EPA/600/R-07/064 

September 2007 

Considerations for Developing a 

Dosimetry-Based Cumulative Risk 

Assessment Approach for Mixtures of 

Environmental Contaminants 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Cincinnati, OH 45268 
NOTICE 

 The U.S. Environmental Protection Agency through its Office of Research and 

Development funded and managed the research described here under contract no. EPA 

3C-R102-NTEX to Colorado State University. It has been subjected to the Agency’s 

peer and administrative review and has been approved for publication as an EPA 

document. Mention of trade names or commercial products does not constitute 

endorsement or recommendation for use. 

ABSTRACT 

For most of its history, the U.S. Environmental Protection Agency (U.S. EPA) has 

assessed risks based on individual contaminants and has often focused on one source, 

pathway or adverse effect. But in reality, the public is exposed to multiple contaminants 

from a variety of sources, and tools are needed to understand the resulting combined 

risks. In keeping with its continuing effort of developing science-based risk assessment, 

a major mission goal and challenge for the U.S. EPA has been underway for the 

development of cumulative risk assessment. The Office of Pesticide Programs (OPP), 

U.S. EPA, took the lead and conducted cumulative risk assessment on 

organophosphorus (OP) pesticides under the Congressional mandate of the Food 

Quality Protection Act (FQPA), addressing only chemicals with the same 

mode/mechanism of action. The present work is complementary to that of OPP. This 

report develops a framework to guide decisions whether to incorporate physiologicallybased pharmacokinetic (PBPK) modeling into the cumulative risk assessment process. 

It is not restricted to FQPA applications and intends to guide the risk assessor through 

choosing to conduct and conducting a dosimetry based cumulative risk assessment, 

addressing the level of toxicant in the target tissue, rather than relying on exposure 

concentrations and the hazard index approach. This report includes data and values for 

several compounds to demonstrate the proposed approach by means of example. It is 

not intended to present an analysis of the internal exposures or risks from these 

mixtures. Some of the example chemicals (trichloroethylene, tetrachloroethylene) are 

the subject of ongoing U.S. EPA risk assessments, and risk values for other chemicals 

are those developed by ATSDR, rather than by the U.S. EPA. The main objectives are 

(1) to improve the science and risk assessment by moving the measure of dose from 

concentration in the environmental contact medium to inside the body (i.e., tissue dose 

rather than exposure dose); (2) to achieve this objective is via PBPK modeling; (3) to 

incorporate PBPK modeling into the cumulative risk assessment process and (4) to 

make the optimal use of in vitro techniques, including the use and application of human 

tissues. The proposed approach retains the 10-step process developed by the OPP; 

these are: (1) Identify Common Mechanism Group (CMG); (2) Identify Potential 

Exposures; (3) Characterize and Select Common Mechanism Endpoint(s); (4) 

Determine The Need For a Dosimetry-Based Cumulative Risk Assessment; (5) 

Determine Candidate Cumulative Assessment Group (CAG); (6) Conduct Doseii
Response Analyses and Determine Relative Potency and Points of Departure; (7) 

Develop Detailed Exposure Scenarios for All Routes and Durations; (8) Establish 

Exposure Input Parameters; (9) Conduct Final Cumulative Risk Assessment; and (10) 

Conduct Characterization of Cumulative Risk. In the present effort, however, the first 

five steps were grouped under Phase I (Initial Analysis) and the next five steps were 

grouped under Phase II (Dosimetry-Based Cumulative Risk Assessment). PBPK 

models are proposed to be developed for the components that are considered in 

Phase II, i.e., those that are in the CAG. This approach stresses the importance of 

initial analysis to eliminate those situations that do not warrant a PBPK-based approach 

to cumulative risk assessment thereby reducing the unnecessary expenditure of 

resources. The development of this framework/approach utilizes two model sets of 

chemical mixtures: Mixture 1 consists of 6 OP pesticides (methyl-parathion, parathion, 

chlorpyrifos, fenthion, diazinon, and fenitrothion) which share the same mode of action 

of inhibiting acetylcholinesterase (AChE); Mixture 2 consists of four chlorinated 

hydrocarbon solvents or volatile organic chemicals (trichloroethylene, 

tetrachloroethylene, 1,1,1-trichloroethane, and chloroform) which have different modes 

of action on a variety of toxic endpoints. The wealth of information available for these 

chemicals was a prominent factor in their choice as two model chemical mixtures for 

this effort. The advantages of utilizing PBPK modeling in cumulative risk assessment 

and the incorporation of credible human tissue studies in PBPK modeling, as well as the 

methodologies involving the incorporation of interactive PBPK models are discussed. 

The intent of this document is to serve as a logical framework upon which to integrate 

information to decide whether to embark on technical and resource-intensive PBPK 

approaches to cumulative risk assessment. 

Preferred citation: 

U.S. EPA. 2007. Considerations for Developing a Dosimetry-Based Cumulative Risk Assessment 

Approach for Mixtures of Environmental Contaminants. U.S. Environmental Protection Agency, National 

Center for Environmental Assessment, Cincinnati, OH. EPA/600/R-07/064. 

iii
TABLE OF CONTENTS 

Page

TABLE OF CONTENTS ..................................................................................................iv 

LIST OF TABLES............................................................................................................vi 

LIST OF FIGURES..........................................................................................................vi 

LIST OF ABBREVIATIONS............................................................................................vii 

GLOSSARY ....................................................................................................................ix 

LIST OF AUTHORS, CONTRIBUTORS AND REVIEWERS...........................................xi 

ACKNOWLEDGMENTS.................................................................................................xii 

1. INTRODUCTION.................................................................................................. 1 

 1.1. BACKGROUND ......................................................................................... 1 

 1.2. STRATEGY: INCORPORTAION OF PBPK MODELING AND 

 HUMAN TISSUE STUDIES IN CUMULATIVE RISK ASSESSMENT........ 3 

 1.3. OBJECTIVES ............................................................................................ 4 

 1.4. TWO MODEL MIXTURES OF DRINKING WATER CONTAMINANTS ..... 5 

 1.5. PHARMACOKINETICS vs. PHARMACODYNAMICS................................ 5 

 1.6. INTERACTIONS: CUMULATIVE RISK AND PHARMACOKINETICS ...... 6 

2. THE PROPOSED APPROACH.......................................................................... 13 

 2.1. OVERVIEW ............................................................................................. 13 

 2.2. PHASE I: INITIAL ANALYSIS .................................................................. 16 

 2.2.1. Step 1. Identify Common Mechanism Group (CMG).................... 16 

 2.2.2. Step 2. Identify Potential Exposures ............................................ 18 

 2.2.3. Step 3. Characterize and Select Health Endpoint(s) for 

Evaluation ..................................................................................... 20 

 2.2.4. Step 4. Determine the Need for a Dosimetry-Based 

 Cumulative Risk Assessment........................................................ 22 

 2.2.5. Step 5. Identify Chemicals in the Candidate Cumulative 

Assessment Group (CAG) for Further Evaluation ......................... 36 

 2.3. PHASE II: DOSIMETRY-BASED CUMULATIVE RISK 

ASSESSMENT ........................................................................................ 37 

 2.3.1. Step 6. Characterize Dose-Response, Point of Departure and 

Determine Relative Potency from Tissue Doses........................... 38 

 2.3.2. Step 7. Develop Detailed Exposure Scenarios for All 

 Routes and Durations ................................................................... 49 

 2.3.3. Step 8. Quantify Parameters for Exposure .................................. 49 

iv
TABLE OF CONTENTS cont. 

Page

 2.3.4. Step 9. Conduct Dosimetry-Based Cumulative Risk 

Assessment .................................................................................. 50 

 2.3.5. Step 10. Characterize Cumulative Risk via Pharmacokinetic 

Analysis......................................................................................... 52 

3. CONCLUSIONS ................................................................................................. 54 

4. REFERENCES................................................................................................... 57 

APPENDIX A: A GENERAL REFERENCE ON PHYSIOLOGICALLY-BASED 

PHARMACOKINETIC/PHARMACODYNAMIC (PBPK/PD) MODELING .....................A-1 

APPENDIX B: A REFERENCE ON HOW TO INCORPORATE CREDIBLE 

HUMAN ENZYME STUDIES INTO THE PHYSIOLOGICALLY-BASED 

PHARMACOKINETIC (PBPK) MODELING AND RISK ASSESSMENT 

PROCESS....................................................................................................................B-1 

APPENDIX C: INFORMATION ON CHEMICAL MIXTURES AND THEIR 

COMPONENT CHEMICALS ....................................................................................... C-1 

APPENDIX D: EXTERNAL REVIEW COMMENTS..................................................... D-1 

v
LIST OF TABLES 

1 Screening Hazard Index Approach..................................................................... 23 

2 Derivation of TTD Values for Chloroform............................................................ 25 

3 Derivation of TTD Values for Tetrachloroethylene.............................................. 26 

4 Derivation of TTD Values for Trichloroethylene.................................................. 27 

5 Derivation of TTD Values for 1,1,1-Trichloroethane ........................................... 28 

6 Target Organ Toxicity Doses, Hazard Quotients and Hazard Indices ................ 30

LIST OF FIGURES 

1 Data Evaluation for Dosimetry-Based Cumulative Risk Assessment ................. 15 

2. A Generalized Metabolic Scheme for OP Pesticides.......................................... 32 

3 Metabolic Pathway for Solvents Indicating Potential Sites for Interactions......... 33 

4 A Preliminary PBPK Model Structure for OPs .................................................... 44 

5 A Pharmacodynamic Submodel for CNS/PNS Compartment............................. 46 

6 A Schematic for Integrating Data for Cumulative Risk Assessment ................... 51 

vi
LIST OF ABBREVIATIONS 

AChE Acetylcholinesterase 

AL Acceptable level 

AUC Area-under the concentration vs. time curve 

BMD Benchmark dose 

BMDL Lower bound benchmark dose 

BMDS Benchmark dose software 

CAG Cumulative Assessment Group 

CE Carboxylesterase 

CHF Chloroform 

CMG Common Mechanism Group 

CNS Central nervous system 

CRA Cumulative Risk Assessment 

DBCRA Dosimetry-Based Cumulative Risk Assessment 

CYP Cytochrome P450 

E Exposure 

FQPA Food Quality Protection Act 

GST Glutathione S-transferase 

HI Hazard Index 

HQ Hazard Quotient 

IRIS Integrated Risk Information System 

LOAEL Lowest-observed-adverse-effect level 

MC 1,1,1-Trichloroethane 

NCEA National Center for Environmental Assessment 

NOAEL No-observed-adverse-effect level 

OP Organophosphorus 

OPP Office of Pesticide Programs 

PBO Piperonyl butoxide 

PBPK Physiologically-based pharmacokinetic 

PD Pharmacodynamic 

PERC Tetrachloroethylene 

PK Pharmacokinetic 

POD Point of departure 

PNS Peripheral nervous system 

RfC Reference concentration 

vii
LIST OF ABBREVIATIONS cont. 

RfD Reference dose 

TCE Trichloroethylene 

TTD Target Organ Toxicity Dose 

U.S. EPA U.S. Environmental Protection Agency 

VOCs Volatile organic chemicals 

WOE Weight of evidence 

viii
GLOSSARY 

Acceptable Level: A measure of the acceptable level of human exposures. For 

cumulative risk assessment, reference values are typically used. 

Aggregate Exposure: The combined exposure of an individual (or defined population) 

to a specific agent or stressor via relevant routes, pathways, and sources. 

Aggregate Risk: The risk resulting from aggregate exposure to a single agent or 

stressor. 

Chemical Mixture: Any set of multiple chemical substances that may or may not be 

identifiable, regardless of their sources, that may jointly contribute to toxicity in the 

target population. May also be referred to as a whole mixture” or as the “mixture of 

concern.” 

Components: Single chemicals that make up a chemical mixture that may be further 

classified as systemic toxicants, carcinogens, or both.

Common Mechanisms Group: The group of chemicals under evaluation that induce a 

common effect via the same mechanism of toxicity. The Initial Analysis begins with the 

identification of this group of chemicals. 

Critical Organ/Effect: The effect or the organ that responds first as dose increases. 

The dose-response for the critical organ serves as the basis for establishing reference 

values (e.g., RfD Values). 

Cumulative Assessment Group: The group of chemicals surviving the Initial Analysis, 

for which a dosimetry-based cumulative Risk Assessment will be conducted. 

Cumulative Risk: The combined risks from aggregate exposures to multiple agents or 

stressors. 

Cumulative Risk Assessment: An analysis, characterization and possible 

quantification of the combined risks to health or the environment from multiple agents or 

stressors. 

Dosimetry-Based Cumulative Risk Assessment: Cumulative risk assessment 

undertaken using tissue concentrations, or internal measures of exposure, rather than 

external measures of exposure. 

Hazard Quotient: The ratio of human exposure to reference values used to estimate 

the potential for non-cancer health effects. HQ = E/AL, where E is exposure and AL is 

an acceptable level of exposure (e.g., RfD value). 

Hazard Index: The summed Hazard Quotient values for a given set of chemicals or 

responding tissues, depending on the analysis. 

ix
GLOSSARY cont. 

Index Chemical: The chemical selected as the basis for standardization of toxicity of 

components in a mixture. The index chemical must have a clearly defined doseresponse relationship. 

Initial Analysis: The first 5 steps of the process of developing a dosimetry-based 

cumulative risk assessment. This initiates with the identification of a common 

mechanism group and culminates with the analysis of pharmacokinetic data and a 

Hazard index analysis. 

Interactions: Interactions among mixture components are demonstrated by responses 

that, when measured, differ from those predicted by additivity. Chemical interactions 

may occur due to commonalities in pharmacokinetic or pharmacodynamic processes. 

Pharmacokinetics: The (study of the) distribution of chemical dosimetry in the body. 

Pharmacodynamics: The process of developing a biological response to a chemical. 

Point of Departure: 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), or a NOAEL or LOAEL 

for an observed incidence, or change in level of response. 

Target Organ: Any organ adversely affected by chemical exposures. 

Target Organ Toxicity Dose Approach: A cumulative risk approach quite similar to 

the Hazard Index Approach. The TTD approach differs in that includes dose-response 

data to estimate AL in the E/AL format for all responding organs, not just the critical 

organ. 

Target Organ Toxicity Dose Values: Hazard quotient values developed for organs 

that are not the critical organ in the IRIS Assessment, or for which a reference value has 

not been formally established. 

Weight of Evidence: A qualitatively useful adjunct to the Hazard Index approach that 

incorporates information available in binary combinations of chemicals. This approach 

is used to account for interactions among mixture components. 

Definitions obtained from U.S. EPA (2000a, 2003c) and Mumtaz and Durkin (1992). 

x
LIST OF AUTHORS, CONTRIBUTORS AND REVIEWERS 

AUTHORS 

John C. Lipscomb 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Cincinnati, OH 

Raymond S.H. Yang 

Colorado State University 

Center for Environmental Toxicology and Technology 

Fort Collins, CO 

James Dennison 

Colorado State University 

Center for Environmental Toxicology and Technology 

Fort Collins, CO 

INTERNAL REVIEWERS 

Rob DeWoskin 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 

Rick Hertzberg 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Cincinnati, OH 

EXTERNAL REVIEWERS 

Richard J. Bull, Ph.D. 

MoBull Consulting 

Richland, WA 

Harvey Clewell (Chair) 

CIIT Centers for Health Research 

Research Triangle Park, NC 

xi
xii

LIST OF AUTHORS, CONTRIBUTORS AND REVIEWERS cont. 

Gary L. Ginsberg, Ph.D. 

Connecticut Department of Public Health 

Hartford, CT 

Margaret MacDonell, Ph.D. 

Argonne National Laboratory 

Argonne, IL 

Moiz M. Mumtaz, Ph.D. 

Agency for Toxic Substances and Disease Registry (ATSDR) 

Chamblee, GA 

Clifford P. Weisel, Ph.D. 

Environmental & Occupational Health Sciences Institute (EOHSI)/UMDNJ 

Piscataway, NJ

ACKNOWLEDGMENTS 

This project was a team effort by members of the Quantitative and Computational 

Toxicology Group at the Center for Environmental Toxicology & Technology (CETT), 

Colorado State University. The literature reviews of component chemicals of the two 

chemical mixtures were carried out by many of the graduate students working toward 

their Ph.D.s in Toxicology and Chemical Engineering at CETT; they are: Ms. Amanda 

Ashley, Mr. Sun Ku Lee, Mr. Ken Liao, Mr. Manupat Lohitnavy, Ms. Ornrat Lohitnavy, 

Mr. Yasong Lu, and Mr. Damon Perez. Their collective effort is gratefully 

acknowledged. Dr. Anna Lowit of OPP, U.S. EPA read the Draft Approach and kindly 

provided valuable comments and suggestions. 
1. INTRODUCTION 

1.1. BACKGROUND 

 On July 3, 1997, the U.S. Environmental Protection Agency (U.S. EPA) 

Administrator, Carol M. Browner, and Deputy Administrator, Fred Hansen, jointly issued 

a memorandum entitled “Cumulative Risk Assessment Guidance - Phase I Planning

and Scoping” to top U.S. EPA officials. The content of this memo, quoted below, 

provided the essence of the reasoning for cumulative risk assessment. 

As you are aware, the processes that EPA and others follow to 

assess environmental risk are of great interest to environmental 

professionals and to the public, and growing attention is being given to the 

combined effects of multiple environmental stressors. Consistent with 

this, EPA and others are asking more questions about the wider and more 

complex issues that define a cumulative approach to risk assessment. 

Today, we are providing guidance for all EPA offices on cumulative risk 

assessment. This guidance directs each office to take into account 

cumulative risk issues in scoping and planning major risk assessments 

and to consider a broader scope that integrates multiple sources, effects, 

pathways, stressors and populations for cumulative risk analyses in all 

cases for which relevant data are available. This assures a more 

consistent and scientifically complete Agency-wide approach to 

cumulative risk assessments in order to better protect public health and 

the environment. 

This approach provides a platform for significant advances in our 

scientific approach to assessing environmental risks. For most of our 

history, EPA has assessed risks and made environmental protection 

decisions based on individual contaminants—such as lead, chlordane, 

and DDT—with risk assessments for these chemicals often focused on 

one source, pathway or adverse effect. Today, better methods and data 

often allow us to describe and quantify the risks that Americans face from 

many sources of pollution, rather than by one pollutant at a time. We are 

increasingly able to assess not simply whether a population is at risk, but 

how that risk presents itself. In addition, we are better able in many cases 

to analyze risks by considering any unique impacts the risks may elicit due 

to the gender, ethnicity, geographic origin, or age of the affected 

populations. Where data are available, therefore, we may be able to 

determine more precisely whether environmental threats pose a greater 

risk to women, children, the elderly, and other specific populations, and 

whether a cumulative exposure to many contaminants, in combination, 

poses a greater risk to the public. 

Of particular importance are the right-to-know implications of this 

guidance, which requires that we build opportunities for citizens and other 

1
stakeholders to understand our ongoing risk assessments, and to provide 

us with their comments. Our goal is to ensure that citizens and other 

stakeholders have an opportunity to help define the way in which an 

environmental or public health problem is assessed, to understand how 

the available data are used in the risk assessment, and to see how the 

data affect decisions about risk management. 

Some Regions and Programs within the Agency are already 

making significant efforts to use integrated or cumulative risk assessment 

techniques, and this guidance both reflects those practices and makes 

them consistent across the Agency. The scope of integrated risk 

assessments often involves coordination across many program offices 

and statutory mandates for risk analysis; for example, those called for 

under the new safe drinking water and food safety laws. Therefore, this 

guidance calls for ongoing communication among risk assessors, risk 

managers, economists, engineers, and other technical experts within the 

Agency. 

While we can more consistently take into account many new factors 

in this approach to risk assessment, many other potentially important 

factors are more difficult to include in our analyses, particularly the social, 

economic, behavioral or psychological factors that also may contribute to 

adverse health effects. These include, among others, such factors as 

existing health conditions, anxiety, nutritional status, crime and 

congestion. Assessment of these factors is often hampered by a lack of 

data to establish plausible cause-and-effect relationships; difficulties in 

measuring exposure, incidence and susceptibilities related to these risks; 

and few methods for assessing or managing these risks. This guidance 

does not address these factors. We expect, nonetheless, that this 

guidance will be updated as our understanding and experience develop; 

and, the Agency is focusing its research to improve our ability to 

incorporate these broader concerns into our cumulative risk assessments 

as new data and methods are brought forward. 

Please take the steps needed to ensure that all major risks 

assessments undertaken in your area embrace this cumulative approach, 

so that we can better advise all citizens about the environmental and 

public health risks they face, and improve our ability to protect the 

environment and public health for the nation. 

 During his tenure as U.S. EPA Science Advisor and Chair, Science Policy 

Council, Dr Paul Gilman underscored the importance of cumulative risk assessment to 

the U.S. EPA in the following memo entitled “Framework for Cumulative Risk 

Assessment” to senior managers at U.S. EPA. 

2
I am very pleased to present EPA’s Framework for Cumulative Risk 

Assessment. For most of our history, EPA assessed risks based on 

individual contaminants and often focused on one source, pathway or 

adverse effect. But in reality, the public is exposed to multiple 

contaminants from a variety of sources, and tools are needed to 

understand the resulting combined risks. The Framework represents an 

important milestone for EPA in expanding our focus from an individual 

chemical-based approach to a community or population-based approach 

for multiple stressors. 

 Development of the Framework is one of a series of Agency 

activities to better address combined risks from multiple stressors. 

Several EPA Programs and Regions have growing experience in 

conducting cumulative risk assessments, such as in evaluating cumulative 

risk for pesticides with a similar mode of action. In addition, the EPA 

Science Policy Council (SPC) has sponsored several efforts designed to 

lead to Agency-wide guidance on planning and scoping for cumulative risk 

assessments. As a next step, a technical panel of the Risk Assessment 

Forum has now completed the Framework, which builds on prior efforts by 

identifying the basic elements and definitions for cumulative risk 

assessment. It also serves as the foundation for future efforts, such as 

evaluating past and emerging case studies in relation to the approach 

outlined in the document. I offer my thanks to this cross Agency panel of 

experts for their efforts to further advance the thinking in this area. 

 Cumulative risk assessment is a major challenge for the Agency. 

This Framework moves us closer to achieving our goal of producing the 

most scientifically rigorous and realistic evaluation of cumulative risk that 

the state-of-the-science can accommodate. The Administrator and I 

encourage Agency personnel to incorporate the thinking embodied in this 

document in the development of cumulative risk assessments that 

address risk management needs. 

1.2. STRATEGY: INCORPORATION OF PBPK MODELING AND HUMAN TISSUE 

STUDIES IN CUMULATIVE RISK ASSESSMENT 

 The present report follows the vision outlined in the above memos by proposing 

an approach which integrates Physiologically-Based Pharmacokinetic (PBPK) modeling, 

particularly the inclusion of credible human tissue studies, into the cumulative risk 

assessment of chemical mixtures. There are several reasons for utilizing the PBPK 

modeling approach: 

1. As indicated in the above memos, U.S. EPA continues to improve its risk 

assessment process by incorporating the state-of-the-art science and 

technology. PBPK modeling, though advancing continuously in the last 30 years 

3
http://www.epa.gov/oscpmont/sap/2003/index.htm). However, due to the lack of 

availability of a PBPK model for all but one chemical, a PBPK modeling approach 

was not taken in the final assessment (U.S. EPA, 2007a). Many advantages, 

which coincide with what’s presented here, were noted in the “Consultation” 

above document (U.S. EPA, 2003a) and the subsequent FIFRA SAP meeting 

minutes (U.S. EPA, 2003b) for utilizing PBPK modeling in cumulative risk 

assessment. 

2. Multiple chemical exposure and interaction in our body is an extremely complex 

phenomenon. PBPK modeling is a powerful tool to integrate the various routes 

and modes of exposure and potential biological interactions with multiple 

chemicals to help resolve the best target tissue dose metrics for risk assessment. 

3. PBPK models support hypothesis testing through experimentation of evaluation 

using in silico methods and serves as a platform upon which to integrate existing 

data, and extend the efficient use of the available data. A well developed PBPK 

model is tested for accuracy in simulating existing study results, and if sufficiently 

able to do so, can be used to simulate other needed data to minimize animal 

usage that would otherwise be required for “unnecessary experiments”. This is 

particularly relevant for the many animal toxicology experiments that would be 

required to evaluate multiple chemical interactions in a cumulative risk 

assessment. 

4. PBPK models can extrapolate dosimetry across dose level, route, species, age, 

or gender, and can characterize the uncertainty in simulation results (which are 

derived from the accuracy of the model in simulating existing data). A PBPK 

model may even provide useful information for the dose-response relationship at 

low doses where experimental studies are impossible to conduct. 

5. PBPK modeling offers a reliable tool for use in exploring the relationship between 

external dose and tissue response in the growing area of systems biology. 

1.3. OBJECTIVES 

The main objectives/emphases are: (1) to improve the science and risk 

assessment by moving the measure of dose from concentration in the environmental 

contact medium to inside the body (i.e., tissue dosimetry rather than exposure dose); (2) 

to achieve the objective of tissue dosimetry determination via PBPK modeling; (3) to 

incorporate the best and most efficient scientific approach, PBPK modeling, into 

cumulative risk assessment; and (4) to make the best use of in vitro techniques, 

4
including the use and application of human tissues. The intent of this document is to 

serve as a logical framework upon which to integrate information to decide whether to 

embark on technical and resource-intensive PBPK approaches to cumulative risk 

assessment. 

1.4. TWO MODEL MIXTURES OF DRINKING WATER CONTAMINANTS 

It should be emphasized that this effort is for establishing a framework/approach 

for integrating PBPK modeling into the cumulative risk assessment process, not the 

actual conduct of cumulative risk assessment. Indeed, two example component 

chemicals, trichloroethylene and tetrachloroethylene, are the subject of ongoing U.S. 

EPA risk assessments. The actual conduct of a cumulative risk assessment is 

understandably much more resource-intensive. As shown below, this report provides a 

description of a recommended approach for integrating PBPK modeling into cumulative 

risk assessment for two groups of drinking water contaminants. The first group is a 

mixture of six organophosphorous (OP) insecticides (methyl-parathion, parathion, 

chlorpyrifos, fenthion, diazinon, and fenitrothion) which share the same mode of action 

of inhibiting acetylcholinesterase (AChE); the second group is a mixture of four 

chlorinated hydrocarbon solvents or volatile organic chemicals (trichloroethylene, 

tetrachloroethylene, 1,1,1-trichloroethane, and chloroform) which have different modes 

of action for a variety of toxic endpoints. We understand that chlorpyrifos and diazinon 

household usage have been cancelled; however, all these OP pesticides can still be in 

drinking water and/or as food contaminants because of residues from earlier 

applications. Furthermore, we use these two model mixtures as a test set for 

demonstrating the establishment of a framework/approach. The risk assessment 

approach is basically similar to the 10-step approach recommended by the U.S. EPA for 

cumulative risks (U.S. EPA, 2002a,b). 

1.5. PHARMACOKINETICS vs. PHARMACODYNAMICS 

 Pharmacokinetics (PK) and pharmacodynamics (PD) are inseparable in the 

sense that they formulate a continuum of a toxicological process from exposure, to 

tissue dose metrics, to molecular interaction(s) and finally to toxic effect(s). This 

document will address the grouping of chemicals according to similarities in 

pharmacokinetics, pharmacodynamics (mode/mechanism of action) and target organ. 

These are mutually separable groupings: chemicals may attack the same target organ 

through different modes of action and have markedly different pharmacokinetic profiles 

with different physiological and or biochemical functions determining the 

5
pharmacokinetic profile. Alternately, chemicals may share the same target organ, and 

may have similar pharmacokinetic profiles, but act on different cellular or organ function 

via different modes or mechanisms of action. Presently, efforts are advancing to guide 

the assessment of chemicals grouped by mode or mechanism of action, and mature 

guidance exists for grouping chemicals according to common target organ (ATSDR, 

2004; U.S. EPA, 1989, 2000a). Because of the rate at which pharmacokinetic analyses 

are becoming directly and quantitatively incorporated into risk assessments, it seemed 

valuable to develop a discourse on some considerations that should be undertaken prior 

to embarking on a complex and resource-intensive pharmacokinetic analysis to support 

a cumulative risk assessment. For those reasons and because of the state of 

development of the area of PBPK modeling, we place more emphasis on PBPK 

modeling in this effort. Thus, at the present time, we would like to focus on 

incorporating PBPK modeling into the cumulative risk assessment process first. As the 

science advances and more relevant information becomes available, we will consider 

PBPD modeling in cumulative risk assessment as well. 

 Because cumulative risk assessment will impact upon our society at large, when 

this process is carried out, it should be transparent and inclusive of all stakeholders 

much the same way as Office of Pesticide Programs (OPP) did it for OP pesticides 

(U.S. EPA, 2002b, 2003c). 

1.6. INTERACTIONS: CUMULATIVE RISK AND PHARMACOKINETICS 

 The term “interaction” has different meanings regarding cumulative risk and 

pharmacokinetics. In cumulative risk parlance, interaction describes the risk outcome 

(toxicity, organism response) when exposure to a chemical mixture results in a degree 

of response not predicted by additivity (e.g., antagonism, synergism, potentiation). If 

the outcome is over or under-predicted by additivity, then the chemicals are said to have 

an “interaction.” With respect to applying an increased understanding of tissue 

dosimetry in risk assessment, assessing interactions becomes problematic when the 

observed response is related to the external dose (mg/kg) and the observation (i.e., 

enzyme levels in blood indicative of heart damage) is made at the level of the intact 

organism. The outcomes are influenced by absorption, distribution, metabolism and 

elimination processes (pharmacokinetics) on production/reduction of the toxic chemical 

species and its delivery to the target organ/tissue, as well as the inherent 

responsiveness of the tissue to the insult (toxicodynamics). 

 Later, this document will present a grouping of chemicals into a Common 

Mechanism Group, which is done so that a cumulative risk assessment can be 

6
performed on that group. Recently, the organophosphate insecticides have been 

grouped according to their common inhibition of acetylcholinesterase (AChE). This is 

the key step in their toxic effect—for which the mode of action might be an accumulation 

of acetylcholine. The distinction between mechanism of action and mode of action can 

be important and has been treated elsewhere: 

The U.S. EPA Guidance for Conducting Health Risk Assessment of 

Chemical Mixtures (U.S. EPA, 2000) defines mode of action (MoA) as a 

series of ‘key events’ and processes starting with interaction of an agent 

with a cell, and proceeding through operational and anatomical changes 

causing disease formation. A ‘key event’, as defined in the 2005 U.S. 

EPA Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005), is an 

empirically observable precursor step that is itself a necessary element of 

the mode of action or is a biologically based marker for such an element. 

In contrast, mechanism of toxic action implies a more detailed 

understanding and description of events, often at the molecular and 

cellular level (U.S. EPA, 1999, 2003a). The U.S. EPA 2005 and U.S. EPA 

2000 Guidelines both emphasize MoA over mechanism, indicating that 

MoA is a critical determinant for evaluation of risk posed by environmental 

hazards. For well defined modes of action, mechanistic data may merely 

provide more detail in support of identified key events. However, for less 

well defined toxic modes of action, mechanistic data may lead to 

identification of previously unknown obligatory steps in the causal pathway 

leading to toxicity (Lambert and Lipscomb, 2007). 

 What is most important is whether chemicals in a mixture can interact, altering 

the toxicity of the mixture. Later, this section presents several examples of chemical 

interactions that would not have been anticipated based only on data describing either 

the mode of action or the mechanism of action. 

 From a pharmacokinetic standpoint, chemicals have the potential to interact in 

either passive (i.e., distribution phenomena; tissue partitioning) or active (i.e., energyrequiring processes; active transport, metabolism) processes. Available evidence 

indicates that chemicals partition into tissues independent of one another, when 

encountered at concentrations that do not disrupt tissue integrity. However, it is widely 

accepted that chemicals (e.g., metabolic substrates) can, and do compete against one 

another for metabolism, and that the likelihood for this competition increases with 

increasing levels of exposure (dose). When substrates are metabolized by the same 

enzyme, or require the same cofactor, interaction is possible. This interaction may be 

based on a suicide inhibition of an enzyme (mechanism based inhibitors) or a simple 

competition for an enzyme (competitive inhibition). Mechanism based inhibitors (suicide 

7
substrates; this type inhibition is demonstrated as non-competitive) have effects that 

cannot be overcome by changing the ratio of substrates. When these substrates are 

metabolized, an irreversible complex is formed between product (or intermediate) and 

enzyme, irreversibly binding the active site and inhibiting catalysis. However, 

competitive inhibition is more common. Even chemicals (competing substrates) that 

share the same enzyme may not demonstrate inhibition under some conditions (levels) 

of exposure. Studies have demonstrated that there are thresholds of exposure, below 

which chemicals that do share a common enzyme do not interfere with the metabolism 

of one another. In the case of chemicals thought to have a metabolic (competitive) 

interaction, the total exposure to the mixture, rather than the exposure to single 

components should be evaluated. Dobrev et al. (2001) constructed an analysis to 

estimate interaction thresholds in the rat for three commonly found environmental 

contaminants, trichloroethylene (TCE), tetrachloroethylene (PERC) and 

1,1,1-trichloroethane (methyl chloroform; MC). PBPK models were constructed for each 

chemical using published values for parameters, including Km values. PBPK modeling 

of gas uptake data was applied, and interactions were characterized as concentrations 

of PERC and MC required to increase blood TCE concentrations by 10%. These 

solvents were chosen because of their ubiquitous occurrence as environmental 

contaminants, as well as their dependence on cytochrome P450 (CYP)-mediated 

oxidation as a primary metabolic process. Interaction models were constructed for 

competitive, non-competitive and uncompetitive inhibition. Although all models fit the 

data to some extent, the similarity between model-predicted (optimized) values for the 

inhibition constant (Ki) and Km values indicated that the competitive model best fit the 

data. This model was implemented to interpret gas uptake data for binary and ternary 

mixtures. In the model, TCE exposure was maintained at the TLV of 50 ppm and 

interactions from binary and ternary exposures were examined. Under binary 

exposures to TCE and either MC or PERC, the threshold for interaction was 175 ppm 

(MC) and there was no interaction when PERC exposure was at 25 ppm. However, 

when the model simulated a ternary exposure to TCE (50 ppm), PERC (25 ppm) and 

MC, interaction was noted at MC concentrations of 130 ppm (compared to 175 ppm 

when encountered in a binary mixture). These results demonstrate the value of PBPK 

modeling in refining the descriptions of toxicologically important (metabolic, 

toxicokinetic) interactions among components of environmentally-important mixtures. 

The above interaction is metabolic, or pharmacokinetic in nature. When a 

mixture or cumulative exposure results in a response different from that predicted from 

additivity and there are no data on tissue dosimetry, it is not possible to determine 

8
whether the interaction is based on toxicokinetic or toxicodynamic interactions, or both. 

From an “interactions” perspective, it is possible that pharmacokinetic interactions may 

explain chemical mixture (cumulative risk) interactions, that may not be “interactions” as 

defined by cumulative risk; such departures from additivity may only be departures 

when considered at the level of the external or applied dose, and may be strictly 

additive when “dose” is expressed as “dose metric”—the concentration of the 

toxicologically active chemical species in the target tissue. 

For example, consider two chemicals, A and B, that share a common metabolic 

pathway. The response from chemical A is dependent on a metabolite, whereas the 

response from chemical B is mediated by the parent compound. When the animal is 

concomitantly exposed to these chemicals at a level where metabolic interactions do 

occur—chemical A out-competes chemical B for metabolism; chemical A is metabolized 

to a greater extent than chemical B, the response from a mixtures exposure is much 

higher than the response predictions made on the assumption of additivity applied to 

single chemical toxicity results. An explanation may include the metabolic interaction, 

and when the “dose” is expressed in terms of the biologically active dose (a metabolite 

of chemical A and the parent form of chemical B), the response may, indeed, be 

predicted by additivity. Appreciating, developing and communicating the distinction, as 

well as its basis, will decrease the level of uncertainty associated with assessments of 

cumulative risk. 

Pharmacodynamics is concerned with the development of the response, 

ultimately at the level of the cell. Processes that determine or modify delivery of the 

toxicologically active chemical species to the cell are addressed as pharmacokinetics. 

The biological response is ultimately mediated at the cellular level and key events may 

be identified, and sorted into those defining the mechanism of toxicity and those 

defining the mode of action. However, when chemicals act on the same biological 

and/or biochemical cellular processes, a toxicodynamic interaction may occur, 

regardless whether the shared biological processes are involved in mode or mechanism 

of toxic action. These are not trivial, and interactions may occur in biochemical 

pathways not directly involved in the critical toxic event for a chemical. Piperonyl 

butoxide (PBO) is a synergist in some insecticides. PBO is not especially toxic, and it 

inhibits enzymes that are important in detoxicating insecticides. Under a singlechemical exposure to PBO, inhibition of these enzymes is not especially important, 

though it does represent a toxicodynamic effect—it represents an adverse interaction of 

the toxic agent with a biomolecule. However, when PBO is co-exposed with an 

insecticide, the result is rather marked potentiation of insecticidal activity. The 

9
interaction is toxicokinetic in this regard, because the potency (when measured as effect 

per applied dose) of the insecticide is increased. However, if the measure of dose is 

expressed relative to the internal dose of the insecticide, the potency (expressed per 

mg/kg of internally circulating insecticide) is not altered. An example of a strictly 

toxicodynamic interaction may occur when two chemicals share a common biological or 

biochemical event. If both chemicals alter cellular communication resulting in an 

outgrowth of damaged cells, then this type of interaction is likely to demonstrate an 

effect characterized by additivity. This is the type of interaction that is anticipated for 

mixtures of organophosphate insecticides. However, if two chemicals act on different 

biochemical processes in the same response pathway, a response other than additive 

might occur. Such would be the case if one chemical caused a decrease in the efficacy 

of DNA repair and/or a release of apoptotic control of cellular growth, and a second 

chemical caused damage to DNA. Under the event of a co-exposure, then the net 

result might be the outgrowth of cells in which DNA damage was responsible for toxicity. 

The effect resulting from the DNA damage might be a further loss of control over cell 

growth leading to tumorigenesis, or may be manifest as the production of a protein 

whose function has been adversely affected, resulting in increased cellular toxicity. 

Whereas some of the above interactions results in potentiation or synergism, 

such is not always the case. Mehendale and colleagues evaluated the interaction 

between thioacetamide and carbon tetrachloride (reviewed in Mehendale, 2005). Both 

compounds are known to induce liver injury. However, it was demonstrated that low 

doses of thioacetamide given in the hours before carbon tetrachloride administration 

actually protected against CCl4-induced hepatotoxicity. Ultimately, the effect was 

demonstrated to reside in the rebound effect of cellular repair following thioacetamide 

exposure. Here, the pre-exposure resulted in a stimulation of cellular repair processes; 

these processes were already active when CCl4 exposure and injury occurred. The 

interaction was significant to the extent that thioacetamide exposure could spare 

animals from doses of CCl4 that would otherwise have been lethal (reviewed in 

Mehendale, 2005). 

The application of pharmacokinetic analyses to chemical interactions can 

suggest the basis for interactions (determine whether and to what extent departures of 

response from additivity can be attributed to pharmacokinetic interactions). An example 

is provided by the joint exposure of kepone and carbon tetrachloride. Mehendale and 

colleagues have demonstrated a significant interaction between these chemicals, 

resulting in an approximate 67-fold induction of CCl4-induced hepatotoxicity. A PBPK 

model was developed to study the interactions between CCl4 and kepone in rats 

10
(El-Masri et al., 1996a). Previous experimental findings demonstrated that kepone 

co-exposure amplified the CCl4-induced lethality 67-fold. In an additional study, CCl4

was administered i.p. to rats fed a control diet or a diet containing Kepone. Data 

describing the pathology and the exhalation of CCL4 (indicative of metabolism) were 

collected and a pharmacodynamic model was developed and incorporated that 

characterized the injury, repair and death sequence for cells and animals. The results 

of this PBPK-PD model demonstrated an approximate 61 to 142-fold increase in CCl4

lethality with kepone treatment. However, when the results were adjusted for PK 

differences in CCl4 metabolism, the increase was approximately 4-fold. These results 

suggest that the kepone-CCl4 interaction is primarily based on a TK interaction, but may 

also rely to some extend on a TD interaction. 

Another example of pharmacokinetic interactions altering response can be 

provided by the organophosphate insecticides. While these compounds have been 

compiled into the same common mechanism group based on inhibition of AChE (the 

toxicodynamic determinant of CNS toxicity), a pharmacokinetic interaction among OPs 

may also influence response. These compounds are degraded by carboxylesterase 

(CE) enzymes. When exposure to OPs includes some compounds with relatively low 

toxic potency (e.g., fenitrothion) as well as more potent compounds (like parathion), the 

lower potency compounds may compete against the other compounds for detoxication 

via CE enzymes. The net result can be that higher levels of internal dose per unit 

external dose are developed than would have been predicted on the basis of single 

chemical studies (see Chambers et al., 1991; Cohen, 1984). In this example, 

interaction would occur at a level that may not have been predicted based on the results 

of toxicity studies. In this regard, this case is similar to that demonstrated by piperonyl 

butoxide: metabolic interactions alter internal dosimetry and are responsible for toxic 

interactions. 

This example highlights another important consideration—that of timing. Timing 

may relate to pharmacokinetics, where dose timing can be different when characterized 

at the level of the external (encountered) dose or the tissue dose. For some chemicals, 

a prolonged tissue exposure can result from a brief environmental contact. In those 

instances, toxicant concentrations in tissues can remain appreciable hours, days or 

even weeks after the cessation of external exposures. Here, a joint exposure might 

occur, even if the person is not concomitantly “exposed” to multiple chemicals. Another 

mechanism through which timing may be complicated is when the effects of an 

exposure persist following cessation of exposure and elimination of the compound from 

the body. An example of such an effect is provided by the enzyme inducing ability of 

11
some compounds. Many compounds can and do induce hepatic cytochrome P450 

enzymes, and the previous administration of these compounds can modify the toxic (or 

therapeutic) response to subsequently-encountered xenobiotics via a modification of 

their internal dosimetry. The metabolism of a compound may represent a bioactivation 

or a detoxication process, and so the effect of enzyme induction (or inhibition) on toxic 

outcome must be evaluated on a chemical by chemical basis. 

12
2. THE PROPOSED APPROACH 

2.1. OVERVIEW 

The existing U.S. EPA guidance for addressing the cumulative risk for pesticide 

mixtures acting through a common mode of action (U.S. EPA, 2002a)1

 suggests a 

series of evaluations that first lead to the determination of the Cumulative Assessment 

Group. The U.S. EPA’s Office of Pesticide Programs has undertaken cumulative risk 

assessments for four groups of compounds (organophosphates, N-methyl carbamates, 

triazines and chloroacetanilides), which are available at 

http://www.epa.gov/pesticides/cumulative/common_mech_groups.htm. The reader of 

this report should not confuse the Cumulative Assessment Group (CAG) with the 

Common Mechanism Group (CMG). A cumulative risk assessment can include 

chemicals for which the mechanism(s) of toxicity may be similar (common) or 

independent. For the purpose of this document, the CAG is the group of chemicals that 

will undergo full risk analysis (the Dosimetry-Based Cumulative Risk Assessment; 

DBCRA) and may not include all chemicals originally considered. For some chemicals, 

data on exposures and/or dose-response may be so limiting, or a comparison of 

anticipated exposures to risk levels may reveal such a gulf that these chemicals would 

be “de-selected” for inclusion in a DBCRA. In some instances, this situation may be 

relevant to the entire mixture. For those chemicals or mixtures, only the conduct of a 

screening level analysis may be warranted. Therefore, steps prior to the determination 

of the CAG may be thought of as an “Initial Analysis” to determine whether the 

exposures to a chemical warrant the DBCRA. DBCRA is an analysis that includes 

evaluation of and inclusion of tissue or organ-specific levels of active toxic agent. 

Tissue levels of toxicant are employed in the determination of toxicokinetic interactions, 

i.e., metabolic competition, and tissue levels of toxicant are employed in the prediction 

of toxic events (toxicodynamics). In some cases, the Initial Analysis may be sufficient. 

For instance, the likelihood of a positive response predicted by additivity my not be of 

concern, and there may appear to be no reason to suspect an interaction (for example, 

if the Target Organ Toxicity Dose-based Hazard Index is appreciably below unity). If 

 

1 In 2002, the U.S. EPA, Office of Pesticide Programs (OPP) finalized guidance that would be relied upon 

to conduct a Congressionally-mandated assessment of the cumulative risks of organophosphate and 

other pesticides, including the risks to children. The guidance specifically addressed the development of 

an FQPA Safety Factor for the mixture of pesticides. The guidance document itself indicates that the 

intent of the document was not to serve as a guide for the conduct of all cumulative risk assessment 

approaches, within or without OPP. Specific provisions were included so that risk assessors may choose 

to depart from specific guidance when data and circumstances warrant doing so. This guidance has 

been subjected to Agency and Public review and is freely available on the World Wide Web.

13
14

there is insufficient evidence to further support a DBCRA, then the results of the Initial 

Analysis may be sufficient, and embarking on a DBCRA may not be deemed necessary. 

Alternately, the screening hazard index approach—that of assessing cumulative risks 

on the basis of external (instead of internal) concentrations/doses, may apply (U.S. 

EPA, 2000a). Thus, the hazard analysis of chemicals in a mixture during the “Initial 

Analysis” requires not only a determination of the population risk presented by the 

chemical alone, but any additional risks that would occur due to interactions in the 

mixture. The 10-step process communicated in this document (Figure 1) is divided into 

the two broad activities, Initial Assessment and the conduct of the Dosimetry-Based 

Cumulative Risk Assessment. In this process, the CAG is determined in step 5; steps 

6-10 describe the specific activities that comprise the DBCRA. 

 A biologically based analysis of risks including evaluations of tissue dosimetry 

can be very resource-intensive and this is the main reason why the “Initial Analysis” is 

carried out to avoid investing resources into analyses that are not worthwhile. Likewise, 

development of PBPK models, particularly interactive PBPK models (PBPK models 

capable of addressing mixtures of chemicals, where additive and non-additive 

interactions at the level of pharmacokinetics and/or pharmacodynamics can be 

addressed) for chemical mixtures, would require additional resources. Thus, it is 

important to apply a screening method (initial analysis) to eliminate from further 

consideration and development of a DBCRA (a CRA whose exposure and dose 

response evaluations are based on tissue concentrations of toxicant, rather than on 

externally encountered concentrations/doses) those chemical mixtures anticipated not 

to cause significant cumulative risk and/or those mixtures for which the available data 

would not support a credible DBCRA. Accordingly, we recommend that the cumulative 

risk assessment (here, for drinking water contaminants) be conducted in a two-phase 

framework (1) Phase I: Initial Analysis; and (2) Phase II: Dosimetry-Based Cumulative 

Risk Assessment. PBPK models would be developed for the components that are 

considered in Phase II, i.e., those that are in the CAG. This approach stresses the 

importance of the initial analysis to eliminate those situations that do not warrant a fullscaled cumulative risk assessment thereby reducing the burden of the Public in 

conducting dosimetry-based cumulative risk assessment. 

The 10 steps described in the U.S. EPA guidance document on cumulative risk 

assessment (U.S. EPA, 2002a) from the Office of Pesticide Programs remained 

unchanged under our proposed two-phase approach because: (1) the steps have been 

established, reviewed, and appear useful as a starting point, (2) we would 
Initial Analysis

1. Group chemicals 2. Identify 3. Characterize 4. Determine need for 5. Id

by similar target potential health dosimetry-based chorgan, exposures endpoints for cumulative risk CApharmacokinetics (Section 2.2.2) evaluation assessment evand mechanism (Section 2.2.3) • Hazard index (S(CMG) • Target organ (Section 2.2.1) toxicity dose

• Weight of evidence

(Section 2.2.4)

Dosimetry-Based Cumulative Risk Assessment

6. Characterize 7. Develop 8. Quantify 9. Conduct dosimetry- 10. C

dose-response, detailed parameters for based CRA c

point of departure exposure exposures to • Develop e

and relative scenarios be evaluated pharmacokinetic p

potency as a (Section 2.3.2) (Section 2.3.3) analysis a

function of tissue • Predict responses (dose as a function of (Section 2.3.1) tissue dose

(Section 2.3.4)

Initial Analysis

1. Group chemicals 2. Identify 3. Characterize 4. Determine need for 5. Id

by similar target potential health dosimetry-based chorgan, exposures endpoints for cumulative risk CApharmacokinetics (Section 2.2.2) evaluation assessment evand mechanism (Section 2.2.3) • Hazard index (S(CMG) • Target organ (Section 2.2.1) toxicity dose

• Weight of evidence

(Section 2.2.4)

Dosimetry-Based Cumulative Risk Assessment

6. Characterize 7. Develop 8. Quantify 9. Conduct dosimetry- 10. C

dose-response, detailed parameters for based CRA c

point of departure exposure exposures to • Develop e

and relative scenarios be evaluated pharmacokinetic p

potency as a (Section 2.3.2) (Section 2.3.3) analysis a

function of tissue • Predict responses (dose as a function of (Section 2.3.1) tissue dose

(Section 2.3.4)

FIGURE 1 

Data Evaluation for Dosimetry-Based Cumulative Risk Assessment 

 15
like to maintain consistency with the published U.S. EPA approach (U.S. EPA, 2002a); 

and (3) for any chemical mixture which is to undergo dosimetry-based cumulative risk 

assessment, it would have gone through the first five initial screening steps. Our 

framework/approach simply organizes the 10 steps into two phases to underscore the 

rationale for introducing PBPK modeling at the 6th step. 

2.2. PHASE I: INITIAL ANALYSIS 

 In this phase, the principal purpose is to determine, from all available information 

on the chemical mixture and its exposure scenarios, whether a dosimetry-based 

cumulative risk assessment is warranted. Central to this determination or decision are 

the answers to the following questions: 

• Is there the likelihood of toxicological interactions among the chemicals in the 

mixture? At what concentration levels? 

• Are these toxicological interactions likely to cause higher toxicity (i.e., synergism 

or potentiation) or lower toxicity (i.e., antagonism) than the simple addition of the 

toxicity from all components of the mixture? 

• Under the exposure conditions and levels, are these toxicological interactions 

possible? 

2.2.1. Step 1. Identify Common Mechanism Group (CMG). A cumulative risk 

assessment begins with the identification of a group of chemicals, a Common 

Mechanism Group (CMG), that induce a common toxic effect by the same mechanism 

of toxicity. When chemicals are identified, data on their interactions should be sought. 

Two sources for such data are the general toxicological literature and the Interactions 

Profiles series developed by ATSDR (http://www.atsdr.cdc.gov/interactionprofiles/). An 

approach developed to guide OPP through conducting a mandated cumulative risk 

assessment of organophosphate pesticides (U.S. EPA, 2002a) refers to the definition of 

mechanism of toxicity from the Food Quality Protection Act (FQPA): “the major steps 

leading to an adverse health effect following interaction of a pesticide with biological 

targets.” However, this definition is somewhat ambiguous regarding whether the 

interactions between the chemical and the biological target are PK or PD in nature. 

Existing U.S. EPA guidance (U.S. EPA, 2002a) considers this issue in Step 3 of the 

CRA process. However, if a chemical is not included in the CMG group because it does 

not share relevant elements of PD processes with other chemicals in the group, but the 

chemical in fact can interact with other chemicals in the group through PK processes, 

16
an underestimation (or overestimation) of the actual cumulative risk may occur. For 

example, suppose four chemicals (Chemicals A, B, C, Q) are being considered for 

inclusion in a CMG based on Effect X. Chemical Q does not cause Effect X, but Q is an 

inhibitor of metabolism of Chemical A, and this inhibition causes an increase in the 

Effect X for the same exposure to Chemical A (see also Section 1.6). If Chemical Q is 

omitted from the CMG due to lack of Effect X, the CRA will not properly characterize the 

cumulative risk for Chemicals A, B, C when Chemical Q is also present. For this 

reason, all chemicals that share common types of PK or PD interactions with biological 

targets should be considered as members of the CMG. This represents a specific 

extension of the process, as recommended by the available (OPP) guidance to address 

commonalities beyond those related to toxic manifestation: those pertaining to tissue 

doses of toxicologically active chemical species (metabolites). While OPP’s guidance 

defines the CMG based only on toxicodynamic events in the mechanism of action itself, 

The CMG is here defined differently. Our definition is broadened to include 

commonalities or interactions in toxicokinetics that may alter tissue dosimetry and 

modify the toxic response. This has been done to optimize the application of PBPK 

modeling to assess cumulative risk. 

For this project, the first mixture consists of 6 OP pesticides: methyl-parathion, 

parathion, chlorpyrifos, fenthion, diazinon and fenitrothion. For one effect of concern, 

neurotoxicity, the common mechanism of these pesticides is inhibition of AChE. Thus, 

the mechanism of toxicity includes a common pharmacodynamic process. However, 

many of these OP pesticides are also metabolized by common enzymes, primarily in 

the liver, including cytochrome P450s and esterases in various tissues. Therefore, this 

CMG includes chemicals that have similar pharmacokinetics, pharmacodynamics and 

target organs. During the CRA process for OP pesticides, the risk assessor will 

obviously also consider other toxicity endpoints, such as developmental neurotoxicity, 

before deciding which endpoint is most relevant. For at least some of the OP pesticides 

being considered here, there is some evidence that exposures during pregnancy or 

during childhood development may be important, because development is occurring 

and chemical may be transferred from the mother (e.g., placental transfer or via breast 

milk), because the child may have increased sensitivity, or because the child may 

exhibit pharmacokinetic differences from adults that lead to greater toxicity (Chanda and 

Pope, 1996; Qiao et al., 2001). Considerations of potentially susceptible 

subpopulations add to the complexity of PBPK modeling, when subpopulations exhibit 

anatomic, physiologic and biochemical differences compared to the general population. 

17
The second mixture consists of four chlorinated hydrocarbons or volatile organic 

chemicals that are often present in drinking water as disinfection byproducts or 

groundwater contaminants: TCE, PERC, MC and chloroform (CHF). The mechanisms 

of toxicity for these chemicals are somewhat different depending on the type of toxic 

endpoints. For instance, at higher doses, particularly through inhalation exposure, all of 

these chemicals cause acute central nervous system (CNS) depression through the 

common mechanism of interference in ion channel function (Bruckner and Warren, 

2001). In general, narcotic effects by solvents are caused by the following mechanisms: 

(1) membrane expansion and associated conformational changes of proteins (Shibata 

et al., 1991); (2) interaction with macromolecules (e.g., membrane lipids and proteins) 

and associated changes of membrane fluidity (Ueda and Kamaya, 1984) and (3) 

interaction with ligand gated ion channels (Franks and Lieb, 1994). Specific 

mechanisms for such effects by the TCE, PERC, MC and CHF have not been revealed 

yet. Since we are specifically interested in the exposure to contaminants found in 

drinking water, the likely exposure levels are low. Thus, acute neurotoxicity is highly 

unlikely and does not need to be considered as an endpoint. However, a hazard 

quotient for such an effect could be developed. For volatile compounds, exposure 

should also consider volatilization and inhalation of compounds during water use. 

Long-term, low-level exposure to this mixture in drinking water is a highly likely 

scenario. Therefore, in addition to other chronic effects, the possible carcinogenic 

potential of this mixture as a critical effect should also be considered. In that sense, 

damage to cellular targets from reactive metabolites from this group of chemicals may 

be considered as a common mode of action. However, the specific targets of PD 

mechanisms that are the critical determinants of toxicity at low dose may vary from one 

chemical to another in this group. All of the four chemicals to be considered here share 

common elements of PK processes, namely shared metabolic pathways by CYP2E1, 

glutathione S-transferase (GST), and possibly other enzymes. These enzymes detoxify 

some of the volatile organic chemicals and their metabolites, while bioactivating others. 

Based on the discussion above, all four chemicals should be considered to be part of 

the CMG based on the potential for PK and/or PD interactions. 

2.2.2. Step 2. Identify Potential Exposures. For each CMG member, evaluate 

proposed and registered uses and use patterns to identify potential exposure pathways 

(i.e., food, drinking water, residential) and routes (oral, inhalation, dermal). For this 

project, the primary exposure is through consumption of drinking water although the 

potential exists for inhalation and dermal exposure (i.e., during showers). Additionally, 

18
pesticide (and other chemical) exposures from food intake and dust or residue through 

home application remains a potential. In this latter case, for instance, Whyatt et al. 

(2002) reported that, of the 72 pregnant women residing in northern Manhattan and 

South Bronx who underwent personal air monitoring for 48 hrs during their third 

trimester, all (100%) had detectable levels of three insecticides: chlorpyrifos, diazinon, 

and propoxur. Fenske et al. (2002) reported that chlorpyrifos was measurable in the 

house dust of all homes they monitored in a central Washington State agricultural 

community; children in these homes had detectable urinary metabolite of chlorpyrifos. 

Here, if the DBCRA is to be conducted, the PBPK modeling has the advantage of being 

able to carry out computer simulations with a variety of exposure scenarios and 

assumptions with relative ease. This translates external concentrations to internal 

(target tissue) doses, representing a valuable advance in dose-response evaluation for 

animal studies, for species extrapolation of dose-response, and for the purpose of 

providing a more useful characterization of human exposure. 

 In Step 2, members of the CMG should be assessed for the potential for 

significant and/or overlapping exposures. Various sources of information on the range 

of levels of each chemical in public and private drinking water supplies should be 

consulted. When analyzing these data, the risk assessor will confront one of the 

conundrums to be encountered in this CRA: data on contaminants are often expressed 

only as ranges observed, with no information indicating which co-occur and which do 

not. A CRA can be based on these data, assuming that exposure to all of the chemicals 

coincide. However, it is likely that there is some correlation between the exposure data, 

such that some exposures do not coincide. Suppose that the concentration of Chemical 

A ranges from 0.01 to 1.2 mg/L in various drinking water supplies and that the 

concentration of Chemical B ranges from 0.005 to 1.3 mg/L, with no data available to 

demonstrate a correlation between the two. Should a worst-case CRA be performed at 

the upper limit of exposure data, or at some confidence interval based on those data? 

Such an approach would likely overstate the cumulative risk if, as is likely the case, the 

geographical coincidence of maximal exposures is not complete. An alternate approach 

would consider using a geographical scenario-based approach. Such an approach 

could perform a CRA based on regional exposure data, such as was previously 

performed for OP pesticides (U.S. EPA, 2002b). A further alternative could take a point 

sampling approach. In this approach, specific risk assessment calculations could be 

performed based on exposure data at discrete locations or at discrete points in the 

concentration distributions (see US EPA, 2006). Statistical analysis of the results could 

then be performed on the CRA output to determine the percentages (and locations) of 

19
the population that are above various hazard indices. This approach would involve an 

iterative approach; the information addressed in step 10 would communicate which 

subpopulations and to what extent their risk may be increased above a given hazard 

index. We note that these latter approaches are more calculation-intensive, and the 

ability of the risk assessor to perform this more detailed analysis would depend on 

resources. We also note that, to the extent that interactions between chemicals are 

found to occur, a non-PBPK modeling approach would have difficulty in accurately 

determining the cumulative risks. This would be due, primarily, to a lack of mechanism 

to address the interactions of chemicals within the body, which may occur between the 

portal of entry and the target tissue. However, once validated interaction-based PBPK 

models have been developed, it is a relatively simple matter to implement them for any 

number of exposure-concentration scenarios (including the point sampling approach); 

this could even be automated in such a way that a large exposure dataset could be 

evaluated. 

 The potential exposures to each component in the mixture under as many 

exposure scenarios as possible (primarily use of drinking water and showering) should 

be evaluated. This analysis should include exposure information that may vary by 

season and geographic location. A formalized approach, similar to the five-step 

process developed by ATSDR (2005) may be undertaken. This systematic approach 

includes: (1) identification of contaminant source(s), (2) evaluation of environmental fate 

and transport, (3) identification of point or area of exposure, (4) identification of 

exposure route(s) and (5) characterization of the exposed population. 

2.2.3. Step 3. Characterize and Select Health Endpoint(s) for Evaluation. For 

each CMG member, evaluate common health effects and affected tissues and affected 

organs attributable to the common mechanism of toxicity across all exposure routes and 

durations of interest, determine the time-frames of expression for the common toxicity, 

and evaluate the quality of the dose-response data for each CMG member. 

Recommend endpoints/species/sex that can serve as a uniform basis for determining 

relative potency. In the case of the OP pesticide mixture, the most reasonable endpoint 

to use should be the inhibition of brain AChE (because they are neurotoxic through this 

mechanism). This endpoint should be correlated with clinical signs which are usually 

available in the toxicology literature. During this step, attention must be given to 

subpopulations or life stages that may be more susceptible to the common toxic effect 

and mechanism (here, inhibition of erythrocyte AChE activity). For example, infants and 

children may not have fully developed metabolic pathways for detoxifying or 

20
bioactivating chemicals in a common mechanism grouping (U.S. EPA, 2002a). The 

importance of describing the potential increased sensitivity of infants and children is 

described in Executive Order 13045, and U.S. EPA’s guidance is provided in EPA’s 

Rule Writer’s Guide to Executive Order 13045: Guidance for Considering Risks to 

Children During the Establishment of Public Health-Based and Risk-Based Standards 

(U.S. EPA, 1998). 

The selection of endpoints requires a consideration, at least semi-quantitatively, 

of the time-course of biological effects so that overlapping pharmacokinetics can be 

considered. Thus, it is necessary to consider “biologically effective dose.” This would 

represent the concentration of toxicant in the target tissue; and in the case of the 

drinking water disinfection byproducts and contaminant mixture, could be taken as the 

area-under the concentration vs. time curve (AUC) of the reactive species or related 

dose metrics. 

Another issue to be determined is the choice of sex and age. Traditionally, risk 

assessments and PBPK models are most commonly developed for adult male humans. 

This is probably because of the preference for performing initial studies in male rodents 

(because the male may be a simpler model from an endocrine standpoint). However, 

U.S. EPA has been clear in its position that work should not stop with the male model 

and should be extended to females as well. Thus, the logical process may be as 

follows: determine the sex for which the most data are available to support the DBCRA 

(PBPK models). Initially develop PBPK models (and DBCRAs) for this sex. This would 

likely be the male model. Subsequently, extend this to female using as much femalespecific data as possible and extrapolating from pooled or male data as required. 

Discuss uncertainty in this context, i.e., there may be more certainty with the initial sex 

selected and more uncertainty with the latter sex. Specific anatomic, physiologic and 

biochemical factors thought to differ among sexes should be identified. These issues 

apply to both the PBPK modeling and the DBCRA itself. 

The same thoughts apply to the issue of age. At this stage, the PBPK models 

and the DBCRA would probably be applied to adults, and possibly to children, although 

it would certainly be preferable if other age groups could be considered as well. Again, 

both PBPK models and DBCRA analysis will probably be supported by more data for 

adults than for children. Therefore, the models and DBCRAs should be initially 

conducted for adults and subsequently extended to children. These issues (issues 

relating to potential differences in sensitivity among subpopulations) should be kept in 

consideration while determining the common mechanism endpoint. 

21
2.2.4. Step 4. Determine the Need for a Dosimetry-Based Cumulative Risk 

Assessment. The relationship between exposure and acceptable exposure limits 

should serve as the trigger in the decision whether to proceed with conducting a more 

technically based dosimetry-based cumulative risk assessment (DBCRA). The 

guidelines established by the U.S. EPA (1986) describe a Hazard Index (HI) screening 

approach that represents the first step in this evaluation (U.S. EPA, 1989). In that 

approach, the exposure (E) and the acceptable level (AL) are determined for routes of 

exposure and expressed in the same units. That approach favors AL measurements 

(reference dose [RfD] and reference concentration [RfC] values) available on the U.S. 

EPA’s Integrated Risk Information System (IRIS). However, in the absence of such 

values, other reliable values (e.g., ATSDR Minimal Risk Level values) may be relied 

upon, or values may be developed from original toxicity findings in the absence of more 

reliable information. A Hazard Quotient (HQ; E/AL) is determined for each component 

of the mixture and a Hazard Index for the mixture is determined as the sum of the HQ 

values. When a group of chemicals is targeted for a CRA, a HI should be developed for 

the entire group, and HI values should also be developed for each CMG, independently. 

A hypothetical example of a four chemical mixture (chloroform, trichloroethylene, 

1,1,1-trichloroethane and tetrachloroethylene) is presented below in Table 1. The 

exposures are hypothetical, for demonstration purposes only. 

This is a conservative approach, based on the assumption of dose additivity for 

the mixtures components, and unconstrained in that the AL values are not segregated 

by target organ or tissue. Although there is no “bright line,” as HI values increase above 

1.0, risk from the mixture is considered to increase. Given the conservative nature of 

the screening approach, if the HI is less than 1.0, additional methods to calculate HI 

should be considered. Subsequent to the development of this approach (U.S. EPA, 

1986), additional works have suggested the employment of a weight of evidence (WOE) 

determination to evaluate the potential for toxicological interactions2

 (ATSDR, 2004; 

Mumtaz and Durkin, 1992; Mumtaz et al., 1998; U.S. EPA, 2000a); a WOE exercise is 

recommended by the U.S. EPA in its Supplementary Guidance for Conducting Health 

Risk Assessment of Chemical Mixtures (U.S. EPA, 2000a). The WOE serves as the 

basis for the decision whether component chemicals interact to alter (increase or 

decrease) the toxicity of other components of the mixture. When HI values are below 

1.0, the WOE will serve as the basis for further consideration of the mixture, when the 

 

2

 A Weight of Evidence Determination is a judgment reflecting the quality of the available information that 

categorizes the most plausible nature of any potential influence of one compound on the toxicity of 

another compound for a given exposure scenario (U.S. EPA, 2000a). 

22
TABLE 1 

Screening Hazard Index Approach 

Chemical Exposure (mg/kg-d) RfD (mg/kg-d) Hazard 

Quotient 

Chloroform 0.0025 0.01 0.25 

Tetrachloroethylene 0.005 0.01 0.5 

Trichloroethylenea,b 0.0025 0.04 0.06 

1,1,1-Trichloroethaneb,c 0.05 0.5 0.1 

Hazard Index: 0.91 

a

 IRIS RfD values were not available. 

b

 RfD value was not available on IRIS; it was developed from a chronic no-observedadverse-effect level (NOAEL) value for renal effects (ATSDR, 1997a) and an 

uncertainty factor of 1000. 

c

 RfD value was not available on IRIS at the time of this report’s completion; it was 

developed from a chronic lowest-observed-adverse-effect level (LOAEL) value for body 

weight reduction (ATSDR, 1995) and an uncertainty factor of 1000. 

23
24

HI value alone would suggest that further consideration is not warranted. Thus, 

developing a weight of evidence increases the basis for decision making. Some points 

that should be considered in developing the WOE include: 

• Information on the chemical mixture or combinations of components that can 

inform the magnitude and direction of toxicologic interactions, 

• Information on mode or mechanism of action of component chemicals, and 

• Information on mode or mechanism of action of related chemicals. 

While the U.S. EPA (2000a) has codified a technical approach to refining the HI 

by incorporating mathematical values for WOE and additional factors, incorporating 

such technical guidance here is beyond the scope of the intended exercise. 

When the HI for the mixture exceeds 1.0 or when the HI is considered in light of 

the WOE and cannot without uncertainty be reduced to a value below 1.0, then the 

mixture should be subjected to a more technical estimation of HI, based on the Target 

Organ Toxicity Dose (TTD) approach (ATSDR, 2004; Mumtaz et al., 1997; U.S. EPA, 

2000a). In this approach, effect levels for multiple organs are compiled and combined 

with uncertainty factors to develop TTD values for each organ system affected whether 

the effect is the critical effect or a secondary effect. These TTDs are analogous to RfD 

values. HQ values are derived, but the AL values can be either TTD values or RfD 

values for the most sensitive tissue or organ. HQ is determined as, E/TTD; HI values 

are developed for each organ, by summing the HQ values for each chemical, within the 

organ. This approach is more technical in that it incorporates organ-specific measures 

of toxicity, expanding considerations of toxicity from only the critical target to additional 

target systems, and it ensures that major systems affected are taken into account. In 

the tables that follow, chronic oral NOAEL values for effects were taken from ATSDR 

Toxicological Profiles for the four respective chemicals (notable exceptions are that the 

IRIS RfD values for chloroform and tetrachloroethylene, where liver was the critical 

target, were incorporated). Information on chronic NOAEL values were durationadjusted (days/week) when necessary, and uncertainty factors (10, 100 or 1000) were 

employed to adjust for animal to human extrapolation, human interindividual variability 

and, in some cases, subchronic to chronic duration). Tables 2, 3, 4 and 5 present the 

derivation of TTD values for chloroform, tetrachloroethylene, trichloroethylene and 

1,1,1-trichloroethane, respectively. 
 25 TABLE 2 Derivation of TTD Values for Chloroform (ATSDR, 1997b) Target Resp CV GI Hemat Musc Liver Kidney 

Species mouse dog rat human rat human human NOAEL (mg/kg-d) 60 30 200 21 200 0.96 0.96 NOAEL Adjusted 51.4 25.7 142 21 143 0.96 0.96 

Uncertainty Factors 100 100 100 10 100 10 10 

TTD (mg/kg-d) 0.51 0.26 1.4 2.1 1.4 0.10 0.10 

Resp = respiratory system CV = cardiovascular GI = gastrointestinal Hemat = hematological Musc = musculoskeletal Repro = reproductive system b.w. = body weight reduction 
 26 TABLE 3 Derivation of TTD Values for Tetrachloroethylene (ATSDR, 1997c) Target Resp CV GI Hemat Musc Liver Kidney 

Species rat rat rat human rat mouse* 

NOAEL (mg/kg-d) 941 941 941 NR NR 20 386 NOAEL Adjusted 672 672 672 14.3 275 Uncertainty Factors 100 100 100 1000** 1000 

TTD (mg/kg-d) 6.7 6.7 6.7 0.01 0.28 

* LOAEL 

** IRIS UF = 1,000. 10 each for UF

S , UF

A , UFH

NR = not reported Additional abbreviations are listed with Table 2. 
 27 TABLE 4 Derivation of TTD Values for Trichloroethylene (ATSDR, 1997a) Target Resp CV GI Hemat Musc Liver Kidney 

Species rat rat rat rat rat rat NOAEL (mg/kg-d) 250 250 250 NR 250 250 50 NOAEL Adjusted 179 179 179 179 179 35.7 Uncertainty Factors 1000 1000 1000 1000 1000 1000 TTD (mg/kg-d) 0.18 0.18 0.18 0.18 0.18 0.036 NR = not reported Additional abbreviations are listed with Table 2.
 28 TABLE 5 Derivation of TTD Values for 1,1,1-Trichloroethane (ATSDR, 1995) Target Resp CV GI Hemat Musc Liver Kidney Repro 

Species rat rat rat rat rat rat rat rat NOAEL 1500 1500 1500 1500 1500 1500 1500 1500 NOAEL Adjusted 1071 1071 1071 1071 1071 1071 1071 1071 Uncertainty Factors 100 100 100 100 100 100 100 100 TTD 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.7 * LOAEL 

Additional abbreviations are listed with Table 2. 
Employing TTD values, rather than simply relying on RfD values gives a more 

complete treatment. For example, liver was the critical target for chloroform and 

trichloroethylene, but kidney and body weight gain were the critical endpoints for 

trichloroethylene and 1,1,1-trichloroethane, respectively. When the HI is calculated 

based on segregated values (i.e., the TTD approach), tissue dose-response information 

is more optimally employed. While liver served as the RfD values for two of the four 

compounds, the HI value changed from 0.91 under the screening approach (Table 1) to 

0.77 for liver under the TTD approach (Table 6), reducing concern for toxicological 

interaction. This change was due to liver not being the most sensitive target for 

trichloroethylene or 1,1,1-trichloroethane. Further, the TTD based HI appears quite low 

for the other potentially affected organs/tissues, serving to focus attention on the liver as 

the site most likely affected. Again, a weight of evidence should be developed, but 

specifically for liver effects, to further refine the decision whether to proceed to a full 

DBCRA for this mixture. This should include the number and types of possible 

exposure scenarios in conjunction with the associated information on the concentration 

of contaminant in food and environmental media available. The NOAEL and LOAEL 

values for the common health endpoints and target tissues should be compiled. This 

evaluation may suggest that an initial analysis for the CMG will indicate that there is no 

risk concern for this group of chemicals and no further detailed assessment will be 

necessary. The available information may also suggest that expending resources on a 

dosimetry-based cumulative risk assessment may not be warranted. 

 One possible scenario is that, for some or all of the component chemicals in the 

mixture of interest, there is insufficient data or a lack of data. In this case, depending on 

the importance of the mixture of interest to public health, the U.S. EPA may decide: (1) 

there are insufficient data to conduct a cumulative risk assessment, and the component 

chemicals in the mixture would be assessed by single chemical risk assessment 

methods; or (2) new research initiatives are to be launched to fill the data gaps for the 

specific purpose of conducting dosimetry-based cumulative risk assessment for the 

mixture of interest. 

 A thorough evaluation of the toxicological literature for each chemical in the CMG 

must be performed. This may be performed in general accordance with risk 

assessment guidance for single chemicals because interactions between chemicals are 

considered separately. The type and quality of toxicological data should be assessed. 

Moreover, the data must be evaluated in relation to the species, gender and age of the 

animal as surrogates for humans. Ultimately, studies must be identified in which data 

describing doses and quantified responses are presented in sufficient detail to serve as 

29
 30 TABLE 6 Target Organ Toxicity Doses, Hazard Quotients and Hazard Indices Exposure Effect Resp CV GI Hemat Musc Liver Kidn

Chloroform 0.0025 TTD 0.51 0.26 1.4 2.1 1.4 0.01* 0.09

Hazard Quotient 0.0049 0.0097 0.0018 0.0012 0.0018 0.25 0.02

Tetrachloroethylene 0.005 TTD 6.7 6.7 6.7 0 0.01* 0.28 

Hazard Quotient 0.00074 0.00074 0.00074 0.5 0.01

Trichloroethylene 0.0025 

TTD 0.18 0.18 0.18 0.18 0.18 0.04 Hazard Quotient 0.014 0.014 0.014 0.014 0.014 0.06

1,1,1-Trichloroethane 0.050 

TTD 11 11 11 11 11 11 11 

Hazard Quotient 0.0047 0.0047 0.0047 0.0047 0.0047 0.0047 0.00

Hazard Index 0.024 0.029 0.021 0.0059 0.020 0.77 0.11 

* Liver is the critical organ for chloroform and for tetrachloroethylene. RfD values were taken from IRIS (U.S. EPA, 2007b)

values (NOAEL values) were taken from ATSDR toxicological profiles for the respective chemicals. 
the basis for the dose-response relationship. If the PBPK modeling approach will 

ultimately be used, PK or PD studies will have to be identified that provide information 

on the dose-response of the animal where the response is expressed relative to 

appropriate markers of exposure. These types of data can be then transformed to 

determine the appropriate NOAEL or benchmark dose (BMD) for each chemical. 

For the two mixtures considered in this project, as an illustration below, the three 

questions are answered and related discussions are provided. 

• Is there the likelihood of toxicological interactions among the chemicals in the 

mixture? At what concentration levels? 

As shown in Figures 2 and 3, respectively for the metabolic pathways of OP 

compounds (Mixture 1) and the drinking water disinfection byproducts and contaminants 

(Mixture 2), there are several possible pharmacokinetic interactions. All OP compounds 

share virtually the same metabolic pathways involving: (1) cytochrome P450 mediated 

oxidative desulfuration, dearylation, or dealkylation; (2) calcium-dependent hydrolysis of 

phosphoester bond; and (3) GST conjugation reactions. Similarly, there are interwoven 

metabolic pathways in the biotransformation of TCE, PERC, MC and CHF (see 

Figure 3). At the pharmacodynamic level, various oxon derivatives would certainly 

compete for the inhibition of brain AChE; thus, toxicological interactions are highly likely. 

Cancer and noncancer effects have been observed following exposure to the 

component chemicals of Mixture 2. During the CRA screening process, both cancer 

and noncancer effects, including reproductive effects, should be reviewed for potential 

(non-additive) interaction and significance. Three (TCE, PERC, CHF) of the four 

chemicals are thought to be carcinogenic at some doses; there is limited evidence that 

MC could be carcinogenic at this time. Thus, if cancer is the endpoint and MC is 

regarded as non-carcinogenic, the other three chemicals will be contributors to the 

overall risk, but the potential for MC to affect the carcinogenic potency of the other 

chemicals through PK interaction needs to be evaluated. The appropriate dose metrics 

for cancer risk assessment for TCE, PERC and CHF are not clearly established, but 

generally involve metabolites such as trichloroacetic acid or phosgene. There is limited 

information regarding potential pharmacodynamic interactions between these chemicals 

that can be addressed within a CRA. On the other hand, there have been several 

studies of pharmacokinetic interactions that will be useful (Dobrev et al., 2001, 2002; 

Haddad et al., 2000; Thrall and Poet, 2000). Indeed, for some of the mixtures of these 

four chemicals, PBPK interaction models exist (see below). A more likely scenario 

31
P

YO S

XO

OZ

P

YO O

XO

OZ

P

YO O

XO

OH

P

OH S

XO

OZ

P

YO S

OH

OZ

P

YO S

XO

OH

P

YO O

XO

OH

P

YO O

OH

OZ

P

OH O

XO

OZ

1

2 AChE Binding

P

YO S

XO

OZ

P

YO O

XO

OZ

P

YO O

XO

OH

P

OH S

XO

OZ

P

YO S

OH

OZ

P

YO S

XO

OH

P

YO O

XO

OH

P

YO O

OH

OZ

P

OH O

XO

OZ

1

2

FIGURE 2 

A Generalized Metabolic Schematic for OP Pesticides. Step 1 is activation and all other 

steps are detoxication. Pharmacokinetic interactions are most likely during Step 1. 

Interactions in other steps can be evaluated using PBPK models. Pharmacodynamic 

interactions may also occur during binding of the active moiety to AChE (Step 2). 

32
H

Cl Cl

Cl

SG

Cl Cl

H S

Cl Cl

H NH3+

H

COOS

Cl Cl

H NHCOCH3

H

COOH

CCl3 H

OH

OH

Fe

O

Cl

H C+

Cl

Cl

gluc O

CCl3

OH

Cl2CH

O

OH

Cl3C

O

SG

Cl H

Cl S

Cl H

Cl NH3+

H

COOS

Cl H

Cl NHCOCH3

H

COOH

C C S

Cl

H SH

ClCl

H

H

Cl2HC

S

OH CCl3

Thioketene

S-1,2-DCVC

CYP450

CYP450

TCOH TCOG

TCA DCA

CHO

S-1,1-DCVC

GSH

GSH

β-Lyase

β-Lyase

Cytotoxicity

Mutagenicity

Cytotoxicity

Mutagenicity

1,2-DVT

Excretion

in Urine

Excretion

in Urine

1

2

2

1

1

1

3

3

Potential interaction between PERC and TCE

Potential interaction between MC and TCE

Potential interaction between CHF and TCE

1

1

3

3

3

3

H

Cl Cl

Cl

SG

Cl Cl

H S

Cl Cl

H NH3+

H

COOS

Cl Cl

H NHCOCH3

H

COOH

CCl3 H

OH

OH

Fe

O

Cl

H C+

Cl

Cl

gluc O

CCl3

OH

Cl2CH

O

OH

Cl3C

O

SG

Cl H

Cl S

Cl H

Cl NH3+

H

COOS

Cl H

Cl NHCOCH3

H

COOH

C C S

Cl

H SH

ClCl

H

H

Cl2HC

S

OH CCl3

Thioketene

S-1,2-DCVC

CYP450

CYP450

TCOH TCOG

TCA DCA

CHO

S-1,1-DCVC

GSH

GSH

β-Lyase

β-Lyase

Cytotoxicity

Mutagenicity

Cytotoxicity

Mutagenicity

1,2-DVT

Excretion

in Urine

Excretion

in Urine

1

2

2

1

1

1

3

3

Potential interaction between PERC and TCE

Potential interaction between MC and TCE

Potential interaction between CHF and TCE

1

1

3

3

3

3

FIGURE 3 

Metabolic Pathway for Solvents Indicating Potential Sites for Interactions. This is 

superimposed on a metabolic schematic for TCE (Dobrev et al., 2001), metabolic steps 

where pharmacokinetic interactions could occur are denoted by numbers as shown. 

Identification of additional potential interactions should occur during CRA Steps 3 and 6. 

33
would be that some of the components could be excluded from the DBCRA (not from 

the cumulative risk assessment; for these chemicals, only a screening level assessment 

may be justified). It is important to avoid the expenditure of extensive resources to 

develop PBPK models for all components, only to find that the models indicate that a 

given component could have been omitted from the analysis based on a careful 

evaluation of data presented in the Initial Analysis. 

As suggested by Figure 3, there are several common metabolic processes that 

overlap for TCE, PERC, MC and CHF. All are initially oxidized by Cytochrome P450, 

mostly by CYP2E1. Several studies have shown competitive inhibition at this step 

(Koizumi et al., 1982; Dobrev et al., 2001). Other steps are common to two or more of 

the chemicals, including metabolism by GST and β-lyase family enzymes. Potential 

interactions at these stages should be considered. 

These interactions are likely to happen in vivo at the tissue levels of 

approximately nmolar to μmolar range which generally correspond to a mmole/kg 

administered dosing range (i.e., see Dobrev et al., 2002). 

 At this point, we believe that it is appropriate to address the toxicological 

interaction issue using a specific example reported in the earlier attempt of cumulative 

risk assessment of OP pesticides. In the Guidance on Cumulative Risk Assessment of 

Pesticide Chemicals That Have a Common Mechanism of Toxicity (U.S. EPA, 2002a), it 

was assumed that at lower levels of exposure typically encountered environmentally, no 

chemical interactions are expected (i.e., simple dose/concentration additivity would 

apply). For additivity to hold true, a further assumption must be that all the common 

mechanism chemicals exhibit similar pharmacokinetic and pharmacodynamic 

characteristics (i.e., distribute to the same target tissue and have similar elimination 

half-lives; exert toxicity via similar biochemical mechanisms, without specific regard to 

which organ is the critical target). In reality though, a case study of cumulative risk 

assessment of 33 organophosphorus pesticides provided BMDL (lower bound 

benchmark dose at ED10) with a range of 3977-fold (female) to 5528-fold (male) 

difference between the highest BMDL (for malathion) to the lowest BMDL for 

(dicrotophos) (U.S. EPA, 2002b; Table I.B.-4). These 3-4 order of magnitude 

differences among “common mechanism chemicals” suggest that the PK and PD may 

each be quantitatively different. For example, if each compound is active through its 

oxon form, and each has a similar potency, then one may expect a 3-4 order of 

magnitude difference in PK, which seems unlikely. If the PK of these two compounds 

are similar, then potency would have to differ some 3-4 orders of magnitude. Inasmuch 

as neither potency nor PK may be envisioned to differ to that degree between these 

34
chemicals, it seems logical to assume that both PK and PD differ between these 

chemicals. Differences in the extent of tissue exposure to toxicologically active 

chemical moieties with markedly difference potencies can be anticipated. These 

toxicological interactions are discussed a bit further in a hypothetical example in the 

following paragraph. Thus, the probability of toxicological interactions at the level of PK 

and PD exists. 

• Are these toxicological interactions likely to cause higher toxicity (i.e., synergism 

or potentiation) or lower toxicity (i.e., antagonism or inhibition) than the simple 

addition of the toxicity (dose addition or response addition) from all components 

of the mixture? 

With the OP-pesticide mixture, synergism/potentiation or antagonism/inhibition 

are likely depending on the rates of pharmacokinetic or pharmacodynamic interactions. 

For instance, all six OP compounds are phosphorothioates (i.e., P=S compounds); they 

require activation to their respective oxon (i.e., P=O) compounds to become potent 

AChE inhibitors. This requires oxidative desulfuration mediated by cytochrome P450. 

Depending on the substrates (e.g., the parent OP insecticides) that can compete among 

themselves for P450-mediated oxidation (bioactivation) and the involvement of other 

metabolic processes (e.g., competing enzymes that may conjugate the parent chemical 

with glutathione, a detoxication reaction), more or less potent oxon compounds may 

emerge (the oxon of one insecticide may be more potent than the oxon of another 

pesticide in the mixture) as the predominant (most prevalent) metabolite. If available, 

information on this competition between parent chemicals for oxidation and competition 

between different enzymes for bioactivation/detoxication will determine if 

synergism/potentiation or antagonism may result. 

In addition to compound differences in activation and detoxication, differences in 

the biological fate of the inhibition complex (Oxon-AChE) exist. Regardless of the OP 

examined, once the oxon form becomes bound to the AChE enzyme, that enzyme 

molecule is no longer capable of metabolizing acetylcholine. Once bound, other events 

occur, leading to either destabilization and ultimate dissociation of the inhibitory 

complex or stabilization and aging of the inhibitory complex. Each of the OP 

compounds identified is an effective inhibitor, but they differ in the half-life of the 

inhibitory complex with some dissociating within hours whereas others may take days. 

Once bound, the inhibitory complex may “age,” and become permanent. Once aging 

occurs, the enzyme cannot be reactivated and AChE activity can only be restored with 

35
the generation of new enzyme. Each of these OP compounds have different inhibition 

half lives and they differ in the extent to which they age, particularly so for dimethyl 

versus diethyl substituted compounds. When encountered in a mixture, compounds 

which bind readily, but which have a short inhibition half-life and relatively reduced 

potential to age, may actually be protective against AChE inhibition produced by other 

OP compounds. 

Similarly, depending on the competitive inhibition of cytochrome P450 2E1 in the 

initial metabolism of TCE by the other chlorinated hydrocarbon solvents/volatile 

organics, the GST conjugation pathway may be favored (Dobrev et al., 2002); this 

would lead to more production of reactive thiols for possible renal carcinogenesis—an 

example of interactive toxicity. 

One of the greatest challenges in dealing empirically with chemical mixture 

toxicology is the exponential rise of experimental groups as the number of component 

chemicals and doses increase. Frequently, systematic experimental studies of 

chemical mixtures become untenable because of the limitation of resources. Here, the 

possible application of reaction network modeling3 (Klein et al., 2002; Liao et al., 2002; 

Reisfeld and Yang, 2004; Yang et al., 2004a,b) can offer a workable solution to the 

complexity of chemical mixture toxicology, particularly relating to metabolism. 

• Under the exposure conditions and levels, are these toxicological interactions 

possible? 

In the cases of pesticide applicators, total exposure comprises both occupational 

exposures and additional exposures from drinking water, foods and household residues. 

For these individuals, total exposures may approximate the range where toxicological 

interactions are possible (for Mixture 1). Similarly, occupational exposures in chemical 

industry or manufacturing plants where TCE, PERC, MC and CHF are used, plus 

additional exposures from drinking water, foods, showers, etc. could also render the 

cumulative exposure concentrations to where toxicological interactions are likely. 

2.2.5. Step 5. Identify Chemicals in the Candidate Cumulative Assessment Group 

(CAG) for Further Evaluation. Select chemicals, chemical uses, routes and pathways 

 

3 Reaction Network Modeling: A chemical/petroleum engineering computer simulation technology to 

model the entire oil refinery based on initial chemical analyses of the feedstocks, as well as using graph 

theory, linear free energy relationship (LFER), computational quantum chemical calculations, quantitative 

structural activity correlation, Monte Carlo and quardrature modeling techniques. It is capable of 

simulating the fate of thousands of chemicals and tens of thousands of reactions simultaneously and 

predicting the outcomes of these chemical reactions.

36
from the CMG that have an exposure and hazard potential to result in cumulative effects 

(joint toxicity) for inclusion in the quantitative estimates of cumulative risk. 

This determination flows from the information acquired during Steps 1-4 and 

compiled in Step 4. For the purpose of an illustration for the present project, we will 

assume that all the component chemicals in the respective Mixtures 1 and 2 are the 

respective CAGs. In the event that (1) exposures are sufficiently small or (2) exposure 

or toxicity data are too limited to support including a chemical in the CAG, it will be 

omitted at this stage. Once again, depending on the importance of the mixture of 

interest to public health, this (the omission) may not be acceptable. In that case, there 

are two alternatives: (1) There are insufficient data to conduct cumulative risk 

assessment and the component chemicals in the mixture should revert back to the 

traditional single chemical risk assessment; or (2) New research initiatives are to be 

launched to fill the data gaps for the specific purpose of conducting dosimetry-based 

cumulative risk assessment for the mixture of interest. 

2.3. PHASE II: DOSIMETRY-BASED CUMULATIVE RISK ASSESSMENT 

 For those chemical mixtures selected to undergo DBCRA, the following 

additional five steps should be carried out. It is proposed that starting from Step 6 (i.e., 

the first step in Phase II), PBPK modeling should be incorporated. Common belief is 

that PBPK modeling is resource-intensive and it is difficult, particularly for models 

involving chemical interactions at the level of either pharmacokinetics or 

pharmacodynamics. This warrants some special discussion: First, all of the component 

chemicals in the mixtures used as examples in this document are industrially or 

environmentally important chemicals with uses and/or exposures of concern. When 

many chemicals reach this stage of commercialization, a substantial number of studies 

may have been conducted on them. Those chemicals have already become resourceintensive during the developmental stage to become successful chemicals in 

commerce. However, this is not the case for all commercially important chemicals. The 

important point is that quantitative, time-course data useful for PBPK model 

development may have already been generated during the product developmental 

phase. If the incentive (i.e., risk assessment-driven scientific studies) exists, such 

quantitative, time-course data would be generated during the product developmental 

phase automatically. In fact, PBPK modeling, being a hypothesis-testing tool in 

toxicology, may be utilized to conduct many different kinds of experiments on computers 

(i.e., in silico toxicology). Development of in silico toxicology such as PBPK modeling 

and other biologically based computer modeling will improve the “attrition rate” of drug 

37
or chemical product development. Thus, it may save precious resources by avoiding 

unnecessary experimental studies or minimizing animal experimentation. Second, while 

PBPK modeling is by no means a very easy technology, it is not any more difficult than 

some of the statistical modeling (e.g., linearized multistage carcinogenesis model) 

carried out in the routine risk assessment process. Furthermore, excellent training 

opportunities are available and the development of software is such that more and more 

user-friendly tools are going to be available. 

The advantages of incorporating PBPK modeling in CRA are many: First, PBPK 

modeling may bring the tissue dose, instead of the applied dose, into dose-response 

assessment. Thus, it is much more accurate in that pharmacokinetics has been 

employed to refine “exposure” in terms of tissue doses, reducing uncertainty. Second, 

PBPK modeling has the capability of incorporating toxicological interactions of multiple 

chemicals, representing a valuable feature for CRA. Third, PBPK modeling has the 

capability of extrapolation, be it dose-, species-, route-, age-, gender-dependent 

extrapolations. In many cases, such extrapolation may reach the region (e.g., very low 

doses) where experimental studies are impossible to conduct. Finally, PBPK modeling, 

in conjunction with Monte Carlo simulation, may estimate the true means (i.e., of tissue 

AUC values) by carrying out numerous repeated exposure simulations via computerbased programs. However, confidence in mean values will be tempered by the degree 

to which the distributions of the variable input parameters (i.e., chemical specific values 

such as partition coefficients and species specific values such as organ blood flows) are 

characterized. 

2.3.1. Step 6. Characterize Dose-Response, Point of Departure and Determine 

Relative Potency from Tissue Doses. The risk assessor should select and apply an 

appropriate method to characterize the dose-response relationship for effects and 

determine the relative toxic potencies of the CAG chemicals by each exposure route and 

duration of interest. Subsequently, the point(s) of departure for extrapolating the risk of 

the CAG should be chosen. As indicated in the guidance document (U.S. EPA, 2002a), 

the preferred point of departure is one derived from modeling the dose-response curve of 

the index chemical to derive a BMD that estimates a pre-specified level of response 

(e.g., BMD10, BMD5 or BMD1). The utility of PBPK modeling in the dose-response 

analyses is to provide tissue dose metrics such that in the BMD estimation, the tissue 

doses rather than the applied doses will be used in the Benchmark Dose Software 

(BMDS). Obviously, this requires identification of the target tissue. If a PBPK model 

capable of addressing chemical interactions (be they additive or not) cannot be 

38
constructed because of data gaps, PBPK models for individual chemicals (e.g., the OP 

pesticides) should be constructed to determine tissue dose metrics (e.g., concentration 

of active metabolite in brain tissue) based on PBPK modeling of given applied doses. 

That is, given the exposure level (applied doses), what would be level of the dose metric 

in the affected tissue (e.g., brain) relative to the level of inhibition of brain AChE (e.g., a 

10% reduction in AChE activity) and the related clinical signs? Pharmacokinetic models 

can simulate concentrations of toxicants in tissues for which biological monitoring cannot 

be conducted in humans and for which concentration data may not be available in test 

animals. Certainty in PBPK-based risk assessments is increased when there are tissue 

toxicant concentration data against which to compare PBPK model predictions. The 

availability of tissue toxicant concentration data for comparison should be considered 

when choosing a pharmacokinetic outcome (tissue concentration, dose metric) for risk 

assessment application. Of course, preferably, an interactive PBPK model is available at 

this point such that pharmacokinetic and pharmacodynamic interactions may be taken 

into consideration and “interaction thresholds” (El-Masri et al., 1996b; Dobrev et al., 

2001, 2002) may be estimated to obtain interactive NOAELs, LOAELs (which may be 

designated as NOAELInt and LOAELInt) and higher dose-response levels. 

When dose additivity is selected as the cumulative risk assessment model and 

once the models have been constructed, the index chemical should be selected and the 

potencies of other chemicals in the CMG should be determined based on dose 

response relationships characterized at the level of the dose metric most associated 

with the response. This might, for example, be the level of the dose metric 

corresponding to the animal ED10. Specifically, the dose-response relationships should 

be developed on the basis of internal, rather than external dose. In this manner, 

pharmacokinetic interactions that alter the dose metrics of interest can be made 

valuable in estimating the response from the mixture. 

2.3.1.1. Recommendations on Modeling ― The U.S. EPA guidance document 

included an informative discussion on “Modeling the Data” (Section 6.2.1.6, U.S. EPA, 

2002a). Although it is not necessarily for PBPK modeling, the discussion is relevant to 

the present effort. Some passages are quoted below and the CRA risk assessors using 

PBPK modeling should abide by these recommendations as much as possible: 

...The selection of a mathematical model structure to fit the data being 

analyzed should be guided by the biology of the common mechanism of 

toxicity, the toxicokinetics of the chemicals, and the observed shapes of 

their dose-response curves and the experimental designs used to 

39
generate the data. If available, pharmacodynamic and pharmacokinetic 

data should be considered in order to account for tissue concentrations 

and to aid in defining dose-response relationships across different 

species, routes and time-frames of exposure... 

...Although it is not possible to recommend the use of specific models, a 

few points that should be considered in modeling the data follow: 

• Modeling of individual animal data is desirable; however, if this is 

not practical, then use of summary data such as means and 

standard deviations can be alternatives 

• Care should be taken with modeling high-dose data (particularly 

extreme doses) because the model shape in the low-dose region 

can be influenced by high-dose data 

• Log transformation of data should be justified because such a 

transformation may distort the dose-response curve 

• Data variability should be described by appropriate statistical 

techniques and reflected in the potency estimate (e.g., by weighting 

the data in the fitting procedure) 

• Confidence intervals or limits should be included in the analysis 

because they can be valuable for evaluating the influence of 

variability on the potency estimates 

• An estimate for the uncertainty of the model used in the analysis 

should be included 

• The statistical fitting method used must be clearly described. 

2.3.1.2. Interactive PBPK Model: General Information ― As this is the first 

step of the dosimetry-based cumulative risk assessment process and the step where 

PBPK modeling initiates, it is appropriate to discuss the issues related to the 

incorporation of PBPK modeling. In addition to the discussion below, a recent paper on 

PBPK/PD modeling is attached as Appendix A. 

From the perspective of interactive PBPK modeling, two aspects need to be 

addressed: pharmacokinetic interactions and pharmacodynamic interactions. As 

indicated earlier under Step 4, pharmacokinetic and pharmacodynamic interactions are 

likely in both Mixtures 1 and 2. Thus, it is necessary to construct interactive PBPK 

models after a thorough review of the existing literature. The most ideal and 

scientifically defensible data requirement for establishing an interactive PBPK model is 

that each component chemical in the mixture already has its respectively established 

PBPK model and that there are many pharmacokinetic datasets in laboratory animals 

40
as well as in humans available for each of these component chemicals. In some cases, 

even more specific and stringent data requirements are needed; an example may be 

the ultralow dose pharmacokinetic data in perinatal developmental stages of laboratory 

animals for extrapolation to human fetuses, neonates and infants using PBPK modeling. 

However, until such time that quantitative time-course data useful for PBPK modeling 

are automatically part of the product developmental process, the most likely scenario is 

imperfect datasets. In Mixture 1, no PBPK models for fenthion, fenitrothion, diazinon, 

and methyl parathion could be located at this time. However, PBPK models do exist for 

parathion (Sultatos, 1990; Gearhart et al., 1994; Abbas and Hayton, 1997; Gentry et al., 

2002) and chlorpyrifos (Timchalk et al., 2002a,b; Kousba et al., 2003). In Mixture 2, 

because all the chemicals are important solvents with huge production volumes, PBPK 

models are available for TCE, PERC, MC and CHF. TCE has obtained perhaps the 

most attention of these chlorinated hydrocarbons/volatile organics in terms of the 

development of PBPK models, many of which have also been used to support risk 

assessment (Sato et al., 1977, 1991; Bogen, 1988; Koizumi, 1989; Fisher, 1993; Fisher 

and Allen, 1993; Fisher et al., 1989, 1990, 1991; Allen and Fisher, 1993; Clewell et al., 

1995; Cronin et al., 1995; Poet et al., 2000; Dobrev et al., 2001, 2002). Several PBPK 

models for the disposition of tetrachloroethylene were presented for animals and/or 

humans (Ward et al., 1988; Koizumi, 1989; Bois et al., 1990; Gearhart et al., 1993; Rao 

and Brown, 1993; Byczkowski et al., 1994; Dallas et al., 1994, 1995; Wilson and Knack, 

1994; Byczkowski and Fisher, 1995; Reitz et al., 1996; Loizou, 2001; Poet et al., 2002).4

 

PBPK models for MC alone or for mixtures of MC and TCE or other chlorinated solvents 

have been published (Reitz et al., 1988; Koizumi, 1989; Tardif and Charest-Tardif, 

1999; Dobrev et al., 2001). Several PBPK models that included progressively more 

sophisticated levels of biochemical complexity (e.g., relating to metabolite formation, 

cellular regeneration, enzyme inhibition) have been developed for chloroform (Corley et 

al., 1990, 2000; Gearhart et al., 1993; Chinery and Gleason, 1993; McKone, 1993; Roy 

et al., 1996; Levesque et al., 2000). 

2.3.1.3. PBPK/PD Models: Data Needs ― What are the specific data needed 

for building PBPK models? And what happens when such data are missing because no 

 

4 PBPK models developed for a single chemical may differ for many reasons, including the species 

addressed, route of exposure simulated, effect evaluated, choice for dose metric developed, differential 

reliance on in vivo datasets and/or in vitro datasets, choices for the values of physiological and chemicalspecific parameter values employed, etc.

41
PBPK modeling has been attempted on some of the components in the mixtures of 

interest? First, the data necessary for establishing a PBPK model for a single chemical 

will be presented. Obviously, well conducted in vivo pharmacokinetic experiments are 

essential and usually the more varied the datasets (e.g., different doses, routes, 

species), the better. Other PBPK modeling-specific information such as the chemicalspecific parameters (e.g., tissue partition coefficients, Vmaxs and Kms) is needed. 

Enzyme kinetic data, particularly human data, of the bioactivation (e.g., P=S to P=O 

conversion) and detoxication (e.g., oxidative cleavage, hydrolysis, oxidative 

dealkylation, etc.) processes will be important for the interactive PBPK model. In vitro

determination of tissue partition coefficients and enzyme kinetic data are relatively 

straightforward. A later section is devoted totally to high quality organ donor liver 

enzyme studies. Alternatively, with modern genetic engineering technologies, many 

human enzymes are available commercially, though their expression, singly, in nonmammalian species may complicate the extrapolation of results obtained from such 

preparations. For instance, several recombinant human CYP enzymes are now 

commercially available, including CYP 1A1, CYP 1A2, CYP 2B6, CYP 2C8, CYP 2C9, 

CYP 2C18, CYP 2C19, CYP 2D6, CYP 3A4, CYP 3A5, and CYP 3A7 (e.g., from 

GenTest [Woburn, MA], Cypex Ltd. [Dundee, Scotland], and Sigma-Aldrich [St. Louis, 

MO]). Thus, heretofore unavailable human enzyme kinetic information for many of the 

environmentally important chemicals is within easy reach for many laboratories. These 

experiments should be performed. 

Pharmacodynamically, for instance, once the oxon (i.e., P=O) derivatives are 

formed, a number of questions arise: What is the competition between the irreversible 

binding of the oxon derivative with red cell AChE, non-specific esterases, and brain 

AChE for any given single pesticide? The same question, although much more 

complex, should be asked about the mixture of 6 oxon derivatives. Here the ultra-low 

dose pharmacokinetic/pharmacodynamic studies in the binding of brain AChE with any 

given OP pesticide in the presence of others similar to those reported by Vogel et al. 

(2002) will be critical for the interactive PBPD modeling in the cumulative risk 

assessment. Both chemical specific and species specific data are required. Chemical 

specific data include metabolic rate constants and measures of solubility (e.g., partition 

coefficients). Species specific data include information on body weight, organ or 

compartment sizes, region/organ blood flow rates and media specific intake rates (i.e., 

alveolar ventilation rates). Body weight and relative contributions of specific organs 

(i.e., liver) to fractional body mass are fairly constant; they are usually modeled as point 

values. Regional or organ specific blood flows may vary, obviously with exercise; 

42
recent evidence suggests that hepatic blood flow may vary three-fold at rest among 

adults. Virtually all species specific data may vary, to some extent, among individuals 

and with age, as may metabolic rate constants. Chemical-specific partition coefficients 

may vary approximately two-fold among individuals, and metabolic parameter values 

may vary markedly, depending on the chemical and enzyme involved. 

2.3.1.4. PBPK/PD Modeling: Model Structures ― While the structure of the 

PBPK/PD model will have to be determined in detail based on the analysis conducted in 

Steps 1-5, a general suggestion on what the model might contain can be provided at 

this time. Figure 4 presents a graphical representation of the PBPK model that could be 

initially considered. The model consists of several familiar compartments. The 

CNS/PNS (central nervous system/peripheral nervous system) compartment should be 

added and indeed, can be split into two compartments if kinetic processes are 

significantly different in the two and if endpoints or validation datasets suggest doing so. 

The liver compartment is where metabolism is usually regarded as occurring, but 

extrahepatic metabolism should also be considered. For the drinking water scenario (or 

other ingestion scenarios) chemical exposure of the liver should be primarily via the 

portal flow from the GI compartment in addition to systemic flow. Portal blood flow may 

play a less prominent role (compared to arterial flow) for substances encountered via 

inhalation. Usually, the chemical is dosed directly to the GI compartment and first order 

uptake is often assumed, but more complex formulations can be used if modeling 

parsimony is not overly compromised. 

In the diagram, the liver compartment includes a schematic of simplified 

metabolic pathways. For metabolism, parsimony is important: only include discrete 

pathways if the data will support developing kinetic parameter estimates. The overall 

metabolism can be simplified in a number of ways: sequential metabolic steps can be 

modeled as one step that represents the rate-limiting step in the sequence. Parallel 

pathways can be lumped together. Insignificant pathways can be ignored. The extent 

to which a given pathway may be deemed insignificant should be judged on the basis of 

all information known for each component of the chemical mixture; a pathway deemed 

minor or insignificant for one chemical may be critical for another. Because the same 

model structure should be employed for each chemical (within reason) and for the 

mixture, the selection of pathways for inclusion should not be taken lightly. Some 

pathways can be multiple steps (e.g., oxidative desulfuration and hydrolysis) while 

others can be single staged (e.g., clearance pathways to non-toxic non-interacting 

metabolites). The importance of parsimony is emphasized by the limited amount of 

43
Lung

Fat

Slowly perfused

Rapidly perfused

CNS/PNS

GI Tract

Liver

Skin

Ingestion

Dermal

P YO S XO

z

P YO O XO

z

P YO O XO

OH

P OH S XO

z

P YO S OH

z

P YO S XO

OH

Arterial blood Binding to esterases

Venous blood Binding to esterases

Lung

Fat

Slowly perfused

Rapidly perfused

CNS/PNS

GI Tract

Liver

Skin

Ingestion

Dermal

P YO S XO

z

P YO O XO

z

P YO O XO

OH

P OH S XO

z

P YO S OH

z

P YO S XO

OH

Arterial blood Binding to esterases

Venous blood Binding to esterases

FIGURE 4 

A Preliminary PBPK Model Structure for OPs 

44
data (particularly in vivo) and the difficulty of extrapolating in vitro kinetic constants to in 

vivo or determining the constants from model optimization. Automated model 

optimization of parameters should be minimized because, for the single chemical 

models, each pathway can require 2 metabolic constants, there can be 2, 3, 4... 

different metabolic steps, some of these steps could actually inhibit each other if the 

same enzymes are used at high enough doses, and inhibition would require additional 

parameters. So for metabolism alone, it would be easy to get over 10 unknown 

parameters, which would be difficult to optimize with any certainty. This is before 

consideration of inter-component PK interactions, which require yet more optimization. 

However, more than one metabolic step will nevertheless be required. 

In the schematic (Figure 4), lung and skin compartments are dotted boxes to 

represent the fact that they may or may not be required for the drinking water 

assessment. If dermal exposure to drinking water sources does not contribute 

significantly (a judgment call) to the aggregate exposures, the dermal compartment may 

be unnecessary for the initial model, but may be desirable for future modeling work 

(especially for occupational exposure PBPK models). Depending on the chemical and 

its volatile properties, only occupational exposure models may require the inhalation 

route of exposure. As with the discussion of age and gender, the model is usually built 

for the route where the most complete datasets are available and then extended to 

other routes. However, for this study, the ingestion route should be given decided 

preference unless data are severely lacking. If inhalation models are developed, 

attention must be paid to the form of the chemical (i.e., aerosols) and non-equilibrium 

processes in uptake kinetics. 

The CNS/PNS compartment will need to include a submodel for binding to AChE. 

A simple conceptualization of this is proposed in Figure 5. As OP binding is considered 

largely irreversible and causes complete inhibition of the enzyme (individual enzymes 

that are bound), the degree of inhibition would reflect the number of bound enzymes. 

However, the activity of AChE is not necessarily proportional to unbound (free) enzyme; 

there may be a “reserve capacity” in that not all enzyme is required to clear 

acetylcholine in synapses. Thus, the response may be a non-linear function of the 

molecular events. PD models will be a useful way to describe these processes once 

developed. 

The PD submodel should include a description of the process of synthesis of 

AChE, an on-going process. The kinetics of this process may be zero order (as is often 

assumed for glutathione synthesis (D’Souza and Andersen, 1988), or could be 

inducible. Binding of free AChE by activated OPs may be a saturable process 

45
CNS/PNS

Native AChE Pool AChE resynthesis

Ko?, Inducible?

Binding by OPs

Competitive inhibition?

)

][ 1(*][

max ][

Ki

I S Kd

SB

++

∗

CNS/PNS

Native AChE Pool AChE resynthesis

Ko?, Inducible?

Binding by OPs

Competitive inhibition?

)

][ 1(*][

max ][

Ki

I S Kd

SB

++

∗

FIGURE 5 

A Pharmacodynamic Submodel for CNS/PNS Compartment 

46
describable by a Bmax or Hill’s equation [Bmax + S/(S+Kd)] with or without inhibition by 

competing OPs. However, since it is usually regarded as irreversible, a linear equation 

may do equally well and will involve fewer parameters. An empirical relationship 

between the concentration of free AChE and neurological effects (e.g., tremors) will 

complete the PD portion of the model. Similar submodels will need to be included for 

binding to other esterases, primarily in the blood. 

In order to attain reasonable level of confidence, the interactive PBPK models for 

mixtures should be developed only after the component single chemical models are 

developed and validated. Likewise, the PD submodels should not be incorporated until 

the PBPK models are as validated as possible. 

The models can be exercised with existing animal data to determine practical 

thresholds for toxicokinetic interactions. In the event that human exposure is at lower 

ranges than the model is validated for, particular attention should be paid to the 

presence or absence of interactions at the lowest validated level. It would be useful to 

perform low-dose PK studies in animals (or humans) to extend the range of the model 

downwards, especially if it can be extended down to the range of the exposures under 

consideration. Moreover, it would be even more important to do the low dose 

experiments if (non-additive) interactions are still occurring at levels below the model 

validation range, to determine the shape of the dose response curve below the point of 

departure. 

2.3.1.5. Human PBPK Modeling: Incorporation of In Vitro Enzyme 

Studies ― The ultimate goal of PBPK modeling is to provide scientifically defensible 

computer simulations of the fate of the chemical or chemical mixtures in humans. There 

are ethical and other problems with in vivo human toxicology studies, particularly with 

highly toxic pesticidal chemicals such as OPs. Thus, the building of human PBPK 

models must rely on allometric extrapolation of animal data and/or in vitro studies using 

human tissues. While many of the physiological parameters on humans are readily 

available in the physiology literature, some parameters which are chemical specific such 

as tissue/blood partition coefficients and metabolic parameters (Km and Vmax) are best 

empirically derived. Recent advances in PBPK modeling have demonstrated the ability 

of the technique to include extrapolations made from data on chemical metabolism and 

enzyme contents derived from in vitro human and research animal tissue preparations 

(Kedderis and Held, 1996; Kedderis, 1997; Lipscomb et al., 1997, 1998, 2003a,b; 

Lipscomb and Garrett, 1998; Snawder and Lipscomb, 2000; Kedderis and Lipscomb, 

2001; Lipscomb and Kedderis, 2002). In particular, careful experimental measurements 

47
and statistical evaluations on the content of microsomal protein, cytochrome P450 

enzymes, in human liver samples derived under organ transplant conditions had been 

carried out (Lipscomb et al., 1997, 1998, 2003a,b; Lipscomb and Garrett, 1998; 

Snawder and Lipscomb, 2000). This is important because some past human studies 

employed liver samples from cadavers, which were metabolically compromised. 

Lipscomb et al. (2003b) recently integrated their various human in vitro studies from 

organ donors and demonstrated the application of such information via PBPK modeling 

in a risk assessment framework. This paper (Lipscomb et al., 2003b) provided a 

stepwise illustration of how to incorporate three different datasets (the microsomal 

protein content of human liver, the CYP2E1 [the principal metabolic enzyme for TCE] 

content of human liver microsomal protein, and the in vitro Vmax for TCE oxidation by 

humans) into a PBPK model for risk-relevant pharmacokinetic outcome in humans. 

Using a variety of statistical analyses, the 5th and 95th percentiles of the resulting 

distribution on Vmax (TCE oxidized per minute per gram liver) differed by approximately 

6-fold. These values were converted to the in vivo Vmax (mg TCE oxidized/hr/kg body 

weight) and incorporated into a human PBPK model for TCE. Model simulation under 

the conditions of 8-hr inhalation exposure to 50 ppm or drinking water exposure at 5 μg 

TCE in 2 L/day revealed that 6-fold variation in Vmax (i.e., Vmax at 5th or 95th percentiles 

levels) resulted in only 2% or less differences in metabolism of a key intoxicating step, 

the formation of chloral hydrate (Lipscomb et al., 2003b). On the surface, this finding, 

which is suggestive of Vmax insensitivity under the model simulation conditions, has 

strong implications in risk assessment. In essence, it suggests that, at low 

environmental or occupational exposure conditions, individual variability of metabolic 

capacity as large as 600% (6-fold) has little or no impact on the toxic outcome of TCE. 

However, upon closer examination, valid scientific explanation is available. Kedderis 

(1997), in studying the effect of enzyme induction on the bioactivation of TCE and other 

volatile organic compounds, indicated that the hepatic blood flow limitation plays an 

important role in the kinetics of bioactivation. What happens in the above situation 

(Lipscomb et al., 2003b) is that, even though the individual metabolic capacity varies 

greatly (i.e., 600%), the rate of hepatic blood flow delivery of TCE and its related 

metabolites (formed from earlier passages through liver cells) to the liver is much slower 

than the rate of bioactivation in the liver. Thus, this “flow-limited process” (that delivers 

concentrations below those that are in the linear range of the metabolic rate versus 

substrate concentration curve) is a more important factor than maximum metabolic rate, 

thus limiting the impact of the large variability in metabolic capacity in the population. 

These results underscore the importance of considering the overall dynamic equilibrium 

48
of all the relevant biological processes in the body—in some sense, a broader 

application of the systems biology approach. The Lipscomb et al. (2003b) paper is 

attached as Appendix B as instructional material for the incorporation of human in vitro

data into the PBPK modeling process. 

Appendix C provides summary information for each of the component chemicals 

in the two mixtures identified for this project. It includes information on the toxicity, 

metabolism and pharmacokinetics for each chemical. 

2.3.2. Step 7. Develop Detailed Exposure Scenarios for All Routes and Durations. 

The contribution to isolated or individual chemical exposures to the potential magnitude 

of exposure should be characterized. Once these are characterized, a decision should 

be reached as to whether these exposure scenarios should be included in a qualitative 

exposure assessment. Consideration should be given to the identification of 

subpopulations and their locations, as well as to co-exposures to multiple chemicals. 

As drinking water is the primary concern for this project, emphasis will be placed on the 

oral consumption of finished drinking water. Review of pertinent literature on the 

component chemicals in the mixture is the first step (see Appendix C). In the guidance 

document (U.S. EPA, 2002a), it was stressed that cumulative risk assessments should 

reflect use patterns and practices on a scale sufficient to capture the variability in 

pesticide use, but not so large as to inappropriately dilute real and significant 

differences. A specific example was given on fenthion, one of the six OP pesticides 

selected by the U.S. EPA for the first mixture. Apparently, fenthion was used for 

localized mosquito control in parts of southern Florida; therefore, it was stated that this 

pesticide should have only limited consideration in an assessment of other OP 

pesticides, including those used for mosquito control (U.S. EPA, 2002a). Exposure 

assessments should take into account those factors that impact the targeted group of 

humans or the geographic region of interest. 

2.3.3. Step 8. Quantify Parameters for Exposure. Exposure should be presented in 

as quantitative a manner as is possible. The magnitude, frequency and duration for all 

pertinent exposure pathway/route combinations should be determined. Appropriate 

sources of use/usage information, chemical concentrations in all appropriate media, and 

any modifying factors necessary should be included in the assessment. Where 

necessary, surrogate datasets developed for similar chemicals, published literature or 

generic datasets should be identified and justified for inclusion. Here, emphasis will be 

placed primarily on drinking water. The guidance document (U.S. EPA, 2002a) 

49
indicated that evidence of the co-occurrence of pesticides within a drinking water source 

for a CAG is a critical piece of information needed prior to making a decision to include 

more than one pesticide in a cumulative drinking water exposure assessment. Direct 

measurements of combinations of pesticides in finished drinking water are rarely 

available. However, U.S. Geological Survey’s National Water Quality Assessment 

Program databases do contain information on the co-occurrence of a wide variety of 

pesticides in ambient surface water, and some registrant-sponsored studies provide 

co-occurrence data for some compounds in drinking water. The exposure scenarios 

may take on one of several forms, as previously described. In accordance with U.S. 

EPA guidance (2002a), the exposure data will be formatted for input into the 

quantitative DBCRA taking into account, as appropriate, seasonal variations, 

geographical variations, and other variations in exposure. The range of exposures 

determined as such must be part of the exposure input. Yet more sophisticated 

approaches, such as the one suggested by Figure 6, can also be considered. In 

Figure 6, the distributions of exposures are determined and used as input to a PBPK 

model. As interactions between component chemicals in the CAG will depend on the 

magnitude and duration of the exposure to each chemical, non-PBPK models would 

necessarily have to attempt to simplify the basis for the interactions in some empirical 

manner. Alternately, PBPK models (should validated ones be developed) can be used 

to estimate the actual extent of toxicity (AChE inhibition or as otherwise desired). The 

model can be run iteratively for as many exposure scenarios as necessary. Indeed, it is 

quite feasible to use a Monte Carlo sampling approach to sample from the distributions. 

For example, within each of 500 communities, the simulation of 1000 exposures (i.e., 

what 1000 persons would be exposed to) would not be an unreasonable task. This 

would allow the determination of cumulative risks in various locations, times of year, 

etc., as well as for the nation as a whole. 

2.3.4. Step 9. Conduct Dosimetry-Based Cumulative Risk Assessment. This is 

the point in the process where the pharmacokinetic data and model are combined with 

the defined exposure scenario(s) to estimate internal dosimetry. The resulting internal 

doses are combined with data describing dose-response and potency defined in step 6. 

To accomplish this, route/duration-specific dose metrics associated with specific risks 

should be identified and internal doses should be expressed in these terms. A trial run 

should be conducted initially and its results evaluated. The model should be subjected 

to a sensitivity analysis, which will identify model assumptions and parameters that most 

influence the production of the risk related dose metric. An assessment of the 

50
3. Geographically defined 

distribution of risk estimates

95% LCL

Fraction of 

population with 

cumulative risk 

>= 1.0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Hazard Index

Probability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

2. Interaction PBPK 

Model-Based 

Determination of Tissue 

Doses and Toxicity

CNS AChE

inhibition resulting 

from all mixture 

components

1. Population-weighted,

geographically-defined

exposure data for each 

mixture component

Monte Carlo sampling of 

exposure data from established distributions

Statistical analysis of 

model outputs for all 

exposure scenarios

Chemical A

Chemical B

Chemical C

Chemical D

Chemical E

Region XYZ

3. Geographically defined 

distribution of risk estimates

95% LCL

Fraction of 

population with 

cumulative risk 

>= 1.0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Hazard Index

Probability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

2. Interaction PBPK 

Model-Based 

Determination of Tissue 

Doses and Toxicity

CNS AChE

inhibition resulting 

from all mixture 

components

1. Population-weighted,

geographically-defined

exposure data for each 

mixture component

Monte Carlo sampling of 

exposure data from established distributions

Statistical analysis of 

model outputs for all 

exposure scenarios

Chemical A

Chemical B

Chemical C

Chemical D

Chemical E

Region XYZ

95% LCL

Fraction of 

population with 

cumulative risk 

>= 1.0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Hazard Index

Probability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

2. Interaction PBPK 

Model-Based 

Determination of Tissue 

Doses and Toxicity

CNS AChE

inhibition resulting 

from all mixture 

components

1. Population-weighted,

geographically-defined

exposure data for each 

mixture component

Monte Carlo sampling of 

exposure data from established distributions

Statistical analysis of 

model outputs for all 

exposure scenarios

Chemical A

Chemical B

Chemical C

Chemical D

Chemical E

Region XYZ

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Hazard Index

Probability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

2. Interaction PBPK 

Model-Based 

Determination of Tissue 

Doses and Toxicity

CNS AChE

inhibition resulting 

from all mixture 

components

1. Population-weighted,

geographically-defined

exposure data for each 

mixture component

Monte Carlo sampling of 

exposure data from established distributions

Statistical analysis of 

model outputs for all 

exposure scenarios

Chemical A

Chemical B

Chemical C

Chemical D

Chemical E

Region XYZ

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4

2. Interaction PBPK 

Model-Based 

Determination of Tissue 

Doses and Toxicity

CNS AChE

inhibition resulting 

from all mixture 

components

1. Population-weighted,

geographically-defined

exposure data for each 

mixture component

Monte Carlo sampling of 

exposure data from established distributions

Statistical analysis of 

model outputs for all 

exposure scenarios

Chemical A

Chemical B

Chemical C

Chemical D

Chemical E

Region XYZ

FIGURE 6 

A Schematic for Integrating Data for Cumulative Risk Assessment. This is a possible 

method for integrating exposure distributions with PBPK models for several interacting 

chemicals and analyzing model output to determine distribution of cumulative 

risk estimates. 

51
subpopulations of concern should be undertaken, and uncertainty and FQPA safety 

factors should be recommended. The documents Cumulative Risk: A Case Study of the 

Estimation of Risk from 24 Organophosphate Pesticides (U.S. EPA, 2000b) and 

Organophosphate Pesticides: Revised OP Cumulative Risk Assessment (U.S. EPA, 

2002b), as well as the documents addressing the cumulative risk of N-methyl 

carbamates, triazines and the chloroacetanilides should be consulted for carrying out 

cumulative risk assessment. The results of the DBCRA should be presented so that the 

reader will understand which dose metrics for which chemicals and effects were 

selected and simulated, and the impact that a cumulative exposure will have on the 

production of these dose metrics and, ultimately, on risk. 

Assigning FQPA Safety Factors is restricted to children and restricted to 

pesticide chemicals only (refer to OPP guidance and to Step 10, below). 

2.3.5. Step 10. Characterize Cumulative Risk via Pharmacokinetic Analysis. 

Describe the results and conclusions of the cumulative risk analysis, including the 

relative confidence in toxicity and exposure data sources and model inputs. Discuss 

major areas of uncertainty, the magnitude and direction of likely bias, and the impact on 

the final assessment. Evaluate the risk contributions from each pathway and route 

individually, as well as in combination. Identify risk contributors with regard to 

chemical(s), pathway, source, time of year, and impacted subpopulation (with particular 

attention to children). Conduct sensitivity analyses to determine those factors most 

likely to impact the risk. Determine need for additional uncertainty and safety factors. 

 A summary of the risk characterization should include a restatement of the scope 

of the issue being addressed, explaining the chemicals under evaluation, data available 

and their strengths and weaknesses including uncertainties, the assumptions made 

during the analysis, and how the results are interpreted for each of the demographic 

groups represented. These data include those pertaining to the temporal and 

geographic nature of exposure, including food and water ingestion rates, contamination 

levels (including non-detects) and the methods used to develop distributions for variable 

data. Special emphasis should be placed on how groups differ, and the likely bases for 

those differences, including anatomic, biochemical and physiologic differences, as well 

as differences related to exposures. With respect to PBPK modeling, these differences 

may be due to age and sex-dependent differences in body composition and/or 

metabolic capacity. The risk characterization section should report biases in datasets 

employed, and a general evaluation of the level of confidence placed in the analysis. 

Significant sources of uncertainty should be communicated, and when possible, the 

52
results of a sensitivity analysis should be used to demonstrate the level of impact that 

these uncertainties may have on the overall outcome. Outcome should be 

communicated as the level of the dose metric(s) most closely associated with the health 

endpoints of concern. Regarding sensitivity within the human population, the 

application of FQPA safety factors is restricted to children and to pesticide chemicals. 

The safety factor and the bases for its derivation (a value of up to a factor of ten) have 

been described in other documents developed by OPP. When data and circumstances 

warrant, the additional FQPA safety factor may not be applied. When applied, however, 

the factor is applied to the results for the chemical mixture, rather than to the individual 

chemicals. 

53
3. CONCLUSIONS 

In this report, we outline some of the considerations that are involved in 

performing CRA using a PBPK-modeling based approach. While this report makes a 

number of specific suggestions and observations, additional details of a PBPK-based 

approach to CRA will depend on the specific chemicals in a CAG, which are not known 

until the initial analysis is complete; and issues that arise during the risk assessment 

process itself, which are largely unknown until the work is undertaken. For 

methodological issues that are decided on the basis of scientific judgment, decisions 

should be made with the assistance of appropriate advisory committees dedicated to 

the subject at hand. 

PBPK modeling-based approaches offer several advantages in CRA. First, U.S. 

EPA has recently advocated using biologically-based approaches in risk assessment. 

Indeed, these biologically based approaches offer the best means of performing many 

of the extrapolations that are necessary in the risk assessment process. Biologically 

based approaches were further recommended by U.S. EPA for use in CRA, when they 

are available. Because of their ability to extrapolate dosimetry across dose, species, 

sex, route and age, PBPK modeling is often the favored biologically-based method for 

determining tissue dosimetry. Second, PBPK modeling is the simplest method for 

characterizing PK interactions in the body that is directly based on the biology of the 

process. Other methods are empirical and not only take a significant effort to develop, 

but have uncertainties when extrapolations are made to scenarios that are untested 

(e.g., empirical methods developed for A+B and B+C do not translate easily into a 

method for A+C). Third, if significant PK or PD interactions occur, DBCRA cannot be 

reasonably performed without addressing the interactions at the level of target tissue 

concentration. In other words, why go from single chemical risk assessments to 

multiple chemical risk assessments when one of the ramifications of multiple chemical 

exposures (toxicokinetic interactions resulting in altered tissue dosimetry) is omitted? 

Implementing PBPK modeling-based approaches to CRA would require the 

development of PBPK models for the individual chemicals in the CAG as well as a 

quantitative definition of the nature of the interactions. While PBPK models exist for 

some of the chemicals considered here, some data are available to support the 

development of PBPK models for the rest. Most likely, the biggest data gap consists of 

data regarding interactions. For the chlorinated solvents, sufficient data may be 

available at this time; indeed some interaction models are already in the literature 

(Dobrev et al., 2001, 2002; Haddad et al., 2000; Thrall and Poet, 2000). For the OP 

54
pesticides, data gaps for some combinations of chemicals are likely. Nevertheless, the 

burden of developing PBPK models for this group is greatly reduced by the fact that the 

same model structure can likely be used for all six chemicals. With sufficient data, 

building the interactive PBPK models is likely to be feasible. 

For drinking water scenarios, multiple route (drinking, inhalation and dermal 

through bathing) exposure is a possibility. Even if the CRA is restricted to direct 

consumption of drinking water itself, the pharmacokinetic and pharmacodynamic 

processes involved may be non-linear, including differing rates of metabolism for 

different chemicals, inhibition and replenishment of AChE, etc. These types of nonlinear processes are well characterized by PBPK models. Moreover, the drinking water 

scenario involves a complex set of exposures. For each chemical, a different 

distribution of exposures, varying by season of the year, spatial location, and other 

factors must be incorporated into the assessment. When this level of complexity is 

added to the variety of issues mentioned above, simple algebraic approaches to 

calculating risks become strained. Other modeling approaches may be better suited to 

address the exposure assessment part of the CRA, and these approaches are easily 

tied into the PBPK model. 

The approach recommended in this report is completely consistent with existing 

U.S. EPA guidance. The PBPK model itself largely affects the dose-response analysis 

of the CRA. In essence, a PBPK modeling approach is one alternative method 

suggested by U.S. EPA in performing dose-response analysis. However, if a PBPK 

model is to be used, other aspects of the CRA are also affected. The approach used for 

exposure assessment, for example, must be tailored in such a way that the structure of 

the dataset is appropriate as an input to the PBPK model. As a second example, the 

criteria used to determine whether exposure to a chemical is sufficient to warrant its 

inclusion in the CAG is also modified by the fact that chemical interactions are 

addressed within the CRA. Specifically, this report points out that a chemical could 

conceivably be retained in the CAG based on an ability to interact with another 

chemical, regardless of whether the retained chemical can directly cause the toxicity 

that is the critical endpoint for the analysis. In this way, a PBPK modeling-based CRA 

may more accurately determine the actual cumulative risk from a set of chemicals than 

an approach that does not quantify the impact of interactions on toxicity. Last but not 

least, the ultimate goal for risk assessment is the protection of public health. Therefore, 

PBPK modeling of chemical and chemical mixtures in humans is absolutely essential. 

Accordingly, the incorporation of quality human tissue studies into the PBPK modeling 

process is of critical importance. As a final concluding remark, as U.S. EPA advances 

55
the science of cumulative risk assessment, PBPK modeling should be incorporated 

where appropriate. 

56
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