vvEPA
United States
Environmental Protection
Agency
600R05043F
Approaches for the
Application of Physiologically
Based Pharmacokinetic (PBPK)
Models and Supporting Data in
Risk Assessment
Inhaled Air Exhaled Air
I t
Lungs
Dermal Contact
i t
Skin
Adipose Tissue
Richly Perfused Tissues
Poorly Perfused Tissues
Liver
, Gut
Ingestion
Metabolism
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EPA/600/R-05/043F
August 2006
Approaches for the Application of Physiologically
Based Pharmacokinetic (PBPK) Models and Supporting
Data in Risk Assessment
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
Preferred citation:
U.S. Environmental Protection Agency (EPA). (2006) Approaches for the Application of Physiologically Based
Pharmacokinetic (PBPK) Models and Supporting Data in Risk Assessment. National Center for Environmental
Assessment, Washington, DC; EPA/600/R-05/043F. Available from: National Technical Information Service,
Springfield, VA, and online at http://epa.gov/ncea.
11
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CONTENTS
LIST OF TABLES v
LIST OF FIGURES vi
LIST OF ABBREVIATIONS AND ACRONYMS vii
PREFACE viii
AUTHORS AND REVIEWERS ix
EXECUTIVE SUMMARY x
1. INTRODUCTION 1-1
1.1. SCOPE OF THE DOCUMENT 1-1
1.2. INTENDED AUDIENCE 1-1
1.3. ORGANIZATION OF THE DOCUMENT 1-2
2. PHARMACOKINETIC DATA AND MODEL NEEDS IN RISK ASSESSMENT 2-1
2.1. PHARMACOKINETICS AND DOSIMETRY MODELING 2-1
2.2. DOSE-RESPONSE AND MEASURES OF DELIVERED DOSE 2-3
2.3. PHARMACOKINETIC DATA NEEDS IN RISK ASSESSMENT 2-5
2.4. PHARMACOKINETIC MODELS IN RISK ASSESSMENT 2-8
2.4.1. Regulatory Needs and Considerations 2-8
2.4.2. Use of PBPK Models in Dose-Response Assessment 2-9
2.4.3. Use of Pharmacokinetic Data and Models in Exposure Assessment 2-11
2.4.4. Pharmacokinetic Models in Risk Assessment: Summary 2-12
3. EVALUATION OF PBPK MODELS INTENDED FOR USE IN RISK
ASSESSMENT 3-1
3.1. MODEL PURPOSE 3-1
3.2. MODEL STRUCTURE 3-2
3.3. MATHEMATICAL REPRESENTATION 3-5
3.4. PARAMETER ESTIMATION 3-8
3.4.1. Physiological Parameters 3-8
3.4.2. Partition Coefficients 3-12
3.4.3. Biochemical Parameters 3-13
3.5. COMPUTER IMPLEMENTATION 3-14
3.6. EVALUATION OF PREDICTIVE CAPACITY 3-18
3.6.1. Model Verification 3-20
3.6.2. Model Validation/Calibration 3-21
3.6.3. Model Documentation 3-26
3.7. SENSITIVITY, VARIABILITY, AND UNCERTAINTY ANALYSES 3-29
3.7.1. Sensitivity Analysis 3-29
3.7.2. Variability Analysis 3-32
3.7.3. Uncertainty Analysis 3-34
3.8. DEVELOPING PBPK MODELS FOR USE IN RISK ASSESSMENT:
STRATEGIES FOR DEALING WITH DATA-POOR SITUATIONS 3-36
3.8.1. Minimal Data Needs for Constructing PBPK Models 3-36
3.8.2. Surrogate Data for Interspecies and Interchemical Extrapolations 3-37
3.9. EVALUATION OF PBPK MODELS: SUMMARY 3-38
in
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CONTENTS (continued)
4. APPLICATION OF PBPK MODELS IN RISK ASSESSMENT 4-1
4.1. CHOOSING PBPK MODELS APPROPRIATE FOR USE IN RISK
ASSESSMENT 4-1
4.2. EVALUATION OF DOSE METRICS FOR PBPK MODEL-BASED
ASSESSMENTS 4-3
4.3. REVIEW OF EXTRAPOLATIONS POSSIBLE WITH PBPK MODELS 4-11
4.3.1. Interspecies Extrapolation 4-11
4.3.2. Estimating Intraspecies Variability 4-13
4.3.3. Route-to-RouteExtrapolation 4-14
4.3.4. Duration Adjustment 4-15
4.3.5. High-Dose to Low-Dose Extrapolation 4-16
4.4. ROLE OF PBPK MODELS IN REFERENCE CONCENTRATION AND
REFERENCE DOSE DERIVATION 4-18
4.4.1. Reference Concentration 4-18
4.4.2. Reference Concentration: Point of Departure 4-20
4.4.3. Reference Concentration: Route-to-Route Extrapolation 4-20
4.4.4. Reference Concentration: Duration Adjustment 4-21
4.4.5. Reference Concentration: Dosimetric Adjustment Factor
(Interspecies Extrapolation) 4-22
4.4.6. Example of PBPK Model Use in Reference Concentration Derivation.... 4-23
4.4.7. Reference Dose 4-24
4.4.8. Reference Dose: Point of Departure 4-24
4.4.9. Reference Dose: Route-to-Route Extrapolation and Duration
Adjustment 4-25
4.4.10. Reference Dose: Interspecies Extrapolation 4-25
4.4.11. Example of PBPK Model Use in Reference Dose Derivation 4-25
4.4.12. Uncertainty Factors: Role of PBPK Models 4-26
4.5. ROLE OF PBPK MODELS IN CANCER RISK ASSESSMENT 4-28
4.5.1. Interspecies Extrapolation 4-28
4.5.2. Intraspecies Variability 4-29
4.5.3. Route-to-Route Extrapolation 4-29
4.5.4. High-Dose to Low-Dose Extrapolation 4-30
4.5.5. Example of PBPK Model Use in Cancer Risk Assessment 4-30
4.6. MIXTURE RISK ASSESSMENT 4-33
4.7. LINKAGE TO PHARMACODYNAMIC MODELS 4-34
GLOSSARY G-l
REFERENCES R-l
IV
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LIST OF TABLES
Table 3-1. Equations of a four-compartment PBPK model to simulate the inhalation
exposure of volatile organic compounds 3-6
Table 3-2. Equations used for describing diffusion-limited uptake in PBPK models 3-7
Table 3-3. Commonly used physiological parameters for mice, rats, and humans 3-9
Table 3-4. Range of values of the volume and perfusion of select tissues in the mouse 3-9
Table 3-5. Range of values of the volume and perfusion of select tissues in the rat 3-10
Table 3-6. Range of values of perfusion of select tissues in humans 3-10
Table 3-7. Examples of simulation software used for PBPK modeling 3-15
Table 4-1. Dose metrics used in PBPK model-based cancer and noncancer risk
assessments 4-6
Table 4-2. Relationship between tumor prevalence and dichloromethane metabolites
produced by microsomal and glutathione pathways for the bioassay
conditions (methylene chloride-dose response in female mice) 4-31
Table 4-3. Examples of biologically based models of endpoints and processes of
toxicological relevance 4-36
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LIST OF FIGURES
Figure 2-1. Relationship between the exposure concentration and adverse response for a
hypothetical chemical 2-4
Figure 2-2. Rat-human extrapolation of exposure concentrations of toluene based on
equivalent dose metrics (AUC [area under the curve] of toluene in blood,
3.8mg/L/hr) 2-7
Figure 2-3. Basic flowchart of PBPK model development 2-9
Figure 3-1. Sample PBPK model structures 3-4
Figure 3-2. Comparison of four PBPK model simulations (left, log scale; right, linear
scale). Solid lines are model simulations overlayed with experimental
data (symbols) 3-24
Figure 3-3. Sensitivity ratios associated with certain input parameters of a hypothetical
PBPK model 3-30
Figure 3-4. Monte Carlo simulation 3-33
Figure 4-1. Flowchart for selecting PBPK models appropriate for use in risk assessment.... 4-2
Figure 4-2. Examples of measure of tissue exposure to toxic moiety for risk assessment
applications 4-4
Figure 4-3. Estimation of an interindividual variability 4-14
Figure 4-4. Oral-to-inhalation extrapolation of the pharmacokinetics of chloroform on the
basis of same area under the curve in blood (7.06 mg/L/hr) 4-16
Figure 4-5. Duration adjustment (4 hr to 24 hr) of toluene exposures in rats, based on
equivalent AUC (2.4 mg/L/hr) 4-17
Figure 4-6. High-dose to low-dose extrapolation of dose metrics using PBPK model for
toluene 4-19
Figure 4-7. PBPK model predictions of glutathione (GST)-pathway metabolites in mouse
liver 4-32
Figure 4-8. Relationship between the dose metric (|imol metabolized/g liver/hr)
simulated by PBPK model and the cell killing inferred from
pharmacodynamic model for chloroform 4-35
VI
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LIST OF ABBREVIATIONS AND ACRONYMS
ADME Absorption, distribution, metabolism, and excretion
AUC Area under the curve
BBDR Biologically based dose-response (model)
BMC Benchmark concentration
BMD Benchmark dose
Cmax Maximal concentration
CFD Computational fluid dynamic(s)
CSF Cancer slope factor
DAF Dosimetric adjustment factor
FIEC Human equivalent concentration
IUR Inhalation unit risk
LOAEL Lowest-observed-adverse-effect level
MCMC Markov chain Monte Carlo
MOA Mode of action
NOAEL No-observed-adverse-effect level
PBPK Physiologically based pharmacokinetic
POD Point of departure
QSAR Quantitative structure-activity relationship
RfC Reference concentration
RfD Reference dose
UF Uncertainty factor
UFA interspecies uncertainty factor
UFH intraspecies uncertainty factor
vn
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PREFACE
Approaches for the Application of Physiologically Based Pharmacokinetic (PBPK)
Models and Supporting Data in Risk Assessment addresses the application and evaluation of
PBPK models for risk assessment purposes. PBPK models represent an important class of
dosimetry models that are useful for predicting internal dose at target organs for risk assessment
applications. This report is primarily meant to serve as a learning tool for U.S. Environmental
Protection Agency (EPA) scientists and risk assessors who may be less familiar with PBPK
modeling. In addition, it can be informative to PBPK modelers within and outside EPA because
it provides an overview of the types of data and models that EPA requires for consideration of a
model for use in risk assessment. A draft of this document underwent a public comment period
and external peer review in 2005, and this final report incorporates many of the relevant
comments and suggestions received in response to the draft report.
Vlll
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AUTHORS AND REVIEWERS
The National Center for Environmental Assessment (NCEA), Office of Research and
Development, was responsible for the preparation of this document. It was developed under
EPA Contract No. 4W-0322-NASX, and initially prepared by Dr. Kannan Krishnan with
significant input from the authors listed below.
EPA PROJECT OFFICERS
Femi Adeshina
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Chadwick Thompson
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
AUTHORS
Hugh Barton, National Center for Computational Toxicology, Research Triangle Park
Weihsueh Chiu, NCEA, Washington, DC
Robert DeWoskin, NCEA, Research Triangle Park
Gary Foureman, NCEA, Research Triangle Park
Kannan Krishnan, University of Montreal, Montreal, Canada
John Lipscomb, NCEA, Cincinnati
Paul Schlosser, NCEA, Research Triangle Park
Babasaheb Sonawane, NCEA, Washington, DC
Chadwick Thompson, NCEA, Washington, DC
INTERNAL EPA REVIEWERS
Jerry Blancato, National Exposure Research Laboratory, Las Vegas
Joyce Donohue, Office of Water
Hisham El-Masri, NCEA, Washington, DC
Marina Evans, National Health and Environmental Effects Research Laboratory, Research
Triangle Park
Lynn Flowers, NCEA, Integrated Risk Information System
Karen Hammerstrom, NCEA, Washington, DC
Allen Marcus, NCEA, Integrated Risk Information System
Dierdre Murphy, Office of Air Quality Planning and Standards
Alberto Protzel, Office of Pesticide Programs, Washington, DC
Woodrow Setzer, National Center for Computational Toxicology, Research Triangle Park
IX
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EXECUTIVE SUMMARY
Physiologically based pharmacokinetic (PBPK) models represent an important class of
dosimetry models that are useful for predicting internal dose at target organs for risk assessment
applications. Approaches for the Application of Physiologically Based Pharmacokinetic (PBPK)
Models and Supporting Data in Risk Assessment addresses the following questions: Why are
risk assessors interested in using PBPK models? How are PBPK models evaluated for use in a
risk assessment? What are the questions or data gaps in a risk assessment that can be addressed
by PBPK models? However, this document is not meant to serve as formal U.S. Environmental
Protection Agency (EPA) guidance.
The text is organized into four chapters. Chapter 1 outlines the scope of the document,
the intended audience, and the topics covered in the remaining chapters. Chapter 2 presents the
rationale for using PBPK models in risk assessment and the pharmacokinetic data and models
needed to derive a reference dose (RfD), a reference concentration (RfC), and unit risk estimates
in cancer risk assessment (e.g., cancer slope factor). Chapter 3 describes how models are
evaluated, the main model characteristics to review, and the on-going development of acceptance
criteria for model use in risk assessment. Chapter 4 discusses applications of PBPK model
simulations within the current EPA risk assessment framework. The appendix contains a
comprehensive list of publications, current as of the end of 2005, relating to PBPK modeling and
its use in health risk assessment.
PBPK models consist of a series of mathematical representations of biological tissues and
physiological processes in the body that simulate the absorption, distribution, metabolism, and
excretion of chemicals that enter the body. PBPK models are designed to estimate an internal
dose of a proposed toxic moiety to a target tissue(s) or some appropriate surrogate dose metric
for a target tissue dose. The choice of an internal dose metric is based on an understanding of the
chemical's mode of action. The internal dose metric (sometimes called the biologically effective
dose) replaces the administered dose in the derivation of the quantitative dose-response
relationship, with the intent of reducing the uncertainty inherent in risk assessments based on an
applied dose. This reduction in uncertainty and the improved scientific basis for the dose-
response value are the main advantages of PBPK models and the reasons for the growing interest
in their use. PBPK models also can simulate an internal dose from exposure conditions of
interest where no data are available, i.e., they can extrapolate to conditions beyond those of the
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data set used to develop the model. An important and active area of research is the
characterization of the uncertainty in risk assessments based on PBPK model results compared
with the uncertainty in results based on the administered dose.
PBPK models exist for a wide range of chemicals with varying properties. The vast
majority of PBPK modeling efforts to date have focused on chemicals that distribute
systemically within the body and cause systemic effects, although the models' applicability for
describing the pharmacokinetics of other chemicals, such as reactive gases, has been successfully
demonstrated. Because this document is intended to describe some of the basic principles of
PBPK modeling and its use in risk assessment, it primarily draws upon the experience and
literature concerning chemicals with systemic distributions.
Examples of PBPK model applications in risk assessments include interspecies
extrapolation of the dose-response relationship (based on estimates of the internal dose), route-
to-route extrapolation, estimation of response from varying exposure condition, estimation of
human variability (within the whole population or subpopulations), and high-to-low dose
extrapolation. PBPK models used in risk assessments would, at a minimum (1) contain a
compartment that is either identified with the target tissue, contains the target tissue, or is
identified as a surrogate for the target tissue; (2) have defensible physiological parameter values
that are within the known plausible range; and (3) have undergone a thorough evaluation for
their structure, implementation, and predictive capability.
Evaluation of PBPK models intended for risk assessments includes a review of the model
purpose, model structure, mathematical representation, parameter estimation (calibration), and
computer implementation. Criteria for acceptance of a PBPK model for use in risk assessment
include the following: (1) the model represents the species and life stage of relevance to a
particular risk assessment, (2) it has been evaluated and peer-reviewed for the adequacy of its
structure and parameters, and (3) it provides adequate simulations of the concentration of the
toxic moiety (parent chemical or metabolite) in the target organ (or a surrogate compartment)
following the relevant route(s) of exposure and over the time-course for which the chemical is
present in that tissue.
When a PBPK model is available for the appropriate test species, it is used to estimate the
value of internal dose metrics, which are then used to derive a given point of departure (e.g., no-
observed-adverse-effect level, lowest-observed-adverse-effect level, benchmark dose,
benchmark concentration) for use in dose-response analyses for toxicity endpoints, including
XI
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cancer, chronic toxicity, and other toxicity endpoints. Some risk assessment applications can be
accomplished using only a model for the test species, e.g., prediction of the toxicity in that
species by another route of exposure for purposes of route extrapolation. For most applications,
a human version of the PBPK model is also developed to estimate an administered dose to a
human that would result in the equivalent internal dose in a human that led to the observed
toxicity in a test species or, less frequently, the biologically effective dose from a human clinical
or epidemiology study. PBPK model analysis is accepted as a scientifically sound approach to
estimating the internal dose of a chemical at a target site and as a means to evaluate and describe
the uncertainty in risk assessments.
xn
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1. INTRODUCTION
1.1. SCOPE OF THE DOCUMENT
The objective of this document is to provide a description of approaches for using
physiologically based pharmacokinetic (PBPK) data and models in human health risk
assessment. Its primary focus is on the evaluation and use of PBPK models for predicting
internal dose at target organs in risk assessment applications. Many of the past efforts on PBPK
modeling have focused on water-insoluble gases that cause systemic toxicity (i.e., producing
effects remote from the site of exposure) and on some nonvolatile organics. This document
primarily draws on the experience and literature resulting from these efforts. These approaches
can also be applied to agents such as reactive gases and paniculate matter where the target organ
is the respiratory tract, generally in conjunction with specialized respiratory tract modeling (e.g.,
computational fluid dynamic [CFD] modeling). Guidance concerning alternative approaches to
dosimetry modeling should also be consulted for determining a reference concentration (RfC)
value (U.S. EPA, 1994). The discussions herein are conceptually applicable, in a broad sense, to
many kinds of dosimetry models and a wide range of substances.
In developing this document, it was assumed that risk assessors are familiar with some
basic concepts of pharmacokinetics and that model developers are familiar with some of the
basic concepts of risk assessment; therefore, the document serves as an overview of PBPK
modeling and its application in risk assessment. Appropriate references to secondary review
articles and reports from which additional information can be obtained are provided.
Finally, it is important to realize that the application of PBPK models in risk assessment
is evolving. Thus, this document does not specify (or recommend) when the effort to construct
and apply PBPK modeling is justified; rather, it highlights some of the benefits of PBPK
modeling in risk assessment.
1.2. INTENDED AUDIENCE
The document was prepared with two primary audiences in mind: (1) risk assessors who
need to know about the potential applications of PBPK models in risk assessments, and (2)
PBPK model developers who need to better understand how their efforts can help improve health
risk assessment.
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1.3. ORGANIZATION OF THE DOCUMENT
The remaining three chapters form the core of this document. They describe what risk
assessors need in terms of pharmacokinetic data, and why (Chapter 2); how to evaluate PBPK
models for use in risk assessments (Chapter 3); and how to use PBPK models in risk assessments
to address specific areas of uncertainty (Chapter 4).
Chapter 2 addresses data needs in terms of reference dose (RfD) and RfC derivation as
well as predictive estimates in cancer risk assessment. It also contains a brief discussion on the
minimal data requirements for constructing PBPK models, as well as the use of pharmacokinetic
data and PBPK models to improve exposure assessments.
Chapter 3 presents an approach and some criteria for evaluating PBPK models intended
for use in risk assessments that will facilitate the assessor's decision regarding whether or not an
available model is adequate and scientifically defensible for use in reducing uncertainties in a
given risk assessment. The PBPK modeling issues are considered under each of the following
topic areas: model structure, mathematical description, parameter estimation (calibration),
computer implementation, and evaluation. Current criteria as well as accepted methods are
identified and then assembled to facilitate the identification of PBPK models that meet the
requirement for use in risk assessment.
Chapter 4 discusses how PBPK models and data can be applied within the current U.S.
Environmental Protection Agency (EPA, or the Agency) risk assessment framework to address
specific areas of uncertainty. The following types of PBPK model applications in risk
assessment are presented in this chapter: high-dose to low-dose, interspecies, intraspecies, route-
to-route, and scenario extrapolations; mixture risk assessment; and linkage with
pharmacodynamic models. This chapter also highlights how PBPK models are used in cancer
and noncancer assessments.
Finally, the appendix provides a list of publications relating to PBPK modeling and its
use in health risk assessment.
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2. PHARMACOKINETIC DATA AND MODEL NEEDS IN RISK ASSESSMENT
2.1. PHARMACOKINETICS AND DOSIMETRY MODELING
Pharmacokinetics (pharmakon + kinetics; pharmakon (Greek) = drugs and poisons;
kinetics = change as a function of time) involves the study of the time course of the parent
chemical or metabolite concentrations or amounts in biological fluids, tissues, and excreta and
the construction of mathematical models to interpret such data (Wagner, 1981). The time course
of the concentration of a chemical or its metabolite in biota is determined by the rate and extent
of absorption, distribution, metabolism, and excretion (ADME). The pharmacokinetics or
ADME of a substance determines the delivered dose or the amount of chemical available for
interaction in the tissues. Relating adverse response observed in biota to an appropriate measure
of delivered dose (e.g., concentration of the toxic chemical in the target tissue) rather than
administered dose or exposure concentration is likely to improve the characterization of many
dose-response relationships (see Section 2.2.).
A range of modeling approaches is used to characterize exposures and the resulting
delivered doses. The variety of approaches reflects differences in chemical and physical
characteristics (e.g., stable or reactive gases, particulate matter, lipophilic organics, water-soluble
compounds), differences in pharmacokinetic properties, and the ability of compounds to cause
contact site or systemic toxic effects (U.S. EPA, 2004, 1994; Andersen and Jarabek, 2001;
Overton, 2001).
Exposure to many drugs and toxicants occurs via the oral route and causes systemic
effects, and many simple (e.g., one- and two-compartment) pharmacokinetic models have been
used to analyze the pharmacokinetics of such exposures (Renwick, 2001; O'Flaherty, 1981).
Generally, these compartment models contain a central compartment that represents the whole
body (or plasma) where distribution occurs nearly instantaneously (one-compartment model) or
an additional compartment (two-compartment model) where the distribution is affected by
additional processes such as metabolism or sequestration into fat. Compartment models help
characterize a chemical's kinetic behavior, and they are useful in deriving values for a chemical
or drug's distribution in the body or clearance from the blood (i.e., half-life).
The values derived from a compartment model analysis, however, apply only to the
conditions of the study from which the experimental data were obtained. To better represent the
biological determinants of a chemical's disposition in the body and predict the internal dose that
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would result from different exposure regimens (including hypothetical exposures where no data
are available), models have evolved with multiple compartments and mathematical descriptions
of the real physiological processes and tissues most likely to affect chemical disposition (e.g.,
absorption from the gut or lung, cardiac output, metabolism in the liver, renal clearance). These
models are called physiologically based pharmacokinetic (PBPK) models, and they are the focus
of this document.
The need to predict behaviors of volatile anesthetics, including compounds now used
exclusively as industrial chemicals, was a driving force for the development of PBPK models
(Krishnan and Andersen, 2001). The general principles developed in these early PBPK
modeling efforts for systemically distributed compounds are also applicable to other compounds.
For example, the respiratory tract is a frequent site of both exposure and toxicity, and it has been
a particular focus for a range of modeling approaches, including those developed to simulate the
kinetics of gases of various reactivities and solubilities, as well as parti culate matter (see U.S.
EPA, 1994). More recently, the kinetics of reactive gases and paniculate matter within the
respiratory tract are simulated with advanced approaches such as two-dimensional and three-
dimensional CFD modeling (U.S. EPA, 2004; Martonen et al., 2001; Overton, 2001; Kimbell et
al., 1993).
The role of metabolism is a significant factor in the development of PBPK models.
Saturable metabolism results in nonlinear relationships between the level of administered dose
and the levels of the internal dose for a parent or metabolite. In combination with other
physiological and chemical events, the resulting administered dose-response relationship can
quickly become difficult to resolve with simple analytical tools. PBPK models provide an
excellent means to account for multiple process interactions and nonlinearities and to provide
insight into the whether the parent chemical or the metabolite is the main form (or toxic moiety)
leading to adverse effects.
Metabolism has also played an important role in the development of models for
respiratory tract toxicities. Nasal or other respiratory tract toxicities following exposure to many
volatile organic compounds has often been linked to the generation of a toxic metabolite in
respiratory tract tissues. To simulate the kinetics and resulting toxicities for these compounds,
CFD models have been coupled with PBPK models. The CFD models describe the deposition of
the chemical in different regions of the nose, and the PBPK model then simulates the tissue
absorption, metabolic, and clearance processes (Frederick et al., 2002; Bogdanffy et al., 2001).
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Other complex kinetic events in the respiratory tract are also being modeled, including the
kinetics for compounds that are relatively water soluble and that exhibit fractional absorption, or
the so-called "wash-in, wash-out" effect (Perkins et al., 1995; Medinsky et al., 1993; Johanson,
1991).
Although the approaches detailed in the earlier RfC methodology (U.S. EPA, 1994) do
not address many of these more recent advances, there is recognition of the need for additional
approaches that address these and other challenging aspects of respiratory tract dosimetry.
The relevant modeling approach, therefore, depends on the physical and chemical
characteristics of the material, the method and route of exposure or delivery, and the toxicities
under consideration. All of these modeling approaches attempt to describe the dose delivered to
the relevant areas of the body, whether that is a region of the respiratory tract or skin or systemic
delivery through the blood supply to target organs. These approaches permit estimation of some
measure of delivered dose for improved understanding of the dose-response relationship.
2.2. DOSE-RESPONSE AND MEASURES OF DELIVERED DOSE
Dose-response relationships that appear unclear or confusing at the administered dose
level can become more understandable when expressed on the basis of internal dose of the
chemical. Figure 2-1 depicts the case of a hypothetical chemical for which the correlation between
dose and response is weak or complex (Panel A). However, once the relationship is based on
internal dose, there emerges a clear and direct relationship between dose and response (Panels B
and C). The major advantage of constructing dose-response relationships on the basis of internal
or delivered dose is that it can provide a stronger biological basis for conducting extrapolations and
for comparing responses across studies, species, routes, and dose levels (Melnick and Kohn, 2000;
Benignus et al., 1998; Aylward et al., 1996; Andersen et al., 1987; Clewell and Andersen, 1985).
Relating blood and tissue concentrations with response in exposed organisms has long
been recognized in pharmacology (e.g., Wagner, 1981). In pharmacokinetics, the target tissue
dose that most closely relates to an adverse response is often referred to as the internal "dose
metric" (Andersen and Dennison, 2001). Dose metrics used in risk assessment applications,
ideally, reflect the biologically active form of the chemical (parent chemical, metabolites, or
adducts), its level (concentration or amount), duration of internal exposure (instantaneous, daily,
lifetime, or a specific developmental period), and intensity (peak, average, or integral), as well as
2-3
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30 i
, linear
Weibull
1000 2000 3000 4000
Exposure concentration (ppm)
B
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50-
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10 100 1000
Exposure concentration (ppm)
10000
10 20 30 40
Rate metabolized (mg/hr)
50
Figure 2-1. Relationship between the exposure concentration and adverse
response for a hypothetical chemical. Panel A depicts the case of a chemical
for which the correlation between dose and response is weak or complex, along
with equally plausible curve fits (linear, Hill, and Weibull). This dose-response
relationship is improved when it is based on an appropriate measure of internal
dose (Panels B and C).
the appropriate biological matrix (e.g., blood, target tissue, surrogate tissues). For assessment of
health risks related to lifetime exposure of systemically acting chemicals, in the absence of mode
of action (MO A) information to the contrary, the integrated concentration of the toxic form of
chemical expressed as the daily average (i.e., average daily area under the concentration vs. time
curve, or area under the curve [AUC]) in target tissue has been considered to be an appropriate
dose metric (Clewell et al., 2002a; Voisin et al., 1990; Collins, 1987).
2-4
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Box 2-1. Examples of dose metrics useful for
exploring dose-response relationships
Parent chemical
• Peak concentration
• Average concentration
• Amount or quantity
• AUC (integral)
Metabolite
Peak concentration
Average concentration
Amount or quantity
Rate of production
Cumulative rate of formed/time/L tissue
AUC (integral)
Miscellaneous
• Receptor occupancy (extent/duration)
• Macromolecular adduct levels
• Depletion of cofactors
When the toxicant is not the parent
chemical but a reactive intermediate, the
amount of metabolite produced per unit
time or the amount of metabolite in target
tissue over a period of time (e.g., mg
metabolite/L tissue during 24 hr) has been
used as the dose metric (Andersen and
Dennison, 2001). For developmental
effects, the dose surrogate is defined in the
context of the window of exposure during a
particular gestational event (e.g., Luecke et
al., 1997;Welschetal., 1995).
Even though the AUC and rate of
metabolite formation are among the most
commonly investigated dose metrics, other
surrogates of tissue exposure may also be appropriate for risk assessment purposes, depending on
the chemical and the MOA (Clewell et al., 2002a). Dose metrics that may be used to derive
dose-response relationships for risk assessment are listed in Box 2-1; evaluation of dose metrics
for use in risk assessment is further discussed in Section 4.2. Finally, it should be noted that
PBPK models can also be useful for hypothesis testing, particularly with regard to choosing
among potential dose metrics. This is discussed further in Chapters 3 and 4, and particularly in
Section 4.5.5.
2.3. PHARMACOKINETIC DATA NEEDS IN RISK ASSESSMENT
The quantitative dose-response assessment portion of the risk assessment process can be
used to determine a point of departure (POD) for one or more of the most sensitive critical
effects. The POD is the dose-response point that marks the beginning of a low-dose
extrapolation, and it can be the no-observed-adverse-effect level (NOAEL) or the lowest-
observed-adverse-effect level (LOAEL) for an observed incidence or the lower bound on dose
for an estimated incidence or change in response level from a benchmark dose (BMD) analysis.
The quantitative characterization relates the administered dose to the observed responses in
laboratory or field studies. In some cases, data are available to relate an internal dose at a target
2-5
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tissue to the response, but generally the internal dose-response relationship is derived from a
model analysis. An understanding of the MOA leading to the most sensitive endpoint is used to
determine the most appropriate dose measure for deriving a POD. Noncancer and nonlinear
cancer assessments derive a POD for use in risk assessment based on the available data. This
process often requires conduct of interspecies, high-dose to low-dose, duration, and/or exposure
route extrapolations of the dose-response from available data to the likely human exposure
conditions and most sensitive human subpopulations.
These extrapolations assume that when the value of the internal dose metric is identical in
two situations (rat vs. human, oral vs. inhalation, 6-hr exposure vs. 24-hr exposure), the two
administered doses are pharmacokinetically equivalent. For example, exposure of rats to 50 ppm
toluene for 6 hr and of humans to 17.7 ppm toluene for 24 hr yields the same blood AUC (3.8
mg/L/hr), implying that these exposure scenarios in rats and humans are pharmacokinetically
equivalent (Figure 2-2). The example in Figure 2-2 demonstrates that pharmacokinetic
equivalence is not always linear equivalence; if one assumes that all aspects of the
pharmacokinetics of toluene in humans is identical to those in rats, an equivalent 24-hr exposure
would be 12.5 ppm (i.e., 6 hr x 50 ppm is equivalent to 24 hr x 12.5 ppm), not 17.7 ppm.
Knowledge of the MOA supports the choice of the dose metric. For example, the most
appropriate dose metric to characterize the dose-response relationship for reactive gases that
cause contact site toxicity is the total amount of chemical in the target tissue over time, whereas
for the anesthetic effects of a volatile organic, the current (or peak) concentration in the blood is
the most appropriate dose metric. For the latter, the acute effects are more (or entirely)
dependent on concentration rather than on total amount over time, so extrapolations are best
conducted using that dose metric.
PBPK models are often intended to estimate target tissue dose in species and under
exposure conditions for which little or no data exist. Thus, if a complete pharmacokinetic data
set were available, then there would be no need to develop a PBPK model. Such an optimal
pharmacokinetic data set for risk assessment would consist of the time-course data on the most
appropriate dose metric associated with exposure scenarios and doses used in the critical studies
chosen for the assessment (e.g., animal bioassays or human clinical and epidemiological studies)
and relevant human exposure conditions. An example of such a dose metric is the concentration
of a toxic metabolite in target tissue over a 24-hr period in the test species and in humans. This
information would be obtained for the window of exposure, route and scenario of exposure
2-6
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RAT
c
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2
c
CD
.
.a
CO
3
o
I
100 -i
10-
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0.1 -
0.01 -
0.001 -
0.0001
12 16
Time (hours)
20
24
HUMAN
o
"ro
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o ^J
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o ^
15
CO
-------
chemical, urinary metabolite levels, or fraction absorbed may be used as a surrogate for the
tissue levels. These and other subsets of pharmacokinetic data can be used to develop a PBPK
model to estimate the level of the toxic moiety of interest, and the uncertainty in those estimates
can be formally characterized.
2.4. PHARMACOKINETIC MODELS IN RISK ASSESSMENT
2.4.1. Regulatory Needs and Considerations
Regulatory agencies such as EPA derive dose-response values based on the current
understanding of a dose-response relationship. Reference values correspond to an estimate of a
daily exposure to the human population (including sensitive subgroups) that is likely to be
without an appreciable risk of deleterious effects during a lifetime. The reference values
developed at EPA include RfC for chronic inhalation exposures and RfD for chronic oral
exposures. For chronic oral and inhalation cancer risk assessments with an unknown or a linear
MOA (e.g., mutagenic carcinogens), EPA develops unit risk estimates, including the cancer
slope factor (CSF) for oral exposures and the inhalation unit risk (IUR). The underlying
assumption in these derivations is that the exposure concentration (or applied dose) of a parent
chemical results in an internal exposure of the putative toxic form of the chemical in a target
organ that will be less than or equal to a level that is not associated with significant adverse
responses during a lifetime (reference value) or that yields a likely risk at or below the estimated
lifetime risk (unit risk).
Even though a key factor in the induction of adverse effects is the presence of the toxic
form of a chemical in the target organ, it is rare that data are available on the time course of the
toxic moiety in the target tissue(s) in humans. Even in animal studies, it is more practical to
obtain measures of blood, plasma, and urinary concentrations of toxic chemicals and their
metabolites than the actual toxic moiety level in the relevant tissue. Pharmacokinetic models are
therefore used to estimate the tissue concentration of toxic substances.
Among the compartmental pharmacokinetic models, PBPK models are the most
appropriate and useful for conducting the extrapolations needed to derive reference values
because they model the underlying physiological and chemical processes that determine
chemical disposition, and they can be used to predict target organ concentrations for hypothetical
exposures (Krishnan and Andersen, 2001; Andersen, 1995; Leung, 1991; Rowland, 1985;
Himmelstein and Lutz, 1979). By simulating the kinetics and dose metric of chemicals, PBPK
2-8
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models can reduce the uncertainty related to interspecies, intraspecies, route-to-route, duration,
and high-dose to low-dose extrapolations needed to derive RfC, RfD, and cancer unit risk
estimates. The following sections discuss how the PBPK models are used in health risk
assessment. Figure 2-3 provides an overview of the development and use of PBPK models for
risk assessment; it should be noted that model development can occur within academic, industry,
and governmental bodies, and often involves collaboration or sharing of information.
Problem
Identification
Literature
Evaluation
Toxic
Mechanisms
Physiological
Constants
Biochemical
or Metabolic
Constants
Model
Formulation
Refine Mode
A
Experimentation
>
not OK
i ^
V
/
Comparison
to Data
„. .
1 5111111 hi
/
OK
•**-
tion
Application in
Risk
Assessment
Figure 2-3. Basic flowchart of PBPK model development.
2.4.2. Use of PBPK Models in Dose-Response Assessment
PBPK models are useful for performing various forms of extrapolations where the data
necessary for predicting risks to humans are not available and cannot easily (or ethically) be
obtained. The primary advantage gained by using PBPK models in risk assessment is their
ability to relate toxicity responses in a test species to humans and outcomes observed in smaller
populations to likely outcomes in the general population. Thus, foremost among the
extrapolations afforded by PBPK models are inter- and intra-species extrapolations.
2-9
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In risk assessments based on nonlinear MO As (e.g., most noncancer assessments), RfC
and RfD values are derived from PODs (i.e., NOAEL, LOAEL, or benchmark concentration
[BMC]) to derive a human equivalent concentration (HEC) or dose. In RfC derivation,
pharmacokinetic adjustments, called dosimetric adjustment factors (DAFs), are applied to
account for species differences in chemical disposition. These factors are dependent on the
nature of the inhaled toxicant and MO A, as well as the endpoint (local effects vs. systemic
effects). Dosimetry data in the test animals and humans (e.g., deposition data, region-specific
dosimetry, blood concentration of systemic toxicants), if available, can help estimate the DAF.
In the absence of such data, knowledge of critical parameters or mathematical models in the test
species and humans can be useful in estimating the DAF. Similar methods are employed in RfD
derivation.
An alternative to the use of DAFs is to employ models to make interspecies
extrapolations. A variety of computational tools are available for determining the uptake and
deposition of gases and parti culates in nasal pathways and the respiratory tract (U.S. EPA, 2004;
Bogdanffy and Sarangapani, 2003; Hanna and Lou, 2001; Iran et al., 1999; Bush et al., 1998;
Asgharian et al., 1995; Jarabek, 1994; Kimbell et al., 1993). Although PBPK models have most
frequently been applied to systemically acting gases and vapors, they have also been applied, in
conjunction with other models (e.g., CFD), to more locally acting gases. Another limitation to
DAFs is that they do not account for metabolism, so PBPK modeling approaches would clearly
be preferable for metabolized compounds if adequate data are available.
PBPK models are also useful for incorporating variability in chemical disposition into a
risk assessment. There are numerous determinants of a chemical's disposition in the body (e.g.,
protein levels, enzyme activity levels, tissue volumes, breathing rates, cell proliferation rates)
(e.g., Dome et al., 2002, 2001a, b; Walton et al., 2001). Focusing on individual determinants of
disposition is useful for understanding their mechanistic basis and potential impacts on dosimetry
and response, but such an approach can easily lead to unrealistic estimates of total variability of a
chemical as well as toxic response (Lipscomb, 2004) because the magnitude of variability
associated with such individual determinants may be neither cumulative nor additive. The net
impact of various determinants on chemical disposition is more properly evaluated by integrating
the available information with a PBPK or biologically based dose-response (BBDR) model.
Two additional forms of extrapolation amenable to PBPK modeling include route and
duration adjustments. The PODs in critical toxicity studies are not always obtained for exposure
2-10
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scenarios of interest to risk assessment. Ideally, the POD used in the RfC process would be the
inhalation route-specific NOAEL, LOAEL, or BMC. Route-to-route extrapolation, however, can
be conducted on the basis of equivalent potential doses when information on the POD is
available only for a noninhalation route of exposure (e.g., oral route) (Pauluhn, 2003).
Historically, simplistic assumptions were used to convert the NOAEL (mg/kg/day) associated
with an oral exposure route to equivalent inhaled concentration, based on breathing rate and
body weight of the test species. Such simplistic approaches, however, incorrectly assume that
the rates of ADME and tissue dosimetry of chemicals are the same for a given total dose,
regardless of the exposure route and intake rate. PBPK modeling is useful for conducting route-
to-route extrapolation on the basis of equivalent delivered dose from PODs identified from the
NOAEL, LOAEL, or BMC (e.g., oral to inhalation).
As mentioned above, RfC and RfD values are intended for continuous exposure of human
populations, such that the POD used in an RfC derivation (for example) would correspond to 24
hr/day exposures (U.S. EPA, 1994). Because the PODs are frequently obtained from animal
exposures or occupational exposures that occur for 6 to 8 hr/day, 5 days/wk, adjustment to a
continuous 24-hr exposure is conducted on the basis of hours per day and days per week (i.e.,
6/24 x 5/7), which essentially results in a lower concentration for continuous exposures (U.S.
EPA, 2002). Depending on the dose metric identified or hypothesized to be the most
appropriate for the chemical and endpoint, the duration-adjusted exposure values can be obtained
with PBPK models (U.S. EPA, 2002; Jarabek, 1994). This approach is based on the expectation
that the pharmacodynamic aspect does not change between the various durations of within-day
exposures (<24 hr).
As discussed in detail in Chapter 4, PBPK models can also be used to convert a POD in a
critical cancer study to an appropriate dose metric. Here again, simpler approaches such as body
weight scaling can be replaced with PBPK models capable of inter- and intra-species
extrapolation as well as route-to-route extrapolation. These models can also facilitate high-dose
to low-dose extrapolation by converting exposure concentrations in critical studies into predicted
dose metric values.
2.4.3. Use of Pharmacokinetic Data and Models in Exposure Assessment
The conventional approach to exposure assessment involves the calculation of applied
dose for each route of exposure based on information about the concentration of the chemical in
2-11
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the medium, frequency and duration of exposure, rate of contact with the medium, and body
weight of the individual (Paustenbach, 2000). With increased data availability, however,
absorbed dose can be calculated (U.S. EPA, 1992). In order to calculate absorbed dose,
pharmacokinetic data such as time-course data on concentration or total quantity in alveolar air,
urine, or blood are required (Paustenbach, 2000). Estimating a delivered dose from biomarker
data, absorbed dose, or applied dose, in fact, may not be straightforward. PBPK models provide
a means to improve these estimates and to fully utilize available data.
PBPK models can be used in conjunction with an exposure assessment to improve the
quantitative characterization of the dose-response relationship and the overall risk assessment.
PBPK models can be used to identify and evaluate the relationship between an applied dose and
biomonitoring or biomarker data, or between an applied dose, biomarker level, and internal
target tissue dose (e.g., Timchalk et al., 2004, 2001; Csanady et al., 1996; Fennell et al., 1992;
Krishnan et al., 1992). PBPK models have also been used to establish biological exposure
indices (e.g., breath, blood, or breath concentrations) to protect workers from harmful exposures
to solvents (Droz et al., 1999; Thomas et al., 1996a; Kumagai and Matsunaga, 1995; Leung,
1992; Perbellini et al., 1990) or in epidemiology studies to reconstruct human exposures over
time (Canuel et al., 2000; Roy and Georgopoulos, 1998; Vinegar et al., 1990). Comprehensive
PBPK models are being developed that provide estimates of an internal tissue dose from
multiroute (oral, inhalation, dermal) or multichemical exposures (Levesque et al., 2002; Liao et
al., 2002; Corley et al., 2000; Rao and Ginsberg, 1997; Roy et al., 1996; Georgopoulos et al.,
1994). The net tissue dose associated with a multiroute (aggregate) and/or multichemical
(cumulative) exposure are especially useful for advancing risk assessment beyond the one
chemical, one exposure route paradigm.
2.4.4. Pharmacokinetic Models in Risk Assessment: Summary
Adverse tissue responses are more directly and closely related to the internal target tissue
dose of the toxic moiety than to the concentration of the parent chemical in the environment.
Therefore, the scientific basis of, and confidence in, risk assessments are enhanced when they are
supported by estimates of the internal tissue dose. Data for the internal tissue dose levels,
however, are generally not available, and the relationship between external and internal dose
may not be easily resolved. PBPK models provide a means of estimating the internal dose for
many different exposure regimens based on what is known about the physiology of the test
2-12
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species and humans and the chemical of interest. PBPK models reduce the uncertainty in dose-
response and exposure assessment and fully utilize the available data.
In the context of dose-response assessment, PBPK models have application in:
1. Interspecies extrapolation of pharmacokinetically equivalent doses (RfD, RfC, CSF,
and IUR),
2. Estimation of the pharmacokinetic component variability (RfC and RfD derivation),
3. Route-to-route extrapolation of the POD (RfC, RfD, CSF, and IUR),
4. Duration adjustment of the POD (RfC and RfD derivation), and
5. High-dose to low-dose extrapolation (CSF and IUR).
In the context of exposure assessment, PBPK models are useful for
1. Converting applied dose into tissue dose,
2. Calculating tissue dose associated with multiroute and multimedia exposures, and
3. Relating biomarker data to tissue dose and potential dose by exposure reconstruction.
PBPK models vary in their quality, transparency, and predictive capability, and they must
undergo a rigorous evaluation if the model is to be used in a risk assessment. The process and
criteria for evaluating a PBPK model are the subject of the next chapter.
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3. EVALUATION OF PBPK MODELS INTENDED FOR USE IN
RISK ASSESSMENT
PBPK models intended for risk assessment applications should be evaluated for quality
and transparency. There are no published criteria or well-defined standards for evaluating PBPK
models; however, several publications have addressed good modeling practices and approaches
for evaluating and documenting biological models intended for risk or safety assessments (Clark
et al., 2004; Andersen et al., 1995; Yates, 1978). Evaluation of PBPK models intended for risk
assessment applications includes considerations for model purpose, model structure,
mathematical representation, parameter estimation, computer implementation, and predictive
capacity as well as sensitivity, variability, and uncertainty analyses. Each of these issues is
discussed in detail in the following sections. Although these considerations are provided in
separate sections, it is important to realize that model evaluation, from development through to
application, can be an iterative process.
3.1. MODEL PURPOSE
Not all PBPK models are developed to support risk assessments. Some are developed to
be used as research tools for testing biological hypotheses or for guiding improved experimental
design. The purpose for which a PBPK model is developed influences its structure, level of
detail, and model parameterization (e.g., species). Thus, the structure of a PBPK model designed
for use in research may not serve the intent and purposes of one applied in risk assessment, and
the complexities and capabilities of PBPK models vary according to their intended use. PBPK
models are generally developed to accomplish one or more of three objectives:
• To integrate diverse sets of pharmacokinetic data on a particular chemical;
• To investigate the pharmacokinetic basis of the toxicity of a chemical that appears to
be complex at the administered dose level; and/or
• To predict tissue dosimetry for situations other than those directly measured in
animals or humans (i.e., extrapolation).
All PBPK models are simplified representations of biological systems of varying
complexities and are designed to predict the behavior and outcome of biological processes
affecting the chemical pharmacokinetics in an in vitro or an in vivo system. Some PBPK models
5-1
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are designed specifically to integrate diverse data sets in efforts to uncover mechanistic
determinants of the pharmacokinetics of a specific chemical and to aid in the interpretation of a
chemical's mode(s) of action (Haddad et al., 1998; Clewell and Andersen, 1987). However,
such PBPK models are not always intended for use in predicting the pharmacokinetic behavior of
a chemical under exposure conditions and species in animal toxicity studies that might be
important to risk assessment. Models designed for such purposes must integrate diverse sets of
pharmacokinetic data and also must be capable of predicting available in vivo pharmacokinetic
data sets. Models with defined "predictive capabilities" are valuable for risk assessment because
they provide confidence that such models can also predict the in vivo pharmacokinetics for
chemicals under exposure conditions where little or no such data exist (e.g., the animals in the
toxicity study or the humans in risk assessment scenarios). Thus, models with this form of
predictive capacity afford risk assessors the ability to extrapolate across species, dose levels, and
exposure scenarios. For more on model purpose, the reader is referred to Clark et al. (2004).
For application in risk assessment, the preferred PBPK model is one that is capable of
predicting the pharmacokinetics and tissue dose of the potential toxic moiety of a chemical under
conditions applicable to critical studies in animals or humans and human environmental
exposures.
3.2. MODEL STRUCTURE
The structure of a PBPK model in large part depends on the purpose for which the model
is developed and the philosophy of the modeler. There is virtually no limit to the number and
complexity of compartments in a model intended to describe molecular/cellular events (see
Figure 3-1). Parsimony in selecting model structures, however, is an important and guiding
principle in developing models for use in risk assessments. The complexity of PBPK models
used in risk assessment is often constrained by limited data available to calibrate and test the
model and the need for risk assessors to defend the model assumptions and the values derived
from model simulations.
The simplest conceptual model represents the organism as a one-compartment system.
One- and two-compartment kinetic models are useful in characterizing the toxicokinetics of a
chemical for any given data set, but they are not useful for extrapolating beyond the data used to
develop the model. PBPK models differ from one- or two-compartment models by representing
many more physiological, physicochemical, and biochemical processes in the species of interest,
5-2
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and they can be used to predict internal dose levels for hypothetical exposure regimens based on
what is known about the toxicokinetics of a chemical. In most PBPK models, tissues are
represented by specific compartments, each with a unique set of physiological (i.e., blood flows)
and physicochemical (i.e., partition coefficients) parameters. Target tissues are generally
represented individually (e.g., brain) and nontarget tissues are lumped together (e.g., slowly
perfused tissues). Depending on the available data, PBPK models intended for risk assessment
applications would, preferably, include the target organ as one of the compartments. More often,
a PBPK model would be capable of estimating blood concentration, which is often used as a
surrogate for tissue concentrations. Major portals of entry (e.g., lung, gastrointestinal tract),
storage organs (e.g., adipose tissue), metabolism/transformation sites (e.g., liver, kidney) as well
as elimination routes (e.g., renal, pulmonary, fecal) would be included if at all possible.
It is often acceptable to mathematically describe ADME of chemicals in PBPK models in
lumped or whole-body surrogate compartments without a highly resolved physically
representation in all of the tissues where these processes occur (Krishnan and Andersen, 2001),
provided that this lack of physical representation does not interfere with a model's use as an
extrapolation tool. When data are available to support more complex representations, the PBPK
model can be elaborated to represent more complex mechanistic and biological interactions. For
example, the liver can be divided into separate compartments depending on the localization of
enzymatic activity. Figure 3-1 provides examples of PBPK model structures that have been
commonly used to simulate the kinetics of volatile and nonvolatile substances. Note that these
models facilitate the simulation of the concentration of chemicals or their metabolites in the
target organ or a surrogate tissue (usually blood).
Frequently, compartments in PBPK models are assumed to be homogenously and
completely mixed reactors. This means that the concentration of the chemical anywhere in the
tissue is the same and related by the partition coefficient to the concentration of the chemical in
venous blood. This assumption is typically used unless there are data to support more elaborated
descriptions, such as diffusion-limited compartments.
Ideally, the structure of a PBPK model intended for risk assessment applications would
contain the target organ (or a surrogate tissue) as well as compartments representing tissues of
unique physiological and biochemical relevance to the pharmacokinetics of the chemical in
question.
5-3
-------
Inhaled Exhaled
chemical chemical
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n
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Figure 3-1. Sample PBPK model structures. (A) A four-compartment model
for simulating perfusion-limited tissue uptake of inhaled chemical. Gas exchange
through lungs is indicated with arrows, and metabolism is described in liver. (B)
A model for simulating diffusion-limited tissue uptake and multi-route exposures.
Dotted lines represent the separation of cellular matrix and tissue blood
components.
5-4
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3.3. MATHEMATICAL REPRESENTATION
Once the qualitative aspects of the model structure are deemed acceptable, the next step
in the evaluation is mathematical representation. Here the focus is on the adequacy of the
number and form of mathematical equations used to represent the tissues and processes in the
real system being modeled. Model code, rationale, and supporting documentation should be
readily available to the reviewer. In PBPK modeling, each tissue compartment is generally
described with a mass-balance ordinary differential equation that describes changes in the
amount of chemical in the tissue over time. These changes result from chemical distribution in
and out of the tissue and clearance processes (e.g., metabolism or excretion) in the tissue. For
chemicals distributing to tissues by passive processes, the tissue:blood partition coefficients
describe the relationship between tissue and blood concentrations. Descriptions of this blood
flow-limited uptake have been used successfully in many of the past efforts in PBPK modeling
that dealt with small-molecular-weight organics. For other compounds, including some with
high-molecular-weight or significant protein binding, membrane diffusion can be the rate-
limiting process; for these chemicals, uptake is described with differential equations for the
tissue blood and cellular matrix subcompartments (Krishnan and Andersen, 2001; Andersen,
1995; Leung, 1991; Rowland, 1985). Tables 3-1 and 3-2 provide examples of commonly used
mathematical representations for tissue compartments and physiological processes that determine
chemical disposition. Note that mass balance differential equations have units of mass per time
(e.g., mg/hr) or sometimes concentration per time (e.g., mg/L/hr).
The rates of metabolism in PBPK models have typically been described as first-order,
saturable Michaelis-Menten (i.e., shifting from first order to zero order) or second-order
processes. At low concentrations, metabolic clearance frequently appears linear, or first order,
with respect to plasma concentration. At higher concentrations, metabolic clearance can become
saturated and a constant amount of chemical is metabolized per unit time (i.e., zero-order
kinetics). Second-order processes and requisite equations are more complex and may be based
on a reversible equilibrium relationship (e.g., macromolecular binding) or the concentrations of
chemical and cofactors required for metabolism (e.g., conjugation reactions). In each case, the
reason for using a particular description should be clearly provided. PBPK models using
particular mathematical descriptions of tissue uptake, metabolism, and protein binding without
any justification cannot be used confidently for risk assessment applications. For example, if
enzyme-mediated metabolism is described as a first-order process in a PBPK model, the
5-5
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Table 3-1. Equations of a four-compartment PBPK model to simulate the
inhalation exposure of volatile organic compounds
Tissue compartments
Equations"
Arterial bloodb
Ca = -
QcxCv
Cinh
Qc
Ft
Liver
JA . \ V x Cv
^- = Q,x(Ca -Cv, —— -
dt v ' Km + Cvt
dA,
l~~dTX + '
C< =Av~
c,
Cv, = —
PI
Fat
dAf
Af =
f dt
C =
Cvr =-
Vf
£L
P,
Richly perfused tissues
dt
dA
4 =^-
dt
-4
-Cv
Poorly perfused tissues
= Qsx(ca -Cv,)
dt
dA
A = — —xdt
dt
P,
-------
Table 3-1. Equations of a four-compartment PBPK model to simulate the
inhalation exposure of volatile organic compounds (continued)
Tissue compartments
Venous blood
Alveolar air
Equations"
Ql x Cv{ + Qf x Cvf +Qrx Cvr +Qsx Cvs
Cnlv —
Pb
a Equations are from Ramsey and Andersen (1984).
b The steady-state arterial blood equation in this example is used for chemicals that reach rapid equilibrium in blood,
such as highly fat-soluble volatile chemicals. In other cases, a detailed mass-balance equation for the arterial blood
may be needed.
A = amount (mg)
a = arterial blood
alv = alveolar air
b = blood:air
C = concentration (mg/L or mmol/L)
c = cardiac output
f=fat
inn = inhaled air
Km = Michaelis-Menten affinity constant (mg/L)
1 = liver
P = partition coefficient
p = pulmonary ventilation
Q = flow rate (L/hr"1)
r = richly perfused tissues
s = slowly perfused tissues
t = tissue :blood
V = volume (L)
v = mixed venous blood
vf = venous blood leaving fat
vl = venous blood leaving liver
Vmax = maximal velocity of enzymatic reaction (mg/hr"1)
vr = venous blood leaving richly perfused tissue
vs = venous blood leaving poorly perfused tissue
Table 3-2. Equations used for describing diffusion-limited uptake in PBPK
models
Subcompartments
Tissue blood
Cellular matrix
Equations
v dCl Ac c \pi\^~c^
tl ^ Qt»( ,n out) [ < \ pt
y dC2 _\piV\Ci-C2)
n dt L ' J'x Pt
A = amount (mg)
C = concentration (mg/L or mmol/L)
in = inflow
out = outflow
PA = permeability-area coefficient
Q = flow rate (L/hr"1)
tl = tissue blood
t2 = cellular matrix
V = volume (L)
Pt = tissue :blood partition coefficient
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scientific rationale for employing such a description is needed before the model can be used for
purposes of extrapolation and prediction. Because PBPK models are simplified representations
of the real systems, the full details and actual complexity of the physiological and biochemical
processes are not incorporated in the equations used. Depending on the level of detail required
and the objective of the modeling effort, appropriate descriptions of the biochemical processes
can be included in these models.
For use in risk assessment, the equations chosen to describe ADME should be
scientifically supported and, where possible, documented.
3.4. PARAMETER ESTIMATION
Chemical-specific and species-specific parameter values are required to solve the
equations constituting a PBPK model. Typically, PBPK models require the numerical values of
physiological parameters such as alveolar ventilation rate, cardiac output, tissue blood flow rates,
and tissue volumes. They also incorporate absorption rate parameters (e.g., for dermal or oral
uptake), clearance parameters related to metabolism, or renal and biliary excretion pathways, as
well as tissue distribution parameters such as partition coefficients (blood:air, skin:water,
skin:air, and tissue:blood), protein binding characteristics, or transporter activities. Additional
parameters (e.g., tissue DNA levels, hematocrit, number and concentration of binding proteins)
may be required in some cases. For many other purposes, knowledge of the average value or the
range of plausible values of model parameters is sufficient; however, for estimating
interindividual differences in tissue dosimetry, knowledge of the distributions of input
parameters is essential.
3.4.1. Physiological Parameters
The physiological parameters used in PBPK models should either correspond to those
obtained in the experimental pharmacokinetic study or be within the range of plausible values for
the species and life stage. Peer-reviewed compilations of ranges and reference values of
physiological parameters for adult animals and humans are available (Brown et al., 1997; Davies
and Morris, 1997; Fiserova-Bergerova, 1995; Leggett and Williams, 1991; Arms and Travis,
1988) (Tables 3-3 through 3-6), yet no such compilations exist with respect to physiological
parameters for specific subgroups of populations (e.g., developing and lactating animals,
-------
Table 3-3. Commonly used physiological parameters for mice, rats, and
humans
Physiological parameters3
Body weight (BW) (kg)
Tissue volume (fraction of BW)
Liver
Fat
Organs
Muscle and skin
Cardiac output (Qc) (L/min)
Tissue perfusion (fraction of Qc)
Liver
Fat
Organs
Muscle and skin
Minute volume (L/min)
Alveolar ventilation (L/min)
Mouse
0.025
0.055
0.1
0.05
0.7
0.017
0.25
0.09
0.51
0.15
0.037
0.025
Rat
0.25
0.04
0.07
0.05
0.75
0.083
0.25
0.09
0.51
0.15
0.174
0.117
Human
70
0.026
0.19
0.05
0.62
6.2
0.26
0.05
0.44
0.25
7.5
5
Many PBPK models often lump certain tissues together into single compartments such as rapidly/richly and
slowly/poorly perfused compartments.
Source: Adapted from Travis and Hattemer-Frey (1991).
Table 3-4. Range of values of the volume and perfusion of select tissues in
the mouse
Tissue
Adipose
Brain
Heart
Kidneys
Liver
Lungs
Muscle
Skin
Volume
(% body weight)
5-14a
1.35-2.03
0.4-0.6
1.35-1.88
4.19-7.98
0.66-0.86
35.8-39.9
15.9-20.8
Regional blood flow
(% cardiac output)
3.1-3.5
5.9-7.2
7-11.1
12.2-19.6
3.3-8.3
a Varies proportionately with body weight.
Source: Adapted from Brown et al. (1997).
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Table 3-5. Range of values of the volume and perfusion of select tissues in
the rat
Tissue
Adipose
Brain
Heart
Kidneys
Liver
Lungs (upper respiratory)
Muscle
Skin
Volume (% body weight)
4.6-12a
0.38-0.83
0.27-0.4
0.49-0.91
2.14-5.16
0.37-0.61
35.4-45.5
15.8-23.6
Regional blood flow
(% cardiac output)
1.5-2.6
4.5-5.1
9.5-19
13.1-22.1
11.1-17.8
a Varies proportionately with body weight.
Source: Adapted from Brown et al. (1997).
Table 3-6. Range of values of perfusion of select tissues in humans
Tissue
Adipose
Brain
Heart
Kidneys
Liver
Muscle
Skin
Regional blood flow
(% cardiac output)
3.7-11.83
8.6-20.4
3.8-8
12.2-22.9
11-34.2
5.7-42.2
3.3-8.6
a Varies proportionately with body weight.
Source: Adapted from Brown et al. (1997).
pregnant women, children). Physiological parameters for specific subgroups is an area of active
research, and there are some published references for some parameter values (Gentry et al.,
2004; Pelekis et al., 2003; Hattis et al., 2003; Price et al., 2003a, b; Haddad et al., 2001a;
Schoeffner et al., 1999; Luecke et al., 1994).
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In PBPK models for organic chemicals, the sum total of the volumes of compartments
corresponding to soft tissues is smaller than the body weight; sometimes 85% of the body weight
(100% [body weight] - 15% [estimate of nonperfused portion based on the weight of
skeletal/structural components as percent body weight]) is used (Brown et al., 1997). Even
though the tissue volumes (expressed in liters) are needed for PBPK modeling, tissue weights
(kg) are usually used with the assumption of unit density (L = kg). This assumption is a
reasonable approximation because tissue densities typically range from 0.9 kg/L for fat to 1.06
kg/L for muscle (Mendez and Keys, 1960).
The tissue blood flow rates in the model should add up to cardiac output. Maintaining
mass balance in PBPK models requires that the sum of all flows to the compartments be equal to
the cardiac output, although other errors in model equations can result in a lack of mass balance.
The ratio of cardiac output to alveolar ventilation rate is roughly 1 in a resting individual but
decreases with activity (Astrand and Rodahl, 1970). The specification of cardiac output
independent of the value of ventilation rate is unacceptable, particularly if the ratio
(ventilation:perfusion) is not in the normal physiological range. Frequently in PBPK models,
ventilation rate, cardiac output, and tissue perfusion rates and tissue volumes are specified for the
individual animal or human being simulated.
The values of common physiological parameters for a test species or human vary
depending on body weight. To simplify the recalibration of certain parameter values when
running the model for a different species, all tissue volumes are expressed as fractions of body
weight such that, for any given body weight, the volumes in liters can be readily calculated by
multiplying the body weight by the corresponding fractional value. Similarly, because the
cardiac output and alveolar ventilation rate are related to basal metabolic rates and/or body
surface rather than to body weight, these models can be specified as a power function of body
weight, with the exponent varying from 0.67 to 0.75 (e.g., Tardif et al., 1997; Andersen et al.,
1987). These scaling functions are based on cross-species analyses for adult animals and may
not be appropriate for all life stages.
An acceptable PBPK model would contain tissue volumes, flow rates, and
ventilation:perfusion ratios that are within physiological limits. The sum total of the tissue
volumes should not exceed the body weight, and the sum total of tissue blood flow rates should
equal cardiac output.
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3.4.2. Partition Coefficients
Tissue distribution is dependent on a variety of processes, including passive diffusion,
active transport, and cellular concentrations of lipid and binding proteins, among others.
Partition coefficients describe the steady-state concentration in the tissue compared with blood or
for volatiles in blood versus air. Calibration of PBPK models for partition coefficients has
sometimes been done by fitting model simulations to in vivo data. In such cases,
pharmacokinetic data collected following a single bolus dose or repeated doses leading to steady
state are analyzed with the PBPK model to estimate the tissue:blood partition coefficients
(Gabrielsson and Bondesson, 1987; Gallo et al., 1987; Lam et al., 1982; Chen and Gross, 1979).
Steady-state data provide the most straightforward data for model calibration; however, they
require correction for tissues in which there are significant specific binding or metabolic
processes. In tissues where there is a significant level of metabolism or binding, the calculation
of an apparent tissue:blood partition coefficient should account for the amount of chemical
consumed by such processes (Chen and Gross, 1979).
The tissue:blood, skin:water, skin:air, and blood:air partition coefficients required for
PBPK modeling of volatile organic chemicals are conveniently determined in vitro using the vial
equilibration method (Beliveau and Krishnan, 2000; Kaneko et al., 1994; Gargas et al., 1989;
Johanson and Dynesius, 1988; Fiserova-Bergerova and Diaz, 1986; Sato and Nakajima, 1979).
Tissue:blood partition coefficients for nonvolatile chemicals may be estimated in vitro using
radioactive chemicals in ultrafiltration, equilibrium dialysis, or vial equilibration procedures
(Murphy et al., 1995; Jepson et al., 1994; Sultatos et al., 1990; Igari et al., 1983; Lin et al., 1982).
The partition coefficients estimated by these in vitro methods are acceptable, provided
equilibrium is attained during the experimental conditions. A time-course analysis should be
conducted to choose an appropriate time point (at which equilibrium is attained) for determining
partition coefficients in vitro, and appropriate studies will help demonstrate that metabolism or
chemical reactions are not depleting the chemical.
Algorithms based on the consideration of solubility and binding of chemicals in
biological matrices have also been developed and applied for predicting tissue:blood, tissue:air,
and blood:air partition coefficients of volatile organic chemicals. This approach requires
knowledge of tissue and blood composition in terms of lipid and water contents, the
octanol: water or oil: water partition coefficients of the chemical, and binding association
constants, if applicable (e.g., Poulin and Thiel, 2000; Poulin and Krishnan, 1996a, b, 1995). At
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the present time, there is no validated animal-replacement approach for predicting association
constants for blood or tissue protein binding of organic chemicals (Poulin and Krishnan, 1996b).
The biologically based algorithms as such are useful in providing initial estimates of tissue:blood
partition coefficients solely on the basis of the consideration of solubility in water and lipid
contents of tissues and blood. A number of other empirical or semiempirical methods relating
molecular structure or physicochemical characteristics to tissue:blood and blood:air partition
coefficients of chemicals are also available (Beliveau et al., 2005, 2003; Payne and Kenney,
2002; Abraham and Weathersby, 1994). Their use is acceptable, as long as the qualitative and
quantitative aspects of the structural features and physicochemical characteristics of the new
chemical are within the range of values that were used to calibrate the algorithm.
Partition coefficients required for PBPK models can be obtained using in vitro methods,
in vivo data obtained at steady state, or theoretical algorithms within the boundary of valid
application.
3.4.3. Biochemical Parameters
Absorption rates, metabolic parameters (e.g., first-order or second-order rate constants,
maximal velocity, and Michaelis constant), tissue diffusion constants (for describing diffusion-
limited uptake), and transporter activity parameters required for PBPK modeling can be
determined by fitting a model to data from in vivo studies. In order to estimate these parameters,
pharmacokinetic data (e.g., time course of tissue or blood concentration of parent chemicals,
urinary metabolite levels) obtained following a single bolus dose or infusion may be used. For
volatile organic chemicals, data from exhaled breath and closed chamber gas uptake studies are
frequently used with success (Gargas et al., 1986; Filser and Bolt, 1981, 1979; Andersen et al.,
1980). Descriptions of serum protein binding have important impacts on tissue distribution and
clearance, with parameters often estimated in vitro (Himmelstein and Lutz, 1979). The rate
constants of chemical reaction with hemoglobin and tissue proteins determined in vitro or in vivo
have been incorporated into the PBPK model to make predictions of these phenomena in vivo
(e.g., Krishnan et al., 1992). Biochemical parameters estimated from in vivo data using a model
are dependent on the model structure and values of other parameters (e.g., physiological), so the
application of these values in models with different structures or parameter values must be
evaluated with care. The use of a Bayesian approach is likely to enhance the precision of
parameter estimations from in vivo data by more formally developing and evaluating the
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uncertainty and accuracy of parameter values and by incorporating multiple data sets into their
derivation (e.g., Vicini et al., 1999).
Appropriate methods for application of in vitro systems (e.g., freshly isolated
hepatocytes, microsomes, post-mitochondrial fractions, cytosols) to provide metabolic constants
for incorporation into PBPK models continues to be an active area of investigation. These data
may be applicable to modeling using the parallelogram approach. For example, chemical-
specific in vitro metabolic data from cultured hepatocytes can be scaled to represent in vivo liver
clearance using in vitro data such as estimates of the number of hepatocytes present per 1 g of
liver tissue and the average liver weight (in grams) of the species and age group of interest. In
vitro data for humans can then extrapolated to in vivo by assuming that the same relationship that
successfully describes the in vitro to in vivo relationship in animals effectively converts the
human in vitro data to the in vivo situation.
There are several examples of successful application based on appropriate in vitro-in vivo
scaling methods (Lipscomb et al., 2003, 1998; Hissink et al., 2002; Cole et al., 2001; Mortensen
and Nilsen, 1998; Mortensen et al., 1997; De Jongh and Blaauboer, 1997, 1996; Reitz et al.,
1996a, 1989; Hwang et al., 1996; Iwatsubo et al., 1996; Kedderis and Held, 1996; Gearhart et al.,
1990), although the extrapolation of in vitro data to intact animal is not clear in all cases (e.g.,
Haddad et al., 1998, 1997). But the in vitro studies are particularly useful for evaluating the
extent of metabolism in target tissues, characterizing interindividual differences in metabolism,
and conducting animal-human extrapolation of metabolism constants based on a parallelogram
approach (Kedderis and Lipscomb, 2001; Thrall et al., 2000; Ploemen et al., 1997; Reitz et al.,
1996b; Andersen et al., 1991). Receptor binding and DNA-binding properties of chemicals have
also been successfully described with PBPK models on the basis of in vitro-derived data (Farris
et al., 1988; Terasaki et al., 1984).
Biochemical parameters for PBPK models can be estimated using in vivo data or on the
basis of adequate scaling of in vitro data.
3.5. COMPUTER IMPLEMENTATION
Most PBPK models require the use of numerical simulation methods because they
contain differential equations and descriptions of nonlinear processes, making exact solutions
difficult or impossible. There are a number of integration algorithms and programming
languages currently available for coding and solving PBPK model equations (Table 3-7). Many
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Table 3-7. Examples of simulation software used for PBPK modeling
Software
Fortran compiler with
IMSL library packages,
C, Pascal, Basic
ACSL, ACSL-Tox , or
acslXtreme (Advance
Continuous Simulation
Language)
SimuSolv
Matlab
Microsoft Excel
ScoP (Simulation
Control Program)
Stella
Developer/vendor
Many vendors sell the
different compiler packages
available on the market
The Aegis Technologies
Group, Inc., Huntsville, AL
Dow Chemical Company,
Midland, MI (no longer
distributed outside the
company)
The MathWorks,
Natick, MA
Microsoft Corporation,
Redmond, WA
Simulation Resources, Inc.,
Redlands, CA
Isee Systems, Lebanon, NH
(formerly High Performance
Systems Inc.)
Salient features
Machine language compiler
packages that require certain
knowledge of computer
programming; models can be
customized to simulate
specific condition
The most commonly used
for PBPK modeling in the
toxicology community;
language designed for
modeling and evaluating the
performance of continuous
systems described by time-
dependent, nonlinear
differential equations
Makes use of ACSL
language to write the
dynamic nonlinear systems
that are translated into
FORTRAN at run time
Mathematical software with
matrix-related computations,
numerical integration
algorithms capable of
solving systems of ordinary
differential equations, and
graphical nonlinear
simulation (Simulink)
Neither translation of the
model nor the compilation
into a program is required,
but the user should specify
integration algorithm and
interval
An interactive control
program for constructing
models; when used with a C
compiler, SCoP greatly
simplifies the construction of
a simulation program
Macintosh, interactive
graphical user interface
software; enables the user to
generate models with
diagrams, where a minimal
knowledge of computer
programming is required
Examples of application
Hoang (1995); Karba et al.
(1990)
Thomas et al. (1996a); Dong
(1994)
Rey and Havranek (1996);
Ramsey and Andersen
(1984)
Easterling et al. (2000)
Haddadetal. (1996);
Johanson and Naslund
(1988)
Menzeletal. (1987)
Hoang (1995)
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Table 3-7. Examples of simulation software used for PBPK modeling
(continued)
Software
Developer/vendor
Salient features
Examples of application
Mathematica
Wolfram Research, Inc..
Champaign, IL
Mathematical software with
matrix-related computations;
numerical integration
algorithms capable of
solving systems of ordinary
differential equations
Burmaster and Murray
(1998)
Berkely Madonna
Robert Macey and George
Oster, University of
California at Berkeley, CA
A general-purpose
differential equation solver
program developed on the
Berkeley campus under the
sponsorship of National
Science Foundation and the
National Institutes of Health;
currently used by academic
and commercial institutions
for constructing
mathematical models for
research and teaching
Reddy et al. (2003)
SONCHES (Simulation
of Nonlinear Complex
Hierarchical Ecological
Systems)
Central Institute of
Cybernetics and Information
Processes, Academy of
Sciences of GDR, Berlin
A computer system where
connections between various
data libraries in the
preparation and post-
processing of simulation are
executed by macro
commands
Wtinscher et al. (1991)
CMATRIX
Robert Ball and Sorell L.
Schwartz, Georgetown
University, Washington, DC
A system that allows the
user to create compartmental
models based on personal
biological knowledge,
leaving the construction and
numerical solution of the
differential equations to the
software
Ball and Schwartz (1994)
BASICA
California Department of
Pesticide Regulation,
Sacramento, CA
Numerical integration
algorithms developed by the
Department for PBPK
modeling
Dong (1994)
AVS
(Application
Visualization System)
Advanced Visual Systems,
Inc., Waltham, MA
A visualization software
package capable of
importing processed
resonance images and
combining the use of ACSL
to create three-dimensional
representations of the PBPK
of a chemical in an organism
Nichols etal. (1994)
MCSim
Drs. Bois and Maszle
Software that facilitates the
conduct of Bayesian analysis
with PBPK models but has
no graphical interface
Jonsson and Johanson
(2003)
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of the commercially available software packages routinely make use of integration algorithms to
obtain numerical solution to differential equations (Reddy et al., 2003; Easterling et al., 2000;
Burmaster and Murray, 1998; Menzel et al., 1987) such that (for most cases) the modeler or the
risk assessor needs only to evaluate that an appropriate algorithm was used. However, if a
programming language (FORTRAN, BASIC) or spreadsheet (Lotus 1-2-3, QuattroPro, Microsoft
Excel) is used for modeling, then the modeler should write the codes for an appropriate
numerical integration algorithm (e.g., Euler, Gear, Runge-Kutta routines; predictor-corrector
methods). In such cases, the integration algorithm as well as the integration interval, i.e., the
time interval at which the calculations of the change in concentration (or amount) of chemical in
various compartments are performed, should be specified (e.g., Haddad et al., 1996; Blancato
and Saleh, 1994).
The modeler also needs to be aware of the optimization routine offered by software
packages, particularly if parameters are to be estimated from experimental data by statistical
optimization (Holmes et al., 2000). The personal and portable computers marketed today
possess sufficient speed, disk space, and run-time memory required for PBPK modeling and
parameter optimization; therefore, this aspect requires no formal evaluation.
The accuracy of computational representation of PBPK models is evaluated by
"debugging," which refers to the process of error detection in computer programs. Errors in
PBPK model code may result either from typing errors or from illogical mathematical
statements. To eliminate these errors, it is essential to carefully verify the model code after entry
into the computer. Commercially available simulation software, while converting the model
codes written in a source language to machine language, can detect syntax/language errors
related to incorrect writing of model codes. However, such error diagnostic features cannot
detect errors associated with incorrect mathematical representation of a process, even if the code
is written in correct programming language and without typing mistakes.
The modeler who uses a PBPK model in risk assessment is ultimately responsible for
ensuring that the code and equations are entered correctly and that the code is subject to routine
error diagnostic checks; this may include re-coding the model. Such verification can initially be
done by the developer, subsequently by individuals not involved in the model development (e.g.,
peer-reviewers and co-workers) (Clark et al., 2004), and again when used for risk assessment
purposes.
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Solution to the differential equations in a PBPK model need not be evaluated if a highly
reputable commercial or open source simulation software has been used, although an
appropriate algorithm should have been selected. However, it is necessary to ensure that the
code and equations are entered correctly and that the code is subject to routine error diagnostic
checks. When the modeler writes his/her own program, the appropriateness of the integration
algorithm and integration intervals should be justified; similar concerns would exist initially for
newly developed commercial or open source software.
3.6. EVALUATION OF PREDICTIVE CAPACITY
The purpose of model evaluation is to assess the adequacy of a model and corresponding
parameters to consistently describe the available pharmacokinetic and dose-response data of a
chemical-biological system, as well as to characterize the uncertainty associated with the
parameter values. In a risk assessment context, this also involves evaluation of the suitability
and applicability of the model for regulatory and health protection purposes. Model evaluation
includes verification and validation (or calibration). In brief, model validation deals with
building the right model, whereas model verification deals with building the model right (Balci,
1997). Model verification includes many of the topics covered in this chapter up to this point. It
involves an evaluation of the accuracy with which a chemical-biological system has been
transformed into a model specification (e.g., the model diagram or equations) and the accuracy to
which such a diagram or set of equations has been coded into an executable computer program.
Model validation/calibration, on the other hand, involves substantiating that the model, within its
domain of applicability, behaves with satisfactory accuracy.
It is important to correct a common misunderstanding about what "validated model"
means. A model that has been calibrated against one data set and adequately simulates a
different data set can be said to be "validated," but it is only validated to the extent to which
those two data sets accurately represent the larger population, not in any global sense
independent of the data used to develop and test the model. PBPK models are used to
extrapolate to hypothetical exposure conditions or dosing regimens but, again, these
extrapolations are only valid to the extent that the data used to calibrate and test the model are of
sufficient quality to support the extrapolations. To avoid giving the impression that a model has
been validated to predict outcomes for which it has not been adequately tested, some modelers
will use the terms "calibrated" to describe a model containing parameters that have been
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optimized to fit one (e.g., an "internal" data set and the term "having predictive utility" to
describe a model that adequately reflects another (e.g., an "external") data set. There are,
however, some advocates for using "all" of the available data to develop parameter values, and
for that approach the calibration and predictive utility distinctions no longer work. This issue of
varying methods and terms used to develop and evaluate PBPK models is a reflection of the
relative newness of the modeling discipline in risk assessment, and the research community is
actively working to advance the methodology.
A potential complexity that may come to light during the model evaluation process is the
existence of discrepancies between data sets or even among different measurements within a data
set. For example, when one dosimetry study reports that the sum of all urinary metabolites
excreted by rats is 20-30% and another study reports 40-50% urinary clearance, no PBPK model
may be able to simulate both data sets. When this occurs the modeler will need to evaluate the
data sets to identify potential sources of these differences and ultimately may need to use expert
judgment to select one over the other or accept the uncertainty implied if both are acceptable.
Obvious sources of discrepancies that a model may be able to explain would be due to dose route
and dose level. Differences that could be more difficult to explain with a single model (and a
single set of parameters) can arise from differences in dose vehicle and animal strain. In the case
of strain differences, if the modeler finds that she/he can describe all the data from one strain of
rats but not a data set from a different strain, there is at least a reasonable chance that some
model parameters (e.g., metabolic rate constants) differ for the second strain. However, if no
potential sources of variation can be identified for discrepancies between data sets for the same
strain, vehicle, route, and dose range, then any model will fail to fit both sets. In such instances,
consideration of the quality of the analysis or other features of the study, or perhaps the one that
is most consistent with all other data sets, can inform the decision of which data to utilize.
It is also possible for a data set to be internally inconsistent. For example, when the sum
of all excreta does not equal the administered dose, or the sum of metabolites measured in the
blood do not equal the total blood level measured using a radiolabel, then there is a lack of mass
balance in the pharmacokinetic data. Here too, the modeler must exercise professional judgment
in determining how to simulate the data and whether some data can be excluded. For example, a
model may be able to describe the data from three doses used in a study but not those from a
fourth dose (that is neither the highest nor the lowest dose). In this case, a classical
pharmacokinetic analysis may show that even with an unstructured model, the parameters for
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that odd dose must be quite distinct from the others, leading the modeler to conclude that there
was an error or unreported variation in the dosing or data collection. If a dosing error is
suggested, then the modeler may try varying the dose for that data set to see whether the model
can then fit the data. In the case of a mass balance discrepancy, the modeler may choose to
normalize the data, forcing it into balance, or to introduce a "loss" term such as binding to tissue
components if that is consistent with the biochemistry.
In the following sections, it is presumed that all such discrepancies in the data themselves
have been dealt with.
3.6.1. Model Verification
As mentioned above, verification of a PBPK model involves evaluation of the biological
plausibility of the model structure and parameters, as described in the documentation, and the
mathematical correctness of the equations. Although these topics are discussed in previous
sections, the issue of verification is being highlighted here in the context of a risk assessor who
may have played no part in the initial development of a model. In this context, the risk
assessor(s) must, in essence, retrace the model development in order to understand the model
well enough for application in regulatory decision making. This includes assessing the model
from purpose and structure all the way to examination of the model code in order to ensure that it
mathematically implements the model as described in the documentation. This examination
includes checking for correctness of statements and functions and correct order of statement
execution (for languages that are not self-sorting). Although trivial, checking the mass balance is
important when evaluating errors in model structure that could lead to erroneous increases or
decreases in the level of the chemical in tissues. Another check on model behavior is to set the
exposure level to zero; this is necessary to ensure that the model can accurately represent steady-
state levels of the chemical and that the background level does not change with time in the
absence of exposure.
Whether a code-based or a graphical-based software interface is used, it is preferable that
the language produce, as one output, the set of equations that constitute the PBPK model. Code-
based representations ease the task of providing sufficient documentation demonstrating that the
model is actually constructed as may be described in prose within a peer-reviewed publication.
To facilitate model verification, the model code would be organized and annotated in such a way
as to facilitate understanding by individuals (e.g., reviewers) other than the original program
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developer. This also affords relatively easy translation into a reviewer's software of choice. In
the case of a model intended for use in a risk assessment application, it is imperative to provide
documentation of the particular parameter values and simulations that are required to reproduce
any validation runs and dose metric calculations. This usually entails the provision of step-by-
step directions, either using the language's scripting capability or in separate documentation, that
allow reproduction of the validation plots and dose-metric calculations by following specific
directions or by invoking specified program blocks.
3.6.2. Model Validation/Calibration
Model evaluation should consider the ability of the model to predict the kinetic behavior
of the chemical under conditions that test the principal aspects of the underlying model structure.
Ideally, a PBPK model would be compared with data that are informative regarding the
parameters to which the dose metric predictions are sensitive (this pre-supposes the use of
sensitivity and uncertainty analysis to identify the parameters of concern; see Section 3.7). For
example, validation of a human model based solely on parent chemical data would not
necessarily provide confidence that the model could be used to predict a metabolite dose metric.
The use of parent chemical kinetic data to validate model estimates of metabolism in the human
can be highly misleading because it can be the case that the metabolism parameters have little
impact on parent chemical concentrations, whereas other uncertain parameters (e.g., ventilation
rate, blood flow, fat content) can strongly influence model predictions of parent chemical kinetic
behavior. However, even in such cases, sensitivity and uncertainty analyses can help to
characterize the confidence (or lack thereof) with which the model makes predictions (see
Section 3.7).
The adequacy of model parameter values may be evaluated in different ways; no single
method has been accepted or endorsed by the modeling or regulatory community as yet.
Statistical methods required for evaluating the adequacy of model parameters are based on
comparison of simulations with experimental data and depend on whether the objective is to
perform internal evaluation (in which all model parameters are estimated from the same data
set), external evaluation (in which different data sets are used for model calibration and testing
the predictive capability of the model), or semi-external evaluation (in which some of the model
parameters are based on the data set). Although no systematic research effort or guidance is
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available in this regard, there is much interest in developing consistent and acceptable evaluation
methods, and progress is being made.
To date, PBPK model evaluation generally has not been conducted rigorously from a
statistical perspective. Although quantitative tests of goodness of fit often may be a useful aspect
of the evaluation process, they generally are not designed to test hypotheses for PBPK models,
which can be highly nonlinear and may contain a large number of parameters. None of the
classical procedures (e.g., t-, Mann-Whitney, two-sample %2, and two-sample Kolmogrov tests)
that determine whether the underlying distributions of the two data sets are similar is applicable
because the output processes of almost all real-world systems and simulations are nonstationary
and autocorrelated. Furthermore, a question exists as to whether the use of statistical hypothesis
tests is even appropriate. Since the model is only an approximation of the actual system, a null
hypothesis that the system and model are the same is clearly false.
Perhaps a more important consideration may be a model's ability to accurately predict the
general behavior of the data in the intended application and whether or not the differences
between the system and the model are significant enough to affect conclusions derived from the
model. In this regard, Haddad et al. (1995) screened various statistical procedures (correlation,
regression, confidence interval approach, lack-of-fit F-test, univariate analysis of variance, and
multivariate analysis of variance) for their potential usefulness in evaluating the degree of
agreement between PBPK model simulations and experimental data. According to these authors,
the multivariate analysis of variance represents the most appropriate classical statistical test for
comparing PBPK model predictions with experimental data.
For now, however, the most common (if not the most rigorous) approach to model
evaluation has involved visual inspection of the plots of model predictions (usually continuous
and represented by solid lines) with experimental values (usually discrete and represented by
symbols) against a common independent variable (usually time). The greater the commonality
between the predicted and experimental data, the greater the confidence in the model structure
and parameters. The correspondence between predictions and experimental data should be not
only at the level of individual values (e.g., blood concentration values) but also at the level of the
profile (i.e., bumps and valleys in the pharmacokinetic curve). In other words, the shape of the
simulated curve should correspond to that of the experimental data and also be one that would
result in a plot of the residuals (i.e., difference between the simulation and the data) without a
systematic deviation from a scatter around zero.
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Figure 3-2 shows several examples of visual evaluations of the adequacy of PBPK model
predictions. The models used in panels A and C could be considered adequate because they
simulate the behavior of the experimental data even though they do not accurately simulate every
single experimental datum. On the other hand, model D would be considered less adequate, and
further work would be required to refine the model. Examination of the model simulation in
panel B suggests that this model is simulating a bolus exposure, whereas the data set (same in all
panels) appears to be from an inhalation study, suggesting that the modeler has chosen either the
wrong exposure parameters for the model or the wrong data set for comparison.
This approach to model evaluation says nothing about the adequacy of the model
structure or parameters; it only reflects an individual's judgment of how closely the model
predicts the observed behavior. Evaluating the adequacy of model structure and equations is
fairly straightforward when compared with the evaluation of the model parameter values. For
example, inadequacies in PBPK model structures can be inferred simply by observing the
simulated and experimental pharmacokinetic profiles (Lilly et al., 1998). If the model cannot fit
the pharmacokinetic profiles for any realistic parameter values, or it can do so only by using
values that are inconsistent with other data, then one can reasonably conclude that the structure is
inadequate.
This evaluation of model structure provides the developer an opportunity to think about
the need for additional compartments, critical determinants of disposition, or different
quantitative descriptions of the phenomena and to improve the capability of the model
accordingly. Again, a useful way of comparing the experimental and simulated data is to plot the
residuals (i.e., difference between experimental and simulated data) as a function of time or as a
function of various controllable variables. If two or more models fit the experimental data
equally well, new experiments may be designed to identify the model that more accurately
predicts the other attributes of the biological system (Kohn, 1995).
One approach for determining whether the level of complexity (number of parameters) in
a model is justified by the data is to use a nested modeling approach, where the model is reduced
to a simpler (nested) model when one or more parameters is set to zero or some other "baseline"
value (Collins et al., 1999); however, additional changes to the model may be needed in order to
maintain mass balance. The increase in goodness-of-fit obtained by allowing those parameters to
be varied can then be evaluated statistically using a j^ statistic to determine whether the
additional degrees of freedom afforded by those parameters are justified (Collins et al., 1999).
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Figure 3-2. Comparison of four PBPK model simulations (left, log scale;
right, linear scale). Solid lines are model simulations overlayed with
experimental data (symbols). Models A and C appear to simulate the data
reasonably well. Model D seems to underpredict all points, suggesting that
refinement is necessary. In model B, there appears to be a mismatch between the
exposure parameters in the model (apparently bolus) and that of the data set
(apparently inhalation).
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There is increasing concern about the relevance and usefulness of external evaluation in
PBPK modeling. External evaluation requires that some of the available pharmacokinetic data
not be used during the model calibration phase, but set aside for evaluating the performance of
the model. Not everyone is in agreement with such an approach. Some investigators argue that
all the data used for model evaluation can be used to improve the parameter estimates, so that no
data are "wasted" toward that end. Such an iterative approach to model evaluation and
calibration maximizes the use of the available data. Others, however, argue that this type of
modeling can become a form of self-fulfilling prophecy.
The issue of external evaluation is particularly problematic for human data because the
actual parameters for each individual in a population might be sufficiently different, such that a
model with a single set of parameters may not be reasonably expected to simulate the observed
kinetics in all individuals. Therefore, the process of modeling not only can take into account
existing information on parameters but also accommodate new information based on fits to
additional data sets. In this context, Bayesian analysis utilizing Markov chain Monte Carlo
(MCMC) calculations is being increasingly explored for use in PBPK modeling (Jonsson and
Johanson, 2002, 2001; Bois, 2000a, b). In the Bayesian approach, the prior information on
parameters is updated on the basis of new pharmacokinetic data, such that the resulting posterior
estimates consistently describe all data and support better characterization of the uncertainty and
distribution in the parameter values. One continuing challenge of the Bayesian approach is how
to use important data or biological information that are not easily amenable to incorporation into
MCMC calculations, the concern being that leaving such information out gives undue priority to
the particular studies that are included. Thus, posterior distributions always need to be
scrutinized as to their consistency with existing biological knowledge; for instance, posteriors
that are strongly inconsistent with (carefully constructed) priors may indicate problems with
model structure or errors or inconsistencies in the data.
Cross-validation is another potentially useful approach for model evaluation (Keys et al.,
2003). In the structure-activity relationship modeling arena (Beliveau et al., 2003), cross-
validation uses all of the available data sets by repeated subsampling. This type of a "leave-one-
out" cross-validation allows the use of available data both for estimation and evaluation of model
parameters.
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It is likely that no single approach will be sufficient or applicable in all contexts. Each of
these approaches has its merits and limitations, and their applicability for PBPK model
evaluation depends on the purpose of the model and data availability.
There are other important issues concerning parameters for which values are estimated by
optimizing model output to experimental data ("fitting"), including parameter values based on
posteriors from a Bayesian analysis. In such instances, the modeler must assess the
identifiability of the parameter given the data. The practical reality of modeling biological
systems is that, regardless of the complexity of the model, there will always be some level of
"model error" in the form of inconsistencies or lack of representation of the real biological
system that can result in systematic discrepancies between the model and the experimental data.
This type of inherent structural deficiency in all models interacts with deficiencies in the
identifiability of the model parameters, potentially leading to misidentification of the parameters.
Due to the confounding effects of model error and parameter correlation, it is quite possible for
an optimization algorithm to obtain a better fit to a particular data set by changing parameters to
values that no longer have any meaningful correspondence to the biological entity the parameter
was initially intended to represent. This problem can be ameliorated in Bayesian analyses
through appropriate prior distributions. It is usually preferable, however, to repeatedly vary the
model parameters manually before performing an optimization to obtain a sense of their
identifiably and correlation under various experimental conditions. Some simulation languages
aid this process by including routines for calculating parameter covariance or for plotting joint
confidence region contours.
Estimates of parameter uncertainty obtained from traditional optimization routines can be
viewed as lower-bound estimates of true parameter uncertainty because only a local, linearized
variance is typically calculated. In characterizing parameter uncertainty, it is probably more
instructive to determine what ranges of parameter values are clearly inconsistent with the data
than to accept a local, linearized variance estimate provided by the optimization algorithm. The
Bayesian approach, in principle, gives a more global characterization of parameter uncertainty
(see Section 3.7.3).
3.6.3. Model Documentation
Adequate documentation is critical in the evaluation of a model. The level of
documentation for a PBPK model depends on its intended use. For models developed for
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research or hypothesis testing, the documentation need only include sufficient information about
the model so that an experienced modeler can accurately reproduce its structure and
parameterization. Usually this documentation would include a combination of one or more "box
and arrow" model diagrams together with any equations that cannot be unequivocally derived
from the diagrams. For simple models, a well-constructed model diagram, together with a table
of the input parameter values and their definitions, may be all that an accomplished modeler
would need to create the mathematical equations defining a PBPK model.
For models submitted in support of a risk assessment, the level of documentation is
considerably greater. These models will be subjected to internal and external peer review, and
their structure, supporting data, simulations, and use in the derivation of reference values must be
completely transparent and reproducible. In addition to graphical representations of the model,
this level of documentation would likely include well-annotated and complete documentation of
the model code, all data (fully referenced) that were used to calibrate and/or test the model, a
description of the calibration and testing procedures used, fully referenced sources for all
parameter values (or the optimization methods, results, and data used in optimizing parameters),
sensitivity analysis or other rationale that guided the choice of which parameters were optimized,
simulation run conditions, any additional analyses that help characterize or support the quality of
the model, and all supporting documentation that would be needed by an experienced modeler to
run the model and accurately reproduce any simulations used (or submitted for use) in deriving
reference values.
The model diagram should be labeled with the names of the key variables associated with
the compartment or process represented by each box and arrow. All tissue compartments,
metabolism pathways, routes of exposure, and routes of elimination must be clearly and
accurately presented. The model diagram should also clearly differentiate blood flow from other
transport (e.g., biliary excretion) or metabolism, and arrows should be used where the direction
of transport could be ambiguous.
In general, there would be a one-to-one correspondence of the boxes in the diagram to the
mass balance equations (or steady-state approximations) in the model. Similarly, the arrows in
the diagram would correspond to the clearance (transport or metabolism processes) in the model.
Each of the arrows connecting the boxes in the diagram should correspond to one of the terms in
the mass balance equations for both of the compartments it connects, with the direction of the
arrow pointing from the compartment in which the term is negative to the compartment in which
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it is positive. Arrows connected only to a single compartment, which represent uptake and
excretion processes, are interpreted similarly.
Interpretation of the model diagram is supplemented by the definition of the model input
parameters in the corresponding table. The definition and units of the parameters can indicate
the nature of the process being modeled (e.g., diffusion-limited vs. flow-limited transport,
binding vs. partitioning, saturable vs. first-order metabolism). The values used for all model
parameters are to be provided, with units. If any of the listed parameter values are based on
allometric scaling, the scaling method should be fully described with body weights used to
obtain the allometric constant and the power of body weight used in the scaling. Any equations
included to supplement the diagram should be dimensionally consistent and in a standard
mathematical notation.
Model documentation plays a critical role in effective transfer of complex biological
models between model developers and potential users, particularly those who will evaluate the
model and implement it in risk assessment applications. Approaches for model documentation
are still evolving, but adequate documentation is essential to the transparency and reproducibility
of risk assessments. One approach that can be implemented in many modeling programs that
have scripting capabilities is to create computer code that reproduces all the key results, although
not necessarily the full procedure to obtain these results (e.g., optimization, Monte Carlo
analysis, etc., which sometimes involve multiple software). The key results are (1) fits to data
used for parameter estimation, (2) calculation of dose metrics for critical studies (e.g., animal
toxicology or occupational epidemiological studies) to be used in developing dose-response
values (e.g., RfC, RfD, CSF, or unit risk), and (3) calculation of human dose metrics used in
developing dose-response values. Such scripts help to quickly recreate critical numerical values
used in the risk assessment and facilitate working with a new model for evaluation or application
in risk assessment. However, that also means that these scripts need to be evaluated carefully to
ensure that they are correct in terms of the computer implementation and the situation they are
modeling. Finally, if model development and implementation in risk assessment are carried out
by different people, not all of these scripts would be expected upon completion of model
development; others would be created during its application in the risk assessment.
PBPK models intended for use in risk assessment should be evaluated to ensure that they
provide simulations of pharmacokinetic profiles consistent with the experimental data and that
the parameters (point estimates, range of values, or distributions) are appropriate for the
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intended application. Scripts facilitate transparency and reproducibility in modeling by
recreating fits to kinetic data, estimation of dose metrics in critical studies, and calculation of
human dose metrics for development of dose-response values (e.g., RfD, CSFs).
3.7. SENSITIVITY, VARIABILITY, AND UNCERTAINTY ANALYSES
In models of biological systems, estimates of the values of model parameters will always
have some variance, due both to biological variation and experimental or model errors. The
interest in having a PBPK model that describes a variety of data with a consistent set of
parameters prevents a model from providing an optimal fit to all sets of experimental data. For
example, a PBPK model of a compound with saturable metabolism is required to reproduce both
the high and low concentration behaviors, which appear qualitatively different, using the same
parameter values. If one were independently fitting single curves with a model, different
parameter values might provide better fits at each concentration, but they would be relatively
uninformative for extrapolation.
Where only some aspects of the model can be evaluated, it is particularly important to
assess the uncertainty associated with the aspects that are untested. For example, a model of a
chemical and its metabolites that is intended for use in cross-species extrapolation to humans
would preferably be verified using data in different species, including humans, for both the
parent chemical and the metabolites. If only parent chemical data are available in the human, the
correspondence of metabolite predictions with data in several animal species could be used as a
surrogate, but this deficiency needs to be carefully considered when applying the model to
predict human metabolism.
One of the values of biologically based modeling is the identification of specific data that
would improve the quantitative prediction of toxicity in humans from animal experiments. The
variability, uncertainty, and sensitivity of parameters constituting the PBPK models can also be
evaluated, which is desirable for models that are used to derive dose-response values.
3.7.1. Sensitivity Analysis
Sensitivity analysis provides a quantitative evaluation of how parameters in input
functions influence the dose metric or outcome. Such an analysis provides insight into how each
parameter influences estimates of the dose metric and the subsequent dose-response value, and
which parameter(s) have the greatest impact on the dose-response value estimate. Sensitivity
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analysis facilitates a focused use of resources for more detailed analysis and for further data
gathering to reduce uncertainty and to better characterize pharmacokinetic variability (Clewell et
al., 1994; Bois et al., 1991; Hetrick et al., 1991).
Sensitivity analysis for PBPK models typically compares the magnitude of change in
output for a defined change in each input parameter. This process of single changes in one
parameter while all others are held constant is called "local" parameter sensitivity analysis. This
analysis yields sensitivity ratios that correspond to the ratio of change in simulation output (e.g.,
tissue dose) to change in parameter value. Figure 3-3 depicts hypothetical sensitivity ratios
associated with some input parameters of a PBPK model. The greater the absolute value of the
sensitivity ratio, the more important the parameter. In this example, the sensitivity ratio for
breathing rate is the highest of all input parameters, indicating that it is the most sensitive model
parameter for the dose metric. The sensitivity ratio of 2 for breathing rate signifies that a 1%
change in the numerical value of this parameter will result in a 2% change in the dose metric.
Sensitivity Ratio
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Figure 3-3. Sensitivity ratios associated with certain input parameters of a
hypothetical PBPK model.
In practice, sensitivity ratios greater than 1 (in absolute value) are of concern because this
results in the amplification of input error (Allen et al., 1996). It is critical that fractional blood
flows sum to cardiac output when they are varied in sensitivity analyses or else mass balance will
be violated and normalized sensitivity ratios much larger than 1 may be obtained for blood flow
parameters.
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There are several caveats when conducting local parameter sensitivity analyses (such as
described above). One is that they only show the sensitivity of the model predictions to a change
in the single parameter when all other parameters are held constant. For example, consider the
sensitivity to breathing rate depicted in Figure 3-3. What would have happened if one had
known, before starting the modeling process, that the breathing rate was 20% higher than the
default value actually used? Would the predicted dose metric have then turned out to be 40%
higher? Only if none of the other parameters were calibrated to the model data during the
modeling process. If in each case (default breathing rate vs. 20% higher) one had started with
that value of the breathing rate and then calibrated the Vmax and other parameters to the data, the
result would be different values of Vmax, etc., that would compensate to some extent for the
change in breathing rate.
As a simple analogy, consider the fit of a straight line to some data, where the intercept is
a "known" parameter and the slope is fitted. After fitting the line, one might determine that the
fit is very sensitive to the intercept by showing that if the intercept is changed while holding the
slope constant (i.e., standard sensitivity analysis), the value of the line equation changes a lot.
But if one had started with a larger value of the intercept at the beginning of the modeling
process, fitting the line to the data would have resulted in a lower value for the slope, such that
the value of the line equation would not change as much as when only the intercept is increased.
In short, the parameter estimation process leads to certain correlations between the values of
parameters that are fixed as inputs and those that are fitted. Thus, standard sensitivity analysis,
although very informative about the importance of individual parameters, can overestimate the
actual impact of changes in individual parameters because it does not account for correlations.
In the case of Figure 3-3, it might be that starting with a different breathing rate and then
calibrating Vmax, etc., would have yielded almost identical values for the dose metric and that the
overall modeling process is insensitive to breathing rate, even though the model predictions are
sensitive to changes in breathing rate when none of the other parameters are changed. Another
example would be that blood concentrations can be highly sensitive to oral absorption rate
constants until steady state is achieved; if the risk assessment is estimating steady-state
concentrations in humans, estimates of the oral absorption rate constants may be essentially
irrelevant. On the other hand, depending on the data at hand, "local" sensitivity analyses may
underestimate the impact of changing individual parameters due to the nonlinear!ty of the model.
For instance, if all the data were obtained at doses where metabolism was saturated, then the
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model fit will be insensitive to the "Km" over a wide range of values, but for making low-dose
predictions, the Km may be a very sensitive parameter. Thus, it is important to carry out
sensitivity analyses under conditions reflecting the studies providing data for model calibration
(i.e., pharmacokinetic studies), under conditions appropriate for estimating dose metrics in
critical studies, and finally under conditions appropriate to the risk assessment. These analyses
help identify the key parameters under the conditions relevant for the various steps in a
dosimetry-based risk assessment.
3.7.2. Variability Analysis
The focus of a variability analysis is to evaluate the range of values that a parameter
expected to be present in individuals may have in a population and the impact of that variability
on variability in the dose metric. PBPK models can account for interindividual differences in
specific parameters (e.g., enzyme levels, tissue volumes, body weights, workload) and simulate
tissue dose variability in populations (Dankovic and Bailer, 1994; Sato et al., 1991).
Alternatively, PBPK models can simulate an average individual representing a specific subgroup
of the population (e.g., adult women, pregnant women, lactating women, children), and thus
evaluate subgroup-specific tissue dose (Corley et al., 2003; Gentry et al., 2003; Price et al.,
2003b; Sarangapani et al., 2003; Krishnan and Andersen, 1998; Fisher et al., 1997), although this
latter approach would not provide the probability or likelihood of a particular output for a
population.
The magnitude of interindividual variability can be characterized using information such
as the estimated tissue dose corresponding to the 95th percentile and 50th percentile. To derive
this information, Monte Carlo simulations based on distributions of input parameters
(physiological parameters, enzyme content/activity with or without the consideration of
polymorphism) have frequently been used (Lipscomb et al., 2003; Gentry et al., 2002; Haber et
al., 2002; Lipscomb and Kedderis, 2002; Timchalk et al., 2002; Bogaards et al., 2001; El-Masri
et al., 1999; Thomas et al., 1996a, b). The Monte Carlo method consists of repeated
computations using inputs selected at random from statistical distributions for each parameter to
generate a statistical distribution for the output, i.e., dose metric (Figure 3-4). The Monte Carlo
approach to variability analysis has been used to evaluate the net impact of the variability of
critical biochemical and physiological parameters (e.g., Clewell and Andersen, 1996; Portier and
Kaplan, 1989).
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Blood flows
Body weights
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Figure 3-4. Monte Carlo simulation. In this approach, the distribution of
internal concentration versus time is simulated by repeatedly (often as many as
10,000 iterations) sampling input values based on the distributions of individual
parameters in a population.
When conducting variability analysis, it is important to identify correlations in model
parameters. For example, cardiac output (Qc) and breathing rate (QA) are expected to vary in
proportion to each other, so using independent distributions that might give a very high value of
Qc with a very low value of QA would be unrealistic. On the other hand, one could consider the
distributions of Qc and the distribution of the f\c = QA/QC and multiply the value selected from
the fAc distribution by the value selected from the Qc distribution to obtain the value of QA to be
used.
Conceptually, the Bayesian framework is particularly well suited for variability analyses
because it allows a "hierarchical" structure in which parameters can be specified at the
"population" (e.g., mean and variance, with uncertainty) and "individual" (i.e., drawn from the
population) levels (Jonsson and Johanson, 2002, 2001), thereby conducting a simultaneous
analysis of variability and uncertainty. Moreover, Bayesian analyses combine prior knowledge
about parameters and their variability and uncertainty with data from new experimental studies
generating "posterior" distributions of the parameters for both the population and individuals
(along with uncertainty) that reflect both preexisting knowledge and the new data (Bernillon and
Bois, 2000; Johanson et al., 1999; Bois, 1999). However, care also must be taken in
implementing these analyses to account for biologically supported correlations among
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parameters, which is as important for Bayesian analyses as it is for "traditional" Monte Carlo
simulation. In addition, because the posteriors from Bayesian analyses are calibrated to a
particular data set, care must be taken when deciding when and how the posterior distributions
for the PBPK parameters are used to make predictions. Consideration must be given as to
whether the subject populations in the data sets represent the population(s) of interest. For
example, data from subjects in a controlled human exposure may be only representative of a
population at rest. Although the Bayesian analysis may correctly estimate a relatively low
ventilation rate for this group, that ventilation rate may not be appropriate for the activity level in
the general population. Thus, one must chose data sets and parameter values carefully to reflect
the population of interest in the risk assessment. In the above example, an additional traditional
Monte Carlo simulation could be performed in which some posterior distributions would be
replaced with distributions considered more representative of the population of interest.
A variability analysis for a PBPK model is not a prerequisite for its use in risk assessment
applications. The assessment of the impact of parameter variability on tissue dose, however, is a
prerequisite for a PBPK model intended for use in estimating the interindividual variability
(pharmacokinetic component).
3.7.3. Uncertainty Analysis
Uncertainty analysis for PBPK models characterizes the impact that a lack of precise
knowledge about the numerical value of a parameter or model structure itself has on estimates of
the dose metric. Uncertainty regarding model structure or parameter values may contribute to
uncertainty in the predicted dose metric, particularly for low-dose exposure situations (Hattis et
al., 1990). Uncertainty analysis is particularly useful when a PBPK model does not adequately
simulate the experimental data. Such a situation may arise due to either lack of precise estimates
of parameter values or inadequacies in the model structure. In such cases, either a quantitative
uncertainty analysis or model-directed mechanistic studies might improve the predictive ability
and robustness of the PBPK model (Haddad et al., 1998; Clewell and Andersen, 1987).
Quantitative uncertainty analyses for specific dose metrics (e.g., amount metabolized,
tissue concentration of parent chemical at a specific time, cancer estimates) have been conducted
using a traditional Monte Carlo approach, a Bayesian MCMC analysis (Elder, 1999; Gelman et
al., 1996; Krewski et al., 1995; Farrar et al., 1989), a stochastic response surface method, or a
fuzzy simulation approach (Nestorov, 2001; Isukapalli et al., 1998). The latter method is
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particularly useful when statistical distributions of parameters cannot be reliably defined and
only semiquantitative, qualitative, and vague information is available.
If there is a lack of confidence regarding the numerical value of a parameter (e.g.,
imprecision due to the method used for parameter estimation), a quantitative analysis of the
uncertainty associated with a parameter(s) of the PBPK model will help characterize the impact
on the dose metric of interest. Uncertainty analysis will be of limited utility if the available data
directly inform the dose metrics of interest, such as the case where a model adequately fits
multiple data sets that directly measure the relevant internal dose following exposure by the
routes and in species of interest. However, even predictions from such "well-calibrated" models
may benefit from uncertainty analysis, particularly if the dose metrics of interest are indirectly
inferred (e.g., total metabolism when only blood concentrations are measured). In addition,
where possible and relevant, uncertainty analysis can be performed to strengthen credibility of
the PBPK model and guide resource allocation for risk assessment-oriented research.
Sensitivity, uncertainty, and variability analyses should be conducted using acceptable
statistical methods. EPA has published guiding principles for Monte Carlo analysis (U.S. EPA,
1997), but no such guidance for Bayesian methods has been released, although active research
and development are ongoing. When using the Bayesian approach, care should be taken to
ensure that the resulting PBPK model simulations respect the following basic conditions:
• The numerical values of physiological parameters (representing prior or posterior
distributions) are within known, plausible limits;
• The sum of tissue volumes is lower than the body weight;
• The sum of tissue blood flows is equal to cardiac output;
• The mass balance is respected (chemical absorbed = chemical in body + chemical
eliminated); and
• The covariant nature of the parameters is appropriately respected (e.g., the person
with lowest breathing rate cannot be the one receiving the highest cardiac output)
While taking advantage of the sophisticated statistical approaches, it is important to
ensure that the resulting model and parameters are within plausible range or representative of the
reality.
Sensitivity, uncertainty, and variability analyses can help improve the credibility of
PBPK models as well as prioritize research needs to improve the model for risk assessment
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applications. However, such analyses may not be required for all PBPK models intended for
risk assessment applications.
3.8. DEVELOPING PBPK MODELS FOR USE IN RISK ASSESSMENT:
STRATEGIES FOR DEALING WITH DATA-POOR SITUATIONS
3.8.1. Minimal Data Needs for Constructing PBPK Models
When an adequately evaluated PBPK model is not available for the species, life stage,
and route relevant to a risk assessment application, significant resources may be needed to
develop such a model, depending on the chemical, the availability of prior information, and the
complexity of disposition mechanisms being modeled. The minimal data required for
developing such models for a chemical in any given species are
• Partition coefficients,
• Biochemical constants,
• Route-specific absorption parameters, and
• In vivo pharmacokinetic data for model evaluation.
As outlined in this chapter, the partition coefficients required for PBPK modeling may be
estimated using the theoretical algorithms found in the literature. Their use, however, should be
limited to the domain of validity and the families of chemicals for which such algorithms have
been developed and validated. Biochemical constants such as metabolism rates or binding
association constants may be obtained using in vitro systems. Other biochemical parameters
may be required, such as renal clearance, which currently can only be determined from in vivo
data. Additionally, route-specific absorption parameters such as the rate of oral absorption and
the skin permeability constant are required to describe oral absorption and dermal absorption,
respectively, prior to achieving steady state. Of these, the skin permeability coefficient can be
obtained using available quantitative structure-activity relationships (QSARs). Such absorption
parameters are not required for simulating intravenous administration and inhalation exposures.
Finally, some in vivo pharmacokinetic data (at a minimum blood concentration time-course data
at two dose levels) are required for evaluating the PBPK model for a particular route of
exposure.
The minimal data set identified above should be available for the species used in the
critical study. Human models, however, may be constructed with knowledge of species-specific
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blood solubility/binding characteristics. Other model parameters, including metabolism rates,
may be either scaled or kept species-invariant, according to the current state of knowledge
(Section 4.5). Of course, the availability of a data set for external evaluation in humans may be a
limiting factor. In such cases, surrogate data sets may be used for model evaluation purposes.
3.8.2. Surrogate Data for Interspecies and Interchemical Extrapolations
In the absence of human data for model evaluation purposes, surrogate data have been
used successfully, although it must be noted that surrogate data may add additional uncertainty to
a risk assessment. A parallelogram approach can be used to generate surrogate data. This
approach uses two data sets: one demonstrating the relationship between in vitro and in vivo
findings in a test species, and the other demonstrating the relationship between in vitro human
and in vitro test species findings; these data are used to predict the in vivo effects in humans.
Accordingly, if human data either cannot be collected or is not available for a chemical of
interest, it may suffice to evaluate a related chemical for which such data are available. Jarabek
et al. (1994) used this parallelogram approach for model development and interspecies
extrapolation of the pharmacokinetics of HCFC-123 (2,2-dichloro-l,l,l-trifluoroethane). In this
case, the authors developed rat PBPK models for HCFC-123 as well as a structural analog
(halothane) by estimating partition coefficients and metabolic constants. Following the
evaluation of the rat PBPK model for each of these chemicals, human models were constructed.
The adequacy of the human model for halothane was evaluated using available human in vivo
data; the model for HCFC-123 was assumed to reasonably simulate the in vivo pharmacokinetics
in humans due to the structural and metabolic similarities between the two chemicals, despite the
absence of in vivo human HCFC-123 pharmacokinetic data (Williams et al., 1996; Jarabek et al.,
1994). This is one practical way of getting around the lack of human data for model evaluation,
particularly when external evaluation is intended.
To deal with situations where there is a lack of data to determine PODs for closely related
chemicals, a family approach has been suggested. This approach, proposed by Barton et al.
(2000), is based on the principle that the acceptable concentrations for related chemicals,
particularly metabolites, can be derived using data on the parent chemical. Thus, if the NOAEL
for the parent chemical is established, there would also have been internal systemic exposures to
its metabolites. By determining the external exposure levels for these compounds that result in
the same systemic exposure, the NOAELs for these compounds can be established. The
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determination of the internal dose and systemic exposures for the parent chemical and
metabolites is accomplished using PBPK models, thus facilitating the derivation and
establishment of the RfD/RfC with a poor database.
QSAR approaches are also available for constructing inhalation PBPK models for
volatile organic chemicals in the rat (Beliveau et al., 2003). Accordingly, the contributions of
various molecular fragments (CH3, CH2, CH, C, C=C, H, Cl, Br, F, benzene ring, and H in
benzene ring) toward the parameters of PBPK models have been determined. With the
knowledge of the number of the fragments occurring in a given molecule, the partition
coefficients and the metabolic constants can be obtained and a first-generation PBPK model can
be constructed. This QSAR approach is useful to initially develop PBPK models for other
chemicals, as long as the number and nature of fragments in the chemical do not differ from the
ones in the calibration set used in the study (Beliveau et al., 2003).
3.9. EVALUATION OF PBPK MODELS: SUMMARY
The basic criteria for evaluation of PBPK models intended for risk assessment
applications, as outlined in Sections 3.1 through 3.7, are summarized below.
• The PBPK model would predict the pharmacokinetics and tissue dose of the toxic
form of a chemical or a surrogate such as parent compound.
• The structure of a PBPK model would contain the target organ or a surrogate tissue,
such as blood.
• The equations chosen to describe ADME would be justified on the basis of known
mechanisms of such processes for the chemical of interest or by analogy with other
chemicals.
• The tissue volumes, flow rates, and ventilation:perfusion ratios specified in the model
would be within reasonable physiological limits.
• The power function frequently assumed for scaling of physiological flows on the
basis of body weight ranges between 0.67 and 0.75 unless species- or individual-
specific data are available.
• Maximal velocities of metabolism may also be scaled on the basis of body weight,
typically raised to the 0.75 power, but measured values for specific enzymes in
humans do not generally correlate with body weight, so the choice of whether and
how to scale metabolism is at the discretion of the modeler.
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• Partition coefficients required for PBPK models can be obtained from steady-state in
vivo or in vitro data or theoretical algorithms in the application domain.
• Biochemical parameters for PBPK models can be estimated using in vivo data or
valid in vitro methods.
• A PBPK model is frequently implemented using commercially available software
requiring that the model code (but not the coding of integration algorithms) be
checked. If the modeler chooses to write his/her own program, then the
appropriateness of the integration algorithm and integration interval should be
justified. The PBPK model code is checked for accuracy of units, mass balance,
blood flow balance, and behavior at zero dose.
• Evaluation of the PBPK model structure and parameters should be conducted to
ensure that the model adequately predicts the pharmacokinetic behavior (i.e., bumps
and valleys in the concentration vs. time plot) of the chemical and that the parameters
(point estimates, range of values or distributions) consistently describe available data.
• A model used in a risk assessment would be accompanied by sufficient
documentation to support an independent evaluation and reconstruction of the model
and simulation results. Scripts facilitate transparency and reproducibility in modeling
by providing computer code to recreate fits to kinetic data, estimation of dose metrics
in critical studies, and calculation of human dose metrics for development of dose-
response values (e.g., RfD, CSFs). A more rigorous verification that may be
considered by the risk assessor is to independently re-code the model to ensure that
the documentation is thorough and that there are no bugs in the code.
• Sensitivity, variability, and uncertainty analyses can help improve the credibility of
PBPK models by identifying the parameters that have the greatest impact on a model
output. In addition, these analyses are useful in prioritizing research needs to improve
a model for risk assessment application. Such analyses, however, may not be
required for all PBPK models intended for risk assessment applications.
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4. APPLICATION OF PBPK MODELS IN RISK ASSESSMENT
4.1. CHOOSING PBPK MODELS APPROPRIATE FOR USE IN RISK ASSESSMENT
PBPK models are most often used in risk assessments to simulate tissue and blood
concentrations of a toxic moiety (parent chemical or metabolite) resulting from the dosing
regimens in the animal toxicity or human studies that are the basis for deriving dose-response
values (e.g., RfC, RfD, CSFs). Specifically, the model would be able to simulate the dose
metrics in the test species and/or in humans for the exposure route and exposure scenario of
relevance. For most applications in risk assessment, a PBPK model
• Would have been developed or calibrated for the species and life stages of relevance
to the risk assessment,
• Would be structured and adequately parameterized to simulate uptake via routes
associated with human exposures as well as the critical study chosen for the
assessment,
• Would be able to provide predictions of the time-course of concentration of the toxic
moiety or appropriate surrogate (parent chemical or metabolite) in the target organ of
interest or a suitable surrogate compartment, and
• Must have been peer-reviewed and evaluated for its quality and predictive capability.
Figure 4-1 depicts how the above criteria can be applied for selecting appropriate PBPK
models. Basically, a peer-reviewed PBPK model for the relevant species and life stage
consisting of parameters for simulating relevant routes of exposure and potentially relevant dose
metrics is appropriate for use in risk assessment.
The first criterion, though appearing self-evident, is quite fundamental, because the
models available in the literature sometimes were not parameterized for the specific species and
life stage used in the critical toxicological study forming the basis of a risk assessment. For
example, PBPK models for volatile organic compounds may have been developed in rats, yet
one of the critical studies in the assessment is in mice. When the PBPK model has not been
developed for the species or life stage used in a critical study, additional work may be needed to
further elaborate the model.
PBPK models chosen for risk assessment applications would be able to provide
simulations of the tissue dose of the toxic moiety or an appropriate dose metric for exposure
scenarios and routes associated with the critical study as well as human exposures. Finally,
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Is the PBP
model available
for the test
species and
humans?
Are the
parameters tor
simulating
relevant routes
available?
Experimental
Data
Collection
Does the model
simulate dose
metrics of relevance
risk assessmen
Has the model
been evaluated and
peer-re viewed?*
Undertake
evaluation and
peer review
Use in
risk assessment
Figure 4-1. Flowchart for selecting PBPK models appropriate for use in risk
assessment. In this context, the model should be evaluated as described in
Chapter 3.
the PBPK model for the relevant species and life stage and exposures corresponding to the
critical study and risk assessment needs should be peer-reviewed; if it has not been, then efforts
may be directed towards such a review (see Chapter 3).
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Peer-reviewed PBPK models that facilitate the prediction of dose metrics for a chemical
through relevant routes of exposure for the life stage and species used in critical studies are a
prerequisite for their use in risk assessments. Most risk assessment applications also require a
model parameterized for humans if the critical studies were in animals.
4.2. EVALUATION OF DOSE METRICS FOR PBPK MODEL-BASED ASSESSMENTS
When using PBPK models in risk assessment (RfD, RfC, and unit risk estimates), the
basic data needed are
1. POD and critical effect from one or more key studies,
2. Peer-reviewed PBPK model for the relevant test species and humans, and
3. Dose metric appropriate for the risk assessment and supported by the MOA (if
known).
The methods and challenges associated with the identification of critical effects and
PODs for an assessment remain the same regardless of whether one uses PBPK models or not.
The approaches for identifying PODs can be found elsewhere (U.S. EPA, 2005a, 1994). The
criteria and issues associated with the selection of PBPK models useful for risk assessment were
considered in the previous section. It is worth noting that although a human model is typically
needed, route extrapolation and some other limited applications can be undertaken with only a
model for the relevant test species. The third data need noted above, i.e., the identification of the
appropriate dose metric, is a key aspect determining the use of PBPK models in risk assessment.
The dose metric, or the appropriate form of chemical most closely associated with the
toxicity, varies from chemical to chemical, depending on the MOA and critical effect. It has two
basic properties: the moiety and the measure thereof. The dose metric for PBPK-based risk
assessment is chosen following the identification of the potential toxic moiety and evaluation of
the relationship with the endpoint of concern. A useful framework for evaluating hypothesized
MO As is included in Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005b). Although
the framework specifically deals with carcinogens, the concepts are broadly applicable to
noncancer MO As. The framework provides useful discussion related to evaluating multiple
MO As (particularly over dose ranges) and for assessing relevance to humans. Furthermore,
available data on closely related chemicals may be used to infer the likely toxic moiety.
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Similarly, the toxicity data for various exposure routes and modes of administration may be
compared to infer the potential toxic moiety (IPCS, 2005).
After the potential toxic moiety has been identified, the appropriate measure of tissue
exposure to the toxic moiety can be selected (Figure 4-2). For example, peak concentration has
been related to some neurotoxic effects of solvents (e.g., MacDonald et al., 2002; Benignus et al.,
1998; Pierce et al., 1998; Bushnell, 1997), such as the correlation of concentration of
trichloroethylene at the time of testing with observed effects on behavioural and visual functions
(Boyes et al., 2000). For tetrachlorodibenzodioxin, tissue concentrations of the chemical
measured during a critical period of gestation have been reported to predict the intensity of
developmental responses (Hurst et al., 2000). The gender-specific genotoxic effects of benzene
in mice are related to differences in the rate of oxidative metabolism (Kenyon et al., 1996).
Potential
Toxic Moiety
(
»
Maximum
1
Parent Chemical
Z^
Concentration
Metabolite Unknown*
^>^
i
Rate of production
7\ TV
Integrated
1
Average
Integrated
1 1 J
r
Cmax
(mg/L. Tissi
AUC
je) (mg/L tissue
xtime)
mg formed
per unit time
(e.g. mg/hr/L tissue)
mg formed AUC
over time specified mg/L tissue x time
(e.g. mg/hr/Ltissue) (p are nt o f me tab o lit e)
Devaluate correlations
DOSE METRICS to toxic responses
Figure 4-2. Examples of measure of tissue exposure to toxic moiety for risk
assessment applications.
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For chronic effects of chemicals, the integrated concentration of the toxic form of
chemical in target tissue over time (i.e., AUC), typically determined as the daily average, is often
considered a reasonable dose metric (Clewell et al., 2002a; Voisin et al., 1990; Andersen et al.,
1987; Collins, 1987). For carcinogens producing reactive intermediates, the amount of
metabolite produced per unit time and the amount of metabolite in target tissue over a period of
time (e.g., mg metabolite/L tissue during 24 hr) have been used as dose metrics (Andersen and
Dennison, 2001; Andersen et al., 1987). For developmental effects, the dose surrogate is defined
in the context of the window of exposure for a particular gestational event (e.g., Welsch et al.,
1995). Although the AUC and rate of metabolite formation figure among the most commonly
investigated dose metrics, other surrogates of tissue exposure may also be appropriate for risk
assessment purposes, depending on the chemical and its MOA (e.g., maximal concentration
[Cmax] of the toxic moiety, duration and extent of receptor occupancy, macromolecular adduct
formation, or depletion of glutathione) (Clewell et al., 2002a). Table 4-1 lists the dose metrics
used in a number of PBPK-based cancer and noncancer risk assessments described in the peer-
reviewed literature.
When the appropriate dose metric cannot readily be identified, evaluation of the
relationship with the endpoint of concern can be undertaken with each of the dose metrics to
identify the one that exhibits the best association (e.g., Andersen et al., 1987; Kirman et al.,
2000). This becomes particularly important when there are limited or confusing data on the
plausible MOA of the chemical. At a minimum, the appropriate dose metric can be identified as
the one that demonstrates a consistent relationship with positive and negative responses observed
at various dose levels, routes, and scenarios in a given species. In other words, the level of the
dose metric would be lower for exposure conditions that elicit no effect and higher for conditions
that elicit toxic responses, regardless of the route and exposure scenario (Clewell et al., 2002a).
Where there is an inadequate basis for prioritizing one dose metric over another, some
suggest using the most conservative one (the dose metric estimating the highest risk or lowest
acceptable exposure level) to be health protective (Clewell et al., 2002a). The use of appropriate
dose metric can help to reconcile route and species differences in cancer responses, provided
there are no pharmacodynamic differences. There has been at least one instance in which PBPK
model-derived dose measures could not reconcile rat and mouse kidney tumor data (Smith et al.,
1995), indicating the significant role of factors other than the target tissue exposure to toxic
moiety.
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Table 4-1. Dose metrics used in PBPK model-based cancer and noncancer
risk assessments
Chemical
Acrylonitrile
Bromotrifluoromethane
Butoxyethanol (2-)
Chloroform
Chloropentafluorobenzene
1,4-Dioxane
Ethyl acrylate
Ethylene glycol ethers
Formaldehyde
Heptafluoropropane
Isopropanol
Methoxyacetic acid
Methyl chloroform
Methyl mercury
Endpoint
Brain tumors
Cardiac sensitization
Forestomach lesions and
tumors
Liver cancer
Hepatic effects and
kidney tumor
Liver toxicity
Liver tumors
Forestomach tumors
Developmental toxicity
Cancer
Cardiac sensitization
Neurobehavioral effects
Developmental/
reproductive effects
Developmental effects
Hepatic effects
Neurological effects
Dose metric
Peak metabolite
concentration in target
tissue
Concentration of parent
chemical at the end of
exposure
Concentration of
butoxyethanol/ butoxy
acetic acid in forestomach
Amount of metabolites
covalently bound to
biological macromolecules
L liver per day; % cell
kill/day
Maximal rate of
metabolism per unit kidney
cortex volume
AUC of parent chemical in
liver
Time-weighted average
concentration in liver over
lifetime
Liver AUC
Tissue-specific glutathione
depletion
Peak concentration and
average daily AUC of the
alkoxyacetic acid
(metabolite) in blood
DNA-protein crosslinks
Concentration of parent
chemical at the end of
exposure
Peak blood concentration
AUC of isopropanol and
its metabolite (acetone)
AUC of parent chemical
(gestational day 11)
Maximal concentration of
parent chemical
(gestational day 8)
Area under the liver
concentration vs. time
curve
Fetal brain concentrations
Reference
Kirman et al. (2000)
Vinegar and Jepson (1996)
Poet et al. (2003)
Reitzetal. (1990a)
Meek et al. (2002)
Clewell and Jarnot (1994)
Leung and Paustenbach
(1990)
Reitzetal. (1990b)
Frederick etal. (1992)
Sweeney etal. (2001)
Schlosser et al. (2003);
Casanova et al. (1996)
Vinegar and Jepson (1996)
Gentry et al. (2002)
Gentry et al. (2002)
Clarke etal. (1993, 1992)
Welsch etal. (1995)
Reitzetal. (1988a)
Gearhart etal. (1995)
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Table 4-1. Dose metrics used in PBPK model-based cancer and noncancer
risk assessments (continued)
Chemical
Methyl methacrylate
Methylene chloride
Styrene
Tetrachlorodibenzodioxin
Toluene
Trichloroethylene
Vinyl acetate
Vinyl chloride
Endpoint
Nasal lesions
Cancer
Lung tumors (mouse)
Biochemical responses
Cancer risk
Behavioral effects
Renal toxicity
Neurotoxicity
Cancer (liver lung and
kidney)
Olfactory degeneration
and tumor development
Angiosarcoma
Dose metric
Amount
metabolized/time/volume
nasal tissue
Rate of glutathione
transferase metabolites
produced/L liver/time
Steady-state concentration
of ring oxidation
metabolite mediated by
CYP2F
Body burden
Time-weighted receptor
occupancy
Up/down regulation of
receptor occupancy
Fraction of cells induced
Brain concentrations at the
time of testing
Metabolite production/L
kidney/day
Blood concentration of
metabolite
(trichloroethanol)
Amount
metabolized/kg/day; AUC
for trichloroacetic acid or
dichloroacetic acid/L
plasma; production of
thioacetylating
intermediate from
dichlorovinylcysteine in
kidney
Intracellular pH change
associated with the
production of acetic acid;
proton concentration in
olfactory tissue
mg metabolized/L liver;
mg metabolite produced/L
liver/day
Reference
Andersen et al. (2002,
1999)
Andersen etal. (1987)
Cruzen et al. (2002)
Kim et al. (2002)
Andersen etal. (1993)
Portier etal. (1993)
Conolly and Andersen
(1997)
Van Asperen et al. (2003)
Barton and Clewell (2000)
Barton and Clewell (2000)
Clewell et al. (2000);
Fisher and Allen (1993)
Bogdanffy etal. (2001,
1999)
Clewell etal. (2001);
Reitzetal. (1996b)
AUC = area under the curve
An important consideration in risk assessments conducted with a PBPK model is that the
critical study (i.e., the study upon which the RfC, RfD, or CSF is based) cannot always be
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selected on the basis of administered dose or exposure concentration. This is because the
relationship of the HEC or human equivalent dose to the administered animal dose depends on
the selected dose metric, which may vary from one endpoint to another and with the nature of the
exposure (species, route of administration, vehicle, duration, etc.). Instead, the pharmacokinetic
model is used to calculate the appropriate dose metrics for each of the endpoints of concern in
each study (Barton and Clewell, 2000). To calculate the dose metrics, the model parameters are
set to those for the species in the toxicity study, whether an experimental animal study or a
human study. In the case of developmental studies, it may be necessary to estimate parameters
for a pregnant female or neonate rather than for an average adult, and physiological and
biochemical parameters may have to be time dependent. To the extent possible, it is best to use
study-specific data on animal strain, body weights, age, and activity when selecting parameters
for the model. The experimental parameters in the model are then set to reproduce the exposure
scenario performed in the study, and the model is run for a sufficient period of time to
characterize the animal exposure to the chemical and, if necessary, its metabolites.
There are often a number of options regarding the way in which the model can be run to
characterize the dose metric (Clewell et al., 2002a). The choices made will depend on the dose
metric(s) selected (e.g., peak vs. average), the nature of the chemical (e.g., volatile vs.
persistent), and the nature of the risk assessment (acute vs. chronic, cancer vs. noncancer).
Frequently, an average daily dose metric such as the average daily AUC is estimated (note that
the average daily AUC is the same metric as the time-weighted average concentration, differing
by only a factor of 24 if the daily AUC was expressed in terms of hours). In general, the
averaging period in the case of cancer is typically taken to be the lifetime, whereas the averaging
period in the case of noncancer risk assessment is usually considered to be the duration of the
exposure or, perhaps, a critical window of exposure.
For short-term exposures, the model must be run for an appropriate period, which
depends on the dose metric being used and the timing of the measurement of toxicity in relation
to the period of exposure. For short exposure, this is easily done by running the model for the
total duration of the exposure (or exposures, for repeated exposure studies) to obtain dose
metrics. If the animals were held for a postexposure period before toxicity was evaluated, the
model must be run either until the end of the postexposure period or for a sufficient duration to
ensure that the parent chemical or metabolite, depending on dose metric, has been completely
cleared from the body. On the other hand, if toxicological evaluations, e.g., neurological tests,
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were performed during or immediately at the end of the exposure period, then the dose metric
might be determined at the time of evaluation. The resulting dose metric obtained for the total
duration of the exposure (including any postexposure period) may need to be divided by the
number of days over which the experiment was conducted to derive the average daily value for
an integrated measure such as AUG.
The same approach (running the model for the total duration of the study) can be used to
calculate dose metrics for longer-term exposures. This approach would typically be necessary
for models that describe changes in physiology or biochemistry during different life stages (e.g.,
children, elderly). An alternative approach, which is often attractive for modeling of chronic
exposures with time-invariant model parameters, is to estimate the steady-state dose metric.
There are two principal methods for calculating a steady-state estimate. In the first, the
model is run until steady state is reached and then the dose metric is calculated by subtraction.
For example, in the case of a chronic oral or inhalation exposure conducted 5 days per week, the
model can be run consecutively for 1 week, 2 weeks, 3 weeks, and so on. To calculate the
average daily AUC for a given week, the value at the end of the previous week is subtracted
from the value at the end of the current week and the result is divided by 7. This process is
repeated until the change in the dose metric from one week to the next is insignificant. For
continuous exposures, the comparison can be made on a daily basis rather than weekly.
The other method for estimating the steady-state dose metric is to estimate it from a
single-day exposure. The model is run for a single-day exposure plus an adequate postexposure
period to capture clearance of the parent compound or relevant metabolite. This value of the
single-day dose metric is then modified by the necessary factors to obtain an average daily value
(e.g., by multiplying by five-sevenths in the case of the 5-day-per-week exposure just described).
This method is faster, but is only approximate if the system is not linear. Typically, it is
sufficiently accurate for estimating average daily AUC when exposures are below the onset of
any nonlinearities. It can be checked against the first method described to determine its accuracy
in a particular case.
The dose metric calculations needed are determined by the method to be used for the
noncancer or cancer analysis. If the NOAEL/uncertainty factor (UF) method is being used in an
assessment, a dose metric needs to be calculated only for the NOAEL or LOAEL exposure for a
particular study and endpoint. On the other hand, if dose-response modeling is going to be
performed, such as in the BMD approach, dose metrics generally would be calculated for all
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exposure groups. The dose metrics are then used in the dose-response model in place of the
usual exposure concentrations or administered doses. It is important to remember that when this
is done, the result of the dose-response modeling will also be in terms of a value of the dose
metric rather than an exposure concentration or administered dose. Dose-response modeling is
more properly conducted on the dose metrics because it is expected that the observed effects of a
chemical will be more simply and directly related to a measure of target tissue exposure than to a
measure of administered dose.
To convert an animal dose metric (e.g., at the BMD) to an equivalent exposure
concentration or administered dose, the pharmacokinetic model must be run repeatedly, varying
the exposure concentration or administered dose, until the dose metric value is obtained. In the
case of calculating the acceptable human exposure corresponding to a given toxicity study, the
physiological, biochemical, and exposure parameters in the model are set to appropriate human
values and the model is iterated until the dose metric obtained for the human exposure of concern
(often continuous or daily lifetime exposure) is equal to the dose metric obtained for the toxicity
study. One effective way to do this is to run the model at regular dose intervals (e.g., log or half-
log) over a wide dose range. These results can be used to generate a regression line describing
the relationship between the internal dose metric and the exposure dose or concentration. This
regression line can be used to accurately estimate the exposure giving a particular internal dose
metric, as can be confirmed by running the model for that exposure. Plotting the relationship
between exposure and the internal dose metric is also valuable because it demonstrates where
nonlinearities occur.
The human dose metrics used for deriving dose-response values can be calculated in an
analogous way to the dose metric for the toxicity study; i.e., if the dose metric in the toxicity
study was expressed in terms of an average daily value, the dose metric used for calculating the
associated human exposure should also represent an average daily value. However, it should be
remembered that the exposure scenarios may be different, e.g., continuous human inhalation in
contrast to a 6-hr/day exposure in the animal toxicity study. When a steady-state dose metric is
used in both an experimental animal and the human, the calculation of a steady-state dose metric
in the human generally requires running the model for a much longer period of time than in the
animal. For short-term exposures, where the model has been run for the total duration of the
toxicity study and the average dose metric value has been calculated, the dose metric used to
calculate associated human exposure is usually obtained for an exposure over the same time
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period. An exception to this rule is the case where it is anticipated that the short-term exposure
of concern for the human may represent a short-term excursion against a background of chronic
exposure. In this case, a more conservative approach may be preferred, in which a steady-state
dose metric calculation is used for the human.
The following section describes applications of PBPK models in risk assessment. These
applications relate to high-dose to low-dose extrapolation, interspecies extrapolation, estimating
intraspecies variability, route-to-route extrapolation, and duration extrapolation as required for
RfD derivation, RfC derivation, and cancer risk assessment.
4.3. REVIEW OF EXTRAPOLATIONS POSSIBLE WITH PBPK MODELS
Risk assessment applications typically require that extrapolations be made from the
critical studies (i.e., animal toxicology or human epidemiology studies) to the human exposure
situation. These extrapolations of the critical studies are the focus of pharmacokinetic modeling.
To a significant extent, the models utilize in vitro or in vivo data to enable the model to address
these extrapolations (e.g., characterize pharmacokinetics at high and low doses, parameterize
models for test species and humans). To a more limited degree, extrapolations are made in the
course of model development, most typically due to limitations on available data for humans,
such that it is assumed parameters scale in some manner from animals to humans. As
pharmacokinetic modeling strives to become increasingly predictive in nature, it is likely that
predictive tools (e.g., methods to predict partition coefficients) will play larger roles in model
development.
4.3.1. Interspecies Extrapolation
The application of PBPK models for interspecies extrapolation of tissue dosimetry is
performed in several steps. First, a model for the appropriate species in potential critical toxicity
studies is developed. Increasingly, a priori predictions of the PBPK model are compared with
experimental observations to evaluate the adequacy of the structure and the parameter estimates
of the rodent model. This sometimes involves refining some or all of the parameters by allowing
modeling software to estimate the best value for these parameters. The next step involves using
species-specific or allometrically scaled physiological parameters in the model and replacement
of the chemical-specific parameters (e.g., metabolic rates, protein binding constants) with
appropriate estimates for the species of interest (e.g., humans). Thus, in this approach, the
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qualitative determinants of pharmacokinetics are considered to be invariant among the various
mammalian species. Qualitative differences between species, if any, can also be factored into
the existing structure of PBPK models (e.g., if different metabolic pathways existed among
species) but, obviously, data describing these species differences are required.
For conducting interspecies extrapolation of pharmacokinetic behavior of a chemical,
quantitative estimates of model parameter values (i.e., partition coefficients, physiological
parameters, and metabolic rate constants) in the second species are required. The tissue:air
partition coefficients of chemicals appear to be relatively constant across species, whereas
blood:air partition coefficients show some species-dependent variability. Therefore, the
tissue:blood partition coefficients for human PBPK models have been calculated by dividing the
rodent tissue:air partition coefficients by the human blood:air partition values (Krishnan and
Andersen, 2001). The tissue:air and blood:air partition coefficients for volatile organic
chemicals may also be predicted using appropriate data on the content of lipids and water in
human tissues and blood (Poulin and Krishnan, 1996a, b).
Whereas the adult physiological parameters vary coherently across species, the kinetic
constants for metabolizing enzymes do not necessarily follow any type of readily predictable
pattern, making the interspecies extrapolation of xenobiotic metabolism difficult. Therefore, the
metabolic rate constants for xenobiotics are best obtained in the species of interest. In vivo
approaches for determining metabolic rate constants are not always feasible for application in
humans. The alternative is to obtain such data under in vitro conditions (e.g., Lipscomb et al.,
1998, 2003). A parallelogram approach may also be used to predict values for the human PBPK
model on the basis of metabolic rate constants obtained in vivo in rodents as well as in vitro
using rodent and human tissue fractions (Lipscomb et al., 1998; Reitz et al., 1988b).
Alternatively, for chemicals exhibiting high affinity (low Km) for metabolizing enzymes, Vmax
has been scaled to the 0.75 power of body weight, keeping the Km species invariant. This
approach may be useful as a crude approximation, but it may be used only when other direct
measurements of metabolic parameters are not available or feasible.
An example of rat-human extrapolation of the kinetics of toluene using a PBPK model is
presented in Figure 2-2 (Chapter 2). Here the structure of the PBPK model developed in rats was
kept unchanged, but the species-specific parameters were determined either by scaling or
experimentally, as described above (Tardif et al., 1997). The model was then able to predict
accurately the kinetics of toluene in humans. Whenever the human data for a particular chemical
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are not available for evaluation purposes, a corollary approach permitting the use of human data
on similar chemicals may be attempted (Jarabek et al., 1994).
There are some instances where PBPK models can be used for interspecies extrapolation
of toxicity studies without the need of an animal PBPK model. For example, an RfC for
methanol has been proposed (Starr and Festa, 2003) using a mice developmental toxicity study
(Rogers et al., 1993) where blood methanol levels were also reported. By using the blood
methanol level at the POD from the mice study, a previously published human methanol PBPK
model (Bouchard et al., 2001) was used to predict the inhalation concentration associated with
the same internal blood methanol level in humans. This example highlights the advantage
afforded by toxicity studies that also include pharmacokinetic measurements.
4.3.2. Estimating Intraspecies Variability
Intraspecies variability for the dose metric can be assessed using PBPK models to
estimate the magnitude of interindividual variability (pharmacokinetic component) for RfC and
RfD derivations. For this purpose, population distributions of parameters, particularly those
relating to physiology and metabolizing enzymes (i.e. genetic polymorphisms), are specified in a
Monte Carlo approach, such that the PBPK model output corresponds to distributions of dose
metric in a population. Using the Monte Carlo approach, repeated computations based on inputs
selected at random from statistical distributions for each input parameter (e.g., physiological
parameters, enzyme content/activity with or without the consideration of polymorphism) are
conducted to provide a statistical distribution of the output, i.e., tissue dose. Using the
information on the dose metric corresponding to a high percentile (e.g., 95th) and the 50th
percentile, the magnitude of interindividual variability can be computed for risk assessment
purposes (Figure 4-3).
Even though past efforts largely have characterized the impact of the distributions of
parameters in the adult population, variability analyses also need to address different life stages
(e.g., pregnancy, children, aged). Generally, age-specific changes in physiology, tissue
composition, and metabolic activity (reviewed in O'Flaherty, 1994) can be incorporated into the
same model structure used for adults (Corley et al., 2003). Published examples of modeling
different ages describe predictions for a range of chemicals with different properties (Clewell et
al., 2004, 2002b; Ginsberg et al., 2004; Sarangapani et al., 2003). However, some life stages,
notably pregnancy and lactation, require different model structures (i.e., describing the mother
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.a
re
.a
o
50th percentile
95th percentile
I interindividual variability i
Concentration
Figure 4-3. Estimation of an interindividual variability. In this example,
interindividual variability describes the variation between the 50th (median) and
95th percentile values of a dose metric simulated with a probabilistic PBPK
model.
and the offspring) (Corley et al., 2003; Gentry et al., 2003, 2002). Characterization of population
variability across ages and life stages as well as adult variability is an ongoing area of
development. PBPK models represent a powerful tool for quantitatively characterizing
population pharmacokinetic variability for application to risk assessment. For more information,
the reader is referred to Use of PBPK Models to Quantify the Impact of Human Age and
Interindividual Differences in Physiology and Biochemistry Pertinent to Risk (U.S. EPA, 2006).
4.3.3. Route-to-Route Extrapolation
There are two different approaches to route extrapolation involving PBPK models. The
first one is to use an animal model to extrapolate a POD for one route to a POD by another route
on the basis of equivalent dose metric. The second approach would involve the estimation of the
human POD for one route from the available animal POD for another route on the basis of
equivalent dose metric.
The extrapolation of the kinetic behavior of a chemical from one exposure route to
another is performed by including appropriate equations to represent each exposure pathway.
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For simulating the intravenous administration of a chemical, a single input representing the dose
administered to the animal can be included in the equation for mixed venous concentration. Oral
gavage of a chemical dissolved in a carrier solvent may be modeled by introducing a first-order
or a zero-order uptake rate constant, and dermal absorption has been modeled by including a
diffusion-limited compartment to represent skin as a portal of entry (Krishnan and Andersen,
2001). After the equations describing the route-specific entry of chemicals into systemic
circulation are included in the model, it is possible to conduct extrapolations of pharmacokinetics
and dose metrics. This approach is illustrated in Figure 4-4 for oral-to-inhalation extrapolation
of the kinetics of chloroform in rats. For simulating the inhalation pharmacokinetics, the oral
dose was set to zero, whereas for simulating chloroform kinetics following oral dosing the
inhaled concentration was set to zero (Figure 4-4). Accordingly, 4-hr inhalation exposure to 83.4
ppm chloroform is equal to an oral dose of 1 mg/kg, as determined with PBPK models on the
basis of equivalent dose metric (i.e., parent chemical AUC in blood) (Figure 4-4). Note that the
peak concentrations differ by about 10-fold; thus, if peak concentration was thought to be the
appropriate dose metric, higher inhalation exposures would be required to produce the same peak
as a 1-mg/kg oral dose.
4.3.4. Duration Adjustment
On the basis of equivalent dose metric, the duration-adjusted exposure values can be
obtained with PBPK models (Simmons et al., 2005; Bruckner, 2004; Brodeur et al., 1990;
Andersen et al., 1987). For example, if the appropriate dose metric were the AUC of a chemical,
it would initially be determined for the exposure duration of the critical study using the PBPK
model and then the atmospheric concentration for a continuous exposure (during a day, window
of exposure, or lifetime) yielding the same AUC is determined by iterative simulation. Figure
4-5 depicts an example of 4-hr to 24-hr extrapolation of the pharmacokinetics of toluene in rats,
based on equivalent 24-hr AUC (2.4 mg/L/hr). The rats exposed to 50 ppm for 4 hr and 9.7 ppm
for 24 hr of toluene would receive the same dose metric. Again, it should be noted that
extrapolations across long durations may not be warranted, as life stage changes and
pharmacodynamic adaptations (e.g., sensitization and desensitization) may be operational
(Clewell et al., 2002a).
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Oral
100 -|
10
1 -
0.1 -
0.01 -
e
ID
> 0.001
2345
Time (hours)
Inhalation
c 100 n
o
1
ID
S
35
•° ?
o g.
o
J2
£
(D
10 -
1 -
0.1
0.01
01 234567
Time (hours)
Figure 4-4. Oral-to-inhalation extrapolation of the pharmacokinetics of
chloroform on the basis of same area under the curve in blood (7.06
mg/L/hr). The oral dose was 1 mg/kg and the inhaled concentration was 83.4
ppm (4 hr).
Source: Adapted from Corley et al. (1990).
4.3.5. High-Dose to Low-Dose Extrapolation
PBPK models facilitate high-dose to low-dose extrapolation of tissue dosimetry by
accounting for the dose-dependency of relevant processes (e.g., saturable metabolism, enzyme
induction, enzyme inactivation, protein binding, and depletion of glutathione reserves) (Clewell
and Andersen, 1987). The description of metabolism in PBPK models has frequently included a
capacity-limited metabolic process that becomes saturated at high doses. Nonlinearity arising
from mechanisms other than saturable metabolism, such as enzyme induction, enzyme
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o E
100-
10-
1 -
0.1 -
0.01 -
0.001 -
0.0001
8 12 16
Time (hours)
20
24
c
o
I
I
8-§
10-1
1 -
0.1 -
0.01 -
0.001 -
0.0001
16 24 32
Time (hours)
40
48
Figure 4-5. Duration adjustment (4 hr to 24 hr) of toluene exposures in rats,
based on equivalent AUC (2.4 mg/L/hr). The rats were exposed to 50 ppm
toluene for 4 hr and 9.7 ppm for 24 hr.
Source: Adapted from Tardif et al. (1997).
inactivation, depletion of glutathione reserves, and binding to macromolecules, have also been
described with PBPK models (e.g., Krishnan et al., 1992; Clewell and Andersen, 1987). A
PBPK model intended for use in high-dose to low-dose extrapolation needs equations and
parameters describing dose-dependent phenomena if they occur in the range of interest for the
assessment. Because the determinants of nonlinear behavior may not be identical across species
and age groups (e.g., maximal velocity for metabolism, glutathione concentrations), these
parameters are required for each species/age group. During the conduct of high-dose to low-
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dose extrapolation, no change in the parameters of the PBPK model is required except for the
administered dose or exposure concentration.
An example of high-dose to low-dose extrapolation is presented in Figure 4-6. In this
figure, both the blood AUC and the amount metabolized over a period of time (12 hr) are plotted
as a function of exposure concentrations of toluene. For conducting high-dose to low-dose
simulation in this particular example, only the numerical value of the exposure concentration
(which is an input parameter for the PBPK model) was changed during every model run. All
other model parameters remained the same. The model simulations shown in Figure 4-6 indicate
the nonlinear nature of blood AUC as well as the amount of toluene metabolized per unit time in
the exposure concentration range simulated. In such cases, the high-dose to low-dose
extrapolation of tissue dosimetry should not be conducted assuming linearity but, rather, should
be performed using tools such as the PBPK models.
4.4. ROLE OF PBPK MODELS IN REFERENCE CONCENTRATION AND
REFERENCE DOSE DERIVATION
4.4.1. Reference Concentration
The RfC corresponds to an estimate (with uncertainty spanning perhaps an order of
magnitude) of continuous inhalation exposure (mg/m3) for a human population, including
sensitive subgroups, that is likely to be without an appreciable risk of deleterious effects during a
lifetime (U.S. EPA, 1994). Notationally, RfC is defined as:
RJC=POD[HEC]/UF
where:
POD[HEc] = POD (NOAEL, LOAEL, or BMC) dosimetrically adjusted to an HEC
UF = uncertainty factors to account for the extrapolations associated with the POD (i.e.,
interspecies differences in sensitivity, human intraspecies variability, subchronic-
to-chronic extrapolation, LOAEL-to-NOAEL extrapolation, and incompleteness of
database)
The starting point for an RfC derivation is the identification of the POD for the critical
effect in a key study. Subsequent steps involve (a) adjustment for the difference in duration
between experimental exposure (e.g., 6 hr) and expected human exposure (24 hr), (b) calculation
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of the HEC, and (c) application of uncertainty factors (UFs). The benefit of using PBPK models
in the RfC process is discussed below. Specifically, the role of PBPK models in determining the
POD, duration adjustment factor, and HEC is presented in Sections 4.4.2 through 4.4.6. It
o
h-
QJ
m
<
300 n
200 -
100 -
500 1000
Exposure Concentration (ppm)
1500
o
_Q
ro
"S
E
15 -,
10 -
o _l
E O
K
5 -
500 1000
Exposure Concentration (ppm)
1500
Figure 4-6. High-dose to low-dose extrapolation of dose metrics using PBPK
model for toluene. Inhalation exposures were for 4 hr, and areas under the curve
and amount metabolized were calculated for 12 hr. Note that there is a slight
curve in the top graph around 125 ppm.
Source: Adapted from Tardif et al. (1997).
should be noted that although the various extrapolations were presented in a hierarchical fashion
earlier in this document (e.g., interspecies, intraspecies, route, duration, and high-dose to low-
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dose), the order of extrapolations is changed in this section to more closely parallel the RfC and
RfD derivation processes.
4.4.2. Reference Concentration: Point of Departure
It is important to realize that currently the POD for RfC derivation cannot be identified or
established with only pharmacokinetic data or PBPK models in the absence of dose-response
data. Integrated pharmacokinetic-pharmacodynamic models (e.g., Timchalk et al., 2002;
Gearhart et al., 1994, 1990) may be capable of predicting response and thus estimating a POD in
the future, but this is a research effort that is not yet ready for risk assessment applications. At
present, PBPK modeling can be useful for conducting route-to-route extrapolation, duration
adjustments, inter- and intraspecies extrapolations on the basis of equivalent delivered dose from
PODs identified in toxicity, epidemiology, or clinical studies.
4.4.3. Reference Concentration: Route-to-Route Extrapolation
Typically, the POD used in the RfC process would be the inhalation route-specific
NOAEL, LOAEL, or BMC. These PODs essentially correspond to exposure concentrations in
an experimental or field study (NOAEL, LOAEL) or to the lower confidence limit (95th
percentile) of the exposure concentration (BMCL) associated with a specified response level
(generally in the range of 1 to 10% above background; e.g., BMCLio%) derived from statistical
analysis of experimental dose-response data (U.S. EPA, 2000a, 1994).
When information on the POD is available only for a noninhalation route of exposure
(e.g., oral route), route-to-route extrapolation can be conducted (Pauluhn, 2003). Historically,
the NOAEL (mg/kg/day) associated with an oral exposure route was converted to milligrams per
day and then to the equivalent inhaled concentration on the basis of human breathing rate and
body weight. Data on the route-specific fraction absorbed, when available, are used to determine
the equivalent inhalation concentration on the basis of equivalent absorbed doses (U.S. EPA,
1999a). Such simplistic approaches, however, assume that the rates of ADME and tissue
dosimetry of chemicals are the same for a given total dose, regardless of the exposure route and
intake rate. These approaches essentially neglect the route-specific differences in
pharmacokinetics, such as first-pass clearance. First-pass clearance can arise when chemicals
undergo extensive metabolism in tissues at portals of entry; this may include the intestines and
liver for orally absorbed compounds or the lungs for inhaled compounds (Benet et al., 1996).
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Therefore, route-to-route extrapolation using a more complete pharmacokinetic modeling
approach, such as PBPK modeling, is preferable, as described in Section 4.3.3.
4.4.4. Reference Concentration: Duration Adjustment
An RfC addresses continuous exposure of human populations, so the POD used in its
derivation should correspond to 24-hr/day exposures (U.S. EPA, 1994). PODs are frequently
obtained from animal exposures or occupational exposures that occur for 6 to 8 hr/day, 5
days/wk, so an adjustment to a continuous 24-hr exposure, resulting in a lower concentration for
continuous exposures, is conducted on the basis of hours per day and days per week (i.e., 6/24 x
5/7) (U.S. EPA, 2002). This simple adjustment assumes that "Haber's Rule" applies, i.e., that
for a given chemical C x t = k, where C and t are the concentration (mass per unit volume) and
time needed (at that concentration) to produce some toxic effect, and k is a constant associated
with that toxic effect. The rule leads to the conclusion, for example, that doubling the
concentration will halve the time needed to produce a comparable effect level. In
pharmacokinetics, the integration of C x t over the exposure-response time frame of interest is
also referred to as the AUG. If the AUC is not the dose metric most associated with the adverse
effect (e.g., sometimes peak concentration is more critical) or various C x t = k regimens do not
result in a comparable effect level, then "Haber's Rule" is not applicable (U.S. EPA, 2002).
When data indicate that a given toxicity is more dependent on concentration than on duration
(time), this adjustment would not be used. If the appropriate measure of internal dose is
uncertain, the Agency uses adjustment to a continuous inhalation exposure based on the C x t
relationship as a matter of health-protective policy (U.S. EPA, 2002). For additional insights
into "Haber's Rule" (as one in a family of power functions) and its use in risk assessment, the
reader is referred to Miller et al. (2000).
PBPK models can be used to estimate the value of a proposed internal dose metric that
would result from various administered doses (U.S. EPA, 2002; Jarabek, 1994). PBPK models
do not address pharmacodynamic events and assume that these events do not alter the kinetics
for within-day exposures (<24 hr). Consistent with the Agency's policy (U.S. EPA, 2002), the
dose metric of a chemical for the exposure scenario of the critical study is initially determined
using the PBPK model (e.g., 6 hr/day, 5 days/wk); then the atmospheric concentration for a
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continuous exposure (24 hr/day) during a lifetime or a particular window of exposure that yields
the same dose metric is determined by iterative simulation.
4.4.5. Reference Concentration: Dosimetric Adjustment Factor (Interspecies
Extrapolation)
In the RfC process, a DAF is applied to the duration-adjusted POD to account for
pharmacokinetic differences between test species and humans to derive an HEC (U.S. EPA,
1994). The DAF depends on the nature of the inhaled toxicant and the MOA as well as the
endpoint (local effects vs. systemic effects). Dosimetry data, if available, in the test animals and
humans (including deposition data, region-specific dosimetry, blood concentration of systemic
toxicants) are used to estimate the DAF. In the absence of such data, knowledge of critical
parameters or mathematical models in the test species and humans can be useful in estimating
the DAF.
For highly reactive or water-soluble gases that do not significantly accumulate in blood
(e.g., hydrogen fluoride, chlorine, formaldehyde, volatile organic esters), the DAF is derived for
estimates of the delivery of chemical to different regions of the respiratory tract, based on
regional mass transfer coefficients and differences in surface area and ventilation rates (U.S.
EPA, 1994). For poorly water-soluble gases that cause remote effects (e.g., xylene, toluene,
styrene), PBPK models are identified as the preferred approach. Absent a PBPK model, the
DAF is determined on the basis of the ratio of blood:air partition coefficients in animals and
humans (U.S. EPA, 1994). For gases that are water soluble with some blood accumulation (e.g.,
acetone, ethyl acetate, ozone, sulfur dioxide, propanol, isoamyl alcohol) and have the potential
for both respiratory and remote effects, some combination of the above approaches may be used.
An alternative to the use of DAFs, discussed in the RfC guidance (U.S. EPA, 1994) is to
employ more elaborate or chemical-specific models to make interspecies extrapolations. A
variety of computational tools are available to determine the uptake and deposition of gases and
particulates in nasal pathways and the respiratory tract (U.S. EPA, 2004; Bogdanffy and
Sarangapani, 2003; Hanna and Lou, 2001; Iran et al., 1999; Bush et al., 1998; Asgharian et al.,
1995; Jarabek, 1994; Kimbell et al., 1993). PBPK models are frequently used for systemically
distributed gases and vapors, but in conjunction with other models (e.g., CFD), they can be used
for locally acting gases with contact site effects. A limitation of DAFs is that they do not
account for metabolism of the more reactive gases, so PBPK modeling approaches would clearly
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be preferable for these compounds if adequate data are available. Further applications of PBPK
models to the more reactive gases and agents are expected to continue to be developed in the
near future.
Intraspecies extrapolations and the application of UFs in RfC derivation are addressed in
Section 4.4.12.
4.4.6. Example of PBPK Model Use in Reference Concentration Derivation
The RfC derivations for m-xylene and vinyl chloride exemplify the application of PBPK
models. In the case of m-xylene, using the adjusted NOAEL of 39 mg/m3 as input to the rat
PBPK model, the steady-state blood concentration was established (0.144 mg/L) (Tardif et al.,
1997). The human model was then run to determine the exposure concentration yielding that
same dose metric (HEC = 41 mg/m3) (U.S. EPA, 2003). In an alternative approach, the dose
metric associated with the unadjusted NOAEL (217 mg/m3, 6 hr/day, 5 days/wk, 13 wks) in the
rat was determined using the PBPK model (time-weighted average blood concentration = 0.198
mg/L). Then, the human PBPK model was used to determine the 24-hr exposure concentration
that would produce this target dose metric (39 mg/m3). Dividing this value by the appropriate
UFs (3 for interspecies pharmacodynamic differences, 10 for interindividual variability, 3 for
subchronic to chronic extrapolation, and 3 for database deficiency), the RfC was determined (0.1
mg/m3).
In the case of vinyl chloride, the RfC was derived from the NOAEL for the oral route
(U.S. EPA, 2000b). The PBPK model was initially used to derive the dose metric associated
with the rat NOAEL (0.13 mg/kg/day). Because systemic toxicity resulted in the same endpoint
regardless of exposure route, a human PBPK model was exercised to determine the continuous
inhalation exposure concentration associated with the same dose metric (2.5 mg/m3) (Clewell et
al., 1995). Using a total UF of 30 (3 for toxicodynamic component of interspecies UF and 10 for
intraspecies variability), the RfC was established (0.1 mg/m3).
If the available human PBPK
Box 4-1. Role of PBPK models in the RfC process
model is probabilistic in nature,
• Route-to-route extrapolation of the point of departure
accounting for the population
distribution of parameters
Duration adjustment calculation
Dosimetric adjustment factor (i.e., interspecies)
Pharmacokinetic component of human variability
(biochemical, physiological, and
physicochemical), the magnitude of the interindividual variability can be estimated (Delic et al.,
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2000). In that case, the intraspecies uncertainty factor might be set to 3 (to account only for
pharmacodynamic differences). The potential role of the PBPK model in the RfC process is
summarized in Box 4-1.
4.4.7. Reference Dose
An RfD is an estimate (with uncertainty spanning perhaps an order of magnitude) of a
daily exposure to the human population (including sensitive subgroups) that is likely to be
without an appreciable risk of deleterious effects during a lifetime (Dourson et al., 1992; Barnes
and Dourson 1988). It is expressed in units of milligrams per kilogram per day. An RfD is
calculated as follows:
RfD = POD/UF
where:
POD = NOAEL, LOAEL, or BMD
UF = uncertainty factors related to extrapolations associated with the POD (i.e.,
interspecies extrapolation, human variability, subchronic-to-chronic extrapolation,
LOAEL-to-NOAEL extrapolation) or incompleteness of the database.
An RfD derivation begins with the identification of the POD for the critical effect.
Subsequently, the UFs are applied as appropriate. PBPK models are potentially useful in
deriving the RfD by estimating the POD and extrapolation factors, as described in Sections 4.4.8
through 4.4.11.
4.4.8. Reference Dose: Point of Departure
As with RfC derivation, PBPK models are not able to establish a POD in the absence of
experimental dose-response data. Development of a BBDR model linked with a PBPK model
could potentially improve the quantification of the dose-response and POD for RfD derivation.
There has been some success in developing BBDR models for simple adverse effects (e.g.,
cholinesterase inhibition, cytotoxicity, hematoxicity) (Ashani and Pistinner, 2004; Cox, 1996;
Gearhart et al., 1994, 1990; Reitz et al., 1990a), but these models are not routinely used to
estimate PODs for RfD derivation, partly due to limitations on data needed to calibrate and test
the models. New technologies (e.g., toxicogenomics) may offer the potential for generating
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needed data, and more integrated PBPK and BBDR models may be an attainable goal in the near
future.
4.4.9. Reference Dose: Route-to-Route Extrapolation and Duration Adjustment
The RfD derivation generally uses an oral NOAEL, LOAEL, or BMD as the POD. The
oral route-specific NOAEL and LOAEL correspond to experimentally tested doses, whereas the
BMD is obtained from statistical modeling of dose-response data (U.S. EPA, 2000a).
When an oral POD is unavailable, PBPK models can be useful in deriving such values on
the basis of results obtained for other dosing routes (e.g., inhalation, intravenous, dermal), as
previously described. Extrapolation of inhalation data using simple assumptions about
ventilation rate, chemical concentration, and body weight will be inaccurate due to
pharmacokinetic factors such as first-pass clearance, discussed above for route-to-route
extrapolation in RfC derivation. In addition, comparisons of oral PODs with dosimetry based
route extrapolation of inhalation results can be valuable because the vehicle (e.g., corn oil) in
oral gavage studies sometimes alters the toxicity response.
As with RfCs, chronic RfDs are intended for continuous exposure of human populations.
For PODs derived from gavage studies, typically administered 5 days/wk, it has been standard
practice to adjust for continuous exposure using a 5/7 adjustment. Alternatively, PBPK models
can be used to model human 7-day/wk exposures and thus estimate a dose metric corresponding
to a POD determined in experimental animal bioassays.
4.4.10. Reference Dose: Interspecies Extrapolation
As with RfC derivation, PBPK models can be employed to account for pharmacokinetic
differences between test species and humans and covert a POD to a human equivalent dose (U.S.
EPA, 2002). Estimation of human pharmacokinetic variability and the application of UFs in RfD
derivation are discussed in Section 4.4.12.
4.4.11. Example of PBPK Model Use in Reference Dose Derivation
The RfD derivation for ethylene glycol monobutyl ether exemplifies the current approach
of PBPK model application (U.S. EPA, 1999b). In this case, the LOAEL identified in an animal
study (59 mg/kg/day) was provided as input to the PBPK model to determine the Cmax of the
metabolite butoxy acetic acid in blood (BAAmax) (Corley et al., 1997). The dose metric
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(BAAmax) associated with the LOAEL was established in the test species (103 uM). The human
PBPK model was then run to determine the exposure dose that would give the target dose metric
(103 uM) (Corley et al., 1997). The resulting human equivalent dose of 7.6 mg/kg/day was
divided by the appropriate UFs (30; 10 for interindividual differences and 3 for LOAEL-to-
NOAEL extrapolation) to establish the RfD (0.3 mg/kg/day). In this particular case, the
interspecies pharmacodynamic factor was set to 1 because in vitro studies suggested that humans
are less sensitive than rats to the hematologic effects of ethylene glycol monobutyl ether (U.S.
EPA, 1999b).
When the BMD is available, a similar approach is used to establish the RfD. In the case
of ethylene glycol monobutyl ether, initially the dose metric associated with the BMD was
established (BAAmax = 64 uM) and then the human PBPK model was used to back-calculate the
equivalent dose (5.1 mg/kg/day). Using the appropriate UF (10 for interindividual variability),
the RfD was derived (0.5 mg/kg/day) (U.S. EPA, 1999a). If the human PBPK model accounted
for the population distribution of
Box 4-2. Role of PBPK models in the RfD process
parameters, the pharmacokmetic
• Route-to-route extrapolation
component of the interindividual variability
could be addressed as illustrated in the
dose-response analysis with methyl
mercury (Clewell et al., 1999). PBPK variability
Duration adjustment
Pharmacokinetic component of interspecies
extrapolations
Pharmacokinetic component of human
models, by facilitating the simulation of tissue dose of the toxic moiety of chemicals, address
specific areas of uncertainty associated with derivation of the RfD, as shown in Box 4-2.
4.4.12. Uncertainty Factors: Role of PBPK Models
The UFs and variability factors used in RfC and RfD derivation account for
extrapolations from test animals to humans (interspecies, UFA), across duration of exposure
(subchronic to chronic), from LOAEL to NOAEL, for variability within the human population to
protect the most sensitive population (intraspecies variability, UFH), and for poor quality or
missing data in the database (database deficiency) (U.S. EPA, 1994; Jarabek, 1994). The total of
all UFs generally should not exceed 3,000 (U.S. EPA, 2002). If the NOAEL for a chemical with
an adequate database has been identified in a chronic study, only the UFA and UFn are used in
the assessment. The conventional default value for UFA of 10 is used in RfC and RfD derivation
as an approximation of cross-species scaling resulting in equivalent effects. Similarly, the
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default value for UFn of 10 is presumed adequate to account for variability in the human kinetic
and dynamic processes following exposure and to protect potentially sensitive human
subpopulations.
The values for UFA and UFH are based on empirical information for pharmacokinetics
and pharmacodynamics (e.g., isoenzyme levels, enzyme activity levels, tissue volumes, breathing
rates, cell proliferation rates) (Dome et al., 2002, 2001a, b; Walton et al., 2001) and science
policy and historical use. Extrapolations across species or estimates of interindividual variability
(e.g., differences arising from genetic polymorphisms), however, are best done on the basis of
chemical specific determinants of disposition and effects. Initially, evaluation of specific
determinants of interspecies differences or human variability is useful, but simple pooling of
these specific determinants without accounting for covariance or nonlinear interactions can lead
to unrealistic estimates for either UFA or UFH (Lipscomb, 2004). The net impact of various
determinants on the UFA and UFH is more properly evaluated within the integrated and
physiologically based context of a PBPK or BBDR model.
When data are available to go beyond default uncertainty values, these UFs can be
subdivided into their toxicokinetic and toxicodynamic components (IPCS, 2005; U.S. EPA,
2005b). The World Health Organization's International Programme on Chemical Safely (IPCS)
has produced guidance on the development of chemical-specific adjustment factors (CSAFs)
(IPCS, 2005). Although the principles of using chemical-specific data in developing values for
UFs has long been endorsed by EPA (e.g., U.S. EPA, 1994), and many of the guiding principles
in the IPCS document are also components of EPA's risk assessment approach, the Agency does
not use CSAFs per se, due in part to differences in calculation methods. For instance, the Agency
often separates the pharmacokinetic and pharmacodynamic components of interspecies
variability equally (i.e., 1005 or 3.16, generally rounded to 3 each), whereas the IPCS advocates
10°'6 (4.0) and 10°'4 (2.5), respectively (IPCS, 2005).
When sufficient chemical-specific data are available for PBPK modeling, such models
are useful for characterizing the magnitude of the pharmacokinetic component of the UFA as well
as the UFH used in the RfC and RfD processes. When using PBPK models to adjust for
pharmacokinetic differences between species, a factor of 3 (one-half order of magnitude) is
generally retained to account for remaining uncertainties (U.S. EPA, 2003, 1994; Clewell et al.,
2002a; Jarabek, 1995a). However, chemical-specific information on the pharmacodynamic
aspect of inter- and intraspecies differences may inform a further reduction or increase of these
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UFs from default values. It should be recognized that PBPK and BBDR models are not currently
suitable for characterizing the magnitude of LOAEL-NOAEL, subchronic-chronic, or database
UFs, although research in these areas is ongoing (Thomas et al., 1996a).
4.5. ROLE OF PBPK MODELS IN CANCER RISK ASSESSMENT
The dose-response assessment portion of cancer risk assessment may vary, depending on
MOA considerations. A CSF can be based on a linear extrapolation from the POD (i.e., high-
dose to low-dose extrapolation), or a nonlinear analysis may be applied (U.S. EPA, 2005b).
Either approach may also require interspecies or route-to-route extrapolations for the POD. The
role of PBPK models in conducting these extrapolations is discussed in Sections 4.5.1 through
4.5.5.
4.5.1. Interspecies Extrapolation
For gases and particulates, the default procedure for interspecies extrapolation involves
the derivation of an HEC, as described in Section 2.5.4 (Jarabek, 1995a, b; U.S. EPA, 1994). For
oral exposures, when a PBPK model is not available, the EPA endorsed scaling of doses for
carcinogens between species (e.g., rat to humans) according to body mass raised to the three-
fourths power (BW0-75) (U.S. EPA, 2005b, 2002, 1992b). This procedure presumes that equal
doses in these units (i.e., in mg/kg°'75/day), when administered daily over a lifetime, will result in
equal lifetime cancer risks across mammalian species. The three-fourths power scaling
relationship (sometimes called "Kleiber's law" from his original proposition in a 1932 article) is
generally attributed to differences in metabolic rate. The leading biological rationale for a less-
than-full-power relationship for general metabolic processes (i.e., < BW1) is that exchange
surfaces and distribution networks constrain the concentration and flux of metabolic reactants
(Enquist et al., 1998; West et al., 1997). There remains considerable dissent as to the generality
of the BW°75 scaling factor, the underlying biological rationale, and the value of the exponent
(i.e., many proponents advocate a BW0'67 scaling based solely on surface area differences),
particularly for toxicological effects of xenobiotic chemicals in contrast to endogenous anabolic
and catabolic processes (Agutter and Wheatley, 2004). Nonetheless, BW0'75 scaling remains the
current EPA default approach (U.S. EPA, 1992b).
The nature and slope of the dose-response relationship for carcinogens may not be
identical in test species and humans due to pharmacokinetic and pharmacodynamic differences
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(Monro, 1994). If appropriate data are available in both the test species and humans (e.g., tissue
or blood concentrations), then interspecies extrapolations of an equivalent carcinogenic or safe
dose can be conducted. In the absence of a complete data set, PBPK models provide a means to
characterize the relationship between the applied dose and the internal dose of a carcinogen in
the species of interest for subsequent extrapolation to humans (Andersen et al., 1987).
4.5.2. Intraspecies Variability
Intraspecies variability in pharmacokinetics or pharmacodynamics has not usually been
considered in cancer risk assessment. CSFs have been used without further adjustment to
account for susceptible populations. The recent supplemental guidance, however, suggests that
an additional adjustment factor to the cancer slope or unit risk value be considered to account for
enhanced susceptibility in early life (i.e., to neonates and young children) from exposure to
carcinogens exhibiting a mutagenic MOA (U.S. EPA, 2005a). Furthermore, when assessing the
less-than-lifetime exposures occurring in childhood, the guidelines stipulate consideration of
adult-children differences in key exposure factors (e.g., skin surface area, drinking water
ingestion rates) (U.S. EPA, 2005c).
PBPK models can be useful in evaluating pharmacokinetic differences among adults and
children and their impact on the internal disposition of chemical carcinogens (Ginsberg et al.,
2004; Clewell et al., 2004; Price et al., 2003b; Gentry et al., 2003; Clewell et al., 2002b).
However, the quantitation of differences in tissue dose between adults and children would not be
account for pharmacodynamic differences related to early-life exposures of neonates and
children.
4.5.3. Route-to-Route Extrapolation
As with RfC and RfD derivation, PBPK models can facilitate the conduct of route-to-
route extrapolation by accounting for the route-specific rate and magnitude of absorption, first-
pass effect, and metabolism (Clewell and Andersen, 1994). The slope factor or the POD
associated with one exposure route can be translated into applied dose for another exposure route
by simulating the tissue dose of toxic moiety associated with the exposures by each route (U.S.
EPA, 2000b; Gerrity et al., 1990).
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4.5.4. High-Dose to Low-Dose Extrapolation
The oral CSF or the IUR can be determined by modeling the relationship between the
cancer response and the administered dose or exposure concentration (U.S. EPA, 2005a).
According to the revised cancer guidelines, either a nonlinear (i.e., RfC or RfD) or linear (i.e.,
unit risk estimate) extrapolation based on the POD can be conducted, as appropriate for the
MOA of the carcinogen (U.S. EPA, 2005a). The use of internal dose or delivered dose in such
analysis has been encouraged.
Because high doses of chemicals are often administered in rodent cancer bioassays, the
number of tumors observed in such studies is not always directly proportional to the exposure
dose. Thus, the dose-response relationships can appear complex, in part due to nonlinearity in
the pharmacokinetic processes occurring at high exposure doses. In other words, the target tissue
dose of the toxic moiety may be disproportional to the administered doses used in animal
bioassays (Figure 2-1, Chapter 2). Therefore, dose-response analysis based on an appropriate
dose metric may result in linearization of the relationship (Clewell et al., 2002a, 1995; Andersen
et al., 1987). The slope factor derived using the dose metric-response curve has units of (dose
metric)"1. For nonlinear analyses, a POD can be converted using a PBPK model to the dose
metric at which no significant incidence of cancer is expected on the basis of MOA of the
chemical and dose-response data.
An integrated PBPK-BBDR model would improve the characterization of a chemical
carcinogen dose-response relationships (e.g., a PBPK model coupled to a clonal expansion and
progression model); however, most such coupled models are still in the development stage (U.S.
EPA, 2005b). PBPK models improve
estimation of the internal dose metric for a
chemical carcinogen and play an important
role in reducing the uncertainties associated
. . ,, . . . 1-1 •» High-dose to low-dose extrapolation
with some of the extrapolations used in the T ° . •.•,•._. + +
» mtraspecies variability to protect sensitive
cancer risk assessment process (Box 4-3).
Box 4-3. Role of PBPK models in cancer risk
assessment
• Interspecies extrapolations of pharmacokinetically
equivalent doses
• Route-to-route extrapolation
subpopulations
4.5.5. Example of PBPK Model Use in Cancer Risk Assessment
For assessing the cancer risk associated with human exposures, the exposure
concentration is used as input to human PBPK models to estimate the dose metric, which is then
multiplied with the dose metric-based slope factor. In the cancer risk assessments using PBPK
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models, it is assumed that the tissue response associated with a given level of dose metric in the
target tissue is the same in test animals and in humans (e.g., Andersen et al., 1987). It is a
reasonable assumption that can be revised as a function of species-specific mechanistic
information available for a given chemical.
The demonstration of the applicability of PBPK models in cancer risk assessment was
first accomplished with dichloromethane, which caused liver and lung tumors in mice exposed to
2,000 or 4,000 ppm 6 hr/day, 5 days/wk for lifetime (Andersen et al., 1987). In this case, the
mouse PBPK model was used to calculate the tissue dose of metabolites and parent chemical
arising from exposure scenarios comparable to those of the cancer bioassay study, and their
relationship to the observed tumor incidence was then examined. Because the parent chemical
was nonreactive, Andersen et al. (1987) considered it an unlikely candidate responsible for the
tumorigenicity. Hence, the relationship between the tissue exposure to its metabolites and tumor
incidence was examined (Table 4-2). Whereas the dose metric based on oxidative pathway
varied little between 2,000 and 4,000 ppm, the flux through the glutathione pathway increased
with increasing dose of dichloromethane and corresponded well with the degree of
dichloromethane-induced tumors at these exposure concentrations.
Table 4-2. Relationship between tumor prevalence and dichloromethane
metabolites produced by microsomal and glutathione pathways for the
bioassay conditions (methylene chloride-dose response in female mice)
Exposure
(ppm)
0
2,000
4,000
Microsomal pathway
dose3
Liver
—
3,575
3,701
(Lung)
—
(1,531)
(1,583)
Glutathione pathway
dose8
Liver
—
851
1,811
(Lung)
—
(123)
(256)
Tumor number
Liver
6
33
83
(Lung)
(60)
(63)
(85)
1 Tissue dose is cumulative daily exposure (mg metabolized/volume tissue/day). Reprinted from Toxicology &
Applied Pharmacology, vol. 87, Andersen et al., Physiologically based pharmacokinetics and the risk assessment
process for methylene chloride, pp. 185-205, 1987, with permission from Elsevier.
The model prediction of the target tissue dose of the glutathione conjugate resulting from
6-hr inhalation exposures to 1-4,000 ppm dichloromethane is presented in Figure 4-7. The
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estimation of target tissue dose of dichloromethane-glutathione conjugate by linear back-
extrapolation gives rise to a 21-fold higher estimate than that obtained by the PBPK modeling
approach. This discrepancy arises from the nonlinear behavior of dichloromethane metabolism
at high-exposure concentrations. At exposure concentrations exceeding 300 ppm, the
10000
0.01
1 10 100 1000 10000
PPM in air
Figure 4-7. PBPK model predictions of glutathione (GST)-pathway
metabolites in mouse liver. The three curves are for a linear extrapolation from
the bioassay exposures of 2,000 and 4,000 ppm (upper curve), the expected tissue
dose, based on model parameters for the mouse (middle line), and the expected
dose expected in humans, based on human model parameters (bottom line). The
curvature occurs because oxidation reactions that are favored at low inhaled
concentrations become saturated as inhaled concentration increases above several
hundred ppm. Reprinted from Toxicology & Applied Pharmacology., vol. 87,
Andersen et al., Physiologically based pharmacokinetics and the risk assessment
process for methylene chloride, pp. 185-205, 1987, with permission from
Elsevier.
cytochrome P-450-mediated oxidation pathway is saturated, giving rise to a corresponding
disproportionate increase in the flux through glutathione conjugation pathway. By accounting
for the species-specific differences in metabolism rates and physiology in the PBPK model, the
target tissue dose for humans was estimated to be some 2.7 times lower than that for the mouse.
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The target tissue dose-based slope factor has subsequently been used for characterizing the
cancer risk associated with human exposures (Haddad et al., 2001a; Reitz et al., 1989; Andersen
et al., 1987). The case of dichloromethane exemplifies how PBPK models can be used to
improve the dose-response relationship on the basis of appropriate dose metrics, thus leading to
scientifically sound conduct of interspecies and high-dose to low-dose extrapolations essential
for cancer risk assessments.
4.6. MIXTURE RISK ASSESSMENT
PBPK models facilitate risk assessment of chemical mixtures by estimating the change in
dose metrics due to multichemical interactions (Haddad et al., 2001b). For conducting tissue
dosimetry-based assessments for mixtures, adequately evaluated PBPK models for the mixture in
the test species and in humans are required and the dose-response values for the individual
chemicals (e.g., CSF, RfD, RfC) known. The approach for using PBPK models in risk
assessment of mixtures of systemic toxicants or carcinogens exhibiting threshold mechanism of
action, would consist of (Haddad et al., 2001b)
1. Characterizing the dose metrics associated with dose-response values for the mixture
components,
2. Obtaining predictions of dose metrics of each mixture component in humans, based
on information on exposure levels provided as input to the mixture PBPK model; and
3. Determining the sum total of the ratios of the results of steps (1) and (2) for each
component during mixed exposures.
Similarly, for carcinogens with slope factor (Haddad et al., 2001b),
1. The dose metric-based slope factor can be established for each component using the
animal PBPK model,
2. The dose metric associated with human exposure concentrations can be established
using mixture PBPK models, and
3. The results of steps (1) and (2) can be combined to determine the potentially altered
cancer response during mixed exposures.
Risk assessments based on the use of PBPK models for single chemicals and mixtures, as
detailed in previous sections, account for only the pharmacokinetic aspect or, more specifically,
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target tissue exposure to toxic moiety. If these tissue exposure simulations are combined with
pharmacodynamic models, then better characterization of dose-response relationships and
prediction of PODs (NOAEL, BMD, BMC) may become possible.
4.7. LINKAGE TO PHARMACODYNAMIC MODELS
The identification of PODs by simulation may become possible with the use of BBDR
models. These models would require the linkage of quantitative descriptions of
pharmacokinetics and pharmacodynamics via mechanism of action. Accordingly, the output of
PBPK models is linked to the pharmacodynamic model using an equation that reflects the
researcher's hypothesis of how the toxic chemical participates in the initiation of cellular
changes leading to measurable toxic responses. For example, certain DNA adducts cause
mutations, cytotoxic metabolites kill individual cells, and expression of growth factors can act as
a direct proliferation stimulus. In each of these cases, the temporal change in the dose metric
simulated by the PBPK model is linked with mathematical descriptions of the process of adduct
formation, cytotoxicity, or proliferation in the BBDR models to simulate the quantitative
influence of these processes on tumor outcome. Figure 4-8 presents an example of the
relationship between dose metric (simulated by the PBPK model) and fraction of liver cells
killed (simulated by pharmacodynamic model) for chloroform. In this case, the
pharmacodynamic model consisted of differential equations to simulate time-dependent changes
in the number of hepatocytes in the liver as a function of basal rates of cell division and death,
chloroform-induced cytolethality, and regenerative replications (Page et al., 1997; Conolly and
Butterworth, 1995).
Table 4-3 presents a list of pharmacodynamic models for cancer and noncancer
endpoints. A characteristic of several of these pharmacodynamic models is that they are able to
simulate the normal physiological processes (e.g., cell proliferation rates, hormonal cycle) and
additionally account for the ways in which chemicals perturbate such phenomena, leading to the
onset and progression of injury. Pharmacodynamic models that can be linked with PBPK
models are not available for a number of toxic effects and modes of action. This situation is a
result, in part, of the complex nature of these models and the extensive data requirements for
development and evaluation of these models for various exposure and physiological conditions.
With the availability of integrated pharmacokinetic-pharmacodynamic models, the
scientific basis of the process of estimating PODs and characterizing the dose-response curve
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will be significantly enhanced. Additionally, such a modeling framework will facilitate a
quantitative analysis of the impact of pharmacodynamic determinants on the toxicity outcome,
such that the magnitude of the pharmacodynamic component of the interspecies and intraspecies
factors can be characterized more confidently. Even though some PBPK models have been used
in RfD, RfC, and unit risk estimate derivation for a number of substances (Table 4-1), the need
for applying such models (where possible) should be continuously explored.
0.30
0.25 - -
1
§.1S •
0,10 -
0.05 - -
0.00
0,00
1.00
2.00
3.00
4.00
i.OO
6.00
Liver dose surrogate ({iittol metabolized/g liver/60 min)
Figure 4-8. Relationship between the dose metric (|Jmol metabolized/g
liver/hr) simulated by PBPK model and the cell killing inferred from
pharmacodynamic model for chloroform. Reprinted from Fundamentals of
Applied Toxicology., vol. 37, Page et al., Implementation of EPA revised cancer
assessment guidelines: incorporation of mechanistic and pharmacokinetic data,
pp. 16-36, 1997, with permission from Oxford University Press.
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Table 4-3. Examples of biologically based models of endpoints and processes
of toxicological relevance
Toxicity endpoint
or process
Cancer
Cholinesterase
inhibition
Developmental
toxicity
Estrus cycle
Gene expression
Granulopoiesis
Nephrotoxicity
Teratogenic effect
Features
Simulation of relative roles of
initiation, promotion,
cytolethality, and proliferation
Simulation of dose-dependent
inhibition of plasma
cholinesterase, red blood cell
acetyl cholinesterase and brain
acetyl cholinesterase, and
nontarget B-esterase
Simulation of altered cell
kinetics as the biological basis
of developmental toxicity
Simulation of interactions of
estradiol and lutenizing
hormone
Simulation of induction of
CYP1A1/2 protein expression
in multiple tissues
Simulation of loss of
proliferating cells and loss of
functional cells
Simulation of induction of
renal 2\i globulin in male rat
kidney as a function of
proteolytic degradation and
hepatic production
Sensitivity distribution of
embryo as a function of age
and stage of development
Chemical studied
2-acetylamino fluorine
Chloroform
Dimethylnitrosamine
Formaldehyde
Polychlorinated biphenyls
Pentachlorobenzene
Saccharin
Organophophates
Methyl mercury
Endocrine-modulating
substances
Tetrachlorodibenzodioxin
Cyclophosphamide
2,2,4-Trimethyl-2 -phenol
Hydroxyurea
References
Conolly et al. (2003); Tan et
al. (2003); Thomas et al.
(2000); Conolly and Andersen
(1997); Conolly and Kimbell
(1994); Chen (1993); Luebeck
etal. (1991); Cohen and
Ellwein (1990); Moolgavkar
and Luebeck (1990);
Moolgavkar and Knudson
(1981); Moolgavkar and
Verizon (1979); Armitage and
Doll (1957)
Timchalk et al. (2002);
Gearhart et al. (1994, 1990)
Faustman et al. (1999); Leroux
etal. (1996)
Andersen etal. (1997)
Santostefano etal. (1998)
Steinbach etal. (1980)
Kohn and Melnick (1999)
Luecke etal. (1997)
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GLOSSARY
Absorbed dose: The amount crossing a specific absorption barrier (e.g., the exchange
boundaries of the skin, lung, and digestive tract) through uptake processes.
Applied dose: The amount presented to an absorption barrier and available for absorption
(although not necessarily having yet crossed the outer boundary of the organism).
Area under the curve (AUC): The concentration of a chemical in tissues or blood integrated
over time. It is a measure of tissue exposure to chemicals over a period of time.
Bayesian statistics: An approach that considers a model's parameters as random variables with
a probability distribution for describing each parameter. The distribution based only on prior
information and assumptions is called the prior distribution. Analysis of new data yields a
posterior distribution that reconciles the prior information and assumptions with the new data.
Benchmark dose (BMD) or benchmark concentration (BMC): A dose or concentration that
produces a predetermined change in response rate of an adverse effect (called the benchmark
response) compared to background.
Biologically based dose-response (BBDR) model: A predictive model that describes biological
processes at the cellular and molecular level linking the target organ dose to the adverse effect.
Cancer slope factor (CSF): An estimate of the increased cancer risk from a lifetime exposure
to an agent. This estimate, usually expressed in units of proportion (of a population) affected, is
generally reserved for use in the low-dose region of the dose-response relationship. It is often an
upper bound, approximating a 95% confidence limit.
Clearance: Volume containing the amount of drug eliminated per unit time by a specified
organ; it has the dimension of a flow per unit time.
Critical effect: The first adverse effect, or its known precursor, that occurs to the most sensitive
species as the dose rate of an agent increases.
Delivered dose: The amount of a substance available for biologically significant interactions in
the target organ.
Diffusion limited uptake: Compounds (typically high molecular weight and those with
significant protein binding) where membrane diffusion is often the rate-limiting process.
Dose metric: The target tissue dose that is closely related to ensuing adverse responses. Dose
metrics used for risk assessment applications should reflect the biologically active form of
chemical, its level, and duration of internal exposure, as well as intensity.
Dose-response assessment: The process of determining the relationship between the magnitude
of administered, applied, or internal doses and biological responses. Response can be expressed
as measured or observed incidence or change in level of response, percent response in groups of
G-l
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subjects (or populations), or the probability of occurrence or change in level of response within a
population.
Exposure assessment: The process of identifying and evaluating the human population exposed
to a toxic agent by describing its composition and size as well as the type, magnitude, frequency,
route, and duration of exposure.
First-order process: A linear metabolic process where a constant fraction of chemical is
metabolized per unit time.
First-pass effects: Metabolism that occurs before a compound can enter the general circulation.
For example, an orally administered compound may undergo metabolism in the intestines and/or
liver prior to systemic distribution.
Flow-limited diffusion: The chemical diffuses readily between blood and tissue compartments
and exchange is limited primarily by blood flow.
Half-life: Interval of time required for one-half of a given substance present in an organ to leave
it through processes other than physical decay. It is a constant only for mono-exponential
functions.
Human equivalent concentration (HEC): The human concentration (for inhalation exposure)
of an agent that is believed to induce the same magnitude of toxic effect as the exposure
concentration in experimental animal species. This adjustment may incorporate pharmacokinetic
information on the particular agent, if available, or use a default procedure.
Integration interval: The time interval at which the calculations of the change in concentration
or amount of chemical in various compartments of the model are performed.
Internal dose: A more general term denoting the amount absorbed without respect to specific
absorption barriers or exchange boundaries. The amount of the chemical available for
interaction by any particular organ or cell is termed the delivered or biologically effective dose
for that organ or cell.
Markov-chain Monte-Carlo simulation: An approach that has frequently been used within a
Bayesian statistical framework to (a) sample each model's parameters from their prior
distributions, (b) fit the model with the sampled parameters to several additional experimental
data sets, and (c) compare the model's predictions with the experimental results to obtain
posterior distributions for the model's parameters that improve the model's fit. These steps are
repeated thousands of times until each parameter's posterior distribution converges to a more
robust distribution that reflects a wider database.
Pharmacokinetic models: Mathematical descriptions simulating the relationship between
external exposure levels and the biologically effective dose at a target tissue over time.
Pharmacokinetic models take into account absorption, distribution, metabolism, and elimination
of the administered chemical and its metabolites.
G-2
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Pharmacodynamic models: Mathematical descriptions simulating the relationship between a
biologically effective dose and the occurrence of a tissue response over time.
Physiologically based pharmacokinetic (PBPK) model: A model that estimates the dose to
target tissue by taking into account the rate of absorption into the body, distribution and storage
in tissues, metabolism, and excretion on the basis of interplay among critical physiological,
physicochemical, and biochemical determinants.
Point of departure (POD): The dose-response point that marks the beginning of a low-dose
extrapolation. This point can be the lower bound on dose for an estimated incidence or a change
in response level from a dose-response model (BMD, BMC), or a no-observed-adverse-effect
level or lowest-observed-adverse-effect level for an observed incidence or change in level or
response.
Potential dose: The amount ingested, inhaled, or applied to the skin.
Reference concentration (RfC): An estimate (with uncertainty spanning perhaps an order of
magnitude) of a continuous inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.
It can be derived from a NOAEL, LOAEL, or benchmark concentration, with uncertainty factors
generally applied to reflect limitations of the data used. Generally used in EPA's noncancer
health assessments. [Durations include acute, short-term, subchronic, and chronic].
Reference dose (RfD): An estimate (with uncertainty spanning perhaps an order of magnitude)
of a daily oral exposure to the human population (including sensitive subgroups) that is likely to
be without an appreciable risk of deleterious effects during a lifetime. It can be derived from a
NOAEL, LOAEL, or benchmark dose, with uncertainty factors generally applied to reflect
limitations of the data used. Generally used in EPA's noncancer health assessments. [Durations
include acute, short-term, subchronic, and chronic].
Steady state: A variable is said to have attained steady state when its value stays constant in a
given interval of time, i.e., when its derivative is zero.
Target organ: The biological organ(s) most adversely affected by exposure to a chemical or
physical agent.
Terminal half-life: The terminal half-life is the interval of time for the concentration of the
drug in a compartment to decrease 50% in its final phase.
Uncertainty: Uncertainty occurs because of lack of knowledge. Uncertainty can often be
reduced with greater knowledge of the system or by collecting more and better experimental or
simulation data.
Uncertainty factors (UFs)/variability factors: Generally, 10-fold default factors used in
operationally deriving the reference dose and reference concentration from experimental data.
The factors are intended to account for (a) variation in sensitivity among the members of the
human population (i.e., interindividual variability), (b) uncertainty in extrapolating animal data
to humans (i.e., interspecies uncertainty), (c) uncertainty in extrapolating from data obtained in a
G-3
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study with less-than-lifetime exposure to lifetime exposure (i.e., extrapolating from subchronic to
chronic exposure), (d) uncertainty in extrapolating from a lowest-observed-adverse-effect level
rather than from a no-observed-adverse-effect level, and (e) uncertainty associated with
extrapolation when the database is incomplete.
Variability: Variability refers to true heterogeneity or diversity. Differences among individuals
in a population are referred to as interindividual variability; differences for one individual over
time are referred to as intraindividual variability.
Volume of distribution: The volume of distribution is the ratio between the administered dose
and plasma or blood concentration of a chemical.
Zero-order process: A saturated metabolic process where a constant amount of chemical is
eliminated per unit time.
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REFERENCES
Abraham, MH; Weathersby, PK. (1994) Hydrogen bonding. 30. Solubility of gases and vapors in biological liquids
and tissues. J Pharm Sci 83:1450-1456.
Allen, BC; Covington, TR; Clevwell, HJ. (1996) Investigation of the impact of pharmacokinetic variability and
uncertainty on risks predicted with a pharmacokinetic model for chloroform. Toxicology 111 :289-303.
Andersen, ME. (1995) Development of physiologically based pharmacokinetic and physiologically based
pharmacodynamic models for applications in toxicology and risk assessment. Toxicol Lett 79:35-44.
Andersen, ME; Dennison, JE. (2001) Mode of action and tissue dosimetry in current and future risk assessments.
Sci Total Environ 274:3-14.
Andersen, ME; Jarabek, AM. (2001) Nasal tissue dosimetry-issues and approaches for "Category 1" gases: a report
on a meeting held in Research Triangle Park, NC, February 11-12, 1998. Inhal Toxicol 13:415-435.
Andersen, ME; Gargas, ML; Jones, RA; et al. (1980) Determination of the kinetic constants for metabolism of
inhaled toxicants in vivo by gas uptake measurements. Toxicol Appl Pharmacol 54:100-116.
Andersen, ME; Clewell, HJ, III; Gargas, ML; et al. (1987) Physiologically-based pharmacokinetics and risk
assessment process for methylene chloride. Toxicol Appl Pharmacol 87:185-205.
Andersen, ME; Clewell, HJ, III; Gargas, ML. (1991) Physiologically-based pharmacokinetic modeling with
dichloromethane, its metabolite carbon monoxide and blood carboxyhemoglobin in rats and humans. Toxicol Appl
Pharmacol 108:14-27.
Andersen, ME; Mills, JJ; Gargas, ML; et al. (1993) Modeling receptor-mediated processes with dioxin: implications
for pharmacokinetics and risk assessment. Risk Anal 13:25-36.
Andersen, ME; Clewell, HJ, III; Frederick, CB. (1995) Applying simulation modeling to problems in toxicology and
risk assessment—a short perspective. Toxicol Appl Pharmacol 133:181-187.
Andersen, ME; Clewell, HJ, III; Gearhart, J; et al. (1997) Pharmacodynamic model of the rat estrus cycle in relation
to endocrine disrupters. J Toxicol Environ Health 52:189-209.
Andersen, ME; Sarangapani, R; Gentry, PR; et al. (1999) Application of a hybrid CFD-PBPK nasal dosimetry
model in an inhalation risk assessment: an example with acrylic acid. Toxicol Sci 57:312-325.
Andersen, ME; Green, T; Frederick, CB; et al. (2002) Physiologically based pharmacokinetic (PBPK) models for
nasal tissue dosimetry of organic esters: assessing the state-of-knowledge and risk assessment applications with
methyl methacrylate and vinyl acetate. Regul Toxicol Pharmacol 36:234-245.
Armitage, P; Doll, R. (1957) A two-stage theory of carcinogenesis in relation to the age distribution of human
cancer. Brit J Cancer 11:161-169.
Arms, AD; Travis, CC. (1988) Reference physiological parameters in pharmacokinetic modeling. U.S.
Environmental Protection Agency, Office of Health and Environmental Assessment, Washington, DC. EPA/600/6-
88/004.
Asgharian, B; Wood, R; Schlesinger, RB. (1995) Empirical modeling of particle deposition in the alveolar region of
the lungs: a basis for interspecies extrapolation. Fundam Appl Toxicol 27:232-238.
Ashani, Y; Pistinner, S. (2004) Estimation of the upper limit of human butylcholinesterase dose required for
protection against organophosphates toxicity: a mathematically based toxicokinetic model. Toxicol Sci 77:358-367.
R-l
-------
Astrand, P; Rodahl, K. (1970) Textbook of work physiology. New York: McGraw-Hill.
Aylward, LL; Hays, SM; Karch, NJ; et al. (1996) Relative susceptibility of animals and humans to the cancer hazard
posed by 2,3,7,8-tetrachlorodibenzo-p-dioxin using internal measures of dose. Environ Sci Technol 30:3534-3543.
Balci, O. (1997) Verification, validation, and accreditation of simulation models. In: Andradottir, S; Healy, K;
Withers, D; et al.; eds. Proceedings of the 1997 Winter Simulation Conference; December 7-10, 1997; Atlanta, Ga.
Piscataway, NJ: Institute of Electrical and Electronics Engineers; pp. 135-141.
Ball, R; Schwartz, SL. (1994) Cmatrix: software for physiologically based pharmacokinetic modeling using a
symbolic matrix representation system. Comput Biol Med 24:269-276.
Barton, HA; Deisinger, PJ; English, JC; et al. (2000) Family approach for estimating reference concentrations/doses
for series of related organic chemicals. Toxicol Sci 54(1):251-261.
Barnes, DG; Dourson, M. (1988) Reference dose (RfD): description and use in health risk assessments. Regul
Toxicol Pharmacol 8:471-495.
Barton, HA; Clewell, HJ, III. (2000) Evaluating noncancer effects of trichloroethylene: dosimetry, mode of action,
and risk assessment. Environ Health Perspect 108(2):323-334.
Beliveau, M; Krishnan, K. (2000) Estimation of rat blood: air partition coefficients of volatile organic chemicals
using reconstituted mixtures of blood components. Toxicol Lett 116:183-188.
Beliveau, M; Tardif, R; Krishnan, K. (2003) Quantitative structure-property relationships for physiologically based
pharmacokinetic modeling of volatile organic chemicals in rats. J Toxicol Appl Pharmacol 189:221-232.
Beliveau, M; Lipscomb, J; Tardif, R; et al. (2005) Quantitative structure-property relationships for interspecies
extrapolation of the inhalation pharmacokinetics of organic chemicals. Chem Res Toxicol 18(3):475-485.
Benet, LZ; Kroetz, DL; Sheiner, LB. (1996) Pharmacokinetics: the dynamics of drug absorption, distribution, and
elimination. In: Hardman, JG; Limbird, LE; eds. Goodman and Oilman's the pharmacological basis of therapeutics.
9th ed. New York: McGraw-Hill; pp. 3-27.
Benignus, VA; Boyes, WK; Bushnell, PJ. (1998) A dosimetric analysis of behavioral effects of acute toluene
exposure in rats and humans. Toxicol Sci 43:186-195.
Bernillon, P; Bois, FY. (2000) Statistical issues in toxicokinetic modeling: a Bayesian perspective. Environ Health
Perspect (Suppl 108):883-893.
Blancato, JN; Saleh, MA. (1994) Physiologically based pharmacokinetic models. Examples of their use in exposure
and risk assessment. In: Saleh, MA; Blancato, JN; Nauman, CH; eds. Biomarkers of human exposure to pesticides.
Washington, DC: American Chemical Society; pp. 264-283.
Bogaards, JJ; Freidig, AP; VanBladeren, PJ. (2001) Prediction of isoprene diepoxide levels in vivo in mouse, rat
and man using enzyme kinetic data in vitro and physiologically-based pharmacokinetic modelling. Chem Biol
Interact 138:247-265.
Bogdanffy, MS; Sarangapani, R. (2003) Physiologically-based kinetic modeling of vapours toxic to the respiratory
tract. Toxicol Lett 138:103-117.
Bogdanffy, MS; Sarangapani, R; Plowchalk, DR; et al. (1999) A biologically risk assessment for vinyl acetate-
induced cancer and noncancer inhalation toxicity. Toxicol Sci 51:19-35.
Bogdanffy, MS; Plowchalk, DR; Sarangapani, R; et al. (2001) Mode-of-action-based dosimeters for interspecies
extrapolation on vinyl acetate inhalation risk. Inhal Toxicol 13:377-396.
R-2
-------
Bois, FY. (1999) Analysis of PBPK models for risk characterization. Ann N Y Acad Sci 895:317-337.
Bois, FY. (2000a) Statistical analysis of Fisher et al. PBPK model of trichloroethylene kinetics. Environ Health
Perspect 108(Suppl 2):275-282.
Bois, FY. (2000b) Statistical analysis of Clewell et al. PBPK model of trichloroethylene kinetics. Environ Health
Perspect 108(Suppl 2):307-316.
Bois, FY; Woodruff, TJ; Spear, RC. (1991) Comparison of three physiologically based pharmacokinetic models for
benzene disposition. Toxicol Appl Pharmacol 110:79-88.
Bouchard, M; Brunei, RC; Droz, PO; et al. (2001) A biologically based dynamic model for predicting the
disposition of methanol and its metabolites in animals and humans. Toxicol Sci 64:169-184.
Boyes, WK; Bushnell, PJ; Crofton, KM; et al. (2000) Neurotoxic and pharmacokinetic responses to
trichloroethylene as a function of exposure scenario. J Toxicol Environ Health 108:317-322.
Brodeur, J; Lapare, S; Krishnan, K; et al. (1990) Le probleme de 1'ajustement des valeurs limites d'exposition pour
des horaires de travail non-conventionnels: utilite de la modelisationpharmacocinetique abase physiologique.
Travail et Sante 6(2):S11-16.
Brown, RP; Delp, MD; Lindstedt, SL; et al. (1997) Physiological parameter values for physiologically based
pharmacokinetic models. Toxicol Ind Health 13:407-484.
Burmaster, DE; Murray DMA. (1998) Trivariate distribution for the height, weight, and fat of adult men. Risk Anal
8:385-389.
Bush, ML; Frederick, CB; Kimbell, JS; et al. (1998) A CFD-PBPK hybrid model for simulating gas and vapor
uptake in the rat nose. Toxicol Appl Pharmacol 150:133-145.
Bushnell, PJ. (1997) Concentration time relationships for the effects of inhaled trichloroethylene on signal detection
behavior in the rats. Fundam Appl Toxicol 36:30-38.
Canuel, G; Viau, C; Krishnan, K. (2000) A modeling framework for back-calculating ambient concentrations from
data onbiomarkers: proceedings of the International Conference on Health Sciences Simulation; January 27-29; San
Diego, CA: The Society for Computer Simulation International.
Casanova, M; Conolly, RB; Heck, HA. (1996) DNA-protein cross-links (DPX) and cell proliferation in B6C3FJ
mice but not Syrian golden hamsters exposed to dichloromethane: pharmacokinetics and risk assessment with DPX
as dosimeter. Fundam Appl Toxicol 31:103-116.
Chen, CW. (1993) Armitage-Doll two-stage model: implications and extension. Risk Anal 13:273-279.
Chen, HSG; Gross, JF. (1979) Estimation of tissue to plasma partition coefficients used in physiological
pharmacokinetic models. J Pharmacokinet Biopharm 7:117-125.
Clarke, DO; Duignan, JM; Welsch, F. (1992) 2-Methoxyacetic acid dosimetry-teratogenicity relationships in CD-I
mice exposed to 2-methoxyethanol. Toxicol Appl Pharmacol 114:77.
Clarke, DO; Elswick, BA; Welsch, F; et al. (1993) Pharmacokinetics of 2-methoxyethanol and 2-methoxyacetic acid
in the pregnant mouse: a physiologically-based mathematical model. Toxicol Appl Pharmacol 121:239-252.
Clark, LH; Setzer, RW; Barton, HA. (2004) Framework for evaluation of physiologically-based pharmacokinetic
models for use in safety or risk assessment. Risk Anal 24(6):1697-1717.
Clewell, HJ, III; Andersen, ME. (1985) Risk assessment extrapolations and physiological modeling. Toxicol Ind
Health 1:111-131.
-------
Clewell, HJ, III; Andersen, ME. (1987) Dose, species and route extrapolation using physiologically-based
pharmacokinetic models. In: Pharmacokinetics in risk assessment: drinking water and health. Vol. 8. Washington,
DC: National Academy Press; pp. 159-182.
Clewell, HJ; Andersen, ME. (1994) Physiologically-based pharmacokinetic modeling and bioactivation of
xenobiotics. Toxicol Ind Health 10(1-2): 1-24.
Clewell, HJ; Andersen, ME. (1996) Use of physiologically based pharmacokinetic modeling to investigate
individual versus population risk. Toxicology lll(l-3):315-329.
Clewell, HJ, III; Jarnot, BM. (1994) Incorporation of pharmacokinetics in noncancer risk assessment: example with
chloropentafluorobenzene. Risk Anal 14:265-276.
Clewell, HJ, III; Lee, TS; Carpenter, RL. (1994) Sensitivity of physiologically based pharmacokinetic models to
variation in model parameters—methylene chloride. Risk Anal 14:521-531.
Clewell HJ; Gentry PR; Gearhart JM; et al. (1995) Considering pharmacokinetic and mechanistic information in
cancer risk assessments for environmental contaminants: examples with vinyl chloride and trichloroethylene.
Chemosphere 31:2561-2578.
Clewell, HJ; Gearhart, JM; Gentry, PR; et al. (1999) Evaluation of the uncertainty in an oral reference dose for
methylmercury due to interindividual variability in pharmacokinetic s. Risk Anal 19(4):547-558.
Clewell, HJ, III; Gentry, PR; Covington, TR; et al. (2000) Development of a physiologically based pharmacokinetic
model of trichloroethylene and its metabolites for use in risk assessment. Environ Health Perspect 108:283-305.
Clewell, HJ, III; Gentry, PR; Gearhart, JM; et al. (2001) Comparison of cancer risk estimates for vinyl chloride
using animal and human data with a PBPK model. Sci Total Environ 274:37-66.
Clewell, HJ, III; Andersen, ME; Barton, HA. (2002a) A consistent approach for the application of pharmacokinetic
modeling in cancer and noncancer risk assessment. Environ Health Perspect 110:85-93.
Clewell, HJ, III; Teeguarden, JG; McDonald, T; et al. (2002b) Review and evaluation of the potential impact of age-
and gender-specific pharmacokinetic differences on tissue dosimetry. Crit Rev Toxicol 32:329-389.
Clewell, HJ, III; Gentry, PR; Covington, TR; et al. (2004) Evaluation of the potential impact of age- and gender-
specific pharmacokinetic differences on tissue dosimetry. Toxicol Sci 79:381-93.
Cohen, SM; Ellwein, LB. (1990) Cell proliferation in carcinogenesis. Science 249:1007-1011.
Cole, CE; Tran, HT; Schlosser, PM. (2001) Physiologically based pharmacokinetic modeling of benzene
metabolism in mice through extrapolation from in vitro to in vivo. J Toxicol Environ Health 62:439^165.
Collins, J. (1987) Prospective predictions and validations in anti-cancer therapy. In: Pharmacokinetics in risk
assessment: drinking water and health. Vol. 8. Washington, DC: National Academy Press; pp. 431-440.
Collins, AS; Sumner, SC; Borghoff, SJ; Medinsky, MA. (1999) A physiological model fortert-amyl methyl ether
and tert-amyl alcohol: hypothesis testing of model structures. Toxicol Sci 49:15-28.
Conolly, RB: Andersen, ME. (1997) Hepatic foci in rats after diethyl-nitrosamine initiation and 2,3,7,8-
tetrachlorodibenzo-p-dioxin promotion: evaluation of a quantitative two-cell model and of CYP 1 Al/1 A2 as a
dosimeter. Toxicol Appl Pharmacol 146:281-293.
Conolly, RB; Butterworth, BE. (1995) Biologically based dose response model for hepatic toxicity: a
mechanistically based replacement for traditional estimates of noncancer risk. Toxicol Lett (82/83):901-906.
R-4
-------
Conolly, RB; Kimbell, JS. (1994) Computer simulation of cell growth governed by stochastic processes: application
to clonal growth cancer models. Toxicol Appl Pharmacol 124:284-295.
Conolly, RB; Limbell, JS; Janszen, D; et al. (2003) Biologically motivated computational modeling of formaldehyde
carcinogenicity in the F344 Rat. Toxicol Sci 75:432-447.
Corley, RA; Mandrela, AL; Smith, FA. (1990) Development of a physiologically based pharmacokinetic model for
chloroform. Toxicol Appl Pharmacol 103:512-527.
Corley, RA; Markham, DA; Banks, C; et al. (1997) Physiologically based pharmacokinetics and the dermal
absorption of 2-butoxyethanol vapor by humans. Fundam Appl Toxicol 39:120-130.
Corley, RA; Gordon, SM; Wallace, LA. (2000) Physiologically based pharmacokinetic modeling of the temperature-
dependent dermal absorption of chloroform by humans following bath water exposures. Toxicol Sci 53:13-23.
Corley, RA; Mast, TJ; Carney, EW; et al. (2003) Evaluation of physiologically based models of pregnancy and
lactation for their application in children's health risk assessments. Crit Rev Toxicol 33:137-211.
Cox, LA, Jr. (1996) Reassessing benzene risks using internal doses and Monte Carlo uncertainty analysis. Environ
Health Perspect 104:1413-1429.
Cruzen, G; Carlson, GP; Johnson, KA; et al. (2002) Styrene respiratory tract toxicity and mouse lung tumors are
mediated by CYP2F-generated metabolites. Regul Toxicol Pharmacol 35:308-319.
Csanady, GA; Kreuzer, PE; Baur, C; et al. (1996) A physiological toxicokinetic model for 1,3-butadiene in rodents
and man: blood concentrations of 1,3-butadiene, its metabolically formed epoxides, and of haemoglobin adducts-
relevance of glutathione depletion. Toxicology 113:300-305.
Dankovic, DA; Bailer, AJ. (1994) The impact of exercise and intersubject variability on dose estimates for
dichloromethane derived from a physiologically based pharmacokinetic model. Fundam Appl Toxicol 22:20-25.
Davies, B; Morris, T. (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10:1093-
1095.
De Jongh, J; Blaauboer, BJ. (1996) Simulation of toluene kinetics in the rat by a physiologically based
pharmacokinetic model with application of biotransformation parameters derived independently in vitro and in vivo.
Fundam Appl Toxicol 32:260-268.
De Jongh, J; Blaauboer, BJ. (1997) Simulation of lindane kinetics in rats. Toxicology 122:1-9.
Delic, JI; Lilly, JD; MacDonald, AJ; et al. (2000) The utility of PBPK in the safety assessment of chloroform and
carbon tetrachloride. Regul Toxicol Pharmacol 32:144-155.
Dong, MH. (1994) Microcomputer programs for physiologically-based pharmacokinetic (PB-PK) modelling.
Comput Methods Programs Biomed 45:213-221.
Dome, JL; Walton, K; Renwick, AG. (200 la) Uncertainty factors for chemical risk assessment: human variability in
the pharmacokinetics of CYP1A2 probe substrates. Food Chem Toxicol 39:681-696.
Dome, JL; Walton, K; Renwick, AG. (200Ib) Human variability in glucuronidation in relation to uncertainty factors
for risk assessment. Food Chem Toxicol 39:1153-1173.
Dome, JL; Walton, K; Slob, W; et al. (2002) Human variability in polymorphic CYP2D6 metabolism: is the kinetic
default uncertainty factor adequate? Food Chem Toxicol 40:1633-1656.
Dourson, ML; Knauf, LA; Swartout, JD. (1992) On reference (RfD) and its underlying toxicity data base. Toxicol
Ind Health 8:171-189.
R-5
-------
Droz, PO; Berode, M; Jang, JY. (1999) Biological monitoring of tetrahydrofuran: contribution of a physiologically
based pharmacokinetic model. Am Ind Hyg Assoc J 60:243-248.
Easterling, MR; Evans, MV; Kenyon, EM. (2000) Comparative analysis of software for physiologically based
pharmacokinetic modeling: simulation, optimization, and sensitivity analysis. Toxicol Methods 10:203-229.
El-Masri, HA; Bell, DA; Portier, CJ. (1999) Effects of glutathione transferase theta polymorphism on the risk
estimates of dichloromethane to humans. Toxicol Appl Pharmacol 158:221-230.
Farrar, D; Allen, B; Crump, K; et al. (1989) Evaluation of uncertainty in input parameters to pharmacokinetic
models and the resulting uncertainty in output. Toxicol Lett 49:371-385.
Farris, FF; Dedrick, RL; King, FG. (1988) Cisplatin pharmacokinetics: applications of a physiological model.
Toxicol Lett 43:117-137.
Faustman, EM; Lewandowski, TA; Ponce, RA; et al. (1999) Biologically based dose-response models for
developmental toxicants: lessons from methylmercury. Inhal Toxicol 11:559-572.
Fennell, TR; Sumner, SC; Waler, VE. (1992) A model for the formation and removal of hemoglobin adducts.
Cancer Epidemiol Biomarkers Prev 1:213-219.
Filser, JG; Bolt, HM. (1979) Pharmacokinetics of halogenated ethylenes in rats. Arch Toxicol 42:123-136.
Filser, JG; Bolt, HM. (1981) Inhalation pharmacokinetics based on gas uptake studies. I. improvement of kinetic
models. Arch Toxicol 47:279-292.
Fiserova-Bergerova, V. (1995) Extrapolation of physiological parameters for physiologically based simulation
models. Toxicol Lett 79:77-86.
Fiserova-Bergerova, V; Diaz, ML. (1986) Determination and prediction of tissue-gas partition coefficients. Int Arch
Occup Environ Health 58:75-87.
Fisher, JW; Allen, BC. (1993) Evaluating the risk of liver cancer in human exposed to trichloroethylene using
physiological models. Risk Anal 13:87-95.
Fisher, J; Mahle, D; Bankston, L; Greene, R; et al. (1997) Lactational transfer of volatile chemicals in breast milk.
AIHAJ 58:425-431.
Frederick, CB; Potter, DW; Chang-Mateu, MI; et al. (1992) A physiologically-based pharmacokinetic and
pharmacodynamic model to describe the oral dosing of rats with ethyl acrylate and its implications for risk
assessment. Toxicol Appl Pharmacol 114:246-260.
Frederick, CB; Lomax, LG; Black, KA; et al. (2002) Use of a hybrid computational fluid dynamics and
physiologically based inhalation model for interspecies dosimetry comparisons of ester vapors. Toxicol Appl
Pharmacol 183:23-40.
Gabrielsson, J; Bondesson, U. (1987) Constant-rate infusion of nicotine andcotinine. I. a physiological
pharmacokinetic analysis of the cotinine disposition, and effects on clearance and distribution in the rat. J
Pharmacokinet Biopharm 15:583-599.
Gallo, JM; Lam, FC; Perrier, DG. (1987) Area method for the estimation of partition coefficients for physiological
pharmacokinetic models. J Pharmacokinet Biopharm 15:271-280.
Gargas, ML; Andersen, ME; Clewell, HJ. (1986) A physiologically-based simulation approach for determining
metabolic rate constants from gas uptake data. Toxicol Appl Pharmacol 86:341-352.
R-6
-------
Gargas, ML; Burgess, RJ; Voisard, DE; et al. (1989) Partition coefficients of low molecular weight volatile
chemicals in various liquids and tissues. Toxicol Appl Pharmacol 98:87-99.
Gearhart, JM; Jepson, GW; Clewell, HJ, III; et al. (1990) Physiologically based pharmacokinetic and
pharmacodynamic model for the inhibition of acetylcholinesterase by diisopropylfluorophosphate. Toxicol Appl
Pharmacol 106:295-310.
Gearhart, JM; Jepson, GW; Clewell, HJ; et al. (1994) Physiologically based pharmacokinetic model for the
inhibition of acetylcholinesterase by organophosphate esters. Environ Health Perspect 102:51-60.
Gearhart, JM; Clewell, HJ; Crump, KS; et al. (1995) Pharmacokinetic dose estimates of mercury in children and
dose-response curves of performance tests in a large epidemiological study. Water Air Soil Pollut 80:49-58.
Gelman, A; Bois, F; Jiang, J. (1996) Physiological pharmacokinetic analysis using population modeling and
informative prior distributions. J Amer Stat Assoc 91:436.
Gentry, PR; Covington, TR; Andersen, ME; et al. (2002) Application of a physiologically based pharmacokinetic
model for isopropanol in the derivation of a reference dose and reference concentration. Regul Toxicol Pharmacol
36:51-68.
Gentry, PR; Covington, TR; Clewell, HJ, III. (2003) Evaluation of the potential impact of pharmacokinetic
differences on tissue dosimetry in offspring during pregnancy and lactation. Regul Toxicol Pharmacol 38:1-16.
Gentry, PR; Haber, L; McDonald, T; et al. (2004) Data for physiologically based pharmacokinetic modeling in
neonatal animals: physiological parameters in mice and Sprague-Dawley rats. J Child Health 2(3-4):363-411.
Georgopoulos, PG; Roy, A; Gallo, MA. (1994) Reconstruction of short-term multiroute exposure to volatile organic
compounds using physiologically based pharmacokinetic models. J Exp Anal Environ Epidemiol 4:309-328.
Gerrity, TR; Henry, CJ; Birnbaum, L. (1990) Principles of route-to-route extrapolation for risk assessment. New
York: Elsevier.
Ginsberg, G; Hattis, D; Russ, A; et al. (2004) Physiologically based pharmacokinetic (PBPK) modeling of caffeine
and theophylline in neonates and adults: implications for assessing children's risks from environmental agents. J
Toxicol Environ Health A 67:297-329.
Haber, LT; Maier, A; Gentry, PR; et al. (2002) Genetic polymorphisms in assessing interindividual variability in
delivered dose. Regul Toxicol Pharmacol 35:177-197.
Haddad, S; Gad, SC; Tardif, R; et al. (1995) Statistical approaches for the validation of physiologically-based
pharmacokinetic (PBPK) models. Abstract. Toxicologist 15:48.
Haddad, S; Pelekis, M; Krishnan. K. (1996) A methodology for solving physiologically based pharmacokinetic
models without the use of simulation softwares. Toxicol Lett 85:113-126.
Haddad, S; Withey, JR; Tardif, R; et al. (1997) Determination of the rate of pyrene metabolism in rat liver post-
mitochondrial fractions. Toxicol Lett 93:177-184.
Haddad, S; Withey, JR; Lapare, S; et al. (1998) Physiologically based pharmacokinetic modeling of pyrene in the
rat. Environ Toxicol Pharmacol 5:245-255.
Haddad, S; Restieri, C; Krishnan, K. (2001a) Characterization of age-related changes in body weight and organ
weights from birth to adolescence in humans. J Toxicol Environ Health A 64:453^4-64.
Haddad, S; Beliveau, M; Tardif, R; et al. (200Ib) A PBPK modeling-based approach to account for interactions in
the health risk assessment of chemical mixtures. Toxicol Sci 63:125-131.
R-7
-------
Hanna, LM; Lou, SR. (2001) Mass transport analysis: inhalation RFC methods framework for interspecies
dosimetric adjustment. InhalToxicol 13:437^163.
Hattis, D; White, P; Marmorstein, L; et al. (1990) Uncertainties in pharmacokinetics modeling for
perchloroethylene. I. comparison of model structure, parameters, and predictions for low dose metabolic rates for
models by different authors. Risk Anal 10:449^158.
Hattis, D; Ginsberg, G; Sonawane, B; et al. (2003) Differences in pharmacokinetics between children and adults. II.
children's variability in drug elimination half-lives and in some parameters needed for physiologically-based
pharmacokinetic modeling. Risk Anal 23:117-142.
Hetrick, DM; Jarabek, AM; Travis, CC. (1991) Sensitivity analysis for physiologically-based pharmacokinetic
models. J Pharmacokinet Biopharm 19:1-20.
Himmelstein, KJ; Lutz, RJ. (1979) A review of the application of physiologically based pharmacokinetic modeling.
J Pharmacokinet Biopharm 7:127-145.
Hissink, EM; Bogaards, JJP; Freidig, AP; et al. (2002) The use of in vitro metabolic parameters and physiologically
based pharmacokinetic (PBPK) modeling to explore the risk assessment of trichloroethylene. Environ Toxicol
Pharmacol 11:259-271.
Hoang, KCT. (1995) Physiologically based pharmacokinetic models-mathematical fundamentals and simulation
implementations. Toxicol Lett 79:87-98.
Holmes, SL; Ward, RC; Galambos, JD; et al. (2000) A method for optimization of pharmacokinetic models.
Toxicol Methods 10:41-53.
Hurst, CH; DeVito, MJ; Setzer, RW; et al. (2000) Acute administration of 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD)in pregnant Long Evans rats: association of measured tissue concentrations with developmental effects.
Toxicol Sci 53:411^120.
Hwang, IY; Reardon, KF; Tessari, JD; et al. (1996) A gas-liquid system for enzyme kinetic studies of volatile
organic chemicals: determination of enzyme kinetic constants and partition coefficients of trichloroethylene. Drug
Metabol Dispos 24:377-382.
Igari, Y; Sugiyama, Y; Sawada, Y; et al. (1983) Prediction of diazepam disposition in rat and man by a
physiologically-based pharmacokinetic model. J Pharmacokinet Biopharm 11:577-593.
IPCS (International Programme on Chemical Safety). (2005) Chemical-specific adjustment factors (CSAFs) for
interspecies differences and human variability in dose/concentration-response assessment: guidance document for
the use of data in dose/concentration-response assessment. International Programme on Chemical Safety. Geneva,
Switzerland: World Health Organization.
Isukapalli, SS; Roy, A; Georgopoulos, PG. (1998) Stochastic response surface methods (SRSMs) for uncertainty
propagation: application to environmental and biological systems. Risk Anal 18:351-363.
Iwatsubo, T; Hiriko, N; Ooie, T; et al. (1996) Prediction of in vivo drug disposition from in vitro data based on
physiological pharmacokinetics. Biopharmaceut Drug Dispos 17:273-310.
Jarabek, AM. (1994) Inhalation RfC methodology: dosimetric adjustments and dose-response estimation of non-
cancer toxicity in the upper respiratory tract. Inhal Toxicol 6:301-325.
Jarabek, AM. (1995a) Interspecies extrapolation based on mechanistic determinants of chemical disposition.
Human Ecol Risk Assess 1:641-662.
Jarabek, AM. (1995b) The application of dosimetry models to identify key processes and parameters for default
dose-response assessment approaches. Toxicol Lett 79:171-184.
R-8
-------
Jarabek, AM; Fisher, JW; Rubenstein, R; et al. (1994) Mechanistic insights aid the search for CFC substitutes: risk
assessment of HCFC-123 as an example. Risk Anal 14:231-250.
Jepson, GW; Hoover, DK; Black, RK; et al. (1994) A partition coefficient determination method for nonvolatile
chemicals in biological tissues. Fundam Appl Toxicol 22:519-524.
Johanson G. (1991) Modelling of respiratory exchange of polar solvents. Ann Occup Hyg 35:323-339.
Johanson, G; Dynesius, B. (1988) Liquid: air partition coefficients for six commonly used glycol ethers. Brit J
Indust Med 45:561-564.
Johanson, G; Naslund, PH. (1988) Spreadsheet programming: a new approach in physiologically based modeling of
solvent toxicokinetics. Toxicol Lett 41:115-127.
Johanson, G; Jonsson, F; Bois, F. (1999) Development of new technique for risk assessment using physiologically
based toxicokinetic models. Am J Ind Med 36(Suppl 1): 101-103.
Jonsson, F; Johanson, G. (2001) Bayesian estimation of variability in adipose tissue blood flow in man by
physiologically based pharmacokinetic modeling of inhalation exposure to toluene. Toxicology 157:177-193.
Jonsson, F; Johanson, G. (2002) Physiologically based modeling of the inhalation kinetics of styrene in humans
using a Bayesian population approach. Toxicol Appl Pharmacol 179:35^9.
Jonsson, F; Johanson, G. (2003) The Bayesian population approach to physiological toxicokinetic-toxicodynamic
models—an example using the MCSim software. Toxicol Lett 143-150.
Kaneko, T; Wang, PY; Sato, A. (1994) Partition coefficients of some acetate esters and alcohols in water, blood,
olive oil, and rat tissues. Occup Environ Med 51:68-72.
Karba, R; Zupancic, B; Bremsak, F. (1990) Simulation tools in pharmacokinetic modelling. Acta Pharm Jugosl
40:247-262.
Kedderis, GL; Held, SD. (1996) Prediction of furan pharmacokinetics from hepatocyte studies: comparison of
bioactivation and hepatic dosimetry in rats, mice, and humans. Toxicol Appl Pharmacol 140:124-130.
Kedderis, GL; Lipscomb, JC. (2001) Application of in vitro biotransformation data and pharmacokinetic modeling
to risk assessment. Toxicol Ind Health 17:315-321.
Kenyon, EM; Kraichely, RE; Hudson, KT; et al. (1996) Differences in rates of benzene metabolism correlate with
observed genotoxicity. Toxicol Appl Pharmacol 136:49-56.
Keys, DA; Bruckner, JV; Muralidhara, S; et al. (2003) Tissue dosimetry expansion and cross-validation of rat and
mice physiologically-based pharmacokinetic models for trichloroethylene. Toxicol Sci 76:35-50.
Kim, AH; Kohn, MC; Portier, CJ; et al. (2002) Impact of physiologically based pharmacokinetic modeling on
benchmark dose calculations for TCDD-induced biochemical responses. Regul Toxicol Pharmacol 36:287-296.
Kimbell, JS; Gross, EA; Joyner, DR; et al. (1993) Application of computational fluid dynamics to regional
dosimetry of inhaled chemicals in the upper respiratory tract of the rat. Toxicol Appl Pharmacol 121:253-263.
Kirman, CR; Hays, SM; Kedderis, GL; et al. (2000) Improving cancer dose-response characterization by using
physiologically based pharmacokinetic modeling: an analysis of pooled data for acrylonitrile-induced brain tumors
to assess cancer potency in the rat. Risk Anal 20:135-151.
Kohn, MC. (1995) Achieving credibility in risk assessment models. Toxicol Lett 79:107-114.
R-9
-------
Kohn, MC; Melnick, RL. (1999) A physiological model for ligand-induced accumulation of alpha 2u globulin in
male rat kidney: roles of protein synthesis and lysosomal degradation in the renal dosimetry of 2,4,4-trimethyl-2-
pentanol. Toxicology 136:89-105.
Krewski, D; Wang, Y; Bartlett, S; et al. (1995) Uncertainty, variability, and sensitivity analysis in physiological
pharmacokinetic models. J Biopharm Stat 5:245-271.
Krishnan, K; Andersen, M. (1998) Physiologically based pharmacokinetic models in the risk assessment of
developmental neurotoxicants. In: Slikker, W; ed. Handbook of developmental neurotoxicology. New York:
Academic Press; pp. 709-725.
Krishnan, K; Andersen, ME. (2001) Physiologically based pharmacokinetic modeling in toxicology. In: Hayes,
AW; ed. Principles and methods of toxicology. Philadelphia, PA: Taylor & Francis; pp. 193-241.
Krishnan, K; Gargas, ML; Fennell, TR; et al. (1992) A physiologically based description of ethylene oxide
dosimetry in the rat. Toxicollnd Health 8:121-140.
Kumagai, S; Matsunaga, I. (1995) Physiologically based pharmacokinetic model for acetone. Occup Environ Med
52:344-352.
Lam, G; Chen, ML; Chiou, WL. (1982) Determination of tissue:blood partition coefficients in physiologically-based
pharmacokinetic models. J Pharmaceut Sci 71:454-456.
Leggett, RW; Williams, LR. (1991) Suggested reference values for regional blood volumes in humans. Health
Physics 60:139-154.
Leroux, BG; Leisenring, WM; Moolgavkar, SH; et al. (1996) A biologically-based dose-response model for
development toxicology. Risk Anal 16(4):449-458.
Leung, HW. (1991) Development and utilization of physiologically based pharmacokinetic models for toxicological
applications. J Toxicol Environ Health 32:247-267.
Leung, HW. (1992) Use of physiologically based pharmacokinetic models to establish biological exposure indexes.
Am Ind Hyg Assoc J 53:369-374.
Leung, HW; Paustenbach, DJ. (1990) Cancer risk assessment for dioxane based upon a physiologically-based
pharmacokinetic modeling approach. Toxicol Lett 51:147-162.
Levesque, B; Ayotte, P; Tardif, R; et al. (2002) Cancer risk associated with household exposure to chloroform. J
Toxicol Environ Health 56:489-502.
Liao, KH; Dobrev, ID; Dennison, JE, Jr; et al. (2002) Application of biologically based computer modeling to
simple or complex mixtures. Environ Health Perspect 110(Suppl 6): 957-963.
Lilly, PD; Andersen, ME; Ross, TM; et al. (1998) A physiologically based pharmacokinetic description of the oral
uptake, tissue dosimetry, and rates of metabolism of bromodichloromethane in the male rat. Toxicol Appl
Pharmacol 150(2):205-217.
Lin, JH; Sugiyama, Y; Awazu, S; et al. (1982) In vitro and in vivo evaluation of the tissue to blood partition
coefficients for physiological pharmacokinetic models. J Pharmacokinet Biopharm 10:637-647.
Lipscomb, JC. (2004) Evaluating the relationship between variance in enzyme expression and toxicant concentration
in health risk assessment. Human Ecol Risk Assess 10:39-55.
Lipscomb, JC; Kedderis, GL. (2002) Incorporating human interindividual biotransformation variance in health risk
assessment. Sci Total Environ 288:13-21.
R-10
-------
Lipscomb, JC; Fisher, JW; Confer, PD; et al. (1998) In vitro to in vivo extrapolation for trichloroethylene
metabolism in humans. Toxicol Appl Pharmacol 152:376-387.
Lipscomb, JC; Teuschler, LK; Swartout, J; et al. (2003) The impact of cytochrome P450 2E1-dependent metabolic
variance on a risk-relevant pharmacokinetic outcome in humans. Risk Anal 6:1221-1238.
Luebeck, EG; Moolgavkar, SH; Buchmann, A; et al. (1991) Effects of polychlorinated biphenyls in rat liver:
quantitative analysis of enzyme-altered foci. Toxicol Appl Pharmacol 111 :469-484.
Luecke, RH; Wosilait, WD; Pearce, BA; et al. (1994) A physiologically based pharmacokinetic computer model for
human pregnancy. Teratology 49:90-103.
Luecke, RH; Wosilait, WD; Young, JF. (1997) Mathematical analysis for teratogenic sensitivity. Teratology
55:373-380.
MacDonald, AJ; Rostami-Hodjegan, A; Tucker, GT; et al. (2002) Analysis of solvent central nervous system
toxicity and ethanol interactions using a human population physiologically based kinetic and dynamic model. Regul
Toxicol Pharmacol 35(2Pt 1): 165-176.
Martonen, TB; Zhang, Z; Yu, G; et al. (2001) Three-dimensional computer modeling of the human upper respiratory
tract. Cell Biochem Biophys 35:255—61.
Medinsky, MA; Kimbell, JS; Morris, JB; et al. (1993) Advances in biologically based models for respiratory tract
uptake of inhaled volatiles. Fundam Appl Toxicol 20:265-272.
Meek, ME; Beauchamp, R; Long, G; et al. (2002) Chloroform: exposure estimation, hazard characterization, and
exposure-response analysis. J Toxicol Environ Health B Crit Rev 5:283-334.
Melnick, RL; Kohn, MC. (2000) Dose-response analyses of experimental cancer data. Drug Metab Rev 32:193-
209.
Mendez, J; Keys, A. (1960) Density and composition of mammalian muscle. Metabolism 9:184-188.
Menzel, DB; Wolpert, RL; Boger, JR; et al. (1987) Resources available for simulation in toxicology: specialized
computers, generalized software, and communication networks. In: Pharmacokinetics in risk assessment: drinking
water and health. Vol. 8. Washington, DC: National Academy Press; pp. 229-254.
Monro, A. (1994) Drug toxicokinetics: scope and limitations that arise from species differences in
pharmacodynamic and carcinogenic responses. J Pharmaceut Biopharm 22:41-57.
Moolgavkar, S; Knudson, A. (1981) Mutation and cancer: a model for human carcinogenesis. J Natl Cancer Inst
66:1037-1052.
Moolgavkar, SH; Luebeck, G. (1990) Two-event model for carcinogenesis: biological, mathematical, and statistical
considerations. Risk Anal 10:323-341.
Moolgavkar, S; Verizon, D. (1979) Two-event models for carcinogenesis: incidence curve for childhood and adult
tumors. Math Biosci 47:55-77.
Mortensen, B; Nilsen, OG. (1998) Allometric species comparison of toluene and n-hexane metabolism: prediction of
hepatic clearance in man from experiments with rodent liver S9 in headspace vial equilibration system. Pharmacol
Toxicol 82:183-188.
Mortensen, B; Lokken, T; Zahlsen, K; et al. (1997) Comparison and in vivo relevance of two different in vitro
headspace metabolic systems: liver S9 and liver slices. Pharmacol Toxicol 81:35-41.
R-ll
-------
Murphy, JE; Janszen, DB; Gargas, ML. (1995) An in vitro method for determination of tissue partition coefficients
of non-volatile chemicals such as 2,3,7,8-tetrachlorodibenzo-p-dioxinandestradiol. J ApplToxicol 15:147-152.
Nestorov, I A. (2001) Modelling and simulation of variability and uncertainty in toxicokinetics and
pharmacokinetics. ToxicolLett 120:411^20.
Nichols, J; Rheingans, P; Lothenbach, D; et al. (1994) Three-dimensional visualization of physiologically based
kinetic model outputs. Environ Health Perspect 102:952-956.
O'Flaherty, EJ. (1981) Toxicants and drugs: kinetics and dynamics. New York: John Wiley & Sons.
O'Flaherty, EJ. (1994) Physiologic changes during growth and development. Environ Health Perspect 102:103-
106.
Overton, JH. (2001) Dosimetry modeling of highly soluble reactive gases in the respiratory tract. Inhal Toxicol
13:347-357.
Page, NP; Singh, DV; Farland, W; et al. (1997) Implementation of EPA revised cancer assessment guidelines:
incorporation of mechanistic and pharmacokinetic data. Fundam Appl Toxicol 37:16-36.
Pauluhn, J. (2003) Issues of dosimetry in inhalation toxicity. Toxicol Lett 140:229-238.
Paustenbach, DJ. (2000) The practice of exposure assessment: a state-of-art review. J Toxicol Environ Health (Part
B) 3:179-291.
Payne, MP; Kenny, LC. (2002) Comparison of models for the estimation of biological partition coefficients. J
Toxicol Environ Health 65:897-931.
Pelekis, M; Nicolich, MJ; Gauthier, JS. (2003) Probabilistic framework for the estimation of the adult and child
toxicokinetic intraspecies uncertainty factors. Risk Anal 23:1239-1255.
Perbellini, L; Mozzo, P; Olivata, D; et al. (1990) Dynamic biological exposure indexes for n-hexane and 2,5-
hexanedione, suggested by a physiologically-based pharmacokinetic model. Amer Indust Hygiene Assoc J 51:356-
362.
Perkins, RA; Ward, KW; Pollack, GM. (1995) A pharmacokinetic model of inhaled methanol in humans and
comparison to methanol disposition in mice and rats. Environ Health Perspect 103:726-33
Pierce, CH; Dills, RL; Morgan, MS; et al. (1998) Biological monitoring of controlled toluene exposure. Int Arch
Occup Environ Health 71:433^44.
Poet, TS; Soelberg, JJ; Weitz, KK; et al. (2003) Mode of action and pharmacokinetic studies of 2-butoxyethanol in
the mouse with an emphasis on forestomach dosimetry. Toxicol Sci 71:176-189.
Portier, CJ; Kaplan, NL. (1989) Variability of safe dose estimates when using complicated models of the
carcinogenic process. A case study: methylene chloride. Fundam Appl Toxicol 13:533-544.
Portier, C; Tritscher, A; Kohn, M; et al. (1993) Ligand/receptor binding for 2,3,7,8-TCDD: implications for risk
assessment. Fundam Appl Toxicol 20:48-56.
Poulin, P; Krishnan, K. (1995) A biologically-based algorithm for predicting human tissue: blood partition
coefficients of organic chemicals. Hum Exp Toxicol 14:273-280.
Poulin, P; Krishnan, K. (1996a) A tissue composition-based algorithm for predicting tissue: air partition coefficients
of organic chemicals. Toxicol Appl Pharmacol 136:126-130.
R-12
-------
Poulin, P; Krishnan, K. (1996b) A mechanistic algorithm for predicting blood:air partition coefficients of organic
chemicals with the consideration of reversible binding in hemoglobin. Toxicol Appl Pharmacol 136:131-137.
Poulin, P; Theil, FP. (2000) A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of
physiologically-based pharmacokinetic models in drug discovery. J Pharma Sci 89:16-35.
Price, PS; Conolly, RB; Chaisson, CF; et al. (2003a) Modeling interindividual variation in physiological factors
used in PBPK models of humans. Crit Rev Toxicol 3 3:469-503.
Price, K; Haddad, S; Krishnan, K. (2003b) Physiological modeling of age-specific changes in the pharmacokinetics
of organic chemicals in children. J Toxicol Environ Health A66:417-433.
Ramsey, JC; Andersen, ME. (1984) A physiologically-based description of the inhalation pharmacokinetics of
styrene in rats and humans. Toxicol Appl Pharmacol 73:159-175.
Rao, HV; Ginsberg, GL. (1997) A physiologically-based pharmacokinetic model assessment of methyl t-butyl ether
in groundwater for a bathing and showering determination. Risk Anal 17:583-598.
Reddy, MB; Andersen, ME; Morrow, PE; et al. (2003) Physiological modeling of inhalation kinetics of
octamethylcyclotetrasiloxane in humans during rest and exercise. Toxicol Sci 72:3-18.
Reitz, RH; McDougal, JN; Himmelstein, MW; et al. (1988a) Physiologically-based pharmacokinetic modeling with
methyl chloroform: implications for interspecies, high-low dose and dose-route extrapolations. Toxicol Appl
Pharmacol 95:185-199.
Reitz, RH; Mandrela, AL; Park, CN; et al. (1988b) Incorporation of in vitro enzyme data into the physiologically-
based pharmacokinetic (PBPK) model for methylene chloride: implications for risk assessment. Toxicol Lett 43:97-
116.
Reitz, RH; Mandrela, AL; Guengerich, FP. (1989) In vitro metabolism of methylene chloride in human and animal
tissues: use in physiologically-based pharmacokinetic models. Toxicol Appl Pharmacol 97:230-246.
Reitz, RH; Mandrela, AL; Corley, RA; et al. (1990a) Estimating the risk of liver cancer associated with human
exposures to chloroform using physiologically-based pharmacokinetic modeling. Toxicol Appl Pharmacol
105:443^59.
Reitz, RH; McCroskey, PS; Park, CN; et al. (1990b) Development of a physiologically-based pharmacokinetic
model for risk assessment with 1,4-dioxane. Toxicol Appl Pharmacol 105:37-54.
Reitz, RH; Gargas, ML; Mendrala, AL; et al. (1996a) In vivo and in vitro studies of perchloroethylene metabolism
for physiologically based pharmacokinetic modeling in rats, mice, and humans. Toxicol Appl Pharmacol 136:289-
306.
Reitz, RH; Gargas, ML; Andersen, ME; et al. (1996b) Predicting cancer risk from vinyl chloride exposure with a
physiologically based pharmacokinetic model. Toxicol Appl Pharmacol 137:253-267.
Renwick, AG. (2001) Toxicokinetics-pharmacokinetics in toxicology. In: Hayes, Wa; ed. Principles and methods of
toxicology, 4th ed. Philadelphia, PA: Taylor & Francis; pp. 137-192.
Rey, TD; Havranek, WA. (1996) Some aspects of using the SimuSolv program for environmental, pharmacokinetics
and toxicological applications. Ecol Model 86:277-282.
Rogers, JM; Mole, ML; Chernodd, N; et al. (1993) The developmental toxicity of inhaled methanol in the CD-I
mouse, with quantitative dose-response modeling for estimation of benchmark doses. Teratology 47:175-188.
Ross, R; Leger, L; Guardo, R; et al. (1991) Adipose tissue volume measured by magnetic resonance imaging and
computerized tomography in rats. J Appl Physiol 70(5):2164-2172.
R-13
-------
Rowland, M. (1985) Physiologic pharmacokinetic models and interanimal species scaling. Pharmacol Ther 29:49-
68.
Roy, A; Georgopoulos, PG. (1998) Reconstructing week-long exposures to volatile organic compounds using
physiologically based pharmacokinetic models. J Expo Anal Environ Epidemiol 8:407-422.
Roy, A; Weisel, CP; Lioy, PJ; et al. (1996) A distributed parameter physiologically-based pharmacokinetic model
for dermal and inhalation exposure to volatile organic compounds. Risk Anal 16:147-160.
Santostefano, MJ; Wang, X; Richardson, VM; et al. (1998) A pharmacodynamic analysis of TCDD-induced
cytochrome P450 gene expression in multiple tissues: dose- and time-dependent effects. Toxicol Appl Pharmacol
151:294-310.
Sarangapani, R; Gentry, PR; Covington, TR; et al. (2003) Evaluation of the potential impact of age- and gender-
specific lung morphology and ventilation rate on the dosimetry of vapors. Inhal Toxicol 15(10):987-1016.
Sato, A; Nakajima, T. (1979) Partition coefficients of some aromatic hydrocarbons and ketones in water, blood and
oil. Brit J Indust Med 36:231-234.
Sato, A; Endoh, K; Kaneko, T; et al. (1991) A simulation study of physiological factors affecting phamacokinetic
behavior of organic solvent vapors. Brit J Indust Med 48:342-347.
Schlosser, PM; Patrick, DL; Conolly, RB; et al. (2003) Benchmark dose risk assessment for formaldehyde using
airflow modeling and a single-compartment, DNA-protein cross-link dosimetry model to estimate human equivalent
doses. Risk Anal 23:473-487.
Schoeffner, DJ; Warren, DA; Muralidhara, S; et al. (1999) Organ weights and fat volume in rats as a function of
strain and age. J Toxicol Environ Health 56:449-462.
Smith, AE; Gray, GM; Evans, JS. (1995) The ability of predicted internal dose measures to reconcile tumor bioassay
data for chloroform. Regul Toxicol Pharmacol 21:33 9-3 51.
Starr, TB; Festa, JL. (2003) A proposed inhalation reference concentration for methanol. Regul Toxicol Pharmacol
38(2):224-231.
Steinbach, KH; Raffler, H; Pabst, G; et al. (1980) A mathematical model of canine granulocytopoiesis. J Math Biol
10:1-12.
Sultatos, LG; Kim, B; Woods, L. (1990) Evaluation of estimations in vitro of tissue: blood distribution coefficients
for organothiophosphate insecticides. Toxicol Appl Pharmacol 103:52-55.
Sweeney, LM; Tyler, TR; Kirman, CR; et al. (2001) Proposed occupational exposure limits for select ethylene
glycol ethers using PBPK models and Monte Carlo simulations. Toxicol Sci 62(1): 124-139.
Tan, YM; Butterworth, BE; Gargas, ML; et al. (2003) Biologically motivated computational modeling of
chloroform cytolethality and regenerative cellular proliferation. Toxicol Sci 75:192-200.
Tardif, R; Charest-Tardif, G; Brodeur, J; et al. (1997) Physiologically based pharmacokinetic modeling of a ternary
mixture of alkyl benzenes in rats and humans. Toxicol Appl Pharmacol 144:120-134.
Terasaki, T; Iga, T; Sugiyama, Y; etal. (1984) Nuclear binding as a determinant of tissue distribution of adriomycin,
daunomycin, adriamycinol, daunorubicinol and actinomycin D. J Pharmacobio-Dynamics 7:269-277.
Thomas, RS; Lytle, WE; Keefe, TJ; et al. (1996a) Incorporating Monte Carlo simulation into physiologically based
pharmacokinetic models using advanced continuous simulation language (ACSL): a computational method. Fundam
Appl Pharmacol 31:19-28.
R-14
-------
Thomas, RS; Yang, RSH; Morgan, DG; et al. (1996b). PBPK modeling/Monte Carlo simulation of methylene
chloride kinetic changes in mice in relation to age and acute, subchronic, and chronic inhalation exposure. Environ
Health Perspect 104:858-865.
Thomas, RS; Conolly, RB; Gustafson, DL; et al. (2000) A physiologically based pharmacodynamic analysis of
hepatic foci within a medium-term liver bioassay using pentachlorobenzene as a promoter and diethylnitrosamine as
an initiator. Toxicol Appl Pharmacol 166:128-137.
Thrall, KD; Vucelick, ME; Gies, RA; et al. (2000). Comparative metabolism of carbon tetrachloride in rats, mice,
and hamsters using gas uptake and PBPK modeling. J Toxicol Environ Health 60:531-548.
Timchalk, C; Poet, TS; Kousba, AA; et al. (2004) Noninvasive biomonitoring approaches to determine dosimetry
and risk following acute chemical exposure: analysis of lead or organophosphate insecticide in saliva. J Toxicol
Environ Health (Part A) 67(8-10):635-650.
Timchalk, C; Poet, TS; Lin, Y; et al. (2001) Development of an integrated microanalytical system for analysis of
lead in saliva and linkage to a physiologically based pharmacokinetic model describing lead saliva secretion.
AIHAJ 62:295-302.
Timchalk, C; Nolan, RJ; Mendrala, AL; et al. (2002) A physiologically based pharmacokinetic and
pharmacodynamic (PBPK/PD) model for the organophosphate insecticide chlorpyrifos in rats and humans. Toxicol
Sci 66:34-53.
Tran, CL; Jones, AD; Cullen, RT; et al. (1999) Mathematical modeling of the retention and clearance of low-toxicity
particles in the lung. Inhal Toxicol 11:1059-1076.
Travis, CC; Hattemer-Frey, HA. (1991) Physiological pharmacokinetic models. In: Krewski, D; Franklin, C; eds.
Statistics in toxicology. New York: Gordon and Breach; p. 170.
U.S. EPA (Environmental Protection Agency). (1992) Guidelines for exposure assessment. Federal Register
57(104):22888-22938. Available online at http://www.epa.gov/ncea/raf/rafguid.htm.
U.S. EPA. (Environmental Protection Agency). (1994). Methods for derivation of inhalation reference
concentrations and application of inhalation dosimetry. Environmental Criteria and Assessment Office, Office of
Health and Environmental Assessment, Cincinnati, OH; EPA/600/8-90/066F. Available from the National
Technical Information Service, Springfield, VA, PB2000-500023, and online at http://epa.gov/ncea.
U.S. EPA (Environmental Protection Agency). (1997) Guiding principles for Monte Carlo analysis. Risk
Assessment Forum, Washington, DC; EPA/630/R-97/001. Available online at http://www.epa.gov/ncea/raf.
U.S. EPA (Environmental Protection Agency). (1999a) Extrapolation of the benzene inhalation unit risk estimate to
the oral route of exposure [draft]. National Center for Environmental Assessment, Washington, DC; NCEA-W-
0517.
U.S. EPA (Environmental Protection Agency). (1999b) Toxicological review of ethylene glycol monobutyl ether.
In support of summary information on the IRIS. National Center for Environmental Assessment, Washington, DC.
Available online at http://www.epa.gov/iris.
U.S. EPA (Environmental Protection Agency). (2000a) Benchmark dose technical guidance document [external
review draft]. Risk Assessment Forum, Washington, DC; EPA/630/R-00/001. Available online at
http://www.epa.gov/ncea/raf.
U.S. EPA (Environmental Protection Agency). (2000b) Toxicological review of vinyl chloride. In support of
summary information on the IRIS. National Center for Environmental Assessment, Washington, DC; EPA/635/R-
00/004. Available online at http://www.epa.gov/iris.
R-15
-------
U.S. EPA (Environmental Protection Agency). (2002) A review of the reference dose and reference concentration
processes. Risk Assessment Forum, Washington, DC; EPA/630/P-02/002-F. Available online at
http ://epa. gov/ncea/raf.
U.S. EPA (Environmental Protection Agency). (2003) Toxicological review of xylenes. In support of summary
information on the IRIS, Washington, DC; EPA/635/R-03/001. Available online at http://www.epa.gov.iris.
U.S. EPA (Environmental Protection Agency). (2004) Air quality criteria for paniculate matter. National Center for
Environmental Assessment, Research Triangle Park, NC; EPA/600/P-99/002aF.
U.S. EPA (Environmental Protection Agency). (2005a) Guidelines for carcinogen risk assessment. Federal Register
70(66)17765-17817. Available online at http://www.epa.gov/cancerguidelines.
U.S. EPA (Environmental Protection Agency). (2005b) Toxicological review of boron and compounds.
EPA/635/04/052.
U.S. EPA (Environmental Protection Agency). (2005c) Supplemental guidance for assessing susceptibility from
early -life exposure to carcinogens. Risk Assessment Forum, Washington, DC; EPA/630/R-03/003F. Available
online at http://www.epa.gov/cancerguidelines.
U.S. EPA (Environmental Protection Agency). (2006) Use of PBPK models to quantify the impact of human age
and interindividual differences in physiology and biochemistry pertinent to risk (final report). National Center for
Environmental Assessment, Washington, DC; EPA/600/R-06/014A.
Van Asperen, J; Rijcken, WRP; Lammers, JHCM. (2003) Application of physiologically based toxicokinetic
modelling to study the impact of the exposure scenario on the toxicokinetics and the behavioural effects of toluene
in rats. Toxicol Lett 138:51-68.
Vicini, P; Pierce, CH; Dills, RL; et al. (1999). Individual prior information in a physiological model of 2H8-toluene
kinetics: an empirical Bayes estimation strategy. Risk Anal 19:1127-1134.
Vinegar, A; Jepson, GW. (1996) Cardiac sensitization thresholds of halon replacement chemicals predicted in
humans by physiologically based pharmacokinetic modeling. Risk Anal 16:571-579.
Vinegar, A; Winsett, DW; Andersen, ME; et al. (1990) Use of a physiologically based pharmacokinetic and
computer simulation for retrospective assessment of exposure to volatile toxicants. Inhal Toxicol 2:119-128.
Voisin, E; Ruthsatz, M; Collins, J; et al. (1990) Extrapolation of animal toxicity to humans: interspecies
comparisons in drug development. Regul Toxicol Pharmacol 12:107-116.
Wagner, JG. (1981) History of pharmacokinetics. Pharmacol Ther 12:537-562.
Walton, K; Dome, JL; Renwick, AG. (2001) Uncertainty factors for chemical risk assessment: interspecies
differences in the in vivo pharmacokinetics and metabolism of human CYP1A2 substrates. Food Chem Toxicol
39:667-680.
Welsch, F; Blumenthal, GM; Conolly, RB. (1995) Physiologically based pharmacokinetic models applicable to
organogenesis: extrapolation between species and potential use in prenatal toxicity risk assessments. Toxicol Lett
(82-83):539-547.
Williams, PJ; Vinegar, A; McDougal, JN; et al. (1996) Rat to human extrapolation of HCFC-123 kinetics deduced
from halothane kinetics-a corollary approach to physiologically based pharmacokinetic modelling. Fundam Appl
Toxicol 30:55-66.
Wtinscher, G; Kersting, H; Heberer, H; et al. (1991) Simulation system SONCHES-based toxicokinetic model and
data bank as a tool in biological monitoring and risk assessment. Sci Total Environ 101:101-109.
R-16
-------
Yates, FE. (1978) Good manners in good modeling: mathematical models and computer simulations of
physiological systems. Am J Physiol 234:R59-R160.
R-17
-------
APPENDIX
LIST OF PUBLICATIONS RELEVANT TO PBPK MODELING OF
ENVIRONMENTAL CHEMICALS AND ITS USE
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1. Abbas, R., and Fisher, J. W. (1997). A physiologically based pharmacokinetic
model for trichloroethylene and its metabolites, chloral hydrate, trichloroacetate,
dichloroacetate, trichloroethanol, and trichloroethanol glucuronide in B6C3F1 mice.
Toxicology and Applied Pharmacology 137, 15-30.
2. Abbas, R., and Hayton, W. L. (1997). A physiologically based pharmacokinetic and
pharmacodynamic model for paraxon in rainbow trout. Toxicology and Applied
Pharmacology 145, 192-201.
3. Abraham, M. H., Kamlet, M. J., Taft, R. W., Doherty, R. M., and Weathershy, P. K.
(1985). Solubility properties in polymers and biological media. 2. The correlation
and prediction of the solubilities of nonelectrolytes in biological tissues and fluids.
Journal of Medicinal Chemistry 28, 865-870.
4. Aggarwal, G., Kohn, M. C., and Melnick, R. L. Development of a physiologically
based pharmacokinetic model for isoprene. Isoprene, NTP TR 486, H -l-H-17.
2000. National Institute of Environmental Health Sciences., North Carolina.
Ref Type: Report
5. Albanese, R. A., Banks, H. T., Evans, M. V., and Potter, L. K. (2002).
Physiologically based pharmacokinetic models for the transport of trichloroethylene
in adipose tissue. Bulletin of Mathematical Biology 64, 97-131.
6. Ali.N, T. R. (1999). Toxicokinetic modeling of the combined exposure to Toluene
and N-Hexane in Rats and Humans. J Occup Health 41, 95-103.
7. Allen, B. C., and Fisher, J. W. (1993). Pharmacokinetic modeling of
trichloroethylene and trichloroacetic acid in humans. Risk Analysis 13, 71-86.
8. Allen, B. C., Covington, T. R., and Clewell, H. J. I. (1996). Investigation of the
impact of pharmacokinetic variability and uncertainty on risks predicted with a
pharmacokinetic model for chloroform. Toxicology 111, 289-303.
9. Altman, P. L., and Dittmer, D. S. (1961). Blood and other body fluids. Federation of
American Society for Experimental Biology, Bethesda.
10. Andersen, M. E., Krewski, D., and Withey, J. R. (1993). Physiological
pharmacokinetics and cancer risk assessment. Cancer.Lett. 69, 1-14.
11. Andersen, M. E., and Clewell, H. J. I. (1994). Gas uptake studies of deuterium
isotope effects on dichloromethane metabolism in female B6C3F1 mice in vivo.
Toxicology and Applied Pharmacology 128, 158-165.
12. Andersen, M. E. (1995). Development of physiologically based pharmacokinetic
and physiologically based pharmacodynamic models for applications in toxicology
and risk assessment. Toxicol Lett. 79, 35-44.
-------
13. Andersen, M. E., Clewell, H. J. I, and Frederick, C. B. (1995). Applying si,ulation
modeling to problems in toxicology and risk assessment-a short perspective.
Toxicology and Applied Pharmacology 133, 181-187.
14. Andersen, M. E., Clewell, H. J. I, Gearhart, J., Allen, B. C., and arton, H. A.
(1997). Pharmacodynamic model of the rat estrus cycle in relation to endocrine
disrupters. Journal of Toxicology and Environmental Health 189-209.
15. Andersen, M. E., Eklund, C. R., Mills, J. J., Barton, H. A., and Birnbaum, L. S.
(1997). A multicompartment geometric model of the liver in relation to regional
induction of cytochrome P450s. Toxicol.Appl.Pharmacol. 144, 135-144.
16. Andersen, M. E., and Barton, H. A. (1999). Biological regulation of receptor-
hormone complex concentrations in relation to dose-response assessments for
endocrine-active compounds. Toxicol.Sci. 48, 38-50.
17. Andersen, M. E., and Jarabek, A. M. (2001). Nasal tissue dosimetry-issues and
approaches for "Category 1" gases: a report on a meeting held in Research Triangle
Park, NC, February 11-12, 1998. Inhal.Toxicol 13, 415-435.
18. Andersen, M. E., and Sarangapani, R. (2001). Physiologically based
clearance/Extraction models for compounds metabolized in the Nose: An example
with Methyl Methacrylate. Inhal.Toxicol. 13, 397-414.
19. Andersen, M. E., Green, T., Frederick, C. B., and Bogdanffy, M. S. (2002).
Physiologically based pharmacokinetic (PBPK) models for nasal tissue dosimetry of
organic esters: assessing the state-of-knowledge and risk assessment applications
with methyl methacrylate and vinyl acetate. Regulatory Toxicology and
Pharmacology 36, 234-245.
20. Andersen, M. E. (2003). Toxicokinetic modeling and its applications in chemical
risk assessment. Toxicol Lett. 138, 9-27.
21. Andersen, M. E. (2004). Computer demonstration of physiological-toxicokinetic
models: demonstrating a computer model that simulates the estradiol (E2)-primed
LH surge during proestrus in the rat. Toxicol.Lett. 138, 179-182.
22. Andersen, M. E., Gargas, M. L., Jones, R. A., and Jenkins, L. J. (1980).
Determination of the kinetic constants for metabolism of inhaled toxicants in vivo
by gas uptake measurements. Toxicology and Applied Pharmacology 54, 100-116.
23. Andersen, M. E. (1981). Pharmacokinetics of inhaled gases and vapors.
Neurobehavioral Toxicol.and Teratol. 3, 383-389.
24. Andersen, M. E. (1981). A physiologically based toxicokinetic description of the
metabolism of inhaled gases and vapors: analysis at steady state. Toxicology and
Applied Pharmacology 60, 509-526.
-------
25. Andersen, M. E. and Clewell, H. J. III. Pharmacokinetic interaction of mixtures.
Dayton, O. H. Proceedings of the 14th Annual Conference on Environmental
Toxicology , 221-238. 1983. AFAMRL-TR-83-099.
Ref Type: Abstract
26. Andersen, M. E., and Keller, W. C. (1984). Toxicokinetic principles in relation to
percutaneous absorption and cutaneous toxicity. In Cutaneous Toxicity (V. A. Drill,
and P. Lazar, Eds.), pp. 9-27. Raven Press, New York.
27. Andersen, M. E., Clewell, H. J. I, Gargas, M. L., Smith, F. A., and Reitz, R. H.
(1987). Physiologically-based pharmacokinetics and risk assessment process for
methylene chloride. Toxicology and Applied Pharmacology 87, 185-205.
28. Andersen, M. E., Gargas, M. L., Clewell, H. J. L, and Severin, K. M. (1987).
Quantitative evaluation of the metabolic interaction between trichloroethylene and
1,1-dichloroethylene in vivo using gas uptake methods. Toxicology and Applied
Pharmacology 89, 149-157.
29. Andersen, M. E., MacNaughton, M. G., Clewell, H. J. L, and Paustenbach, D. J.
(1987). Adjusting exposure limits for long and short exposure period using a
physiological pharmacokinetic model. American Industrial Hygiene Association
JournaUS, 335-343.
30. Andersen, M. E. (1988). Quantitative risk assessment and occupational carcinogens.
Applied Industrial Hygiene 3, 267-273.
31. Andersen, M. E. (1988). Quantitative risk assessment and occupational carcinogens.
Applied Industrial Hygiene 3, 267-273.
32. Andersen, M. E. (1991). Physiological modeling of organic chemicals. Annals of
Occupational Medecine 35, 305-321.
33. Andersen, M. E., Clewell, H. J. L, and Gargas, M. L. (1991). Physiologically-based
pharmacokinetic modeling with dichloromethane, its metabolite carbon monoxide
and blood carboxyhemoglobin in rats and humans. Toxicology and Applied
Pharmacology 108, 14-27.
34. Andersen, M. E., Krishnan, K., Conolly, R. B., and McClellan, R. O. (1992).
Biologically based modeling in toxicology research. Arch.Toxicol Suppl 15, 217-
227.
35. Andersen, M. E., Krishnan, K., Conolly, R. B., and McClellan, R. O. (1992).
Mechanistic toxicology research and biologically-based modeling: partners for
improving quantitative risk assessments. Chemical Industry Institute of Toxicology
12, 1-8.
-------
36. Andersen, M. E., Mills, J. J., and Gargas, M. L. (1993). Modeling receptor-mediated
processes with dioxin: Implications for pharmacokinetics and risk assessment. Risk
Analysis 13, 25-36.
37. Andersen, M. E., and Krishnan, K. (1994). Physiologically based pharmacokinetics
and cancer risk assessment. Environ.Health Perspect. 102 Suppl 1, 103-108.
38. Andersen, M. E., and Krishnan, K. (1995). Relating In Vitro to In Vivo Exposures
with Physiologically Based Tissue Dosimetry and Tissue Response Models. In
Animal Test Alternatives: Refinement, Reduction, Replacement (H. Salem, Ed.), pp.
9-25. Marcel Dekker, Inc., New York.
39. Andersen, M. E., Clewell, H. J. L, and Frederick, C. B. (1995). Contemporary issues
in toxicology. Applying simulation modeling to problems in toxicology and risk
assessment - a short perspective. Toxicology and Applied Pharmacology 133, 181-
187.
40. Andersen, M. E. (1995). Physiologically based pharmacokinetic (PB-PK) models in
the study of the disposition and biological effects of xenobiotics and drugs.
Toxicology Letters 82-83, 341-348.
41. Andersen, M. E., Clewell, H., Ill, and Krishnan, K. (1995). Tissue dosimetry,
pharmacokinetic modeling, and interspecies scaling factors. Risk Anal. 15, 533-537.
42. Andersen, M. E., and Krishnan, K. (1995). Relating in vitro to in vivo exposures
with physiologically-based models of tissue dosimetry and tissue response. In
Animal test alternatives:refinement, reduction and replacement. (H. Salem, Ed.), pp.
9-25. Marcel Dekker, Inc., New York, Basel, Hong Kong.
43. Andersen, M. E., Birnbaum, L. S., Barton, H. A., and Eklund, C. R. (1997).
Regional hepatic CYP1A1 and CYP1A2 induction with 2,3,7,8-tetrachlorodibenzo-
p-dioxin evaluated with a multicompartment geometric model of hepatic zonation.
Toxicology and Applied Pharmacology 144, 145-155.
44. Andersen, M. E., Sarangapani, R., Gentry, P. R., Clewell, H. J. I, Covington, T. R.,
and Frederick, C. B. (1999). Application of a hybrid CFD-PBPK nasal dosimetry
model in an inhalation risk assessment: an example with acrylic acid. Toxicological
Sciences 57, 312-325.
45. Andersen, M. E., and Sarangapani, R. (1999). Clearance concepts applied to the
metabolism of inhaled vapors in tissues lining the nasal cavity. Inhalation
Toxicology 11, 873-897.
46. Andersen, M. E., Sarangapani, R., Frederick, C. B., and Kimbell, J. S. (2000).
Dosimetric adjustment factors for methyl methacrylate derived from a steady-state
analysis of a physiologically based clearance-extrapolation model. Inhalation
Toxicology 11, 899-926.
-------
47. Andersen, M. E., Sarangapani, R., Reitz, R. H., Gallavan, R. H., Dobrev, I. D., and
Plotzke, K. P. (2001). Physiological modeling reveals novel pharmacokinetic
behavior for inhaled octamethylcyclotetrasiloxane in rats. Toxicol Sci. 60, 214-231.
48. Andersen, M. E., and Dennison, J. E. (2001). Mode of action and tissue dosimetry in
current and future risk assessments. Science of the Total Environment 274, 3-14.
49. Andersen, M.E., Scimeca, J., Olin, S.S. (2005). Kinetic and mechanistic data needs
for a human phsiologically based pharmacokinetic (PBPK) model for acrylamide:
pharmacokinetic model for acrylamide. Adv Exp Med Biol 561,117-25. Review.
PMID: 16438294 [PubMed - indexed for MEDLINE]
50. Anderson, M. W., Eling, T. E., Lutz, R. L., and Mattews, H. B. (1977). The
construction of a pharmacokinetic model for the disposition of PCBs in the rat.
Clinical Pharmacology and Therapeutics 22, 765-773.
51. Angelo, M. J., and Pritchard, A. B. (1987). Route to route extrapolation of
dichloromethane exposures using a physiological pharmacokinetic model. Drinking
Water and Health 8, 254-264.
52. Apostolou, A. (1998). What's in a name: toxicokinetics, pharmacokinetics, or just
kinetics? Regulatory Toxicology and Pharmacology 27, 82-83.
53. Arms, A. D. and Travis, C. C. Reference Physiological Parameters in
Pharmacokinetic Modeling. Office of Health and Environmental Assessment. EPA.
EPA/600/6-88/004, 1-1-7.16. 1988. Washington, DC., US EPA ( United State
Environmental Protection Agency).
Ref Type: Report
54. Asgharian, B., Wood, R., and Schlesinger, R. B. (1995). Empirical modeling of
particle deposition in the alveolar region of the lungs: A basis for interspecies
extrapolation. Fundamental and Applied Toxicology 27, 232-238.
55. Auton, M. J., and Woollen, B. H. (1991). A physiologically based mathematical
model for the human inhalation pharmacokinetics of 1,1,2-trichloro-1,2,2-
trifluoroethane. International Archives of Occupational and Environmental Health
63, 133-138.
56. Auton, T. R., Ramsey, J. D., and Wollen, B. H. (1993). Modelling dermal
pharmacokinetics using in vitro data. Part II. Fluazifop-butyl in man. Human &
Experimental Toxicology 12, 207-213.
57. Auton, T. R., Ramsey, J. D., and Woollen, B. H. (1993). Modelling dermal
pharmacokinetics using in vitro data. Part I. Fluazifop-butyl in the rat. Human &
Experimental Toxicology 12, 199-206.
58. Aylward, L. L., Hays, S. M., Karch, N. J., and Paustenbach, D. J. (1996). Relative
susceptibility of animals and humans to the cancer hazard posed by 2,3,7,8-
-------
tetrachlorodibenzo-p-dioxin using internal measures of dose. Environmental Science
and Technology 30, 3534-3543.
59. Balaz, S., and Lukacova, V. (2000). A model-based dependence of the human
tissue/blood partition coefficients of chemicals on lipophilicity and tissue
composition. Quantitative Structure-Activity Relationships 18, 361-368.
60. Ball, R., and Schwartz, S. L. (1994). Cmatrix: software for physiologically based
pharmacokinetic modeling using a symbolic matrix representation system.
Computers in Biology and Medicine 24 , 269-276.
61. Banks, H. T., Musante, C. J., and Tran, H. T. (1997). A dispersion model for the
hepatic uptake and elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin. CRSC-
TR97-29 1-24.
62. Barton, H. A., Creech, J. R., Godin, C. S., Randall, G. M., and Seckel, C. S. (1995).
Chloroethylene mixtures: pharmacokinetic modeling and in vitro metabolism of
vinyl chloride, trichloroethylene, and trans-1,2-dichloroethylene in rat. Toxicology
and Applied Pharmacology 130, 237-247.
63. Barton, H. A., and Andersen, M. E. (1998). A model for pharmacokinetics and
physiological feedback among hormones of the testicular-pituitary axis in adult
male rats: a framework for evaluating effects of endocrine active compounds.
Toxicological Sciences 45, 174-187.
64. Barton, H. A., and Clewell, H. J., Ill (2000). Evaluating noncancer effects of
trichloroethylene: dosimetry, mode of action, and risk assessment. Environ.Health
Perspect. 108 Suppl 2, 323-334.
65. Basak, S. C., Mills, D., Hawkins, D. M., and El-Marsi, H. A. (2002). Prediction of
tissue-air partition coefficients: A comparison of structure-based and property-based
methods. SAR QSAR Environ Res. 13, 649-665.
66. Basak, S. C., Mills, D., Hawkins, D. M., and El-Marsi, H. A. (2003). Prediction of
human blood: air partition coefficient: a comparison of structure-based and
property-based methods. Risk.Anal. 23, 1173-1184.
67. Bass, L., and Keiding, S. (1988). Physiologically based models and strategic
experiments in hepatic pharmacology. Biochemical Pharmacology 37, 1425-1431.
68. Batterman, S., Zhang, L., Wang, S., and Franzblau, A. (2002). Partition coefficients
for the trihalomethanes among blood, urine, water, milk and air. Sci.Total Environ.
284, 237-247.
69. Baxter, L. T., Zhu, H., Mackensen, D. G., and Jain, R. K. (1994). Physiologically
based pharmacokinetic model for specific and nonspecific monoclonal antibodies
and fragments in normal tissues and human tumor xenografts in nude mice. Cancer
Research 54, 1517-1528.
-------
70. Baxter, L. T., Zhu, H., Mackensen, D. G., Butler, W. F., and Jain, R. K. (1995).
Biodistribution of monoclonal antibodies: scale-up from mouse to human using a
physiologically based pharmacokinetic model. Cancer Research 55, 4611-4622.
71. Beck, B. D., Mattuck, R. L., Bowers, T. S., Cohen, J. T., and OTlaherty, E. (2001).
The development of a stochastic physiologically-based pharmacokinetic model for
lead. Sci.Total Environ. 274, 15-19.
72. Beliles, R. P., and Totman, L. C. (1989). Pharmacokinetically based risk assessment
of workplace exposure to benzene. Regulatory Toxicology and Pharmacology 9,
186-195.
73. Beliveau, M., and Krishnan, K. (2000). Estimation of rat blood: air partition
coefficients of volatile organic chemicals using reconstituted mixtures of blood
components. Toxicology Letters 116, 183-188.
74. Beliveau, M., Tardif, R., and Krishnan, K. (2003). Quantitative structure-property
relationships for physiologically based pharmacokinetic modeling of volatile
organic chemicals in rats. J.Toxicol.Appl.Pharmacology. 189, 221-232.
75. Benignus, V. A., Boyes, W. K., and Bussnell, P. J. (1998). A dosimetric analysis of
behavioral effects of acute toluene exposure in rats and humans. Toxicological
Sciences 43, 186-195.
76. Bernillon, P., and Bois, F. Y. (2000). Statistical issues in toxicokinetic modeling: a
Bayesian perspective. Environmental Health Perspectives Suppl 108, 883-893.
77. Beliveau, M., Charest-Tardif, G., and Krishnan, K. (2001). Blood: air partition
coefficients of individual and mixtures of trihalomethanes. Chemosphere 44, 377-
381.
78. Birnbaum, L., Bischoff, K., Blancato, J., Clewell, H., Dedrick, R., Delp, M.,
Rhomberg, L., Schaeffer, V., Brown, R., Foran, J., Olin, S., and Robinson, D.
Physiological parameter values for PBPK models. A report prepared by the
international life sciences institute risk science institute. 1-137. 1994. U.S.
Environmental Protection Agency, Office of Health and Environmental Assessment.
Ref Type: Report
79. Bischoff, K. B., Dedrick, R. L., Zaharko, D. S., and Longstreth, J. A. (1971).
Methotrexate pharmacokinetics. Journal of Pharmaceutical Sciences 60, 1128-1133.
80. Bischoff, K. B. (1987). Physiogically-based pharmacokinetic modeling. Drinking
Water and Health 8, 36-64.
81. Bjorkman, S. (2003). Reduction and lumping of physiologically based
pharmacokinetic models: prediction of the disposition of fentanyl and pethidine in
humans by sucessively simplified models. J.Pharmacokinet.Pharmacodyn. 30, 285-
307.
-------
82. Bjorkman, S., Wada, R., and Stanski, D. R. (1998). Application of physiologic
models to predict the influence of changes in body composition and blood flows on
the pharmacokinetics of fentanyl and alfentanil in patients. Anesthesiology 88, 657-
667.
83. Bjorkman, S., Wada, R. D., Berling, B. M., and Benoni, G. (2001). Prediction of the
disposition of midazolam in surgical patients by a physiologically based
pharmacokinetic model. Journal of Pharmaceutical Sciences 90, 1226-1241.
84. Blaauboer, B. J. (2004). The integration of data on physio-chemical properties, in
vitro-derived toxicity data and physiologicaly based kinetic and dynamic as
modelling a tool in hazard and risk assessment. A commentary. Toxicol Lett. 138,
161-171.
85. Blakey, G. E., Nestorov, I. A., Arundel, P. A., Aarons , L. J., and Rowland, M.
(1997). Quantitative structure-pharmacokinetics relationships: 1. Development of a
whole-body physiologically based model to characterize changes in
pharmacokinetics across a homologous series of barbiturates in the rat. Journal of
Pharmacokinetics and Biopharmaceutics 25, 277-312.
86. Blancato, J. N., and Bischoff, K. B. (1993). The application of pharmacokinetic
models to predict target dose. In Dermal risk assessment. Dermal and inhalation
exposure and absorption of toxicants. (R. G. M. Wang, J. B. Knaak, and H. I.
Macbach, Eds.), pp. 31-46. CRC Press Inc..
87. Blancato, J. N., and Saleh, M. A. (1994). Physiologically based pharmacokinetic
models. Examples of their use in exposure and risk assessment. In Biomakers of
Human Exposure to Pesticides. (M. A. Saleh, J. N. Blancato, and C. H. Nauman,
Eds.), pp. 264-283. American Chemical Society, Washington, DC.
88. Blesch, K. S., Gieschke, R., Tsukamoto, Y., reigner, B. G., Burger, H. U., and
Steimer, J. L. (2003). Clinical pharmacokinetic/pharmacodynamic and
physiologically based pharmacokinetic modeling in new drug development: the
capecitabine experience. Invest New Drugs 21, 195-223.
89. Blumenthal, G. M., Kohn, M. C., and Portier, C. J. (1997). A mathematical model of
production, distribution, and metabolism of melatonin in mammalian systems.
Toxicology and Applied Pharmacology 147, 83-92.
90. Bogaards, J. J., Freidig, A. P., and van Bladeren, P. J. (2001). Prediction of isoprene
diepoxide levels in vivo in mouse, rat and man using enzyme kinetic data in vitro
and physiologically-based pharmacokinetic modelling. Chem.Biol.Interact. 138,
247-265.
91. Bogdanffy, M. S., and Sarangapani, R. (2003). Physiologically - based kinetic
modeling of vapours toxic to the respiratory tract. Toxicol.Lett. 138, 103-117.
-------
92. Bogdanffy, M. S., Sarangapani, R., Kimbell, J. S., Frame, S. R., and Plowchalk, D.
R. (1998). Analysis of vinyl acetate metabolism in rat and human nasal tissues by an
in vitro gas uptake technique. Toxicological Sciences 46, 235-246.
93. Bogdanffy, M. S., Sarangapani, R., Plowchalk, D. R., Jarabek, A. M., and Andersen,
M. E. (1999). A biologically risk assessment for vinyl acetate-induced cancer and
noncancer inhalation toxicity. Toxicological Sciences 51, 19-35.
94. Bogdanfy, M. S., Plowchalk, D. R., Sarangapani, R., Starr, T. B., and Andersen, M.
E. (2001). Mode-of-Action-Based dosimeters for interspecies extrapolation on vinyl
acetate inhalation risk. Inhal.Toxicol. 13, 377-396.
95. Bogen, K. T. (1988). Pharmacokinetics for regulatory risk analysis: the case of
trichloroethylene. Regulatory Toxicology and Pharmacology 8, 447-466.
96. Bogen, K. T., and McKone, T. E. (1988). Linking indoor air and pharmacokinetic
models to assess tetrachloroethylene risk. Risk Analysis 8, 509-519.
97. Bogen, K. T., and Gold, L. S. (1997). Trichloroethylene cancer risk: simplified
calculation of PBPK-Based MCLs for cytotoxic end points. Regulatory Toxicology
and Pharmacology 25, 26-43.
98. Bois, F. Y. (1999). Analysis of PBPK models for risk characterization. Ann N Y
AcadSci. 895, 317-337.
99. Bois, F. Y., Zeise, L., and Tozer, T. N. (1990). Precision and sensitivity of
pharmacokinetic models for cancer risk assessment. Tetrachloroethylene in mice,
rats and humans. Toxicology and Applied Pharmacology 102, 300-315.
100. Bois, F. Y., Smith, M. T., and Spear, R. C. (1991). Mechanism of benzene
carcinogenesis. Application of a physiological model of benzene pharmacokinetics
and metabolism. Toxicology Letters 56, 283-298.
101. Bois, F. Y., Woodruff, T. J., and Spear, R. C. (1991). Comparison of three
physiologically-based pharmacokinetic models for benzene disposition. Toxicology
and Applied Pharmacology 110, 79-88.
102. Bois, F. Y., and Paxman, D. G. (1992). An analysis of exposure rate effects for
benzene using a physiologically based pharmacokinetic model. Regulatory
Toxicology and Pharmacology 15, 122-136.
103. Bois, F. Y., Gelman, A., Jiang, J., Maszle, D. R., Zeise, L., and Alexeef, G. (1996).
Population toxicokinetics of tetrachloroethylene. Archives of Toxicology 70, 347-
355.
104. Bois, F. Y., Jackson, E. T., Pekari, K., and Smith, M. T. (1996). Population
toxicokinetics of benzene. Environmental Health Perspectives 104, 1405-1411.
-------
105. Bois, F. Y. (2000). Statistical analysis of Fisher et al. PBPK model of
trichloroethylene kinetics. Environmental Health Perspectives 108, 275-282.
106. Bois, F. Y. (2000). Statistical analysis of Clewell et al. PBPK model of
trichloroethylene kinetics. Environmental Health Perspectives 108, 307-316.
107. Bois, F. Y. (2001). Applications of population approaches in toxicology.
Toxicol.Lett. 120, 385-394.
108. Bonate, H. A., Swann, A., and Silverman, P. B. (1996). Simulation of toluene
kinetics in the rat by a physiologically based pharmacokinetic model with
application of biotyransformation parameters derived independently in vitro and in
vivo. Journal of Pharmaceutical Sciences 85, 878-883.
109. Bond, J. A., Himmelstein, M. W., Seaton, M., Boogaard, P., and Medinsky, M. A.
(1996). Metabolism of butadiene by mice, rats, and humans: a comparison of
physiologically based toxicokinetic model predictions and experimental data.
Toxicology 113, 48-54.
110. Bookout, R. L. J., McDaniel, C. R., and Quinn, D. W. M. J. H. (1996). Multilayered
dermal subcompartments for modeling chemical absorption. SAR QSAR Environ
Res. 5, 133-150.
111. Boom, S. P. A., Meyer, L, Wouterse, A. C., andRussel, F. G. M. (1998). A
physiologically based kidney model for the renal clearance of ranitidine and the
interaction with cimetidine and probenecid in the dog. Biopharmaceutics & Drug
Disposition 19, 199-208.
112. Borghoff, et. al., Gargas, M. L., Andersen, M. E., and Conolly, R. B. (1995).
Development of a Mechanism-Based Dosimetry Model for 2,4,4-Trimethyl-2-
pentanol-Induced & 2U-Globulin Nephropathy in Male Fischer 344 Rats.
Fundam.Appl.Toxicol 25, 124-137.
113. Borghoff, S. J., Murphy, J. E., and Medinsky, M. A. (1996). Development of a
physiologically based pharmacokinetic model for methyl tertiaty-butyl ether and
tertiary-butanol in male Fischer-344 rats. Fundamental and Applied Toxicology 30,
264-275.
114. Bouchard, M., Brunet, R. C., Droz, P.-O., and Carrier, G. (2001). A Biologically
based dynamic model for predicting the disposition of methanol and its metabolites
in animals and humans. Toxicol.Sci. 64, 169-184.
115. Bouchard, M., Gosselin, N. H., Brunet, R. C., Samuel, O., Dumoulin, M. J., and
Carrier, G. (2003). A Toxicokinetic model of malathion and its metabolites as a tool
to assess human exposure and risk through measurements of urinary biomarkers.
Toxicol.Sci. 194.
10
-------
116. Boxenbaum, H. (1992). Pharmacokinetics: philosophy of modeling. Drug
Metabolism and Disposition 24, 89-120.
117. Boyes, W. K., Bussnell, P. J., Crofton, K. M., Evans, M., and Simmons, J. E.
(2000). Neurotoxic and pharmacokinetic responses to trichloroethylene as a function
of exposure scenario. Journal of Toxicology and Environmental Health 108, 317-
322.
118. Brightman, F.A., Leahy, D.E., Searle, G.E., Thomas, S. (2006). Application of a
generic physiologically based pharmacokinetic model to the estimation of
xenobiotic levels in rat plasma. Drug Metab Dispos 34(1), 84-93. Epub 2005 Oct
12. PMID: 16221757 [PubMed - indexed for MEDLINE]
119. Brightman, F.A., Leahy, D.E., Searle, G.E., Thomas, S. (2006). Application of a
generic physiologically based pharmacokinetic model to the estimation of
xenobiotic levels in human plasma. Drug Metab Dispos 34(1), 94-101. Epub 2005
Oct 12. PMID: 16221756 [PubMed - indexed for MEDLINE]
120. Brochot, C., Toth, J., Bois, F.Y. (2006). Lumping in pharmacokinetics. J
PharmacokinetPharmacodyn32(5-6),719-736. PMID: 16341473 [PubMed - in
process]
121. Brocklebank, J. R., Namdari, R., and Law, F. C. P. (1997). An oxytracycline residue
depletion study to assess the physiologically based pharmacokinetic (PBPK) model
in farmed Atlantic salmon. Canadian Veterinary Journal 38, 645-646.
122. Brodeur, J., Lapare, S., Krishnan, K., Tardif, R., and Goyal, R. (1990). Le probleme
de I'ajustement des valeurs limites d'exposition pour des horaires de travail non-
conventionnels: utilite de la modelisation pharmacocinetique a base physiologique.
Travail et Sante 6, SI 1-16.
123. Brodeur, J., Tardif, R., Lapare, S., Charest-Tardif, G., and Krishnan, K. (1994). Use
of kinetic modeling to predict metabolic interaction for combinations of binary
mixtures of industrial solvents. In Recent advances in researchs on the combined
effects of environmental factors. (M. Kasuya, Ed.), pp. 101-107. Toyama Medical &
Pharmaceutical University Press, Toyama, Japan.
124. Bronaugh, R. L., Collier, S. W., Macpherson, S. E., and Kraeling, M. E. K. (1994).
Influence of metabolism in skin on dosimetry after topical exposure. Environmental
Health Perspectives 102 , 71-74.
125. Brown, E. A., Shelley, M. L., and Fisher, J. W. (1998). A pharmacokinetic study of
occupational and environmental benzene exposure with regard to gender. Risk
Analysis 18,205-213.
126. Brown, R. N. (1994). Analytic solution of a linear physiologically based
pharmacokinetic model prototype useful in risk assessment. In Biomarkers of
11
-------
human exposure to pesticides. (M. A. Saleh, J. N. Blancato, and C. H. Nauman,
Eds.), pp. 301-317. American Chemical Society, Washington.
127. Brown, R. P., Delp, M. D., Lindstedt, S. L., Rhomberg, L. R., and Bellies, R. P.
(1997). Physiological parameter values for physiologically based pharmacokinetic
models. Toxicology and Industrial Health 13, 407-484.
128. Bruckner, J. V. (2000). Differences in sensitivity of children and adults to chemical
toxicity: the NAS panel report. Regul.Toxicol Pharmacol. 31, 280-285.
129. Buchanan, J. R., Burka, L. T., and Melnick, R. L. (1997). Purpose and guidelines for
toxicokinetic studies within the National Toxicology Program. Environmental
Health Perspectives 105, 468-471.
130. Bucher, J. R., and Lucier, G. (1998). Current approaches toward chemical mixture
studies at the National Institute of Environmental Health Sciences and the U.S.
National Toxicology Program. Environmental Health Perspectives 106, 1295-1298.
131. Buckley, L. A. (1995). Biologically-based models of dioxin pharmacokinetics.
Toxicology 102, 125-131.
132. Bungay, P. M., Dedrick, R. L., and Guarino, A. M. (1978). Pharmacokinetic
modeling of the dogfish shark (squalus acanthias): distribution and urinary and
biliary excretion of phenol red and its glucuronide. Journal of Pharmacokinetics and
Biopharmaceutics 4, 377-388.
133. Bungay, P. M., Dedrick, M. L., and Mattews, H. B. (1981). Enteric transport of
chlordecone in the rat. Journal of Pharmacokinetics and Biopharmaceutics 9, 309-
341.
134. Bush, M. L., Frederick, C. B., Kimbell, J. S., and Ultman, J. S. (1998). A CFD-
PBPK hybrid model for simulating gas and vapor uptake in the rat nose. Toxicology
and Applied Pharmacology 150 , 133-145.
135. Byczkowski, J. Z., and Fisher, J. W. (1994). Lactational transfer of
tetrachloroethylene in rats. Risk Analysis 14, 339-349.
136. Byczkowski, J. Z., Kinkead, E. R., Leahy, H. F., Randall, G. M., and Fisher, J. W.
(1994). Computer simulation of the lactational transfer of tetrachloroethylene in rats
using a physiologically based model. Toxicology and Applied Pharmacology 125,
228-236.
137. Byczkowski, J. Z., Gearhart, J., and Fisher, J. W. (1994). Occupational exposure of
infants to toxic chemicals via breast milk. Nutrition 10, 43-48.
138. Byczkowski, J. Z., and Fisher, J. W. (1995). A computer program linking
physiologically based pharmacokinetic model with cancer risk assessment for
breast-fed infants. Computer Methods and Programs in Biomedecine 46, 155-163.
12
-------
139. Byczkowski, J. Z., and Lipscomb, J. C. (2001). Physiologically based
pharmacokinetic modeling of the lactational transfer of methylmercury. Risk Anal.
21, 869-882.
140. Cahill, T. M., Cousins, I, and Mackay, D. (2003). Development and application of a
generalized physiologically based pharmacokinetic model for multiple
environmental contaminants. Environmental Toxicology and Chemistry 22, 26-34.
141. Cai, H., Stoner, C., Reddy, A, Freiwald, S., Smith, D., Winters, R., Stankovic, C.,
Surendran, N. (2006). Evaluation of an integrated in vitro-in silico PBPK
(physiologically based pharmacokinetic) model to provide estimates of human
bioavailability. Int J Pharm 308(1-2),133-139. Epub 2005 Dec 13. PMID:
16352407 [PubMed - in process]
142. Calabrese, E. J., and Baldwin, L. A. (1995). A Toxicological basis to derive generic
interspecies uncertainty factors for application in human and ecological risk
assessment. Human & Ecological Risk Assessment. 1, 555-564.
143. Cantoreggi, S., and Keller, D. A. (1997). Pharmacokinetics and metabolism of vinyl
fluoride in vivo and in vitro. Toxicology and Applied Pharmacology 143, 130-139.
144. Canuel, G., Viau, C., and Krishnan, K. A modeling framework for back-calculating
ambient concentrations from data on biomarkers.. The Society for Computer
Simulation International. Proceeding of the International Conference on Health
Sciences Simulation , 97-102. 2000. San Diego, CA.
Ref Type: Conference Proceeding
145. Cardus, J., Burgos, F., Diaz, O., Roca, J., Barbera, J. A., Marrades, R. M.,
Rodriguez-Roisin, R., and Wagner, P. D. (1997). Increase in pulmonary ventilation-
perfucion inequality with age in healthy individuals. Am.J.Respir.Crit.Care.Med.
156, 648-653.
146. Carfagna, M. A., and Kedderis, G. L. (1992). Isolated hepatocytes as in vitro models
for the biotransformation and toxicity of chemicals in vivo. CUT Activities 12, 1-6.
147. Carlton, L. D., Pollack, G. M., and Brouwer, K. L. R. (1996). Physiologic
pharmacokinetic modeling of gastrointestinal blood flow as a rate-limiting step in
the oral absorption of digoxin: implications for patients with congestive heart failure
receiving epoprostenol. Journal of Pharmaceutical Sciences 85, 473-477.
148. Carpenter, R. L. (1999). Aerosol deposition modeling using ACSL. Drug and
Chemical Toxicology 22, 73-90.
149. Carpenter, R. L., Eger, E. L, Johnson, B. H., Unadkat, J. D., and Sheiner, L. B.
(1987). Does the duration of anesthetic administration affect the pharmacokinetic or
metabolism of inhaled anaesthetics in humans? Anesthesiology and Analgesia 66, 1-
13
-------
150. Carrier, G., Brunei, R. C., and Brodeur, J. (1995). Modeling of the toxicoketics of
polychlorinated dibenzo-p-dioxins and dibenzofurans in mammalians, including
Hymans. Toxicol.Appl.Pharmacol. 131, 267-276.
151. Carrier, G., Brunet, R. C., and Brodeur, J. (1995). Modeling of the toxicokinetics of
polychlorinated dibenzo-p-dioxins and dibenzofurans in mammalians, including
Humans. I. Nonlinear distribution of PCDD/PCDF body burden between liver and
adipose tissues. Toxicol.Appl.Pharmacol. 131, 253-266.
152. Carrier, G., and Brunet, R. C. (1995). A toxicokinetic model to assess the risk of
azinphosmethyl exposure in humans through measures of urinary elimination of
alkylphosphates. Toxicological Sciences 47, 23
32.
153. Casanova, M., Conolly, R. B., and Heck, H. d. (1996). DNA-protein cross-links
(DPX) and cell proliferation in B6C3F1 mice but not Syrian golden hamsters
exposed to dichloromethane: pharmacokinetics and risk assessment with DPX as
dosimeter. Fundamental and Applied Pharmacology 31, 103-116.
154. Caster, W. O., Poncelet, J., Simon, A. B., and Armstrong, W. B. (1956). Tissue
weights of the rat. I. Normal values determined by dissection and chemical methods.
Proceedings of the Society for Experimental Biology and Medicine 91, 122-126.
155. Chan, K. K., Cohen, J. L., Gross, J. F., Himmelstein, K. J., Bateman, J. R., Tsu-Lee,
Y., and Marlis, A. S. (1978). Prediction of adriamycin disposition in cancer patients
using a physiologic pharmacokinetic model. Cancer Treatment Report 62, 1161-
1171.
156. Chapel, A. M., and Sanders, P. (1996). Physiologically-based pharmacokinetic
modelling in veterinary medecine. Revue de Medecine Veterinaire 147, 359-366.
157. Charnick, S. B., Kawai, R., Nedelman, J. R., Lemaire, M., Niederberger, W., and
Sato, H. (1995). Perspectives in pharmacokinetics. Physiologically based
pharmacokinetic modeling as a tool for drug development. Journal of
Pharmacokinetics and Biopharmaceutics 23, 217-229.
158. Chen, C. W. (1993). Armitage-Doll Two-Stage Model: Implications and Extension.
Risk.Anal. 13, 273-279.
159. Chen, H. S. G., and Gross, J. F. (1979). Estimation of tissue to plasma partition
coefficients used in physiological pharmacokinetic models. Journal of
Pharmacokinetics and Biopharmaceutics 7, 117-125.
160. Chow, H. H. (1997). A physiologically based pharmacokinetic model of zidovudine
(AZT) in the mouse - model development and scale-up to humans. Journal of
Pharmaceutical Sciences 86, 1223-1228.
14
-------
161. Clarke, D. O., Elswick, B. A., Welsch, F., and Conolly, R. B. (1993).
Pharmacokinetics of 2-methoxyethanol and 2-methoxyacetic acid in the pregnant
mouse: a physiologically-based mathematical model. Toxicology and Applied
Pharmacology 121, 239-252.
162. Clausen, J., and Bickel, M. H. (1993). Prediction of drug distribution in dialysis and
in vivo from binding to tissues and blood. Journal of Pharmaceutical Sciences 82,
345-349.
163. Clewell, H. J. I, Lee, T. S., and Carpenter, R. L. (1994). Sensitivity of
physiologically based pharmacokinetic models to variation in model parameters -
methylene chloride. Risk Analysis 14, 521-531.
164. Clewell, H. J. L, Gentry, P. R., Gearhart, J. M., Allen, B. C., and Andersen, M. E.
(1995). Considering pharmacokinetic and mechanistic information in cancer risk
assessments for environmental contaminants: examples with vinyl chloride and
trichloroethylene. Chemosphere 31, 2561-2578.
165. Clewell, H. J. I. (1995). The application of physiologically based pharmacokinetic
modeling in human health risk assessment of hazardous substances. Toxicology
Letters 79, 207-217.
166. Clewell, H. J. L, Gentry, P. R., and Gearhart, J. M. (1997). Investigation of the
potential impact of benchmark dose and pharmacokinetic modeling in noncancer
risk assessment. Journal of Toxicology and Environmental Health 52, 475-515.
167. Clewell, H. J. L, Gentry, P. R., Covington, T. R., and Gearhart, J. M. (2000).
Development of a physiologically based pharmacokinetic model of
trichloroethylene and its metabolites for use in risk assessment. Environmental
Health Perspectives 108, 283-305.
168. Clewell, H. J. L, Gentry, P. R., Gearhart, J. M., Allen, B. C., and Andersen, M. E.
(2001). Comparison of cancer risk estimates for vinyl chloride using animal and
human data with a PBPK model. Sci.Total Environ. 274, 37-66.
169. Clewell, H. J. L, Teeguarden, J. G., McDonald, T., Sarangapani, R., Lawrence, G.
S., Covington, T. R., Gentry, R., and Shipp, A. (2002). Review and evaluation of the
potential impact of age- and gender-specific pharmacokinetic differences on tissue
dosimetry. Critical Reviews in Toxicology 32, 329-389.
170. Clewell, H. J., Ill, Gentry, P. R., Gearhart, J. M., Covington, T. R., Banton, M. L,
and Andersen, M. E. (2001). Development of a physiologically based
pharmacokinetic model of isopropanol and its metabolite acetone. Toxicol Sci. 63,
160-172.
171. Clewell, H. J. L, and Andersen, M. E. (1987). Dose, species and route extrapolation
using physiologically-based pharmacokinetic models. Drinking Water and Health 8,
159-182.
15
-------
172. Clewell, H. J. I. (1993). Coupling of computer modeling with in vitro
methodologies to reduce animal usage in toxicity testing. Toxicology Letters 68,
101-117.
173. Clewell, H. J. I, and Jarnot, B. M. (1994). Incoporation of pharmacokinetics in
noncancer risk assessment: example with chloropentafluorobenzene. Risk Analysis
14, 265-276.
174. Clewell, H. J. I, and Andersen, M. E. (1994). Physiologically-based
pharmacokinetic modeling and bioactivation of xenobiotics. Journal of Toxicology
and Environmental Health 10, 1-24.
175. Clewell, H. J. I, and Andersen, M. E. (1996). Use of physiologically-based
pharmacokinetic modeling to investigate individual versus population risk.
Toxicology 111, 315-329.
176. Clewell, H. J. I, Andersen, M. E., Wills, R. J., and Latriano, L. (1997). A
physiologically based pharmacokinetic model for retinoic acid and its metabolites.
Journal of American Academy of Dermatology 36, S77-S85.
177. Clewell, H. J. L, Gearhart, J. M., Gentry, P. R., Covington, T. R., VanLandingham,
C. B., Crump, K. S., and Shipp, A. M. (1999). Evaluation of the uncertainty in an
oral reference dose for methylmercury due to interindividual variability in
pharmacokinetics. Risk Anal. 19, 547-558.
178. Clewell, H. J. L, Andersen, M. E., and Barton, H. A. (2002). A Consistent Approach
for the Application of Pharmacokinetic Modeling in Cancer and Noncancer Risk
Assessment. Environmental Health Perspectives 110, 85-93.
179. Clewell, R. A., Merrill, E. A., and Robinson, P. J. (2001). The use of
physiologically based models to integrate diverse data sets and reduce uncertainty in
the prediction of perchlorate and iodide kinetics across life stages and species.
Toxicology and Industrial Health 17, 210-222.
180. Clewell, R. A., and Gearhart, J. M. (2002). Pharmacokinetics oftoxic chemicals in
breast milk: use of PBPK models to predict infant exposure. Environmental Health
Perspectives 110, A333-A337.
181. Clewell, R. A., Merrill, E. A., Yu, K. O., Mahle, D. A., Sterner, T. R., Fisher, J. W.,
and Gearhart, J. M. (2003). Predicting neonatal perchlorate dose and inhibition of
iodide uptake in the rat during lactation using physiologically-based
pharmacokinetic modeling. Toxicol Sci. 74, 416-436.
182. Clewell, R. A., Merrill, E. A., Yu K.O, Mahle, D. A., Sterner, T. R., Mattie, D. R.,
Robinson, P. J., Fisher, J. W., and Gearhart, J. M. (2003). Predicting fetal
perchlorate dose and inhibition of iodide kinetics during gestation: a
physiologically-based pharmacokinetic analysis of perchlorate and iodide kinetics in
the rat. Toxicol.Sci. 73, 235-255.
16
-------
183. Clewell, H.J., Gentry, P.R., Kester, I.E., Andersen, M.E. (2005). Evaluation of
physiologically based pharmacokinetic models in risk assessment: an example with
perchloroethylene. Crit Rev Toxicol 35(5), 413-433. Review. PMID: 16097137
[PubMed - indexed for MEDLINE]
184. Cohen, S. M., and Ellwein, L. B. (1990). Cell proliferation in carcinogenesis.
Science 249, 1007-1011.
185. Cohn, M. S. (1987). Sensitivity analysis in pharmacokinetic modeling. Drinking
Water and Health 8, 265-272.
186. Cole, C. E., Iran, H. T., and Schlosser, P. M. (2001). Physiologically based
pharmacokinetic modeling of benzene metabolism in mice through extrapolation
from in vitro to in vivo. Journal of Toxicology and Environmental Health 62, 439-
465.
187. Collins, A. S., Sumner, S. C. I, Borghoff, S. I, and Medinsky, M. A. (1999). A
physiological model for tert-amyl methyl ether and tert-amyl alcohol: hypothesis
testing of model structures. Toxicological Sciences 49, 15-28.
188. Collins, J. M., Dedrick, R. L., Flessner, M. F., and Guarino, A. M. (1982).
Concentration dependent disappearance of fluorouracil from peritoneal fluid in the
rat: Experimental observations and distributed modeling. Journal of Pharmaceutical
Sciences 71, 735-738.
189. Collins, J. M., and Dedrick, M. L. (1982). Contribution of lungs to total body
clearance: linear and nonlinear effects. Journal of Pharmaceutical Sciences 71, 66-
69.
190. Connell, D. W., Braddock, R. D., and Mani, S. V. (1993). Prediction of the partition
coefficient of lipophilic compounds in the air-mammal tissue system.
Sci.Total.Environ. Supplement. 1383-1396.
191. Conolly, R. B., and Kimbell, J. S. (1994). Computer simulation of cell growth
governed by stochastic processes: Application to clonal growth cancer models.
Toxicology and Applied Pharmacology 124, 284-295.
192. Conolly, R. B., and Butterworth, B. E. (1995). Biologically based dose response
model for hepatic toxicity: a mechanistically based replacement for traditional
estimates of noncancer risk. Toxicol.Lett. 82/83, 901-906.
193. Conolly, R. B., Limbell, J. S., Janszen D, Schlosser, P. M., Kalisak, D., Preston, J.,
and Miller, F. J. (2003). Biologically motivated computational modeling of
formaldehyde carcinogenicity in the F344 Rat. Toxicol.Sci. 75, 432-447.
194. Conolly, R. B., Reitz, R. H., Clewell, H. J. L, and Andersen, M. E. (1988).
Pharmacokinetics, biochemical mechanism and mutation accumulation: a
comprehensive model of chemical carcinogenesis. Toxicology Letters 43, 189-200.
17
-------
195. Conolly, R. B., and Andersen, M. E. (1991). Biologically Based Pharmacodynamic
Models: Tools for Toxicological Research And Risk Assessment.
Annu.Rev.Pharmacol.Toxicol. 31, 303-323.
196. Conolly, R. B., Krishnan, K., and Andersen, M. E. (1992). An overview of the
outstanding issues in the risk assessmnent of methylene chloride. In Oncogene and
transgenics correlates of cancer risk assessment. (C. Zervos, Ed.), pp. 217-229.
Plenum Press, New York.
197. Conolly, R. B., Lilly, P. D., and Kimbell, J. S. (2000). Simulation modeling of the
tissue disposition of formaldehyde to predict nasal DNA-protein cross-links in
Fischer 344 rats, rhesus monkeys, and humans. Environ.Health Perspect. 108 Suppl
5, 919-924.
198. Conolly, R. B. (2001). Biologically motivated quantitative models and the mixture
toxicity problem. Toxicol.Sci. 63, Review.
199. Corley, R. A., Mast, T. J., Carney, E. W., Rogers, J. M., and Daston, G. P. (2003).
Evaluation of physiologically based models of pregnancy and lactation for their
application in children's health risk assessments. Critical Reviews in Toxicology 33,
137-211.
200. Corley, R. A., Mandrela, A. L., and Smith, F. A. (1990). Development of a
physiologically-based pharmacokinetic model for chloroform. Toxicology and
Applied Pharmacology 103, 512-527.
201. Corley, R. A., Bormett, G. A., and Ghanayem, B. I. (1994). Physiologically based
pharmacokinetics of 2-butoxyethanol and its major metabolite, 2-butoxyacetic acid,
in rats and humans. Toxicology and Applied Pharmacology 129, 61-79.
202. Corley, R. A., Markham, D. A., Banks, C., Delorme, P., Masterman, A., and Houle,
J. M. (1997). Physiologically based pharmacokinetics and the dermal absorption of
2-butoxyethanol vapor by humans. Fundamental and Applied Toxicology 39, 120-
130.
203. Corley, R. A., Gordon, S. M., and Wallace, L. A. (2000). Physiologically based
pharmacokinetic modeling of the temperature-dependent dermal absorption of
chloroform by humans following bath water exposures. Toxicological Sciences 53,
13-23.
204. Corley, R. A., English, J. C., Hill, T. S., Fiorica, L. A., and Morgott, D. A. (2000).
Development of a physiologically based pharmacokinetic model for hydroquinone.
Toxicology and Applied Pharmacology 165, 163-174.
205. Cox Jr L.A (1996). Reassessing benzene risks using internal doses and Monte-Carlo
uncertainty analysis. Environmental Health Perspectives 104, 1413-1429.
18
-------
206. Cox, L. A. Jr., and Ricci, P. F. (1992). Reassessing benzene cancer risks using
internal doses. Risk Analysis 12, 401.
207. Cox, L. A. Jr. (1995). Simple relations between administered and internal doses in
compartmental flow models. Risk Analysis 15, 197-204.
208. Craigmill, A. L. (2003). A physiologically based pharmacokinetic model for
oxytetracycline residues in sheep. J Vet Pharmacol Ther. 26, 55-63.
209. Crank, W. D., and Vinegar, A. (1992). A physiologically-based pharmacokinetic
model for chloropentafluorobenzene in primates to be used in the evaluation of
protective equipment against toxic gases. Toxicology and Industrial Health 8, 21-35.
210. Cronin, W. J., Oswald, E. J., Shelley, M. L., Fisher, J. W., and Flemming, C. D.
(1995). A trichloroethylene risk assessment using a Monte Carlo analysis of
parameter uncertainty in conjunction with physiologically based pharmacokinetic
modeling. Risk Analysis 15, 555-565.
211. Cruzen, G., Carlson, G. P., Johnson, K. A., Andrews.L.S, Banton, M. I, Bevan, C.,
and Cushman, J. R. (2002). Styrene respiratory tract toxicity and mouse lung tumors
are mediated by CYP2F-generated metabolites. Regulatory Toxicology and
Pharmacology 35, 308-319.
212. Csanady, G. A., and Laib, R. J. (1990). Use of linear free energy relationships in
toxicology: prediction of partition coefficients of volatile lipophilic compounds.
Arch.Toxicol. 64, 594-596.
213. Csanady, G. A., Laib, R. J., and Filser, J. G. (1995). Metabolic transformation of
halogenated and other alkenes- a theoretical approach, Estimation of metabolic
reactivities for in vivo conditions. Toxicol.Lett. 75, 217-223.
214. Csanady, G. A., Kessler, W., Hoffmann, H. D., and Filser, J. G. (2003). A
toxicokinetic model for styrene and its metabolite styrene - 7,8-oxide in mouse, rat
and human with special emphasis on the lung. Toxicol.Lett. 138, 75-102.
215. Csanady, G. A., Mendrala, A. L., Nolan, R. J., and Filser, J. G. (1994). A
physiologic pharmacokinetic model for styrene and styrene-7,8-oxide in mouse, rat
and man. Archives of Toxicology 68, 143-157.
216. Csanady, G. A., Kreuzer, P. E., Baur, C., and Filser, J. G. (1996). A physiological
toxicokinetic model for 1,3-butadiene in rodents and man: blood concentrations of
1,3-butadiene, its metabolically formed epoxides, and of haemoglobin adducts -
relevance of glutathione depletion. Toxicology 113, 300-305.
217. Csanady, G. A., Denk, B., Putz, C., Kreuzer, P. E., Kessler, W., Baur, C., Gargas,
M. L., and Filser, J. G. (2000). A physiological toxicokinetic model for exogenous
and endogenous ethylene and ethylene oxide in rat, mouse, and human: formation of
19
-------
2-hydroxyethyl adducts with hemoglobin and DNA. Toxicology and Applied
Pharmacology 165, 1-26.
218. Cuddihy R.G., Griffith, W. C., and Boecker, B. B. Tissue weight in adult beagle
dogs. Lovelage foundation for medical education and research, november 1972,
106-111. 1971. Albuquerque ,New Mexico., Staff of the fission product inhalation
program. Fission product inhalation program annual report.
Ref Type: Report
219. Cuddihy R.G., Hall, R. P., Hobbs, C. H., Boecker, B. B., and Muggenburg, B. A.
Tissue blood volumes and weights in adult beagle dogs. Lovelage foundation for
medical education and research, november 1972, 119-134. 1971. Albuquerque, New
Mexico, Staff of the fission product inhalation program. Fission product inhalation
program annual report.
Ref Type: Report
220. D'Souza, R. W., and Andersen, M. E. (1988). Physiologically-based
pharmacokinetic model for vinylidine chloride. Toxicology and Applied
Pharmacology 95, 230-240.
221. D'Souza, R. W., Francis, W. R., and Andersen, M. W. (1988). Physiological model
for tissue glutathione depletion and increased resynthesis after ethylene dichloride
exposures. Journal of Pharmacology and Experimental Therapeutics 245, 563-568.
222. D'Souza, R. W., Francis, W. R., Bruce, R. D., and Andersen, M. E. (2000).
Physiologically based pharmacokinetic model for ethylene dichloride and its
application in risk assessment, inconnu 286-301.
223. da Silva, M. L., Charest-Tardif, G, Krishnan, K., and Tardif, R. (1999). Influence of
oral administration of a quaternary mixture of trihalomethanes on their blood
kinetics in the rat. Toxicology Letters 106, 49-57.
224. da Silva, M. L., Charest-Tardif, G., Krishnan, K., and Tardif, R. (2000). Evaluation
of the pharmacokinetic interactions between orally administered trihalomethanes in
the rat. Journal of Toxicology and Environmental Health 60, 343-353.
225. Dallas, C. E., Bruckner, J. V., Megden, J. L., and Weir, F. W. (1986). A method for
direct measurement of systemic uptake and elimination of volatile organics in small
mammals. Journal of Pharmacological Methods 16, 239-250.
226. Dallas, C. E., Ramanthan, R., Muralidhara, S., Gallo, G. M., and Bruckner, J. V.
(1989). The uptake and elimination of 1,1,1-trichloroethane during the following
inhalation exposures in rats. Toxicology and Applied Pharmacology 98, 385-397.
227. Dallas, C. E., Gallo, J. M., Ramanthan, R., Muralidhara, S., and Bruckner, J. V.
(1991). Physiological pharmacokinetic modeling of inhaled trichloroethylene in rats.
Toxicology and Applied Pharmacology 110, 303-314.
20
-------
228. Dallas, C. E., Muralidhara, S., Chen, X. M., Ramanthan, R., Varkonyl, P., Gallo, J.
M., and Bruckner, J. V. (1994). Use of a physiologically based model to predict
systemic uptake and respiratory elimination of perchloroethylene. Toxicology and
Applied Pharmacology 128 , 60-68.
229. Dallas, C. E., Chen, X. M., O'Barr, K., Muralidhara, S., Varkonyl, P., and Bruckner,
J. V. (1994). Development of a physiologically based pharmacokinetic model for
perchloroethylene using tissue concentration-time data. Toxicology and Applied
Pharmacology 128, 50-59.
230. Dallas, C. E., Chen, X. M., Muralidhara, S., Varkonyl, P., Tackett, R. L., and
Bruckner, J. V. (1994). Use of tissue disposition data from rats and dogs to
determine species differences for a physiological model for perchloroethylene.
Environmental Research 67, 54-67.
231. Dallas, C. E., Chen, X. M., Muralidhara, S., Varkonyl, P., Tackett.L., and Bruckner,
J. V. (1995). Physiologically based pharmacokinetic model useful in prediction of
the influence of species, dose, and exposure route on perchloroethylene
pharmacokinetics. Journal of Toxicology and Environmental Health 44, 301-317.
232. Dalley, J. W., Gupta, P. K., and Hung, C. T. (1990). A physiological
pharmacokinetic model describing the disposition of lead in the absence and
presence of 1-ascorbic acid in rats. Toxicology Letters 50, 337-348.
233. Dankovic, D. A., and Bailer, A. J. (1994). The impact of exercise and intersubject
variability on dose estimates for dichloromethane derived from a physiologically
based pharmacokinetic model. Fundamental and Applied Toxicology 22, 20-25.
234. Davies, B., and Morris, T. (1993). Physiological parameters in laboratory animals
and humans. Pharmaceutical Research 10, 1093-1095.
235. Davis, N. R., and Mapleson, W. W. (1993). A physiological model for the
distribution of injected agents, with special reference to pethidine. British Journal of
Anaesthesia 70, 248-258.
236. de Jongh, J., and Blaauboer, B. J. (1996). In vitro-based and in vivo-based
simulations of benzene uptake and metabolism in rats. ALTA 24, 179-190.
237. de Jongh, J., and Blaauboer, B. J. (1996). Simulation of toluene kinetics in the rat by
a physiologically based pharmacokinetic model with application of
biotransformation parameters derived independently in vitro and in vivo.
Fundamental and Applied Toxicology 32, 260-268.
238. de Jongh, J., and Blaauboer, B. J. (1997). Simulation of lindane kinetics in rats.
Toxicology 122, 1-9.
239. de Jongh, J., and Blaauboer, B. J. (1997). Evaluation of in vitro-based simulations of
toluene uptake and metabolism in rats. Toxicology in Vitro 11, 485-489.
21
-------
240. de Jongh, I, Verhaar, H. J. M., and Hermens, J. L. M. (1998). Role of kinetic in
acute lethality of nonreactive volatile organic compounds (VOCs). Toxicological
Sciences 45, 26-32.
241. Dedrick, R. L., and Bischoff, K. B. (1968). Pharmacokinetics in applications of the
artificial kidney. Chemical Engineering Progress Symposium Series 64, 32-44.
242. Dedrick, R. L., Forrester, D. D., and Ho, D. H. W. (1972). In vitro-in vivo
correlation of drug metabolism: Deamination of l-|3-D-arabinosyl cytosine.
Biochemical Pharmacology 21, 1-16.
243. Dedrick, R. L., Zaharko, D. S., and Lutz, R. J. (1973). Transport and binding of
methotrexate in vivo. Journal of Pharmaceutical Sciences 62, 882-890.
244. Delic, J. I, Lilly, P. D., MacDonald, A. J., and Loizou, G. D. (2000). The utility of
PBPK in the safety assessment of chloroform and carbon tetrachloride. Regulatory
Toxicology and Pharmacology 32, 144-155.
245. 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. American
Journal of Physiology 261, H1487-1493.
246. Dennison, J. E., Andersen, M. E., and Yang, R. S. (2003). Characterization of the
phramacokinetics of gasoline using PBPK modeling with a complex mixtures
chemical lumping approach. Inhal.Toxicol. 15, 961-986.
247. Derks, H. J. G. M., Berende, P. L. M., Oiling, M., Everts, H., Liem, A. K. D., and de
Jongh, A. P. J. M. (1994). Pharmacokinetic modeling of poly chlorinated dibenzo-p-
dioxins (PCDDs) and furans (PCDFS) in cows. Chemosphere 28, 711-715.
248. Derr, R. F. (1993). Simulation studies on ethanol metabolism in different human
populations with a physiological pharmacokinetic model. Journal of Pharmaceutical
Sciences 82, 677-682.
249. Dietz, K. F., Rodriguez-Giaxola, M., Traiger, G. J., Stella, V. J., and Himmelstein,
K. J. (1981). Pharmacokinetics of 2-butanol and its metabolites in the rat. Journal of
Pharmacokinetics and Biopharmaceutics 9, 553-576.
250. Dills, R. L., Ackerlund, W. S., Kalman, D. A., and Morgan, M. S. (1993). Blood/air
partition coefficient determinations: automation and improvement of the equilibrium
partitioning in closed systems method. J.Exposure Analysis.Environ.Epidemiology.
3, 471-489.
251. Dills, R. L., Enderlein, C., Ackerlund, W. S., Kalman, D. A., and Morgan, M. S.
(1994). Investigation of the relationship between chemical concentration and the
blood/air partition coefficient. J.Exposure Analysis.Environ.Epidemiology. 4, 343-
353.
22
-------
252. Dills, R. L., Ackerlund, W. S., Kalman, D. A., and Morgan, M. S. (1994). Inter-
individual variability in blood/air partitioning of volatile organic compounds and
correlation with blood chemistry. Journal of Exposure Analysis and Environmental
Epidemiology 4, 229-245.
253. DiStefano, J. J. I. (1982). Noncompartmental vs. compartmental analysis: some
basis for choice. American Journal of Physiology 243, R1-R6.
254. Do Luu, H. M., and Hutter, J. C. (2000). Pharmacokinetic modeling of 4,4'-
methylenedianiline released from reused polyurethane dialyzer potting materials.
Journal of Biomedical Materials Research 53, 276-286.
255. Dobbs, A. J., and Williams, N. (1983). Fat solubility : a property of environmental
relevance? Chemosphere 12 , 97-104.
256. Dobrev, I. D., Andersen, M. E., and Yang, R. S. (2002). In silico toxicology:
simulating interaction thresholds for human exposure to mixtures of
trichloroethylene, tetrachloroethylene, and 1,1,1-trichloroethane. Environmental
Health Perspectives 110, 1031-1039.
257. Dobrev, I. D., Reddy, M. B., Plotzke, K. P., Varaprath, S., McNett, D. A., Durham,
J., and Andersen, M. E. (2003). Closed- chamber inhalation pharmacokinetic studies
with hexamethyldisiloxane in the rat. Inhal.Toxicol 15, 589-617.
258. Dobrev, I. D., Andersen, M. E., and Yang, R. S. (2001). Assessing interaction
thresholds for trichloroethylene in combination with tetrachloroethylene and 1,1,1-
trichloroethane using gas uptake studies and PBPK modeling. Arch.Toxicol 75,
134-144.
259. Domenech, R. J., Hoffman, J. E., Noble, M. M., Saunder, K. B., Hensen, J. R., and
Subijanto, S. (1969). Total and regional coronary blood flow measured by
radioactive microsphere in conscious and anesthetized dogs. Circulation Research
25, 581-596.
260. Dong, M. H. (1994). Microcomputer programs for physiologically-based
pharmacokinetic (PB-PK) modelling. Computer Methods and Programs in
Biomedecine 45, 213-221.
261. Dome, J. L. C. M., Walton, K., and Renwick, A. G. (2001). Uncertainty factors for
chemical risk assessement: human variability in the pharmacokinetics of CYP1A2
probe substrates. Food and Chemical Toxicology 39, 681-696.
262. Dome, J. L. C. M., Walton, K., and Renwick, A. G. (2001). Human variability in
glucuronidation in relation to uncertainty factors for risk assessment. Food and
Chemical Toxicology 39, 1153-1173.
23
-------
263. Dome, J. L. C. M., Walton, K., Slob, W., and Renwick, A. G. (2002). Human
variability in polymorphic CYP2D6 metabolism: is the kinetic default uncertainty
factor adequate? Food and Chemical Toxicology 40, 1633-1656.
264. Dourson, M. L., Felter, S. P., and Robinson, D. (1996). Evolution of science-based
uncertainty factors in noncancer risk assessment. Regulatory Toxicology and
Pharmacology 24, 108-120.
265. Dourson, M. L., Andersen, M. E., Erdreich, L. S., and MacGregor, J. A. (2001).
Using human data to protect the public's health. Regulatory Toxicology and
Pharmacology 33, 234-256.
266. Droz, P. O. (1986). Simulation models for organic solvents. In Progress in clinical
and biological research, pp. 73-87. Alan R. Liss Inc., New York.
267. Droz, P. O., Berode, M., and Jang, J. Y. (1999). Biological monitoring of
tetrahydrofuran: contribution of a physiologically based pharmacokinetic model.
American Industrial Hygiene Association Journal 60, 243-248.
268. Dunn, W. J., and Wold, S. (1978). Statistical analysis of the partition coefficient.
Acta Chemica Scandinavia B32, 536-542.
269. Dybing, E. (2003). Panel discussion: application of physiological-toxicokinetic
modelling. Toxicol Lett. 138, 173-178.
270. Easterling, M. R., Evans, M. V., and Kenyon, E. M. (2000). Comparative analysis
of software for physiologically based pharmacokinetic modeling : simulation,
optimization, and sensitivity analysis. Toxicology Methods 10, 203-229.
271. Ebling, W. F., Wada, D. R., and Stanski, D. R. (1994). From piecewise to full
physiologic pharmacokinetic modeling - Applied to thiopental disposition in the rat.
Journal of Pharmacokinetics and Biopharmaceutics 22, 259-292.
272. Edler, L. (1999). Uncertainty in biomonitoring and kinetic modeling. Ann N Y Acad
Sci. 895, 80-100.
273. Edler, L., and Portier, C. J. (1992). Uncertainty in physiological pharmacokinetic
modeling and its impact on statistical risk estimation of 2,3,7,8-TCDD.
Chemosphere 25, 239-242.
274. El-Marsi, H. A., Reardon, K. F., and Yang, R. S. H. (1997). Integrated approaches
for the analysis of toxicologic interactions of chemical mixtures. Critical Reviews in
Toxicology 27, 175-197.
275. El-Masri, H. A., Bell, D. A., and Portier, C. J. (1999). Effects of glutathione
transferase theta polymorphism on the risk estimates of dichloromethane to humans.
Toxicology and Applied Pharmacology 158, 221-230.
24
-------
276. El-Masri, H. A., Thomas, R. S., Benjamin, S. A., and Yang, R. S. H. (1995).
Physiologically based pharmacokinetic/pharmacodynamic modeling of chemical
mixtures and possible applications in risk assessment. Toxicology 105, 275-282.
277. El-Masri, H. A., Thomas, R. S., Sabados, G. R., Phillips, J. K., Constan, A. A.,
Benjamin, S. A., Andersen, M. E., Mehendale, H. M., and Yang, R. S. H. (1996).
Physiologically based pharmacokinetic/pharmacodynamic modeling of the
toxicologic interaction between carbon tetrachloride and kepone. Archives of
Toxicology 70, 704-713.
278. El-Masri, H. A., Tessari, J. D., and Yang, R. S. H. (1996). Exploration of an
interaction threshold for the joint toxicity of trichloroethylene and 1,1-
dichloroethylene: utilization of a PBPK model. Archives of Toxicology 70, 527-
539.
279. El-Masri, H. A., Constan, A. A., Ramsdell, H. S., and Yang, R. S. H. (1996).
Physiologically based pharmacokinetic modeling of an interaction threshold
between trichloroethylene and 1,1-dichloroethylene in Fisher 344 rats. Toxicology
and Applied Pharmacology 141, 124-132.
280. El-Masri, H. A., and Portier, C. J. (1998). Physiologically based pharmacokinetics
models of primidone and its metabolites phenobarbital and phenylethymalonamide
in humans, rats, and mice. Drug Metabolism and Disposition 26, 585-594.
281. Ellis, M. K., Trebilcock, R., Naylor, J. L., Tseung, K., Collins, M. A., Hext, P. M.,
and Green, T. (1996). The inhalation toxicology, genetic toxicology, and
metabolism of difluoromethane in the rat. Fundamental and Applied Toxicology 31,
243-251.
282. Emond, C., Michalek, I.E., Birnbaum, L.S., DeVito, M.J. (2005) Comparison of the
use of a physiologically based pharmacokinetic model and a classical
pharmacokinetic model for dioxin exposure assessments. Environ Health Perspect
113(12),1666-1668. PMID: 16330344 [PubMed - in process]
283. Erickson, R. J., and McKim, J. M. (1990). A simple flow-limited model for
exchange of organic chemicals at fish gills. Environmental Toxicology and
Chemistry 9, 159-165.
284. Evans, M. V., Boyes, W. K., Simmons, J. E., Litton, D. K., and Easterling, M. R.
(2002). A comparson of Haber's rule at different ages using a physiologically based
pharmacokinetic (PBPK) model for chloroform in rats. Toxicology. 176, 11-23.
285. Evans, M. V., Crank, W. D., Yang, H. M., and Simmons, J. E. (1994). Applications
of sensitivity analysis to a physiologically based pharmacokinetic model for carbon
tetrachloride in rats. Toxicology and Applied Pharmacology 128, 36-44.
286. Evans, M. V., and Andersen, M. E. (1995). Sensitivity analysis and the design of
gas uptake inhalation studies. Inhalation Toxicology 7, 1075-1094.
25
-------
287. Evans, M. V., and Simmons, J. E. (1996). Physiologically based pharmacokinetic
estimated metabolic constants and hepatotoxicity of carbon tetrachloride after
methanol pretreatment in rats. Toxicology and Applied Pharmacology 140, 245-253.
288. Evans, M. V., and Andersen, M. E. (2000). Sensitivity analysis of a physiological
model for 2,3,7,8-tetrachlorocdibenzo-p-dioxin (TCDD):assessing the impact of
specific model parameters on sequestration in liver and fat in the rat. Toxicological
Sciences 54, 71-80.
289. Evelo, C. T., Oostendorp, J. G., ten Berge, W. F., and Borm, P. J. (1993).
Physiologically based toxicokinetic modeling of 1,3-butadiene lung metabolism in
mice becomes more important at low doses. Environmental Health Perspectives
101, 496-502.
290. Farrar, D., Allen, B., Crump, K., and Shipp, A. (1989). Evaluation of uncertainty in
input parameters to pharmacokinetic models and the resulting uncertainty in output.
Toxicology Letters 49, 371-385.
291. Farris, F. F., Dedrick, R. L., and King, F. G. (1988). Cisplatin pharmacokinetics:
Applications of a physiological model. Toxicology Letters 43, 117-137.
292. Farris, F. F., Dedrick, R. L., Allen, P. V., and Smith, J. C. (1993). Physiological
model for the pharmacokinetics of methylmercury in the growing rat. Toxicology
and Applied Pharmacology 119, 74-90.
293. Faustman, E. M., Lewandowski, T. A., Ponce, R. A., and Bartell, S. M. (1999).
Biologically based dose-response models for developmental toxicants:essons from
Methylmercury. Inhal.Toxicol 11, 559-572.
294. Ferl, G.Z., Wu, A.M., DiStefano, J.J. 3rd. (2005). A predictive model of therapeutic
monoclonal antibody dynamics and regulation by the neonatal Fc receptor (FcRn).
Ann Biomed Eng 33(11), 1640-1652. PMID: 16341929 [PubMed - indexed for
MEDLINE]
295. Fernandez, J. G., Droz, P. O., Humbert, B. E., and Caperos, J. R. (1977).
Trichloroethylene exposure simulation of uptake, excretion and metabolism using a
mathematical model. British Journal of Industrial Medicine 34, 43-55.
296. Filser, J. G., and Bolt, H. M. (1981). Inhalation pharmacokinetics based on gas
uptake studies. I. Improvement of kinetic models. Archives of Toxicology 47, 279-
292.
297. Filser, J. G., Johanson, G., Kessler, W., Kreuzer, P. E., Stei, P., Baur, C., and
Csanady, G. A. (1993). A pharmacokinetic model to describe toxicokinetic
interactions between 1,3-butadiene and styrene in rats: predictions for human
exposure. IARC Scientific Publications 127, 65-78.
26
-------
298. Filser, J. G., Csanady, G. A., Kreuzer, P. E., and Kessler, W. (1995). Toxicokinetic
models for volatile industrial chemicals and reactive metabolites. Toxicology
Letters 82/83, 357-366.
299. Filser, J. G., Csanady, G. A., Denk, B., Hartmann, M., Kauffman, A., Kessler, W.,
Kreuzer, P. E., Putz, C., Shen, J. H., and Stei, P. (1996). Toxicokinetics of isoprene
in rodents and humans. Toxicology 113, 278-287.
300. Filser, J. G., Schmidbauer, R., Rampf, F., Baur, C. M., Putz, C., and Csanady, G. A.
(2000). Toxicokinetics of inhaled propylene in mouse, rat, and human. Toxicology
and Applied Pharmacology 169, 40-51.
301. Fiserova-Bergerova, V. (1975). Mathematical modeling of inhalation exposure.
Journal of Combustion Toxicology 32, 201-210.
302. Fiserova-Bergerova, V., Tichy, M., and Di Carlo, F. J. (1984). Effects of
biosolubility on pulmonary uptake and disposition of gases and vapors of lipophilic
chemicals. Drug Metabolism Reviews 15, 1033-1070.
303. Fiserova-Bergerova, V. Toxicokinetics of organic solvents. Scandinavian Journal of
Work Environment and Health ll(suppl. 1), 7-21. 1985.
Ref Type: Abstract
304. Fiserova-Bergerova, V., and Diaz, M. L. (1986). Determination and prediction of
tissue-gas partition coefficients. International Archives of Occupational and
Environmental Health 58, 75-87.
305. Fiserova-Bergerova, V. (1992). Inhalation anesthesia using physiologically based
pharmacokinetic models. Drug Metabolism Reviews 24, 531-557.
306. Fiserova-Bergerova, V. (1995). Extrapolation of physiological parameters for
physiologically based simulation models. Toxicology Letters 79, 77-86.
307. Fisher, D. M. (1996). (Almost) Everything You Learned About Pharmacokinetics
Was (Somewhat) Wrong ! Anesthesiology and Analgesia 83, 901-903.
308. Fisher, J., Mahle, D., Bankston, L., Greene, R., and Gearhart, J. (1997). Lactational
transfer of volatile chemicals in breast milk. Industrial Hygiene Association Journal
58,425-431.
309. Fisher, J. W. (2003). PBPK modeling advances understanding of D4
Pharmacokinetics. Toxicol Sci. 72, 2.
310. Fisher, J. W., Whittaker, T. A., Taylor, D. H., Clewell, H. J., and Andersen, M. E.
(1989). Physiologically-based pharmacokinetic modeling of the pregnant rat:
Multiroute exposure model for trichloroethylene and trichloroacetic acid.
Toxicology and Applied Pharmacology 99, 395-414.
27
-------
311. Fisher, J. W., Whittaker, T. A., Taylor, D. H., Clewell, H. 1, and Andersen, M. E.
(1990). Physiologically-based pharmacokinetic modeling of the lactating rat and
nursing pup: a multiroute exposure model for trichloroethylene and its metabolite,
trichloroacetic acid. Toxicology and Applied Pharmacology 102, 497-513.
312. Fisher, J. W., Gargas, M. L., Jepson, G. W., Allen, B., and Andersen, M. E. (1991).
Physiologically-based pharmacokinetic modeling with trichloroethylene and its
metabolite, trichloroacetic acid in the rat and mouse. Toxicology and Applied
Pharmacology 109, 183-195.
313. Fisher, J. W., and Allen, B. C. (1993). Evaluating the risk of liver cancer in human
exposed to trichloroethylene using physiological models. Risk Analysis 13, 87-95.
314. Fisher, J. W., Mahle, D., and Abbas, R. (1998). A human physiologically based
pharmacokinetic model for trichloroethylene and its metabolites, trichloroacetic acid
and free trichloroethanol. Toxicology and Applied Pharmacology 152, 339-359.
315. Fisher, J. W. (2000). Physiologically based pharmacokinetic models for
trichloroethylene and its oxidative metabolites. Environmental Health Perspectives
108,265-273.
316. Fisher, J. W. (2003). PBPK modeling advances understanding of D4
pharmacokinetics. Toxicol.Sci. 72, 1-2.
317. Fisher, J.W., Campbell, J., Muralidhara, S., Bruckner, J.V., Ferguson, D., Mumtaz,
M., Harmon B, Hedge JM, Crofton KM, Kim H, Almekinder TL. (2006). Effect of
PCB 126 on hepatic metabolism of thyroxine and perturbations in the hypothalamic-
pituitary-thyroid axis in the rat. Toxicol Sci 90(1), 87-95. Epub 2005 Dec 9.
PMID: 16339789 [PubMed - in process]
318. Flemming, D. E. B., Chettle, D. R., Webber, C. E., and O'Flaherty, E. J. (1999). The
O'Flaherty model of lead kinetics: an evaluation using data from a lead smelter
population. Toxicology and Applied Pharmacology 161, 100-109.
319. Fouchecourt, M. O., Beliveau, M., and Krishnan, K. (2001). Quantitative structure-
pharmacokinetic relationship modelling. Sci.Total Environ. 274, 125-135.
320. Franks, S.J., Spendiff, M.K., Cocker, J., Loizou, G.D. (2006). Physiologically based
pharmacokinetic modelling of human exposure to 2-butoxyethanol. Toxicol Lett
162(2-3),164-173. Epub 2005 Oct 24. PMID: 16246510 [PubMed - in process]
321. Frederick, C. B. (1995). Summary of panel discussion on the
advantages/limitations/uncertainties in the use of physiologically based
pharmacokinetic and pharmacodynamic models in hazard identification and risk
assessment of toxic substances. Toxicol.Lett. 79, 201-209.
322. Frederick, C. B., Lomax, L. G., Black, K. A., Finch, L., Scribner, H. E., Kimbell, J.
S., Morgan, K., Subramaniam, R. P., and Morris, J. B. (2002). Use of a hybrid
28
-------
computational fluid dynamics and physiologically based inhalation model for
interspecies dosimetry comparisons of ester vapors. Toxicology and Applied
Pharmacology 183, 23-40.
323. Frederick, C. B., Potter, D. W., Chang-Mateu, M. I, and Andersen, M. E. (1992). A
physiologically-based pharmacokinetic and pharmacodynamic model to describe the
oral dosing of rats with ethyl acrylate and its implications for risk assessment.
Toxicology and Applied Pharmacology 114, 246-260.
324. Frederick, C. B. (1993). Limiting the uncertainty in risk assessment by the
development of physiologically based pharmacokinetic and pharmacodynamic
models. Toxicology Letters 68, 159-175.
325. Frederick, C. B., Morris, J. B., Kimbell, J. S., Morgan, K. T., and Scherer, P. W.
(1994). Comparison of four biologically based dosimetry models for the deposition
of rapidly metabolized vapors in the rodent nasal cavity. Inhalation Toxicology 6,
135-157.
326. Frederick, C. B., Bush, M. L., Lomax, L. M., Black, K. A., Finch, L., Kimbell, J. S.,
Morgan, K. T., Subramaniam, R. P., Morris, J. B., and Ultman, J. S. (1998).
Application of a hybrid computational fluid dynamics and physiologically based
inhalation model for interspecies dosimetry extrapolation of acidic vapors in the
upper airways. Toxicology and Applied Pharmacology 152, 211-231.
327. Frederick, C. B., Gentry, P. R., Bush, M. L., Lomax, L. G., Black, K. A., Finch, L.,
Kimbell, J. S., Morgan, K. T., Subramaniam, R. P., Morris, J. B., and Ultman, J. S.
(2001). A hybrid computational fluid dynamics and physiologically based
pharmacokinetic model for comparison of predicted tissue concentrations of acrylic
acid and other vapors in the rat and human nasal cavities following inhalation
exposure. Inhal.Toxicol 13 , 359-376.
328. Freeman, R. A., Rozman, K. K., and Wilson, A. G. E. (1989). Physiological
pharmacokinetic model of hexachlorobenzene in the rat. Health Physics 57, 139-
147.
329. Furtaw, E. J. Jr. (2001). An overview of human exposure modeling activities at the
USEPA's National Exposure Research Laboratory. Toxicology and Industrial Health
17,302-314.
330. Gabrielsson, J., and Bondesson, U. (1987). Constant-rate infusion of nicotine and
cotinine. 1. A physiological pharmacokinetic analysis of the cotinine disposition,
and effects on clearance and distribution in the rat. Journal of Pharmacokinetics and
Biopharmaceutics 15, 583-599.
331. Gabrielsson, J. L., Paalkow, L. K., and Nordstrom, L. (1987). A physiologically-
based pharmacokinetic model for theophylline disposition in the pregnant and
nonpregnant rat. Journal of Pharmacokinetics and Biopharmaceutics 12, 149-165.
29
-------
332. Gallo, J. M., Lam, F. C., and Perrier, D. G. (1987). Area method for the estimation
of partition coefficients for physiological pharmacokinetic models. Journal of
Pharmacokinetics and Biopharmaceutics 15, 271-280.
333. Gallo, J. M., Cheung, L. L., Kim, H. J., Bruckner, J. V., and Gillespie, W. R. (1993).
A physiological and system analysis hybrid pharmacokinetic model to characterize
carbon tetrachloride blood concentrations following administration in different oral
vehicles. Journal of Pharmacokinetics and Biopharmaceutics 21, 551-574.
334. Gargas, M. L., Andersen, M. E., and Clewell, H. J. (1986). A physiologically-based
simulation approach for determining metabolic rate constants from gas uptake data.
Toxicology and Applied Pharmacology 86, 341-352.
335. Gargas, M. L., Clewell, H. J., and Andersen, M. E. (1986). Metabolism of inhaled
dihalomethanes in vivo: Differentiation of kinetic constants for two independent
pathways. Toxicology and Applied Pharmacology 82, 211-223.
336. Gargas, M. L., Seybold, P. G., and Andersen, M. E. (1988). Modeling the tissue
solubilities and metabolic rate constants (Vmax) of halogenated methanes, ethanes
and ethylenes. Toxicology Letters 43, 235-256.
337. Gargas, M. L., and Andersen, M. E. (1988). Physiologically based approaches for
examining the pharmacokinetics of inhaled vapors. In Toxicology of the lung. (D. E.
Gardner, J. D. Crapo, and E. J. Massaro, Eds.), pp. 449-476. Raven Press, New
York.
338. Gargas, M. L., and Andersen, M. E. (1989). Determinating the kinetic constants of
chlorinated ethane metabolism in the rat from rates of exhalation. Toxicology and
Applied Pharmacology 99, 344-353.
339. Gargas, M. L., Burgess, R. J., Voisard, D. E., Cason, G. H., and Andersen, M. E.
(1989). Partition coefficients of low molecular weight volatile chemicals in various
liquids and tissues. Toxicology and Applied Pharmacology 98, 87-99.
340. Gargas, M. L. (1990). An exhaled breath chamber system for assessing rates of
metabolism and rates of gastrointestinal absorption with volatile chemicals. Journal
of American College of Toxicology 9, 447-453.
341. Gargas, M. L., Clewell, H. J., and Andersen, M. E. (1990). Gas uptake inhalation
techniques and the rates of metabolism of chloromethanes, chloroethanes and
chloroethylenes in the rat. Inhalation Toxicology 2, 295-319.
342. Gargas, M. L., Medinsky, M. A., and Andersen, M. E. (1995). Pharmacokinetic
modeling approaches for describing the uptake, systemic distribution, and
disposition of inhaled chemicals. Critical Reviews in Toxicology 25, 237-254.
343. Gargas, M. L., Andersen, M. E., Teo, S. K., Batra, R., Fennell, T. R., and Kedderis,
G. L. (1995). A physiologically based dosimetry description of acrylonitrile and
30
-------
cyanoethylene oxide in the rat. Toxicology and Applied Pharmacology 134, 185-
194.
344. Gargas, M. L., Tyler, T. R., Sweeney, L. M., Corley, R. A., Weitz, K. K., Mast, T.
J., Paustenbach, D. J., and Hays, S. M. (2000). A toxicokinetic study of inhaled
ethylene glycol monomethyl ether (2-ME) and validation of a physiologically based
pharmacokinetic model for the pregnant rat and human. Toxicology and Applied
Pharmacology 165, 53-62.
345. Gargas, M. L., Tyler, T. R., Sweeney, L. M., Corley, R. A., Weitz, K. K., Mast, T.
J., Paustenbach, D., and Hays, S. M. (2000). A toxicokinetic study of inhaled
ethylene glycol ethyl ether acetate and validation of a physiologically based
pharmacokinetic model for rat and human. Toxicology and Applied Pharmacology
165, 63-73.
346. Gear, C. W. (1971). Numerical Initial Value Problems in Ordinary Differential
Equations. Prentice-Hall, Englewoods Cliffs.
347. Gearhart, J. M., Jepson, G. W., Clewell, H. J., Andersen, M. E., and Conolly, R. B.
(1990). Physiologically based pharmacokinetic and pharmacodynamic model for the
inhibition of acetylcholinesterase by diisopropylfluorophosphate. Toxicology and
Applied Pharmacology 106 , 295-310.
348. Gearhart, J. M., Mahle, D. A., Greene, R. J., Seckel, C. S., Flemming, C. D., Fisher,
J. W., and Clewell, H. J. I. (1993). Variability of physiologically based
pharmacokinetic (PBPK) model parameters and their effects on PBPK model
predictions in risk assessment for perchloroethylene (PCE). Toxicology Letters 68,
131-144.
349. Gearhart, J. M., Seckel, C., and Vinegar, A. (1993). In vivo metabolism of
chloroform in B6C3F1 mice determined by the method of gas uptake: the effects of
body temperature on tissue partition coefficients and metabolism. Toxicology and
Applied Pharmacology 119, 258-266.
350. Gearhart, J. M., Jepson, G. W., Clewell, H. J., Andersen, M. E., and Conolly, R. B.
(1994). Physiologically based pharmacokinetic model for the inhibition of
acetylcholinesterase by organophosphate esters. Environmental Health Perspectives
102,51-60.
351. Gearhart, J. M., Clewell, H. J. L, Crump, K. S., Shipp, A. M., and Silvers, A.
(1995). Pharmacokinetic dose estimates of mercury in children and dose-response
curves of perfomance tests in a large epidemiological study. Water,Air ,and Soil
Pollution 80, 49-58.
352. Gelman, A., Bois, F., and Jiang, J. (1996). Physiological pharmacokinetic analysis
using population modeling and informative prior distributions. Journal of the
American Statistical Association 91, 436.
-------
353. Gentry, P. R., Covington, T. R., Andersen, M. E., and Clewell, H. J. I. (2002).
Application of a physiologically based pharmacokinetic model for isopropanol in
the derivation of a reference dose and reference concentration. Regulatory
Toxicology and Pharmacology 36, 51-68.
354. Gentry, P. R., Covington, T. R., Clewell, H. J. I, and Andersen, M. E. (2003).
Application of a physiologically based pharmacokinetic model for reference dose
and reference concentration estimation for acetone. J.Toxicol Environ.Health A 66,
2209-2225.
355. Gentry, P. R., Covington, T. R., and Clewell, H. J. I. (2003). Evaluation of the
potential impact of pharmacokinetic differences on tissue dosimetry in offspring
during pregnancy and lactation. Regulatory Toxicology and Pharmacology 38, 1-16.
356. Gentry, P. R., Covington, T. R., Mann, S., Shipp, A. M., Yager, J. W., and Clewell,
H. J., Ill (2004). Physiologically based pharmacokinetic modeling of arsenic in the
mouse. J.Toxicol Environ.Health A 67, 43-71.
357. Georgopoulos, P. G., Roy, A., and Gallo, M. A. (1994). Reconstruction of short-
term multi-route exposure to volatile organic compounds using physiologically
based pharmacokinetic models. Journal of Exposure Analysis and Environmental
Epidemiology 4, 309-328.
358. Georgopoulos, P. G., Walia, A., Roy, A., and Lioy, P. J. (1997). Integrated exposure
and dose modeling and analysis system. I. Formulation and testing of
microenvironmental and pharmacokinetic components. Environmental Science and
Technology 31, 17-27.
359. Gerde, P., and Dalh, A. R. (1991). A model for the uptake of inhaled vapors in the
nose of the dog during cyclic breathing. Toxicology and Applied Pharmacology
109, 276-288.
360. Gerde, P., Muggenburg, B. A., Sabourin, P. J., Harkema, J. R., Hotchkiss, J. A.,
Hoover, M. D., and Henderson, R. F. (1993). Disposition of polycyclic aromatic
hydrocarbons in the respiratory tract of the beagle dog. II. The conducting airways.
Toxicology and Applied Pharmacology 121, 319-327.
361. Gerde, P., Muggenburg, B. A., Hoover, M. D., and Henderson, R. F. (1993).
Disposition of polycyclic aromatic hydrocarbons in the respiratory tract of the
beagel dog. I. The alveolar region. Toxicology and Applied Pharmacology 121,
313-318.
362. Gerde, P., Muggenburg, B. A., and Henderson, R. F. (1993). Disposition of
Polycyclic Aromatic Hydrocarbons in the Respiratory Tract of the Beagle Dog. Ill
Mechanism of the Dosimetry. Toxicology and Applied Pharmacology 121, 328-334.
32
-------
363. Gerlowski, L. E., and Jain, R. K. (1983). Physiologically based pharmacokinetic
modeling principles and applications. Journal of Pharmaceutical Sciences 72, 1103-
1127.
364. Ghanem, A., and Shuler, M. L. (2000). Combining cell culture analogue reactor
designs and PBPK models to probe mechanisms of naphtalene toxicity.
Biotechnology Progress 16, 334-345.
365. Gibaldi, M., and Perrier, D. (1982). Pharmacokinetics. New York.
366. Ginsberg, G., Hattis, D., Sonawane, B., Russ, A., Banati, P., Kozlak, M., Smolensk!,
S., and Goble, R. (2002). Evaluation of child/adult pharmacokinetic differences
from a database derived from the therapeutic drug literature. Toxicol.Sci. 66, 185-
200.
367. Ginsberg, G., Hattis, D., Russ, A., and Sonnawane, B. (2004). Physiologically based
pharmacokinetic (PBPK) modeling of caffeine and theophylline in neonates and
adults: implications for assessing children's risks from environmental agents.
J.Toxicol Environ.Health A 67 , 297-329.
368. Goldstein, A., Arovow, L., and Kalman, S. M. (1974). The absorption,distribution,
and elimination of drugs. In Principles of drug action: the basis of pharmacology.
pp. 129-225. John Wiley & Sons., New York, London, Sydney, Toronto.
369. Grass, G. M., and Sinko, P. J. (2002). Physiologically-based pharmacokinetic
simulation modelling. Adv.Drug Deliv.Rev. 54, 433-451.
370. Gray, D. G. (1995). A physiologically based pharmacokinetic model for methyl
mercury in the pregnant rat and fetus. Toxicology and Applied Pharmacology 132,
91-102.
371. Green, T. (1997). Methylene chloride induced mouse liver and lung tumours: An
overview of the role of mechanistic studies in human safety assessment. Human &
Experimental Toxicology 16, 3-13.
372. Greenberg, M. S., Burton, G. A., and Fisher, J. W. (1999). Physiologically based
pharmacokinetic modeling of inhaled trichloroethylene and its oxidative metabolites
inB6C3Fl mice. Toxicology and Applied Pharmacology 154, 264-278.
373. Greene, D. S., Quintiliani, R., and Nightingale, G. H. (1978). Physiological
perfusion model for cephalosporin antibiotics 1: model selection based on blood
drug concentrations. Journal of Pharmaceutical Sciences 67, 191-196.
374. Greim, H. (2003). Mechanistic and toxicokinetic data reducing uncertainty in risk
assessment. Toxicol.Lett. 138, 1-8.
33
-------
375. Grille, J. A., Venitz, J., and Ornato, J. P. (2001). Prediction of lidocaine tissue
concentrations following different dose regimes during cardiac arrest using a
physiologically based pharmacokinetic model. Resuscitation 50, 331-340.
376. Gundert-Remy, U., Sonich-Mullin, C., and IPCS Uncertainty and Variability
Planning Workgroup and Drafting Group. (2002). The use of toxicokinetic and
toxicodynamic data in risk assessment: an international perspective. Science of the
Total Environment 288, 3-11.
377. Haber, L. T., Maier, A., Gentry, P. R., Clewell, H. J., and Dourson, M. L. (2002).
Genetic polymorphisms in assessing interindividual variability in delivered dose.
Regulatory Toxicology and Pharmacology 35, 177-197.
378. Haddad, S., Gad, S. C., Tardif, R., and Krishnan, K. Statistical approaches for the
validation of physiologically-based pharmacokinetic (PBPK) models. The
Toxicologist 15(1), 48. 1995.
Ref Type: Abstract
379. Haddad, S., Pelekis, M., and Krishnan, K. (1996). A methodology for solving
physiologically based pharmacokinetic models without the use of simulation
softwares. Toxicology Letters 85, 113-126.
380. Haddad, S., and Krishnan, K. (1998). Physiological modeling of toxicokinetic
interactions: implications for mixture risk assessment. Environmental Health
Perspectives 106, 1377-1384.
381. Haddad, S., Tardif, R., and Krishnan, K. Physiological modeling of higher order
interactions in complex chemical mixtures. Anderson, J. G. and Katzper, M. 110-
115. 1998. The Society for Computer Simulation International. 1998 Medical
Sciences Simulation Conference.
Ref Type: Conference Proceeding
382. Haddad, S., Withey, J. R., Lapare, S., Law, F. C. P., and Krishnan, K. (1998).
Physiologically-based pharmacokinetic modeling of pyrene in the rat.
Environmental Toxicology and Pharmacology 5, 245-255.
383. Haddad, S., Tardif, R., Viau, C., and Krishnan, K. (1999). A modeling approach to
account for toxicokinetic interactions in the calculation of biological hazard index
for chemical mixtures. Toxicology Letters 108, 303-308.
384. Haddad, S., Tardif, R., Charest-Tardif, G., and Krishnan, K. (1999). Physiological
modeling of the toxicokinetic interactions in a quaternary mixture of aromatic
hydrocarbons. Toxicology and Applied Pharmacology 161, 249-257.
385. Haddad, S., Charest-Tardif, G., Tardif, R., and Krishnan, K. (2000). Validation of a
physiological modeling framework for simulating the toxicokinetics of chemicals in
mixtures. Toxicology and Applied Pharmacology 167, 199-209.
34
-------
386. Haddad, S., Charest-Tardif, G., and Krishnan, K. (2000). Physiologically based
modeling of the maximal effect of metabolic interactions on the kinetics of
components of complex chemical mixtures. Journal of Toxicology and
Environmental Health 61 Part A, 209-223.
387. Haddad, S., Beliveau, M., Tardif, R., and Krishnan, K. (2001). A pbpk modeling-
based approach to account for interactions in the health risk assessment of chemical
mixtures. Toxicol Sci. 63 , 125-131.
388. Haenen, B., Rompelberg, C., Van Twillert, K., Hamzink, M., Dorman, J., and Van
Eijkeren, J. (2002). Utility of rat liver slices to estimate hepatic clearance for
application in physiologically based pharmacokinetic modeling: a study with
tolbutamide, a compound with low extraction efficiency. Drug Metabolism and
Disposition 30, 307-313.
389. Haggard, H. W. (1924). The absorption, distribution and elimination of ethyl ether.
Analysis of the mechanism of the absorption and elimination of such a gas or vapor
as ethyl ether. Journal of Biological Chemistry 59, 753-770.
390. Hallenbeck, W. H. (1992). Cancer risk assessment for the inhalation of 1,3-
butadiene using PBPK modeling. Bulletin of Environmental Contamination and
Toxicology 49, 66-70.
391. Hallier, E., Filser, J. G., and Bolt, H. M. (1981). Inhalation pharmacokinetics based
on gas uptake studies. II. Pharmacokinetics of acetone in rats. Archives of
Toxicology 47, 293-304.
392. Hanano, M., Sawada, Y., Iga, T., and Sugiyama, Y. (1987). The integration of in
vitro data with physiological data. pp. 63-77.
393. Hanna, L. M., and Lou, S.-R. S. S. (2001). Mass Transport Analysis: Inhalation
RFC methods framework for interspecies dosimetri adjustment. Inhal.Toxicol 13,
437-463.
394. Harashima, H., Sawada, Y., Sugiyama, Y., Iga, T., and Hanano, M. (1985). Analysis
of nonlinear tissue distribution of quinidine in rats by physiologically based
pharmacokinetics. Journal of Pharmacokinetics and Biopharmaceutics 13, 425-440.
395. Harashima, H., Sawada, Y., Sugiyama, Y., Iga, T., and Hanano, M. (1986).
Prediction of serum concentration time course of quinidine in human using a
physiologically based pharmacokinetic model developed from the rat. Journal of
Pharmacobio-Dynamics 9, 132-138.
396. Hattis, D., Banati, P., Goble, R., and Burnmaster, D. E. (1999). Human
interindividual variability in parameters related to health risks. Risk.Anal. 19, 711-
726.
35
-------
397. Hattis, D., Ginsberg, G., Sonawane, B., Smolensk!, S., Russ, A., Kozlak, M., and
Goble, R. (2003). Differences in pharmacokinetics between children and adults - II.
Children's variability in drug elimination half-lives and in some parameters needed
for physiologically-based pharmacokinetic modeling. Risk.Anal. 23, 117-142.
398. Hattis, D., White, P., Marmorstein, L., and Koch, P. (1990). Uncertainties in
pharmacokinetics modeling for perchloroethylene. I. Comparison of model
structure, parameters, and predictions for low dose metabolic rates for models by
different authors. Risk Analysis 10, 449-458.
399. Hattis, D. (1990). Phamacokinetics principles for dose-rate extrapolation of
carcinogenic risk from genetically active agents. Risk Analysis 10, 303-316.
400. Hattis, D., White, P., and Koch, P. (1993). Uncertainties in pharmacokinetic
modeling for perchloroethylene .2. comparison of model predictions with data for a
variety of different parameters. Risk Analysis 13, 599-610.
401. Hays, S. M., Aylward, L. L., Karch, N. I, and Paustenbach, D. J. (1997). The
relative susceptibility of animals and humans to the carcinogenic hazard posed by
exposure to 2,3,7,8-TCDD: an analysis using standard and internal measures of
dose. Chemosphere 34, 1507-1522.
402. Hays, S. M., Elswick, B. A., Blumenthal, G. M., Welsch, F., Conolly, R. B., and
Gargas, M. L. (2000). Development of a physiologically based pharmacokinetic
model of 2-methoxyethanol and 2-methoxyacetic acid disposition in pregnant rats.
Toxicology and Applied Pharmacology 163, 67-74.
403. Hetrick, D. M., Jarabek, A. M., and Travis, C. C. (1991). Sensitivity analysis for
physiologically-based pharmacokinetic models. Journal of Pharmacokinetics and
Biopharmaceutics 19, 1-20.
404. Hilderbrand, R. L., Andersen, M. E., and Jensen, L. J. (1981). Prediction of in vivo
kinetic constants for metabolism of inhaled vapors from kinetic constants measured
in vitro. Fundamental and Applied Toxicology 1, 403-409.
405. Hiramatsu, Y., Nagler, R. M., Fox, P. C., and Baum, B. J. (1994). Rat salivary gland
blood flow and blood-to-tissue partition coefficients following x-irradiation.
Archs.oral.Biol. 39, 77-80.
406. Hissink, A. M., Van Ommen, B., Kruse, J., and van Bladeren, P. J. (1997). A
physiologically based pharmacokinetic (PB-PK) model for 1,2-dichlorobenzene
linked to two possible parameters of toxicity. Toxicology and Applied
Pharmacology 145, 301-310.
407. Hissink, E. M., Bogaards, J. J. P., Freidig, A. P., Commandeur, J. N. M.,
Vermeulen, N. P. E., and van Bladeren, P. J. (2002). The use of in vitro metabolic
parameters and physiologically based pharmacokinetic (PBPK) modeling to explore
the risk assessment of trichloroethylene. Environ.Toxicol.Pharmacol. 11, 259-271.
36
-------
408. Hoang, K. C. T. (1995). Physiologically based pharmacokinetic models -
Mathematical fundamentals and simulation implementations. Toxicology Letters 79,
87-98.
409. Hoener, B. A. (1994). Predicting the hepatic clearance of xenobiotics in humans
from in vitro data. Biopharmaceutics & Drug Disposition 15, 295-304.
410. Hoffman, A. D., Bertelsen, S. L., and Gargas, M. L. (1992). An in vitro
equilibration method for determination of chemical partition coefficients in fish.
Comparative Biochemistry and Physiology 101A, 47-51.
411. Hogan, K., Marcus, A., Smith, R., and White, P. (1998). Integrated exposure uptake
biokinetic model for lead in children: empirical comparisons with epidemiologic
data. Environmental Health Perspectives Suppl 106, 1557-1567.
412. Holford, N. H. G. (1995). The target concentration approach to clinical drug
development. Clinical Pharmacokinetics 29, 287-291.
413. Holman, H.-Y. N., Goth-Goldstein, R., Aston, D., Yun, M., and Kengsoontra, J.
(2002). Evaluation of Gastrointestinal Solubilization of Petroleum hydrocarbon
residues in soil using an IN Vitro Physiologically Based Model. Environmental
Science and Technology 36, 1281-1286.
414. Holmes, S. L., Ward, R. C., Galambos, J. D., and Strickler, D. J. (2000). A method
for optimization of pharmacokinetic models. Toxicology Methods 10, 41-53.
415. Hopkins, J. C., and Leipold, R. J. (1996). On the dangers of adjusting the parameter
values of mechanism-based mathematical models. Journal of Theorical Biology
183, 417-427.
416. Horton, V. L., Higuchi, M. A., and Rickert, D. E. (1992). Physiologically based
pharmacokinetic model for methanol in rats, monkeys and humans. Toxicology and
Applied Pharmacology 117, 26-36.
417. Houston, J. B., and Carlile, D. J. (1997). Incorporation of in vitro drug metabolism
data into physiologically-based pharmacokinetic models. Toxicology in Vitro 11,
473-478.
418. Hull, C. J. (1998). How far can we go with compartmental models? Anesthesiology
72, 399-402.
419. Hurst, C. H., DeVito, M. J., Setzer, R. W., and Birnbaum, L. S. (2000). Acute
administration of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)in pregnant long
evans rats: association of measured tissue concentrations with developmental
effects. Toxicological Sciences 53, 411-420.
420. Hutter, J. C., Luu, H. M. D., Mehlhaff, P. M., Killam, A. L., and Dittrich, H. C.
(1999). Physiologically based pharmacokinetic model for fluorocarbon elimination
37
-------
after the administration of an octafluoropropane-albumin microsphere sonographic
constrast agent. Journal of Ultrasound in Medecine 18, 1-11.
421. Hwang, I. Y., Reardon, K. F., Tessari, J. D., and Yang, R. S. H. (1996). A gas-liquid
system for enzyme kinetic studies of volatile organic chemicals. Determination of
enzyme kinetic constants and partition coefficients of trichloroethylene. Drug
Metabolism and Disposition 24, 377-382.
422. ICRP (1975). International Commission on Radiation Protection: Report of the task
group on reference man. Pergamon Press, New York.
423. Igari, Y., Sugiyama, Y., Sawada, Y., Iga, Y., and Hanano, M. (1983). Prediction of
diazepam disposition in rat and man by a physiologically-based pharmacokinetic
model. Journal of Pharmacokinetics and Biopharmaceutics 11, 577-593.
424. Iman, R., and Helton, J. (1988). An investigation of uncertainty and sensitivity
analysis techniques for computer models. Risk Analysis 8, 71-90.
425. Ishida, S., Sakiya, Y., Ichikawa, T., Taira, Z., and Awazu, S. (1990). Prediction of
glycyrrhizin disposition in rat and man with liver failure by a physiologically based
pharmacokinetic model. Journal of Pharmacobio-Dynamics 13, 142-157.
426. Ishida, S., Sakiya, Y., Ichikawa, T., Taira, Z., and Awazu, S. (1990). Prediction of
glycyrrhizin disposition in rat and man by a physiologically based pharmacokinetic
model. Chemical and Pharmaceutical Bulletin 38, 212-218.
427. Ishizaki, J., Nakashima, E., Yokogawa, K., Nagano, T., Takayasu, T., and Ichimura,
F. (1992). A physiologically based pharmacokinetic model for (-)-quinuclidinyl
benzylate using nonlinear irreversible tissue binding parameters in rats. Drug
Metabolism and Disposition 20, 485-489.
428. Ishizaki, J., Yokogawa, K., Nakashima, E., and Ichimura, F. (1997). Relationships
between the hepatic intrinsic clearance or blood cell-plasma partition coefficient in
the rabbit and the lipophilicity of basic drugs. Journal of Pharmacy and
Pharmacology 49, 768-772.
429. Ishizaki, J., Yokogawa, K., Nakashima, E., and Ichimura, F. (1997). Prediction of
changes in the clinical pharmacokinetic of basic drugs on the basis of octanol-water
partition coefficients. Journal of Pharmacy and Pharmacology 49, 762-767.
430. Islam, M. S., Zhao, L., Zhou, J., Dong, L., McDougal, J. N., and Flynn, G. L.
(1996). Systemic uptake and clearance of chloroform by hairless rats following
dermal exposure.I.Brief exposure to aqueous solutions. Risk Analysis 16, 349-357.
431. Isukapalli, S. S., Roy, A., and Georgopoulos, P. G. (1998). Stochastic response
surface methods (SRSMs) for uncertainty propagation:application to environmental
and biological systems. Risk Analysis 18, 351-363.
38
-------
432. Ito, K., Iwatsubo, T., Kanamitsu, S., Nakajima, Y., and Sugiyama, Y. (1998).
Quantitative prediction of in vivo drug clearance and drug interactions from in vitro
data on metabolism, together with binding and transport.
Annu.Rev.Pharmacol.Toxicol. 38, 461-499.
433. Ito, S., Kawamura, T., Inada, M., Inoue, Y., Hirao, Y., Koga, T., Kunizaki, J.,
Shimizu, T., Sato, H. (2005). Physiologically based pharmacokinetic modelling of
the three-step metabolism of pyrimidine using C-uracil as an in vivo probe. Br J
Clin Pharmacol 60(6), 584-593. PMID: 16305582 [PubMed - in process]
434. Iwatsubo, T., Hiriko, N., Ooie, T., Suzuki, H., and Sugiyama, Y. (1996). Prediction
of in vivo drug disposition from in vitro data based on physiological
pharmacokinetics. Biopharmaceutics & Drug Disposition 17, 273-310.
435. lyengar, S., and Rao, M. S. (1983). Statistical techniques in modeling of complex
systems: single versus multiresponse models. IEEE Trans. Syst.Man.Cybernet. 13,
175-189.
436. Jain, R. K., Gerlowski, L. E., Weissbrod, J. M., Wang, J., and Pierson, R. N. (1982).
Kinetics of uptake, distribution and excretion of zinc in rats. Annals of Biomedical
and Engineering 9, 347-361.
437. Jang, J. Y., and Droz, P. O. (1996). Simulation of toluene in venous blood with a
physiologically based pharmacokinetic model: its application to biological exposure
index development. Applied Occupational Environmental Hygiene 11, 1092-1095.
438. Jang, J. Y., and Droz, P. O. (1997). Ethnic differences in biological monitoring of
several organic solvents II. A simulation study with a physiologically based
pharmacokinetic model. International Archives of Occupational and Environmental
Health 70, 41-50.
439. Jang, J. Y., Droz, P. O., and Chung, H. K. (1999). Uncertainties in physiologically
based pharmacokinetic models caused by several input parameters. International
Archives of Occupational and Environmental Health 72, 247-254.
440. Jang, J. Y., Droz, P. O., and Kim, S. (2001). Biological monitoring of workers
exposed to ethylbenzene and cO-exposed to xylene. Int. Arch.Occup.Environ.Health
74,31-37.
441. Jarabek, A. M. (1995). Interspecies extrapolation based on mechanistic determinants
of chemical disposition. Human & Ecological Risk Assessment. 1, 641-662.
442. Jarabek, A. M. (1994). Inhalation RfC methodology: dosimetric adjusments and
dose-response estimation of non-cancer toxicity in the upper respiratory tract.
Inhalation Toxicology 6, 301-325.
39
-------
443. Jarabek, A. M., Fisher, J. W., Rubenstein, R., Lipscomb, J. C., Wills, R. J., Vinegar,
A., and McDougal, J. N. (1994). Mechanistic insinghts aid the search for CFC
substitutes:risk assessment of HCFC-123 as an example. Risk Analysis 14, 231-250.
444. Jarabek, A. M. (1995). The application of dosimetry models to identify key
processes and parameters for default dose-response assessment approaches.
Toxicology Letters 79, 171-184.
445. Jarnberg, J., and Johanson, G. (1995). Liquid/air partition coefficients of the
trimethylbenzenes. Toxicol.Ind.Health. 11, 81-88.
446. Jarnberg, J. (1998). Toxicokinetics of inhaled trimethylbenzenes in man. Uppsala
Universitet.
447. Jarnberg, J., and Johanson, G. (1999). Physiologically based modeling of 1,2,4-
trimethylbenzene inhalation toxicokinetics. Toxicology and Applied Pharmacology
155, 203-214.
448. Jepson, G. W., Hoover, D. K., Black, R. K., McCafferty, J. D., Mahle, D. A., and
Gearhart, J. M. (1994). A partition coefficient determination method for nonvolatile
chemicals in biological tissues. Fundamental and Applied Toxicology 22, 519-524.
449. Jepson, G. W., and McDougal, J. N. (1999). Predicting vehicle effects on the dermal
absorption of halogenated methanes using physiologically based modeling.
Toxicological Sciences 48, 180-188.
450. Johanson, G. (1986). Physiologically-based pharmacokinetic modeling of inhaled 2-
butoxyethanol in man. Toxicology Letters 34, 23-31.
451. Johanson, G., and Dynesius, B. (1988). Liquid: air partition coefficients for six
commonly used glycol ethers. British Journal of Industrial Medicine 45, 561-564.
452. Johanson, G., and Naslund, P. H. (1988). Spreadsheet programming: A new
approach in physiologically based modeling of solvent toxicokinetics. Toxicology
Letters 41, 115-127.
453. Johanson, G. (1991). Modelling of respiratory exchange of polar solvents. Annals of
Occupational Hygiene 35, 323-339.
454. Johanson, G., and Filser, J. G. (1992). Experimental data from closed chamber gas
uptake studies in rodents suggest lower uptake rate of chemical than calculated from
literature values on alveolar ventilation. Archives of Toxicology 66, 291-295.
455. Johanson, G., and Filser, J. G. (1993). A physiologically based pharmacokinetic
model for butadiene and its metabolite butadiene monoxide in rat and mouse and its
significance for risk extrapolation. Archives of Toxicology 67, 151-163.
40
-------
456. Johanson, G., and Filser, J. G. (1996). PBPK model for butadiene metabolism to
epoxides: quantitative species differences in metabolism. Toxicology 113, 40-47.
457. Johanson, G., Jonsson, F., and Bois, F. (1999). Development of new technique for
risk assessment using physiologically based toxicokinetic models. American Journal
of Industrial Medecine Suppl 1, 101-103.
458. Johanson, G. (2000). Toxicity review of ethylene glycol monomethyl ether and its
acetate ester. Critical Reviews in Toxicology 30, 307-345.
459. Jonsson, F., and Johanson, G. (2001). Bayesian estimation of variability in adipose
tissue blood flow in man by physiologically based pharmacokinetic modeling of
inhalation exposure to toluene. Toxicology 157, 177-193.
460. Jonsson, F., Bois, F., and Johanson, G. (2001). Physiologically based
pharmacokinetic modeling of inhalation exposure of humans to dichloromethane
during moderate to heavy exercise. Toxicol.Sci 59, 209-218.
461. Jonsson, F., and Johanson, G. (2002). Physiologically based modeling of the
inhalation kinetics of styrene in humans using a bayesian population approach.
Toxicology and Applied Pharmacology 179, 35-49.
462. Jonsson, F., and Johanson, G. (2003). The bayesian population approach to
physiological toxicoketic - toxicodynamic models - an example using the MCSim
software. Toxicol.Lett. 143-150.
463. Jonsson, F., Sandborgh-Englund, G., and Johanson, G. (1999). A compartmental
model for the kinetics of mercury vapor in humans. Toxicology and Applied
Pharmacology 155, 161-168.
464. Jonsson, F., and Johanson, G. (2001). A Bayesian analysis of the influence of
GSTT1 polymorphism on the cancer risk estimate for dichloromethane. Toxicology
and Applied Pharmacology 174, 99-112.
465. Jonsson, F. (2001). Physiologically based pharmacokinetic modeling in risk
assessment.Development of Bayesian population methods. National Institute for
Working Life, Stockholm.
466. Jonsson, F., Bois, F. Y., and Johanson, G. (2001). Assessing the reliability of PBPK
models using data from methyl chloride-exposed, non-conjugating human subjects.
Arch.Toxicol 75, 189-199.
467. Jonsson, F., and Johanson, G. (2002). Physiologically based modeling of the
inhalation kinetics of styrene in humans using a bayesian population approach.
Toxicol Appl.Pharmacol. 179, 35-49.
468. Kac, M. (1969). Some mathematical models in science. Science 166, 695-699.
41
-------
469. Kamrin, M. A., Fischer, L. J., Suk, W. A., Fouts, J. R., Pellizzari, A., and Thornton,
K. (1994). Assessment of human exposure to chemicals from Superfund sites.
Environmental Health Perspectives 102, 221-228.
470. Kanamitsu, S., Ito, K., and Sugiyama, Y. (2000). Quantitative prediction of in vivo
drug-drug interactions from in vitro data based on physiological pharmacokinetics:
use of maximum unbound concentration of inhibitor at the inlet to the liver.
Pharm.Res. 17, 336-343.
471. Kaneko, T., Wang, P. Y., and Sato, A. (2000). Relationship between blood/air
partition coefficients of lipophilic organic solvents and blood triglyceride levels.
Toxicology 143, 208.
472. Kaneko, T., Endoh, K., and Sato, A. (1991). Biological monitoring of exposure to
organic solvent vapors. I. A physiological simulation model of m-xylene
pharmacokinetics in man. Yamanashi Medicine Journal 6, 137-149.
473. Kaneko, T., Wang, P. Y., and Sato, A. (1994). Partition coefficients of some acetate
esters and alcohols in water, blood, olive oil, and rat tissues. Occupational and
Environmental Medicine 51, 68-72.
474. Kaneko, T., Horiuchi, J., and Sato, A. (2000). Development of a physiologically
based pharmacokinetic model of organic solvent in rats. Pharmaceutical Research
42, 465-470.
475. Kang, H. J. K., Wientjes, M. G., and Au, J. L. S. (1997). Physiologically based
pharmacokinetic models of 2',3'-dideoxynosine. Pharmaceutical Research 14, 337-
344.
476. Karba, R., Zupancic, B., and Bremsak, F. (1990). Simulation tools in
pharmacokinetic modelling. Acta Pharm Jugosl 40, 247-262.
477. Katayama, K., Ohtani, H., Murai, A., Kakemi, M., and Koizumi, T. (1990). Kinetic
studies on drug disposition in rabbits. II. Dose dependent pharmacokinetics of
sulfamethizole. Journal of Pharmacobio-Dynamics 13, 142-157.
478. Kawahara, M., Nanbo, T., and Tsuji, A. (1998). Physiologically based
pharmacokinetic prediction of p-phenylbenzoic acid disposition in the pregnant rat.
Biopharmaceutics & Drug Disposition 19, 445-453.
479. Kawahara, M., Sakata, A., Miyashita, T., Tamai, I, and Tsuji, A. (1999).
Physiologically based pharmacokinetics of digoxin in mdrla knochout mice. Journal
of Pharmaceutical Sciences 88, 1281-1287.
480. Kawai, R., Mathew, D., Tanaka, C., and Rowland, M. (1998). Physiologically based
pharmacokinetics of cyclosporine A: extension to tissue distribution kinetics in rats
and scale-up to human. Journal of Pharmacology and Experimental Therapeutics
287, 457-469.
42
-------
481. Kedderis, G. L., and Lipscomb, J. C. (2001). Application of in vitro
biotransformation data and pharmacokinetic modeling to risk assessment.
Toxicology and Industrial Health 17, 315-321.
482. Kedderis, G. L., Carfagna, M. A., Held, S. D., Batra, R., Murphy, J. E., and Gargas,
M. L. (1993). Kinetic analysis of furan biotransformation by F-344 rats in vivo and
in vitro. Toxicol Appl.Pharmacol. 123, 274-282.
483. Kedderis, G. L., and Held, S. D. (1996). Prediction of furan pharmacokinetics from
hepatocyte studies: comparison of bioactivation and hepatic dosimetry in rats, mice,
and humans. Toxicology and Applied Pharmacology 140, 124-130.
484. Kedderis, G. L., Teo, S. K., Batra, R., Held, S. D., and Gargas, M. L. (1996).
Refinement and verification of the physiologically based dosimetry description for
acrylonitrile in rats. Toxicology and Applied Pharmacology 140, 422-435.
485. Kedderis, L. B., Mills, J. J., Andersen, M. E., and Birnbaum, L. S. (1993). A
physiologically-based pharmacokinetic model of 2,3,7,8-tetrabromo dibenzo-p-
dioxin (TBDD) in the rat: Tissue distribution and CYPIA induction. Toxicology and
Applied Pharmacology 121, 87-98.
486. Keller, F., Emde, C., and Schwarz, A. (1988). Exponential function for calculating
saturable enzyme kinetics. Clinical Chemistry 34, 2486-2489.
487. Kenyon, E. M., Kraichely, R. E., Hudson, K. T., and Medinsky, M. A. (1996).
Differences in rates of benzene metabolism correlate with observed genotoxicity.
Toxicology and Applied Pharmacology 136, 49-56.
488. Kety, S. S. (1951). The theory and application of the exchange of inert gas at the
lungs. Pharmacological Reviews 3, 1-41.
489. Keys, D. A., Bruckner, J. V., Muralidhara, S., and Fisher, J. W. (2003). Tissue
dosimetry expansion and cross-validation of rat and mouse physiologically based
pharmacokinetic models for trichloroethylene. Toxicol.Sci. 76, 35-50.
490. Keys, D. A., Wallace, D. G., Kepler, T. B., and Conolly, R. B. (1999). Quantitative
evaluation of alternative mechanisms of blood and testes disposition of di(2-
ethylhexyl) phthalate and mono(2-ethylhexyl) phthalate in rats. Toxicological
Sciences 49, 172-185.
491. Khor, S. P., and Mayersohn, M. (1991). Potential error in the measurement of tissue
to blood distribution coefficients in physiological pharmacokinetic modeling:
residual tissue blood. I. Theoretical considerations. Drug Metabolism and
Disposition 19, 478-485.
492. Kim, A. H., Kohn, M. C., Portier, C. J., and Walker, N. J. (2002). Impact of
physiologically based pharmacokinetic modeling on benchmark dose calculations
43
-------
for TCDD-induced biochemical responses. Regulatory Toxicology and
Pharmacology 36, 287-296.
493. Kim, C., Manning, R. O., Brown, R. P., and Bruckner, J. V. (1996). Use of vial
equilibration technique for determination of metabolic rate constants for
dichloromethane. Toxicology and Applied Pharmacology 139, 243-251.
494. Kim, C. S., Binienda, Z., and Sandberg, J. A. (1996). Construction of a
physiologically based pharmacokinetic model for 2,4-dichlorophenoxyacetic acid
dosimetry in the developing rabbit brain. Toxicology and Applied Pharmacology
136, 250-259.
495. Kim, C. S., Gargas, M. L., and Andersen, M. E. (1994). Pharmacokinetic modeling
of 2,4-dichlorophenoxyacetic acid (2,4-D) in rat and in rabbit brain following single
dose administration. Toxicology Letters 74, 189-201.
496. Kim, C. S., Slikker, W., Ninienda, Z., Gargas, M. L., and Andersen, M. E. (1995).
Development of a physiologically based pharmacokinetic model for 2,4-
dichlorophenoxyacetic acid dosimetry in discrete areas of the rabbit brain.
Neurotoxicology and Teratology 17, 111-120.
497. Kim, C. S., Ross, I. A., Sandberg, J. A., and Preston, E. (1998). Quantitative low-
dose assessment of seafood toxin, domoic acid, in the rat brain - application of
physiologically-based pharmacokinetic (PBPK) modeling. Environmental
Toxicology and Pharmacology 6, 49-58.
498. Kim, C. S., Sandberg, J. A., Slikker, W., Binienda, Z., Schlosser, P. M., and
Patterson, T. A. (2001). Quantitative exposure assessment:application of
physiologically-based pharmacokinetic (PBPK) modeling of low-dose, long-term
exposures of organic acid toxicant in the brain. Environmental Toxicology and
Pharmacology 9, 153-160.
499. Kimbell, J. S., Gross, E. A., Joyner, D. R., Godo, M. N., and Morgan, K. T. (1993).
Application of computational fluid dynamics to regional dosimetry of inhaled
chemicals in the upper respiratory tract of the rat. Toxicology and Applied
Pharmacology 121, 253-263.
500. Kimbell, J. S., Godo, M. N., Gross, E. A., Joyner, D. R., Richardson, R. B., and
Morgan, K. T. (1997). Computer simulation of inspiratory airflow in all regions of
the F344 rat nasal passages. Toxicology and Applied Pharmacology 145, 388-398.
501. Kimbell, J. S., and Subramaniam, R. P. (2001). Use of computational fluid
dynamics models for dosimetry of inhaled gases in the nasal passages. Inhal.Toxicol
13, 325-334.
502. Kimmel, E. C., Carpenter, R. L., Smith, E. L., Reboulet, J. E., and Black, B. H.
(1998). Physiologic models for comparaison of inhalation dose between laboratory
44
-------
and field-generated atmospheres of a dry powder fire suppressant. Inhalation
Toxicology 10, 905-922.
503. King, F. G., Dedrick, R. L., Collins, J. M., Mattews, H. B., and Birnbaum, L. G.
(1983). Physiological model for the pharmacokinetics of 2,3,7,8-tetra-chloro
dibenzofuran in several species. Toxicology and Applied Pharmacology 67, 390-
400.
504. Kinkead, E. R., Wall, H. G., Hixson, C. J., Tice, R. R., Kutzman, R. S., and
Vinegar, A. (1990). Chloropentafluorobenzene: short-term inhalation toxicity,
genotoxicity and physiologically-based pharmacokinetic model development.
Toxicology and Industrial Health 6, 533-550.
505. Kiriyama, A., Nishiura, T., Yamaji, H., and Takada, K. (1999).
Physiologicallybased pharmacokinetics of KNI-272, atripeptide HIV-1 protease
inhibitor. Biopharmaceutics & Drug Disposition 20, 199-205.
506. Kirman, C. R., Sweeney, L. M., Meek, M. E., and Gargas, M. L. (2003). Assessing
the dose-dependency of allometric scaling performance using physiologically based
pharmacokinetic modeling. Regulatory Toxicology and Pharmacology 38, 345-367.
507. Kirman, C. R., Gargas, M. L., Deskin, R., Tonner-Navarro, L., and Andersen, M. E.
(2003). A physiologically based pharmacokinetic model for acrylamide and its
metabolite, glycidamide, in the rat. J.Toxicol Environ.Health A 66, 235-274.
508. Kirman, C. R., Hays, S. M., Kedderis, G. L., Gargas, M. L., and Strother, D. E.
(2000). Improving cancer dose-response charaterization by using physiologically
based pharmacokinetic modeling: an analysis of pooled data for acrylonitrile-
induced brain tumors to assess cancer potency in the rat. Risk Analysis 20, 135-151.
509. Kirman, C.R., Gargas, M.L., Marsh, G.M., Strother, D.E., Klaunig, I.E., Collins,
J.J., Deskin, R. (2005). Cancer dose—response assessment for acrylonitrile based
upon rodent brain tumor incidence: use of epidemiologic, mechanistic, and
pharmacokinetic support for nonlinearity. Regul Toxicol Pharmacol 43(1), 85-103.
PMID: 16099568 [PubMed - in process]
510. Kissel, J. C., and Robarge, G. M. (1998). Assessing the elimination of 2,3,7,8-
TCDD from humans with a physiologically based pharmacokinetic model.
Chemosphere 17, 2017-2027.
511. Klein, M. T., Hou, G., Quann, R. J., Wei, W., Liao, K. H., Yang, R. S., Campain, J.
A., Mazurek, M. A., and Broadbelt, L. J. (2002). Bio MOL: a computer-assisted
biological modeling tool for complex chemical mixtures and biological processes at
the molecular level. Environmental Health Perspectives 110, 1025-1029.
512. Knaak, J. B., Al-Bayati, M. A., and Raabe, O. G. (1995). Development of partition
coefficients, Vmax and Km values, and allometric relationships. Toxicology Letters
79, 87-98.
45
-------
513. Knaak, J. B., Smith, L. W., Fitzpatrick , R. D., Olson, J. R., and Newton, P. E.
(1998). In vitro hepatic metabolism of PCBTF: development of Vmax and Km
values and partition coefficients and their use in an inhalation PBPK. Inhalation
Toxicology 10, 65-85.
514. Kohn, M. C., Sewall, C. H., Lucier, G. W., and Portier, C. J. (1996). A mechanistic
model of effects of Dioxin on Thyroid Hormones in the Rat. Toxicol
Appl.Pharmacol. 136, 29-48.
515. Kohn, M. C., and Melnick, R. L. (1993). Species differences in the production and
clearance of 1,3-butadiene metabolites: A mechanistic model indicates
predominantly physiological, not biochemical control. Carcinogenesis 14, 619-628.
516. Kohn, M. C., Lucier, G. W., Clark, G. C., Sewall, C., Tritscher, A. M., and Portier,
C. J. (1993). A Mechanistic model of effects of dioxin on gene expression in the rat
liver. Toxicology and Applied Pharmacology 120, 138-154.
517. Kohn, M. C., and Portier, C. J. (1993). Effects of the mechanism of receptor-
mediated gene expression on the shape of the dose-response curve. Risk Analysis
13, 565-572.
518. Kohn, M. C. (1995). Achieving credibility in risk assessment models. Toxicology
Letters 79, 107-114.
519. Kohn, M. C., and Melnick, R. L. (1996). Effects of the structure of a toxicokinetic
model of butadiene inhalation exposure on computed production of carcinogenic
intermediates. Toxicology 113, 31-39.
520. Kohn, M. C. (1997). The importance of anatomical realism for validation of
physiological models of disposition of inhaled toxicants. Toxicology and Applied
Pharmacology 147, 448-458.
521. Kohn, M. C., and Melnick, R. L. (1999). A physiological model for ligand-induced
accumulation of alpha 2u globulin in male rat kidney: roles of protein synthesis and
lysosomal degradation in the renal dosimetry of 2,4,4-trimethyl-2-pentanol.
Toxicology 136, 89-105.
522. Kohn, M. C., and Melnick, R. L. (2000). The privileged acces model of 1,3-
butadiene disposition. Environmental Health Perspectives Suppl 108, 911-917.
523. Kohn, M. C. (2000). Current directions in physiological modeling for environmental
health sciences: an overview. Environ.Health Perspect. 108 Suppl 5, 857-859.
524. Kohn, M. C., and Melnick, R. L. (2001). Physiological modeling of butadiene
disposition in mice and rats. Chem.Biol.Interact. 135-136, 285-301.
46
-------
525. Koizumi, A. (1989). Potential of physiological pharmacokinetics to amalgamate
kinetic data of trichloroethylene and tetrachloroethylene obtained in rats and man.
British Journal of Industrial Medicine 46, 239-249.
526. 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. Bulletin of Mathematical Biology 48, 427-441.
527. Kousba, A., A, Poet, T. S., and Timchalk.C (2004). Characterization of the in vitro
kinetic interaction of chlorpyrifos-oxon with rat salivary cholinesterase: A potential
biomonitoring matrix. Toxicology 188, 219-232.
528. Krewski, D., Whithey, J. R., Ku, L. F., and Andersen, M. E. (1994). Applications of
physiologic pharmacokinetic modeling in carcinogenic risk assessment.
Environmental Health Perspectives Suppl 102, 37-50.
529. Krewski, D., Wang, Y., Bartlett, S., and Krishnan, K. (1995). Uncertainty,
variability, and sensitivity analysis in physiological pharmacokinetic models.
J.Biopharm.Stat. 5, 245-271.
530. Krishnan, K., Haddad, S., Beliveau, M., and Tardif, R. (2002). Physiological
modeling and extrapolation of pharmacokinetic interactions from binary to more
complex chemical mixtures. Environmental Health Perspectives 110, 989-994.
531. Krishnan, K., and Andersen, M. E. (1991). Interspecies scaling in pharmacokinetics.
In New Trends in Pharmacokinetics (A. Rescigno, and A. K. Thakkur, Eds.), pp.
203-226. Plenum Press, New York.
532. Krishnan, K., Gargas, M. L., Fennell, T. R., and Andersen, M. E. (1991). Ethylene
oxide risk assessment: incorporating dosimetry and mechanistic information.
Chemical Industry Institute of Toxicology 11, 1-8.
533. Krishnan, K., and Andersen, M. E. (1991). The role of physiological modeling in
reducing animal use in toxicology research. In In vitro toxicology: mechanisms and
new technology. (A. M. Goldberg, Ed.), pp. 113-133. Mary Ann Liebert, Inc, New
York.
534. Krishnan, K., and Andersen, M. E. (1991). Physiological modeling and cancer risk
assessment. In New Trends in Pharmacokinetics (A. Rescigno, and A. K. Thakur,
Eds.), pp. 335-354. Plenum Press, New York.
535. Krishnan, K., Gargas, M. L., Fennell, T. R., and Andersen, M. E. (1992). A
physiologically-based description of ethylene oxide dosimetry in the rat. Toxicology
and Industrial Health 8, 121-140.
536. Krishnan, K., Conolly, R. B., and Andersen, M. E. Biologically based models in risk
assessment. Clewell, H. J. 109-115. 1992. Cincinnati, OH., American Conference
47
-------
of Governmental Industrial Hygienists (ACGIH).
Ref Type: Conference Proceeding
537. Krishnan, K., Gargas, M. L., and Andersen, M. E. (1993). In vitro toxicology and
risk assessment. Alternative Methods in Toxicology 9, 185-203.
538. Krishnan, K., and Andersen, M. E. (1993). Pharmacokinetics, individual
differences. In Handbook of Hazardous Materials pp. 577-589. Academic Press.
539. Krishnan, K., Andersen, M. E., Clewell, H. J. L, and Yang, R. S. H. (1994).
Physiologically-based pharmacokinetic modeling of chemical mixtures. In
Toxicology of Chemical Mixtures. (R. S. A. Yang, Ed.), Academic Press, New
York.
540. Krishnan, K., Clewell, H. J. L, and Andersen, M. E. (1994). Physiologically based
pharmacokinetic analysis of simple mixtures. Environmental Health Perspectives
102, 151-155.
541. Krishnan, K., and Andersen, M. E. (1994). Physiologically based pharmacokinetic
modeling in toxicology. In Principles and methods of toxicology (A. W. Hayes,
Ed.), pp. 149-188. Raven Press, New York.
542. Krishnan, K., and Pelekis, M. (1995). Hematotoxic interactions:
occurrence,mechanisms and predictability. Toxicology 105, 355-364.
543. Krishnan, K., Haddad, S., and Pelekis, M. (1995). A simple index for representing
the discrepancy between simulations of physiological pharmacokinetic models and
experimental data. Toxicol Ind.Health 11, 413-422.
544. Krishnan, K., and Andersen, M. (1998). Physiologically based pharmacokinetic
models in the risk assessment of developmental neurotoxicants. In Handbook of
Developmental Neurotoxicology (W. Slikker, Ed.), pp. 709-725. Academic Press.
545. Krishnan, K., and Andersen, M. E. (2001). Physiologically based pharmacokinetic
modeling in toxicology. In Principles and methods of toxicology (A. W. Hayes,
Ed.), pp. 193-241. Taylor & Francis, Philadelphia.
546. Krishnan, K., Johanson, G. (2005). Physiologically-based pharmacokinetic and
toxicokinetic models in cancer risk assessment. J Environ Sci Health C Environ
Carcinog Ecotoxicol Rev 23(1), 31-53. Review. PMID: 16291521 [PubMed-
indexed for MEDLINE]
547. Kubic, V. L., Anders, M. W., Engel, R. R., Barlow, C. H., and Caughey, W. S.
(1974). Metabolism of dihalomethanes to carbon monoxide. Drug Metabolism and
Disposition 2, 53-57.
548. Kuempel, E. D., Tran, C. L., Bailer, A. J., Smith, R. J., Dankovic, D. A., and
Stayner, L. T. (2001). Methodological issues of using observational human data in
48
-------
lung dosimetry models for particulates. Science of the Total Environment 274, 67-
77.
549. Kumagai, S., Matsunaga, I, and Tabuchi, T. (1998). Effects of variation in exposure
to airborne acetone and difference in work load on acetone concentration in blood,
urine, and exhaled air. American Industrial Hygiene Association Journal 59, 242-
251.
550. Kumagai, S., and Matsunaga, I. (1995). Effect of variation of exposure to airborne
chlorobenzene on internal exposure and concentrations of urinary metabolite.
Occupational and Environmental Medicine 52, 65-70.
551. Kumagai, S., and Matsunaga, I. (1995). Physiologically based pharmacokinetic
model for acetone. Occupational and Environmental Medicine 52, 344-352.
552. Kumagai, S., and Matsunaga, I. (2000). A lung model describing uptake of organic
solvents and roles of mucosal blood flow and metabolism in the bronchioles.
Inhal.Toxicol 12,491-510.
553. Lam, C. W., Galen, T. 1, Boyd, J. F., and Pierson, D. L. (1990). Mechanism of
transport and distribution of organic solvents in blood. Toxicology and Applied
Pharmacology 104, 117-129.
554. Lam, G., Chen, M. L., and Chiou, W. L. (1982). Determination of tissue: blood
partition coefficients in physiologically-based pharmacokinetic models. Journal of
Pharmaceutical Sciences 71, 454-456.
555. Lapare, S., Tardif, R., and Brodeur, J. (1993). Effect of various exposure scenarios
on the biological monitoring of organic solvents. I. Toluene and xylene.
International Archives of Occupational and Environmental Health 64, 569-580.
556. Lapare, S., Tardif, R., and Brodeur, J. (1995). Effect of various exposure scenarios
on the biological monitoring of organic solvents in alveolar air. II. 1,1,1-
trichloroethane and trichloroethylene. International Archives of Occupational and
Environmental Health 67 , 375-394.
557. Law, F. C. P., Abeini, S., and Kennedy, C. J. (1991). A biologically-based
toxicokinetic model for pyrene in rainbow trout. Toxicology and Applied
Pharmacology 110, 390-402.
558. Lawrence, G. S., and Gobas, F. A. P. C. (1997). A pharmacokinetic analysis of
interspecies extrapolation in dioxin risk assessment. Chemosphere 35, 427-452.
559. Leavens, T. L., and Bond, J. A. (1996). Pharmacokinetic model describing the
disposition of butadiene and styrene in mice. Toxicology 113, 310-313.
49
-------
560. Leavens, T. L., Moss, D. R., and Bond, J. A. (1996). Dynamic inhalation system for
individual whole-body exposure of mice to volatile organic chemicals. Inhalation
Toxicology 8, 655-677'.
561. Lee, K. M., Bruckner, J. V., Muralidhara, S., and Gallo, J. M. (1996).
Characterization of Presystemic elimination of trichloroethylene and its nonlinear
kinetics in rats. Toxicology and Applied Pharmacology 139, 262-271.
562. Lee, K. M., Muralidhara, S., Dallas, C. E., and Bruckner, J. V. (1997). Lack of
volatilization and escape of orally administered trichloroethylene from the
gastrointestinal tract of rats. Toxicology and Industrial Health 13, 81-89.
563. Lee, K. M., Dill, J. A., Chou, B. J., and Roycroft, J. H. (1998). Physiologically
based pharmacokinetic model for chronic inhalation of 2-butoxyethanol. Toxicology
and Applied Pharmacology 153, 211-226.
564. Lee, S. K., Ou, Y. C., and Yang, R. S. (2002). Comparison of Pharmacokinetic
Interactions and Physiologically Based Pharmacokinetic Modeling of PCB 153 and
PCB 126 in Nonpregnant Mice, Lactating Mice, and Suckling Pups. Toxicol Sci. 65,
26-34.
565. Leggett, R. W., and Willams, L. R. (1991). Suggested reference values for regional
blood volumes in humans. Health Physics 60, 139-154.
566. Lerou, J. G., and Booij, L. H. (2001). Model-based administration of inhalation
anaesthesia. 1. Developing a system model. Br.J.Anaesth. 86, 12-28.
567. Leung, H. W., and Paustenbach, D. J. (1995). Physiologically based
pharmacokinetic and pharmacodynamic modeling in health risk assessment and
characterization of hazardous substances. Toxicol.Lett. 79, 55-65.
568. Leung, H. W., Ku, R. H., Paustenbach, D. J., and Andersen, M. E. (1988). A
physiologically-based pharmacokinetic model for 2,3,7,8-tetrachloro dibenzo-p-
dioxin in C57BL/6J and DBA/2J mice. Toxicology Letters 42, 15-28.
569. Leung, H. W., and Paustenbach, D. J. (1990). Cancer risk assessment for dioxane
based upon a physiologically-based pharmacokinetic modeling approach.
Toxicology Letters 51, 147-162.
570. Leung, H. W., Paustenbach, D. J., Murray, F. J., and Andersen, M. E. (1990). A
physiologically-based pharmacokinetic description and enzyme-inducing properties
of 2,3,7,8-tetrachlorodibenzo-p-dioxin in the rat. Toxicology and Applied
Pharmacology 103, 399-410.
571. Leung, H. W., Poland, A. P., Paustenbach, D. J., and Andersen, M. E. (1990). Dose-
dependent pharmacokinetics of (125-l)-2-iodo-3,7,8-trichlorodibenzo-p-dioxin in
mice: Analysis with a physiological modeling approach. Toxicology and Applied
Pharmacology 103, 411-419.
50
-------
572. Leung, H. W. (1991). Development and utilization of physiologically based
pharmacokinetic models for toxicological applications. Journal of Toxicology and
Environmental Health 32, 247-267.
573. Leung, H. W. (1992). Use of physiologically based pharmacokinetic models to
establish biological exposure indexes. American Industrial Hygiene Association
Journal 53, 369-374.
574. Leung, H. W. (1993). Physiologically-based pharmacokinetic modelling. In General
& applied toxicology. (B. Ballantyne, T. Marrs, and P. Turner, Eds.), pp. 153-164.
M. Stockton Press.
575. Levesque, B., Ayotte, P., Tardif, R., Charest-Tardif, G., Dewailly E, P. D., Gingras,
G., Allaire, S., and Lavoie, R. (2000). Evaluation of the health risk associated with
exposure to chloroform in indoor swimming pools. Journal of Toxicology and
Environmental Health 61, 225-243.
576. Levitt, D. G. (2002). PK-Quest: volatile solutes - application to enflurane, nitrous
oxide, halothane, methoxyflurane and toluene pharmacokinetics. BMC Anesthesiol
2,5.
577. Levitt, D. G. (2002). PK Quest: measurement of intestinal absorption and first pass
metabolism - application to human ethanol pharmacokinetics. BMC
Clin.Pharmacol. 2, 4.
578. Levitt, D. G. (2002). PK Quest: a general physiologically based pharmacokinetic
model. Introduction and application to propranolol. BMC Clin.Pharmacol. 2, 5.
579. Levitt, D. G. (2003). The pharmacokinetics of the interstital space in humans. BMC
Clin.Pharmacol. 3, 3.
580. Levitt, D. G. (2003). The use of a physiologically based pharmacokinetic model to
evaluate deconvolution measurements of systemic absorption. BMC
Clin.Pharmacol. 3, 1.
581. Li, H., Watanbe, K., Auslander, D., and Spear, C. R. (1994). Model parameter
estimation and analysis: understanding parametric structure. Annals of Biomedical
and Engineering 22, 97-111.
582. Liao, K. H., Dobrev, I. D., Dennison, J. E. J., Andersen, M. E., Reisfeld, B.,
Reardon, K. F., Campain, J. A., Wei, W., Klein, M. T., Quann R.J, and Yang, R. S.
(2002). Application of biologically based computer modeling to simple or complex
mixtures. Environmental Health Perspectives 110, 957-963.
583. Licata, A. C., Dekant, W., Smith, C. E., and Borghoff, S. J. (2001). A
physiologically based pharmacokinetic model for methyl tert-butyl ether in humans:
implementing sensitivity and variability analyses. Toxicol Sci. 62, 191-204.
51
-------
584. Lien, G. 1, Nichols, J. W., McKim, J. M., and Gallinat, C. A. (1994). Modeling the
accumulation of three waterborne chlorinated ethanes in fathead minnow
(Pimephales promelas): a physiologically based approach. Environmental
Toxicology and Chemistry 13, 1195-1205.
585. Lien, G. J., McKim, J. M., Hoffman, A. D., and Jenson, C. T. A physiologically
based toxicokinetic model for lake trout (salvelinus namaycush). Aquatic
Toxicology 51, 335-350. 2001.
Ref Type: Abstract
586. Liira, J., Johanson, G., and Riihimaki, V. (1990). Dose-dependent kinetics of
inhaled methylethylketone in man. Toxicol Lett. 50, 195-201.
587. Lilly, P. D., Andersen, M. E., Ross, T. M., and Pegram, R. A. (1997).
Physiologically based estimation of in vivo rates of bromodichloromethane
metabolism. Toxicology 124, 141-152.
588. Lilly, P. D., Andersen, M. E., Ross, T. M., and Pegram, R. A. (1998). A
physiologically based pharmacokinetic description of the oral uptake, tissue
dosimetry, and rates of metabolism of bromodichlorom ethane in the male rat.
Toxicology and Applied Pharmacology 150, 205-217.
589. Lin, J. H. (1995). Species similarities and differences in pharmacokinetics.
Am.Society.Pharam.Experimental Therap. 23, 1008-1021.
590. Lin, J. H., Hayashi, M., Awazu, S., and Hanano, M. (1978). Correlation between in
vitro and in vivo drug metabolism rate: Oxidation of ethoxybenzamide in rat.
Journal of Pharmacokinetics and Biopharmaceutics 6, 327-337.
591. Lin, J. H., Sugiyama, Y., Awazu, S., and Hanano, M. (1982). In vitro and in vivo
evaluation of the tissue to blood partition coefficients for physiological
pharmacokinetic models. Journal of Pharmacokinetics and Biopharmaceutics 10,
637-647.
592. Lin, J. H., Sugiyama, Y., Awazu, S., and Hanano, M. (1982). Physiological
pharmacokinetics of ethoxybenzamine based on biochemical data obtained in vitro
as well as on physiological data. Journal of Pharmacokinetics and Biopharmaceutics
10,649-661.
593. Lin, Y.-S., Smith, T. J., Wypij, D., Kelsey K.T, and Sacks, F. M. (2002).
Association of the blood/airpartition coefficient of 1,3-butadiene with blood lipids
and albumin. Environmental Health Perspectives 110, 165-168.
594. Linstrom, F. T., Gillette, J. W., and Rodecap, S. E. (1974). Distribution of HEOD
(dieldrin) in mammals. I. Preliminary model. Archives of Environmental
Contamination and Toxicology 2, 9-42.
52
-------
595. Lipscomb, J. C., and Kedderis, G. L. (2002). Incorporating human interindividual
biotransformation variance in health risk assessment. Science of the Total
Environment 288, 13-21.
596. Lipscomb, J. C., Fisher, J. W., Confer, P. D., and Byczkowski, J. Z. (1998). In vitro
to in vivo extrapolation for trichloroethylene metabolism in humans. Toxicology
and Applied Pharmacology 152, 376-387.
597. Lipscomb, J. C., Teuschler, L. K., Swartout, J., Popken, D., Cox, T., and Kedderis,
G. L. (2003). The impact of cytochrome P450 2El-dependent metabolic variance on
a risk-relevant pharmacokinetic outcome in humans. Risk Analysis 6, 1221-1238.
598. Loizou, G. D., Urban, G., Dekant, W., and Anders, M. W. (1994). Gas-uptake
pharmacokinetics of 2,2-dichloro-1,1,1-trifluoroethane (HCFC-123). Drug
Metabolism and Disposition 22, 511-517.
599. Loizou, G. D., and Anders, M. W. (1995). Gas-uptake pharmacokinetics and
metabolism of 2-chloro-1,1,1,2-tetrafluoroethane (HCFC-124) in the rat, mouse, and
hamster. Drug Metabolism and Disposition 23, 875-880.
600. Loizou, G. D., Eldirdiri, N. I, and King, L. J. (1996). Physiologically based
pharmacokinetics of uptake by inhalation of a series of 1,1,1-trihaloethanes:
correlation with various physicochemical parameters. Inhalation Toxicology 8, 1-
19.
601. Loizou, G. D., Tran, C. L., and Anders, M. W. (1997). Physiologically based
pharmacokinetic analysis of the concentration-dependent metabolism of halothane.
Xenobiotica 27, 87-100.
602. Loizou, G. D., Jones, K., Akrill, P., Dyne, D., and Cocker, J. (1999). Estimation of
the dermal absorption of m-xylene vapor in humans using breath sampling and
physiologically based pharmacokinetic analysis. Toxicological Sciences 48, 170-
179.
603. Loizou, G. D. (2001). The application of physiologically based pharmacokinetic
modelling in the analysis of occupational exposure to perchloroethylene. Toxicol
Lett. 124, 59-69.
604. Lorber, M. A pharmacokinetic model for estimating exposure of Americans to
dioxins-like compounds in the past, present, and future. Sci.Total Environ. 288, 81-
95. 2002.
Ref Type: Abstract
605. Ludbrook, G. L., and Upton, R. N. (1997). A physiological model of induction of
anaesthesia with propofol in sheep. 2. Model analysis and implications for dose
requirements. British Journal of Anaesthesia 79, 505-513.
53
-------
606. Luebeck, E. G., Moolgavkar, S. H., Buchmann, A., and Schwarz, M. (1991). Effects
of poly chlorinated biphenyls in rat liver: Quantitative analysis of enzyme-altered
foci. Toxicology and Applied Pharmacology 111, 469-484.
607. Luecke, R. H., Wosilait, W. D., Pearce, B. A., and Young, J. F. (1994). A
physiological based pharmacokinetic computer model for human pregnancy.
Teratology 49, 90-103.
608. Luecke, R. H., Wosilait, W. D., and Young, J. F. (1997). Mathematical analysis for
teratogenic sensitivity. Teratology 55, 373-380.
609. Luecke, R. H., Wosilait, W. D., Pearce, B. A., and Young, J. F. (1997). A computer
model and program for xenobiotic disposition during pregnancy. Computer Methods
and Programs in Biomedecine 53, 201-224.
610. Luttringer, O., Theil, F. P., Poulin, P., Schmitt-Hoffmann, A. H., Guentert, T. W.,
and Lave, T. (2003). Physiologically based pharmacokinetic (PBPK) modeling of
disposition of epiroprim in humans. Journal of Pharmaceutical Sciences 92, 1990-
2007.
611. Lutz, R. J., Dedrick, R. L., Matthews, H. B., Eling, T. E., and Anderson, M. W.
(1977). A preliminary pharmacokinetic model for several chlorinated biphenyls in
the rat. Drug Metabolism and Disposition 5, 386-396.
612. Lutz, R. J., Dedrick, R. L., Tuey, D., Sipes, I. G., Andersen, M. W., and Mattews,
H. B. (1984). Comparison of the pharmacokinetics of several polychlorinated
biphenyls in the mouse, rat, dog, and monkey by means of a physiological
pharmacokinetic model. Drug Metabolism and Disposition 12, 527-535.
613. Lutz, R. J., and Dedrick, R. L. (1985). Physiological pharmacokinetics: relevance to
human risk assessment. In New Approaches in Toxicity Testing and Their
Application in Human Risk Assessment. (A. P. Li, Ed.), p. 129. Raven Press, New
York.
614. Luu, H. M., and Hutter, J. C. (2001). Bioavailability of
octamethylcyclotetrasiloxane (D(4)) after exposure to silicones by inhalation and
implantation. Environ.Health Perspect. 109, 1095-1101.
615. Luu, H. M. D., Hutter, J. C., and Bushar, H. F. (1998). A physiologically based
pharmacokinetic model for 2,4-toluenediamine leached from polyurethane foam-
covered breast implants. Environmental Health Perspectives 106, 393-400.
616. MacDonald, A. J., Rostami-Hodjegan, A., Tucker, G. T., and Linkens, D. A. (2002).
Analysis of solvent central nervous system toxicity and ethanol interactions using a
human population physiologically based kinetic and dynamic model. Regulatory
Toxicology and Pharmacology 35, 165-176.
54
-------
617. Macpherson, S. E., Barton, C. N., and Bronaugh, R. L. (1996). Use of in vitro skin
penetration data and a physiologically based model to predict in vivo blood levels of
benzoic acid. Toxicology and Applied Pharmacology 140, 436-443.
618. Mann, S., Droz, P. O., and Vahter, M. (1996). A physiologically based
pharmacokinetic model for arsenic exposure. I. Development in hamsters and
rabbits. Toxicology and Applied Pharmacology 137, 8-22.
619. Mann, S., Droz, P. O., and Vahter, M. (1996). A physiologically based
pharmacokinetic model for arsenic exposure. II. Validation and application in
humans. Toxicology and Applied Pharmacology 140, 471-486.
620. Manuilov, K. K. (1992). Use of a physiologically-based pharmacokinetic model for
analysis of antibiotic distribution in tissue. International Journal of Clinical
Pharmacology, Therapy and Toxicology 30, 548-549.
621. Mapleson, W. W. (1963). An electric analog for uptake and elimination in man.
Journal of Applied Physiology 18, 197-204.
622. Mapleson, W. W. (1972). KINETICS. Absorption, distribution and excretion. In
Handbook of experimental pharmacology . Modern inhalation anesthetics. (M. B.
Chenoweth, Ed.), pp. 326-344. Springer-Verlag, Berlin-Heidelberg, New York.
623. Mapleson, W. W. (1973). Ciculation-time models of the uptake of inhaled
anaesthetics and data for quantifying them. British Journal of Anaesthesia 45, 319-
334.
624. Marchand, S., Chenel, M., Lamarche, I, Couet, W. (2005). Pharmacokinetic
modeling of free amoxicillin concentrations in rat muscle extracellular fluids
determined by microdialysis. Antimicrob Agents Chemother 49(9), 3702-3706.
PMID: 16127043 [PubMed - indexed for MEDLINE]
625. Mari, A. (1993). Circulatory models of intact-body kinetics and their relationship
with compartmental and non-compartmental analysis. Journal of Theorical Biology
160,509-531.
626. Maruyama, W., Yoshida, K., Tanaka, T., and Nakanishi, J. (2002). Possible range of
dioxin concentration in human tissues: simulation with a physiologically based
model. J.Toxicol Environ.Health A 65, 2053-2073.
627. Maruyama, W., Yoshida, K., Tanaks, T., and Nakanishi, J. (2003). Simulation of
dioxin accumulation in human tissues and analysis of reproductive risk.
Chemosphere 53, 301-313.
628. Maruyama, W., Yoshida, K., Tanaka, T., and Nakanishi, J. (2002). Determination of
tissue-blood partition coefficients for a physiological model for humans, and
estimation of dioxin concentration in tisssues. Chemosphere 46, 975-985.
55
-------
629. Mason, H., and Wilson, K. (1999). Biological monitoring: the role of toxicokinetics
and physiologically based pharmacokinetic modeling. American Industrial Hygiene
Association Journal 60, 237-242.
630. Mattie, D. R., Grabau, J. H., and McDougal, J. N. (1994). Significance of the dermal
route of exposure to risk assessment. Risk. Anal. 14, 277-284.
631. Mauderly, J. L. (1990). Measurement of respiration and respiratory responses during
inhalation exposures. Journal of American College of Toxicology 9, 397-406.
632. Mayer, J. M., and Van de Waterbeemd, H. (1985). Development of quantitative
structure-pharmacokinetic relationships. Environmental Health Perspectives 61,
295-306.
633. McCarley, K. D., and Bunge, A. L. (1998). Physiologically relevant one-
compartment pharmacokinetic models for skin. 1. Development of models. Journal
of Pharmaceutical Sciences 87, 470-481.
634. McDougal, J. N., and Robinson, P. J. (2002). Assessment of dermal absorption and
penetration of components of a fuel mixture (JP-8). The Science of the Tot.Environ.
288, 23-30.
635. McDougal, J. N., Jepson, G. W., Clewell, H. J., and Andersen, M. E. (1985).
Dermal absorption of dihalomethane vapors. Toxicology and Applied
Pharmacology 79, 150-158.
636. McDougal, J. N., Jepson, G. W., Clewell, H. J., MacNaughton, M. G., and
Andersen, M. E. (1986). A physiological pharmacokinetic model for dermal
absorption of vapors in the rat. Toxicology and Applied Pharmacology 85, 286-294.
637. McKim, J., Schieder, P., and Veith, G. (1985). Absorption dynamics of organic
chemical transport across trout gills as related to octanol-water partition coefficient.
Toxicology and Applied Pharmacology 77, 1-10.
638. McKim, J. M., and Goeden, H. M. (1982). A direct measure of the uptake efficiency
of a xenobiotic chemical across the gills of brook trout (salvelinus fontin alis) under
normoxic and hypoxic conditions. Comparative Biochemistry and Physiology 72C,
65-74.
639. McKim, J. M., Nichols, J. W., Lien, G. J., Hoffman, A. D., Gallinat, C. A., and
Stokes, G. N. (1996). Dermal absorption of three waterborne chloroethanes in
rainbow trout (Oncorhynchus mykiss) and channel catfish (Ictalurus punctatus).
Fundam.Appl.Toxicol 31, 218-228.
640. McKone, T. E. (1993). Linking a PBPK model for chloroform with measured breath
concentrations in showers: implications for dermal exposure models. Journal of
Exposure Analysis and Environmental Epidemiology 3, 339-365.
56
-------
641. McMahon, T. F., Medinsky, M. A., and Birnbaum, L. S. (1994). Age-related
changes in benzene disposition in male C57BL/6N mice described by a
physiologically based pharmacokinetic model. Toxicology Letters 74, 241-253.
642. Medinsky, M. A., Bechtold, W. E., Birnbaum, L. S., Chico, D. M., Gerlach, F. G.,
and Henderson, R. F. (1988). Uptake of vinylidene fluoride in rats simulated by a
physiological model. Fundamental and Applied Toxicology 11, 250-260.
643. Medinsky, M. A., Sabourin, P. J., Lucier, G., Birnbaum, L. S., and Henderson, R. F.
(1989). A physiological model for simulation of benzene by rats and mice.
Toxicology and Applied Pharmacology 99, 193-206.
644. Medinsky, M. A., Bond, J. A., Hunsberger, S., and Griffith, W. C. Jr. (1989). A
physiologically based model of 1-nitropyrene metabolism after inhalation or
ingestion. Health Physics 57, 149-155.
645. Medinsky, M. A., Kimbell, J. S., Morris, J. B., Gerde, P., and Overton, J. H. (1993).
Advances in biologically based models for respiratory tract uptake of inhaled
volatiles. Fundamental and Applied Toxicology 20, 265-272.
646. Medinsky, M. A., Leavens, T. L., Csanady, G. A., Gargas, M. L., and Bond, J. A.
(1994). In vivo metabolism of butadiene by mice and rats: a comparison of
physiological model predictions and experimental data. Carcinogenesis 15, 1329-
1340.
647. Medinsky, M. A. (1995). The application of physiologically based
pharmacokinetic/pharmacodynamic (PBPK/PD) modeling to understanding the
mechanism of action of hazardous substances. Toxicology Letters 79, 185-191.
648. Medinsky, M. A., Kenyon, E. M., Seaton, M. J., and Schlosser, P. M. (1996).
Mechanistic considerations in benzene physiological model development.
Environmental Health Perspectives 104, 1399-1404.
649. Meek, B., Renwick, A., and Sonich-Mullin, C. (2003). Practical application of
kinetic data in risk assessment - an IPCS initiative. Toxicol.Lett. 138, 151-160.
650. Melnick, R. L., and Kohn, M. C. (2000). Dose-response analyses of experimental
cancer data. Drug.metab.Reviews. 32, 193-209.
651. Menzel, D. B., Wolpert, R. L., Boger, J. R., and Kootsey, J. M. (1987). Resources
available for simulation in toxicology: specialized computers, generalized software
and communication networks. Drinking Water and Health 8, 229-254.
652. Menzel, D. B. (1988). Planning and using PBPK models: An integrated inhalation
and distribution model for nickel. Toxicology Letters 43, 67-83.
653. Merrill, E. A., Clewell, R. A., Gearhart, J. M., Robinson, P. J., Sterner, T. R., Yu
K.O, Mattie, D. R., and Fisher, J. W. (2003). PBPK predictions of perchlorate
57
-------
distribution and its effect on thyroid uptake of radioiodide in the male rat.
Toxicol.Sci. 73, 256-269.
654. Miller, F. 1, Overton, J. H., Jaskot, R. H., and Menzel, D. B. (1985). A model for
the regional uptake of gaseous pollutants in the lung. I. The sensitivity of the uptake
of ozone in the human lung to lower respiratory tract secretions and exercise.
Toxicology and Applied Pharmacology 79, 11-27.
655. Miller, S. C., Himmelstein, K. J., and Patton, T. F. (1981). A physiologically based
pharmacokinetic model for the intraocular distribution of pilocarpine in rabbits.
Journal of Pharmacokinetics and Biopharmaceutics 9, 653-677.
656. Mills, J. J., and Andersen, M. E. (1993). Dioxin hepatic carcinogenesis: Biologically
motivated modeling and risk assessment. Toxicology Letters 68, 177-189.
657. Molino, G., Avagnina, P., Belforte, G., and Bircher, J. (1998). Assessment of the
hepatic circulation in humans: New concepts based on evidence derived from a D-
sorbitol clearance method. Journal of laboratory and clinical medecin 131, 393-405.
658. Monro, A. (1994). Drug toxicokinetics: Scope and limitations that arise from
species differences in pharmacodynamic and carcinogenic responses.
J.Pharam.Biopharm. 22, 41-57.
659. Moolgavkar, S. H., and Luebeck, G. (1990). Two-Event model for carcinogenesis:
Biological, Mathematical, and Statistical Considerations. Risk Anal. 10, 323-341.
660. Mork, A.K., Johanson, G. (2005). A human physiological model describing acetone
kinetics in blood and breath during various levels of physical exercise. Toxicol
Lett. [Epub ahead of print] PMID: 16364574 [PubMed - as supplied by publisher]
661. Morris, J. B., Hassett, D. N., and Blanchard, K. T. (1993). A physiologically based
pharmacokinetic model for nasal uptake and metabolism of nonreactive vapors.
Toxicology and Applied Pharmacology 123, 120-129.
662. Mortensen, B., andNilsen, O. G. (1997). Optimization and application of the head
space liver S9 equilibration technique for metabolic studies of organic solvents.
Pharm.Toxicol. 82, 142-146.
663. Mortensen, B., andNilsen, O. G. (1997). Allometric species comparison of toluene
and n-haxane metabolism: Prediction of hepatic clearance in man from experiments
with rodent liver S9 in a head space vial equilibration system. Pharm.Toxicol. 82,
183-188.
664. Mortensen, B., Osvoll, P. O., Woldbaek, T., Zahlsen, K., Eide, I, and Nilsen, O. G.
(1998). In vitro screening for metabolic interactions among frequently occurring
binary mixtures of volatile organic chemicals in norwegian occupational
atmosphere. Pharmacol.Toxicol. 83, 49-56.
58
-------
665. Mortensen, B., Zahlsen, K., and Nilson, O. G. (1998). Metabolic interaction of n-
hexane and methyl ethyl ketone in vitro in a head space rat liver S9 vial
equilibration system. Pharmacol.Toxicol. 82, 67-73.
666. Morzorati, S. L., Ramchandani, V. A., Li, T. K., and O'Connor, S. (2002).
Maintaining steady state arterial alcohol levels in rats by using a physiologically
based pharmacokinetic model. Alcohol 28, 189-195.
667. Murphy, J. E., Janszen, D. B., and Gargas, M. L. (1995). An in vitro method for
determination of tissue partition coefficients of non-volatile chemicals such as
2,3,7,8-tetrachlorodibenzo-p-dioxin and estradiol. Journal of Applied Toxicology
15, 147-152.
668. Nagata, O., Murata, M., Kato, H., Terasaki, T., Sato, H., and Tsuji, A. (1990).
Physiological pharmacokinetics of a new muscle-relaxant, inaperisone, combined
with its pharmacological effects on blood flow rate. Drug Metabolism and
Disposition 18, 902.
669. Nakajima, Y., Hattori, K., Shinsei, M., Matsunaga, N., lizasa, H., Sasabe, H.,
Akiyama, H., Miyamoto, G., and Nakashima, E. (2000). Physiologically-based
pharmacokinetic analysis of grepafloxacin. Biological and Pharmaceutical Bulletin
23, 1077-1083.
670. Nakashima, E., Yokogawa, K., Ichimura, F., Kurata, K., Kido, H., Yamaguchi, N.,
and Yamana, T. (1987). A physiologically based pharmacokinetic model for
biperiden in animals and its extrapolation to humans. Chemical and Pharmaceutical
Bulletin 35, 718-725.
671. Naumann, B. D., Silverman, K. C., Dixit, R., Faria, E. C., and Sargent, E. V. (2001).
Case studies of categorical data-derived adjustment factors. Human and Ecological
Risk Assessment 7, 61-105.
672. Nestorov, I. (2001). Modelling and simulation of variability and uncertainty in
toxicokinetics and pharmacokinetics. Toxicol Lett. 120, 411-420.
673. Nestorov, I. (2003). Whole body pharmacokinetic models. Clinical
Pharmacokinetics 42, 883-908.
674. Nestorov, I. A., Aarons , L. J., and Rowland, M. (1997). Physiologically based
pharmacokinetic modeling of a homologous series of barbiturates in the rat: a
sensitivity analysis. Journal of Pharmacokinetics and Biopharmaceutics 25, 413-
447.
675. Nestorov, I. A., Aarons , L. J., Arundel, P. A., and Rowland, M. (1998). Lumping of
whole-body physiologically based pharmacokinetic models. Journal of
Pharmacokinetics and Biopharmaceutics 26, 21-46.
59
-------
676. Nestorov, I. A., and Rowland, M. (1998). Quantitative structure-pharmacokinetics
relationships: II. A mechanistically based model to evaluate the relationship
between tissue distribution parameters and compound lipophilicity. Journal of
Pharmacokinetics and Biopharmaceutics 26, 521-545.
677. Nestorov, I. A. (1998). A WWW resource for physiologically based modelling in
pharmacokinetics, pharmacodynamics, toxicology and risk assessment.
Med.Inform.(Lond) 23, 193-198.
678. Nestorov, I. A. (1999). Sensitivity analysis of pharmacokinetic and
pharmacodynamic systems: I. A structural approach to sensitivity analysis of
physiologically based pharmacokinetic models. J.Pharmacokinet.Biopharm. 27,
577-596.
679. Nichlos, J. W., Fitzsimmons, P. N., and Whiteman, F. W. (2004). A Physiologically
Based Toxicokinetic Model for Dietary Uptake of Hydrophobic Organic
Compounds by Fish. II. Simulation of chronic Exposure Scearious. Toxicol.Sci. 77,
219-229.
680. Nichols, J., Rheingans, P., Lothenbach, D., McGeachie, R., Skow, L., and McKim,
J. (1994). Three-Dimentional Visualization of Physiologically Based Kinetic Model
Outputs. Environmental Health Perspectives 102, 952-956.
681. Nichols, J. W., Fitzsimmons, P. N., Whiteman, F. W., Dawson, T. D., Babeu, L.,
and Juenemann, J. (2004). A physiologically Based Toxicokinetic Model for
Dietary Uptake of Hydrophobic Organic Compounds by Fish. 1. Feeding Studies
with 2,2', 5,5' - Tetrachlorobiphenyl. Toxicol.Sci. 77, 206-218.
682. Nichols, J. W., McKim, J. M., Andersen, M. E., Gargas, M. L., Clewell, H. J., Ill,
and Erickson, R. J. (1990). A physiologically based toxicokinetic model for the
uptake and disposition of waterborne organic chemicals in fish. Toxicol
Appl.Pharmacol. 106, 433-447.
683. Nichols, J. W., McKim, J. M., Lien, G. J., Hoffman, A. D., and Bertelsen, S. L.
(1991). Physiologically based toxicokinetic modeling of three waterborne
chloroethanes in rainbow trout. Toxicology and Applied Pharmacology 110, 374-
389.
684. Nichols, J. W., McKim, J. M., Lien, G. J., Hoffman, A. D., Bertelsen, S. L., and
Gallinat, C. A. (1993). Physiologically-based toxicokinetic and modeling of three
waterborne chloroethanes in channel catfish, Ictalurus punctatus. Aquatic
Toxicology 27, 83-112.
685. Nichols, J. W., McKim, J. M., Lien, G. J., Hoffman, A. D., Bertelsen, S. L., and
Elswick, B. A. (1996). A physiologically based toxicokinetic model for dermal
absorption of organic chemicals by fish. Fundamental and Applied Toxicology 31,
229-242.
60
-------
686. Nichols, J. W., Jensen, K. M., Tietge, J. E., and Johnson, R. D. (1998).
Physiologically based toxicokinetic model for maternal transfer of 2,3,7,8-
tetrachlorodibenzo-p-dioxin in brook trout (salvelinus fontinalis). Environmental
Toxicology and Chemistry 17, 2422-2434.
687. Nihlen, A., Lof, A., and Johanson, G. (1995). Liquid/air partition coefficients of
methyl and ethyl t-butyl ethers, t-amyl methyl ether, and t-butyl alcohol. Journal of
Exposure Analysis and Environmental Epidemiology 5, 573-582.
688. Nihlen, A., and Johanson, G. (1999). Physiologically based toxicokinetic modeling
of inhaled ethyl ternary-butyl ether in humans. Toxicological Sciences 51, 184-194.
689. O'Flaherty, E. J. (1981). Toxicant and Drugs: Kinetics and Dynamics. New York.
690. O'Flaherty, E. J. (1991). Physiologically-based models for boneseeking elements. II.
Kinetics of lead disposition in rats. Toxicology and Applied Pharmacology 111,
313-331.
691. O'Flaherty, E. J. (1991). Physiologically based models for bone-seeking elements. I.
Rat skeletal and bone growth. Toxicol Appl.Pharmacol. Ill, 299-312.
692. O'Flaherty, E. J. (1991). Physiologically Based Models for Bone-Seeking Elements.
Ill: Human Skeletal and Bone Growth. Toxicology and Applied Pharmacology 111,
332-341.
693. O'Flaherty, E. J., Scott, W., Schreiner, C., and Beliles, R. P. (1992). A
physiologically-based kinetic model of rat and mouse gestation: Disposition of a
weak acid. Toxicology and Applied Pharmacology 112, 245-256.
694. O'Flaherty, E. J. (1993). A pharmacokinetic model for chromiun. Toxicology
Letters 68, 145-158.
695. O'Flaherty, E. J. (1993). Physiologically based models for bone-seeking elements.
IV. Kinetics of lead disposition in humans. Toxicology and Applied Pharmacology
118, 16-29.
696. O'Flaherty, E. J. (1994). Physiologic changes during growth and development.
Environmental Health Perspectives Suppl 102, 103-106.
697. O'Flaherty, E. J. (1994). Physiologically based pharmacokinetic models in
developmental toxicology. Risk Analysis 14, 605-611.
698. O'Flaherty, E. J. (1995). PBK modeling for metals. Examples with lead, uranium,
and chromium. Toxicology Letters 82-83, 367-372.
699. O'Flaherty, E. J., Nau, H., McCandless, D., Beliles, R. P., Schreiner, C. M., and
Scott, W. J. Jr. (1995). Physiologically based pharmacokinetics of methoxyacetic
acid: dose-effect considerations in C57BL/6 mice. Teratology 52, 78-89.
61
-------
700. O'Flaherty, E. J. (1995). Physiologically based models for bone-seeking elements.
V. Lead absorption and disposition in childhood. Toxicology and Applied
Pharmacology 131, 297-308.
701. O'Flaherty, E. J. (1996). A physiologically based model of chromium kinetics in the
rat. Toxicology and Applied Pharmacology 138, 54-64.
702. O'Flaherty, E. J. (1998). Physiologically based models of metal kinetics. Critical
Reviews in Toxicology 28, 271-317.
703. O'Flaherty, E. J. (1998). A physiologically based kinetic model for lead in children
and adults. Environmental Health Perspectives Suppl 106, 1495-1503.
704. O'Flaherty, E. J., Inskip, M. J., Franklin, C. A., Durbin, P. W., Manton, W. I, and
Baccanale, C. L. (1998). Evaluation and modification of a physiologically based
model of lead kinetics using data from a sequential isotope study in cynomolgus
monkeys. Toxicology and Applied Pharmacology 149, 1-16.
705. Oliver, R. E., Jones, A. F., and Rowland, M. (2001). A whole - body physiologically
based pharmacokinetic model incorporating dispersion concepts: short and long
time characteristics. Journal of Pharmacokinetics and Biopharmaceutics 28, 27-55.
706. Overton, J. H., Kimbell, J. S., and Miller, F. J. (2001). Dosimetry modeling of
inhaled formaldehyde: the human respiratory tract. Toxicol Sci. 64, 122-134.
707. Overton, J. H., Graham, R. C., and Miller, F. J. (1987). Mathematical modeling of
ozone absorption in the lower respiratory tract. Drinking Water and Health 8, 302-
311.
708. Overton, J. H., Graham, R. C., and Miller, F. J. (1987). A model of the regional
uptake of gaseous pollutants in the lung.II. The sensitivity of ozone uptake in
laboratory animal lungs to anatomical and ventilatory parameters. Toxicology and
Applied Pharmacology 88, 418-432.
709. Overton, J. H., and Jarabek, A. M. (1989). Estimating equivalent human
concentrations of no observed adverse effect levels: a comparison of several
methods. Experimental Pathology 37, 89-94.
710. Parham, F. M., Kohn, M. C., Matthews, H. B., DeRosa, C., and Portier, C. J. (1997).
Using structural information to create physiologically based pharmacokinetic
models for all polychlorinated biphenyls. I. Tissue: blood partition coefficients.
Toxicology and Applied Pharmacology 144, 340-347.
711. Parham, F. M., and Portier, C. J. (1998). Using structural information to create
physiologically based pharmacokinetic models for all polychlorinated biphenyls. II.
Rates of metabolism. Toxicology and Applied Pharmacology 151, 110-116.
62
-------
712. Parham, F. M., Matthews, H. B., and Portier, C. J. (2002). A physiologically based
pharmacokinetics model of p,p'-dichlorodiphenylsulfone. Toxicology and Applied
Pharmacology 181, 153-163.
713. Pastino, G. M., Sultatos, L. G., and Flynn, E. J. (1996). Development and
application of a physiologically-based pharmacokinetic model for ethanol in the
mouse. Alcohol and Alcoholism 31, 365-374.
714. Pastino, G. M., Asgharian, B., Roberts, K., Medinsky, M. A., and Bond, J. A.
(1997). A comparison of physiologically based pharmacokinetic model predictions
and experimental data for inhaled ethanol in male and female B6C3F1 mice, F344
rats, and humans. Toxicology and Applied Pharmacology 145, 147-157.
715. Pastino, G. M., and Conolly, R. B. (2000). Application of a physiologically based
pharmacokinetic model to estimate the bioavailability of ethanol in male rats:
distinction between gastric and hepatic pathways of metabolic clearance.
Toxicological Sciences 55, 256-265.
716. Pastino, G. M., Flynn, E. J., and Sultatos, L. G. (2000). Genetic polymorphism in
ethanol metabolism: issues and goals for physiologically based pharmacokinetic
modeling. Drug and Chemical Toxicology 23, 179-201.
717. Paterson, S., and Mackay, D. (1986). A pharmacokinetic model of styrene
inhalation with the fugacity approach. Toxicology and Applied Pharmacology 82,
444-453.
718. Paustenbach, D., Andersen, M. E., Clewell, H. J., and Gargas, M. L. (1988). A
physiologically-based pharmacokinetic model for inhaled carbon tetrachloride in the
rat. Toxicology and Applied Pharmacology 96, 191-211.
719. Paustenbach, D., Layard, M. W., Wenning, R. J., and Keenan, R. E. (1991). Risk
assessment of 2,3,7,8-TCDD using a biologically based cancer model: a
reevaluation of the Kociba et al. bioassay using 1978 and 1990 histopathology.
Journal of Toxicology and Environmental Health 34, 11-26.
720. Paustenbach, D. J., Bruce, G. M., and Chrostowski, P. (1997). Current views on the
oral bioavailability of inorganic mercury in soil: Implications for Health Risk
Assessments. Risk Analysis 17, 544.
721. Payne, J. A. (1982). Verification and validation procedures. In Introduction to
simulation. Programming techniques and methods of analysis (J. A. Payne, Ed.), pp.
225-242. McGraw-Hill Book Company, New York, St. Louis, San Francisco, & al.
722. Payne, M. P., and Kenny, L. C. (2002). Comparison of Models for the Estimation of
Biological Partition Coefficients. Journal of Toxicology and Environmental Health
65,897-931.
63
-------
723. Pelekis, M., Gephart, L. A., and Lerman, S. E. (2001). Physiological - model-based
derivation of the adult and child pharmacokinetic intraspecies uncertainty factors for
volatile organic compounds. Regulatory Toxicology and Pharmacology 33, 12-20.
724. Pelekis, M., Nicolich, M. I, and Gauthier, J. S. (2003). Probabilistic framework for
the estimation of the adult and child toxicokinetic intraspecies uncertainty factors.
Risk Anal. 23, 1239-1255.
725. Pelekis, M., Poulin, P., and Krishnan, K. (1995). An approach for incorporating
tissue composition data into physiologically based pharmacokinetic models. Toxicol
Ind.Health 11, 511-522.
726. Pelekis, M., Krewski, D., and Krishnan, K. (1997). Physiologically based algebraic
expressions for predicting steady-state toxicokinetics of inhaled vapors. Toxicology
Methods 7, 205-225.
727. Pelekis, M., and Krishnan, K. (1997). Assessing the relevance of rodent data on
chemical interactions for health risk assessment purposes: a case study with
dichloromethane- toluene mixture. Regul.Toxicol Pharmacol. 25, 79-86.
728. Pelekis, M. and Krishnan, K. Physiologically-based modeling of the
pharmacokinetics and pharmacodynamics of aldicarb in humans. Anderson, J. G.
and Katzper, M. 124-128. 1999. Proceeding of the 1999 Medical Science
Simulation Conference, Society for Computer, Simulation International, San diego,
CA.
Ref Type: Conference Proceeding
729. Perbellini, L., Brugnone, F., Caretta, D., and Maranelli, G. (1985). Partition
coefficients of some industrial aliphatic hydrocarbons (C5-C7) in blood and human
tissues. British Journal of Industrial Medicine 42, 162-167.
730. Perbellini, L., Mozzo, P., Brugnone, F., and Zedde, A. (1986).
Physiologicomathematical model for studying human exposure to organic solvents:
Kinetics of blood/tissue n-hexane concentrations and of 2,5-hexanedione in urine.
British Journal of Industrial Medicine 43, 760-768.
731. Perbellini, L., Mozzo, P., Turri, P. V., Zedde, A., and Brugnone, F. (1988).
Biological exposure index of styrene suggested by a physiologico-mathematical
model. International Archives of Occupational and Environmental Health 60, 187-
193.
732. Perbellini, L., Mozzo, P., Olivata, D., and Brugnone, F. (1990). Dynamic biological
exposure indexes for n-hexane and 2,5-hexanedione, suggested by a
physiologically-based pharmacokinetic model. American Industrial Hygiene
Association Journal 51, 356-362.
64
-------
733. Perleberg, U.R., Keys, D.A., Fisher, J.W. (2004). Development of a physiologically
based pharmacokinetic model for decane, a constituent of jet propellent-8. Inhal
Toxicol 16(11-12), 771-783. PMID: 16036747 [PubMed - indexed for MEDLINE]
734. Perico, A., Cassinelli, C., Brugnone, F., Bavazzano, P., and Perbellini, L. (1999).
Biological monitoring of occupational exposure to cyclohexane by urinary 1,2-and
1,4-cyclohexanediol determination. International Archives of Occupational and
Environmental Health 72, 115-120.
735. Perkins, R. A., Ward, K. W., and Pollack, G. M. (1995). A pharmacokinetic model
of inhaled methanol in humans and comparison to methanol disposition in mice and
rats. Environmental Health Perspectives 103, 726-733.
736. Perkins, R. A., Ward, K. W., and Pollack, G. M. (1996). Methanol inhalation:site
and other factors influencing absorption, and an inhalation toxicokinetic model for
the rat. Pharmaceutical Research 13, 749-755.
737. Pierce, C. H., Dills, R. L., Silvey, G. W., and Kalman, D. A. (1996). Partition
coefficients between human blood or adipose tissue and air for aromatic solvents.
Scand.J.Environ.Health. 22, 112-118.
738. Pierce, C. H., Dills, R. L., Morgan, M. S., Nothstein, G. L., Shen, D. D., and
Kalman, D. A. (1996). Interindividual differences in 2H8-toluene toxicokinetics
assessed by a semi empirical physiologically based model. Toxicology and Applied
Pharmacology 139, 49-61.
739. Pierce, C. H., Dills, R. L., Morgan, M. S., Vicini, P., and Kalman, D. A. (1998).
Biological monitoring of controlled toluene exposure. International Archives of
Occupational and Environmental Health 71, 433-444.
740. Pierce, C. H., Becker, C. E., Tozer, T. N., Owen, D. I, and So, Y. (1998). Modeling
the acute neurotoxicity of styrene. J.Occup.Environ.Med. 40, 230-240.
741. Piotrovskij, V. K., Shvatchko, E. V., and Trnovec, T. (1994). The use of
physiologically based models to simulate enantioselective differences in
pharmacokinetics. Methods and Findings in Experimental and Clinical
Pharmacology 16, 263-269.
742. Pleil, J. D., and Lindstrom, A. B. (1998). Sample timing and mathematical
considerations for modeling breath elimination of volatile organic compounds. Risk
Analysis 18, 585-602.
743. Ploeger, B., Mensinga, T., sips, A., Meulenbelt, J., and Dejongh, J. (2000). A human
physiologically - based model for glycyrrhzic acid, a compound subject to
presystemic metabolism and enterohepatic cycling. Pharmaceutical Research 17,
1516-1525.
65
-------
744. Ploeger, B., Mensinga, T., Sips, A., Deerenberg, C., Meulenbelt, J., and de Jongh, J.
(2001). A population physiologically based pharmacokinetic/pharmacodynamic
model for the inhibition of 1 l-|3-hydroxysteroid dehydrogenase activity by
glycyrrhetic acid. Toxicology and Applied Pharmacology 170, 46-55.
745. Ploeger, B., Mensinga, T., Sips, A., Seinen, W., Meulenbelt, J., and DeJongh, J.
(2001). The pharmacokinetics of glycyrrhizic acid evaluated by physiologically
based pharmacokinetic modeling. Drug Metab Rev. 33, 125-147.
746. Ploeger, B. A., Meulenbelt, J., and de Jongh, J. (2000). Physiologically based
pharmacokinetic modeling of glycyrrhizic acid, a compound subjet to presystemic
metabolism and enterohepatic cycling. Toxicology and Applied Pharmacology 162,
177-188.
747. Ploemen, J. P. H. T. M., Wormhoudt, L. W., Haenen, G. R. M. M., Oudhoorn, M. J.,
Commandeur, J. N. M., Vermeulen, N. P. E., De Wazier, L, Beaune, P. H., Watabe,
T., and van Bladeren, P. J. (1997). The use of human in vitro metabolic parameters
to explore the risk assessment of hazardous compounds: the case of ethylene
dibromide. Toxicology and Applied Pharmacology 143, 56-69.
748. Plowchalk, D. R., and Teeguarden, J. (2002). Development of a physiologically
based pharmacokinetic model for estradiol in rats and humans: a biologically
motivated quantitative framework for evaluating responses to estradiol and other
endocrineactive compounds. Toxicol Sci. 69, 60-78.
749. Plowchalk, D. R., Andersen, M. E., and Bethizy, J. D. (1992). A physiologically-
based pharmacokinetic model for nicotine disposition in the Sprague-Dawley rat.
Toxicology and Applied Pharmacology 116, 177-188.
750. Plowchalk, D. R., Andersen, M. E., and Bogdanffy, M. S. (1997). Physiologically
based modeling of vinyl acetate uptake, metabolism, and intracellular pH changes in
the rat nasal cavity. Toxicology and Applied Pharmacology 142, 386-400.
751. Poet, T. S., Weitz, K. K., Gies, R. A., Edwards, J. A., Thrall, K. D., Corley, R. A.,
Tanojo, H., Hui, X., Maibach, H. L, and Wester, R. C. (2002). PBPK modeling of
the percutaneous absorption of perchloroethylene from a soil matrix in rats and
humans. Toxicol Sci. 67 , 17-31.
752. Poet, T. S., Soelberg, J. J., Weitz, K. K., Mast, T. J., Miller, A. A., Thrall, B. D., and
Corley, R. A. (2003). Mode of Action and pharmacokinetic studies of 2-
butoxyethanol in the mouse with an Emphasis on Forestomach dosimetry.
Toxicol.Sci. 71, 176-189.
753. Poet, T. S., and Borghoff, S. J. (1997). In vitro uptake of methyl tert-butylether in
male rat kidney: use of a two-compartment model to describe protein interactions.
Toxicology and Applied Pharmacology 145, 340-348.
66
-------
754. Poet, T. S., Corley, R. A., Thrall, K. D., Edwards, J. A., Tanajo, H., Weitz, K. K.,
Hui, X. Y., Maibach, H. I, and Wester, R. C. (2000). Assessment of the
percutaneous absorption of trichloroethylene in rats and humans using MS/MS real-
time breath analysis and physiologically based pharmacokinetic modeling.
Toxicological Sciences 56, 61-72.
755. Poet, T. S., Thrall, K. D., Corley, R. A., Hui, X. Y., Edwards, J. A., Weitz, K. K.,
Maibach, H. I, and Wester, R. C. (2000). Utility of real time breath analysis and
physiologically based pharmacokinetic modeling to determine the percutaneous
absorption of methyl chloroform in rats and humans. Toxicological Sciences 54, 42-
51.
756. Portier, C. J., and Kaplan, N. L. (1989). Variability of safe estimated when using
complicated models of carcinogenic processes. A dose study: methylene chloride.
Fundamental and Applied Toxicology 13, 533-544.
757. Portier, C. J., and Koop-Schneider, A. (1991). A multistage model of carcinogenesis
incorporating DNA damage and repair. Risk Analysis 11, 535-543.
758. Poulin, P., and Krishnan, K. (1995). A biologically-based algorithm for predicting
human tissue: blood partition coefficients of organic chemicals. Human &
Experimental Toxicology 14, 273-280.
759. Poulin, P., and Krishnan, K. (1996). A tissue composition-based algorithm for
predicting tissue: air partition coefficients of organic chemicals. Toxicology and
Applied Pharmacology 136, 126-130.
760. Poulin, P., and Krishnan, K. (1996). A mechanistic algorithm for predicting blood:
air partition coefficients of organic chemicals with the consideration of reversible
binding in hemoglobin. Toxicology and Applied Pharmacology 136, 131-137.
761. Poulin, P., and Krishnan, K. (1996). Molecular structure-based prediction of the
partition coefficients of organic chemicals for physiological pharmacokinetic
models. Toxicology Methods 6, 117-137.
762. Poulin, P., and Krishnan, K. (1998). A quantitative structure-toxicokinetic
relationship model for highly metabolised chemicals. Alternatives to Laboratory
Animals 26, 45-59.
763. Poulin, P., and Krishnan, K. (1999). Molecular structure-based prediction of the
toxicokinetics of inhaled vapors in humans. International Journal of Toxicology 18,
7-18.
764. Poulin, P., Beliveau, M., and Krishnan, K. (1999). Mechanistic animal-replacement
approaches for predicting pharmacokinetics of organic chemicals. In Toxicity
assessment alternatives: methods, issues,opportunities. (H. Salem, and S. A. Katz,
Eds.), pp. 115-139. Humana Press Inc., Totowa, NJ.
67
-------
765. Poulin, P., and Theil, F. P. (2000). A priori prediction of tissue:plasma partition
coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic
models in drug discovery. Journal of Pharmaceutical Sciences 89, 16-35.
766. Poulin, P., and Theil, F. P. (2002). Prediction of pharmacokinetics prior to in vivo
studies. 1. Mechanism- based prediction of volume of distribution. J.Pharm.Sci. 91,
129-156.
767. Poulin, P., and Theil, F. P. (2002). Prediction of pharmacokinetics prior to In Vivo
studies. II. Generic physiologically based pharmacokinetic models of drug
disposition. J.Pharm.Sci. 91, 1358-1370.
768. Pounds, J. G., and Leggett, R. W. (1998). The ICRP age-specific biokinetic model
for lead: validations, empirical comparisons, and explorations. Environmental
Health Perspectives 106, 1505-1511.
769. Price, K., Haddad, S., and Krishnan, K. (2003). Physiological modeling of age-
specific changes in the pharmacokinetics of organic chemicals in children. J.Toxicol
Environ.Health 66, 417-433.
770. Price, P. S., Conelly, R. B., Chaisson, C. F., Gross, E. A., Young, J. S., Mathis, E.
T., and Tedder, D. R. (2003). Modeling interindividual variation in physiological
factors used in PBPK models of humans. Critical Reviews in Toxicology 33, 469-
503.
771. Purcell, K. J., Cason, G. H., Gargas, M. L., Andersen, M. E., and Travis, C. C.
(1990). In vivo metabolic interactions of benzene and toluene. Toxicology and
Applied Pharmacology 105, 37-54.
772. Quick, D. J., and Shuler, M. L. (2000). Use of in vitro data for construction of a
physiologically based pharmacokinetic model for naphthalene in rats and mice to
probe species differences. Biotechnology Progress 15, 540-555.
773. Ramchandani, V. A., Bolane, J., Li, T. K., and O'Connor, S. (1999). A
physiologically-based pharmacokinetic (PBPK) model for alcohol facilitates rapid
BrAC clamping. Alcoholism: Clinical and Experimental Research 23, 617-623.
774. Ramsey, J. C., and Andersen, M. E. (1984). A physiologically-based description of
the inhalation pharmacokinetics of styrene in rats and humans. Toxicology and
Applied Pharmacology 73, 159-175.
775. Rane, A., Wilkinson, G. R., and Shand, D. G. (1977). Prediction of hepatic
extraction ratio from in vitro measurement of intrinsic clearance. Journal of
Pharmacology and Experimental Therapeutics 200, 420-424.
776. Rao, H. V., and Brown, D. R. (1993). A physiologically based pharmacokinetic
assessment of tetrachloroethylene in groundwater for a bathing and showering
determination. Risk Analysis 13, 37-49.
68
-------
777. Rao, H. V., Bellies, R. P., Whitford, G. M., and Turners, C. H. (1995). A
physiologically based pharmacokinetic model for fluoride uptake by bone.
Regulatory Toxicology and Pharmacology 22, 30-42.
778. Rao, H. V., and Ginsberg, G. L. (1997). A physiologically-based pharmacokinetic
model assessment of methyl t-buthyl ether in groundwater for a bathhing and
showering determination. Risk Analysis 17, 583-598.
779. Reddy, M. B., McCarley, K. D., and Bunge, A. L. (1998). Physiologically relevant
one-compartment pharmacokinetic models for skin. 2. Comparison of models when
combined with a systemic pharmacokinetic. Journal of Pharmaceutical Sciences 87,
482-490.
780. Reddy, M. B., Andersen, M. E., Morrow, P. E., Dobrev, I. D., Varaprath, S.,
Plotzke, K. P., and Utell, M. J. (2003). Physiological modeling of inhalation kinetics
of octamethylcyclotetrasiloxane in humans during rest and exercise. Toxicol Sci. 72,
3-18.
781. Reinoso, R. F., Telfer, B. A., and Rowland, M. (1997). Tissue water content in rats
measured by desiccation. Journal of Pharmacological Methods 38, 87-92.
782. Reitz, R. H., Mandrela, A. L., Park, C. N., Andersen, M. E., and Guengerich, F. P.
(1988). Incorporation of in vitro enzyme data into the physiologically-vased
pharmacokinetic (PBPK) model for methylene chloride: Implications for risk
assessment. Toxicology Letters 43, 97-116.
783. Reitz, R. H., McDougal, J. N., Himmelstein, M. W., Nolan, R. J., and Schumann, A.
M. (1988). Physiologically-based pharmacokinetic modeling with methyl
chloroform: Implications for interspecies, high-low dose and dose-route
extrapolations. Toxicology and Applied Pharmacology 95, 185-199.
784. Reitz, R. H., Mandrela, A. L., and Guengerich, F. P. (1989). In vitro metabolism of
methylene chloride in human and animal tissues: Use in physiologically-based
pharmacokinetic models. Toxicology and Applied Pharmacology 97, 230-246.
785. Reitz, R. H., Mandrela, A. L., Corley, R. A., Quast, J. F., Gargas, M. L., Andersen,
M. E., Staats, D. A., and Conolly, R. B. (1990). Estimating the risk of liver cancer
associated with human exposures to chloroform using physiologically-based
pharmacokinetic modeling. Toxicology and Applied Pharmacology 105, 443-459.
786. Reitz, R. H., McCroskey, P. S., Park, C. N., Andersen, M. E., and Gargas, M. L.
(1990). Development of a physiologically-based pharmacokinetic model for risk
assessment with 1,4-dioxane. Toxicology and Applied Pharmacology 105, 37-54.
787. Reitz, R. H., Gargas, M. L., Andersen, M. E., Provan, W. M., and Green, T. L.
(1996). Predicting cancer risk from vinyl chloride exposure with a physiologically
based pharmacokinetic model. Toxicology and Applied Pharmacology 137, 253-
267.
69
-------
788. Reitz, R. H., Gargas, M. L., Mendrala, A. L., and Schumann, A. M. (1996). In vivo
and in vitro studies of perchloroethylene metabolism for physiologically based
pharmacokinetic modeling in rats, mice, and humans. Toxicology and Applied
Pharmacology 136, 289-306.
789. Renwick, A. G. (1999). Subdivision of uncertainty faactors to allow for
toxicokinetics and toxicodynamics. Human and Ecological Risk Assessment 5,
1035-1050.
790. Renwick, A. G. (1994). Pharmacokinetics in toxicology. In Principles and Methods
of Toxicology. (W. A. Hayes, Ed.), pp. 101-147. Raven Press, New York.
791. Rescigno, A., Beck, J. S., and Thakur, A. K. (1987). Perspectives in
Pharmacokinetics. The use and abuse of models. Journal of Pharmacokinetics and
Biopharmaceutics 15, 327-344.
792. Rescigno, A. (1996). Pharmacokinetics, science or fiction? Pharmacological
Research 33, 227-233.
793. Rescigno, A. (1997). Fundamental concepts in pharmacokinetics. Pharmacological
Research 35, 364-390.
794. Rey, T. D., and Havranek, W. A. (1996). Some aspects of using the SimuSolv
program for environmental, pharmacokinetics and toxicological applications.
Ecological Modeling 86, 277-282.
795. Rhomberg, L. (1995). Use of quantitative modelling in methylene chloride risk
assessment. Toxicology 114.
796. Rhomberg, L. R. (2000). Dose-response analyses of the carcinogenic effects of
trichloroethylene in experimental animals. Environ.Health Perspect. 108, 343-358.
797. Rideout, V. C. (1991). Mathematical and Computer Modeling of'Physiological
Systems. Prentice-Hall, New York.
798. Riggs, D. S. (1970). The Mathematical Approach to Physiological Problems: A
Critical Treatise. MIT Press, Cambridge.
799. Riihimaki (1990). Toxi.Letters. 50, 195-201.
800. Robinson, D. E., Baiter, N. J., and Schwartz, S. L. (1992). A physiologically based
pharmacokinetic model for nicotine and cotinine in man. Journal of
Pharmacokinetics and Biopharmaceutics 20, 591-609.
801. Robinson, P. J. (1991). Effect of microcirculatory heterogeneity in the determination
of pharmacokinetic parameters: Implications for risk assessment. Drug Metabolism
Reviews 23, 43-64.
70
-------
802. Robinson, P. J. (1992). Physiologically-based liver modeling and risk assessment.
Risk Analysis 12, 139-148.
803. Robinson, P. J. (1992). Hepatic modeling and risk assessment: compartmental
versus tube models and interspecies scaling. Drug Metabolism Reviews 23, 601-
617.
804. Ross, R., Leger, L., Guardo, R., de Guise, J., and Pike, B. G. (1991). Adipose tissue
volumes measured by magnetic resonance imaging and computerized tomography
in rats. Journal of Applied Physiology 70, 2164-2172.
805. Roth, R. A., and Vinegar, A. (1990). Action by the lungs on circulating xenobiotic
agents with a case study of physiologically-based pharmacokinetic modeling of
benzo(a)pyrene disposition. Pharmacology and Therapeutics 48, 143-155.
806. Roth, W. L., Freeman, R. A., and Wilson, A. G. (1993). A physiologically based
model for gastrointestinal absorption and excretion of chemicals carried by lipids.
Risk Anal. 13, 531-543.
807. Roth, W. L., Freeman, R. A., and Wilson, A. G. (1993). A physiologically based
model for gastrointestinal absorption and excretion of chemicals carried by lipids.
Risk Anal. 13, 531-543.
808. Roth, W. L., Ernst, S., Weber, L. W. D., Kerecsen, L., and Rozman, K. K. (1994). A
pharmacodynamically responsive model for 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) transfer between liver and fat at low and high doses. Toxicology and
Applied Pharmacology 127, 151-162.
809. Roth, W. L., Weber, L. W. D., and Rozman, K. K. (1995). Incorporation of first-
order uptake rate constants from simple mammillary models into blood-flow limited
physiological pharmacokinetic models via extraction efficiencies. Pharmaceutical
Research 12, 263-269.
810. Rowland, M. (1985). Physiologic pharmacokinetic models and interanimal species
scaling. Pharmacology and Therapeutics 29, 49-68.
811. Roy, A., Weisel, C. P., Gallo, M. A., and Georgopoulos, P. G. (1996). Studies of
multiroute exposure/dose reconstruction using physiologically based
pharmocokinetic models. Toxicology and Industrial Health 12, 153-163.
812. Roy, A., Weisel, C. P., Lioy, P. J., and Georgopoulos, P. G. (1996). A distributed
parameter physiologically-based pharmacokinetic model for dermal and inhalation
exposure to volatie organic compounds. Risk Analysis 16, 147-160.
813. Roy, A., and Georgopoulos, P. G. (1998). Reconstructing week-long exposures to
volatile organic compounds using physiologically based pharmacokinetic models.
Journal of Exposure Analysis and Environmental Epidemiology 8, 407-422.
71
-------
814. Runciman, W. B., Ilsley, A. H., Mather, L. E., Carapetis, R., and Rao, M. M.
(1984). A sheep preparation for study interaction between blood flow and drug
disposition. I; physiological profile. British Journal of Anaesthesia 56, 1015-1028.
815. Russel, F. G., Wouterse, A. C., and Van Ginneken, C. A. (1987). Physiologically
based pharmacokinetic model for the renal clearance of salicyluric acid and the
interaction with phenolsulfonphthalein in the dog. Drug Metabolism and Disposition
15,695-701.
816. Russel, F. G., Wouterse, A. C., and Van Ginneken, C. A. (1987). Physiologically
based pharmacokinetic model for the renal clearance of phenolsulfonnphthalein and
the interaction with probenecid salicyluric acid in the dog. Journal of
Pharmacokinetics and Biopharmaceutics 15, 349-368.
817. Russel, F. G., Wouterse, A. C., and Van Ginneken, C. A. (1989). Physiologically
based pharmacokinetic model for the renal clearance of iodopyracet and the
interaction with probenecid in the dog. Biopharmaceutics & Drug Disposition 10,
137-152.
818. Salvan, A., Thomaseth, K., Bortot, P., and Sartori, N. (2001). Use of a toxicokinetic
model in the analysis of cancer mortality in relation to the estimated absorbed dose
of dioxin (2,3,7,8-tetrachlorodibenzo-p-dixin, TCDD). Sci.Total Environ. 274, 21-
35.
819. Sancho, E., Ferrando, M. D., Lleo, C., and Andreu-Moliner, E. (1998). Pesticide
toxicokinetics in fish: accumulation and elimination. Ecotoxicology and
Environmental Safety 41, 245-250.
820. Santostefano, M. J., Wang, X., Richardson, V. M., Ross, D. G., DeVito, M. J., and
Birnbaum, L. S. (1998). A pharmacodynamic analysis of TCDD-induced
cytochrome P450 gene expression in multiple tissues: dose- and time-dependent
effects. Toxicology and Applied Pharmacology 151, 294-310.
821. Sarangapani, R., Teeguarden, J. G., Cruzan, G., Clewell, H. J., and Andersen, M. E.
(2002). Physiologically based pharmacokinetic modeling of styrene and styrene
oxide respiratory-tract dosimetry in rodents and humans. Inhal.Toxicol 14, 789-834.
822. Sarangapani, R., Gentry, P. R., Covington, T. R., Teeguarden, J. G., and Clewell, H.
J. I. (2003). Evaluation of the potential impact of age- and gender- specific lung
morphology and ventilation rate on the dosimentry of vapors. Inhal.Toxicol. 15,
987-1016.
823. Sarangapani, R., Teeguarden, J., Andersen, M. E., Reitz, R. H., and Plotzke, K. P.
(2003). Route-specific differences in distribution characteristics of
octamethylcyclotetrasiloxane in rats: analysis using PBPK models. Toxicol Sci. 71,
41-52.
72
-------
824. Sarangapani, R., Clewell, H. J., Cruzan, G., and Andersen, M. E. (2002). Comparing
respiratory - tract and hepatic exposure - dose relationships for metabolized inhaled
vapors: a pharmacokinetic analysis. Inhal.Toxicol 14, 835-854.
825. Sarangapani, R., Teeguarden, J.G., Gentry, P.R., Clewell, HJ. 3rd, Barton, H.A.,
Bogdanffy, M.S. (2004). Interspecies dose extrapolation for inhaled dimethyl
sulfate: a PBPK model-based analysis using nasal cavity N7-methylguanine
adducts. Inhal Toxicol 16(9), 593-605. PMID: 16036752 [PubMed - indexed for
MEDLINE]
826. Sathirakul, K., Suzuki, H., Yasuda, K., Hanano, M., and Sugiyama, Y. (1993).
Construction of a physiologically based pharmacokinetic model to describe the
hepatobiliary excretion process of ligands: quantitative estimation of intracellular
diffusion. Biological and Pharmaceutical Bulletin 16, 273-279.
827. Sato, A., and Nakajima, T. (1979). A vial equilibration method to evaluate the drug
metabolizing enzyme activity for volatile hydrocarbons. Toxicology and Applied
Pharmacology 47, 41-46.
828. Sato, A., and Nakajima, T. (1979). Partition coefficients of some aromatic
hydrocarbons and ketones in water, blood and oil. British Journal of Industrial
Medicine 36, 231-234.
829. Sato, A., and Nakajima, T. (1987). Pharmacokinetics of organic solvent vapors in
relation to their toxicity. Scandinavian Journal of Work Environment and Health 13,
81-93.
830. Sato, A., Endoh, K., and Johanson, G. (1990). The use of models to investigate the
toxicokinetic behavior of organic solvents. Exposure assessment for epidemiology
and hazard control. In Industrial hygiene science series. (S. M. Rapport, Ed.), pp.
131-152. Smith TJ.Lewis publishers..
831. Sato, A., Endoh, K., Kaneko, T., and Johanson, G. (1991). A simulation study of
physiological factors affecting phamacokinetic behavior of organic solvent vapors.
British Journal of Industrial Medicine 48, 342-347.
832. Sawada, Y., Harashima, H., Hanano, M., Sugiyama, Y., and Iga, T. (1985).
Prediction of the plasma concentration time courses of various drugs in humans
based on data from rats. Journal of Pharmacobio-Dynamics 8, 757-766.
833. Schlosser, P. M., Lilly, P. D., Conolly, R. B., Janszen, D. B., and Kimbell, J. S.
(2003). Benchmark dose risk assessment for formaldehyde using airflow modeling
and a single-compartment, DNA-protein cross-Link Dosimetry model to estimate
human equivalent doses. Risk.Anal. 473-487.
834. Schoeffner, D. J., Warren, D. A., Muralidhara, S., Bruckner, J. V., and Simmons, J.
E. (1999). Organ weights and fat volume in ats as a function of strain and age.
Journal of Toxicology and Environmental Health 56, 449-462.
73
-------
835. Schreiber, J. S. (1993). Predicted infant exposure to tetrachloroethene in human
breastmilk. Risk Analysis 13 , 515-524.
836. Schultz, I. R., and Hayton, W. L. (2000). Interspecies scaling of the
bioaccumulation of lipophilic xenobiotics in fish: an example using trifluralin.
Environmental Toxicology and Chemistry 18, 1440-1449.
837. Schumann, A. M., Fox, T. R., and Watanabe, P. G. (1982). 14C Methyl chloroform
(1,1,1-Trichloroethane): Pharmacokinetics in rats and mice following inhalation
exposure. Toxicology and Applied Pharmacology 62, 390-401.
838. Schwartz, S. (2001). Providing toxicokinetic support for reprductive toxicology
studies in pharmaceutical development. Arch.Toxicol. 75, 381-387.
839. Sear, J. W. (1993). Why not model physiologically ? British Journal of Anaesthesia
70, 243-245.
840. Semino, G., Lilly, P., and Andersen, M. E. (1997). A pharmacokinetic model
describing pulsatile uptake of orally-administered carbon tetrachloride. Toxicology
117,25-33.
841. Sharma, M., Maheshwari M., Morisawa S. (2005). Dietary and inhalation intake of
lead and estimation of blood lead levels in adults and children in Kanpur, India.
Risk Anal 25(6),1573-1588. PMID: 16506983 [PubMed - in process]
842. Shatkin, J. A., and Brown, H. S. (1991). Pharmacokinetics of the dermal route of
exposure to volatile organic chemicals in water: A computer simulation model.
Environmental Research 56, 90-108.
843. Shelley, M. L., Harris, R. L., and Boehlecke, B. A. (1996). A mathematical model
of bronchial absorption of vapors in the human lung and its significance in
pharmacokinetic modeling. SAR QSAR Environ Res. 5, 221-253.
844. Shelley, M. L., Andersen, M. E., and Fisher, J. W. (1988). An inhalation distribution
model for the lactating mother and nursing child. Toxicology Letters 43, 23-29.
845. Shelley, M. L., Andersen, M. E., and Fisher, J. W. (1989). A risk assessment
approach for nursing infants exposed to volatile organics through the mother's
occupational inhalation exposure. Applied Industrial Hygiene 4, 21-26.
846. Sherman, C. D., Portier, C. J., and Kopp-Schneider, A. (1994). Multistage models of
carcinogenesis: an approximation for the size and number distribution of late-stage
clones. Risk Analysis 14, 1039-1049.
847. Sherwood, R. J., and Sinclair, G. C. (1999). New PBPK model applied to old
occupational exposure to benzene. American Industrial Hygiene Association Journal
60, 259-265.
74
-------
848. Shibata, N., Gao, W., Okamoto, H., Kishida, T., Iwasaki, K., Yoshikawa, Y., and
Takada, K. (2002). Drug interactions between HIV protease inhibitors based on
physiologically-based pharmacokinetic model. J.Pharm.Sci. 91, 680-689.
849. Shipp, A. M., Gentry, P. R., Lawrence, G. S., Van Landingham, C., Covington, T.,
Clewell, H. J., Gribben, K., and Crump, K. (2000). Determination of a site-specific
reference dose for methylmercury for fish-eating populations. Toxicol.Ind.Health.
16, 335-438.
850. Shuler, M. L., Ghanem, A., Quick, D., Wong, M. C., and Miller, P. (1996). A self-
regulating cell culture analog device to mimic animal and human toxicological
responses. Biotechnology and Bioengineering 52, 45-60.
851. Shyr, L. J., Sabourin, P. J., Medinsky, M. A., Birnbaum, L. S., and Henderson, R. F.
(1993). Physiologically based modeling of 2-butoxyethanol disposition in rats
following different routes of exposure. Environmental Research 63, 202-218.
852. Sielken, R. L., Reitz, R. H., and Hays, S. M. (1996). Using PBPK modeling and
comprehensive realism methodology for the quantitative cancer risk assessment of
butadiene. Toxicology 113, 231-237.
853. Sijm, D. T. H. M., Schipper, M., and Opperhuizen, A. (1993). Toxicokinetics of
halogenated benzenes in fish: lethal body burden as a toxicological end point.
Environmental Toxicology and Chemistry 12, 1117-1127.
854. Silverman, K. C., Naumann, B. D., Holder, D. J., Dixit, R., Faria, E. C., Sargent, E.
V., and Gallo, M. A. (1999). Establishing data-derived adjustment factors from
published pharmaceutical clinical trial data. Human and Ecological Risk
Assessments, 1059-1089.
855. Simmons, J. E. (1996). Application of physiologically based pharmacokinetic
modelling to combination toxicology. Food and Chemical Toxicology 34, 1067-
1073.
856. Simon, T. W. (1997). Combining physiologically based pharmacokinetic modeling
with Monte Carlo simulation to derive an acute inhalation guidance value for
trichloroethylene. Regulatory Toxicology and Pharmacology 26, 257-270.
857. Sinclair, G. C., Gray, C. N., and Sherwood, R. J. (1999). Structure and validation of
a pharmacokinetic model for benzene. American Industrial Hygiene Association
Journal 60, 249-258.
858. Singh, D. V., Spitzer, H. L., and White, P. D. Addendum to the health risk
assessment for dichloromethane. Updated carcinogenicity assessment for
dichloromethane. (EPA/600/8-82/004F). 1987.
Ref Type: Serial (Book,Monograph)
75
-------
859. Singh, P., and Roberts, M. S. (1993). Dermal and underlying tissue
pharmacokinetics of salicyclic acid after topical application. Journal of
Pharmacokinetics and Biopharmaceutics 21, 337-373.
860. Sirianni, G. L., and Pang, K. S. (1997). Organ clearance concepts: new perspectives
on old principles. J.Pharmacokinetic.Biopharm. 25, 449-469.
861. Slob, W., Janssen, P. H. M., and van den Hof, J. M. (1997). Structural identifiability
of PBPK models: Practical consequences for modeling strategies and study desings.
Critical Reviews in Toxicology 27, 261-272.
862. Smith, A. E., Gray, G. M., and Evans, J. S. (1995). The ability of predicted internal
dose measures to reconcile tumor bioassay data for chloroform. Regulatory
Toxicology and Pharmacology 21, 339-351.
863. Smith, A. E., and Evans, J. S. (1995). Uncertainty in fitted estimates of apparent in
vivo metabolic constants for chloroform. Fundamental and Applied Toxicology 25,
29-44.
864. Smith, A. E., Evans, M. V., and Davidian, M. (1998). Statistical properties of fitted
estimates of apparent in vivo metabolic constants obtained from gas uptake data .
I.lipophilic and slowly metabolized VOCs. Inhalation Toxicology 10, 383-409.
865. Smith, J. M. (1987). Difference equation derivation by numerical integration
substitution. In Mathematical modeling and digital simulation for engineers and
scientists (M. T. Smith, Ed.), pp. 154-160. John Wiley & Sons, New
York,Chichester,Brisbane,Toronto, Singapore.
866. Soldin, O. P. (2002). Proposed PBPK model to predict infant exposure to toxic
chemicals in breast milk. Environmental Health Perspectives 110, A663-A664.
867. Spear, R. C., Bois, F. Y., Woodruff, T., Auslander, D., Parker, J., and Selvin, S.
(1991). Modeling benzene pharmacokinetics across three sets of animal data:
parametric sensitivity and risk implications. Risk Analysis 11, 641-654.
868. Spear, R. C., and Bois, F. Y. (1994). Parameters variability and the interpretation of
physiologically based pharmacokinetic modeling results. Environmental Health
Perspectives 102, 61-66.
869. Srinivasan, R. S., Bourne, D. W., and Putcha, L. (1994). Application of
physiologically based pharmacokinetic models for assessing drug disposition in
space. J Clin Pharmacol. 34, 692-698.
870. Staats, D. A., Fisher, J. W., and Conolly, R. B. (1991). Gastrointestinal absorption
of xenobiotics on physiologicaly-based pharmacokinetic models. A two
compartmental description. Drug Metabolism and Disposition 19, 144-149.
76
-------
871. Steinbach, K. H., Raffler, H., Pabst, G., and Fliedner, T. M. (1980). A mathematical
model of Canine Granulocytopoiesis. J.Math.Biology. 10, 1-12.
872. Stenner, R. D., Merdink, J. L., Fisher, J. W., and Bunge, A. L. (1998).
Physiologically-based pharmacokinetic model for trichloroethylene considering
enterohepatic recirculation of major metabolites. Risk Analysis 18, 261-269.
873. Stern, A. H., and Smith, A. E. (2003). An assessment of the Cord Blood: Maternal
blood methylmercury ratio: Implications for risk assessment. Environ.Health
Perspect. Ill, 1465-1470.
874. Stickney, J. A., Sager, S. L., Clarkson, J. R., Smith, L. A., Locey, B. J., Bock, M. J.,
Hartung, R., and Olp, S. F. (2003). An updated evaluation of the carcinogenic
potential of 1,4-dioxane. Regulatory Toxicology and Pharmacology 38, 183-195.
875. Stoughton, R. W., and Lamson, P. D. (1936). The relative anaesthetic activity of the
butanes and pentanes. Journal of Pharmacology and Experimental Therapeutics 58,
74-77.
876. Sultatos, L. G. (1990). A physiologically-based pharmacokinetic model for
parathion based on chemical specific parameters determined in vitro. Journal of
American College of Toxicology 9, 611-617.
877. Sultatos, L. G., Kim, B., and Woods, L. (1990). Evaluation of estimations in vitro of
tissue: blood distribution coefficients for organothiophosphate insecticides.
Toxicology and Applied Pharmacology 103, 52-55.
878. Sum, D. T. H. M., Schipper, M., and Opperhuizen, A. (1993). Toxicokinetis of
halogenated benzenes in fish: lethal body burden as a toxicological end point.
Environmental Toxicology and Pharmacology 12, 1117-1127.
879. Suzuki, H., Iwatsubo, T., and Sugiyama, Y. (1995). Applications and prospects for
physiologically based pharmacokinetic (PB-PK) models involving pharmaceutical
agents. Toxicology Letters 82/83, 349-355.
880. Sweeney, L. M., Shuler, M. L., Babish, J. G., and Ghanem, A. (1995). A cell culture
analogue of rodent physiology - Application to naphtalene toxicity. Toxicology in
Vitro 9, 307-316.
881. Sweeney, L. M., Shuler, M. L., Quick, D., and Babish, J. G. (1996). A preliminary
physiologically based pharmacokinetic model for naphthalene and naphthalene
oxide in mice and rats. Annals of Biomedical and Engineering 24, 305-320.
882. Sweeney, L. M., Himmelstein, M. W., Schlosser, P. M., and Medinsky, M. A.
(1996). Physiologically based pharmacokinetic modeling of blood and tissue
epoxide measurements for butadiene. Toxicology 113, 318-321.
77
-------
883. Sweeney, L. M., Schlosser, P. M., Medinsky, M. A., and Bond, J. A. (1997).
Physiologically based phamacokinetic modeling of 1,3-butadiene, l,2-epoxy-3-
butene, and l,2:3,4-diepoxybutane toxicokinetics in mice and rats. Carcinogenesis
18,611-625.
884. Sweeney, L. M., Tyler, T. R., Kirman, C. R., Corley, R. A., Reitz, R. H.,
Paustenbach, D. J., Holson, J. F., Whorton, M. D., Thompson, K. M., and Gargas,
M. L. (2001). Proposed occupational exposure limits for select ethylene glycol
ethers using PBPK models and Monte Carlo simulations. Toxicol Sci. 62, 124-139.
885. Sweeney, L. M., Gargas, M. L., Strother, D. E., and Kedderis, G. L. (2003).
Physiologically Based Pharmacokinetic Model Parameter Estimation and Sensitivity
and Variability Analyses for Acrylonitrile Disposition in Humans. Toxicological
Sciences 71, 27-40.
886. Sweeney.L.M (2000). Comparing occupational and environmental risk assessment
methodologies using pharmacokinetic modeling. Human & Ecological Risk
Assessment. 6, 1101-1124.
887. Tan, Y. M., Butterworth, B. E., Gargas, M. L., and Conolly, R. B. (2003).
Biologically motivated computational modeling of chloroform cytolethality and
regenerative celluar proliferation. Toxicol.Sci. 75, 192-200.
888. Tardif, R., Lapare, S., Krishnan, K., and Brodeur, J. (1993). A descriptive and
mechanistic study of the interaction between toluene and xylene in humans.
Int.Arch.Occup.Environ.Health 65, S135-S137.
889. Tardif, R., Lapare, S., Krishnan, K., and Brodeur, J. (1993). Physiologically based
modeling of the toxicokinetic interaction between toluene and m-xylene in the rat.
Toxicol Appl.Pharmacol. 120, 266-273.
890. Tardif, R., Lapare, S., Charest-Tardif, G., Brodeur, J., and Krishnan, K. (1995).
Physiologically-based pharmacokinetic modeling of a mixture of toluene and xylene
in humans. Risk Anal. 15, 335-342.
891. Tardif, R., Lapare, S., Charest-Tardif, G., Krishnan, K., and Brodeur, J. (1995).
Utilisation des modeles cinetiques a base physiologique pour la prediction des
interactions metaboliques consecutives aux expositions a des melanges binaires de
solvants. In ler Colloque de 1'Universite de Montreal sur I'environment: analyse et
intervention. (C. E. Delisle, M. A. Bouchard, P. Andre, and J. Zayed, Eds.), pp. 581-
590. Montreal.
892. Tardif, R., Charest-Tardif, G., Brodeur, J., and Krishnan, K. (1997). Physiologically
based pharmacokinetic modeling of a ternary mixture of alkyl benzenes in rats and
humans. Toxicology and Applied Pharmacology 144, 120-134.
78
-------
893. Tardif, R., and Charest-Tardif, G. (1999). The importance of measured end-points in
demonstrating the occurence of interactions: a case study with methyl chloroform
and m-xylene. Toxicological Sciences 49, 312-317.
894. Teo, S. K., Kedderis, G. L., and Gargas, M. L. (1994). Determination of tissue
partition coefficients for volatile tissue-reactive chemicals: acrylonitrile and its
metabolite 2-cyanoethylene oxide. Toxicology and Applied Pharmacology 128, 92-
96.
895. Teorell, T. (1937). Kinetics of distribution of substances administered to the body. I.
The extravascular modes of administration. Archives Internationales de
Pharmacodynamie 57, 205-225.
896. Teorell, T. (1937). Kinetics of distribution of substances administered to the body.
II. The intravascular modes of administration. Archives Internationales de
Pharmacodynamie 57, 226-240.
897. Terasaki, T., Iga, T., Sugiyama, Y., Sawada, Y., and Hanano, M. (1984). Nuclear
binding as a determinant of tissue distribution of adriomycin, daunomycin,
adriamycinol, daunorubicinol and actinomycinD. Journal of Pharmacobio-
Dynamics 7, 269-277.
898. Terry, K. K., Elswick, B. A., Welsch, F., and Conolly, R. B. (1995). Development
of a physiologically based pharmacokinetic model describing 2-methoxyacetic acid
disposition in the pregnant mouse. Toxicology and Applied Pharmacology 132, 103-
114.
899. Thakore, K. N., Gargas, M. L., Andersen, M. E., and Mehendale, H. M. (1991). PB-
PK derived metabolic constants, hepatotoxicity, and lethality of BrCCL3 in rats
pretreated with chlordecone, phenobarbital, or mirex. Toxicology and Applied
Pharmacology 109, 514-528.
900. Theil, F. P., Guentert, T. W., Haddad, S., and Poulin, P. (2003). Utility of
physiologically based pharmacokinetic models to drug development and rational
drug discovery candidate selection. Toxicology Letters 138, 29-49.
901. Thomas, R. S., Bigelow, P. L., Keefe, T. 1, and Yang, R. S. H. (1996). Variability
in biological exposure indices using physiologically based pharmacokinetic
modeling and Monte Carlo simulation. American Industrial Hygiene Association
Journal 57, 23-32.
902. Thomas, R. S., Lytle, W. E., Keefe, T. J., Constan, A. A., and Yang, R. S. H.
(1996). Incorporating Monte Carlo simulation into physiologically based
pharmacokinetic models using advanced continuous simulation language (ACSL): a
computational method. Fundamental and Applied Pharmacology 31, 19-28.
903. Thomas, R. S., Yang, R. S. H., Morgan, D. G., Moorman, M. P., Kermani, H. R. S.,
Sloane, R. A., O'Connor, R. W., Adkins, B. Jr., Gargas, M. L., and Andersen, M. E.
79
-------
(1996). PBPK modeling / Monte Carlo simulation of methylene chloride kinetic
changes in mice in relation to age and acute, subchronic, and chronic inhalation
exposure. Environmental Health Perspectives 104, 858-865.
904. Thomas, R. S., Conolly, R. B., Gustafson, D. L., Long, M. E., Benjamin, S. A., and
Yang, R. S. H. (2000). A physiologically based pharmacodynamic analysis of
hepatic foci within a medium-term liver bioassay using pentachlorobenzene as a
promoter and diethylnitrosamine as an initiator. Toxicology and Applied
Pharmacology 166, 128-137.
905. Thomaseth, K., and Salvan, A. (1998). ERRATUM. Estimation of occupational
exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin using a minimal physiologic
toxicokinetic model. Environmental Health Perspectives Suppl 106, 743-753.
906. Thrall, B. D., and Woodstock, A. D. (2003). Evaluation of the dermal bioavailability
of aqueous xylene in F344 rats and human volunteers. Journal of Toxicology and
Environmental Health 66, 1267-1281.
907. Thrall, K. D., Gies, R. A., Muniz, I, Woodstock, A. D., and Greg Higgins (2002).
Route-of-entry and brain tissue partition coefficients for common superfund
contaminants. J.Toxicol Environ.Health 65, 2075-2086.
908. Thrall, K. D., and Woodstock, A. D. (2002). Evaluation of the dermal absorption of
aqueous toluene in F344 rats using real-time breath analysis and physiologically
based pharmacokinetic modeling. Journal of Toxicology and Environmental Health
65, 2087-2100.
909. Thrall, K. D., Weitz, K. K., and Woodstock, A. D. (2002). Use of real-time breath
analysis and physiologically based pharmacokinetics modeling to evaluate dermal
absorption of aquous toluene in human volunteers. Toxicol Sci. 68, 280-287.
910. Thrall, K. D., Soelberg, J. J., Weitz, K. K., and Woodstock, A. D. (2002).
Development of a physiologically based pharmacokinetic model for methyl ethyl
ketone in F344 rats. J.Toxicol Environ.Health A 65, 881-896.
911. Thrall, K. D., and Kenny, D. V. (1996). Evaluation of a carbon tetrachloride
physiologically based pharmacokinetic model using real-time breath-analysis
monitoring. Inhalation Toxicology 8, 251-261.
912. Thrall, K. D., and Poet, T. S. (2000). Determination of biokinetic interactions in
chemical mixtures using real-time breath analysis and physiologically based
pharmacokinetic modeling. Journal of Toxicology and Environmental Health 59,
653-670.
913. Thrall, K. D., Vucelick, M. E., Gies, R. A., Zangar, R. C., Weitz, K. K., Poet, T. S.,
Springer, D. L., Grant, D. M., and Benson, J. M. (2000). Comparative metabolism
of carbon tetrachloride in rats, mice, and hamsters using gas uptake and PBPK
modeling. Journal of Toxicology and Environmental Health 60, 531-548.
80
-------
914. Timchalk, C., Kousba, A., and Poet, T. S. (2002). Monte Carlo analysis of the
human chlorpyrifos-oxonase (PONI) polymorphism using a physiologically based
pharmacokinetic and pharmacodynamic (PBPK/PD) model. Toxicol Lett. 135, 51-
59.
915. Timchalk, C., Nolan, R. 1, Mendrala, A. L., Dittenber, D. A., Brzak, K. A., and
Mattsson, J. L. (2002). A physiologically based pharmacokinetic and
pharmacodynamic (PBPK/PD) model for the organophosphate insecticide
chlorpyrifos in rats and humans. Toxicol Sci. 66, 34-53.
916. Timchalk, C., Poet, T. S., Lin, Y., Weitz, K. K., Zhao, R., and Thrall, K. D. (2001).
Development of an integrated microanalytical system for analysis of lead in saliva
and linkage to a physiologically based pharmacokinetic model describing lead saliva
secretion. AIHAJ. 62, 295-302.
917. Timchalk, C., Nolan, R. J., Mendrala, A. L., Dittenber, D. A., Brzak, K. A., and
Mattsson, J. L. (2002). A Physiologically Based Pharmacokinetic and
Pharmacodynamic (PBPK/PD) Model for the Organophosphate Insecticide
Chlorpyrifos in Rats and Humans. Toxicol Sci. 66, 34-53.
918. Trachsel, D., Tschudi, P., Portier, C. J., Kuhn, M., Thormann, W., Scholtysik, G.,
and Mevissen, M. (2004). Pharmacokinetics and pharmacodynamic effects of
amiodarone in plasma of ponies after single intravenous administration. Toxicology
and Applied Pharmacology 195, 113-125.
919. Travis, C. C., Quillen, J. L., and Arms, A. D. (1990). Pharmacokinetics of benzene.
Toxicology and Applied Pharmacology 102, 400-420.
920. Travis, C. C., White, R. K., and Ward, R. C. (1990). Interspecies extrapolation of
pharmacokinetics. Journal of Theorical Biology 142, 285-304.
921. Travis, C. C., and Hattemer-Frey, H. A. (1990). Pharmacokinetics and its
application to risk assessment. In Hasard assessment of chemicals (J. Saxena, Ed.),
pp. 39-82. Hemisphere Publishing Corporation, New York, Washington,
Philadelphia, London.
922. Travis, C. C., and Hattemer-Frey, H. A. (1991). Physiological pharmacokinetic
models. In Statistics in Toxicology (D. Krewski, and C. Franklin, Eds.), p. 170.
Gordon and Breach, New York.
923. Travis, C. C., McClain, T. W., and Birkner, P. D. (1991). Diethylnitrosamine-
Induced Hepatocarcinogenesis in Rats: A Theoretical Study. Toxicology and
Applied Pharmacology 109, 289-304.
924. Travis, C. C., Zeng, C., and Nicholas, J. (1996). Biological model of ED01
hepatocarcinogenesis. Toxicology and Applied Pharmacology 140, 29.
81
-------
925. Tsuji, A., Nishide, K., Minami, H., Nakashima, E., Terasaki, T., and Yamana, T.
(1985). Physiologically based pharmacokinetic model for cefazolin in rabbits and its
preliminary extrapolation to man. Drug Metabolism and Disposition 13, 729-739.
926. Tsukamoto, Y., Kato, Y., Ura, M., Horii, I, Ishitsuka, H., Kusuhara, H., and
Sugiyama, Y. (2001). A physiologically based pharmacokinetic analysis of
capecitabine, a triple prodrug of 5-FU, in humans: the mechanism for tumor-
selective accumulation of 5-FU. Pharm.Res. 18, 1190-1202.
927. Tsukamoto, Y., Kato, Y., Ura, M., Horii, I, Ishikawa, T., Ishitsuka, H., and
Sugiyama, Y. (2001). Investigation of 5-FU disposition after oral administration of
capecitabine, a triple-prodrug of 5-FU, using a physiologically based
pharmacokinetic model in a human cancer xenograft model: comparison of the
simulated 5-FU exposures in the tumor tissue between human and xenograft model.
Biopharmaceutics & Drug Disposition 22, 1-14.
928. Tsuruta, H. Percutaneous absorption of organic solvents. III. On the penetration
rates of hydrophobic solvents through the excised rat skin. Industrial Health 20,
335-345. 1982.
Ref Type: Abstract
929. Tuey, D. B., and Mattews, D. H. (1980). Use of a physiological compartmental
model for the rat to describe the pharmacokinetics of several chlorinated biphenyls
in the mouse. Drug Metabolism and Disposition 8, 397-403.
930. Tuey, D. B., and Mattews, D. H. (1980). Distribution and excretion of 2,2',4',4',5,5'-
hexabromobiphenyls in rats and man: Pharmacokinetic model predictions.
Toxicology and Applied Pharmacology 53, 420-430.
931. Uemitsu, N. (1986). Inhalation pharmacokinetics of carbon tetrachloride in rats
based on arterial blood: inhaled air concentration ratios. Toxicology and Applied
Pharmacology 83, 20-29.
932. Upton, R. N., and Ludbrook, G. L. (1997). A physiological model of induction of
anaesthesia with propofol in sheep. 1. Structure and estimation of variables. British
Journal of Anaesthesia 79, 497-504.
933. Van Asperen, 1, Rijcken, W. R. P., and Lammers, J. H. C. M. (2003). Application
of physiologically based toxicokinetic modelling to study the impact of the exposure
scenario on the toxicokinetics and the behavioural effects of toluene in rats.
Toxicol.Lett. 138, 51-68.
934. van der Merwe, D., Brooks, J.D., Gehring, R., Baynes, R.E., Monteiro-Riviere,
N. A., Riviere, I.E. (2006). A physiologically based pharmacokinetic model of
organophosphate dermal absorption. Toxicol Sci 89(1), 188-204. Epub 2005 Oct
12. PMID: 16221965 [PubMed - indexed for MEDLINE]
82
-------
935. van der Molen, G. W., Kooijman, S. A. L. M., and Slob, W. (1996). A generic
toxicokinetic model for persistent lipophilic compounds in humans: an application
to TCDD. Fundamental and Applied Pharmacology 31, 83-94.
936. van der Woude, H., Alink, G.M., Rietjens, I.M. (2005). The definition of hormesis
and its implications for in vitro to in vivo extrapolation and risk assessment. Crit
Rev Toxicol 35(6), 603-607. Review. PMID: 16422398 [PubMed - indexed for
MEDLINE]
937. Van Eijkeren, J. C. (2002). Estimation of metabolic rate constants in PBPK - model
from liver slice experiments: what are the experimental needs? Risk Analysis 22,
159-173.
938. Van Ommen, B., de Jongh, J., van de Sandt, J., Blaauboer, B., Hissink, E.,
Bogaards, S., and van Bladeren, P. (1995). Computer-aided biokinetic modelling
combined with in vitro data. Toxicology in Vitro 9, 537-542.
939. van Vliet, P. W., and de Jongh, J. (1996). Biokinetics and biokinetic models in risk
assessment. Human & Experimental Toxicology 15, 799-809.
940. Vander, A. J., Sherman, J. H., and Luciano, D. S. (1990). Respiration, pp. 440-452.
Me Graw-Hill, New York, Montreal, Toronto & al.
941. Varkonyl, P., Bruckner, J. V., and Gallo, J. M. (1995). Effect of parameter
variability on physiologically-based pharmacokinetic model predicted drug
concentrations. Journal of Pharmaceutical Sciences 84, 381-384.
942. Verhaar, H. J. M., Morroni, J. R., Reardon, K. F., Hays, S. M., Gaver, D. P.,
Carpenter, R. L., and Yang, R. S. H. (1997). A proposed approach to study the
toxicology of complex mixtures of petroleum products: the integrated use of QSAR,
lumping analysis and PBPK/PD modeling. Environ.Health Perspect. 105, 179-195.
943. Vicini, P., Pierce, C. H., Dills, R. L., Morgan, M. S., and Kalman, D. A. (1999).
Individual prior information in a physiological model of 2H8-toluene kinetics: an
empirical bayes estimation strategy. Risk.Anal. 19, 1127-1134.
944. Vielle, B., and Chauvet, G. (1993). An ACSL simulation of the respiratory system.
International Journal of Biomedical Computing 33, 45-54.
945. Vinegar, A. (2001). PBPK modeling of canine inhalation exposures to halogenated
hydrocarbons. Toxicol Sci. 60 , 20-27.
946. Vinegar, A., Auten, K. L., Seckel, C. S., Reed, Y. M., and Conolly, R. B. (1990).
Physiologically based pharmacokinetic model of the metabolism of
trichloroethylene by an isolated ventilated perfused lung. Inhalation Toxicology 2,
285-294.
83
-------
947. Vinegar, A., Winsett, D. W., Andersen, M. E., and Conolly, R. B. (1990). Use of a
physiologically based pharmacokinetic and computer simulation for retrospective
assessment of exposure to volatile toxicants. Inhalation Toxicology 2, 119-128.
948. Vinegar, A., Seckel, C. S., Pollard, D. L., Kinkead, E. R., Conolly, R. B., and
Andersen, M. E. (1992). Polychlorotrifluoroethylene oligomer pharmacokinetics in
F-344 rats: Development of a physiologically-based model. Fundamental and
Applied Toxicology 18, 504-514.
949. Vinegar, A., Williams, R. I, Fisher, J. W., and McDougal, J. N. (1994). Dose-
dependant metabolism of 2,2-dichloro-l,l,l-trifluoroethane: a physiologically based
pharmacokinetic model in the male Fisher 344 rat. Toxicology and Applied
Pharmacology 129, 103-113.
950. Vinegar, A., and Jepson, G. W. (1996). Cardiac sensitization thresholds of halon
replacement chemicals predicted in humans by physiologically based
pharmacokinetic modeling. Risk Analysis 16, 571-579.
951. Vinegar, A., Jepson, G. W., and Overton, J. H. (1998). PBPK modeling of short-
term ( 0 to 5 min) human inhalation exposures to halogenated hydrocarbons.
Inhalation Toxicology 10, 411-429.
952. Vinegar, A., Jepson, G. W., Hammann, S. J., Harper, G., Dierdorf, D. S., and
Overton, J. H. (2000). Simulated blood levels of CF3I in personnel exposed during
its release from an F-15 jet engine nacelle and during intentional inhalation.
American Industrial Hygiene Association Journal 60, 403-408.
953. Vinegar, A., Jepson, G. W., Cisneros, M., Rubenstein, R., and Brock, W. J. (2000).
Setting safe acute exposure limits for halon replacement chemicals using
physiologically based pharmacokinetic modeling. Inhalation Toxicology 12, 751-
763.
954. Volp, R. F., Sipes, I. G, Falcoz, C., Carter, D. E., and Gross, J. F. (1984).
Disposition of 1,2,3-trichloropropane in the Fischer-344 rats: Conventional and
physiological pharmacokinetics. Toxicology and Applied Pharmacology 75, 8-17.
955. von Bertalanffy, L. (1968). The model of open system. In General system theary
foundations, development,applications pp. 139-154.
956. Wada, D. R., and Ward, D. S. (1994). The hybrid model - a new pharmacokinetic
model for computer-controlled infusion pumps. IEEE Transaction on Biomedical
Engineering 41, 134-142.
957. Wada, D. R., Stanski, D. R., and Ebling, W. F. (1995). A PC-based graphical
simulator for physiological pharmacokinetic models. Computer Methods and
Programs in Biomedecine 46, 245-255.
84
-------
958. Wada, D. R., Bjorkman, S., Ebling, W. F., Harashima, H., Harapat, S. R., and
Stanski, D. R. (1997). Computer simulation of the effects of alterations in blood
flows and body composition on thiopental pharmacokinetics in humans.
Anesthesiology 87, 884-899.
959. Wagner, J. G. (1973). Properties of the Michaelis-Menten equation and its
integrated form which are useful in pharmacokinetics. Journal of Pharmacokinetics
and Biopharmaceutics 1, 103-121.
960. Wagner, J. G. (1975). Fundamentals of Clinical Pharmacokinetics. Drug
International, Hamilton,IL.
961. Wagner, J. G. (1981). History of pharmacokinetics. Pharmacology and Therapeutics
12, 537-562.
962. Wagner, P. D. (1981). Ventilation-perfusion relationships. Clin.Physiology. 1, 437-
451.
963. Wakefield, J. (1996). The bayesian analysis of population pharmacokinetic models.
J Am Statistical Ass 91, 62-75.
964. Waller, C. L., Evans, M. V., and Mckinney, J. C. (1996). Modeling the cytochrome
P450-mediated metabolism of chlorinated volatile organic compounds.
Drug.metab.Dispos. 24, 203-210.
965. Walton, K., Dome, J. L., and Renwick, A. G. (2001). Uncertainty factors for
chemical risk assessment: interspecies differences in the in vivo pharmacokinetics
and metabolism of human CYP1A2 substrates. Food and Chemical Toxicology 39,
667-680.
966. Wang, X., Santostefano, M. J., Evans, M. V., Richardson, V. M., Diliberto, J. J., and
Birnbaum, L. S. (1997). Determination of parameters responsible for
pharmacokinetic behavior of TCDD in female Sprague-Dawley rats. Toxicology
and Applied Pharmacology 147, 151-168.
967. Wang, X., Santostefano, M. J., DeVito, M. J., and Birnbaum, L. S. (2000).
Extrapolation of a PBPK model for dioxins acros dosage regimen, gender, strain,
and species. Toxicological Sciences 56, 49-60.
968. Wang, Y., Kupper, L. L., Lof, A., and Rappaport, S. M. (1996). Comparison of
average estimated metabolic rates for styrene in previously exposed and unexposed
groups with pharmacokinetic modelling. Occupational and Environmental Medicine
53, 601-605.
969. Ward, K. W., Blumenthal, G. M., Welsch, F., and Pollack, G. M. (1997).
Development of a physiologically based pharmacokinetic model to describe the
disposition of methanol in pregnant rats and mice. Toxicology and Applied
Pharmacology 145, 311-322.
85
-------
970. Ward, R. C., Travis, C. C., Hetrick, D. M., Andersen, M. E., and Gargas, M. L.
(1988). Pharmacokinetics of tetrachloroethylene. Toxicology and Applied
Pharmacology 93, 108-117.
971. Warner, P. D. (1982). Calculating the distribution of ventilation-perfusion ratios
from inert gas elimination data. Federation.Proc. 41, 136-139.
972. Watanabe, K. H., Bois, F. Y., Daisey, J. M., Auslander, D. M., and Spear, R. C.
(1994). Benzene toxicokinetics in humans: exposure of bone marrow to metabolites.
Occupational and Environmental Medicine 51, 414-420.
973. Watanabe, K. H., and Bois, F. Y. (1996). Interspecies extrapolation of physiological
pharmacokinetic parameter distributions. Risk Analysis 16, 741-754.
974. Welsch, F., Blumenthal, G. M., and Conolly, R. B. (1995). Physiologically based
pharmacokinetic models applicable to organogenesis: extrapolation between species
and potential use in prenatal toxicity risk assessments. Toxicology Letters 82/83,
539-547.
975. Welsch, F., Blumenthal, G. M., and Conolly, R. B. (1995). Physiologically based
pharmacokinetic models applicable to organogenesis: extrapolation between species
and potential use in prenatal toxicity risk assessments. Toxicol Lett. 82-83, 539-547.
976. Willems, B. A., Melnick, R. L., Kohn, M. C., and Portier, C. J. (2001). A
physiologically based pharmacokinetic model for inhalation and intravenous
administration of naphthalene in rats and mice. Toxicol Appl.Pharmacol. 176, 81-
91.
977. Williams, R. J., Vinegar, A., McDougal, J. N., Jarabek, A. M., and Fisher, J. W.
(1996). Rat to human extrapolation of HCFC-123 kinetics deduced from halothane
kinetics-a corollary approach to physiologically based pharmacokinetic modelling.
Fundamental and Applied Toxicology 30, 55-66.
978. Wilson, A. G. E., Frantz, S. W., and Keifer, L. C. (1994). A tiered approach to
pharmacokinetic studies. Environmental Health Perspectives Suppl 102, 5-11.
979. Woodruff, T. J., and Bois, F. Y. (1993). Optimization issues in physiological
toxicokinetic modeling: a case study with benzene. Toxicol.Lett. 69, 181-196.
980. Woodruff, T. J., Bois, F. Y., Auslander, D., and Spear, R. C. (1992). Structure and
parametrization of pharmacokinetic models: Their impact on model predictions.
Risk Analysis 12, 189-201.
981. Wu, K. Y., Smithers, B. M., and Roberts, M. S. (1997). Melphalan dosing regimens
for management of recurrent melanoma by isolated limb perfusion: application of a
physiological pharmacokinetic model based on melphalan distribution in the isoled
perfused rat hindlimb. Melanoma Research 7, 252-264.
86
-------
982. Wunscher, G., Kersting, H., Heberer, H., Westmeier, I, Wenzel, V., Flechsig, M.,
and Matthaeus, E. (1991). Simulation system SONCHES-based toxicokinetic model
and data base bank as a tool in biological monitoring and risk assessment. Science
of the Total Environment 101, 101-109.
983. Yamaguchi, T., Yabuki, M., Saito, S., Watanabe, T., Nishima, H., Isobe, N., Shono,
F., and Matsuo, M. (1996). Research to develop a predicting system of mammalian
subacute toxicity (3) construction of a predictive toxicokinetics model.
Chemosphere 33, 2441-2468.
984. Yang, R., Thurston, V., Neuman, 1, and Randall, D. J. (2000). A Physiological
model to predict xenobiotic concentration in fish. Aquatic Toxicology 48, 109-117.
985. Yang, R. S. H., El-Masri, H. A., Thomas, R. S., and Constan, A. A. (1995). The use
of physiologically-based pharmacokinetic pharmacodynamic dosimetry models for
chemical mixtures. Toxicology Letters 82-83, 497-504.
986. Yang, R. S. H., El-Masri, H. A., Thomas, R. S., Constan, A. A., and Tessari, J. D.
(1995). The application of physiologically based
pharmacokinetic/pharmacodynamic (PBPK/PD) modeling exploring risk assessment
approaches of chemical mixtures.. Toxicology Letters 79, 193-200.
987. Yang, R. S. H., Thomas, R. S., Gustafson, D. L., Campain, J., Benjamin, S. A.,
Verhaar, H. J. M., and Mumtaz, M. M. (1998). Approaches to developing
alternative and predictive toxicology based on PBPK/PD and QSAR modeling.
Environmental Health Perspectives Suppl 106, 1385-1393.
988. Yesair, D. W., Feder, P. I, and Chin, A. E. (1986). Development, evaluation and
use of a pharmacokinetic model for hexachlorobenzene. In Hexachlorobenzene:
Proceedings of an International Syposium. (C. R. Morris, and J. R. P. Cabral, Eds.),
pp. 297-318. Oxford University Press, New York.
989. Yoshikawa, T., Oguma, T., Ichihashi, T., Kinoshita, H., Hirano, K., and Yamada, H.
(1999). Epimerization of moxalactam by albumin and simulation of in vivo
epimerization by a physiologically based pharmacokinetic model. Chirality 11, 309-
315.
990. You, L., Gazi, E., Archibeque-Engle, S., Casanova, M., Conolly, R. B., and Heck,
H. A. (1999). Transplacental and lactational transfer of p,p'-DDE in sprague-dawley
rats. Toxicology and Applied Pharmacology 157, 134-144.
991. Young, J. F., Branham, W. S., Sheehan, D. M., Baker, M. E., Wosilait, W. D., and
Luecke, R. H. (1997). Physiological "constants" for PBPK models for pregnancy.
Journal of Toxicology and Environmental Health 52, 385-401.
992. Young, J. F. (1998). Physiologically-based pharmacokinetic model for pregnancy as
a tool for investigation of developmental mechanisms. Computers in Biology and
Medicine 28, 359-364.
87
-------
993. Young, J. F., Wosilait, W. D., and Luecke, R. H. (2001). Analysis of methylmercury
disposition in humans utilizing a PBPK model and animal pharmacokinetic data.
J.Toxicol Environ.Health A 63, 19-52.
994. Yu, D., and Kim, J. K. (2004). A physiologically based assessment of human
exposure to radon released from groundwater. Chemosphere 54, 639-645.
995. Yu, D. (1998). Uncertainties in a pharmacokinetic modeling for inorganic arsenic.
Journal of Environmental Science & Health.Part A 33, 1369-1390.
996. Yu, D. (1999). A physiologically based pharmacokinetic model of inorganic arsenic.
Regulatory Toxicology and Pharmacology 29, 128-141.
997. Yu, D. H. (2000). A pharmacokinetic modeling of inorganic arsenic: a short-term
oral exposure model for humans. Chemosphere 39, 2737-2747.
998. Yu, R. C., Hattis, D., Landaw, E. M., and Froines, J. R. (2002). Toxicokinetic
interaction of 2,5-hexanedione and methyl ethyl ketone. Arch.Toxicol. 75, 643-652.
999. Yu, X., Johanson, G., Ichihara, G., Shibata, E., Kamijima, M., Ono, Y., and
Takeuchi, Y. (1998). Physiologically Based Pharmacokinetic Modeling of
Metabolic Interactions between n-Hezane and Toluene in Humans. J Occup Health
40,293-301.
1000. Zaharko, D. S., Dedrick, R. L., and Oliverio, V. T. (1972). Prediction of the
distribution of methotrexate in the sting rays; use of a model developed in mice.
Comparative Biochemistry and Physiology 42A, 183-194.
1001. Zellner, D., Frankewitsch, T., Simon, S., and Keller, F. (1996). Statistical analysis
of heterogeneous pharmacokinetic data from the literature. European Journal of
Clinical Chemistry and Clinical Biochemistry 34, 585-589.
1002. Zierler, K. L. (1961). Theory of the use of arteriovenous concentration differences
for measuring metabolism in steady and non-steady states. Journal of Clinical
Investigation 40, 2111-2125.
1003. Zwart, A., Lommen, J. G. J., and Feron, V. J. (1992). Multi-compartment model to
study the effect of air-blood and blood-tissue partition coefficients on concentration-
time-effect relationships. Arch.Toxicol Suppl 15, 249-252.
88
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APPENDIX A
LIST OF REVIEWERS AND OBSERVERS
-------
Panel Peer Review of the Draft NCEA Document "Approaches for the Application of
Physiologically Based Pharmacokinetic Models and Supporting Data in Risk Assessment"
Arlington, VA
November 10-11, 2005
List of Participants
Gregory M. Blumenthal, Ph.D.
GMB Consulting
Chapel Hill, NC
James V. Bruckner, Ph.D.
University of Georgia
Athens, GA
Janusz Z. Byczkowski, Ph.D., D.Sc., D.A.B.T.
Consultant
Fairborn, OH
Harvey Clewell
CUT Centers for Health Research
RTF, NC
Gary L. Ginsberg, Ph.D.
Connecticut Department of Public Health
Hartford CT, 06134
A-l
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Panel Peer Review of the Draft NCEA Document "Approaches for the
Application of Physiologically Based Pharmacokinetic Models and Supporting
Data in Risk Assessment"
November 10-11, 2005
Observer Registration
Name
Tammie Covington
George Cruzan
Rob DeWoskin
Paul Dugard
Jane Eickhoff
Kate Z. Guy ton
C. Eric Hack
Douglas Johns
Russ Keenan
Trevor Knowblich
Thomas McDonald
Robert McGaughy
Gary Mihlan
Beth E. Mileson
Pat Phibbs
Micah Reynolds
Bob Sonawane
Chadwick Thompson
David Bottimore
Amanda Jacob
Organization
ENVIRON International Corp
ToxWorks
US EPA/NCEA
HSIA
TOXCEL LLC
US EPA
TERA
US EPA/NCEA
AMEC Earth & Environmental, Inc.
Inside EPA News
Arysta LifeScience North America Corporation
US EPA/NCEA
Bayer CropScience
Technology Sciences Group, Inc.
BNA, Inc.
TOXCEL LLC
US EPA/NCEA
US EPA/NCEA
Versar, Inc.
Versar, Inc.
Nov. 10
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
A-2
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APPENDIX B
AGENDA
-------
United States
Environmental Protection Agency
Panel Peer Review of the Draft NCEA Document "Approaches for
the Application of Physiologically Based Pharmacokinetic Models
and Supporting Data in Risk Assessment"
Hyatt Arlington
1325 Wilson Boulevard
Arlington, VA 22209
Agenda
THURSDAY, NOVEMBER 10, 2005
8:30AM Registration Begins
9:OOAM Welcome, Goals of Meeting, and Introductions
David Bottimore, Versar, Inc.
9:10AM Welcome
Bob Sonawane, EPA/NCEA
9:15AM Background on "Approaches for the Application of Physiologically Based
Pharmacokinetic Models and Supporting Data in Risk Assessment"
Chad Thompson, EPA/NCEA
9:30AM Chair's Introduction and Review of Charge
Gary Ginsberg, Chair
9:40AM Reviewer Roundtable of Overview Comments
Gary Ginsberg, Chair
10:OOAM Observer Comment Period
10:20AM Discussion Session and Responses to Charge Questions (with break as
appropriate)
12:00PM Lunch
1:OOPM Discussion Session and Responses to Charge Questions (continues, with
break as appropriate)
4:15PM Discussion of Public Comments
4:30PM Recap of Comments/Recommendations and Plans for Writing
5:00PM Adjourn
B-l
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United States
Environmental Protection Agency
Panel Peer Review of the Draft NCEA Document "Approaches for
the Application of Physiologically Based Pharmacokinetic Models
and Supporting Data in Risk Assessment"
Hyatt Arlington
1325 Wilson Boulevard
Arlington, VA 22209
Agenda
FRIDAY, NOVEMBER 11, 2005
9:OOAM Welcome and Goals of Panel Writing Session
David Bottimore, Versar, Inc.
9:15AM Panel Writing Session
Gary Ginsberg, Chair
11:45AM Closing Remarks and Next Steps
12:00PM Adjourn
B-2
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APPENDIX C
CHARGE QUESTIONS
-------
Panel Peer Review of the Draft NCEA Document "Approaches for the Application of
Physiologically Based Pharmacokinetic Models and Supporting Data in Risk Assessment"
November 10-11, 2005
External Peer Review Panel Workshop Charge Questions
Purpose of the Document:
The purpose of the draft document is to describe some approaches for the use of
physiologically-based pharmacokinetic (PBPK) models in risk assessment. PBPK models
represent an important class of dosimetry models that are useful for predicting internal dose at
target organs for risk assessment applications. Dose-response relationships that appear unclear
or confusing at the administered dose level can become more understandable when expressed on
the basis of internal dose of the chemical. To predict internal dose level, PBPK models use
pharmacokinetic data to construct mathematical representations of biological processes
associated with the absorption, distribution, metabolism, and elimination of compounds. With
the appropriate data, these models can be used to extrapolate across species and exposure
scenarios, and address various sources of uncertainty in risk assessments. This report addresses
the following questions: (1) Why do risk assessors need PBPK models; (2) How can these
models be used in risk assessments; and (3) What are the characteristics of acceptable PBPK
models for use in risk assessment?
Workshop Purpose:
The purpose of the workshop is to carry out an independent external peer review of the draft
framework document entitled, "Approaches for the Application of Physiologically Based
Pharmacokinetic Models and Supporting Data in Risk Assessment." Independent external
experts in PBPK modeling and risk assessment have been invited as panelists to provide review
and comment on the document, as well as address the particular charge questions (see below).
Further, we ask that you consider and provide remarks on the public comments. We hope your
feedback will improve the usefulness and clarity of the final document.
Questions:
1. What is the panel's overall view of the thoroughness, clarity, and applicability of this report?
2. Are the graphical examples explaining various concepts clear and helpful? If not, do you have
suggestions for improving clarity?
3. Does this document reasonably describe the major potential uses and advantages of PBPK
modeling in risk assessment, are there risk assessment applications of PBPK modeling that have
not been addressed?
4. Are there improvements to the document that would substantially help risk assessors who are
less familiar with PBPK modeling better understand the potential strengths and limitations of
PBPK modeling?
C-l
-------
5. Do you think that PBPK modelers outside the EPA, and those less familiar with risk
assessment practices, would find this document useful as far as fostering the kinds of research
and model development useful for risk assessment?
6. Are there current research needs and data gaps not highlighted in this report that would
improve the utilization of PBPK models in risk assessment?
7. Are there future reports that you could envision which would compliment or expand upon the
topics covered in the current document? For example, would a report focused on dosimetry
models for reactive gases be helpful? Or a report focused on extrapolation across life stages
using PBPK modeling?
8. Do you have additional comments/suggestions that were not covered in the above questions?
C-2
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APPENDIX D
POWERPOINT PRESENTATIONS
-------
David Bottimore
Welcome, Goals of Meeting, and Introductions
D-l
-------
Chad Thompson, EPA/NCEA
Background on "Approaches for the Application of Physiologically Based Pharmacokinetic
Models and Supporting Data in Risk Assessment"
D-2
-------
APPENDIX E
WRITTEN COMMENTS FROM REVIEWERS
-------
PRE-MEETING COMMENTS SUMMARY REPORT
Panel Peer Review of the Draft NCEA Document "Approaches for the Application of
Physiologically Based Pharmacokinetic Models and Supporting Data in Risk Assessment'
Prepared for:
U.S. Environmental Protection Agency
National Center for Environmental Assessment
808 17th Street, N.W.
Washington, DC 20074
Prepared by:
Versar, Inc.
6850 Versar Center
Springfield, Virginia 22151
Contract No. C68-C02-061
Task Order 89
Reviewers:
Gregory M. Blumenthal, Ph.D.
James V. Bruckner, Ph.D.
Janusz Z. Byczkowski, Ph.D.,D.Sc.,D.A.B.T.
Harvey J. Clewell
Gary L. Ginsberg, Ph.D.
November 9, 2005
Specific Comments Section Revised with Additions on November 16, 2005
E-l
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TABLE OF CONTENTS
I. INTRODUCTION 3
II. CHARGE TO THE PEER REVIEWERS 4
III. GENERAL COMMENTS 6
IV. RESPONSE TO CHARGE QUESTIONS 9
V. SPECIFIC OBSERVATIONS 25
E-2
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I. INTRODUCTION
The objective of this Task Order is to perform a panel peer review of the draft EPA document,
developed by the National Center for Environmental Assessment (NCEA), entitled "Approaches
for the Application of Physiologically Based Pharmacokinetic Data and Models in Risk
Assessment." Pharmacokinetics is the study of the biological processes that affect the
absorption, distribution, metabolism and excretion of a substance, such as a drug or toxicant.
Pharmacokinetic data and models have important applications in risk assessment. Given
sufficient physiological and pharmacokinetic data, physiologically based pharmacokinetic
(PBPK) models, which mathematically represent pharmacokinetic processes based on known
biological properties, can be developed. Such models can then predict an internal dose (generally
blood level or target tissue level) that would result from different exposure regimens or in
different species. Pharmacokinetic data and PBPK models can also support quantitative
estimates of intraspecies internal dose variability. Examples include the variability in
physiological values (e.g., tissue volumes or fluid flows), or in metabolic capacity associated
with the presence of enzyme polymorphisms. Predicted internal dosimetry from PBPK models
can also be used with benchmark dose or other empirical approaches for dose-response analysis.
The objective of this document is to provide a description of approaches for using PBPK data
and models in human health risk assessment. The document primarily focuses on the evaluation
and use of these models in predicting internal doses at target organs for risk assessment
applications, based on EPA guidelines. The document assumes that risk assessors are familiar
with the basic concepts of PBPK modeling, and that model developers are familiar with basic
concepts of risk assessment. Hence, exhaustive descriptions of PBPK modeling and/or risk
assessment methods are not presented. However, brief descriptions of both disciplines, as well
as appropriate references to secondary review articles and reports, are included in the document.
E-3
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II. CHARGE TO THE PEER REVIEWERS
Purpose of the Document:
The purpose of the draft document is to describe some approaches for the use of physiologically-
based pharmacokinetic (PBPK) models in risk assessment. PBPK models represent an important
class of dosimetry models that are useful for predicting internal dose at target organs for risk
assessment applications. Dose-response relationships that appear unclear or confusing at the
administered dose level can become more understandable when expressed on the basis of
internal dose of the chemical. To predict internal dose level, PBPK models use pharmacokinetic
data to construct mathematical representations of biological processes associated with the
absorption, distribution, metabolism, and elimination of compounds. With the appropriate data,
these models can be used to extrapolate across species and exposure scenarios, and address
various sources of uncertainty in risk assessments. This report addresses the following
questions: (1) Why do risk assessors need PBPK models; (2) How can these models be used in
risk assessments; and (3) What are the characteristics of acceptable PBPK models for use in risk
assessment?
Workshop Purpose:
The purpose of the workshop is to carry out an independent external peer review of the draft
framework document entitled, "Approaches for the Application of Physiologically Based
Pharmacokinetic Models and Supporting Data in Risk Assessment." Independent external
experts in PBPK modeling and risk assessment have been invited as panelists to provide review
and comment on the document, as well as address the particular charge questions (see below).
Further, we ask that you consider and provide remarks on the public comments. We hope your
feedback will improve the usefulness and clarity of the final document.
Questions:
1. What is the panel's overall view of the thoroughness, clarity, and applicability of this report?
2. Are the graphical examples explaining various concepts clear and helpful? If not, do you have
suggestions for improving clarity?
3. Does this document reasonably describe the major potential uses and advantages of PBPK
modeling in risk assessment, are there risk assessment applications of PBPK modeling that have
not been addressed?
4. Are there improvements to the document that would substantially help risk assessors who are
less familiar with PBPK modeling better understand the potential strengths and limitations of
PBPK modeling?
5. Do you think that PBPK modelers outside the EPA, and those less familiar with risk
assessment practices, would find this document useful as far as fostering the kinds of research
and model development useful for risk assessment?
6. Are there current research needs and data gaps not highlighted in this report that would
improve the utilization of PBPK models in risk assessment?
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7. Are there future reports that you could envision which would compliment or expand upon the
topics covered in the current document? For example, would a report focused on dosimetry
models for reactive gases be helpful? Or a report focused on extrapolation across life stages
using PBPK modeling?
8. Do you have additional comments/suggestions that were not covered in the above questions?
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III. GENERAL COMMENTS
Gregory M. Blumenthal
The report is relatively thorough and applicable, although it presupposes that the readers already
have a good grasp of such pharmacokinetic concepts as "first-pass effect"; zero-order, first-order,
and second-order metabolic processes; and metabolic saturation. The clarity of this report to EPA
scientists and risk assessors without a thorough background in these pharmacokinetic concepts
may be severely limited.
An additional minor improvement could be made by heavily emphasizing the term "dose
metric". This massively useful concept is the key to the applicability and utility of PBPK models
in risk assessment and its importance cannot be overstated.
In general, the graphical examples are appropriate and useful. However, the authors should
provide text for each figure, even if it repeats material in the main text.
Innovation in computational biology and its application to risk assessment is an ongoing process.
This report should be envisioned as a living document, with room to expand as novel methods
and applications arise. Key among the areas with explosive growth occurring are acute
dosimetry, point-of-contact dosimetry, whole-life and developmental modeling, maternal-fetal
dosimetry, and metabolic network modeling to identify sensitive subpopulations.
James V. Bruckner
Janusz Z. Byczkowski
The reviewed document "Approaches for the Application of Physiologically Based
Pharmacokinetic (PBPK) Models and Supporting Data in Risk Assessment" seems to be the first
comprehensive publication that systematically reviews application of PBPK modeling in risk
assessment. The authors of this document should be commanded for preparing thorough, clear
and well organized report.
This document reads smoothly. It clearly states the scope and intent of the report in the
introductory section. The PBPK model applications, issues and potential pitfalls are presented
transparently and discussed appropriately. The "boxes", tables and figures are easily readable
and informative. The schematic diagrams are appropriate and well designed. The rest of the
figures adequately illustrate the issues discussed in the text. However, because the mathematical
equations in the text are kept to the absolute minimum, it would be informative if an example of
the computer codes, for instance of the "classic" Ramsey and Andersen's PBPK model, was
provided in an appendix to the document. The extent of literature review, supplemented by the
list of all pertinent references in the Appendix 2, seems to be adequate and complete - up to the
beginning of the year 2003. An exhaustive list of partition coefficients and metabolic constants
for PBPK modeling of many classes of chemical compounds is provided in the Appendix 3.
While this document is addressed to risk assessors rather than pharmacokineticists, it contains
many clarifications and suggestions that may be useful in developing both PBPK and
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pharmacokinetic models in general. For example, the Appendices provide extremely useful for
pharmacokineticist compilation of references and parameters.
A public discussion, initiated over the Internet, proved that there is a genuine interest in this
report among the international experts from both fields - pharmacokinetics and risk analysis
(involved were members from three mailing lists: PharmPK < http://www.boomer.org/pkin/ >,
Risk Anal , and DRSG
). The participants of this discussion emphasized that in addition to
physiological modeling of pharmacokinetics, there is a need for "...pharmacodynamic and
disease progress modelling to get the full picture..."
This is also this reviewer's belief, that although this document represents an opportunity for the
Agency and a good beginning of the process to depart from defaults in order to base its chemical
risk assessment paradigm on physiologically realistic and scientifically defensible methodology,
much more work needs to be done. Particularly, linking the internal dosimetry with the effects of
environmental contaminants represents a real challenge as it needs further research. Even though,
the section 4.4 of this report attempts to address the issue of linkage of PBPK to
pharmacodynamic models, it also demonstrates that still additional science is needed to be able
to predict and quantify the health effects of many classes of chemical compounds.
If the U.S. EPA chose to continue exploring this trend, this reviewer would suggest that the
future report by the Agency, perhaps entitled "Approaches for the Application of Physiologically
Based Pharmacodynamic (PBPD) Models and Supporting Data in Risk Assessment", may be
essential in order to stimulate research in the PBPD, to summarize the endeavor so far, and to
support progress in this field.
Harvey J. Clewell
Gary L. Ginsberg
This is a well written document, primarily of use to those experienced in pharmacokinetics,
PBPK modeling and risk assessment. It makes many cogent points, describing both general
principles and special considerations needed to successfully develop, evaluate and apply PBPK
models. It does not provide a great deal of detail in any area which makes the document easier to
read cover to cover, and get an overview of the subject matter. However, the lack of details and
specific case examples prevents this from being a PBPK "cookbook" which one could use as a
stand alone resource to actually construct a PBPK analysis. One would already have to know
about modeling software, model structure, mathematical relationships, optimization/backfitting
techniques, evaluation techniques (e.g., residuals calculations, mass balance calculations), and
probabilistic techniques (Monte Carlo, Bayesian) to really make use of this document. However,
for those in the field or at EPA who are applying PBPK to risk assessment, this should be a
helpful set of principles. To some extent, the document has the potential to standardize modeling
practice, not in terms of which parameters to use, but in terms of basic approaches and
applications.
The document focuses upon the use of PBPK modeling to address certain extrapolation issues in
the setting of reference values and slope factors: cross-species, dose route, high dose to low dose,
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temporal adjustments, and degree of variability. However, it doesn't provide anything of
substance on several other uses of PBPK modeling: across age group extrapolations; in utero
modeling; variability due to genetic polymorphisms, lactational models, etc. Additionally, the
document does not put PBPK modeling into an overall context of pharmacokinetic analysis in
which classical one and two compartment models have been used for many years to provide
adequate description of chemical fate, and which have been applied to risk assessment (e.g.,
methyl mercury, dioxin).
The appendices are a useful compilation of equations that can be used to derive parameter inputs
if empirical data are not available. It is also helpful to have parameter data sheets for a variety of
xenobiotics. However, these parameter data sheets are not optimally useful because the model
structure for which these values were derived is not specified. In some cases, references are
provided so that the reader has a head start if he/she needs to develop a model for that analyte.
However, in many cases references are not provided.
In spite of the aforementioned, the document does a good job of introducing and providing
perspective on many important topics. In a number of cases, it doesn't go far enough. For
example, it introduces the concept that PBPK modeling can be used to help interpret
biomonitoring data. This is an emerging and essential application for PBPK modeling because it
offers the opportunity to convert a urinary or blood biomarker level into an exposure dose from
which key questions about population and individual risk may begin to be assessed. The
document may be enhanced by a more complete description of this application including its
methodological and interpretative issues.
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IV. RESPONSE TO CHARGE QUESTIONS
1. What is the panel's overall view of the thoroughness, clarity, and applicability of
this report?
Gregory M. Blumenthal
The report is relatively thorough and applicable, although it presupposes that the readers already
have a good grasp of such pharmacokinetic concepts as "first-pass effect"; zero-order, first-order,
and second-order metabolic processes; and metabolic saturation. The clarity of this report to EPA
scientists and risk assessors without a thorough background in these pharmacokinetic concepts
may be severely limited.
James V. Bruckner
Janusz Z. Byczkowski
From this reviewer's point of view, this is the first comprehensive publication that systematically
reviews application of PBPK modeling in risk assessment. The authors of this document should
be commanded for preparing thorough, clear and well organized report.
Harvey J. Clewell
In general, I was very impressed with the thoroughness and clarity of the report. It is well
written, easy to read, and covers most of the important issues for applying PBPK modeling in
risk assessment. There are, however, a few additional areas that should be discussed in the
report:
- Sections 2.5 and 2.6 should include multiple references to the IPCS document on chemical
specific adjustment factors (CSAFs) in order to clarify the relationship between the model
applications discussed in these sections with the description of the CSAF approach in the IPCS
document.
- Section 2.5.5 (p. 2-11) incorrectly states that "The IVF of 10 conventionally used in RfC
dereivation implies that for the same level of response or nonresponse, the potential doses among
individuals may differ by as much as - but not more than - an order of magnitude." This is a
common misunderstanding. In fact, the IVF of 10 is associated with the potential ratio of the
equitoxic doses for an average individual as compared to a sensitive individual. Therefore, an
IVF of 10 is consistent of a range of equitoxic doses across the population of about two orders of
magnitude.
- The first paragraph in Section 3.3 is incorrect: a mass-balance differential equation consists of a
series of rate terms (not clearance terms), and the units are mass per time (not volume per time).
These rate terms are often calculated as the product of a clearance (in units of volume per time)
and a concentration (in units of mass per volume). Also, the uptake of a chemical in systemic
circulation by a tissue is proportional to its activity gradient, where the tissue: blood relationship
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of activities is related to the concentration difference by the partition coefficient. It just confuses
things to mention Pick's law of diffusion since the transport is generally blood-flow limited, not
diffusion limited.
- Section 3.5 should include a description of the process of internal verification of the model
code. I disagree completely with the suggestion in the document that the risk assessor is not
responsible for ensuring that the computer implementation of the model is free from error.
Curiously, the summary (Section 3.9) actually includes considerations relevant to internal
verification (in the second to last bullet) that are not mentioned at all in this section.
- Section 3.6 should include a discussion of the necessity of evaluating the model specifically for
the dose metric that will be used in the risk assessment. For example, if the validation of the
human model is based solely on parent chemical kinetic data, it may not be valid at all for a
metabolism dose metric.
- Section 4.3 should include (perhaps between sub-sections 4.3.5 and 4.3.6, a much more
detailed explanation of the alternative approaches for calculating a dose metric for a repeated
exposure, as discussed in Clewell et al. (2002):
- Calculation of total dose metric over entire study divided by length of study
- Single dose estimate (total AUC for single dose adjusted for exposure frequency)
- Steady-state estimate (subtraction of consecutive periods after steady state or periodicity
is achieved)
Gary L. Ginsberg
For the most part, this is a clear presentation of how PBPK modeling can be used in cancer and
non-cancer risk assessment, building a logical progression from basic uses and of PBPK
modeling in risk assessment (Chapter 2) to the ingredients, design and implementation of PBPK
models (Chapter 3), to evaluation of PBPK models once constructed (later in Chapter 3), and
finally to briefcase examples of PBPK model use in risk assessment (Chapter 4). The report
provides especially useful sections on parameter estimation (Section 3.4), model evaluation
(Section 3.6), constructing models in data-poor situations (Section 3.8). Any risk assessment
document is likely to leave out some application or question. In the case of this document there
are several important cases which are largely unexplored but which are topics of considerable
interest to risk assessors: 1) Extrapolation across age groups and for in utero and lactational
exposure; 2) Extrapolation to those with genetic polymorphisms; 3) Use of PBPK modeling for
interpreting human biomonitoring data; 4) Use of simpler compartmental models to address risk
assessment questions. Further, other topics are treated incompletely such as chemical mixtures
(covered to some extent in Section 4.3.9) and how models differ for different types of
xenobiotics (most of discussion is based upon non-reactive volatile organics; Figure 3-1 shows
different models for different chemicals but doesn't explain whats going on).
While the document provides excellent background information and general principles, it is not
detailed enough to constitute a cookbook or user's manual for PBPK modeling. As such, it is
most useful for those already in the field to ensure consistency of approach. It is also perhaps a
way for non-modeler risk assessors to become better able to understand and interpret the output
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of PBPK models. On the last point, it may be very useful to include a section on "Interpreting
the Output of PBPK Models." This could help the generalist and the modeler alike.
As described in the ES and Introduction, this document relies on experience with water-insoluble
gases and some non-volatile organics. The explanation given is that PBPK modeling has thus far
focused on these types of chemicals. There is a wide range of PBPK models from dioxins/PCBs
to pesticides, to inorganics (metals, perchlorate) to VOCs. It would be good to start the
document off with a broad rather than narrow description of PBPK uses thus far and provide
descriptions of how PBPK modeling may differ for chemicals with very different properties
(e.g., metals, bone or binding sites may be important to disposition; lipid binding and metabolism
less important; lipophilic slowly metabolized organics (PCBs) - lipid partitioning important
determinant of fate; perchlorate - transport via symporter, etc. etc.).
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2. Are the graphical examples explaining various concepts clear and helpful? If not, do
you have suggestions for improving clarity?
Gregory M. Blumenthal
In general, the graphical examples are appropriate and useful. However, several minor changes
might be made to improve the clarity of some of the examples:
Figure 2-1 - Use bold type for the axis labels of the graphs in order to highlight the role of the
dose metric (rate of amount metabolized, in this case) in linearizing the dose-response function.
Figure 2-2 - Shade the area under the curves to emphasize the AUC as dose metric.
Figure 3-1 - More closely associate each diagram (A, B, C, D) with its respective literature
citation.
Figure 3-2 - Provide text for the figure, even if it repeats material in the main text.
Figure 3-3 - Provide text for the figure, even if it repeats material in the main text. Additionally,
note that this is just an illustration and not real data from any particular model.
Figure 3-4 - Provide text for the figure, even if it repeats material in the main text. Additionally,
some indication of how many Monte Carlo iterations are involved may be useful.
Figure 4-1 - If the parameters are not available, you want benchwork to precede model
development, if at all possible.
Figure 4-2 - Use the phrase "dose metric" whenever possible.
Figure 4-4 - Shade the area under the curves to emphasize the AUC as dose metric.
Figure 4-5 - Shade the area under the curves to emphasize the AUC as dose metric.
Figure 4-6 - Replace the diagonally-oriented curly brackets for the interindividual variability
factor with horizontally-oriented square brackets at the x-axis.
Figure 4-8 - Add a note that this is an illustration, not actual data from a specific chemical.
James V. Bruckner
Janusz Z. Byczkowski
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The "boxes", tables and figures are easily readable and informative. The schematic diagrams are
appropriate and well designed. The rest of the figures adequately illustrate the issues discussed in
the text.
Harvey J. Clewell
Figures 2-2 and 4-5 give a misleading illustration of a human equivalent exposure calculation or
temporal adjustment. The comparison shown, which appears to accumulate the AUC over just
24 hours, would only be appropriate if the animals/subjects were exposed only for a single day
and were sacrificed/tested at exactly 24 hours. The comparison that appears to be intended, i.e.,
adjustment to continuous exposure, should either continue to accumulate the AUC after the 24-
hour exposure until the chemical is completely cleared, or should calculate an AUC for
consecutive 24 hour time-points after steady-state is achieved.
Figure 3-2, panel B should show an inhalation profile of the correct duration rather than an oral
profile. I would suggest a case where the end-exposure concentration is overpredicted by an
order of magnitude and the predicted post-exposure concentrations decrease much too rapidly so
they cross through the data.
Figure 3-3 should not include a parameter sensitivity as high as 2 without some additional
discussion explaining that, in actual practice, it is unusual for a PBPK model to have a parameter
sensitivity much greater than zero in absolute value, and that such a condition is a cause for
concern since it indicates amplification of input error (Allen and Clewell 1996).
Figure 4-2 should be revised to eliminate any extensive variables (amounts/quantities). Only
intensive variables (concentrations, AUCs, amounts produced per unit volume) should be
considered for use as dose metrics.
On p. 4-12, there is a reference to Figure 2-2 indicating that the model accurately predicts the
observed kinetics in the human, but there is no data shown in the Figure.
Figure 4-5 shows rat and human panels, but the caption and text refers to both panels as being for
rats.
Gary L. Ginsberg
The figures have good content and purpose and are generally simple enough for the casual reader
to learn from. The following are some points for improving the figures:
Figure 2-2 (pg 2-6)- For direct interspecies comparison of kinetics, it would be good to show the
human 6 hr exposure curve at 50 ppm and then show in legend the concentration needed in
humans for 6 hrs to match the rodent AUC from 50 ppm. Then the use of PBPK models to
simulate different scenarios (continuous exposure) with the bottom chart, along with different
species, would be clearer.
Section 2.5.1 - a graphic from the RfC document or elsewhere showing different modeling
approaches for different types of gases/particles would be helpful.
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Figure 3-1 - this is a potentially important figure and can set the stage for comparison of PBPK
modeling approaches for different types of chemicals. However, the text and legend fall short of
this treatment and it makes one wonder about the point of the figure. Also, the "D" part of the
figure is cramped and difficult to read.
Section 3.4.2 - Partition coefficients - a graphic of vial equilibration technique and how one gets
to tissue:blood from separate measurement of tissue:air and blood:air. In fact the text does not
talk about this common approach for deriving tissue:blood.
Figure 3-2-pg 3-18- can the figures be represented on a linear scale to more clearly see
goodness of fit or at least discuss the issue of log vs linear y axis. The text that goes with this
figure (3-18 bottom) promises to show how adequacy of model structure can be judged from the
graph. However, the text states that the poor fit in Fig 3-2B may be due to either inadequate
structure or problems with model parameters. So this example falls short of teaching us how to
judge structural issues from the data. On top of that, it appears that the data and models in A,B,
and D are describing a steady state situation in which chemical is continuously taken up and
eliminated, followed by a break point wherein chemical is no longer taken up but just eliminated.
Figure B appears to show a much different uptake pattern (bolus) and may be off not because of
model structure but because the exposure parameters are wrong.
Figure 3-4 - it's a good figure. An enhancement would be to show how an input distribution
(e.g., bimodality in metabolic enzymes) can be transmitted thru to the distribution of AUC
results.
Figure 4-2 - very busy, not easy to follow. Perhaps remove the Amount-^quantity in tissue
boxes under parent and metabolite, and the Average -^ mg formed boxes under rate of
production. Also need a new box under Unknown-^AUC--^ "Evaluate Correlations to Tox
Response". Also the bottom right box should be in units of per tissue volume.
Figure 4-3 - should the toluene parent compound plot have more of an upward bend to it to
match saturation above 500 ppm?
Page 4-12 - 2nd para - discussion of Figure 2-2 which allegedly demonstrates predictiveness of
a toluene rat model for humans. However, Figure 2-2 has no data points, only simulated lines, so
one cannot tell good the fit is or the predictiveness of the model.
Figure 4-5 - heading for lower figure should be rat and break point in upper figure when
concentration starts decending should be 4 hr, not later. Also, the conversion from 4 hr to 24 hr
dose for equivalent AUC is not quite linear - it would be helpful to point out why the lower dose
over longer time is somewhat less efficient (is there saturation at the higher dose?).
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Figure 4-6, Page 4-15 - nicely laid out figure. However, its important to indicate that 99th to
50th can also be used to indicate size of variability factor. The figure represents the degree of
intraspecies variability as being determined on the basis of the 95th to 50% comparison.
However, some have used 99th % as another upper bound for evaluating population variability.
This may be important to ensure that low percentage subgroups which have distinct
pharmacokinetics are not diluted out of the analysis. (Ginsberg, et al. 2001 - ALDH analysis
which showed that a metabolic polymorphism in an ethnic minority (Asians) that leads to much
less acetaldehyde detoxification and greater risk of ethanol toxicity could be overlooked when
that population is averaged in the general pop. To see this subgroup in variability statistics, one
needed to compare 99th to 50th, not 95th.)
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3. Does this document reasonably describe the major potential uses and advantages of
PBPK modeling in risk assessment, are there risk assessment applications of PBPK
modeling that have not been addressed?
Gregory M. Blumenthal
The use of PBPK models in quantifying age-dependent pharmacokinetic adjustments, allowing
departure from ADAFs for mutagenic carcinogens, should also be addressed.
James V. Bruckner
Janusz Z. Byczkowski
Yes. This document seems to be reasonable, thorough and complete.
Harvey J. Clewell
I think the document adequately describes the major potential uses and advantages of PBPK
modeling in risk assessment.
Gary L. Ginsberg
Overall, the answer is yes. See answer to #1 for comments on what applications have not been
fully covered.
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4. Are there improvements to the document that would substantially help risk
assessors who are less familiar with PBPK modeling better understand the
potential strengths and limitations of PBPK modeling?
Gregory M. Blumenthal
A chapter with a basic introduction to critical pharmacokinetic concepts is necessary.
James V. Bruckner
Janusz Z. Byczkowski
Yes. This reviewer would suggest to include an example of source codes for a PBPK model, for
instance, codes of the "classic" Ramsey and Andersen (1984) PBPK model for styrene.
Harvey J. Clewell
Perhaps a few case-studies where the strengths and limitations of a particular model used in a
risk assessment were evaluated.
Gary L. Ginsberg
As mentioned above, a section on interpretation of model output could be helpful for non-
modeling risk assessors in reviewing the results of a modeling exercise. Areas of interpretation
can be confidence in the model (extent to which it is calibrated and tested against external
datasets, goodness of fit, number of parameters needing back-fit), more on sensitivity analysis
(what is level of confidence and key uncertainties in the most sensitive parameters; what are
reasonable bounds for these parameters; how much might they influence the assessment), more
on non-linearities and how they affect results, etc.
The document could do a better job of educating the reader on the source of toxicokinetic non-
linearities. Michaelis-Menten kinetics are not described, perhaps because this is too technical a
subject? There are numerous opportunities to provide background on non-linear kinetics,
whether due to saturable absorption, binding, metabolism, or excretion (e.g., Section 3.4.3 -
biochemical parameters).
The RfC (1994) methodology comes up in several locations. One clear presentation of the role
of PBPK modeling within the RfC approach would be useful, especially in terms of
simplications taken that are not normally used in full PBPK modeling assessments.
Additionally, it would be good to explain some of the differences between reactive gas and non-
reactive gas models (local vs. systemic effect).
Other improvements would be to consolidate and clarify some aspects of Section 2. Specifically:
Page 2-12 to 2-15 - RfD Development Section - much of this material is redundant with RfC
section. It would be more efficient to combine the bulk of the RfC and RfD sections into one
general principles section and then have subsections that discuss any techniques specific to the
inhalation or oral dose route.
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Also: Page 2-15 - the box - has essentially the same rules as in the RfD box, but here they are
worded differently - any reason for this? It can confuse the reader. Also, left out of Box 2-3 is
PBPK use in duration adjustment. There are needs for duration adjustment in oral studies (e.g., 5
day a week gavage dosing to 7 day per week continuous exposure in drinking water). The RfD
section should discuss the utility of PBPK models to extrapolate from one exposure scenario in
animals (e.g., bolus dosing which may involve saturation of activation or detoxification systems)
to a more continuous exposure scenario in humans to achieve the same AUC dose.
Also, Page 2-18 - Section 2.8 - This section should come before the RfD/RfC sections. PBPK
models, at their most basic and primary level, are an advanced form of exposure assessment.
The improved exposure metric is then more effectively used in non-cancer and cancer risk
assessment. Therefore, the exposure assessment application should come before the risk
assessment discussion. This section brings up an important and evolving use of PBPK modeling
- conversion of biomonitoring data to exposure doses and application to risk assessment. This
merits considerably more attention in this document. There are several case studies (dioxin,
mercury, PFOA, phthalates) where this approach is used.
The differences between flow-limited and diffusion-limited kinetics as introduced on Page 3-3,
bottom should be spelled out more fully.
Page 3-17, 2nd full para - near the end - "PBPK modeling is not a fitting exercise" - this is
debatable since some parameters are fitted to an initial dataset. Better to state that since many
parameters are not fitted, can't expect perfect match to the underlying PK dataset.
There can be more discussion of limitations of fitting procedures (e.g., metabolic constants)
wherein one may have a fairly insensitive parameter and so the parameter estimate is still fairly
uncertain. This may affect the predictiveness of the model since the uncertain parameter may not
be very reliable for different conditions of exposure. It would be good to describe a bit more the
iterative approach and use of multiple datasets to optimize the fit. Finally, the situation where
there are multiple backfitted parameters (typically an absorption coefficient and metabolic
constants) should be described as being particularly uncertain in terms of the optimal value of
the individual parameters.
Same para on page 3-17 - where does the 2 SD rule for evaluating results come from?What other
choices are there? An option is to use best model fit when considering multiple underlying
datasets and iterative recalibration.
Issue of model calibration vs. validation (e.g., Page 3-19) - its confusing to say that a model
which has been cross-checked against external datasets isn't really "validated" but is only
"calibrated". Calibration signifies the adjustment of a model to match an underlying
observation, not an independent test of the model predictiveness (without adjustment) against a
new dataset. A different term is needed to described this activity and the confidence it provides.
Perhaps "confirmation of predictive ability" for certain types of data.
More on model calibration - Page 3-21 - top section - should discuss case where underlying PK
datasets disagree with one another. A single model will not be able to simulate both; must
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decide which underlying dataset to trust more or consider 2 different versions of the model - one
that fits one dataset optimally and the other fits the 2nd dataset.
Need for peer-reviewed model: Page 4-1 - 2nd bullet and page 4-3 - requirement that a PBPK
model must be peer-reviewed before being used in risk assessment seems excessive, or at least
should be caveated to state that the applications for which a model must be peer-reviewed to be
considered for risk assessment. The point of this guidance is to describe the principles for
developing and evaluating a model to enable it to be used in risk assessment. If one follows
these principles the need for external peer review prior to the risk assessment is diminished.
Perhaps it is USEPA policy that the model must first be peer reviewed to be used in deriving any
value that goes onto IRIS. The best approach for this document may be to say that the policy at
certain agencies or for certain risk applications may be to peer review a model prior to use.
Description of PBPK for Mixtures - Page 4-21 - this section would be helped by citing
examples where mixtures were analyzed with PBPK models and how this was accomplished
(cite work by Ray Yang), and by expanding the description of the HI approach. This appears to
be a simple addition approach to His for multiple contaminants in a mixture in which the PBPK
model is used to adjust the ingredient concentration as influenced by the other chemicals. The
description should include how chemicals may interact (at metabolic enzymes or binding sites)
and how this can be simulated in a model. Further, the description at the top of Page 4-22 is
confusing due to the word "POD". I recommend replacing it with "environmental exposure
concentration".
As described above, it would be good to relate PBPK to the classical PK frame of reference.
This is only done in passing in this document. However, risk assessors will come across one
compartment models from time to time (e.g., mercury, dioxin, chlorpyrifos) and will want to be
able to distinguish these from pBPK and why is PBPK preferable (or are there cases where one
compartment is just fine for RA?).
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5. Do you think that PBPK modelers outside the EPA, and those less familiar with risk
assessment practices, would find this document useful as far as fostering the kinds of
research and model development useful for risk assessment?
Gregory M. Blumenthal
This document excels at describing the role that PBPK modeling plays in EPA risk assessment
practices. This should greatly assist non-EPA PBPK modelers in developing and presenting
models that serve EPA risk assessment purposes.
James V. Bruckner
Janusz Z. Byczkowski
Yes. While this document is addressed to risk assessors rather than pharmacokineticists, it
contains many clarifications and suggestions that may be useful in developing PBPK and
pharmacokinetic models in general. For example, the Appendices provide extremely useful for
pharmacokineticists compilation of references and parameters. A public discussion, initiated over
the Internet, proved that there is a genuine interest in this report among the international experts
from both fields - pharmacokinetics and risk analysis.
Harvey J. Clewell
Yes, I think the document is particularly valuable for those who would like to develop a PBPK
description for a chemical that would be of value to the agency for a risk assessment.
Gary L. Ginsberg
As stated above, the document is technical but fairly readable and gives risk assessors the whys
and some of the hows for doing PBPK modeling. Without actually having an operative model in
front of you to work with, one cannot fully appreciate what is involved. It is a very hands on
activity. Perhaps a section that takes apart a case example could make this all more concrete. It
could show the construction of a real world model from choice of structure, through
parameterization (showing sources of parameter values), different simulations with different
exposure inputs, matches to key datasets, and how the model is affected by sensitive parameters.
The document may also describe how one learns how to do it (attending workshops; working
with other modelers).
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6. Are there current research needs and data gaps not highlighted in this report that
would improve the utilization of PBPK models in risk assessment?
Gregory M. Blumenthal
Innovation in computational biology and its application to risk assessment is an ongoing process.
This report should be envisioned as a living document, with room to expand as novel methods
and applications arise. Key among the areas with explosive growth occurring are acute
dosimetry, point-of-contact dosimetry, whole-life and developmental modeling, maternal-fetal
dosimetry, and metabolic network modeling to identify sensitive subpopulations.
James V. Bruckner
Janusz Z. Byczkowski
No. This report represents the current state- of-the-art in the PBPK modeling.
Harvey J. Clewell
Not that I can think of.
Gary L. Ginsberg
This document doesn't really address research needs and data gaps, as it is more of an
informational rather than an analytical document. Many of the research needs are chemical
specific or host specific (age groups, polymorphisms, etc.), or have to do with chemical
mixtures. I suppose a section that outlines these key areas of data needs would be helpful.
A related issue is where does the PK data come from to construct and calibrate models. The
discussion in section 2.3 doesn't really cover this. There could be a discussion that this is not a
regulatory-driven activity but the data are developed for specific risk assessment needs. It would
be helpful to describe the PK data gathering that is required for FIFRA and TSCA and discuss
the limitations for its use in PBPK modeling. Risk assessors familiar with these programs may
wonder about the utility of these data for modeling.
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7. Are there future reports that you could envision which would compliment or expand
upon the topics covered in the current document? For example, would a report
focused on dosimetry models for reactive gases be helpful? Or a report focused on
extrapolation across life stages using PBPK modeling?
Gregory M. Blumenthal
See answer to #6.
James V. Bruckner
Janusz Z. Byczkowski
Yes. A report focused on PBPK modeling of gestational and lactational transfers of chemicals
would be helpful in addressing unique problems of assessing risk from exposures of children to
environmental pollutants.
Moreover, this reviewer can envision the future report by the Agency, perhaps entitled
"Approaches for the Application of Physiologically Based Pharmacodynamic (PBPD) Models
and Supporting Data in Risk Assessment".
Harvey J. Clewell
Both of the suggested reports would be of value.
Gary L. Ginsberg
As stated above, there are various areas which are not fully covered in this document. These
could conceivably form the material of other in depth PBPK / risk reports. Even if this were the
case, consideration should be given to providing more information on these areas in this report to
make it more complete and representative of the field. In addition to the above, a document that
is more of a "How To", providing a step-by-step methodology for conducting PBPK modeling
and explains how some of the simulation software works might encourage more people to use
the technology.
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8. Do you have additional comments/suggestions that were not covered in the above
questions?
Gregory M. Blumenthal
Appendix 1 requires significant explanation and discussion. Those formulas should not be
published without appropriate descriptions and caveats.
James V. Bruckner
Janusz Z. Byczkowski
Yes. Because numerous typing errors were spotted in the Appendices (see below), this reviewer
would suggest a thorough "quality control" of the Appendices (at least, a "quality reading" by
the professional technical proofreader).
Harvey J. Clewell
Table 3-1: I would remove the subscripts for current and previous simulation time. These
subscripts are only accurate for explicit integration algorithms such as the Euler method. They
are not correct for the case of implicit algorithms such as the Gear (backward differentiation)
method.
Similarly, specifying the "integration interval" is only relevant to constant step-size algorithms.
For variable step-size algorithms, the appropriate element is the "error criteria". There are a
number of places in the document where this oversight should be corrected.
P. 3-17:1 would suggest also mentioning an alternative rule of thumb often used by modelers:
that a model is successful if it is within a fator of two to three of the data more than half the time.
I don't understand, and probably don't agree with, the 6th bullet in section 3.9. I could agree
with scaling metabolism by tissue weight, but not by body weight.
I have made a number of minor suggested edits to my copy of the document, which I will
provide to the peer review organizers.
I have also suggested a number of additional references at various places in main document.
They are listed here:
Abraham, MH; Weathersby, PK. JPharm Sci. 1994, 83, 1450-1456.
Allen BC, Covington TR, Clewell HJ. 1996. Investigation of the impact of pharmacokinetic
variability and uncertainty on risks predicted with a pharmacokinetic model for chloroform.
Toxicology 111:289-303.
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Astrand, P., and Rodahl, K. Textbook of Work Physiology. McGraw-Hill, New York, 1970.
Beliveau, M; Lipscomb, J; Tardif, R; Krishnan, K. Chem Res Toxicol. 2005, 18, 475-485.
Clewell HJ, Andersen ME. 1985. Risk assessment extrapolations and physiological modeling.
Toxicol Ind Health 1:111-131.
Clewell HJ, Andersen ME. 1996. Use of physiologically-based pharmacokinetic modeling to
investigate individual versus population risk. Toxicology 111:315-329.
Clewell, H.J., Gentry, PR., Covington, T.R., Sarangapani, R., and Teeguarden, J.G 2004.
Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on
tissue dosimetry. Toxicol. Sci. 79:381-393.
Gentry, PR., Hack, C.E., Haber, L., Maier, A., and Clewell, III, HJ. 2002. An Approach for the
Quantitative Consideration of Genetic Polymorphism Data in Chemical Risk Assessment:
Examples with Warfarin and Parathion. Toxicological Sciences 70:120-139.
Lilly, P.O., Thornton-Manning, J.R., Gargas, M.L., Clewell, H.J., and Andersen, M.E. 1998.
Kinetic characteristics of CYP2E1 inhibition in vivo and in vitro by the chloroethylenes. Arch
Toxicol 72:609-621.
Sarangapani R, Teeguarden J, Andersen ME, Reitz RH, Plotzke KP. 2003a. Route-specific
differences in distribution characteristics of octamethylcyclotetrasiloxane in rats: analysis using
PBPK models. Toxicol Sci. 71(l):41-52.
Sarangapani, R., Gentry, PR., Covington, T.R., Teeguarden, J.G, and Clewell, HJ. 2003b.
Evaluation of the potential impact of age- and gender-specific lung morphology and ventilation
rate on the dosimetry of vapors. Inhal Toxicol 15(10):987-1016.
Gary L. Ginsberg
Yes, see below for a variety of broad and specific comments.
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V. SPECIFIC OBSERVATIONS
Gregory M. Blumenthal
James V. Bruckner
p. xi, pgr. 3, line 7; p. xii, pgr. 1, line 7; and p. xiii, line 3: Replace the word "applied" with
"administered".
p. xii, pgr. 4, lines 1 & 2: What is meant by the term "value" in the phrase "it is used to estimate
the value of internal dose metrics"?
p. 1-1, pgr. 1, line 8: It should be noted here for less familiar readers that RfC methodology
applies to inhalation exposures.
p. 2-1, pgrs. 1 & 2: It might be useful to include a short, transitional paragraph that clarifies the
difference(s) between classical pharmacokinetic (PK) analysis (of data) and PK modeling.
p. 2-2, lines 25-28: I assume that Fig. 2-1 illustrates (is an example of) a chemical that is
metabolically activated. If this is the case, it should be mentioned in the text and noted that the
modeling provides evidence of this phenomenon.
p. 2-3, pgr. 1: It may be useful here to note the importance of being able to relate the mode of
action to the biologically-active form of the chemical.
p. 2-5, pgr. 1: It would be worthwhile to point out that PK equivalence may not equate to
toxicity equivalence, due to the potential existence of thresholds and toxicodynamic differences.
p. 2-5, line 21: The term "window of susceptibility" should be succinctly defined.
p. 2-7, line 11: Substitute "administered" for "applied" dose.
p. 2-9, pgr. 1: It should be recognized (and pointed out) here that estimation of equivalent
absorbed doses (by inhalation and ingestion) does not take into account an important factor;
namely the frequent differences in the duration of the oral (usually bolus) and inhalation
(frequently hours) exposures. Bruckner and his colleagues have assessed the influence of first-
pass effect with equivalent inhalation and oral exposures to VOCs by: (1) measuring the total
absorbed dose for 2- to 3-hour inhalation exposures in rats; (2) administering this total dose by
constant gastric infusion over the same time-frame; and (3) monitoring blood and tissue
concentrations of the compound of interest during and post exposure [e.g., see Sanzgiri et al.
Toxicol. Appl. Pharmacol. 134: 148-154 (1995)].
p. 2-9, lines 26 - 30: The magnitude of CNS depression is an example of an effect that is
dependent upon the momentary concentration of a VOC in the blood/brain. PBPK models and
objective measurements of trichloroethylene-induced CNS dysfunction in rats have been utilized
to demonstrate that Haber's Rule and ten Berge et al's. J. Hazard. Mater. JJ: 301-309 (1986) Cn
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X t approaches to time scaling result in overestimation of risks when extrapolating from shorter
to longer exposure times and underestimation when extrapolating from longer to shorter
exposures [Boyes et al. Environ. Health Per spec. 108 (Suppl 2.): 317-322, 2000; Bruckner et al.
J. Toxicol. Environ. Health 67 (Part A}: 621-634, 2004].
p. 2-12, Box 2-2: Should "Interspecies uncertainty factor" be added?
p. 2-14, lines 26-29: This sentence is a bit unclear. I assume the authors intend to state that
default uncertainty factors of 3 are used for potential interspecies and intraspecies
pharmacodynamic differences in conjunction with PBPK modeling. See suggested editorial
changes.
p. 2-15, line 26: Has the meaning of the abbreviation "IUR" been stated previously?
p. 2-17. lines 11-13: The paper of Lee et al. Toxicol. Appl. Pharmacol. 139: 262-271 (1996)
might be cited here as an example of the influence of dose on the extent of first-pass hepatic and
of first-pass pulmonary elimination of orally-administered trichloroethylene.
p. 2-18. line 1: Should drinking water "rate" be "volume"?
p. 2-18, lines 3 - 17: The authors seem unnecessarily tentative in stating that "PBPK models are
potentially useful in evaluating the pharmacokinetic basis ...". Furthermore, it has been clearly
established a number of times in this document that potential pharmacokinetic and
pharmacodynamic differences are separate/different entities.
p. 2-18, Box 2-4: Can't PBPK models be useful in certain intraspecies pharmacokinetic
extrapolations (e.g., child to adult)?
p. 3-2, line 15: What is meant by the statement that there is no limitation to the size of a
compartment? Doesn't a compartment's size have to be physiologically realistic?
p. 3-3, line 10: Is use of the term "reactors" necessary? It may not be familiar to the general
reader.
p. 3-3, lines 25 & 26: Concentration gradient is, of course, just one factor in Pick's law of
diffusion. See suggested editorial change in the text.
p. 3-8, lines 7-9: An ILSI committee is currently finalizing a series of manuscripts that include
a compilation of some physiological values for immature rodents and humans.
p. 3-10, Table 3-5: The range of values given for pulmonary blood flow (as a % of cardiac
output) is 11.1-17.8%. Doesn't 100% of the cardiac output pass through the pulmonary
circulation?
p. 3-11, lines 14: The phrase "model validation" might be utilized rather than "external
evaluation".
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p. 3-13, lines 11 & 12: What do the authors mean when they state that in vitro to in vivo
extrapolations are not clear?
p. 3-19, pgr. 2: It would be useful to briefly address the question of the quality of experimental
PK data sets. Many factors can influence the accuracy of experimental measurements in animals
and humans, from stress on the subjects to analytical chemistry problems. My primary role in
PBPK model development has been to provide quality empirical time-course data, so I am aware
of many of the potential problems.
p. 3-27, line 27: I assume that in vivo kinetic data may also be useful for model calibration.
p. 3-29, lines 1-3: Should the words "The model for" be inserted at the beginning of this
sentence? It is problematic as presently written.
p. 3-29, lines 32: Some sort of disclaimer sentence should probably be added at the end of this
section, emphasizing that a relatively high degree of uncertainty is usually inherent in the use of
surrogate data.
p. 3-30, line 25: The term "steady-state" can be inserted between the words "/« vivo" and
"data".
p. 3-31, lines 3-6: Another bullet/point should be added addressing the use of kinetic data for
model construction/calibration and the need for additional data sets for model validation.
p. 3-31, line 7: Still another bullet/point should be added at or near the beginning of this section.
It should point that the model should include/focus on the proximate toxic moiety, if it and/or the
MOA is/are understood.
p. 4-1, lines 6& 7: Types of human studies, that may serve as the critical study, should be
expanded to include clinical and experimental studies. See the recommended change in the text.
p. 4-3. Box 4-1. 2—bullet: "Binding" should be included with storage.
p. 4-4, lines 24-26: Treatment with an inhibitor of the metabolism of a toxic parent compound
may enhance its toxicity by decreasing its rate of metabolic clearance.
p. 4-5, pgr. 1: It would be worthwhile pointing out that the concentration of ligand at a
particular point in time is often considered to be the most appropriate dose metric for adverse
effects associated with receptor interactions.
p. 4-12, lines 26 & 27: The words "oral" and "gavage" are redundant.
p. 4-13, Fig. 4-4: Obviously, as shown here, exposure conditions that yield equivalent AUCs
can be forecast, but the oral Cmax is about 10-fold higher in the oral group. This relatively high
concentration may exceed a toxicity threshold. Alternatively, the adverse effect may depend
upon the momentary concentration.
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p. 4-14, pgr. 1: It would be worthwhile to point out that PBPK models are now being used to
make duration adjustments for derivation of acute exposure guideline levels (AEGLs) for a
number of chemicals. AEGLs are established for 10- and 30-minute inhalation exposures, as
well as 1-, 4- and 8-hour exposures. Typically, a critical study with a single duration of exposure
serves as the basis for derivation of AEGLs for the other exposure durations. A PBPK model is
used to forecast the inhaled concentration required for each time-period to produce the same dose
metric as that associated with an adverse effect in the critical study. Bruckner et al. (2004)
(referenced previously) described the AEGLs program and illustrated the use of PBPK for time
scaling with trichloroethylene.
p. 4-20, Table 4-2: How is tumor prevalence expressed here? No units are given.
p. 4-23. line 11: Shouldn't the word "difference" be "differential"?
Janusz Z. Byczkowski
P. xii Line 29 - Quote: "...which are then use to derive..."
Change to: "...which are then used to derive..."
P. 2-2 Line 21 - please add a short paragraph on modeling of dermal exposures. Some of
the following references, listed in the Appendix 2, may be used:
Auton, T. R., Ramsey, J. D., and Wollen, B. H. (1993). Modelling dermal
pharmacokinetics using in vitro data. Part II. Fluazifop-butyl in man. Human &
Experimental Toxicology 12, 207-213.
Auton, T. R., Ramsey, J. D., and Woollen, B. H. (1993). Modelling dermal
pharmacokinetics using in vitro data. Part I. Fluazifop-butyl in the rat. Human &
Experimental Toxicology 12, 199-206.
Blancato, J. N., and Bischoff, K. B. (1993). The application of pharmacokinetic
models to predict target dose. In Dermal risk assessment. Dermal and inhalation
exposure and absorption of toxicants. (R. G. M. Wang, J. B. Knaak, and H. I.
Macbach, Eds.), pp. 31-46. CRC Press Inc.
Bookout, R. L. J., McDaniel, C. R., and Quinn, D. W. M. J. H. (1996).
Multilayered dermal subcompartments for modeling chemical absorption. SAR
QSAR Environ Res. 5, 133-150.
Corley, R. A., Markham, D. A., Banks, C., Delorme, P., Masterman, A., and
Houle, J. M. (1997). Physiologically based pharmacokinetics and the dermal
absorption of 2-butoxyethanol vapor by humans. Fundamental and Applied
Toxicology 39, 120-130.
Corley, R. A., Gordon, S. M., and Wallace, L. A. (2000). Physiologically based
pharmacokinetic modeling of the temperature-dependent dermal absorption of
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chloroform by humans following bath water exposures. Toxicological Sciences
53, 13-23.
Jepson, G. W., and McDougal, J. N. (1999). Predicting vehicle effects on the
dermal absorption of halogenated methanes using physiologically based
modeling. Toxicological Sciences 48, 180-188.
Loizou, G. D., Jones, K., Akrill, P., Dyne, D., and Cocker, J. (1999). Estimation
of the dermal absorption of m-xylene vapor in humans using breath sampling and
physiologically based pharmacokinetic analysis. Toxicological Sciences 48, 170179.
McDougal, J. N., Jepson, G. W., Clewell, H. J., MacNaughton, M. G., and
Andersen, M. E. (1986). A physiological pharmacokinetic model for dermal
absorption of vapors in the rat. Toxicology and Applied Pharmacology 85, 286294.
McKone, T. E. (1993). Linking a PBPK model for chloroform with measured
breath concentrations in showers: implications for dermal exposure models.
Journal of Exposure Analysis and Environmental Epidemiology 3, 339-365.
Nichols, J. W., McKim, J. M., Lien, G. J., Hoffman, A. D., Bertelsen, S. L., and
Elswick, B. A. (1996). A physiologically based toxicokinetic model for dermal
absorption of organic chemicals by fish. Fundamental and Applied Toxicology
31,229-242.
Roy, A., Weisel, C. P., Lioy, P. J., and Georgopoulos, P. G. (1996). A distributed
parameter physiologically-based pharmacokinetic model for dermal and
inhalation exposure to volatie organic compounds. Risk Analysis 16, 147-160.
Shatkin, J. A., and Brown, H. S. (1991). Pharmacokinetics of the dermal route of
exposure to volatile organic chemicals in water: A computer simulation model.
Environmental Research 56, 90-108.
Thrall, K. D., and Woodstock, A. D. (2002). Evaluation of the dermal absorption
of aqueous toluene in F344 rats using real-time breath analysis and
physiologically based pharmacokinetic modeling. Journal of Toxicology and
Environmental Health 65, 2087-2100.
P. 2-14 Line 24 - Please add a sentence or two, explaining that Monte Carlo modeling
coupled with PBPK represents a useful approach to quantify impact of
parameter variability on simulated dose metrics (e.g., excerpt from page 3-
24).
P. 2-15 Line 28 - Quote: " ...either a nonlinear (i.e., RJC or RfD) or linear..''
Please delete "(i.e., RfC or RfD)"
P. 2-16 Line 1 - Quote: " ...the tumors observed in such studies are..''
E-29
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Please add "the number of tumors observed in such studies is"
P. 3-2 Line 24 - Quote: "...biochemicalparameters (i.e., partition coefficients)...'"
Please change to "physicochemical parameters (i.e., partition
coefficients)"
P. 3-6 Line 12 - Please change in the equation for Tissue blood expression "(Ci+C2)" to
"(C, - C,/PH,)"
and in the equation for Cellular matrix "(Ci-C2)" to "(Ci - C2/P_tb)"
Add below the table 3-2: "PH,_= partition coefficient tissue:blood"
P. 3-7 Line 18 - Quote: "...(mg/L-hf1)..?
This expression of unit, mathematically, does not make sense. Please
change either to exponential "(ma x L— x hr^-)" or proportional
"(mg/L/hr)" expression.
P. 3-17 Line 28 - Quote: "...every single data..."
Please change to "every single datum"
P. 3-29 Line 28 - Quote: "... a web-basedresource for PBPK modeling
(http://www.capkr.man.ac.uk). The site provides instant access to
resources such as data, methodology, and tools necessary to start PBPK
modeling effort..."
Unfortunately, the provided URL, points to the password-protected
"members only" university web site, without public access to any
resources.
Please either, provide URL to publicly accessible web site (for example,
http://www.pbpk.org/ ) or delete this information.
P. 4-13 Line 4; 4-14 Lines 8 and 14 - Quote: "...mg/L-hr..."
This expression of unit, mathematically, does not make sense. Please
change either to exponential "(mg x L- x hr1)" or proportional
"(mg/L/hr)" expression.
P. 4-16 Lines 23 - 26 - Quote: "...Because the Agency has traditionally applied the
uncertainty factors to the external dose and not to the internal dose, it may
be useful to undertake a systematic evaluation of the outcome of applying
the uncertainty factors to the external and internal doses for various
chemicals and situations..."
The Agency traditionally applied the uncertainty factors to the external
dose because before accepting PBPK models it could not calculate
realistically internal doses, delivered to the target. The very goal of
applying PBPK models in risk assessment is to determine the internal dose
metrics under relevant exposures, and thus, reverting to "external dose
divided by uncertainty factor" as a basis for calculation of RfD and/or RfC
would nix most of the advantage from using the PBPK model. Even
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though, as already noted in the document, for the linear range of PK, the
result is numerically the same, no matter whether uncertainty factors are
applied to internal or external dose, the right method is to divide internal
dose metrics by uncertainty factor(s), and then, to calculate human
exposure (external) dose. Undertaking the "systematic evaluation", as
suggested in the document, would just waste time and resources as its
outcome can be easily predicted right now: it is always appropriate to
divide internal dose metrics by uncertainty factor(s); while it is only
sometimes appropriate to divide external dose by uncertainty factor(s) -
only when dealing with linear pharmacokinetics.
This reviewer suggests to substitute the above quoted sentence with the
recommendation to appropriately apply uncertainty factor(s) to internal
dose metrics, before calculating human exposure (external) dose for RfD
and/or RfC, applicable for both linear and nonlinear pharmacokinetics.
P. 4-17 Lines 7 and 15; 4-18 Line 15 - Quote: "...mg/kg-d..."
This expression of unit, mathematically, does not make sense. Please
change either to exponential "(mg x kgr^-x d—)" or proportional
"(mg/kg/d)" expression.
P. 4-23 Line 11 - Quote: "...difference equations..''
Please change to: "differential equations"
P. G-l Line 23 - Quote: "...Cancer scope factor..''
Please change to: "Cancer slope factor"
P. G-l Line 34 - Quote: "...Delivered dose: The amount of a substance available for
interactions with biologically significant receptors in the target
organ..''
Although this definition is correct, risk assessors usually use the term
"receptors" differently than pharmacologists. For the risk assessor,
"receptor" often means the individual (human subject) exposed to a
potentially harmful chemical compound.
To avoid confusion, this reviewer would suggests changing the definition
of Delivered dose, to: "The amount of a substance available for
interactions with biologically significant targets in the affected tissues".
P. G-2 Line 11 - Quote: "...This adjustment may incorporate..."
Please change to: "The adjustment of concentration from animal to human
may incorporate".
P. G-2 Line 27 - Quote: "...the relationship between external exposure level and the
biologically effective dose at a target tissue..."
Please add: "the relationship between external exposure level and the
biologically effective dose at a target tissue overtime".
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P. G-2 Line 27 - Quote: "...the relationship between a biologically effective dose and the
occurrence of a tissue response..''
Please add: "the relationship between a biologically effective dose and the
occurrence of a tissue response overtime".
P. G-2 Line 42 - "Reference concentration (RfCf:
Please change to revised IRIS definition (effective July 2005 <
http://www.epa.gov/iris/gloss8.htm >: "An estimate of a continuous
inhalation exposure for a given duration to the human population
(including susceptible subgroups) that is likely to be without an
appreciable risk of adverse health effects over a lifetime. It is derived from
a BMCL, a NOAEL, a LOAEL, or another suitable point of departure,
with uncertainty/variability factors applied to reflect limitations of the data
used."
P. G-3 Line 5 - "Reference dose (R/Df:
Please change to revised IRIS definition (effective July 2005
< http://www.epa.gov/iris/gloss8.htm >: "An estimate of a daily oral
exposure for a given duration to the human population (including
susceptible subgroups) that is likely to be without an appreciable risk of
adverse health effects over a lifetime. It is derived from a BMDL, a
NOAEL, a LOAEL, or another suitable point of departure, with
uncertainty/variability factors applied to reflect limitations of the data
used."
P. G-3 Line 22 - Please explain the term "window of susceptibility".
P. R-12 ref. Price et al. (2003a) - Change "Conelly" to "Conolly"
P. R-12 ref. Price et al. (2003b) - Add in "J Toxicol Environ Health A66:"
Appendix 3, P. 6 Line 13 - Quote: "...Oxydation..."
Please correct to "Oxidation"
Appendix 3, P. 6 Line 16 and P. 7 Line 22 - Quote: "...conjugaison..."
Please change to "conjugation"
Appendix 3, P. 8 Lines 24, 25 - Quote: "...Oxydation..."
Please correct to "Oxidation"
Appendix 3, P. 19 Lines 33, 34, 35 37, 39, 40, 41, 43 - Quote: "...Oxydation..."
Please correct to "Oxidation"
Appendix 3, P. 20 Lines 18, 20, 22 - Quote: "...Oxydation..."
Please correct to "Oxidation"
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Appendix 3, P. 22 - Please correct illegible fonts
Appendix 3, Pp. 25, 36 and 51 Line 5 - Quote: "...coumpound..."
Please change to "compound"
Appendix 3, P. 28 Line 17 - Quote: "...conjugaison..."
Please change to "conjugation"
Appendix 3, P. 46 Line 1 - Quote: "...CHLOROFLUOROHYDROCARBONE..."
Please change to "CHLOROFLUOROHYDROCARBON"
Appendix 3, P. 57 Line 14 - Quote: "...(yountK).."
Please change to "(youth)"
Appendix 3, P. 81 Line 15 - Quote: "...disapearance ..."
Please change to "disappearance"
Appendix 3, P. 83 Lines 36 and 39 - Quote: "...conjugaison..."
Please change to "conjugation"
Appendix 3, Pp. 100, 103 and 106 Line 19 - Quote: "...caracteristics..."
Please change to "characteristics"
Appendix 3, Pp. 119 and 120 Line 2 - Please translate into English.
Harvey J. Clewell
p. 1-2, lines 26-27: change "a compilation... environmental chemicals." to "and a case study
based on the evaluation of a PBPK model for isopropanol (Clark et al. 2004)"
p. 2-2, line 31: Clewell and Andersen 1997 should be 1985
p. 2-4, lines 5 and 35; p. 2-14, line 29p. 2-16, line 7: Clewell et al., 2002 should be 2002a
(Clewell Andersen and Barton)
p. 2-7, line 12: add "exposure" at beginning of line
p. 2-9, line 2: change was to could be
p. 2-13, line 8: change "overly" to "highly"
p. 2-18, line 4: add Clewell et al. 2004
p. 3-1, line 31: Clewell and Andersen 1997 should be 1987
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p. 3-2, line 15: change "size" to "complexity"
p. 3-3, line 11: change "equal" to "related by the partition coefficient"
p. 3-3, line 22: delete "clearance" and change "volume" to "mass"
p. 3-3, line 23: change "liters" to "mg" and "milliliters" to "mg" and delete "clearance"
p. 3-3, line 25-26: delete "is described according to Pick's law of simple diffusion, which states
that the flux of a chemical" and change "concentration" to "activity (concentration adjusted by
partition coefficient"
p. 3-3, line 27: delete "passive and"
p. 3-5, table: remove all subscripts "n" and "n-1" and related definitions in footmote
p. 3-6, table 3-1, footnote b: add "steady-state" before "arterial"
p. 3-6, table 3-2: correct equations (should be Cl - C2/P2)
p. 3-7, line 1: add "a" after "by"
p. 3-7, line 2: add "a" after "on"
p. 3-7, line 3: add "typically" before "been"
p. 3-7, line 4: delete "second order"
p. 3-7, lines 4-7: delete from "Conjugation" to "successfully"
p. 3-7, line 28: after "volumes", add "absorption rate parameters (e.g., for dermal or oral
uptake),"
p. 3-8, line 12: add Gentry et al. 2004; Gentry et al. 2006 (ILSI human perinatal parameters)
p. 3-8, line 23: delete "(e.g., Andersen et al., 1987)." and add "but decreases with activity
(Astrand and Rodahl, 1970)."
p. 3-8, lines 14-16: change 91% to 85% and 9% to 15% and add reference to Brown et al. 1997.
p. 3-8, lines 18-19 change "which may seem questionable...in risk assessment." to "is a
reasonable approximation since tissue densities typically range from 0.9 kg/L for muscle to 1.06
kg/L for fat (Ross et al. 1991, Mendez and Keys, 1960)
p. 3-9, table 3-3 title: change "Reference" to "Typical"
E-34
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p. 3-10, Table 3-5: add "upper respiratory tract" as footnote for Lungs
p. 3-10, Table 3-6: add volumes
p. 3-11, line 11: delete "or binding"
p. 3-12, line 6: add Beliveau et al. 2005 and Abraham and Weathersby 1994.
p. 3-14, table: move Ramsey and Andersen (1984) from ACSL row, last column to SimuSolv
row, last column. Add Clewell et al. 2000 to ACSL row, last column.
p. 3-17, line 5: change "levels" to "amounts" in three places
p.3-18, line 17: after "profiles", add (Lilly et al.1998)"
p. 3-20, line 23: after "zero", add (Sarangapani et al. 2003a)
p. 3-22, line 4: change "the" to "hypothetical"
p.3-22, line 11: add "In actual practice, it is a matter of concern for sensitivity ratios for be
greater than one in absolute value, since this results in the amplification of input error (Allen et
al. 1996).
p. 3-22, Figure 3-3 caption: add "hypothetical" before "PBPK"
p. 3-24, line 6: add Sarangapani et al. 2003b, Gentry et al. 2003.
p. 3-24, line 14: add Gentry et al. 2002.
p. 3-24, line 18: add Clewell and Andersen 1996.
p. 3-24, line 23: change "/Q" to "/Qc"
p. 3-25, line 24: change Clewell and Andersen 1997 to 1987.
p. 3-28, line 26: change "suffices" to "may suffice"
p. 3-30, lines 29-31: delete
p. 4-1, line 7: delete "epidemiological"
p. 4-1, line 9: change "as well as" to "and/or"
p. 4-5, line 15: add Andersen et al. 1987.
p. 4-7, table: add acrylic acid ; nasal lesions ; average nasal concentration ; Andersen et al. 1999.
E-35
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p. 4-7, table, butoxyethanol row: change "Levels" to "Concentration"
p. 4-7, table, methylmethacrylate row: change "amount metabolized/time" to "amount
metabolized/time/volume nasal tissue" and delete 1999 reference
p. 4-8, table, top row: add "transferase" after "glutathioine
p. 4-8, table, TCDD row: change "Number" to "Fraction"
p. 4-8, table, TCE row: change "in" to "per L"
p. 4-8, table, VC row: delete "mg metabolite produced/L liver"
p. 4-9, lines 22 and 26: change Clewell and Andersen 1997 to 1987.
p. 4-12, line 26: change "is" to "can be"
p. 4-15, lines 11-12: delete "for unimodel, normal distribution (Naumann et al. 2001)"
p. 4-17, line 32: delete "Subsequently" and change "conventional" to "case of the RfD"
p. 4-19, line 11: change "simulate" to "estimate"
References:
Abraham, MH; Weathersby, PK. JPharm Sci. 1994, 83, 1450-1456.
Allen BC, Covington TR, Clewell HJ. 1996. Investigation of the impact of pharmacokinetic
variability and uncertainty on risks predicted with a pharmacokinetic model for chloroform.
Toxicology 111:289-303.
Astrand, P., and Rodahl, K. Textbook of Work Physiology. McGraw-Hill, New York, 1970.
Beliveau, M; Lipscomb, J; Tardif, R; Krishnan, K. Chem Res Toxicol. 2005, 18, 475-485.
Clark, LH, Setzer RW, and Barton, HA. 2004. Framework for evaluation of physiologically-
based pharmacokinetic models for use in safety or risk assessment. Risk Analysis, 24(6): 1697-
1717.
Clewell HJ, Andersen ME. 1985. Risk assessment extrapolations and physiological modeling.
Toxicol Ind Health 1:111 131.
Clewell HJ, Andersen ME. 1996. Use of physiologically-based pharmacokinetic modeling to
investigate individual versus population risk. Toxicology 111:315-329.
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Clewell, H.J., Gentry, P.R., Covington, T.R., Sarangapani, R., and Teeguarden, J.G. 2004.
Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on
tissue dosimetry. Toxicol. Sci. 79:381-393.
Gentry, P.R., Hack, C.E., Haber, L., Maier, A., and Clewell, III, HJ. 2002. An Approach for the
Quantitative Consideration of Genetic Polymorphism Data in Chemical Risk Assessment:
Examples with Warfarin and Parathion. Toxicological Sciences 70:120-139.
Gentry, PR. et al. 2004. Data for physiologically based pharmacokinetic modeling in neonatal
animals: physiological parameters in mice and Sprague-Dawley rats. J. of Children's Health 2(3-
4):363-411.
Gentry, PR. et al. 2006. Physiological parameters for early life stages. Report of an ILSI
working group. In preparation.
Lilly, P.O., Thornton-Manning, J.R., Gargas, M.L., Clewell, H.J., and Andersen, M.E. 1998.
Kinetic characteristics of CYP2E1 inhibition in vivo and in vitro by the chloroethylenes. Arch
Toxicol 72:609-621.
Mendez J, Keys A, 1960. Density and composition of mammalian muscle. Metabolism 9:184-
188.
Ross R, Leger L, Guardo R, De Guise J, Pike BG 1991 Adipose tissue volume measured by
magnetic resonance imaging and computerized tomography in rats. J Appl Physiol 70: 2164-
2172
Sarangapani R, Teeguarden J, Andersen ME, Reitz RH, Plotzke KP 2003a. Route-specific
differences in distribution characteristics of octamethylcyclotetrasiloxane in rats: analysis using
PBPK models. Toxicol Sci. 71(l):41-52.
Sarangapani, R., Gentry, PR., Covington, T.R., Teeguarden, J.G, and Clewell, HJ. 2003b.
Evaluation of the potential impact of age- and gender-specific lung morphology and ventilation
rate on the dosimetry of vapors. Inhal Toxicol 15(10):987-1016.
Gary L. Ginsberg
ES, 2nd Page (2nd para) and later in the document - 3 aspects of PBPK models are called out as
being essential to include. However, there are others. For example, PBPK model structure be
must be appropriate to the exposure route (oral vs. dermal vs. inhalation vs. lactation vs. trans-
placental) and MOA (only parent compound simulated or both parent compound and
metabolites; GSH depletion or other defense mechanisms simulated?). Further, a good general
principle is that the PBPK model must include all major elimination pathways (renal, exhalation,
liver metabolism, as appropriate).
ES- same para as last entry, last sentence - "available animal-alternative algorithms" - the word
animal seems out of place given that many of the algorithms in the appendix are for humans as
well as animals.
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Page 2-1, last sentence of 1st para and Page 2-2 to 2-3: assertion is made that internal dose
metric provides a better relationship to toxic effect than does external dose. While this is
typically true and may be self-evident to some, the text needs to say why this should be so. For
example, it can be stated that internal dose is obviously closer to biologically effective dose than
is the applied dose, and that internal dose and applied dose may not be directly proportional due
to saturable processes, distributional phenomena, secondary clearance pathways that are active at
higher doses, etc.
Page 2-1, 2nd para - 3rd sentence - seems illogical - I think what it is trying to say is that orally
absorbed compounds tend to exert their effects at systemic rather than local sites so PBPK
models have focused on modeling systemic compartments. However, the sentence is poorly
written. The next sentence appears to be referring to classical one and two compartment models
in the first clause, with the more highly evolved modeling referring to PBPK. This sentence
should be unpacked into one or several paragraphs which gives appropriate background for the
more basic models and their potential utility in risk assessment (e.g., mercury, dioxin), and why
the more complex PBPK models are needed in other applications.
Page 2-1 - last para - obviously the document is focusing on modeling of systemic sites and
steering clear of local dosimetry at portal of entry. It would probably be best to state this more
clearly and then refer to the RfC methodology for particles and gases, the more refined CFD
models, and the oral models of g.i. dosimetry (e.g., Frederick, et al., 1992) as important
applications for chemicals which produce contact site toxicity but will not be the focus of this
document. Then state whether certain principles developed in this document would tend to apply
to the contact site modeling approaches.
Page 2-4 - top - states that default approach is to use lifetime AUC in target tissue as the most
appropriate dose metric for chronic exposure. However, how is the lifetime AUC derived -
running the model for 2 years for a rat or 70 years for a human? That would be require large
time steps that may miss important periods of greater exposure when there may also be windows
of susceptibility that affect chronic risk. Therefore, the concept of windows of vulnerability
should be included when considering a lifetime AUC approach.
Page 2-5, 2nd para - replace the word "current" with peak or Cmax??
Last 2 paras of 2-5: appears to be describing optimal dataset vs. limited dataset with need for
PBPK modeling. It would be helpful to set up discussion this way. Lead in phrase "the most
robust PK dataset needed for RA would consist of..." is awkward and indirect.
Page 2-7, 2nd para, last line - what is a non-compartmental model?? Even one compartment
models are "compartmental". This sentence appears to, once again, gloss over the important
distinctions between non-physiological compartmental models vs. PBPK models.
Page 2-12 - Box of bullet points - consider changing "dosimetric adjustment factor" to "cross-
species" AF to be more specific and contrast this bullet from the one that follows.
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Page 2-10, last para - a more complete discussion of how the default RfC methodology
addresses cross-species dosimetric adjustments via PBPK modeling is needed. That
methodology relies upon a steady state equation in which the HEC calculation is simplified down
to a ratio of the animal to human blood/air partition coefficient. The current document should
present this and describe whether there are situations in which RfC derivation should involve
more complete PBPK modeling.
Page 2-16 - Interspecies Extrapolation for carcinogens - this section should make the point that
there are no uncertainty factors in carcinogen toxicity assessment with the interspecies scaling
factor the only factor used to modify the slope factor derived from animal studies. Therefore,
PBPK models are valuable to extend the dose response analysis to human receptors that may
have unusual exposure and pharmacokinetics (e.g., children, elderly) to address intraspecies
variability, and to use PBPK models to improve upon the interspecies scaling factor approach.
The existing text does not make these points.
Page 2-18, top para, 2nd sentence - "However ..." This sentence is unnecessary - PK is not
expected to adjust for PD differences across species or individuals under any circumstances.
This does not diminish the value of PBPK model adjustment of cancer risk assessments for
dosimetry differences at different life stages.
Page 2-20 -first sentence - very broad statement about circumstances under which PBPK models
wouldn't be needed. Should add the caveat: and if the same relationships between external and
internal dose exist across species, age groups, dose routes, etc., then wouldn't need PBPK
modeling.
Page 2-20 - second set of numbered points - consider changing "potential dose" to "intake
dose".
Page 3-1 to 3-2 - model purpose - should include the use of PBPK models to help interpret
MOA by distinguishing between parent compound and metabolite as active toxicant. Ginsberg
and Rao 2000 publication is a good example of this for MTBE. Discussion in Section 4 (page 4-
5) gets at this but this purpose or utility should also be stated up front.
Page 3-1, end of first para - need to include another step in PBPK model evaluation: "evaluation
of model predictiveness against external datasets".
Page 3-7, top para - general principles are described for a variety of important topics - binding,
metabolism. However, much more can be stated, especially with regards to how metabolism is
represented. No mention is made of Km or Vmax or possible involvement of multiple CYPS
having different activity. Ping-pong mechanism needs definition.
Page 3-9, Table 3-3. Should include rapidly perfused tissues as a line in the table since many
models have a simplified lumped compartment of this nature.
Page 3-12, Section 3.4.3 - Biochemical Parameters - should describe how in vivo data are used
to derive metabolic constants - how backfitting (optimization) techniques are iteratively used.
E-39
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Should also discuss issue where multiple parameters need backfitting (e.g., absorption
coefficient, metabolic constants) and how this can lead to uncertain parameter estimates (Getting
the right answer for the wrong reason).
Page 3-18 - "structure may be inadequate" not "is inadequate". Figure 3-2 uses log scale. Text
should make the point that linear scale highlights data to model differences more clearly and
should be used over log scale if possible.
Page 3-20, end of 1st full para - while visual inspection is a convenient evaluation procedure
commonly used, the method of residuals is a relatively easy approach that is also used and
should be described.
Page 3-20, next para - level of complexity evaluation - set one parameter to zero and see if it
impacts model outcome - this isn't so simple as removing one parameter (e.g., a tissue
compartment) may require adjusting the size of another compartment to maintain physiological
sense; in this way would be changing more than one parameter.
Page 3-23 - bottom - should spell out what is meant by "individual-specific" parameters - are
these values for actual people or values picked from a random distribution.
This section (Variability Analysis) should describe possible form of M-C results - normal,
lognormal, multimodal.
Page 3-26 - should describe how uncertainty analysis is run - how is M-C analysis used
differently here than in variability analysis?
Page 3-29, top - description of parallelogram approach for CFC and halothane a bit confused -
needs rewording and clarification (e.g., The human CFC model was assumed to behave as well
as the human halothane model because they were similarly developed and calibrated from in
vitro data.
Page 3-30, 4th and 5th bullets - there is no apparent difference between them.
6th bullet is a new rule not stated previously in Section 3 and is not well developed.
Page 3-31 - there is no final bullet for variability, sensitivity, uncertainty. For completeness, one
(or several) should be provided.
Page 4-1 - should add another bullet stating that the PBPK model should be reflective of the
MO A in terms of simulating key activation/detoxi cation steps, stores of key factors such as GSH
or metallothionen, and estimating concentrations in lexicologically important tissues.
Page 4-3 - the list of bullets should include one evaluating the fit to external datasets to
determine predictive nature of the model. This listing overlaps with the information presented in
Section 3 and may not be needed or just be merged into Section 3.
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Page 4-9 - 1st full para, last sentence - what chemical is referered to in this example where rat
and mouse kidney tumor data could not be reconciled? Is this simply a matter of species
differences in pharmacodynamics? This is what is implied on the basis that PK evaluation using
various dose metrics couldn't resolve the difference.
Page 4-9, last para - the term dose-dependence used in a confusing manner. I think they are
referring to non-linearities - better to use that term if this is what is meant. Dose dependence
often implies something else.
Page 4-10, 1st para under Interspecies Extrap - the protocol described leaves out the iterative
model fitting step that is often needed to "tweak" parameters to obtain optimum fit to the
calibration dataset. That should come before the 4th sentence. Also, in the next step, it would
be good to clarify what is meant by chemical-specific parameters (biochemical parameters such
as metabolic and transport rates, partition coefficients).
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APPENDIX F
OBSERVER COMMENTS
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Comments by Eric Hack
Toxicology Excellence for Risk Assessment (TERA) was sponsored by the Department of
Defense to prepare oral comments for this peer review meeting. The comments provided are
those of TERA.
General comments
This document gives a good description of PBPK modeling, and covers the basics of how it can
be used to reduce uncertainty in risk assessment. The compilations of existing models for
kinetics and dynamics are also useful. The document will have merit in that it will help increase
the understanding and acceptance of the need for quantitative risk analysis methods among
qualitative risk assessors. EPA should be applauded for undertaking this effort.
Addressing the issues highlighted below will add to the value of the document.
Significant Issues:
There were a number of issues (such as the application of PBPK models to use data to replace
uncertainty factors) that were addressed briefly in Chapter 2, but much more thoroughly in
Chapters 3 and 4. It would be useful to note in Chapter 2 that these issues are addressed in more
detail later. It would also be useful to include one integrated discussion of some topics, such as
the use of PBPK models for interspecies and intraspecies extrapolation or variability and
uncertainty analysis, since some people may use the document more as a lookup document.
While the document does mention the term chemical-specific adjustment factors (CSAFs) in
Section 2.6.3, it would be useful to use this term, and to cite the IPCS (2005) guidance when the
specific examples are provided in Chapters 3 and 4. This discussion should note that one may
be able to obtain in vivo data (e.g., blood concentration) to address interspecies differences in
pharmacokinetics, it is very unlikely that one could obtain enough samples to adequately
characterize human variability in kinetics, and so the PBPK model is essentially the only way to
do so (as discussed in IPCS, 2005). The Gentry et al. (2002) article cited in the detailed
comments carried the calculations for dose variability resulting from polymorphisms through to
the calculation of CSAFs. I was glad to see the IPCS 2001 citation, but it would be useful to
include the citation in the text (e.g., rather than only citing the Naumann paper), in the context of
the discussion of uncertainty factors. In addition, the 2001 document has been superseded by the
finalized (2005) document, at
http://www.who.int/ipcs/methods/harmonization/areas/uncertainty/en/index.html
In the model evaluation section, the discussion of assessing model fit is oversimplified. Visual
fitting and a criterion that the predictions should be within 2 standard deviations of the mean is
described, but other goodness of fit metrics also exist. For example, Krishnan et al. (1995)
proposed a PBPK index based on root mean square of the error between predictions and data.
The description of the 2 standard deviations rule should be offered as perhaps one measure of
goodness of fit, but it is too simplistic to be relied upon to evaluate all PBPK models under all
circumstances.
F-l
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The sections on uncertainty and variability analysis needs more detail. More description of the
methods and especially the issues associated with the interpretation of the results of these
analyses and their use in risk assessment would be helpful. For example, the importance of
accounting for parameter correlations in MC or MCMC analyses, issues with the direct use of
MCMC results in estimating dose metric distributions, the impact of model error on the results of
MCMC analyses, or the proper handling of mixed group and individual observations in MCMC
analysis.
More discussion of the use of surrogates for target doses should be included. Using surrogates,
such as blood concentrations rather than target tissue concentrations, the use of parent chemical
concentrations or flux through a metabolic pathway instead of toxic metabolite concentrations, is
sometimes necessary when the knowledge of the mode of action is more detailed than the kinetic
knowledge, or when the toxic moiety cannot be measured directly.
Discussion of fetal or neonatal modeling should be included in the document. This is an
important contribution of PBPK modeling to the assessment of developmental toxicity.
There is a list of possible dose metrics is given in Box 2-1. This choice is critical for dose metric
estimation, and more discussion of when each choice is appropriate would be helpful.
Although it is mentioned later in the document, the description of the uses of PBPK models in
chapter 2 should include mode of action hypothesis formulation and testing.
Editorial and minor comments:
Using PBPK for duration adjustment may also be useful for the oral route, not just inhalation. It
can be used to account for gavage vs. drinking water dosing, or dosing 5 days/week vs. 7
days/week.
P. 2-4, line 5. There are multiple Clewell et al. 2002 papers in the references. Which one is
cited?
P 2-7, line 9. Should mutagenic be replaced with genotoxic?
P 2-8, line 32. Specify excess risk above background in the BMDL definition.
P 2-10, line 29. Define flow-limited perfusion.
Section 2.5.5. Should use standard notation for interspecies UF (i.e. UFA) and the intraspecies
variability factor (i.e. UFH).
P 3-6, Table 3-2. The equations are incorrect. The rate of change in the cellular matrix should
be [PA]*(C1/V1 - C2/PtiSSue:biood), and this term is subtracted in the equation for the change in
blood.
F-2
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Section 3.4.1. Consider adding a citation for compilation of rodent neonatal physiological
parameters:
Gentry, P.R., Haber, L.T., McDonald, T.B., Zhao, Q. Covington, T., Nance, P., Clewell III, H.J.,
Lipscomb, J.C., and Barton, H.A. 2004. Data for physiologically based pharmacokinetic
modeling in neonatal animals: physiological parameters in mice and sprague-dawley rats. Journal
of Children's Health. 2(3-4): 363-412.
ILSI also has a major project underway for compiling human and rodent neonatal physiological
parameters.
Section 3.5. Since this section includes ways to verify that the model is coded properly, this is a
good section for saying that a mass balance calculation should be included and checked.
Table 3-7. I believe there is an X-windows graphical user interface for MCSim. I was surprised
to see no mention of the EPA software (is it under development). Even if under development, it
would be useful to mention it.
P 3-18. There are some inconsistencies with the discussion of model evaluation. On line 12, the
document says that the visual evaluation approach says nothing about the adequacy of the model
structure or parameters. I disagree with this statement. On lines 16-17, it correctly says that
inadequacies in the model structure can be inferred using this approach. Later, on page 3-20,
line 19, the document says that visual inspection is the best approach to model evaluation.
Figure 3-3. It should be emphasized that one should also consider the uncertainty in the different
parameters. For example, even though the model is sensitive to blood:air PC, if this parameter is
very well known, then it will be changed very little in an uncertainty analysis, and will result in
little uncertainty about the dose metric calculations. On the other hand, VMax is often known
with little certainty, and although the model is less sensitive to this parameter, it will be very
much more widely in an uncertainty analysis and contribute much more to the uncertainty in the
dose metrics.
P. 3-23, lines 3 to 27. This is a very good point, and it is nice to see it given some discussion.
P. 3-23, line 12. Should this be "compensate to some degree for the change in breathing rate"?
P. 3-24, line 13. While we appreciate the citation of Haber et al. 2002, this paper only provided
the background information about the polymorphisms. The PBPK/Monte Carlo simulation was
published as
Gentry, P.R., C.E. Hack, L. Haber, A. Maier and H.J. Clewell, 3rd. 2002. An Approach for the
Quantitative Consideration of Genetic Polymorphism Data in Chemical Risk Assessment:
Examples with Warfarin and Parathion. Toxicol Sci. Nov, 70(1): 120-39.
P. 3-24, line 31. Point out that one of the advanatages of the MCMC approach is that the
posterior parameter distribution obtained is a joint, multivariate distributions that accounts for
correlations between the parameters.
F-3
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P. 3-27, line 24. Include physiological parameters in the bulleted list of minimal data required to
build a model.
P. 3-28, line 13. Expand that the critical study is the critical study used for development of the
risk value (RfC, RfD, or cancer unit risk).
P. 4-1, lines 21-23. This is very simplified. I guess the peer review is expected to capture all of
the model evaluation criteria described earlier? Bullet 2 above includes 'and evaluated for its
structure and parameters'.
Box 4-1. There are bullets in this box that were not in the chapter 3 summary (e.g., major sites
of storage).
Figure 4-4. A description of how the 4-hour inhalation exposure duration was selected is needed
in the text describing this figure, or in a footnote.
Section 4.3.4. Add a caveat regarding extrapolation over very long durations and
pharmacodynamic factors that may dominate (see Clewell, Andersen, and Barton, 2002).
Section 4.3.5. Consistent with other language in the document, intraspecies variability sounds
more appropriate as a header than intraspecies extrapolation.
P. 4-15, line 12. Why does the distribution have to be a unimodel normal distribution? I
understand why you would want a unimodel distribution, but it does not have to be normal.
P. 4-18. It would be useful to use vinyl chloride as a case example for the requirements and
capabilities of PBPK for route-to-route extrapolation (mentioned earlier, but not with an
example.) First, VC met the requirement that systemic toxicity was the concern, and that the
same endpoint was applicable for the oral and inhalation routes. Second, use of route-to-route
allowed the use of a better study for the derivation of the RfC. A NOAEL was available for the
oral route, but not for the inhalation route.
F-4
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APPENDIX G
ADDITIONAL REFERENCES AND SUGGESTIONS
-------
PBPK Modeling Process Flowchart:
Mechanisms
of ToxJcity
Refine Model
Problem
Identification
Literature
Evaluation
Physiological
Constants
Model Formulation
Simulation
Compare to
Kinetic Data
Des%n/Conduct
Critical Experiments
Biochemical
Constants
Vatidate Model
I
Extrapolation
to Humans
G-l
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Proposed Figure 4-1 Modification:
Is the PBP
model available
for the test
species and
humans?
Are the
parameters tor
simulating
relevant routes
available?
Experimental
Data
Collection
Does the model
simulate dose
metrics of relevance
o risk assessmen
Has the model
been evaluated and
peer-reviewed?
risk assessment
G-2
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Validation of Dose Metric Prediction:
Ideally, a PBPK model should be compared with data that is informative regarding the parameters
to which the dose metric predictions are sensitive. This pre-supposes the use of sensitivity and
uncertainty analysis to identify the parameters of concern (those that have the most influence on
the dose metric estimate and are the least certain). For example, validation of a human model
based solely on parent chemical data would not necessarily provide confidence that the model
could be used to predict a metabolite dose metric. The use of parent chemical kinetic data to
validate model estimates of metabolism in the human can be highly misleading, because it is
often the case that the metabolism parameters have little impact on parent chemical
concentrations, while other uncertain parameters (e.g., ventilation rate, blood flow, fat content)
can strongly influence model predictions of parent chemical kinetic behavior. Time-dependent
sensitivity analysis could be conducted with the model under the conditions of the "validation"
exposure to determine whether the metabolic parameter values are identifiable from the data.
G-3
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Calculation of Dose Metrics:
An important consideration in risk assessments conducted with a PBPK model is
that the critical study (the study showing effects at the lowest exposure) cannot always be
selected on the basis of administered dose or exposure concentration. This is because the
relationship of the HEC or HED to the administered animal dose depends on the selected
dose metric, which may vary from one endpoint to another, and the nature of the
exposure (species, route of administration, vehicle, duration, etc.). Instead, the
pharmacokinetic model is used to calculate the appropriate dose metrics for each of the
endpoints of concern in each study (Barton and Clewell, 2000). To calculate the dose
metrics, the model parameters are set to those for the species in the toxicity study,
whether an experimental animal study or a human study. In the case of developmental
studies, it will be necessary to estimate parameters for a pregnant female or neonate
rather than an average adult, and physiological and biochemical parameters may have to
be time-dependent. To the extent possible, study-specific data on animal strain, body
weights, age, and activity should be used in selecting parameters for the model. The
experimental parameters in the model are then set to reproduce the exposure scenario
performed in the study, and the model is run for a sufficient period of time to characterize
the animal exposure to the chemical and, if necessary, its metabolites.
There are often a number of options regarding the way in which the model should
be run to characterize the dose metric Clewell et al. 2002a). These will depend upon the
dose metric(s) selected (e.g., peak vs, average), the nature of the chemical (e.g., volatile
vs. persistent), and the nature of the risk assessment (acute vs. chronic, cancer vs.
noncancer). Usually, the desire is to estimate an average daily dose metric, such as the
average daily AUG. (Note that the average daily AUC is the same metric as the time-
weighted average concentration, differing only by a factor of 24.) In general, the
averaging period in the case of cancer is typically taken to be the lifetime, while the
averaging period in the case of noncancer risk assessment is usually considered to be the
duration of the exposure or, perhaps, a critical window of susceptibility.
For short-term exposures, the model must be run for an appropriate period, which
depends on the dose metric being used and the timing of the measurement of toxicity in
relation to the period of exposure. For short exposure, this is easily done by running the
model for the total duration of the exposure (or exposures, for repeated exposure studies)
to obtain dose metrics. If the animals were held for a post-exposure period before
toxicity was evaluated, the model must be run either till the end of the post-exposure
period or for a sufficient duration to ensure that the parent chemical has been completely
cleared from the body or, for a dose metric based on a metabolite, a long enough time to
ensure the complete clearance of the metabolite. On the other hand, if neurological tests
were performed immediately at the end of the exposure period, then the dose metric
should be determined at, or up to, that time. The resulting dose metric obtained for the
total duration of the exposure (including any post-exposure period) can then be divided
by the number of days over which the experiment was conducted in order to derive the
average daily value.
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The same approach (running the model for the total duration of the study) can be
used to calculate dose metrics for longer-term exposures. This approach would typically
be necessary for models that describe changes in physiology or biochemistry during
different lifestages (e.g., children, elderly). However, an alternative approach, which is
often attractive for modeling of chronic exposures with time-invariant model parameters,
is to estimate the steady-state dose metric. There are two principal methods for
calculating a steady-state estimate. In the first, the model is run until steady state is
reached and then the dose metric is calculated by subtraction. For example, in the case of
a chronic oral or inhalation exposure conducted 5 days per week, the model can be run
consecutively for 1 week, 2 weeks, 3 weeks, and so on. To calculate the average daily
AUC for a given week, the value at the end of the previous week is subtracted from the
value at the end of the current week and the result is divided by 7. This process is
repeated until the change in the dose metric from one week to the next is insignificant.
For continuous exposures, the comparison can be made on a daily basis instead of
weekly. The other method for estimating the steady-state dose metric is to estimate it
from a single day exposure. The model is run for a single-day exposure plus an adequate
post-exposure period to capture clearance of the parent compound or relevant metabolite.
This value of the single-day dose metric is then modified by the necessary factors to
obtain an average daily value (e.g., by multiplying by five-sevenths in the case of the 5-
day per week exposure just described). This method is faster, but is only approximate if
the system is not linear. Typically, it is sufficiently accurate for estimating average daily
AUC when exposures are below the onset of any nonlinearities. It can be checked
against the first method described to determine its accuracy in a particular case.
The dose metric calculations needed are determined by the method to be used for
the noncancer or cancer analysis. If the NOAEL/UF method is being used in a noncancer
risk assessment, a dose metric only needs to be calculated for the NOAEL or LOAEL
exposure for a particular study and endpoint. On the other hand, if dose-response
modeling is going to be performed, such as in the Benchmark Dose approach, dose-
metrics must be calculated for all exposure groups. The dose metrics are then used in the
dose-response model in place of the usual exposure concentrations or administered doses.
It is important to remember that when this is done, the result of the dose-response
modeling will also be in terms of a value of the dose metric rather than an exposure
concentration or administered dose. Dose-response modeling is more properly conducted
on the dose metrics, since it is expected that the observed effects of a chemical will be
more simply and directly related to a measure of target tissue exposure than to a measure
of administered dose.
In order to convert an animal dose metric (e.g., at the Benchmark dose) to an
equivalent exposure concentration or administered dose, the pharmacokinetic model must
be either run repeatedly, varying the exposure concentration or administered dose until
the dose metric value is obtained. In the case of calculating the acceptable human
exposure corresponding to a given toxicity study, the physiological, biochemical, and
exposure parameters in the model are set to appropriate human values and the model is
iterated until the dose metric obtained for the human exposure of concern, often
continuous or daily lifetime exposure, is equal to the dose metric obtained for the toxicity
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study, or, once again by dose estimation by a line-search regression. The dose metric
should be calculated in an analogous way to the dose metric for the toxicity study; that is,
if the dose metric in the toxicity study was expressed in terms of an average daily value,
the dose metric used for calculating the associated human exposure should also represent
an average daily value. When a steady-state dose metric is used in both an experimental
animal and the human, it should be noted that the calculation of a steady-state dose metric
in the human generally requires running the model for a much longer period of time than
in the animal. For short-term exposures, where the model has been run for the total
duration of the toxicity study and the average dose metric value has been calculated, the
dose metric used for calculating the associated human exposure should usually be
obtained for an exposure over the same time period. An exception to this rule is the case
where it is anticipated that the short-term exposure of concern for the human may
represent a short-term excursion against a background of chronic exposure. In this case,
a more conservative approach may be preferred, in which a steady-state dose metric
calculation is used for the human.
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Model Evaluation:
Model evaluation should consider the ability of the model to predict the kinetic
behavior of the chemical under conditions which test the principal aspects of the
underlying model structure. While quantitative tests of goodness of fit may often be a
useful aspect of the evaluation process, the more important consideration may be the
ability of the model to provide an accurate prediction of the general behavior of the data
in the intended application.
In models of biological systems, estimates of the values of model parameters will
always have uncertainty, due both to biological variation and experimental error. The
demand that the PBPK fit a variety of data with a consistent set of parameters limits its
ability to provide an optimal fit to a specific set of experimental data. For example, a
PBPK model of a compound with saturable metabolism is required to reproduce both the
high and low concentration behaviors, which appear qualitatively different, using the
same parameter values. If one were independently fitting single curves with a model,
different parameter value might provide better fits at each concentration, but would be
relatively uninformative for extrapolation.
Where only some aspects of the model can be evaluated, it is particularly
important to assess the uncertainty associated with the aspects which are untested. For
example, a model of a chemical and its metabolites which is intended for use in cross-
species extrapolation to humans would preferably be verified using data in different
species, including humans, for both the parent chemical and the metabolites. If only
parent chemical data is available in the human, the correspondence of metabolite
predictions with data in several animal species could be used as a surrogate, but this
deficiency should be carefully considered when applying the model to predict human
metabolism. One of the values of biologically based modeling is the identification of
specific data which would improve the quantitative prediction of toxicity in humans from
animal experiments.
In some cases it may be considered necessary or preferable to use all of the
available data to support model development and parameterization. Unfortunately, this
type of modeling can easily become a form of self-fulfilling prophecy: models are
logically strongest when they fail, but psychologically most appealing when they succeed
(Yates, 1978). Under these conditions, model evaluation can be particularly difficult,
putting an additional burden on the investigators to substantiate the trustworthiness of the
model for its intended purpose. Nevertheless, a combined model development and
verification can often be successfully performed, particularly for models intended for
interpolation, integration, and comparison of data rather than for true extrapolation.
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Parameter "Fitting":
When parameter estimation has been performed by optimizing model output to
experimental data ("fitting"), the investigator must assure that the parameter is adequately
identifiable from the data (Carson et al., 1983). Moreover, the practical reality of
modeling biological systems is that regardless of the complexity of the model there will
always be some level of "model error" (lack of homomorphism with the biological
system) which can result in systematic discrepancies between the model and
experimental data. This model structural deficiency interacts with deficiencies in the
identifiability of the model parameters, potentially leading to mis-identification of the
parameters. Due to the confounding effects of model error and parameter correlation, it
is quite possible for an optimization algorithm to obtain a better fit to a particular data set
by changing parameters to values that no longer correspond to the biological entity the
parameter was intended to represent. It is usually preferable, prior to performing an
optimization, to repeatedly vary the model parameters manually to obtain a sense of their
identifiability and correlation under various experimental conditions, although some
simulation languages include routines for calculating parameter covariance or for plotting
joint confidence region contours. Estimates of parameter uncertainty obtained from
optimization routines should be viewed as lower bound estimates of true parameter
uncertainty since only a local, linearized variance is typically calculated. In
characterizing parameter uncertainty, it is probably more instructive to determine what
ranges of parameter values are clearly inconsistent with the data than to accept a local,
linearized variance estimate provided by the optimization algorithm. Although MCMC
parameter estimation may lead to systematic errors, it might be useful in calculating
parameter uncertainty.
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Model Verification:
It is important to differentiate model verification and model validation. In brief,
model validation deals with building the right model, while model verification deals with
building the model right (Balci 1997). The accuracy of transforming the chemical-
biological system into a model specification (e.g. the model diagram or equations), and
the accuracy of converting the model representation from a diagram or equations into an
executable computer program is evaluated in model verification. Model validation, on
the other hand, is substantiating that the model, within its domain of applicability,
behaves with satisfactory accuracy.
Verification of a PBPK model involves evaluation of the biological plausibility
of the model structure and parameters as described in the documentation, and the
mathematical correctness of the equations. PBPK model verification also involves
examination of the model code to assure that it mathematically implements the model as
described in the documentation. This examination includes checking for correctness of
statements and functions, and correct order of statement execution (for languages that are
not self-sorting). Improper statement order in a numerical integration code can result in a
model that appears to run normally but gives the wrong results. A problem common to
some graphic model representations is the inadvertent mis-specification of a parameter as
local vs. global in one of the compartments, again resulting in a model that appears to run
normally but gives the wrong results.
Whether a code- or graphical-based model is used, it is preferable that the
language produce as one possible output the set of equations that constitute the PBPK
model. Code-based representations ease the task of insuring that the model is actually
constructed as described in the documentation. To facilitate model verification, the
model code should be organized and commented in such a way as to facilitate
understanding by individuals other than the original program developer. In the case of a
model intended for use in a risk assessment application, it is imperative to provide
documentation of the particular parameter values and simulations that are required to
reproduce any validation runs and dose metric calculations. This usually entails the
provision of step-by-step directions, either using the language's scripting capability or in
separate documentation, that allow reproduction of the validation plots and dose-metric
calculations by following specific directions or by invoking specified program blocks.
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Model Documentation:
Adequate documentation is critical for evaluation of a model. The documentation
for a PBPK model should include sufficient information about the model so that an
experienced modeler could accurately reproduce its structure and parameterization.
Usually the suitable documentation of a model will require a combination of one or more
"box and arrow" model diagrams together with any equations which cannot be
unequivocably derived from the diagrams. In fact, for simple models a well-constructed
model diagram, together with a table of the input parameter values and their definitions,
is all that an accomplished modeler should need in order to create the mathematical
equations defining a PBPK model. The model diagram should be labeled with the names
of the key variables associated with the compartment or process represented by each box
and arrow. All tissue compartments, metabolism pathways, routes of exposure, and
routes of elimination should be clearly and accurately presented. The model diagram
should also clearly differentiate blood flow from other transport (e.g., biliary excretion)
or metabolism, and arrows should be used where the direction of transport could be
ambiguous.
In general, there should be a one-to-one correspondence of the boxes in the
diagram to the mass balance equations (or steady-state approximations) in the model.
Similarly, the arrows in the diagram correspond to the clearance (transport or metabolism
processes) in the model. Each of the arrows connecting the boxes in the diagram should
correspond to one of the terms in the mass balance equations for both of the
compartments it connects, with the direction of the arrow pointing from the compartment
in which the term is negative to the compartment in which it is positive. Arrows only
connected to a single compartment, which represent uptake and excretion processes, are
interpreted similarly.
Interpretation of the model diagram is supplemented by the definition of the
model input parameters in the corresponding table. The definition and units of the
parameters can indicate the nature of the process being modeled (e.g., diffusion-limited
vs. flow-limited transport, binding vs. partitioning, saturable vs. first-order metabolism,
etc.). The values used for all model parameters should be provided, with units. If any of
the listed parameter values are based on allometric scaling, a footnote should provide the
body weight used to obtain the allometric constant as well as the power of body weight
used in the scaling. Any equations included to supplement the diagram should be
dimensionally consistent and in a standard mathematical notation. Generic equations
(e.g., for tissue "i") can help to keep the description brief but complete.
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Model Calibration:
A critical issue in the use of Bayesian approaches, such as Markov Chain Monte
Carlo (MCMC) analysis, for the "calibration" of PBPK models in risk assessment
applications is whether the posterior distributions for the PBPK parameters should be
used to estimate dose metrics in place of the prior estimates based on the scientific
judgment of the model developers. One concern is that the Bayesian approach may give
undue priority to the particular studies included in the Bayesian analysis (as compared to
potentially more appropriate studies that may have been used to inform the various
parameters in the model, but which were not amenable to incorporation in the MCMC
analysis.
Another consideration is whether the subject population in the data sets included
in the MCMC analysis is representative of the population for which the risk assessment is
being performed. For example, the subjects in controlled human exposures may be at
rest, and the MCMC my correctly estimate a relatively low ventilation rate; however, this
ventilation rate may not not be appropriate for the activity level in the general population.
Therefore, calculations of dose metrics and uncertainty/variability analyses performed in
support of an environmental risk assessment would more properly use a ventilation rate
suitable for the general population, not the posterior obtained from the experimental
subjects.
The greatest value of MCMC analysis is its ability to characterize the variability
and uncertainty in the model predictions, and as such is most appropriately used in the
risk characterization segment of a risk assessment. Its use in the dose-response
assessment is more problematic.
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