-0.00652)
0.00170
(0.000620-0.00124)
0.0373
(0.0298-0.0435)
0.00726
(0.0059-0.0109)
0.0505
(0.0519-0.0711)
0.0107
(0.0118-0.0161)
1000
dosing 5 days/week for 1 week
Liver 0.000898
(0.0)
Fat 0.0004063
(0.0)
0.0141
(0.00870-0.0155)
0.00302
(0.000932-0.00466)
0.147
(0.143-0.165)
0.0249
(0.0217-0.0404)
0.302
(0.227-0.457)
0.0586
(0.0497-0.0963)
0.329
(0.471-0.796)
0.0766
(0.0767-0.305)
a. Concentrations in nmole/g tissue; observed values (Rose et al., 1976) in parentheses
37
-------
Table 3: Fit of model to observed TCDD concentrations following single doses3
Dose, ng/kg
Liver, nmole/g
Fat, nmole/g
22 days following gavage
1000
4 days following gavage0
0.25
0.5
10
100
0.0114
(0.00869-0.0145)
0.00000366
(0.0000035)
0.00000734
(0.0000035)
0.0000147
(0.0000078)
0.000155
(0.000066)
0.00198
(0.0011)
7 days following subcutaneous injection
1 0.0000119
(0.0000096)
3 0.0000365
(0.000032)
10
30
100
300
1000
3000
0.000129
(0.000126)
0.000456
(0.000503)
0.00218
(0.00217)
0.00928
(0.0105)
0.0377
(0.0332)
0.116
(0.087)
0.00389
(0.0010-0.0222)
0.0000101
(not detected)
0.0000302
(0.000043)
0.0000995
(0.000153)
0.000290
(0.000432)
0.000885
(0.00104)
0.00234
(0.0025)
0.00699
(0.0063)
0.0201
(0.0114)
38
-------
a. Observed values in parentheses
b. Rose et al. (1976)
c. J. Vanden Heuvel, NIEHS, personal communication
d. Abraham etal. (1988)
39
-------
Table 4; Ah receptor and induced cytochrome P450 isozymes
Dose,
ng/kg/day
0.0
0.01
0.1
0.3
1.0
3.5
7.0
10.7
15.0
20.0
25.0
30.0
35.7
45.0
55.0
65.0
75.0
85.0
100.0
Ah Receptor,
pmole/g
2.09
(2.1)b
(2.1-3.2)c
2.10
2.14
2.23
2.45
3.21
4.01
4.68
(3.0-5.7)c
5.25
5.77
6.16
6.50
(5.5-7.8)c
6.80
7.16
7.43
7.63
7.82
7.95
8.11
(4.9-10.5)c
CYPlAl,nmole/ga
0.0234
(0.0081-0.0351)
0.0271
0.0591
0.126
0.328
0.881
(0.269-0.953)
1.501
2.011
(1.89-3.13)
2.443
2.841
3.135
3.377
3.589
(2.91-4.47)
3.845
4.038
4.183
4.314
4.409
4.519
CYP1 A2, nmole/ga
0.557
(0.352-0.714)
0.561
0.583
0.628
0.771
1.190
(0.840-2.31)
1.699
2.145
(1.39-3.56)
2.544
2.927
3.224
3.476
3.704
(2.39-4.21)
3.990
4.211
4.380
4.537
4.652
4.785
40
-------
Table 4: Ah receptor and induced cytochrome P450 isozymes
Dose,
ng/kg/day
115.0
125.
Ah Receptor,
pmole/g
8.24
8.32
(8.5)d
CYP1A1, nmole/g3
4.612
4.672
(3.72-5.99)
CYP1 A2, nmole/g3
4.899
4.975
(2.86-10.3)
a. Experimental data from Tritscher et al. (1992) given in parentheses
b. Poland and Knutson (1982)
c. Sloop and Lucier (1987)
d. Maximal induction from Poland and Knutson (1982)
41
-------
Table 5: Effects of TCDD on the EGF receptor
Dose,
ng/kg/day
0.0
0.01
0.1
0.3
1.0
3.5
7.0
10.7
15.0
20.0
25.0
30.0
35.7
45.0
55.0
65.0
75.0
85.0
100.0
115.0
125.0
C^CD t , . a TGF-a, Internalized EGF
EGF Receptor, pmole/ga ' D . , .
r ° pmole/g Receptor, pmole/g
2.553
(2.06-2.61)
2.553
2.545
2.53
2.48
2.34
(1.45-2.95)
2.16
2.01
(1.18-2.45)
1.88
1.75
1.65
1.55
1.46
(0.899-1.64)
1.36
1.28
1.22
1.15
1.11
1.07
1.02
0.990
(0.702-1.13)
0.0
0.00000995
0.0000999
0.000291
0.000901
0.00280
0.00540
0.00806
0.0107
0.0138
0.0165
0.0194
0.0222
0.0263
0.0299
0.0326
0.0361
0.0386
0.0429
0.0446
0.0470
0.0
0.000849
0.00854
0.0246
0.0747
0.219
0.391
0.541
0.674
0.807
1.00
1.09
1.20
1.28
1.34
1.40
1.44
1.49
1.53
1.56
42
-------
a. Experimental data from Sewall et al. (1992) given in parentheses
43
-------
Table 6: Effects of TCDD on estrogen metabolism in liver
Dose,
ng/kg/day
0.0
0.01
0.1
0.3
1.0
3.5
7.0
10.7
15.0
20.0
25.0
30.0
35.7
45.0
55.0
65.0
75.0
85.0
100.0
115.0
125.0
Estrogen Receptor,
pmole/g*
5.19
(5.1)
5.19
5.17
5.12
4.98
4.59
4.18
3.85
3.58
3.33
3.16
3.01
2.88
2.73
2.62
2.54
2.46
2.41
2.35
2.30
2.26
(2.3)
Estradiol,
pmole/g
0.154
0.154
0.153
0.151
0.145
0.130
0.114
0.103
0.0962
0.0878
0.0828
0.0792
0.0760
0.0722
0.0692
0.0670
0.0653
0.0638
0.0642
0.0610
0.0601
Catechol Estrogen,
pmole/g
0.610
0.613
0.633
0.672
0.785
1.067
1.336
1.517
1.641
1.734
1.792
1.828
1.854
1.877
1.888
1.893
1.896
1.895
1.891
1.889
1.888
Estradiol 2-
Hydroxylase,
nmole/g/day
0.0347
0.0348
0.0359
0.0381
0.0446
0.0605
0.0757
0.0860
0.0931
0.0984
0.102
0.104
0.105
0.106
0.107
0.107
0.107
0.107
0.107
0.107
0.107
a. Experimental data of Clark et al. (1991) given in parentheses
44
-------
Figure Captions
Figure 1. Flowchart of the NIEHS model of TCDD distribution and consequent effects on gene
expression in the rat liver.
Figure 2. Computed time courses of the accumulation of TCDD in liver and fat of rats treated with
125 ng TCDD/kg body weight/day.
Figure 3. Fit to the time courses in rats given a subcutaneous injection of 300 ng/kg body weight
of TCDD (Abraham et al., 1988). Circles are for observations in liver, squares are for
observations in fat.
Figure 4. Computed relationship between liver TCDD concentration and administered dose.
Experimental data points (filled circles) are the average values of Tritscher et al. (1992).
Error bars denote the range of observed values.
Figure 5. Induced CYP1A1 concentration vs. administered dose of TCDD. Experimental data
points (filled circles) are the average values of Tritscher et al. (1992). Error bars denote
the range of observed values.
Figure 6. Induced CYP1A2 concentration vs. administered dose of TCDD. Experimental data
points (filled circles) are the average values of Tritscher et al. (1992). Error bars denote
the range of observed values.
Figure 7. Reduction of fimai of EOF receptor vs. administered dose of TCDD. Experimental data
points (filled circles) are the average values of Sewall et al. (1992). Error bars denote the
range of observed values.
Figure 8. Calculated fraction of EGF receptor redistributed from the plasma membrane into the
cytosol as a function of the concentration of TGF-a. Vertical bars indicate model's pre-
dictions for the four dose groups of Sewall et al. (1992).
45
-------
Fat
TCDD <-
Muscle
TCDD
Viscera
TCDD
Blood
Bound
TCDD
t
y
^TCDI
1 t
Metabolite
t
D
Urine
>• M
M
etabolite
Oral
Dose
TCDD
Feces
TCDD -
Mptahnlitp «
*
^
4
^.
T(
Mi
Ml
J
DDD
Btabol
A
Gut
ite
> Ah + TCDD <
-------
TCDD, nmole/g
o
o
p
b
en
H
1 o-
CD o
**
Q.
0)
*<
0)
N>
CD-
CD
i i i i I i
o>
o
I I I I I
-------
Tissue TCDD, nmole/g
00
-------
Liver TCDD, nmole/g
o o
O Oi
I I I 1 I 1 I
O
O
CO
CD
a
a
^
:
CD
o-
o
o
I I I I I I
O
•
Ui
-------
.
o
E
Q.
O
0
100
Dose TCDD, ng/kg/day
-------
10-
o
E
c
*• r-
CM 5-
CL
O
0-
I I
J I
r
o
T
T
50 100
Dose TCDD, ng/kg/day
51
-------
B EGF receptor, pmole/g
o—
a
o
V)
CD
N)
O
D
o—
o
I I I I
IV)
i i I i i
CO
. I
I I I I
-------
p
b
o-
o
H
O o
~n b-l
"O
3
g_
0
o
•
o-
Fraction EOF receptor internalized
p
I I I I I
I I I I
3.5 ng/kg/day
10.7ng/kg/day
35.7 ng/kg/day
I ,
125 ng/kg/day
\ \ I I
-------
DRAFT-DO NOT QUOTE OR CITE
APPENDIX B
MODELLING RECEPTOR-MEDIATED PROCESSES WITH DIOXIN:
IMPLICATIONS FOR PHARMACOKINETICS AND RISK ASSESSMENT
Melvin E. Andersen1 Jeremy J. Mills1 , Michael L. Gargas1, Lorrene Kedderis2,
Linda S. Bimbaum3, Diether Neubert4, and William F. Greentee5
Abbreviated title: PB-PK Modelling with Dioxin
Corresponding Author:
Dr. Melvin E. Andersen
Chemical Industry Institute of Toxicology
P.O. Box 12137
Research Triangle Park, NC 27709
(919) 641-2070
Chemical Industry institute of Toxicology, PO Box 12137, Research
Triangle Park, NC 27709
Curriculum in Toxicology, Center for Environmental Medicine, University
of North Carolina, Chapel Hill, NC 27599
Health Effects Research Laboratory, US Environmental Protection
Agency, Research Triangle Park, NC 27711
Institute of Toxicology and Embryopharmacology, Free University Berlin,
Garystr 5, D-1000 Berlin 33
Department of Pharmacology and Toxicology, School of Pharmacy and
Pharmacal Sciences, Purdue University, West Lafayette, IN 47907-7880
To be published in Risk Analysis
-------
Abstract:
Dioxin (2,3,7,8-tetrachIorodibenzo-p-dioxin; TCDD), a widespread polychlorinated
aromatic hydrocarbon, caused tumors in the liver and other sites when
administered chronically to rats at doses as low as 0.01 pg/kg/day. It functions in
combination with a cellular protein, the Ah receptor, to alter gene regulation, and
this resulting modulation of gene expression is believed to be obligatory for both
dioxin toxic'rty and carcinogenic'rty. The US EPA is re-evaluating its dioxin risk
assessment and, as part of this process, will be developing risk assessment
approaches for chemicals, such as dioxin, whose toxicrty is receptor-mediated.
This paper describes a receptor-mediated physiologically-based pharmacokinetic
(PB-PK) model for the tissue distribution and enzyme inducing properties of
dioxin and discusses the potential role of these models in a biologically-motivated
risk assessment. In this model ternary interactions between the Ah receptor,
dioxin, and DMA binding sites lead to enhanced production of specific hepatic
proteins. The model was used to examine the tissue disposition of dioxin and the
induction of both a dioxin binding protein (presumably cytochrome P4501A2),
and cytochrome P4501A1. Tumor promotion correlated more closely with
predicted induction of P4501A1 than with induction of hepatic binding proteins.
Although increased induction of these proteins is not expected to be casually
related to tumor formation, these physiological dosimetry and gene induction
response models will be important for biologically motivated dioxin risk
assessments in determining both target tissue dose of dioxin and gene products
and in examining the relationship between these gene products and the cellular
events more directly involved in tumor promotion.
Keywords:
PB-PK modelling, Dioxin, Gene regulation, Cytochrome P450, Risk assessment,
Pharmacokinetics, Pharmacodynamics.
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1. INTRODUCTION: Dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin; TCDD), a
carcinogen1,2 and tumor promoter3 in rodents, also causes immunotoxic4,
reproductive5, and teratogenic6 effects. The present US EPA cancer risk
assessment, based on the incidence of liver tumors in female rats dosed with
dioxin for two-years1, utilizes the standard linearized multistage (LMS) approach,
with a one in a million risk level, to derive a virtually safe dose of 6 femtograms
dioxin/kg body weight per day7. Other Western countries treat dioxin as a tumor
promoter, with a threshold for its effects, and arrive at acceptable or tolerable
daily intake values up to 1000 fold higher than that used by the US8.
All of the toxic effects of dioxin noted above are believed to be dependent on the
interaction of dioxin with a specific cellular protein, the Ah (aryl Arydrocarbon)
receptor. The Ah receptor-dioxin complex binds to specific sites on DNA
modifying regulation of various genes, some of which can alter cell growth and
differentiation, while others affect metabolic processes (Fig. 1). Due to the
obligate role of the Ah receptor in its toxic effects, dioxin has been referred to as
a "receptor-mediated" carcinogen. Dioxin is only one of many polyhalogenated
dibenzo-p-dioxins, dibenzofurans, and biphenyls that alter cell growth
characteristics via interactions with the A/7-receptor. The US EPA is reviewing
the current dioxin risk assessment with the stated intention of developing a
generic, biologically-realistic approach to risk assessment for these "receptor-
mediated" agents9. With improved knowledge of the biological basis of the
action of dioxin at the molecular level, it may well prove possible to reconcile the
present disparity among different countries.
Dioxin demonstrates dose-dependent kinetics: as the administered dose
increases, a larger proportion of the total dose is found in the liver10. Among the
hepatic proteins induced by dioxin is a cytochrome, P4501A2, which has a high
affinity for dioxin11'12. It appears likely that dose-dependent hepatic
sequestration is related to induction of P4501A2, but other proteins may also be
involved. Any comprehensive model of dioxin pharmacokinetics must include the
induction of these dioxin binding proteins, mediated by the interaction of a dioxin-
Ah receptor complex with specific binding sites on DNA. Leung and co-workers
developed a physiologically-based pharmacokinetic (PB-PK) model for dioxin and
congeners in mice13 and rats14. Their model included induction of a single
hepatic binding species occurring in direct proportion to the fractional occupancy
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4
of the available Ah receptor by dioxin, but did not include direct interactions of the
Ah-JCDD complex with DNA. In mice, dioxin pretreatment increased metabolic
clearance of dioxin-like analogs15.
This paper extends the earlier PB-PK models by including induction of binding
proteins/enzymes and of dioxin metabolism in response to ternary interactions of
dioxin, the Ah receptor, and DNA binding sites and analysis of repeated dose
exposure situations. This physiological model for dioxin disposition and enzyme
induction is discussed in relation to its potential role for estimating dosimetry and
gene regulation in receptor-based risk assessment strategies for dioxin
2. PB-PK MODEL:
a. Model Structure: The dioxin PB-PK model (Rg. 2) consists of five
compartments - liver, fat, slowly perfused tissues (e.g., muscle, skin, etc.), richly
perfused tissues (e.g., kidney, brain, etc.), and blood. The relevant mass-balance
differential equations appear in Appendix 1 and the terms are defined in Table 1.
Each of the 4 tissue compartments (denoted by subscript, t) has both a specified
blood flow (Qt), tissue compartment volume (Vt) and a tissue blood volume (Vtb).
The tissue blood volumes were estimated from Bischoff and Brown16. Movement
of chemical from the tissue blood into the tissue is modelled to be proportional to
a permeation coefficient-surface area cross-product (PA) for the tissue (t).
Tissue uptake is diffusion-limited when PAt < Qt. Each tissue has a specified
volume, blood flow, PA product, and partition coefficient (Pt). In the Leung et al.
models13'14, tissues had no specified blood volume and the tissue/tissue blood
aggregates were described as flow-limited compartments. In addition, there was
binding in the blood compartment which effectively decreased the rate of tissue
uptake. Our revised model, because of the diffusion-limited tissue
compartments, does not require blood binding to match tissue uptake time
course behavior.
Initial estimates of partition coefficients were obtained from Leung et al.14 and
were adjusted in fitting the data sets here. As expected, the fat has the highest
partition coefficient due to the highly lipophilic nature of dioxin. In addition,
metabolism and protein binding are included in the liver, where both the Ah
receptor and the inducibie binding protein act to sequester dioxin via capacity
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limited binding processes. The binding protein was assumed to be cytochrome
P4501A2. The concentrations of P4501A2 in naive and induced rats differ by
about 10-fold17. The differential equation for the liver (equation 5) is solved for
the total amount of dioxin in liver, which is related to partitioned dioxin, dioxin
bound to the Ah receptor, and dioxin bound to P4501A2 (equation 6). This
conservation equation is used to solve for the free dioxin in the liver tissue, Ctf,
which is then utilized in calculating the concentration of the Ah receptor-dioxin
complex available for inducing cytochrome P4501A2 and cytochrome P4501A1
activity. This latter protein, an oxidative microsomal enzyme induced by dioxin, is
frequently used as a marker of Ah receptor-mediated protein induction. All these
binding interactions are described by simple, reversible equilibrium relationships.
This approach is valid as long as the rate constants for association/dissociation
are large (i.e. the processes are rapid). This description, like all models, is really
a simplification of the more complex series of biological events. The model can
readily be extended to include new information on these biological associations
as it becomes available.
b Enzyme/Protein Induction: The induction process is described by assuming
that the Ah-JCDD complex is formed based on Ah receptor binding parameters
(BM-| and KBi) and free dioxin in the liver (C[f). This complex then binds to
unspecified sites on DNA with an affinity, Kd. Since the binding to DNA does not
directly alter the Ah-JCDD concentration in this present model configuration, it is
tacitly assumed that the DNA sites are present at much lower concentration than
the Ah-JCDD complex. The induction is modelled with a Hill plot binding
relationship - equation 6 - where n provides a measure of interaction for multiple
Ah-JCDD complex binding sites. The value of n is estimated by comparison of
model simulations to data on the dose-dependence of liver sequestration of
dioxin or on the induction of P4501A1 activity (Fig. 3 and 4). Basal levels of
P4501A1 are described by a zero-order production rate (Krj) and a first order
elimination rate constant (k-|). The induction process increases the production
rate as specified by the Hill relationship and the maximum observable
enhancement (Komax) - equation 7. Data on the time course of cytochrome
P4501A1 from Abraham et al.10 were used to estimate the first order degradation
rate constant (M) for P4501A1 degradation.
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Induction of hepatic binding capacity was aJso modelled as if It were
instantaneously altered by the Ah receptor-dioxin-DNA interactions, (equation 8).
As data becomes available for the time course of P4501A2 induction, s^rrthesis
and degradation rate constants for this protein can also be directly included in
this description. For both cytochrome P4501A1 and 1A2 induction, H was
assumed that Ah-TCDD complex formation was equivalent (similar Kbi and BM-j
values), but that the n value and Kd for the two responses differed (see Table I).
Our model also allows an induction of dioxin metabolism following dioxin
treatment. However, in contrast to the mouse, induction of metabolism in the rat
if present at all, was negligible. The constants employed assume at most only a
doubling of the metabolic rate.
c. Data Analysis: The data used for this analysis were from two previously
published studies with Wistar rats. The first study10, with female rats, provides
both dose-response characterization of liver concentrations and of liver P4501A1
activity and time course characterization of dioxin tissue concentrations and
enzyme activities. The second study18 examined liver and fat concentrations in
male Wistar rats dosed weekly for periods up to 6 months.
Model simulations were performed using ACSL®, Advanced Continuous
Simulation Language, Mitchell and Gauthier Assoc., Concord, MA.
3. RESULTS:
a. Dose-Response: In this study10 rats received a single subcutaneous dose of
dioxin and were killed 7 days later. The disposition of dioxin in liver and fat was
highly dose-dependent in the concentration range between 1 ng/kg and 10,000
ng/kg (Fig. 3). Normalized concentrations (% dose/gm tissue) would be
horizontal lines if disposition were dose-independent. The curvature appears to
be due to the induction of a dioxin binding protein, presumably cytochrome
P4501A2. The smooth curves were obtained with the PB-PK model based on
the parameters in Table I. For induction of the binding protein, n and Kd were
estimated by fitting the data from Wistar rats10 and were, respectively, 1.0 and 50
pM. Using measured concentration estimates of basal and induced P4501A217
the affinity of the binding protein (KB2) was estimated from the curve fitting to be
6.5 nM. A value of n close to 1.0 suggests little interaction among dioxin
responsive DMA binding sites involved in expression of this particular gene. The
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complex behavior above 10 ng/kg bw is commented on more fully in
DISCUSSION
The dose response curve for cytochrome P4501A1 induction (Fig. 4) was
described in a similar manner, but required a larger value for ni (2.3) to fit the
data, indicating possible interactions among DMA binding sites for the Ah
receptor-TCDD complex with this gene. The half maximal induction response for
P4501A1 occurred at about a 10-fold higher dose than the half maximal response
of the binding protein (Kd-j = 180 pM).
The PB-PK model configured for these dose-response curves was also used to
examine time course elimination/induction after a single dose of 300 ng/kg (Fig. 5
and 6). Analysis of these results were especially useful for setting the rate
constant for metabolism of dioxin and the PAt cross products (i.e., the diffusion
limitations) for liver and fat. The simulation of these curves requires inclusion of
time dependent growth parameters over the 100 days of the experiment. Growth
rates and volumes of fat were estimated from growth curves for these rats
available from suppliers.
b. Repeated dosing: The primary health concerns with dioxin are associated
with repeated low or chronic exposures. We analyzed data from a study in which
liver and fat concentrations were determined in male rats for up to 6 months
during weekly dosing with dioxin18. This study (Rg. 7) used a loading dose (15
pg/kg) followed by a five-fold smaller dose (5 M9/kg) every week. The
simulations for this dosing scenario were conducted with model parameters very
similar to those used in the single dose exposures (Fig. 3-6). Small differences in
fat and slowly perfused tissue compartment parameters are noted in the figure
legends.
c. Correlation with Responses: The present US EPA dioxin risk assessment is
based on liver tumors in female rats1. Liver tumors are also increased in female
and male mice2. Liver initiation-promotion studies3'19-20 demonstrate that dioxin
is a promoter and that the dose-response curve for its promoting action on
altered hepatic foci closely follows the dose response curve of its
hepatocarcinogenic activity (Table II). Both of these dose-effect relationships are
very steep.
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8
Based on our preliminary repeated dose PB-PK model for these Wistar rats,
several measures of dose can be calculated for comparison with the promotional
efficacy and carcinogenic'rty of dioxin in Sprague-Dawley rats(Table Il).-These
include integrated total liver dioxin concentration during the treatment period, or
integrated free liver dioxin concentration. In addition, measures of tissue dose
related to enhanced expression of cytochrome P4501A1 and hepatic binding
proteins can be calculated for the duration of the sub-chronic exposure. The
measures of tissue dose associated with enhanced gene expression (assumed to
be related to P4501A1 activity or the sequestration of dioxin in liver by
cytochrome P4501A2) are the integrated level of these gene products (as protein
concentration of 1A2, or activity of 1A1) over time. Enhanced expression refers
to the increase over any basal levels of expression of these gene products. This
correlative approach does not assume that there should be a direct relationship
between induction of these cytochromes and the promotional action of dioxin at
all dose levels.
The tumor promotional response of dioxin in the rat liver is most closely
correlated with the integrated expression of the P4501A1 gene (Table II) under
these exposure conditions. For instance, the responses (columns 2 and 3)
increase rapidly with increasing dose between 0.01 and 0.1 pg/kg/day. The
integrated level of cytochrome P4501A1 increases by a factor of about 10 in this
range, while the integrated amounts of binding protein (P4501A2), whose
induction is already nearly saturated by 0.01 ng/kg/day, only increases by 50% in
this region of dose. Dioxin concentrations increase in this dose range, but dioxin
itself is not believed to be responsible for toxic effects
4. DISCUSSION:
a. Pharmacokinetics: The half-life of dioxin in male rats was initially reported to
be 20-30 days21. These early studies were conducted at high, inducing doses;
elimination curves were obtained for only about 2 half-lives; and analysis relied
on chemical detection of dioxin in tissues. The half-lives in hamsters (10-15
days) and guinea pigs (30-40 days) were also estimated from time course data
with a simple one-compartment model for dioxin kinetics but used radiochemical
detection of labelled dioxin22'23. These various studies were relatively
insensitive to the dose-dependent effects apparent only when disposition is
examined at much lower doses (Fig. 3) and most apparent when both fat and
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9
liver concentrations are measured. The alteration in the ratio of liver-to-fat
concentration ratio is the most sensitive marker for this dose-dependence
(compare Fig. 3 and Fig. 8b). These low dose effects can now be readily
evaluated by use of high specific activity 3H-labelled dioxin. Single doses of 1
ng/kg caused minimal induction of P4501A1 (Fig. 4) or of the dioxin binding
protein (Fig. 3). Protein induction, however, becomes significant at a dose of 30-
50 ng/kg dose and markedly alters dioxin disposition. Simple linear
compartmental analyses of dioxin pharmacokinetics have completely ignored
dose-dependent protein binding.
PB-PK models have been reported for polychlorinated biphenyls24, 2,3,7,8-
tetrachlorodibenzofuran25, and, in a preliminary form, for dioxin itself26. With the
exception of the work by Leung and colleagues,13'14 none of these descriptions
have included specific hepatic binding, in all these other models, chemicals were
distributed simply by non-specific, dose-invariant partitioning and focused on
behavior at higher doses with substantial induction of binding proteins.
Because of the limited dioxin binding capacity of the liver, even in fully-induced
animals, the liver/fat concentration ratio reaches a maximum and is predicted to
decrease at higher body burdens (Fig. 3) where hepatic binding to induced
proteins approaches saturation. While this region of dose was not examined by
Abraham et al.10, the time course of elimination has been examined after a single
dose of 600 pg/kg in hamsters, a species which is more resistant to the acute
toxic effects of dioxin than are rats. In this study the liver/fat ratio increased from
1.0 to 2.7 as the body burden fell from 600 to 100 pg/kg22, consistent with the
predictions in Fig. 3 for the dose range above 10 jag/kg bw
Several people ingested relatively large amounts of dioxin-like polychlorinated
dibenzofurans in the Yusho and Yu Cheng poisoning incidents in Japan and
Taiwan, respectively. In several instances liver and fat samples were obtained at
autopsy, and the proportion of dose in the liver was found to be dependent on the
total body burden. The % dose/liver increased from about 5 at ambient exposure
levels to over 60 at a body burden of the furans toxicoiogicaily equivalent to a
burden of 3-5 ng dioxin/kg body weight. The body burden for half maximal
sequestration in the liver in people was estimated by Carrier et al.27 to be 0.5-1.5
pg dioxin/kg body weight. Thus, this dose-dependent hepatic distribution is
common both to rats and people with very similar body burdens required for half-
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10
maximal sequestration. The liver/Tat concentration ratio however varies for
different isomers. Higher chlorinated isomers tend to have ratios higher than for
TCDD itself. The reasons for these differences are unknown; however,j>ne
suggestion derived from modelling efforts is that KB£, the dioxin binding protein
dissociation constant, is lower for the higher-chlorinated analogs (i.e. they have
higher affinities for the binding proteins than does dioxin itself).
b. Protein Binding/Induction: The challenge in providing a biologically-realistic
PB-PK model for dioxin is the need to not only account for the determinants of
disposition (i.e., tissue partitioning, biotransformation rates, and protein binding
constants), but also to describe the pharmacodynamic events related to the
induction of specific dioxin binding proteins in the liver. Knowledge of the
molecular mechanisms of the interactions of the dioxin-Ah receptor complex with
regulatory regions of specific genes is growing rapidly28-29, and shows clearly a
role for the ternary dioxin-Ah receptor-DNA complexes in regulating gene
transcription. Our present model of disposition is based on a ternary complex
being obligatory for the pharmacodynamic activity of dioxin at the genomic level.
The description of these interactions requires estimates of binding constants
between the Ah receptor and dioxin and between the Ah receptor-dioxin complex
and sites on DMA. The ternary interactions with DNA are believed to enhance
transcription of mRNA leading eventually to increased amounts of specific dioxin
binding proteins, particularly cytochrome P4501A2.
The binding maximum (BM1) and binding affinity (KB1) for TCDD binding to the
Ah receptor have been previously reported. The values used in our model differ
significantly: our estimate of BM1 is only about 10% of the value determined by
Gasiewicz and Rucci30 and our KB1 is also much lower than the value reported
by Bradfield and Poland31. The discrepancy in KB1 is due simply to our decision
to reference binding to the free dioxin in the liver. If we had used total partitioned
dioxin, our number would increase by a factor of 20 and be more similar to the in
vitro value31. However, the lower BM1 value represents a more fundamental
discrepancy between in vitro measurements and in vivo pharmacokinetics.
In the present study the binding parameters of the Ah receptor were largely
estimated from the tissue concentrations of TCDD at non-inducing levels, shown
in Rg. 3. The liver concentration at doses below 0.01 ng/kg is especially
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11
sensitive to BMi, the Ah binding maximum. In addition, the dose at which
hepatic sequestration is half maximal is extremely sensitive to KB 1, the Ah
binding affinity. If BM1 were increased to the literature value, the liver
concentration of TCDD in the low dose region (i.e. non-induced animals) would
be greatly overestimated. One possibility to explain this discrepancy is that only
a relatively small proportion of the available receptors participate in binding dioxin
and maintaining induction at physiologically-realistic concentrations of TCDD in
vivo. In mouse hepatoma cell lines, the total receptor concentration decreases
after exposure to dioxin32.
The other important binding constant Kd, for the DNA interactions, also affects
the placement of the induction curve, whether for 1A2 or 1A1, along the dose
axis. The Hill-type coefficients control the steepness of the induction response.
With the two responses examined, induction of liver sequestration (i.e., 1A2)
shows higher affinity (lower Kd value) but lower cooperativity (smaller n value)
than does induction of 1A1. This apparent cooperativity with 1A1 (n-| = 2.3) is
consistent with the observation that there are at least 4 dioxin responsive
elements in the regulatory region of this gene26.
The PB-PK model structure developed here assumes that a single type of Ah
receptor-dioxin complex interacts with DNA binding sites of variable affinity to
regulate different genes. More complex models with different Ah receptor-dioxin
complexes (due to other protein interactions, for instance) might lead to different
conclusions about cooperativity in these two responses, but such models do not
seem warranted by available biological data at this time.
c. Pharmacokinetics of the Ah receptor The biological activity of dioxin is a
consequence of both delivery of dioxin to target cells and the dynamics of the
processes that regulate Ah receptor concentration and Ah receptor binding
characteristics. The present model extends our understanding of the biological
determinants of dioxin action in vivo; however, it does not account for time variant
changes in the receptor. In completely induced animals, only a relatively small
fraction of total basal receptor binds to nuclear sites, even with complete
induction of P450 1A1 activity33. Furthermore, in mouse hepatoma cells in vitro,
the total cellular receptor concentration decreases after treatment of the cells with
dioxin32. In contrast, there is an apparent increase in Ah receptor in the rat liver
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12
following TCDD treatment34. These complexities in Ah receptor kinetics should
have minimal effects on our analysis which focuses on longer term behaviors,
after 7 days or over the 100 day excretion period. New experimental techniques
with appropriate antibodies35 should soon be available for measuring the total
amount of cellular Ah receptor. With these techniques, Ah receptor kinetics can
be more fully investigated by examining total amount of receptor, receptor
distribution, tissue dioxin, mRNA and protein for P4501A2 over the first several
hours to days after intravenous administration of dioxin. Such studies will
improve both our appreciation of the pharrnacokinetics/pharmacodynamics of the
Ah receptor and, of the dosimetry of dioxin itself. It bears repetition that the
determinants of dioxin pharmacokinetics cannot be examined in the absence of
additional information. The pharmacokinetics of dioxin, of the Ah receptor, and of
hepatic binding proteins are inextricably coupled36. Continued progress in
physiological modelling of dioxin pharmacokinetics now relies on the progress in
uncovering the biology of the Ah receptor37, especially regarding its ability to
affect the expression of dioxin binding proteins.
The current PB-PK model greatly simplifies the sequence of processes involved
in activation of the receptor by dioxin. Normally, the receptor is complexed with
heat shock protein(s) 90, which probably dissociate after the receptor binds
dioxin38-39. Another protein species is then required for the interaction of the
complex with DNA sites40. In the future it will be necessary to model these
events K they are found to be critical control elements for gene regulation,
however, they are not understood in sufficient detail to justify their inclusion in the
dosimetry model at this time.
d. Risk Assessment Implications: Greenlee et al.29. in a recent perspective on
biologically-based risk assessment for dioxin outlined the biological steps
involved in receptor-mediated growth modutatory effects. They are recognition,
transaction, and response (Fig. 1). Our PB-PK model includes elements of
recognition (binding of dioxin to the Ah receptor), transduction (binding of the
complex to ON A), and response (the changes in rate of transcriptionAranslation
of P4501A1 and the binding protein). The responses of these two proteins are
not thought to be causal linked to the adverse effects of dioxin. Specific
mftogenic and mito-inhibitory cellular processes altered by dioxin treatment are
more likely causally related to toxicrty41-42. Even If we had modelled regulation of
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13
growth factor proteins, it is unclear what specific role they play, singly or in
combination, in cell replication, cell differentiation, or toxiclty. It will be of interest
to examine the kinetic characteristics of induction (or repression) of these other
proteins to see if they are controlled in a manner similar to P4501A1 or the
sequestering protein. Since data on modulation of growth regulatory genes is not
yet available, we have correlated various tissue dose measures from the present
model with the promotional efficacy of dioxin. Of the several measures of tissue
dose examined (Table li), the integrated exposure to P4501A1 is most closely
correlated with promotion and with hepatic tumors. However there is no
expectation of causality between tumor responses and these induced proteins
and this correlation should be should be regarded cautiously.
In using the integrated P4501A1 exposure, the low dose extrapolation slope
approaches the value of the Hill coefficient (n-| = 2.3), reflecting the cooperativity
which appears to exist for induction of this gene by the ternary TCDD-Ah
receptor-DNA interactions (Fig. 8a). The differences in the low dose
extrapolation with measures of tissue dose related to protein induction (Fig. 8b) is
related both to their respective slopes (n values) and the ease of induction by
dioxin (the values of Kd and Kch). The mathematical formulation of these co-
operativity interactions is based on a (n+1) order interaction. Such interactions
are highly unlikely since the rate of these association processes fall sharply with
decreasing ligand concentration. Normally, co-operative relationships arise due
to enhanced affinity for a second ligand molecule after the binding of the first
ligand. In this way all events involved are bimolecular. The Hill equation is
simply a convenient way to treat this more complex series of events. With these
multiple bimolecular steps, the low dose induction behavior should have a slope
of 1 and be characterized by the binding parameters of the first ligand. Dose
measures related to free or total dioxin in liver are also complexly related to
administered dose: neither curve is strictly linear with dose and the departures
from linearity are in opposite directions.
Appropriate measures of tissue dose for risk assessment must focus on specific
cellular events, such as cell proliferation rates within developing preneoplastic
foci in the livers of dioxin treated rodents. Among the important ongoing efforts to
improve the biological basis of dioxin risk assessment are studies analyzing
regulation of newly identified dioxin-responsive growth regulatory genes in liver43
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14
and the effect of these genes on replication and focal growth. Eventually,
biological response models will have to predict the relationship between
replication or differentiation and the tissue exposure to these growth factors.
Only when this level of resolution is achieved will it be possible to more fully
justify any particular approach to low-dose extrapolation with dioxin.
The basic behavior of dose-dependent hepatic sequestration is a characteristic of
many congeners of dioxin44 and is observed in several species including
people45. This basic PB-PK model structure appears to be applicable to many
chemicals that act via interactions with the Ah receptor and should prove, in time,
useful for risk assessment applications with this broad group of important
contaminants with common receptor-mediated mechanisms of toxicrty. These
dosimetry models will have to be combined with emerging quantitative
descriptions of cell and tissue responses to develop a complete biologically-
motivated risk assessment model. Some of the challenges of this approach have
been outlined in this paper.
Acknowledgments: The work on dioxin pharmacokinetics at CUT has been
generously sponsored by a grant from the American Paper Industry. We also
thank Drs. R. Conolly and R.J. Preston for helpful comments and discussions.
Although the research described in this article has been supported by the United
States Environmental Protection Agency, ft has not been subjected to agency
review and therefore does not necessarily reflect the views of the Agency and no
official endorsement should be inferred. Mention or trade names or commercial
products does not constitute an endorsement or recommendation for use.
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15
Appendix I:
There are two mass balance differential equations for each tissue, one for tissue
blood (tb) and another for tissue (t). Respectively,
dAtb/dt = Qt (Ca-Cvt) + PAt (Ct/Pt • Cvt) (1)
dAt/dt = PAt (Cvt - Ct/Pt) (2)
For fat, slowly perfused, and richly perfused tissues, these equations are
integrated for amount and tissue concentrations calculated by dividing amount
(At) by volume (Vt).
(3)
Ct = At/Vt (4)
The tissue free (diffusible) concentration is calculated by dividing the tissue
concentration (Ct) by the tissue partition coefficient (Pt) - equations (1) and (2).
The liver equation -equation (5)- also contains a term for loss due to metabolism:
dA|/dt = PA| (Cvl - Ctf) - V| kf (1+ fold (Clf / (Ctf + Kbi)) Clf (5)
In the development of the mathematical description used here there is the
possibility for inducing metabolism in direct proportion to the fractional occupancy
of the Ah receptor by dioxin. The extent of maximal induction as increase over
the basal rate is controlled by the fold term in the above equation. For rat, this
value was set to 1.0, ft may vary in other species, such as the mouse13.
The total mass in the liver is then apportioned between free dioxin (Ctf) and
bound forms of dioxin.
A| = P|V|C|f +[ BMiCtfAKBi + Ctf)]+ [BM2tC|f/(KB2 + Ctf)] (6)
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16
The second term on the right of the equality is the concentration of the Ah-JCDD
receptor complex (Ah-TCDD) and the third term is the concentration of Jhe
complex of TCDD and the induced binding protein.
The activity of P4501A1 at time, t, is calculated from the basal synthesis rate
(Ko), the maximum increase in synthesis rate (Komax). the Ah receptor-TCDD
complex concentration, the complex-DNA dissociation constant (Kdi), the
appropriate Hill term (n-|), and the P4501A1 degradation rate constant (ki).
d(P4501 A1t)/dt = Ko (1 + KQmax
-kiP4501A1t (7)
The concentration of P4501 A2 at any time, BM2t, was calculated assuming an
instantaneous adjustment of binding protein concentrations and Ah-JCDD levels,
BM2t = BM2o + BM2| (Ah-JCDD)n/((Ah-JCDD)n + Kdn)) (8)
where BM2| is the maximum induction of the binding protein. This formulation
assumes very rapid induction of the binding proteins in these studies.
For the longer duration studies the changes in total body weight and proportion
of weight as fat compartment volume were included via table functions available
in the ACSL®, software package.
For the single dose studies in female rats, Simulated Time (hr)
0 840 1344
Body Weight (kg) 0.215 0.3 0.34
Fat(%bw) 0.07 0.09 0.13
For the repeated dose studies in male rats, Simulated Time (hr)
0 840 1344
Body Weight (kg) 0.35 0.39 0.41
Fat(%bw) 0.10 0.12 0.13
-------
17
Table 1
Parameters in the Physiological Dosimetry Model for Oioxin1
Model Parameters Abbreviations Wistar Rat2
Body weight (kg) bw 0.215
Volumes3 (Liters)
Liver V| 0.0375 x bw
Fat Vf 0.07 x bw
Richly perfused (Viscera) Vr 0.0525 x bw
Slowly perfused (Muscle/skin) Vs 0.75 x bw
Blood Vb 0.05 x bw
Tissue blood volumes
Liver V|D 0.01 x Vf
Fat Vfb 0.05 x Vf
Richly Perfused Vrb 0.01 x Vr
Slowly perfused Vsb 0.05 x Vs
Cardiac output (liters/hr) QC 4.4
Tissue blood flow (% Qc)
Liver (portal and arterial) Q| 0.25
Richly perfused Or 0.51
Fat Of 0.09
Slowly perfused QS 0.15
Dlffusiona! tissue clearance (liters/hr)
Liver PA| 0.5 x Q|
Fat PAf 0.2 x Of
Richly perfused PAr 0.5 x Or
Slowly perfused PAS 0.5 x QS
Metabolic Constants
Metabolism (hr1) kfc 1.65
Induction (fold over basal rate) fold 1.00
-------
18
Partition Coefficients
Liver/blood
Fat/blood
Richly perfused/blood
Slowly perfused/blood
Protein Binding
Ah maximum (pmoles/liver)
Ah affinity (pM)
1A2 basal level (nmoles/liver)
1A2 maximum (nmbles/liver)
1A2 affinity (nM)
1A2 - Hill term
1A2 - Hill binding constant (pM)
1A1 - Hill term
1A1 - Hill binding constant (pM)
1A1 - degradation rate constant (hr-1)
1A1 synthesis-basal rate (unrts/hr)
1A1 maximum induction (fold)
Pi
Pf
Pr
PS
BMi
KB1
BM2Q
BM2|
KB2
n
Kd
ni
Kdi
kl
KO
Komax
2Q_
375
20
30
3.75
35
10
85
6.5
1.0
50
2.3
180
0.035
0.7
50
1 - Deviations from this base parameter set are noted in the legends to each
Figure.
2 - Parameters are for the 215g rat at the start of the dose-response study in Fig.
3 - Tissue volumes are given as liters and referenced to tissue as a proportion of
the total body weight, assuming unit density (1 kg = 1 L)
-------
19
Table II
RELATIONSHIP BETWEEN HEPATIC EXPOSURE TO DIOXIN, HEPATIC PROTEIN
INDUCTION, AND HEPATIC TOXICITY DURING SUBCHRONIC EXPOSURES
Dose1 Altered Foci2 Tumors3
^
control
0.0001
0.001
0.01
0.1
1
10
Volume
0.8
0.2
0.3
0.7
2.8
»
-
(Liver)
1/B6
-
0/50
2/50
11/49
.
-
AUCL-free4 AUCL-total5 AU-1A26
C|f
0
6.7x1 O*1
6.4x10°
5.3x1 01
4.2x1 02
3.8x103
3.8x1 04
C|
0
3.5x101
5.9x1 02
1.1x104
1.2x105
1.0x106
5.4x1 06
(BM2|)
0
T.OxlO3
5.7x1 04
2.1x105
3.2x105
3.5x105
3.5x105
.... ...... ...... .y
AU-1A17
(IND)
0
2.3x1 01
3.9x103
2.7x105
2.0x106
3.0x106
3.1x106
1- Biweekly doses used; dose expressed as average dose per day in pg/kg.
2 - Volume as percent of liver occupied by altered cells, from Pitot et al.18
3 - Liver tumors observed in female rats in the bioassay study of Kociba et al.1.
4 - Total area under the curve for free dioxin in the liver, units are nmoles-hr/liters.
5 - Total area under the curve for total dioxin in the liver, units are nmoles-hr/liter.
6 - Integrated exposure to induced levels of binding protein • units are nrnotes-
hr/liter • and the calculation integrates the third term on the right side of equation
(6).
7 - Integrated exposure to induced levels of cytochrome P4501A1 - units are
enzyme un'rt-hr - and the calculation only considers the enhanced level of activity
due to induction.
-------
20
FIGURE CAPTIONS
Fig. 1: Schematic of the molecular mechanisms of action of dioxin: Dioxin binds
to a cellular protein, the Ah receptor, and the dioxin-receptor complex interacts
with DNA sites in regulatory regions of certain genes. Interaction with these
sites, called dioxin responsive elements, DREs, leads to changes in rates of gene
transcription, i.e., the rate at which mRNA is produced from these genes. The
mRNAs serve as templates for protein synthesis. Changes in cellular mRNA
levels lead to altered protein concentrations for cytochrome P4501A1, hepatic,
dbxin-binding proteins, metabolizing enzymes, and growth factors. The change
in concentration of certain of these proteins is believed to be associated with the
various toxic effects of dioxin.
Fig. 2: The PB-PK model for dioxin: A five compartment diffusion-limited model
was developed with metabolism and protein binding included in the liver. Uptake
after sub-cutaneous administration was described as a first-order process with
the chemical appearing in mixed venous blood. Symbol definitions are in Table
1.
Fig. 3: Dose-dependence of dioxin tissue disposition in female Wistar rats 7 days
following single sub-cutaneous doses: Tissue concentrations from Abraham et
al.10 are expressed normalized to dose, as %dose/gram tissue. Model
parameters are given in Table 1.
Fig. 4: Dose-dependence of cytochrome P4501A1 induction in female rats 7
days following single sub-cutaneous doses: Data from Abraham et al.10 for
activity of EROD (Ethoxyresorufin-O-deethylase) in pmoles resorufin
formed/min/mg protein.
Fig. 5: Time course of liver and fat tissue dioxin concentrations following a
single, sub-cutaneous dose of 300 ng/kg in female Wistar rats. Concentrations
are expressed as ng/g tissue, and the data are from Abraham et al.10.
Parameters used were the same as in Figs. 3 & 4, however, growth of the rat and
fat compartments were also included.
-------
21
Fig. 6: Time course of cytochrome P4501A1 activity following a single dose of
300 ng dioxin/kg in female Wistar rats. Data are from Abraham et al.10 and
parameters as in Fig. 5.
Fig. 7: Time course liver and fat concentrations in rats dosed weekly with 5 pg
dioxin/kg starting 7 days after a loading dose of 25 pg/kg. These data, expressed
as ng/g tissue, are from Krowke et al.18. The model parameters were as
specified in other figures with several changes: Pf, Ps, Pafc, and kfc were,
respectively, 250, 50,0.1, and 1.5. These contrast to values of 375, 30, 0.20,
and 1.65.
Fig. 8 : Low dose extrapolation of measures of tissue dose for dioxin in sub-
chronic exposures. Panel A: Dose measures related to induction of cytochrome
P4501A1 and the liver binding protein. The steeper line is for enhanced
P4501A1 which has a co-operativity term of 2.3. Two extrapolations are depicted
with Cytochrome P4501A1; the case of a Hill coefficient of 2.3 throughout the
dose range, and for the case of a linear relationship below several percent
response (dashed line). The latter is more biologically realistic. Panel B: Dose
measures for total liver dioxin and for free dioxin in liver. The total increases in a
greater than linear fashion with dose in the region where induction of the binding
protein occurs. In that same dose region the tissue dose of free dioxin does not
increase in direct proportion to exposure level. These calculations used the
model applied to data in Fig 7 and calculated exposures for a 6 month period.
-------
22
References
1. Kociba R.f Keyes D., Beyer J., Carreon R., Wade C., DittenberD,, Kalnins
R., Frauson L., Park C., Barnard S., Hummel R., and Humiston C. (1979)
Results of a two year chronic toxicrty and oncogenicity study of 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) In rats. Toxicol. Appl. Pharmacol. 46:
279
2. NIH (1982) Carclnogenesis bloassay of 2,3,7,8-tetrachlorodibenzo-p-
dioxin in Osboume-Mendel rats and B6C3f1 mice (gavage study). NTP
Report Series No. 209
3. Pitot H., Goldsworthy T., Campbell H., and Poland A. (1980) Quantitative
evaluation of the promotion by 2,3,7,8-tetrachlorodibenzo-p-dioxin
hepatocarcinogenesis from diethylnitrosamine. Cancer Res. 40: 3616
4. Buu-Hoi N., Chanh P., Sesque G., Azum-Gelade M., and Saint-Ruf G.
(1972) Organs as targets of dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin)
intoxication. Naturwissenschaften 59:174
5. Murray F., Smith F., Nrtschke K., Humiston C., Kociba R., and Schwetz B.
(1979) Three generation reproduction study of rats given 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) in the diet. Toxicol. Appl.
Pharm&col.SO: 241
6. Courtney K., and Moore J., (1971) Teratology studies with 2,4,5-
trichlorophenoxyacetic acid and 2,3,7,8-tetrachlorodibenzo-p-dioxin.
Toxicol. Appl. Pharmacol. 20: 396
7. US EPA Office of Health and Environmental Development (1988) Review
Draft, EPA/600/6-88/007Aa
8. Kociba R., (1991) Rodent bioassays for assessing chronic toxicity and
carcinogenic potential of TCDD. In "Banbury Report 35: Biological Basis
for Risk Assessment of Dioxins and Related Compounds". Cold Spring
Harbour Laboratory Press, p 3
-------
23
9. Federal Registar, Wed Oct 9,1991, 56:50903
10. Abraham K., Krowke R., and Neubert D. (1988) Pharmacokinetics and
biological activity of 2,3,7,8-tetrachlorodibenzo-p-dioxin. 1. Dose-
dependent tissue distribution and induction of hepatic ethoxy resorufin O-
deethylase in rats following a single injection. Arch. Toxicol. 62: 359
11. Poland A., Tertelbaum P., and Glover E., (1989) [125|j2-|0do-3,7f8-
trichiorodibenzo-p-dioxin-binding species in mouse liver induced by
agonists for the Ah receptor: characterisation and identification. Mol.
Pharmacol. 36:113
12. Voorman R., and Aust S.D., (1989) TCDD (2,3,7,8-tetrachlorodibenzo-p-
dioxin) is a tight binding inhibitor of cytochrome P450d. J. Biochem.
Toxicol. 4:105
13. Leung H., Ku R., Paustenbach D., and Andersen M. (1988) A
physiologically based pharmacokinetic model for 2,3,7,8-
tetrachlorodibenzo-p-dioxin in C57 BI/6J and DBA/2J mice. Tox. Lett. 42:
15
14. Leung H., Paustenbach D., Murray F., and Andersen M. (1990) A
physiological pharmacokinetic dsecription of the tissue distribution and
enzyme inducing properties of 2,3,7,8-tetrachlorodibenzo-p-dioxin in the
rat. Toxicol. Appl. Pharmacol. 103: 399
15. Leung H., Poland A., Paustenbach D., Murray P., and Andersen M. (1990)
Pharmacokinetics of p25IJ-2-iodo-3,7,8-trichlorodibenzo-p-dioxin in mice:
Analysis with aphysioiogical modelling approach. Toxicol. Appl.
Pharmacol. 103: 411
16. Bischoff K., and Brown R. (1966) Drug metabolism in mammals. Chem.
Eng. Prog. Symp. Ser. 62: 33
-------
24
17. Kedderis L, Diliberto J., Linko P., Goldstein J., and Bimbaum L. (1991)
Disposition of 2,3,7,8-tetrabromodibenzo-p-dioxin and 2,3,7,8-
tetrachlorodibenzo-p-dioxin in the rat: biliary excretion and induction of
cytochromes CYP1A1 and CYP1A2. Toxicol. Appl. Pharmacol. 111:163
18. Krowke R., Chahoud I., Baumann-Wilschke I., and Neubert D. (1989)
Pharmacokinetics and biological activity of 2,3,7,8-tetrachlorodibenzo-p-
dioxin. 2. Pharmacokinetics in rats using a loading-dose/maintenance-
dose regime with high doses. Arch. Toxicol. 63: 356
19. Pitot H., Goldsworthy T., Moran S., Kennan W., Glaubert H., Maronpot R.,
>
and Campbell H., (1987) A method to quantitate the relative initiating and
promoting potencies of hepatocarcinogenic agents in their dose-response
relationships to altered foci. Carcinogenesis 8:1491
20. Lucier G., Trrtscher A., Goldsworthy T., Foley J., Clark G., Goldstein J.,
and Maronpot R., (1991) Ovarian hormones enhance TCDD-mediated
increases in cell proliferation and preneoplastic foci in a two stage model
for hepatocarcinogenesis. Cancer Res. 51:1391
21. Rose J., Ramsey J., Wentzler T., Hummel R., and Gehring P. (1976) The
fate of 2,3,7,8-tetrachlorodibenzo-p-dioxin following single and repeated
oral doses to the rat. Toxicol. Appl. Pharmacol. 36: 209
22. Olson J., Gasiewicz T., and Neal R., (1980) Tissue distribution, excretion
and metabolism of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in the
golden Syrian Hamster. Toxicol. Appl. Pharmacol. 56: 78
23. Gasiewicz, T. and Neal, R. (1979) 2,3,7,8-tetrachlorodibenzo-p-dioxin
tissue distribution, excretion, and effect on clinical chemical parameters in
the guinea pig. Toxicol. Appl. Pharmacol., 51: 329
24. Matthews, H.B. and Dedrick, R.L. (1980). Pharmacokinetics of PCBs. Ann.
Rev. Pharmacol. Toxicol., 24: 85
-------
25
25. King P., Dedrick R., Collins J., Matthews H., and Birnbaum L, (1983) A
physiological model for the pharmacokinetics of 2,3,7,8-
tetrachlorodibenzofuran in several species. Toxicol. Appl. Pharmacol. 67:
390
26. Kissel J., and Rombarge G., (1988) Assessing the elimination of 2,3,7,8-
TCDD from humans with a physiologically based pharmacokinetic model.
Chemosphere 17: 2017
27. Carrier G., (1991) In "Response de L'organisme humain aux BPC,
dioxines et furannes et analyse des risques toxiques" Le Passeur Press,
Canada
28. Whrtlock J. Jr., (1990) Genetic and molecular aspects of 2,3,7,8-
tetrachlorodibenzo-p-dioxin activity. Ann. Rev. Pharmacol. Toxicol. 30: 251
29. Greenlee W., Andersen M., and Lucier G., (1991) A perspective on
biologically-based approaches to dioxin risk assessment. Risk Analysis
11:565
30. Gasiewicz T. and Rucci G., (1984) Cytosolic receptor for 2,3,7,8-
tetrachlorodibenzo-p-dioxin: Evidence for a homologous nature amoung
mammalian species. Mol. Pharmacol. 26:90
31. Bradfield C., and Poland A., (1988) A competitive binding assay for
2,3,7,8-tetrachlorodibenzo-p-dioxin and related ligands of the Ah receptor.
Mol. Pharmacol. 34: 682
32. Prokipcak R., and Okey A., (1991) Downregulation of the Ah receptor in
mouse heptaoma cells treated in culture with 2,3,7,8-tetrachlorodibenzo-p-
dioxin. Can. J. Physiol. Pharmacol. 69:1204
33. Rucci G., and Gasiewicz T., (1988) In vivo kinetics and DNA-binding
properties of the A/vreceptor in the golden Syrian Hamster. Arch.
Biochem. Biophys, 265:197
-------
26
34. Sloop T.t and Lucier G., (1987) Dose-dependent elevation of Ah receptor
binding by TCDD in rat liver. Toxicol. Appl. Pharmacoi. 88: 329
35. Poland, A., Glover, E., and Bradfield, C.A. (1991) Characterisation of
polyclonal anybodies to the Ah receptor prepared by immunization with a
synthetic polypeptide hapten. Mo!. Pharmacoi. 39:20
36. Mills J., Gargas M., and Andersen M., (1992) Biological and physiological
factors involved in disposition of dioxin and related compounds.
Chemospere (in press)
37. Mills J., and Andersen M., (1992) Toxicokinetics of dioxin and related
compounds. In "Proceedings of The Workshop on Risk Assessment and
Risk Management Of Toxic Chemicals", Natl. Inst. Environ. Stud, p 102
38. Wilhelmsson A., Cuthill S., Denis M., Wikstrom A-C., Gustafsson J-A.,
(1991) The specific DMA-binding activity of the dioxin receptor is
modulated by the 90 Kd heat shock protein. EMBO J. 9: 69
39. Perdew G.H., (1991) Comparison of the nuclear and cytosblic forms of the
Ah receptor from Hepa 1c1c7 cells: charge heterogeneity and ATP binding
properties. Arch. Biochem. Biophys. 291: 284
40. Hoffman E., Reyes H., Chu F-F., Sander F., Conley L.H., Brooks B.A.,
Johnson B.S., Bannister R.M., Weir-Brown K., Watson A.J., and
Hankinson O., (1991) Analysis of genes affecting Ah receptor functioning.
Abstract book, Dioxin '91. p. 16
41. SutterT.R., Guzman K., Dold K.M., and Greenlee W.F., (1991) Targets for
dioxin: genes for plasminogen activator inhibrtor-2 interleukin-1 0. Science
254:415
42. Gaido K.W., Maness S.C., and Greenlee W.F., (1991) 2,3,7,8-
tetrachlorodibenzo-p-dioxin-dependent regulation of TGF-o1,f31 and f32
gene expression by translational and post-translational mechanisms.
Abstract book, Dioxin '91. p. 183
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27
43. Fox T.R., Best L.L., Goldsworthy S.M., Mills J.J., and Goldsworthy T.L,
Expression of a dioxin-specific gene in the liver of Sprague-Dawiey rats.
Abstract Dioxin '92
44. Brunner H., Wiesmuller T., Hagenmaier H., Abraham K., Krowke R., and
Neubert D., (1989) Distribution of of PCDDs and PCDFs in rat tissues
following various routes of administration. Chemosphere 19: 907
45. Carrier G., and Brodeur J., (1991) Non-linear toxicokinetic behaviour of
TCDD-like halogenated polcyclic aromatic hydrocarbons (H-PAH) in
various species). The Toxicologist 11: 895
-------
Ah + TCDD
t;
Ah•TCDD
CYPIA2 - TCDD
TCDD
Ah-TCDD
Mil
III!
D
Gene:
HEPATIC BINDING PROTEIN
(CYPIA2)
mRNAj
CYPIA1
(EROD)
DNA
Transcription
RNA
Translation
PROTEIN
Figl.
-------
BLOOD
PF=375
FAT
TCDD
TCDD
SLOW
DD
TCDD
RICH
TCDD
_fy__
TCDD
LIVER
Ah-TCDD R>TCDD
TCDD
TCDD
Metabolism
Fig. 2
-------
% Dose/gram tissue
O
g
n>
g
i
CO
-------
EROD activity
Oi -q
CO
O
o
«?
n>
I
r ~
.3
-------
TCDD concentration - ng/g tissue
Oi
-------
EROD Activity
•3
o>
-------
1000 g
0)
to
CO
to
bo
C
I
S
O
-a
03
£
S
o
O
O
Laver
100:
Fat
64 96 128
Time - days
160
Fig. 7
-------
Proportion of maximum response
00
<=?
00
linn
9 S 9 8
en it*. jo o
i IIIIH i nma i HIM i IHIB i iinii i nun i IIIIBI
a
-------
Hepatic TCDD Concentration - Jig/kg bw
------- |