1 

Source-to-Outcome Modeling Physiologically Based 

Pharmacokinetic/Pharmacodynamic (PBPK/PD) Model Linked to a 

Dietary Exposure Model: Chlorpyrifos as a Case Study 

Science Issues Paper for SAP Meeting on 

Chlorpyrifos Physiologically Based Pharmacokinetic/Pharmacodynamic (PBPK/PD) 

Model linked to the Cumulative and Aggregate Risk Evaluation System (CARES) 

February 15-18, 2011, Washington, D.C. 

Dow AgroSciences 

The Dow Chemical Company 

Battelle Pacific Northwest National Laboratory 

January 7, 2011 
2 

Table of Contents

Page 

Terminology............................................................................................................................... 5

Project Contributors ................................................................................................................... 8

Executive Summary ................................................................................................................... 9

1. Introduction.................................................................................................................. 13

1.1 Report objectives..................................................................................................... 13

1.2 Document organization ........................................................................................... 15

1.3 The use of a source-to-outcome model and the future of risk assessment.............. 17

2. CPF metabolism/mechanism of action......................................................................... 21

Section overview...................................................................................................................... 21

2.1 Chemical properties................................................................................................. 21

2.2 Metabolism studies.................................................................................................. 23

Dose-dependent kinetics................................................................................................. 25

2.3 Sites of metabolism ................................................................................................. 26

Metabolizing enzymes.................................................................................................... 26

Age-dependent kinetics .................................................................................................. 27

2.4 New data on age-dependent metabolic rates ........................................................... 28

Materials and methods for in vitro study........................................................................ 28

Results for in vitro study ................................................................................................ 29

Section summary ............................................................................................................ 35

3. PBPK/PD modeling in typical humans of various ages............................................... 36

Section overview ............................................................................................................ 36

3.1 History of PBPK/PD model .................................................................................... 37

3.2 Development of the LifeStage model...................................................................... 40

Summary of Tissue Ontogeny ........................................................................................ 53

Blood flows .................................................................................................................... 55

Enzyme activity in individual......................................................................................... 55

3.3 Comparison of LifeStage model output to human data........................................... 58

3.4 Comparison of LifeStage model to Timchalk et al. 2002 ....................................... 63

3.5 Predicted dose response in typical individuals at 6 months, 3 and 30 years........... 64

Section summary ............................................................................................................ 65

4. Creation of the Variation PBPK/PD model ................................................................. 66

Section overview ............................................................................................................ 66

4.1 Step 1. Determining the relative importance of model parameters using a 

sensitivity analysis................................................................................................... 66

4.2 Step 2. Developing distributions for sensitive parameters...................................... 69

Physiology ...................................................................................................................... 69

Total Blood flow (Cardiac Output) ................................................................................ 70
TABLE OF CONTENTS (continued) 

Page 

3 

Effect of activity on total compartment specific blood flows ........................................ 70

Modeling enzyme activity levels.................................................................................... 72

Combination of enzyme and tissue volumes .................................................................. 76

Variation in Response to CPF and Ethnicity.................................................................. 77

4.3 Step 3. Accounting for correlation .......................................................................... 78

4.4 Step 4. Comparison of variation model’s predictions to observed variation 

in human volunteer studies...................................................................................... 79

4.5 Description of the Variation model......................................................................... 80

4.6 Applying the Variation model to the assessment of the toxicity of CPF ................ 81

4.7 Evaluating the contribution of the different sources of variation on human 

response to CPF....................................................................................................... 81

Methodology................................................................................................................... 81

Factors that drive variation in RBC AChE inhibition .................................................... 83

Evaluation of the factors that drive variation in the level of brain AChE 

inhibition............................................................................................................. 89

4.8 Interindividual variation in response- predictions by the Variation model............. 91

Section summary ............................................................................................................ 95

5. Modeling variation in dietary exposure for different age groups ................................ 97

Section overview ............................................................................................................ 97

5.1 Dietary exposure assessments ................................................................................. 97

5.2 Results of the dietary exposure modeling prediction of daily doses....................... 99

5.3 Simulating longitudinal exposures ........................................................................ 101

Section summary .......................................................................................................... 104

6. Connection of exposure and PBPK/PD models......................................................... 105

Section overview .......................................................................................................... 105

6.1 Assigning characteristics to each individual ......................................................... 105

6.2 Definition of dose in the source-to-outcome model.............................................. 106

6.3 Design of the source-to-outcome model ............................................................... 108

Section summary .......................................................................................................... 109

7. Predictions of one and multiple day (longitudinal) dietary exposures on 

internal doses and cholinesterase inhibition............................................................... 110

Section overview .......................................................................................................... 110

7.1 Model predictions of the impacts of identical daily exposures over time............. 110

7.2 Modeling the impacts of time varying dietary exposures –determining when 

a population reaches quasi-steady state conditions ............................................... 114

7.3 Impact of three different longitudinal approaches of dietary exposure................. 118

7.4 Determining the impact of longitudinal exposures on the impacts of high 

level dietary exposures (the 99.9th percentile dose) on RBC and brain AChE 

inhibition ............................................................................................................... 120

Section summary .......................................................................................................... 124

8. Evaluation in the uncertainty in the predictions of source-to-outcome model by 

comparison to measured values ................................................................................. 126
TABLE OF CONTENTS (continued) 

Page 

4 

Section overview .......................................................................................................... 126

8.1 Finding data for model evaluations....................................................................... 126

8.2 Using biomonitoring data of CPF and its metabolites to evaluate the dietary 

and PBPK portions of the source-to-outcome model............................................ 129

8.3 PBPK/PD model predictions of the relationship between TCPy levels in 

urine and butyryl cholinesterase in plasma in occupationally exposed adults...... 134

Section summary .......................................................................................................... 137

9. Model predictions of dietary dose impact on brain and RBC inhibition. .................. 138

Section overview .......................................................................................................... 138

9.1 Model predictions.................................................................................................. 138

Section summary .......................................................................................................... 144

10 Using the source-to-outcome model to estimate compound and age-specific 

uncertainty factors...................................................................................................... 146

Section overview .......................................................................................................... 146

Example development of interspecies (UFA) uncertainty factor for adults and 

infants ............................................................................................................... 148

Example development of interindividual (UFH) uncertainty factor for adults and 

infants ............................................................................................................... 155

Section summary .......................................................................................................... 158

11 Summary .................................................................................................................... 159

Acknowledgements................................................................................................................ 163

Attachments ........................................................................................................................... 164

References.............................................................................................................................. 165
5 

Terminology

AChE – acetylcholinesterase 

ADME – adsorption, distribution, metabolism, and excretion 

BuChE – butyrylcholinesterase 

CARES: Cumulative and Aggregate Risk Evaluation System, a dietary exposure software 

program 

CNS - Central Nervous System

CPF – Chlorpyrifos 

CPF-oxon – Chlorpyrifos oxon 

CYP – Cytochrome P450 

Dietary exposure software: A software program that relates data on pesticide residues in 

agricultural commodities to daily doses for one or more consecutive days in populations of 

individuals of a specific age. Examples of these models include CARES, LifeLine, DEEMTM

and SHEDS. 

Gompertz function: A mathematical equation of a specific form. Used for fitting increases 

in volume as s function of weight or age. 

LifeLine: A dietary exposure software program

NHANES – National Health and Nutrition Examination Survey 

PBPK/PD- physiologically based pharmacokinetic and pharmacodynamic 

RBC – red blood cell 
6 

Sensitivity: a) A measure of the correlation of a change in one variable and another in a 

model, and b) A measure of the toxicological change from a given dose. 

SHEDS: Stochastic Human Exposure and Dose Simulation, a dietary exposure program

Source-to-outcome model: a model (composed of exposure and PBPK/PD software) that 

allows the direct determination of the potential for the occurrence of apical effects from 

levels of residues in foods. 

TCPy - 3, 5, 6-trichloro-2-pyridinol 

Typical Adult model: A PBPK/PD program that describes the pharmacokinetics and 

pharmacodynamics of CPF, its major metabolites, and cholinesterase for a typical adult from 

oral doses of CPF, based on the model first described by Timchalk et al, (2002). 

LifeStage model: PBPK/PD program expanded to include the ontogeny of enzymes and 

tissue compartments with age to describe the pharmacokinetics and pharmacodynamics of 

CPF from oral doses. 

Longitudinal dietary simulations: The statistical processes used to construct individuals’ 

dietary intakes over 2 or more consecutive days based on data on multiple individuals 

collected in dietary surveys that only determined data on two non-consecutive days. 

Uncertainty: Epistemic uncertainty arising from incomplete knowledge. 

Variation model: a PBPK/PD program that includes interindividual variation in response to 

oral doses of CPF in populations of individuals of specified ages, an expansion of the 

LifeStage model. 

Variability: Aleatory uncertainty, arising from actual differences in dose received or 

response to those doses due to differences in individuals. 

Z-score: A method of describing variation in terms of a distribution’s mean and standard 

deviation. Z-scores of -2, -1, 0, 1, 2 are values that fall at two standard deviations below the 

mean, one standard deviation below the mean, the mean value, one standard deviation above 
7 

the mean and two standard deviations above the mean. The following table gives the Zscores that correspond to various percentiles for normal distributions. 

Percentiles Z‐score

1  ‐2.3

5  ‐1.6

10  ‐1.3

25  ‐0.7

50 0

75 0.7

90 1.3

95 1.6

99 2.3

Plotting the values of a distribution against their Z-score produces a straight line if the data 

are normally distributed. Plotting the logs of data against Z-scores produces a straight line 

when the data are log-normally distributed. 
8 

Project Contributors

A variety of scientists made important contributions to the successful development 

and implementation of this project. 

Dow AgroSciences 

Dr. Daland Juberg 

Dr. Cheryl Cleveland 

The Dow Chemical Company 

Paul Price M.S. 

Dr. Michael Bartels 

 

Battelle Pacific Northwest National Laboratory 

Dr. Torka Poet 

Dr. Paul Hinderliter 

Dr. Charles Timchalk 
9 

Executive Summary

This case study presents the results of the continued development of the scientific 

database for a particular chemical and shows how using new tools and conceptual 

approaches can bring objective, data driven insights, to the risk assessment process. The 

development of data to replace uncertainty represents advancement in knowledge. 

Specifically, this project links data, computer simulation models, and new analyses to 

improve risk assessment for a chemical, chlorpyrifos (CPF), which has as database that is 

sufficiently robust to support these new modeling techniques. 

In this paper we describe the methodologies and data used to create a process that 

captures the events in the pathway that begins with data on residue levels and ends with 

predictions of cholinesterase inhibition across the U.S. population, a source-to-outcome 

model. The source-to-outcome model consists of two publically available software 

programs. The first program can be any of the dietary exposure software programs that 

produce estimates of longitudinal dietary exposures (the specific model used here is 

CARES). The second program is the Variation model, a program created to run on 

acslXtreme, a publically available software platform for PBPK modeling. A copy of the 

Variation model is provided in Attachment C of this report. 

The steps in the creation of the source-to-outcome model are outlined in detail in this 

report and this approach can be applied to other chemicals with other toxicological 

endpoints of interest. Using this approach, key events in the toxicity pathway are identified 

that are as close to the initial interaction between the biological targets/molecules in the 

body and the chemical, or its active metabolite (see Section 2). Once this has occurred, a 

physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model is developed 

and used to predict the dose response for the key event (see Section 3). Once the PBPK/PD 

model is in place, a sensitivity analysis is performed that identifies the factors that drive 

variation in response (see Section 4). Information on the interindividual variation in these 

factors is obtained from published sources and is used to create simulation models of 

interindividual variation in the relationship between dose and the key event. 
10 

The two models define dose and the individual in very specific ways. The exposure 

portions of the source-to-outcome models need to be matched to the specific needs of the 

PBPK/PD models of variation. In this project an existing dietary model was used to 

characterize exposure (see Section 5). The linkage between the two models was achieved 

by a careful consideration of the physiological processes involved in the exposure event 

(see Section 6). 

In Sections 3, 4, 5, and 8 we investigated the ability of the model to predict changes 

in levels of cholinesterase inhibition and findings such as distribution of blood 

concentrations of chlorpyrifos and its metabolites. These predictions can be evaluated 

using other modeled and measured data. In general, we have demonstrated that the various 

predictions of the source-to-outcome model are consistent with empirical data reported in 

the literature. However, these model-to-measurement comparisons were limited by the 

relevance of the measurement data. Because of this, we conclude that the final predictions 

of impacts on AChE in the general population are likely to be within an order of magnitude 

of actual levels but additional monitoring data are required in order to determine if the 

models are in fact more precise. 

Once the source-to-outcome model is completed, it can be used in a variety of ways. 

We believe that the value of a source-to-outcome model is fully realized when the model is 

used as a tool for exploring the processes that determine the source-to-outcome pathway 

rather than just as a risk “calculator.” In this project the source-to-outcome model has been 

used in a number of ways and has demonstrated a number of novel and potentially valuable 

findings. 

•Age related differences in sensitivity to CPF were found to be small (less than 2 fold) and 

to differ with dose and endpoint (Section 4). 

•The models found that variation in metabolism but not variation in physiology or level of 

physical activity were important for infants. For adults, variation in physiology had 

some effect but was still minor compared to metabolism (see Section 4). 
11 

•The source-to-outcome model allowed the investigation of the impact of repeated doses 

of CPF on blood levels of CPF and CPF–oxon and the impact on cholinesterases (see 

Section 7). 

o This work indicated that neither CPF nor CPF-oxon accumulated over time. 

o The inhibition of AChE increased over time when the same doses occurred on 

multiple days. At the dose levels that currently occur from exposure to dietary 

residues the inhibition of RBC AChE was approximately 3-fold. Increases of 

inhibition in brain AChE inhibition less than 3-fold across all doses. 

o When day-to-day variation in dietary exposure was considered, the increase in 

impact from one to multiple days was found to be less than 3-fold. 

o The impact of multi-day exposures was much smaller for individuals with high 

exposures. The impact of dietary exposures on the RBC and brain AChE 

inhibition for these individuals was dominated (90% of the impact) by the dose 

received from a single day. 

•At current dietary levels of exposure the difference between the responses of the typical 

and sensitive individual (resulting from differences in metabolism, physiology, and 

activity) was less than 2 (Section 10). 

The source-to-outcome model was used to evaluate the impact of the current dietary 

exposure on RBC and brain AChE inhibition. This was done in two ways. First the model 

investigated the impacts in three age groups after quasi-steady state conditions were 

obtained (see Section 9). The peak inhibitions did not exceed 0.01% inhibition for either 

RBC or brain inhibition. The second way the models were used was to determine agespecific values for the interspecies and interindividual uncertainty factors (Section 10). 

This assessment found that models prediction of variation in response overlapped with the 

responses seen in recent animal studies (Attachment G). In addition, human variation in 

response was found to be modest (less than a factor of 2 between the responses in a typical 

adult or infant and a sensitive adult or infant) for doses that cause 10% or less RBC AChE 

inhibition in humans. As a result, the values of age-specific interspecies and interindividual 

uncertainty factors were small and ranged between 1 and 2. 
12 

The case study achieved its goals. A key event, AChE inhibition, was identified and 

models of interindividual variation in both dose and response were constructed. The 

combined models form a complete source-to-outcome model. The final source-to-outcome 

model’s quantitative predictions of the AChE inhibition and internal concentrations of CPF 

and its metabolites were evaluated. The final source-to-outcome model provided a 

framework that has allowed diverse information from in vitro studies of metabolism, 

biomonitoring studies in humans, test animal data, and statistical tools to come together to 

provide quantitative insights on the impacts posed by a chemical to the general U.S. 

population. 

The value in this project relates not to CPF as such, but rather in showcasing how 

both empirical and modeled data can be leveraged and linked to inform and advance our 

insight on the risk assessment of specific chemicals. The hope is that continued discussion 

and refinement of the techniques and thinking brought forward in this project will allow 

multiple stakeholders to utilize these and similar approaches for advancing risk assessment 

and aiding regulatory decision-making, goals that align with and that have been embraced 

by the broader scientific community. 
13 

1. Introduction

1.1 Report objectives

Risk assessments of dietary exposures to pesticides have used detailed 

characterization of variation in age-specific dietary exposures, but have relied on data from 

test animals and default values for uncertainty factors when characterizing the toxicity of 

pesticides. These default values do not necessarily reflect specific knowledge of a 

chemical’s mechanism of action, or our increased ability to quantitatively predict internal 

doses and responses in humans. 

This paper summarizes the development of a source-to-outcome model that provides 

a single consistent analysis of both toxicity and exposure and specifically includes data on 

mechanism of action. The source-to-outcome model provides a quantitative relationship 

between the amount of a pesticide, in this case chlorpyrifos (CPF), in food, and the 

probability of the occurrence of cholinesterase inhibition in humans. CPF is used as the 

example chemical because the molecule has a rich data set including: 

• A well-defined metabolism and mechanism of action 

• Multiple markers for exposure and effect 

• A well-defined pharmacokinetic/pharmacodynamic model that quantitatively predicts 

markers of early effects (e.g., inhibition of RBC and plasma cholinesterases) 

• Multiple human data sets available for model calibration and evaluation of model 

predictions 

This document and its attachments provide a detailed description of the source-tooutcome model and information on the uncertainty in the model’s predictions. The sourceto-outcome model is composed of any of the existing dietary exposure software that can 

produce estimates of longitudinal exposure and a pharmacokinetic/pharmacodynamic 

model of response. Together these programs relate data on residues on crops to predictions 

of inhibition of cholinesterases (Figure 1). 
14 

‘PBPK/PD model of

interindividual

variation in

response to CPF in

any age’

‘Variationmodel’ Dietary exposure

software

Relatesdata on

pesticide residue on

consumed foods

(CARES, LifeLine,

CalendexTM , SHEDS)

Source‐to‐Outcome Model

+

LinkingData File

Contains data on

individual’s characteristics

and exposure histories

+

Figure 1. Components of the source-to-outcome model 

The source-to-outcome model is used to investigate of the impact of current dietary 

exposures on three different age groups (thirty year-olds, three year-olds, and six montholds). In addition, the document presents examples by which predictions of the model 

could be used in an assessment of the potential effects using dietary residues of CPF as an 

example. Other examples showcasing the utility of the model include an analysis of the 

factors that influence the relative sensitivity of individuals, predictions of the impacts of 

longitudinal dietary exposures, and the development of chemical- and age-specific 

uncertainty factors. 

The analysis in this document focuses on measures of internal dose and key events in 

the toxicity pathway that lead to inhibition of cholinesterases. The specific targets are the 

characterization of the dose response for the inhibition of acetyl cholinesterase (AChE) in 

red blood cells and brain from exposures to dietary residues by the oral route. The 

document does not address effects that may be associated with non-cholinergic 
15 

mechanisms1, nor does it directly address exposures that occur from other sources, by other 

routes, or to other age groups. 

1.2   Document organization

The paper begins with a review of the relevant information on the absorption, 

distribution, metabolism, and excretion (ADME) and mechanism of action for CPF 

(Section 2). We next describe the creation and final characteristics of the various 

components of the source-to-outcome model (Sections 3 through 7). 

One component in the source-to-outcome model is the software program that 

determines the relationship of the oral dose to the inhibition of cholinesterase. The 

development of this component is presented in a series of three steps (Figure 2). 

Step 1 (Section 3): a PBPK/PD model of the relationship between oral doses and 1) 

internal concentrations of CPF and its metabolites and 2) markers of the key event, brain 

AChE, is described for a generic adult (Typical Adult model). 

Step 2 (Section 3): the expansion of this model to investigate age-related changes and 

specifically to predict dose-response relationships in children and infants is described. 

This expanded model is called the “LifeStage” model. 

Step 3 (Section 4): the LifeStage model is expanded to address interindividual variability 

in dose response that occurs from differences in physical activity, physiology, and 

metabolism in specific age groups. This final version is called the “Variation” model, and 

is used as the PBPK/PD component of the final source-to-outcome model. 

 

1 This project has determined internal concentrations of CPF and CPF oxon that are measures of internal dose that 

could be used to evaluate the potential for the occurrence of other endpoints. 
16 

Figure 2. Steps in the creation of the Variation model. The variation model consists of the 

original “Typical Adult Model” with added parameterization to describe growth in tissues 

and enzymes with age and finally variability around important physiological and biochemical 

factors. 

Sections 3 and 4 also include a series of model-to-measurement exercises that 

demonstrate the LifeStage and Variation’s models’ ability to predict age-related variation 

in physiology and the effects of CPF in human volunteer studies. Finally, in Section 4 the 

Variation model is used to investigate the interindividual variation of the dose response of 

RBC and brain AChE. 

Section 5 presents the dietary exposure component of the source-to-outcome model. 

The modeling of CPF is described along with the data on residues used in this analysis. 

The issue of the uncertainty in characterizing longitudinal exposures is addressed and 

multiple approaches of characterizing longitudinal exposure are investigated. Section 6

presents the process used to link the dietary exposure software and the PBPK/PD 

component of the Variation model. 

The uncertainty in the source-to-outcome model’s predictions of internal dose and 

biomarkers of exposure are evaluated in Section 7 using a series of model-to-measurement 
17 

exercises. The purpose of these exercises is to demonstrate how data can be used to 

evaluate the predictions of present and future source-to-outcome models. 

The document then describes the use of the complete source-to-outcome model to 

characterize the impacts of dietary exposures using CPF as a case study. This description 

begins in Section 8 with the use of the source-to-outcome model to analyze the impact of 

longitudinal exposures on predictions of impact of dietary doses. The model is then used to 

directly predict the impacts of current dietary exposures on AChE in different age groups 

(Section 9). Finally, the source-to-outcome model is used to develop age-specific 

uncertainty factors for the interspecies and interindividual uncertainty factors (Section 10). 

The paper concludes with a summary of work presented in this paper (Section 11). 

1.3 The use of a source­to­outcome model and the future of risk assessment  

Risk assessments of chemicals are undergoing a transition. New tools and 

approaches are enabling assessors to directly characterize the impact of factors such as 

extrapolation from toxicity in animals to humans, age-related differences, human variation, 

and variation in dose over time. At the same time the expectations for risk assessments 

have increased. Assessments are expected to produce quantitative estimates of risk and 

descriptions of how these risks vary across individuals. Finally, future risk assessments 

will be expected to minimize the use of animals and maximize the use of in vitro and in 

silico tools. These changes have been discussed in two recent National Academies Reports 

(National Research Council, 2007; National Research Council, 2008). The source-to 

outcome model described in this paper meets a number of the goals identified in these 

reports. 

In the National Academies of Science report, Toxicity Testing in the 21st Century,

toxicology is challenged to understand toxicity in terms of toxicity pathways (National 

Research Council, 2007). These pathways begin with early perturbations of cellular 

response networks that precede the apical events in time and are necessary for the 

occurrence of the apical effects. The authors state that toxicological investigations of a 

chemical should focus on the early events in the pathways rather than on apical effects and 

that dose response modeling should be informed by data on the mechanism of action rather 
18 

than the simple empirical fitting of response data. This may be achieved by constructing 

quantitative dose response relationships for those perturbations using PBPK/PD models. 

The concept of a perturbation in a cellular response network that is “obligatory” for the 

occurrence of apical effects is also parallel to the “key event” concept in the carcinogen 

mode of action risk assessment (U.S. EPA, 2005). A key event is defined as an empirically 

observable precursor step that is itself a necessary element of the mode of action or is a 

biological marker for such an element (U.S. EPA, 2005). 

As the NAS report indicates creating a dose response model for key events in a 

toxicity pathway provides a number of advantages. First, early key events provide more 

tractable targets for building pharmacodynamic (PD) models. This allows the PD models 

to avoid having to predict the complexity of the complete pathway. Second, early key 

events are easier to study with in vitro techniques. Finally, for chemicals in commerce, 

human biomonitoring of the key event (or the absence of the event) may provide data to 

inform the PD modeling. 

In this assessment we use an organophosphorus insecticide, CPF, as an example to 

illustrate the methodology of the source-to-outcome model. Similar to numerous 

organophosphorous pesticides, high doses of CPF may be associated with a range of apical 

effects in mammals that occur as a result of the compound’s effect on the cholinergic 

system. However the initial perturbation of the cholinergic system that leads to these apical 

effects is the inhibition of AChE in the central nervous system and in other tissues. Thus, 

this change is an example of the “obligatory perturbation” concept identified in the 

National Academies of Science report. 

The source-to-outcome model discussed in this paper is based on three key events. 

The first is brain AChE inhibition, which is a necessary event in the toxicity pathway for 

CNS apical effects. The other two key events (inhibition of AChE in RBCs and BuChE in 

plasma) are not part of the toxicity pathway related to cholinergic effects but represent 

conservative markers of exposure, since inhibition of these blood enzymes likely occurs 

presystemically in the liver during oxon metabolite production. 
19 

Figure 3 presents a source-to-outcome model built on the mode of action for CPF’s 

effects on the cholinergic system. While a detailed description of CPF’s mode of action is 

described in Section 2, the process can be summarized as follows. CPF is converted to the 

oxon which inhibits a variety of cholinesterases. Inhibition of AChE in the central nervous 

system leads to accumulation of acetylcholine at neuronal junctions in target tissues. As 

Figure 3 indicates, the PBPD models do not include processes in the toxicity pathway 

beyond the key event. The PBPD model does make quantitative predictions of when the 

key event occurs in an individual based on the mechanisms of metabolism and interaction 

with cholinesterase. Thus the PBPD model fulfills the goal of extrapolation as stated on 

page 9 of the National Academies of Sciences report Toxicity Testing in the 21st Century, 

to provide a quantitative, mechanistic understanding of the dose-response relationship. 

Figure 3. Source-to-outcome model for the key events of CPF and the subsequent portions of 

the toxicity pathway. Portions enclosed in shaded areas are included in the source-to-outcome 

model. 
20 

This project also addresses concerns raised in the National Academy of Sciences 

report Science and Decisions: Advancing Risk Assessment (National Research Council, 

2008). In chapter 5 of the report, the panel members expressed concern that current RfDbased risk characterizations do not provide information on the fraction of the population 

adversely affected by a given dose or on any other direct measure of risk. 

The source-to-outcome model produces quantitative estimates of key events 

(inhibition of AChE) that are required steps in the pathway that lead to cholinergic effects 

or are conservative markers for such steps. By demonstrating when blood and brain AChE 

are unchanged, the models are able to predict when cholinergic effects may or may not 

occur. 

A second issue raised in Science and Decisions: Advancing Risk Assessment, is the 

importance of considering population variability in humans. As discussed in detail in 

Sections 3 and 4 of this paper, the source-to-outcome model allows for a systematic 

examination of the factors that affect variation in dose, ADME, and responses in the key 

events. These models allow the direct investigation of sensitive subpopulations defined by 

age, variation in enzyme activity, or other factors. 

Finally, Science and Decisions: Advancing Risk Assessment, raises the issue of 

default assumptions in risk assessment and the level of theory and evidence that is required 

to deviate from a default. In Section 10 of this paper, we present an illustration of how the 

source-to-outcome models can be used for setting values of interspecies and interindividual 

uncertainty factors that differ from the default values. This presentation occurs at the end 

of the paper after the technical basis for the model and its predictions are presented. Thus, 

the findings of the modeling project provide a basis for determining if the proposed 

uncertainty factor values meet the suggested criteria of “transparency, clarity, consistency, 

and reasonableness” suggested by the Academy panel report. 

In summary, while the basic mechanism of action for CPF effects on the cholinergic 

system has been understood for decades, the development of quantitative models of the 

key event is an example of the future of toxicology as envisioned by recent National 

Academies of Sciences reports. 
21 

2. CPF metabolism/mechanism of action  

Section overview

This section presents information on the pharmacokinetics and metabolism of CPF. 

The chapter begins with a review of the previously reported animal and human 

PK/metabolism data, then discusses new in vitro metabolism data obtained with human 

donor tissues from humans of various ages that was used in the development of the 

LifeStage PBPK model. 

CPF exerts its biological effects through a complex pathway that includes metabolic 

bioactivation to CPF-oxon and detoxification to trichloropyridinol (TCPy) and 

dialkylphosphate metabolites (diethylphosphate (DEP) and diethylthiophosphate (DETP)) 

(Figure 4). Since the concentration of CPF-oxon is the crucial dose metric leading to the 

toxicity of CPF (Amitai et al., 1998; Chambers et al., 1994), it is important to understand 

the metabolic fate of CPF in humans and any dose- or species-differences in activation and 

detoxification of CPF. In vitro studies of metabolism of CPF were performed to better 

understand interindividual and age related variation. The results of the studies are also 

presented. 

2.1 Chemical properties

CPF is one of a series of phosphate triesters that are used as pesticides. The specific 

structure of CPF (below) includes a phosphothioate functionality, two ethoxy moieties, and 

a trichloropyridinol leaving group. CPF is fairly lipophilic with a log Kow value of 4.96 

(Sangster, 1994), whereas the CPF-oxon and hydrolysis metabolites TCPy, DETP and 

DEP are substantially more polar. The parent compound; containing the P=S bond is more 

chemically stable than the reactive CPF-oxon, due to the P=S bond being less 

electronegative than the corresponding P=O moiety of the oxon metabolite, which 

stabilizes CPF from loss of TCPy (Ballantyne and Marrs, 1992; Chambers and Levi, 

1992). As a result, toxicological effects are generally ascribed to the more reactive CPFoxon than the parent compound (Sultatos, 1994). 
22 

Toxicological effects arising from formation of the CPF-oxon metabolite are 

primarily via inhibition of acetylcholinesterase (AChE) activity, through covalent binding 

of CPF-oxon to the serine active site of this class of enzymes (Thompson et al., 2010). 

This inhibition of the enzyme results in accumulation of acetylcholine at neuronal 

junctions. Covalent binding of CPF-oxon has also been shown to occur to serine, tyrosine 

or lysine residues of butyrylcholinesterase (BuChE), albumin, tubulin and numerous other 

proteins (Albers et al., 2010; Chiu et al., 2007; Grigoryan et al., 2009; Grigoryan et al., 

2008; Noort et al., 2009). These non-ChE protein targets are consistent with reports of 

effects of CPF in various in vivo or in vitro test systems that are not mediated by inhibition 

of AChE (Moreira et al., 2010; Nomura and Casida, 2010; Ray et al., 2010; Yang et al., 

2008). 

Figure 4. Metabolic scheme for CPF in mammals. 

Cl

Cl

Cl

HO N

R

Cl

Cl

Cl

O N

Phosphooxythiiran Intermediate

Trichloropyridinol (TCPy)

Chlorpyrifos Oxon

Diethylphosphate (DEP)

Diethylthiophosphate (DETP)

Trichloropyridinol Conjugate

Cytochrome P450

A & B-Esterases

S

O

Cl

Cl

Cl

H3C

H3C

P

O

O

O

N

Chlorpyrifos (CPF)

O

Cl

Cl

Cl

H3C

H3C

P

O

O

O

N

H3C O

H3C

P

O

O

OH

H C S 3

H3C

P

O

O

OH

S

Cl

Cl

Cl

H3C

H3C

P

O

O

O

N
23 

2.2 Metabolism studies

In rats, CPF is well absorbed and excreted primarily in the urine as conjugates of 

TCPy (Bakke and Price, 1976; Smith et al., 1967). Nolan et al. (1987) also studied CPF 

metabolism in the rat and found that it undergoes high first-pass metabolism to TCPy, with 

no parent compound excreted in urine. These authors reported the major urinary metabolite 

to be TCPy-glucuronide, with lesser amounts of TCPy-sulfate and free TCPy (Nolan et al., 

1987). Sunaga, et al. (1989) report similar findings, following inter-peritoneal 

administration of CPF to rats, showing urinary TCPy accounted for >85% of the 

administered dose of CPF, with lower percentages of the DETP and DEP metabolites 

recovered. A subsequent study by Mendrala and Brzak was conducted to evaluate the timecourse of blood metabolites following oral administration of 0.5-100 mg CPF/kg body 

weight. The authors found 99% of blood metabolites as TCPy with only 1% as parent 

compound. Trace levels of CPF-oxon levels were found, generally 100-fold lower than 

CPF levels (Mendrala and Brzak, 1998). These trace levels of the oxon metabolite are 

consistent with the report of high first-pass metabolism of CPF by the liver (Sultatos, 

1994). 

The metabolism and pharmacokinetics of CPF have also been evaluated in several 

human volunteer studies. Nolan et al. found at least 70% of an orally administered dose of 

CPF (0.5 mg/kg) was absorbed and excreted in the urine, primarily as acid-labile 

conjugates of TCPy. Trace levels of CPF were found in this study, with no measurements 

conducted for CPF-oxon. Griffin et al. measured the urinary DEP metabolites of CPF 

following oral or dermal administration of the parent compound, showing these 

dialkylphosphate metabolites represent 93% of the administered dose in humans (Griffin et 

al., 1999). In a later multi-dose level pharmacokinetic study, Kisicki et al., (1999) found 

that following oral administration of 0.5, 1.0 or 2.0 mg CPF/kg body weight, TCPy was 

again the major metabolite in blood, with CPF levels <1% of TCPy concentrations. No 

CPF-oxon was detected in blood samples from this study, with the 1 ng/ml LOQ value 3-

18-fold lower than the highest CPF concentrations observed. These CPF-oxon results are 

quite consistent with the rat pharmacokinetic results of Mendrala, discussed above, and 

indicate that CPF undergoes high first-pass metabolism in humans as well as animals. 
24 

Any minor variations in the hepatic or extrahepatic formation and/or degradation of 

CPF-oxon would have an impact on systemically available levels of this trace, reactive 

metabolite. It is therefore important to understand the differences in enzymatic activity that 

affect the rates of CPF-oxon formation and hydrolysis to be able to accurately predict 

variations in systemic CPF-oxon levels and the resulting biological effects across the 

population. This evaluation is further discussed in Section 5 of this report. 

Minor metabolites of CPF have also been reported. Additional metabolic routes, 

involving GSH conjugation with the TCPy ring and glucuronide phenolic conjugates of 

TCPy have been seen in human liver S9 and or hepatocyte incubations with CPF (Choi et 

al., 2006). GSH conjugates have been also seen in vivo (Bicker et al., 2005) and in mice 

dosed with 100 mg CPF/kg (Fujioka and Casida, 2007). While the potential toxicity of 

these GSH conjugates is not known, the fact that they have not been seen in lower dose 

animal studies and that protein adducts identified so far arise from reaction with CPFoxon, would suggest that GSH conjugation of CPF or CPF-oxon is not relevant for the 

mechanism of action for this compound at doses commonly used in toxicity studies or 

from environmental exposures. 

Direct evaluation of the in vivo stability of the CPF metabolites has also been 

examined. Timchalk et al., (2007) conducted in vivo pharmacokinetic studies with TCPy, 

DETP and DEP in rats, and compared the results to animals dosed directly with CPF. 

These authors found 100% of the administered TCPy was recovered in the 0-72 hr urine, 

and 65% and 86% of the DETP and DEP, respectively, remained unchanged. These data 

indicate that the majority of these CPF hydrolysis products are excreted without further 

Phase I metabolism. In a more recent Comparative Cholinesterase Assay study, CPF or 

CPF-oxon was administered to adult or PND-11 rats (Marty and Andrus, 2010). At 6-8 hr 

post-dosing, detectable levels of CPF were found in the blood of rats dosed with 0.5 mg/kg 

CPF, while no detectable CPF-oxon was found 4 hr post-dosing in adult or neonatal rats 

dosed with 0.5 mg CPF-oxon/kg. In addition, TCPy was found to be a major blood 

metabolite in the CPF-oxon dosed rats as well as the CPF-dose group. Since CPF has been 

previously shown to be highly metabolized in the rat, and these new data show comparable 

TCPy blood levels from CPF and CPF-oxon, these results indicate that CPF-oxon also 
25 

undergoes high first-pass metabolism in the adult and neonatal rat. A more thorough 

overview of these animal and human metabolism/kinetic studies can be found in recent 

reviews of CPF metabolism (US EPA, 2008b; Eaton et al., 2008). 

Dose­dependent kinetics

Since the first steps in CPF metabolism involve CYP oxidation, it would be expected 

that the rate of metabolism of this compound could be dependent on concentration. A 

comparison of the ratio of blood TCPy/CPF concentrations from various toxicokinetic 

studies in rats (Mendrala et al., 1998; Timchalk et al., 2006; Marty et al., 2010) was 

therefore conducted to evaluate this issue (Figure 5). 

TCP/CPF ratios

0.01 0.1 1 10 100

10

100

1000 PND-5 (Timchalk 2006)

PND-11 (Marty 2010)

PND-12 (Timchalk 2006)

Adult (Marty 2010)

Adult (Mendrala 1998)

PND-17 (Timchalk 2006)

Ratios derived from AUC values (closed symbols) or single sampling time

of 3 hr (Mendrala), 6 hr (Marty, PND-11) or 8 hr (Marty, Adult)

Dose Level (mg/kg)

Ratio of TCP/CPF levels in blood

Figure 5. Dose-dependent pharmacokinetics of CPF and TCPy in the rat. 

As shown in Figure 5, the ratio of TCPy vs. CPF decreases with increased dose in 

each of the six datasets from the three studies evaluated. Overall, the PND-5 to PND-12 

rats show lower metabolism rates than the PND-17 and adult datasets. However, all of the 

experimental results show a good correlation of increased CPF metabolism as dose levels 

decrease. The most complete data are the PND-11 blood data from the recent Marty 2010 

study. The TCPy/CPF ratio increased from 21 to 300 for the dose levels of 5 mg/kg down 
26 

to 0.05 mg/kg. Examination of both the adult and PND-11 data from this study show a 

point of inflection for this metabolite ratio above 1 mg/kg dose levels. One would expect 

that a similar phenomenon of CYP-based metabolic saturation would occur in humans. 

Therefore, an important validation check for the LifeStage model will be to evaluate this 

metabolic saturation in simulations of human systemic dose. 

2.3 Sites of metabolism

The primary site of CPF metabolism has been shown to be the liver in numerous in 

vivo and in vitro studies (Chambers and Chambers, 1989; Sultatos, 1994; Poet et al., 2003). 

Poet et al. have also found that the enterocytes of the small intestine have measurable 

cytochrome P450 (CYP) activity to form both CPF-oxon and TCPy from CPF (Poet et al., 

2003). Numerous groups have reported on the various esterases in blood that can 

hydrolyze CPF-oxon (Pond et al., 1995; Jokanović, 2001). Finally, several researchers 

have postulated or provided data supporting some oxidation of CPF to the oxon or esterase 

hydrolysis of oxon in peripheral tissues such as brain and intestines (Chambers and 

Chambers, 1989; Poet et al., 2003). 

Metabolizing enzymes

The hepatic metabolism of CPF occurs via CYP-mediated desulfurylation and 

dearylation, affording CPF-oxon and TCPy, respectively. Many investigators have studied 

both the rates of microsomal metabolism of CPF, as well as the specific CYPs responsible 

(Ma and Chambers, 1994; Tang et al., 2001; Buratti et al., 2003; Sams et al., 2004; Mutch 

and Williams, 2006; Foxenberg et al., 2007; Croom et al., 2010). CPF desulfurylation is 

most likely due to CYP2B6, 3A4/5, and 2D6 (Croom et al., 2010; Foxenberg et al., 2007; 

Mutch and Williams, 2006; Sams et al., 2004; Tang et al., 2001). For CPF dearylation, 

CYP2C19 was the primary CYP, CYP2D6 and 3A4/5 apparently have significant capacity 

to metabolize CPF. Overall, these investigators have found that CPF metabolism in human 

liver microsomes favors dearylation to TCPy over desulfurylation to CPF-oxon, by a factor 

of 3 to 6-fold, based on Vmax or pseudo-first order rates of metabolism. What is important 

to population modeling, however, is the inter-subject variability in metabolic rates. The 

highest variability seen in microsomal formation of CPF-oxon was that of Buratti et al. 
27 

(2003), in which the authors report a 30-fold difference in oxon production between the 

least and most active human hepatic microsomes. While this variability definitely should 

be taken into account in the Variation model, Foxenberg notes that some variability seen in 

metabolic rates between different sources may be due to artifacts in microsomal viability 

because of varying tissue isolation and microsomal preparation conditions. 

The degradation of CPF-oxon occurs primarily via esterase activity, in the liver, blood and 

various tissues. Some authors report microsomal-based hydrolysis of CPF-oxon (Sams et 

al., 2004). The primary enzymes responsible for CPF-oxon hydrolysis are the calciumdependent A-esterases (PON1) and B-esterases (cholinesterases) (Pond et al., 1995). Other 

proteins like serum albumin have also been shown to have esterase activity against 

CPF-oxon (Sogorb et al., 2008). As with the CYP activities discussed above, substantial 

variability has been observed in PON1 activity between human donors. Mutch et al. 

evaluated variation in CPF-oxon hydrolysis in liver microsomes from 27 human donors, 

with various SNP 192 PON1 phenotypes (Mutch et al., 2006). A 16-fold variation was 

observed between low and high activities, with a high degree of overlap between 

phenotypes. Huen et al. discuss 4 to 34-fold variation in CPF-oxon hydrolysis in plasma 

from newborns, 7-year-old s and pregnant mothers, who express one of the five known 

phenotypes of the PON-1 enzyme (Huen et al., 2010). The variations in PON1 and other 

esterase-mediated hydrolysis of CPF-oxon have been included in the Variation model (see 

Section 4). Recent in vitro experiments, discussed below, provide data on the esterase 

capacity of liver and plasma from numerous human donors, both infant and adult. 

Age­dependent kinetics

Several groups have published data showing age-dependent differences in sensitivity 

to CPF in animals (Timchalk et al., 2006; Marty et al., 2010; Pope et al., 1991; Moser et 

al., 1998; Padilla et al., 2000; Timchalk et al., 2006). This differential sensitivity may be 

due to CYP or esterase deficiencies in the young, affording higher levels of CPF and/or 

CPF-oxon. Researchers studying the ontogeny of metabolic systems have reported that 

individual CYP enzymes maturate at different rates (Alcorn and McNamara, 2002; 

Johnson et al., 2006). Some CYPs (e.g. CYP2C9) will mature to adult levels in as little as 

half a week, while others (e.g. CYP2C19) will take 2 years or longer to reach adult levels 
28 

(Johnson et al., 2006; Koukouritaki et al., 2004; Tateishi et al., 1997; Treluyer et al., 

1997). Other CYP enzymes yet (e.g. CYP3A7) may be present during infancy and not 

present in adults (Tateishi et al., 1997). In a similar vein, PON1 and other esterase 

activities have been shown to increase with age, in both animals and humans 

(Augustinsson and Barr, 1963; Timchalk et al., 2006). Any age-based differences in CPF 

metabolism need to be included in the LifeStage model for appropriate predictions of 

systemic dose in infants, children and adults. Age-specific metabolic parameters for this 

model were therefore obtained by a new in vitro metabolism study, described in the 

following subsection. 

2.4 New data on age­dependent metabolic rates

As discussed above, age-dependent changes in CPF metabolism have been reported 

in rodents (Zemke et al., 2009). While age-dependent changes have been indicated in 

rodents, there has not been a full characterization of enzymatic activity (i.e. MichaelisMenten enzymatic kinetics) with regards to age in humans. Therefore, a new study was 

conducted to quantify age-dependent enzymatic metabolism of CPF and CPF-oxon in vitro 

using human hepatic microsomes and plasma. The rates of CPF conversion to CPF-oxon 

and TCPy in liver microsomes, as well as the hydrolysis of CPF-oxon to TCPy in 

microsomes and plasma were measured. A detailed report on this study is given in 

Attachment A of this report. The following is a brief summary of the study and its 

findings. 

Materials and methods for in vitro study

Thirty human hepatic microsome samples were obtained from XenoTech, LLC 

(Lenexa, KS, USA). Microsomes were selected to maximize the number of samples for 

humans less than 12 years of age (n=20), and match those pediatric samples with 10 adult 

(17 years or older) samples. Overall age range of hepatic microsome donors was 13 d-75 y. 

Both oxidative desulfurylation (CPF to CPF-oxon) and dearylation (CPF to TCPy) 

pathways of CPF metabolism were measured by quantifying product formation from in 

vitro incubations of hepatic microsomes with CPF. Microsomes (0.5 mg protein) were 

incubated in 50 mM HEPES buffer (7.4 pH) containing 15 mM MgCl2•6H2O (Poet et al., 
29 

2003; Lee et al., 2009) and 1 mM ethylenediaminetetraacetic acid (EDTA, to inhibit PON1 

activity (Furlong et al., 1989) with 20, 50, 125, 315.5, or 781.25 μM CPF. Incubations 

were conducted at 37o C, initiated with the addition of 1 mM nicotinamide adenine 

dinucleotide phosphate (NADPH) after a 3 min pre-incubation to allow samples to reach 

temperature equilibrium, and terminated with the addition of 0.25 ml of 3 M HCl saturated 

with NaCl at 15 min after the addition of NADPH. Incubation volume was 0.5 ml. Blanks, 

containing microsomes inactivated by addition of HCl solution prior to incubation, were 

included to quantify non-enzymatic product formation. 

Twenty individual human plasma samples were obtained from Bioreclamation, Inc. 

(Liverpool, NY, USA). Plasma samples were obtained from donors 3 days of age to 43 

years. CPF-oxon metabolism by PON1 was measured in both hepatic microsomal and 

plasma samples by quantifying product formation (TCPy) from in vitro incubations with 

CPF-oxon. Microsome (0.5 mg protein) or plasma (2.5 μL) samples were incubated in 0.1 

M Tris-HCl buffer (8.5 pH) containing 2 M NaCl and 2 mM CaCl2 (Furlong et al., 1989; 

Poet et al., 2003) with 72.3, 173.6, 416.7, 1000, or 2400 μM CPF-oxon. Incubations were 

conducted at 37o C, initiated with the addition of substrate (CPF-oxon) at 3 min, and 

terminated with the addition of 0.25 ml of 3 M HCl saturated with NaCl 5 min after the 

addition of CPF-oxon. Incubation volume was 0.5 ml. Blanks, containing microsomes or 

plasma inactivated by addition of HCl solution prior to incubation, were included to 

quantify non-enzymatic product formation. Metabolite levels were determined via GC/MS 

analysis, using methods comparable to those published previously (Brzak et al., 1998). 

Results for in vitro study

The in vitro hepatic CYP metabolism, including Vmax values, Km values, pseudo 

first-order rates (Vmax/Km), protein concentrations in plasma, and marker substrate 

activities, was evaluated for age dependence using linear regression models in “R: A 

language and environment for statistical computing”, version 2.9.0 or 2.11.1 (R, 2004). All 

statistical tests used an α value of 0.05. No age dependence was noted for either pathway 

(Figure 6). Given the sample size (samples from 30 individuals), linear regression was 

considered a sufficient test of age dependence. The dearylation Vmax and Km were 0.73 ± 

0.38 nmol/min/mg microsomal proteins and 81 ± 73 μM, respectively. Neither Vmax nor 
30 

Km demonstrated age dependence (p = 0.60 and 0.17 respectively). The desulfurylation 

Vmax and Km were 0.35 ± 0.21 nmol/min/mg microsomal protein and 96 ± 62 μM, 

respectively, and again neither were age dependent (p = 0.90 and 0.16, respectively). 

Pseudo first-order rates were estimated by dividing the Vmax by the respective Km. The 

mean (± SD) pseudo first-order rates were 5.8 ± 6.4 μL/min/mg and 16 ± 13 μL/min/mg 

microsomal protein and, again, were not significantly different over age (p = 0.19 and 0.15, 

respectively, data not shown). 

These metabolic rates for CPF are comparable with those published previously by 

other groups. Previously reported CPF dearylation Vmax rates (0.70 nmol/min/mg 

microsomal (Tang et al., 2001) and 0.65 nmol/min/mg microsomal protein (Sams et al., 

2004) were also consistent with the mean value determined in this study (0.73 

nmol/min/mg microsomal protein), although the Km values previously reported were, 

lower than the mean value determined here (12-14 vs. 81 μM, respectively). Tang et al., 

(2001) and Buratti et al., (2003) reported CPF desulfation Vmax rates from pooled human 

microsomes of 0.40 and 0.49 nmol/min/mg microsomal protein, respectively, and Sams et 

al., (2004) reported a mean Vmax of 0.35 nmol/min/mg microsomal protein using individual 

human microsomes. All three values are consistent with the mean value determined here 

(0.35 nmol/min/mg microsomal protein). Reported Km values from those studies were 

lower than those reported here (0.9-30 μM vs. 96 μM, respectively). Since no age-based 

differences in either of these metabolic rates were observed, the overall mean values are 

used in the LifeStage model (Section 3) and Variation model (Section 4) to predict hepatic 

metabolism of CPF in humans. 
31 

0.01 0.1 1 10 100

0.0

0.5

1.0

1.5

2.0

age (yr)

Vmax (nmol/min/mg)

0.01 0.1 1 10 100

0.0

0.5

1.0

1.5

2.0

age (yr)

Vmax (nmol/min/mg)

CPF -> TCPy (Liver CYP)

CPF -> CPF-oxon (Liver CYP)

Figure 6. In vitro metabolism of CPF to TCPy (top) or CPF-oxon (bottom) in human liver 

microsomes from individuals from 13 days to 75 years of age. Solid lines show mean values 

used to extrapolate the rates for the LifeStage model, with 95% confidence intervals (dashed 

lines) around the data displayed. 

Both TCPy and CPF-oxon formation rates were measured simultaneously from each 

donor liver tissue sample. A comparison of the metabolic rates of TCPy and CPF-oxon 
32 

formation was therefore made and shown in Figure 7. Although there is some variability, a 

fairly good correlation is seen between desulfurylation and dearylation in all of the 

samples. However, to be conservative, these parameters were varied independently in the 

Variation model (below). 

0.00 0.25 0.50 0.75 1.00 1.25

0.0

0.5

1.0

1.5

2.0

Metabolism to CPF-oxon (nmol/min/mg)

Metabolism to TCPy (nmol/min/mg)

Figure 7. Correlation between CYP-based metabolism of CPF to CPF-oxon and to TCPy in 

individual human liver samples. Solid line is best fit regression. 

Hepatic metabolism rates of CPF-oxon to TCPy are shown in Figure 8. The mean (± 

SD) Vmax and Km values were 78 ± 44 nmol/min/mg microsomal protein and 520 ± 204 

μM, respectively. None of the CPF-oxon hepatic metabolism measures changed 

significantly with age (p = 0.17 and 0.75, for Vmax, and Km, respectively). In contrast to 

hepatic CPF metabolism, there have been fewer studies quantifying in vitro hepatic CPFoxon metabolism to TCPy in humans. Sams et al., (2004) published a mean human hepatic 

CPF-oxon Vmax of 233 nmol/min/mg microsomal protein, somewhat higher than the value 

reported here (78 nmol/min/mg microsomal protein). This difference could potentially be 
33 

explained by differing incubation buffers. Sams et al., (2004) used a phosphate buffer (pH 

7.4), while a high salt Tris-HCl buffer was utilized in this study (pH 8.5), although others 

have reported greater PON1 activity with high salt buffers (Costa and Furlong, 2002). 

Since no age-based differences were observed in CPF-oxon metabolism to TCPy, the 

overall mean values are used in the LifeStage model to predict hepatic metabolism of CPF 

in humans. 

0.01 0.1 1 10 100

0

50

100

150

200

age (yr)

Vmax (nmol/min/mg)

CPF-oxon -> TCPy (Liver PON1)

Figure 8. In vitro PON1 metabolism of CPF-oxon to TCPy in human liver microsomes from 

individuals aged 13 days to 75 years. Solid lines show mean value used in LifeStage model, 

with 95% confidence intervals (dashed lines) around the data displayed. 

Rates of both CPF-oxon formation from CPF and subsequent CPF-oxon hydrolysis 

to TCPy were measured simultaneously from each donor liver sample. A correlation was 

observed between the metabolic rates of desulfurylation and oxon hydrolysis (Figure 9). 

However, we could find neither a biological nor a literature basis for this correlation and 

thus conservatively varied the CYP-based desulfurylation and liver PON1 hydrolysis rates 

independently in the Variation model. 
34 

0.00 0.25 0.50 0.75 1.00 1.25

0

50

100

150

200

P450 Metabolism to Oxon (Vmax)

PON1 metabolism to TCPy (Vmax)

Figure 9. Relationship between hepatic CYP-based metabolism of CPF to CPF-oxon and 

hepatic PON1 metabolism of CPF-oxon to TCPy in individual human liver samples. 

In contrast to PON1 activity in liver microsomes, CPF-oxon metabolism in plasma 

demonstrated an age-dependent increase in Vmax rates on a per ml plasma basis (p = 0.03, 

Figure 10). Mean (± SD) Vmax rates were 1901 ± 663 nmol/min/ml in children < 6 months 

of age (n=5) and 6828 ± 1614 nmol/min/ml protein in adults (individuals > 12 years of 

age; n=5). Plasma protein concentration also significantly increased with age (p = 0.005). 

Overall, CPF-oxon metabolism Vmax rates on a per ml plasma basis demonstrated over a 

3.5-fold increase from < 6 months in age to adulthood (p = 0.01). With a linear regression 

analysis, Km constants did not demonstrate any age-dependence (p = 0.87); however, 4 out 

of the 5 lowest Km values were measured in children < 6 months of age (data not shown). 

These age-based changes in plasma PON1 activity are quite consistent with previous 

reports of ontogeny of this enzyme (Furlong et al., 2005; Huen et al., 2010). This agedependence in PON1 activity is included in the LifeStage model. 
35 

0.01 0.1 1 10 100

0

2000

4000

6000

8000

10000

CPF-oxon -> TCPy (Plasma PON1)

age (year)

Vmax (nmol/min/ml)

Figure 10. In vitro PON1 metabolism of CPF-oxon to TCPy in human plasma samples from 

individuals aged 3 days to 43 years. Solid lines show mean values used to extrapolate to the 

LifeStage model with 95% confidence intervals (dashed lines) around the data displayed. 

Confidence intervals were determined using GraphPad Prism (version 5.03 for Windows, 

GraphPad Software, San Diego California USA, www.graphpad.com) 

Section summary

The metabolism of CPF and its relationship to the active metabolite CPF-oxon have 

been the subject of a number of studies. Data on age-related variation in metabolism and 

variation across individuals at different ages is important to modeling the effects of CPF on 

AChE. In the studies described in this section, hepatic metabolism of CPF to CPF-oxon 

and TCPy and CPF-oxon to TCPy did not vary across age on a microsomal protein basis, 

and CPF-oxon /TCPy ratios were consistent across ages. In contrast, CPF-oxon 

metabolism to TCPy in plasma demonstrated an age-dependent increase on a per ml 

plasma basis, increasing over 3.5 times from <6 months in age to adulthood. These results 

are integrated into the LifeStage model discussed below. 
36 

3. PBPK/PD modeling in typical humans of various ages

Section overview

This section presents information on the PBPK/PD modeling of CPF. The chapter 

begins with a review of the development of the CPF PBPK/PD model over the last 10 

years, then discusses the development of the LifeStage model, and concludes with an 

evaluation of the model and its predictions for adults, children and infants. 

The section begins with a description of the original model’s (Timchalk et al., 

2002b)structure. This structure has remained fundamentally unchanged in all of the 

subsequent modeling. The sources of data for the model’s inputs are also presented. As 

discussed in the introduction of this paper, the development of the PBPK/PD model occurs 

in three stages, the modeling of a “typical” adult (the Typical Adult model), the modeling 

of typical individuals at different ages (the LifeStage model), and the modeling of 

interindividual variation in populations of a specific age (the Variation model). This 

section presents a description of the development of the PBPK/PD model as it pertains to 

the Typical Adult and the LifeStage capabilities. A description of the creation of the 

Variation model is provided in Section 4. In addition there is an assessment of the changes 

in estimates between the original model (Timchalk et al., 2002b) and the LifeStage model. 

The LifeStage model’s predictions and the assumptions on the values of 

physiological parameters used for the typical individuals of various ages are evaluated by 

comparing the predictions to observed data. In the case of the predictions of age-related 

changes in physiology, the values are compared to published data on physiology. The 

predictions of internal dose and cholinesterases are evaluated by comparisons to data from 

human volunteers. Finally, the section presents predictions of the dose response curves for 

RBC and brain AChE inhibitions in typical humans at three different ages, 6 months, 3 

years, and 30 years. 
37 

3.1 History of PBPK/PD model

The extensive understanding of both the metabolism and biochemical interactions 

between the oxon metabolite of CPF and acetylcholinesterase has been used to develop a 

PBPK/PD model for CPF (the Typical Adult model). The procedures for developing and 

using PBPK models have been extensively reviewed (Andersen, 2003; Clewell et al., 

2005; Krishnan and Johanson, 2005; Reddy et al., 2005; Yang et al., 1995). Models have 

been developed for a broad range of applications including: drug development (Bjorkman, 

2005; Blesch et al., 2003; Slikker et al., 2005), biomonitoring and reverse dosimetry (Tan 

et al., 2006), species extrapolation (Corley et al., 2005; Poet et al., 2002; Reitz et al., 1988; 

Teeguarden and Barton, 2004; Timchalk et al., 2002b), mechanism of action assessment 

(Conolly et al., 2004; DeWoskin and Thompson, 2008; Medinsky, 1995; Peters and Hultin, 

2008), cancer and non-cancer risk assessments (Andersen, 1995; Andersen et al., 1994; 

Bois, 1999; Chiu et al., 2007; Dixit et al., 2003; el Masri et al., 1995; Gearhart et al., 1993; 

Welsch et al., 1995), and estimating human variability and polymorphisms (Licata et al., 

2001; Tardif et al., 2002; Timchalk et al., 2002a). 

Robust PBPK models that are capable of exploiting large experimentally-derived 

data for model parameterization and validation offer an elegant solution to determine 

dosimetry and investigate potential mechanisms of action under a broad range of 

conditions. PD models are not as common as PBPK models since the mechanism (or 

mode) of action for many chemicals is not sufficiently well understood to allow the 

quantitative characterization of toxicological outcomes. The development of such models 

is challenging in animals, and human data are generally not available. 

As described in Section 2, the mechanism of CPF action on AChE is sufficiently 

well defined to allow the development of a combined PBPK/PD model. The original CPF 

PBPK model was published in 2002 (Timchalk et al., 2002b). Since then, the model has 

been applied and expanded to describe research results, test hypotheses, and determine the 

effects of possible perturbations of the physiological and biochemical system (BusbyHjerpe et al., 2010; Cole et al., 2005; Garabrant et al., 2009; Lee et al., 2009; Lowe et al., 

2009; Lu et al., 2010; Marty et al., 2007; Timchalk and Poet, 2008; Timchalk et al., 2005; 

Timchalk et al., 2006). The Typical Adult model includes a complete description of the 
38 

pharmacokinetics and pharmacodynamics of CPF-oxon, the active metabolite of CPF, and 

a more simplified submodel that describes the major metabolite, TCPy, which is of 

importance for biomonitoring (Figure 11). The model contains compartments describing 

CPF and CPF-oxon concentrations over time in fat, slowly perfused tissue, rapidly 

perfused tissue, diaphragm, brain, and liver. Model parameterization was achieved most 

often by extrapolation from in vitro studies (animal and human tissues), in some incidences 

by extrapolation from rat studies, and, for TCPy pharmacokinetics, by fitting to human 

data. TCPy pharmacokinetics are based on a single compartment with a volume of 

distribution and first-order urinary elimination of total urinary TCPy. 

In the case of CPF and other organophosphorus pesticides, the AChE inhibiting 

mechanism of action is well understood and biomarkers of exposure, inhibition of plasma 

cholinesterase, and biomarkers of early effects such as AChE inhibition in RBC, are 

available (Carr and Nail, 2008; Eaton et al., 2008; Kaushik et al., 2007; Lassiter et al., 

1999; Mattsson et al., 2000; Nigg and Knaak, 2000). Pharmacodynamics of AChE 

inhibition as mediated by CPF-oxon are included in blood (plasma and RBC), diaphragm, 

brain, and liver compartments. The PBPD model for CPF is a direct extension of the PBPK 

model and relates the prediction of the formation of CPF-oxon to changes in BuChE 

(plasma cholinesterase), RBC AChE, and brain AChE. This model has been provided to a 

number of researchers over the last eight years and has formed the basis of a number of 

publications on the kinetics of CPF by academics, government, and industry (Cole et al., 

2005; Furlong et al., 2005; Garabrant et al., 2009; Lowe et al., 2009; Lu et al., 2010; 

Marty et al., 2007). 
39 

Figure 11 PBPK/PD model (Typical Adult model) structure. The model structure has the same foundation as 2002 (Timchalk et al., 2002b). The shaded compartments denote tissues which contain B-esterases (bottom paand enzyme activities (Vmax) change with age based on liver and/or blood compartmental growth (Table 1). 
40 

Since 2002, the ongoing development of the CPF PBPK/PD model has been 

informed by the availability of new environmental, toxicological, and physiological data. 

Since its original publication (Timchalk et al., 2002b), the model has been further refined 

with measured in vivo and in vitro parameters (i.e. cholinesterase inhibition rates and CYP 

metabolism rates) and has had additional complexity added (i.e. metabolism in the 

intestines). Specific modifications include: 

• Improvement of the modeling of extrahepatic metabolism both at the site of 

cholinesterase inhibition or presystemically (in the liver). In vitro metabolism of CPF 

was measured in microsomes prepared from rat enterocytes and compared to 

microsomes prepared from whole liver (Poet et al., 2003). 

• Incorporation of in vitro measurement of CYP metabolism to TCPy or CPF-oxon 

and PON1 metabolism of CPF-oxon to TCPy in human liver microsomes from 

individuals ranging in age from 13 days to 75 years and in the PON1 metabolism in 

plasma from humans ranging in age from 3 days to 43 years. (Described in more 

detail in Appendix A.) 

• Measurements of partitioning of CPF between plasma and vegetable oil (a fat 

surrogate) to determine blood:tissue partitioning using a QSAR approach (Lowe et 

al., 2009) 

• Verification of the bimolecular inhibition rate constant by directly determining the 

AChE and BuChE inhibition dynamics in rat plasma, brain, and saliva (Kousba et 

al., 2004; Kousba et al., 2007). 

3.2 Development of the LifeStage model

The Typical Adult model provides very useful information for purposes like 

understanding mechanism or for comparison across species. But the model cannot address 

differences in response in different age groups or gender that arise from differences in 

physiology or metabolism that occur with growth (ontogeny). To address this issue, 

gender-specific human growth was incorporated into the LifeStage version of the 

PBPK/PD model, based on the equations described by Leucke et al. (2007) and Young et 

al., (2009). In this subsection we present the specific equations used in the LifeStage 
41 

model. The subsection also presents an evaluation of the predictions using data on age 

specific body weights and weight - tissue volumes relationships reported in the published 

literature. 

The development of the LifeStage model begins with the development of an agespecific model of body weight. The predictions of body weight for each age are used in 

turn to predict the volumes of the compartments based on specific ontogeny of each tissue. 

Once the age-related physiology is defined, it is combined with the age-specific 

metabolism data discussed in Section 2. Data that are tissue specific are combined with the 

age-specific tissue volumes to capture changes in metabolism from birth to adulthood. 

Cholinesterase (A- and B-) are scaled with age-specific tissue volume. Blood flows are 

likewise scaled based on tissue volume changes with age. This approach provides an 

excellent means of dynamically modifying PBPK physiological parameters based on age 

and gender. 

A generalized, age- and gender-specific Gompertz function was used to calculate a 

body weight for modeled individuals (Luecke et al., 2007). A comparison of the modelpredicted body weights of men and women to NHANES-reported body weights is shown 

in Figure 12. 

0 10 20 30 40 50 60 70 80

0

50

100

150

200

250

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

° °

°

° °

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°°

° °

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

° ° ° °

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

° °

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°° ° °

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

° °

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°° °

°

° ° °

°

°

°

°

°

°

°

° °

°°

°

°

°

°

° °

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

° °

°

° ° °

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° ° ° °

°

°

°

° °

°

°

°

°

° ° °

°

°

°

°

° °

° °

°

° °

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

° ° °

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

° °

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°° °

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° ° ° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

° °

°

°

°

°

°

° °°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

° ° ° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

° ° °

°

°

°

°

°

°

° °° °

°

° °

°

° °

° °

°

°

°

°°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° ° °

°

° °

°

°

°

°

°

° °

°

° °

°

° °

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

° °

° °

°

°

° °

°

° °

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° ° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

° °

°

°°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

° ° °

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

° ° ° °

°

°

°

°

° °

°

°

° ° °

°

°

°

°

°

°

° °

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

° °

°

° ° ° ° °

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°° ° ° °

°

°

° °

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

° °

°

°

°

° °

°

° ° ° °

°

°

°

° °

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °°

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

° ° °

° °

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

° °

° °

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

° °

° °

°

°

°

°

°

°

° °

° °

° °

°

°

°

°

°

°

°

° °

°

°

° °

°

°

° °

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

° °

° °

°

°°

°

°

°

° °

°

°°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

° °°

° °

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

° ° ° °°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°°

° ° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

° ° °

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

° °

°

°

°

°

° ° ° °

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°°

°

°

° °

°

° ° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

° °

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

° °

°

°

° °

° °

°

°

°

°

°°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

B. Male

Age (yr)

Body Weight (kg)

0 10 20 30 40 50 60 70 80

0

50

100

150

200

250

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

° °

°

°

°°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

° °

°

° ° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

° °

°

°

°

°

° °

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

° °

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

° °

°

°

° °

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

° °

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

° °

° ° ° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° ° °

° ° °

° °

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

° ° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

° °

°

°

° °

°

°°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

° ° ° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°° °

° °

°

°

°

°

°

° °

°

°

°

°

° °°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

° °

°

°

°

°

°

°

°

°

° ° °

°

°

°

° ° °

°

° °

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

° °

° °

° °

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

° °

°

°

°

°

°

°°

°

° ° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

° °

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

° ° ° °

°

°

°

° °

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

° °

°

° °

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

° °

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° ° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° ° °

°

°

°

°

° °

° ° °

°

° °

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

° °

° °

°

°

°

°

° ° ° °

°

°

° °

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°°

°

°

°

°°

°

°

°

°

° °

°

° °

° °

°

° °

°

°

°

° ° °

° ° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

°

° °

°

°

° °

°

°

°

°

°

° °

°

°

°

° °

°

°

°

° °

°

°

° °

°

° ° °

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° ° ° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

° °

°

° ° °

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

° °

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° ° °

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° ° °

°

°

° ° °

°

°

°

° °

°

°

° °

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

° ° ° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

° °

°

°

° °

°

°

°

°

° °

°

°

°

°

°

° °

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

° °

° °

°

°

°

°

°

°

°

° °

°

° °

°

° °

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° ° °

°

° °

°

°

° °

°

°

° °

° °

° °

°

°

° °

°

° °

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

° °

°

°

°

°

° °

° °

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

° °

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

° °

° ° °

°

°

°

° °

°

°

°

° °

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

° °

° °

°

°

°

°

° °

°

°

°

°

°

°

° ° °

° °

°

°

°

°°

°

°

° °

°

°

° °

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°°

°

°

°

°

°

°

° ° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

° °

°

°

°

°

° °

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° ° °

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

° ° °

°

° °

°

°

°

°

°

° °

°

°

° °

°

° ° °

°

°

° ° °

°

°

° °

°

°

° ° °

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

°°

°

°

°

°

°

° °

°

°

° °

°

°

°

° °

°

°

°

° ° °

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° °

°

°

°

° °

°

°

°

°

°

°

°

° ° °

°°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° ° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

°

°

° °

°

°

° °

°

° °

°

° °

° °

°

°

°

°°

°

°

°

°

°

°

° °

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

°

°

°

°

°

°

°

° ° °

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

° °

° ° ° °

°

°

°

°

°

°

°

° °

°

°

° °

° °

°

°

°

°

° °

°

°

°

° °°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

° °

°

°

°

°

°

°

°

°

° ° °

°

°

° °

°

°

°

°

°

°

° °

° °

°

°

°

° °

°

°

°

° °

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

° ° °

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

°

° °

°

°

°

° °

° °

°

°°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

° °

° ° °

°

°

° °

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

° °

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

°

°

°

° °

° °

°

°

°

°

°

° ° °

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

° °

° °

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° ° °

° °

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

° °

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

°

°

° °

°

° °

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

° °

°

° °

°

°

°

°

°

° ° °

°

°

°

°

°

°

° ° °

°

°

°

°

°

° °

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

°

° °

°

°

°

°

° °

°

° °

°

° °

°

°

°

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

° ° °

°

°

°

°

°

°

°

°

°

°

°

°

°

° °

°

°

°

°

° °

°

°

°

° °

°

A. Female

Age (yr)

Body Weight (kg)

Figure 12. Model prediction for a typical human body weight (lines) compared to NHANES 

body weight data from 2007-2008 for the U.S. population. 

To incorporate linked physiological data into the LifeStage PBPK/PD model, the 

compartmental volumes, on a fractional body weight basis, are scaled with body weight 
42 

dependent polynomial equation. This approach provides a flexible framework that has been 

used to define and switch among compartments of various species and genders with ease 

within the framework of the LifeStage model (Luecke et al., 2007; Young et al., 2009).

Compartments existing within the LifeStage model are equivalent to the Typical Adult 

model (Timchalk et al., 2002b), discussed previously) and include brain, blood, 

diaphragm, fat, liver, rapid, and slow compartments (Tables 1 and 2). The rapidly perfused 

compartment consists of a summation of kidney, spleen, lung, gastrointestinal tract (GI), 

and pancreas volumes, and the slowly perfused compartment consists of a summation of 

muscle, skin, bone marrow, and non-fat adipose tissue volumes. 

Figure 13 presents a summary of the compartments and the basis for modeling 

variation with age that results from the approach outlined above. 

Figure 13. Schematic of age and body weight dependences in the LifeStage model. 

All compartment volumes and blood flows vary with age and body weight. In vivo metabolic 

rates are scaled based on tissue size (measured in vitro values scaled to describe tissue-specific 

(brain, blood, and liver) metabolism); in blood, PON1 metabolism of oxon is not only blood 

volume but also age-dependent. 
43 

Table 1. Pharmacokinetic model parameters. 

Parameter Value Source 

Tissue Ontogeny 

All Scaled by age and body 

weight See Table 2

Flows (L/hr/kg tissue volume) 

Cardiac Output Summed from total tissue 

flow See “Blood flows” in this section

Brain 30.6 Price et al., 2003 

Diaphragm 85.2 Luecke et al., 2007 

Fat 1.45 Luecke et al., 2007, Cowles et al., 1971, Price et 

al., 2003 

Liver 50.4 Price et al., 2003 

Rapidly Perfused 61.8 Adrenal/Spleen average: Price et al., 2003 

Slowly Perfused 1.80 Bone: Price et al., 2003 

Partition Coefficients for CPF (tissue:blood) 

Brain 16.5 Lowe et al., 2009 

Diaphragm 3.85 Lowe et al., 2009 

Fat 250 Lowe et al., 2009 

Liver 12.8 Lowe et al., 2009 

Rapidly Perfused 16.5 Lowe et al., 2009 

Slowly Perfused 3.85 Lowe et al., 2009 

Partition Coefficients for CPF-oxon (tissue:blood) 

Brain 5.6 Lowe et al., 2009 

Diaphragm 1.8 Lowe et al., 2009 

Fat 75 Lowe et al., 2009 

Liver 4.5 Lowe et al., 2009 

Rapidly Perfused 5.6 Lowe et al., 2009 

Slowly Perfused 1.8 Lowe et al., 2009 

Hepatic CYP Metabolic Constants (per tissue wt) 

CPF → TCPy Vmax 

(nmol/hr/kg) 1580 Measured in vitro, Median: See Table 2, Section 2: CPF metabolism/mechanism of action 

CPF → TCPy Km (μM) 53.8 Measured in vitro, Median: See Table 2, Section 

2: CPF metabolism/mechanism of action 

CPF → Oxon Vmax 

(μmol/hr/kg) 689 Measured in vitro, Median: See Table 2, Section 2: CPF metabolism/mechanism of action 

CPF → Oxon Km (μM) 85.8 Measured in vitro, Median: See Table 2, Section 

2: CPF metabolism/mechanism of action 

Intestinal CYP Metabolic Constants (per tissue wt) 

CPF → TCPy Vmax 

(μmol/hr/kg) 36.8 Poet et al., 2003 

CPF → TCPy Km (μM) 55 Poet et al., 2003 

CPF → Oxon Vmax 

(μmol/hr/kg) 10.0 Poet et al., 2003 

CPF → Oxon Km (μM) 8.1 Poet et al., 2003 

Brain CYP Metabolic Constants (per tissue wt)
44 

Parameter Value Source 

CPF → TCPy Vmax 

(μmol/hr/kg) 3.85 Extrapolated: See Section 2: CPF metabolism/mechanism of action 

CPF → TCPy Km (μM) 5.38 See Section 2: CPF metabolism/mechanism of 

action 

CPF → Oxon Vmax 

(μmol/hr/kg) 0.91 Extrapolated: See Section 2: CPF metabolism/mechanism of action 

CPF → Oxon Km (μM) 8.60 See Section 2: CPF metabolism/mechanism of 

action 

PON1 Metabolic Constants 

Plasma Vmax 

(μmol/hr/kg) Logistic Fit Measured in vitro, Median: See Table 2 and Section 2 of this report 

Plasma Km (μM) 192 Measured in vitro, Median: See Table 2 and 

Section 2 of this report 

Liver Vmax (μmol/hr/kg) 3902 Measured in vitro, Median: See Table 2 and 

Section 2 of this report 

Liver Km (μM) 498 Measured in vitro, Median: See Table 2 and 

Section 2 of this report 

Intestine Vmax 

(μmol/hr/mg) 246 Poet et al., 2003 

Intestine Km (μM) 328 Poet et al., 2003 

Oral Absorption 

Stomach →Intestine 0.5 Fitted: Nolan et al., Timchalk et al., 2002 

Intestinal Absorption 0.2 Fitted: Nolan et al., Timchalk et al., 2002 

Plasma Protein Binding (%) 

CPF 99 Lowe et al., 2009 

Oxon 99 Lowe et al., 2009 

TCPy Compartmental Model 

Vd (L) 0.2×BW1.117 Fitted: Nolan et al., 1984, Timchalk et al., 2003 

Ke (/hr) 0.013 Fitted: Nolan et al., 1984, Timchalk et al., 2002 

Cholinesterase Degradation Rates (hr-1) 

Butyryl 0.004 Fitted (repeat dose data in rats: DOW 

unpublished) 

Acetyl 0.01 Standardized, Timchalk et al., 2002b 

Bimolecular Inhibition Rate (μM hr-1) 

Butyryl 2000 Timchalk et al., 2002 

Acetyl 220 Kousba et al. (Date) 

Enzyme Turnover Rate (hr-1) 

Butyryl 1.17x107 Maxwell et al., 1987, Timchalk et al., 2002b 

Acetyl 3.66x106 Maxwell et al., 1987, Timchalk et al., 2002b 

Carboxyl 1.086x105 Maxwell et al., 1987, Timchalk et al., 2002b 

Enzyme Turnover Rate (hr-1) 

Enzyme Turnover Rate (hr-1) 

Butyryl 11700000 Maxwell et al., 1987, Timchalk et al., 2002b 

Acetyl 3660000 Maxwell et al., 1987, Timchalk et al., 2002b 

Carboxyl 108600 Albers et al., 2009 

Enzyme Activity (μmol/kg/hr) 
45 

Parameter Value Source 

Brain ACHE 440000 Hojring et al., 1976 

Diaphragm ACHE 77400 Maxwell et al., 1987, Timchalk et al., 2002b 

Liver Carboxyl 1920000 Maxwell et al., 1987, Timchalk et al., 2002b; 

Pope et al., 2005 

Plasma Carboxyl NA Li et al., 2005 

Brain Butyryl 46800 Maxwell et al., 1987, Timchalk et al., 2002b 

Diaphragm Butyryl 26400 Maxwell et al., 1987, Timchalk et al., 2002b 

Liver Butyryl 30000 Maxwell et al., 1987, Timchalk et al., 2002b 

Plasma Butyryl 263000 Sidell et al., 1975 

Enzyme Reactivation Rate (hr-1) 

Butyryl 0.0014 Carr and Chambers, 1996, Timchalk et al., 2002b 

Acetyl 0.014 Carr and Chambers, 1996, Timchalk et al., 2002b 

Carboxyl 0.014 Carr and Chambers, 1996, Timchalk et al., 2002b 

Enzyme Aging Rate (hr-1) 

Butyryl 0.0113 Carr and Chambers, 1996, Timchalk et al., 2002b 

Acetyl 0.0113 Carr and Chambers, 1996, Timchalk et al., 2002b 

Carboxyl 0.0113 Carr and Chambers, 1996, Timchalk et al., 2002b 

Utilization of the LifeStage model required extrapolation of some compartmental 

polynomial equations beyond those body weights that were used to fit them by Luecke et al.

(2007) and Young et al. (2009). The equations of Luecke et al. (2007) and Young et al., 

(2009) were fit to individuals less than 100 kg. A goal of the LifeStage model was to also 

simulate individuals over 100 kg, so additional data including children and larger 

individuals were used to re-fit the polynomials for the LifeStage model. Compartments 

existing within the LifeStage model are equivalent to the Typical Adult model (Timchalk et 

al., 2002b), discussed previously) and include brain, blood, diaphragm, fat, liver, rapid, and 

slow compartments (Figure 13). The rapidly perfused compartment consists of a summation 

of kidney, spleen, lung, gastrointestinal tract (GI), and pancreas volumes; and the slowly 

perfused compartment consists of a summation of muscle, skin, bone marrow, and non-fat 

adipose tissue volumes. Modified equations include fat (both male and female), adipose, 

brain, liver, and muscle and were refit with additional data or extrapolations to achieve 

adequate fits up to a 170 kg individual. For all other compartments, previously published 

polynomials or constant values were used to define compartmental volumes (Table 2). 
46 

Table 2. Compartmental Growth. 

Compartment Fraction of Body Weight Equation1 Sour

 Eqn. Format V0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6 (BW V0 V1 V2 V3 V4 V5 V6 

Leucke 9.15e-2 -8.59e-7 1.25e-11 -6.46e-17 - - - Young 8.97e-2 -3.50e-7 6.54e-13

Blood 

LifeStage 8.970e-2 -3.500e-7 6.540e-13 - - - - 

Youn

Leucke 1.19e-1 -3.51e-6 4.28 e-11 -1.82 e-16 - - Young 1.41e-1 -5.54e-6 9.30 e-11 -6.83e e-16 1.80e-21

Brain (Figure 14) 

LifeStage 1.216e-1 -3.465e-6 4.354e-11 2.463e-16 5.132e-22

Fit to (bwt≤

data extraTable 

Leucke 3.000e-4 - - - - - - Young NA - - - - - - 

Diaphragm 

LifeStage 3.000e-4 - - - - - - 

Luec

Fat* 

Leucke 5.91e-2 1.20e-5 -5.80e-10 1.12e-14 -6.36e-20 - - Young 1.84e-2 -6.86e-6 2.46e-10 -2.11e-15 7.58e-21 -9.94e-27 - 

Female (Figure 16) 

LifeStage 9.217e-02 1.401e-05 -6.787e-10 1.540e-14 -1.558e-19 7.249e-25 1.273e-30

Fit to Laforextraequatal. 20
47 

Compartment Fraction of Body Weight Equation1 Sour

 Eqn. Format V0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6 (BW V0 V1 V2 V3 V4 V5 V6 

Leucke 3.95e-2 1.59e-5 -6.99e-10 1.09e-14 -5.26e-20 - - Young 1.61e-2 -3.59e-6 -8.28e-11 -3.57e-16 4.73e-22 - - 

Male (Figure 16) 

LifeStage 3.484e-2 2.803e-5 -1.422e-9 2.892e-14 -2.718e-19 1.20

3e-24

-

2.036e-30

Fit to Laforextraequatal. 20

Leucke 3.49e-2 -3.23e-7 2.13e-12 - - - - Young 4.25e-2 -1.01e-6 1.99e-11 -1.66e-16 4.83e-22 - - 

Liver (Figure 15) 

LifeStage 3.917e-2 -6.789e-7 1.082e-11 -7.393e-17 1.701e-22 - - 

Fit to weig

extraTable 

Rapid 

Leucke 1.67e-2 -9.96e-8 -1.09e-13 1.13e-17 - - - Young 1.860e-2 -4.550e-8 - - - - - 

Lung 

LifeStage 1.860e-2 -4.550e-8 - - - - - 

Youn

Leucke 7.31e-3 -8.29e-8 2.13e-12 - - - - Young 7.26e-3 -6.69e-8 3.33e-13 - - - - 

Kidney 

LifeStage 7.260e-3 -6.690e-8 3.330e-13 - - - - 

Youn

Leucke 1.17e-3 -1.18e-8 1.81e-13 - - - - Young 1.48e-3 - - - - - - 

Pancreas 

LifeStage 1.480e-3 - - - - - - 

Browal. 20

Leucke 3.05e-3 -2.09e-8 1.24e-13 - - - - Young 3.12e-3 5.57e-9 - - - - - 

Spleen 

LifeStage 3.120e-3 5.570e-9 - - - - - 

Youn
48 

Compartment Fraction of Body Weight Equation1 Sour

 Eqn. Format V0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6 (BW V0 V1 V2 V3 V4 V5 V6 

Leucke 1.93e-2 -4.42e-7 9.28e-12 -4.88e-17 - - - Young NA - - - - - - 

GI 

LifeStage 1.650e-2 - - - - - - 

Brow

Slow 

Leucke NA NA NA NA NA NA NA Young NA NA NA NA NA NA NA 

Non-Fat adipose* 

LifeStage 2.044e-1 2.617e-5 -1.542e-9 3.268e-14 -3.116e-19 1.387e-24 -2.35e-30

Fit to Lafor(Valeextraequatal. 20

Leucke 9.61e-2 -4.88e-6 3.05e-10 -3.62e-15 1.22e-20 - - Young 9.68e-1 -3.32e-6 1.83e-10 -1.24e-15 - - - 

Muscle 

LifeStage 1.251e-1 1.458e-5 -2.927e-10 2.114e-15 -5.250e-21 - - 

Fit to Janssextra2A (J

Leucke 1.07e-1 -3.26e-6 6.11e-11 -5.43e-16 1.83e-21 - - Young 1.03e-1 -2.56e-6 3.68e-11 -2.580e-16 8.620e-22 -1.100e-27 - 

Skin 

LifeStage 1.030e-1 -2.560e-6 3.680e-11 -2.580e-16 8.620e-22 -1.100e-27 - 

Youn

Leucke 5.19e-2 8.06e-7 -1.96e-10 7.63e-15 -1.08e-19 5.14e-25 NA Young NA NA NA NA NA NA NA 

Bone Marrow 

LifeStage 2.100e-2 - - - - - - 

Browyello
49 

Similar to Luecke et al., (2007) and Young et al., (2009), polynomial models of 

orders 1-6 were fit to the data (“R: A language and environment for statistical computing”, 

version 2.9.0 or 2.11.1 (R, 2004)), and then the best fit model was chosen based on the 

Bayesian information criterion (Schwartz, 1978). Bayesian information criterion is very 

similar to Akaike's information criterion, in that it is a measure of the goodness of fit in 

terms of both accuracy (positively) and model complexity (negatively), but differs, in that 

it penalizes for the number of parameters in a model more rigorously than Akaike's 

information criterion2

. 

When selecting data for fitting the polynomials first choice was to use the reference 

values for various compartments as defined by the International Commission on 

Radiological Protection (ICRP) Publication 89 (ICRP, 2003), which presents detailed 

information on age- and gender-related differences in anatomical and physiological 

characteristics of reference individuals. This source was used for data on body weights 

from birth to ~70 kg, while other references were used for compartmental data at higher 

body weights. Data for some compartments were not available up to the desired 170 kg. 

For those compartments, extrapolations were made with published relationships, 

extrapolating for body weights 150-175 kg by 5 kg intervals. These compartment refits in 

addition to the other compartments from various sources provide a total volume of 

distribution that is ~75-95% of body weight. 

 2 In selecting a “best-fit” model for any data set, models with more parameters will always fit the 

data set better (have a higher likelihood), especially in the case of polynomial models. For example, a 

polynomial model with (n-1) parameters will connect all data points with a function. However, the 

usefulness of such a function is usually minimal. Thus for selection of a “best-fit” model that is useful, 

standard approaches have been developed to quantitatively select the best fit model by balancing 

goodness-of-fit (by maximum likelihood) and parsimony (penalize by the number of parameters in the 

model [complexity]) Akaike, H., 1974. A New Look at the Statistical Model Identification. I.E.E.E. 

Transactions on Automatic Control. AC 19, 716-723, Akaike, H., 1976. Canonical Correlation Analysis of 

Time Series and the Use of an Information Criterion. in: Lainotis, R. K. M. a. D. G., (Ed.), System 

Identification: Advances and Case Studies. Academic Press, New York, pp. 52-107. In general, these 

criteria (c) are described mathematically below, where Ln() is the maximum likelihood, k is the number of 

parameters of a function, n is the sample size of data, φ(n)=2 for AIC, and φ(n)=ln(k) for BIC. 

cn(k)=-2×ln(Ln(k))/n+k×φ(n)/n 
50 

The following sections describe the modeling of the key compartments and tissues 

for the LifeStage model of CPF. These include, brain, liver, adipose and fat, and muscle. 

The resulting equations are given in Table 2. 

Brain

Luecke et al. have published equations for brain compartment growth as a function 

of body weight (Luecke et al., 2007; Young et al., 2009). The function was refit using data 

from the ICRP Publication 89 (ICRP, 2003) and Young et al. (2009) for body weights 

greater than 70 kg. Extrapolations of brain volume were made using Table 4A in Young et 

al., (2009) for both the body weights presented in that table and extrapolated for 

150-175 kg body weights. The resulting fit was a 4th order polynomial equation (Figure 

14). As Figure 14 demonstrate refitting the equations of Luecke et al. (2007) and Young et 

al., (2009) was required in order to assess individuals with body weights greater than 

100 kg. Symbols are published measurements from Valentin, 2002 (blue), or Young et al., 

2009 (black). The lines show the implementation of the polynomial equations as published 

in Young et al., (Green dashed line), Leucke et al., 2007 (green dotted line) or predicted 

using the LifeStage model (solid black line). The equations of Leucke et al., 2007 and 

Young et al., 2009, were designed to be used up to 100 kg. BTW is body weight in kg. 
51 

0 25 50 75 100 125 150

0

1

2

3

Body Weight (kg)

Tissue Volume (L)

Figure 14. Comparisons of equations to describe ontogeny of brain growth. Symbols are 

published measurements from Valentin, 2002 (blue), or Young et al., 2009 (black). The lines 

show the implementation of the polynomial equations as published in Young et al., (Green 

dashed line) or Leucke et al., 2007 (green dotted line). Brain volume as predicted using the 

LifeStage model is shown with the black line. The equations of Leucke et al., 2007 and Young 

et al., 2009, were designed to be used up to 100 kg. 

Liver

The function for the liver compartment ontogeny was refitted using the ICRP 

Publication 89 (ICRP, 2003) and Young et al. (Young et al., 2009) data presented in Table 

4A of that paper. This refitting allows for the prediction of liver volumes up to 175 kg 

body weights. Initial attempts in developing the function were based on data from Young 

et al. (2009). These data resulted in fitted equations with undesirably high variation across 

narrow body weight ranges. Fitting the function to the reference values for liver from 

Table 4A in Young et al. (2009) resulted in a 4th order polynomial that was a much 

smoother fit (Figure 15, presented with all data). 
52 

0 25 50 75 100 125 150

0

1

2

3

4

Body Weight (kg)

Tissue Volume (L)

Figure 15. Fit of the LifeStage model-predicted ontogeny of the liver (Black line) to the 

published measurements from Valentin, 2002 (blue), or Young et al., 2009 (black). BTW is 

body weight in kg. 

Adipose and fat

The ICRP Publication 89 (ICRP, 2003) describes adipose tissue as a combination of 

fat and connective tissue. Reference fat content of adipose tissue is 40% at birth and 

increases to ~80% in adults (ICRP, 2003). Failure to account for these differences could 

lead to inaccurate compartmental volume estimates of fat or fat-partitioning coefficients 

which are estimated based on chemical makeup of tissue. These potential inaccuracies 

could have substantial implication on overall pharmacokinetics for lipophilic compounds, 

like CPF. In the LifeStage model, the fat compartment is defined as the lipid content of 

adipose tissue. The non-fat adipose tissue (connective tissue and the adipose compartment 

subtracted from the fat compartment) is included in the slow compartment. Data to fit the 

fat compartment came from ICRP Publication 89 (ICRP, 2003) and Lafortuna et al., 

(2005) (Figure 16). Extrapolations were made for 150-175 kg body weights using the 

respective equations presented in those figures. Data for the adipose tissue came from the 

ICRP, (2003) and Lafortuna et al., (2005) based on the assumption that 80% of adipose 

tissue in adults is fat (ICRP, 2003). 
53 

0 25 50 75 100 125 150 175

0

25

50

75

100

125

Female

Male

Body Weight (kg)

Fat Content (L)

Figure 16. Fit of the LifeStage model-predicted ontogeny of the fat in males and females to 

the published measurements from Lafortuna, 2005, Butte, 2000; or Valentin, 2002. 

Muscle

The function for muscle was based on data obtained from Figure 4 A of ICRP 

Publication 89 (ICRP, 2003) and from Janssen et al., (2000). The final function was a 4th

order equation that is capable of predicting the muscle compartment upto 170 kg. The 

volume of the muscle is used in the estimate of the volume of the slowly perfused 

compartment of the individual. 

Summary of Tissue Ontogeny

This LifeStage model includes polynomial equations that fit the available tissue 

growth data from the literature (see Figures 13-16). The ontogeny of the above organs as 

well as the general PBPK model compartments are shown in Figure 17. The benefit of 

describing tissue volumes correctly as a function of age and weight, rather than based only 

on weight (fixed percent of body weight) can be seen for the predictions of the volume of 

the brain. Brain is a much larger fraction of total body weight in infants than adults and 

cannot be accurately predicted from body weight alone.
54 

Figure 17. LifeStage model tissue growth. The LifeStage model predictions for typical adult 

individuals are shown with the lines, the dashed lines show tissue volumes for females, solid 

lines are males. The symbols represent tissue volumes predicted for at typical male using the 

original Typical Adult model, and default tissue volumes as a percentage of body weight 

(Timchalk, et al., 2002; Brown et al., 1997). Arrows indicate typical body weights at each age 

of interest for this analysis. 

0 50 100 150

0.1

1

10

100 FAT

LIVER

BRAIN

6 mo

3 yr30 yr

Body Weight (kg)

Tissue Volume (L)

0 50 100 150

0.1

1

10

100

BLOOD

SLOW

RAPID

6 mo

3 yr30 yr

Body Weight (kg)

Tissue Volume (L)
55 

Blood flows

Once the volumes of the organs and tissues are defined, tissue-specific perfusion 

rates are used to define the blood flows for each organ. These values are summed to predict 

the total cardiac output. Total cardiac output is consistent with predictions of total cardiac 

output in humans that are based on surface area (Brown, 1997) (Figure 18). 

0 25 50 75 100 125 150

0

200

400

600

800

1000

LifeStage Model

Constant fraction

of Body Surface Area

Body Weight (kg)

Total Cardiac Output (L/hr)

Figure 18. Comparison of the total cardiac output used in the original Adult model 

(Timchalk, et al., 2002) to the summed tissue-specific perfusion rates from the LifeStage 

model. The dashed line represents a cardiac output calculated using the formula: 

Cardiac output =15*(Body wt)0.7 (Brown, 1997). 

Enzyme activity in individual

The age-dependent enzymatic metabolism of CPF and CPF-oxon was measured in 

vitro using human hepatic microsomes (CYP and PON1) and plasma (PON1), as described 

previously (Section 2). These in vitro Vmax rates were extrapolated to in vivo values for use 

in the model by multiplying the in vitro rates times the levels of microsomal protein per 

unit volume and times liver (or plasma) volumes (see the following equation). 

[μmole/hr] [μmole/hr/mg protein] [mg protein/L tissue] [L tissue]

Enzymeactivity in individual = Enzymelevelsin tissue*Protein levelin organ *organ volume
56 

Total Vmax of human enzymatic metabolism of CPF to TCPy, CPF to CPF-oxon, and 

CPF-oxon to TCPy in the liver over various ages were scaled to age-dependent volume of 

the liver (Table 2) and concentration of microsomes in the liver, which is approximately 37 

mg/g tissue (Barter et al., 2008).3

Thus, even in cases where the enzymatic measures show no in vitro age- or 

physiological-dependences (on a per mg protein basis), the total enzymatic capacity of the 

individual varies with age. Figure 19 showing the impact of the age-related changes in liver 

volume. The values in this figure were generated by multiplying the observed Vmax times the 

age-specific liver volume. 

0.01 0.1 1 10 100

1×100 1

1×100 2

1×100 3

1×100 4

1×100 5

1×100 6

PON1

oxon

TCPy

Age (yr)

Vmax (μmol/hr)

Figure 19. Hepatic metabolism of CPF to TCPy (CYP), CPF-oxon (CYP) and of CPF-oxon to 

TCPy (PON1) increases with age. Note: total Vmax is on a logarithmic scale. 

 

3 There is limited evidence that microsomal protein content may change with age Barter, Z. E., et al., 2008. 

Covariation of Human Microsomal Protein Per Gram of Liver with Age: Absence of Influence of Operator and Sample 

Storage May Justify Interlaboratory Data Pooling. Drug Metabolism and Disposition. 36, 2405-2409.but this study is 

based on n=4 “pediatric” samples from individuals aged 2, 4, 9 and 13 and uses data from multiple laboratories after 

“correcting for losses” of microsomal protein during preparation. The adult population from this same study included 

separate individuals that were as young as 11 years old, overlapping the age of the pediatric samples. In rats, an 

increase in microsomal protein has been shown from post natal day 1-14, but by day 7 the protein content in the 

microsomes of neonatal rats were not significantly different than adults, whereas liver weight continued to increase 

through day 42 Alcorn, J., et al., 2007. Evaluation of the assumptions of an ontogeny model of rat hepatic cytochrome 

P450 activity. Drug Metab Dispos. 35, 2225-31. Overall, the evidence that microsomal protein in human children will 

vary from adults was not felt to be compelling, but the range in protein content suggested by these data was accounted 

for in the variability model to address any age- related differences in microsomal protein content (if they exist). 
57 

In plasma, however, in vitro PON1 metabolism of CPF-oxon to TCPy was agespecific on a per ml plasma volume basis, as described previously (Section 2). Therefore, 

the age-specific Vmax was scaled to age-dependent volume of the blood (Table 2), resulting 

in plasma metabolism varying by nearly 2 orders of magnitude (Figure 20). 

0.01 0.1 1 10 100

1×100 3

1×100 4

1×100 5

1×100 6

1×100 7

Age (yr)

Vmax (μmol/hr)

Figure 20. Total Vmax values of human enzymatic metabolism of CPF-oxon to TCPy in plasma 

over various ages. Symbols represent in vivo rates extrapolated from the in vitro data (See 

Figure 10). The line shows the Vmax used in the model for standard individuals. 

Metabolism of CPF in the brain was estimated based on an extrapolation from 

limited studies in rats. Incubation of brain microsomes with CPF demonstrated metabolism 

to TCPy 115-fold lower than in liver microsomes. Metabolism to oxon was below the limit 

of quantification. Based on this assessment, brain metabolism was conservatively 

estimated to be 115-fold lower than liver on a per mg protein basis for both metabolism to 

oxon and to TCPy. The microsomal yield in these studies was 5.2 mg/g brain and 35 mg/g 

liver, so the final in vivo metabolic rate constants were estimated to be approximately 760-

fold higher in whole liver than whole brain. Because brain size changes much less than 
58 

liver size over ontogeny, the difference in predicted Vmax for the bioactivation of CPF to 

CPF-oxon in the liver is only 3.7-fold less in newborns compared to adults. 

3.3 Comparison of LifeStage model output to human data

The estimates of the relationship between oral doses and impacts on blood 

concentrations of TCPy and cholinesterase inhibition in blood from the LifeStage model 

can be evaluated by comparing the predictions to the observed values in volunteer human 

studies. The LifeStage model code includes within it the Typical Adult model (See Figure 

2) and is designed such that either a body weight or an age can be put into the model. For 

these examples, the LifeStage model was employed by assigning body weights instead of 

ages to the simulated individuals. As indicated in Tables 1 and 2, the majority of the inputs 

for the LifeStage model are based on measured values and new in vitro data (Section 2)

4

. 

Therefore, results from the human volunteer study can be used to evaluate the predictions 

of the LifeStage model. 

Two controlled human exposure studies are available to compare model output; these 

studies were evaluated for both scientific and ethical considerations by the EPA Human 

Studies Review Board (See Attachment B). In the first study (Nolan et al., 1984), human 

volunteers were administered 0.5 mg/kg CPF, and blood and urinary TCPy and plasma 

cholinesterase were measured. Plasma cholinesterase inhibition was more than 80% 24 hr 

after exposure (Figure 21). The in vitro-derived metabolic constants were applied to the 

LifeStage model as described previously and the parameters optimized from the first study 

were held constant and validated using the results from a second study in which 6 male and 

6 female volunteers were administered 0.5, 1 or 2 mg/kg in a capsule (Kisicki et al., 1999; 

Timchalk et al., 2002b). Based on the urinary output of TCPy from the original study 

(Nolan et al., 1984), approximately 100% of the dose is expected to be excreted as TCPy. 

Based on this and TCPy plasma levels in the second study, oral absorption in the Kisicki et 

al. study is believed to be only ~30%. 

The LifeStage model’s predictions for adults compared well to the human data. The 

blood and urinary concentrations of TCPy are very good with the prediction following the 

 

4 The one exception to this is the TCPy compartmental parameters. 
59 

mean response of the groups at each dose in both studies (Figures 22 and 23). The 

predictions of the LifeStage PBPK/PD model for BuChE in plasma from the Noland et al.

study were also very good with the peak inhibition accurately predicted (Figure 21). The 

LifeStage model’s predictions of RBC AChE inhibition are consistent with the finding of 

Kisicki et al. (Figure 24). In this study no statistically significant differences were 

observed in RBC AChE activity in the volunteers, regardless of dose. The LifeStage 

predicts a peak inhibition of approximately 7% for the 2 mg/kg exposure and lower 

inhibition levels for the other groups. This level would not have been detectable given the 

relatively high day-to-day variation that occurs in humans. 

0 48 96 144 192 240 288 336

0

25

50

75

100 Nolan et al., 1984

Time (hr)

Plasma ChE

(Percent of Control)

Figure 21. Comparison of the model predictions of plasma cholinesterase inhibition for a 

typical adult to the data of Nolan et al. (1984). Data represent mean ± SD for 11-12 

individuals as described in Nolan et al. (1984). 
60 

0 24 48 72 96 120 144 168

0.001

0.01

0.1

1

10

100

Nolan et al., 1984

Time (hr)

TCPy in Plasma (μmol/L)

0 24 48 72 96 120 144 168

0.001

0.01

0.1

1

10

100

Kisicki et al., 1999

Time (hr)

TCPy in Plasma (μmol/L)

Figure 22. Comparison of the model predictions of plasma TCPy concentration for a typical 

adult to the data of Nolan et al. (1984) and Kisicki et al. (1999). Data represent mean ± SD for 

5-12 individuals as described in Nolan et al. (1984) and Kisicki et al., (1999). In Kisicki et al., 

data for 0.5 mg/kg dose is given in blue, 1.0 mg/kg in green, and 2 mg/kg in black. 
61 

0 24 48 72 96 120 144 168

0

50

100

150

Nolan et al., 1984

Time (hr)

TCPy in Urine (μmol)

0 24 48 72 96 120 144 168

0

50

100

150

Kisicki et al., 1999

Time (hr)

TCPy in Urine (μmol)

Figure 23. Comparison of the model predictions of urinary TCPy concentrations for a typical 

adult to the data of Nolan et al. (1984) and Kisicki et al. (1999). Data represent mean ± SD for 

5-12 individuals as described in Nolan et al. (1984) and Kisicki et al., (1999). In Kisicki et al., 

data for 0.5 mg/kg dose is given in blue, 1.0 mg/kg in green, and 2 mg/kg in black. Kisicki et 

al. fits are based on a predicted 30% uptake from the coated pills. Nolan et al. is based on 

100% uptake, model predictions presented below all assume 100% oral bioavailability. 
62 

0 24 48 72 96 120 144 168

80

85

90

95

100

105

110

Kisicki et al., 1999

0.5 mg/kg

Time (hr)

RBC ChE

(Percent of Control)

0 24 48 72 96 120 144 168

80

85

90

95

100

105

110

Kisicki et al., 1999

1 mg/kg

Time (hr)

RBC ChE

(Percent of Control)

0 24 48 72 96 120 144 168

80

85

90

95

100

105

110

Kisicki et al., 1999

2 mg/kg

Time (hr)

RBC ChE

(Percent of Control)

Figure 24. Comparison of the model predictions of RBC cholinesterase inhibition for a 

typical adult to the data of Kisicki et al. (1999). Data represent mean ± SD for 5 individuals as 

described in Kisicki et al., (1999). Kisicki et al. fits are based on a predicted 30% uptake from 

the coated pills. No statistically significant inhibition was measured after 0.5, 1, or 2 mg/kg 

dosing: the model predicts about 7 percent inhibition after a 2 mg/kg dose. Model predictions 

presented in all analyses below all assume 100% oral bioavailability. 
63 

3.4 Comparison of LifeStage model to Timchalk et al. 2002

The core structure of the LifeStage model is based on the model published in 2002 

(Timchalk et al., 2002b), with modifications as described in this Section. In the LifeStage 

model, more human-specific data have been used to parameterize the model whereas the 

initial PBPK/PD model used parameters extrapolated from rat data. Thus, our confidence 

in the parameterization of the human model and its predictive abilities are much greater 

than in earlier models. 

The LifeStage model predictions for adults were compared to those of the original 

standard adult model and found to be more conservative (Figure 25). In this figure the 

solid lines represent the predictions from the LifeStage model while the earlier model 

results are given using dotted lines. While the predictions for plasma BuChE inhibition in 

the two models are very similar, the predictions of RBC and brain AChE inhibition are 

approximately 2-3 fold lower. The RBC and brain AChE models have been updated using 

in vitro data for the bimolecular inhibition rate constant for AChE (Ki), PON1 metabolism 

(liver and blood), and CYP metabolism (brain). 

0.0001 0.001 0.01 0.1 1 10 100

0

20

40

60

80

100

Timchalk et al., 2002

Lifestage, 2010

Plasma

RBC

Brain

Plasma

RBC

Brain

TCPy in urine (μmol/hr)

Percent Activity

0.1 1 10 100 1000 10000

0

20

40

60

80

100

CPF Dose (ug/kg/day)

Percent Activity

Figure 25. Comparison of 2002 PBPK/PD model output (Timchalk, et al., 2002b) to LifeStage 

model. Simulations were conducted assuming a zero order (constant) oral uptake. 
64 

3.5 Predicted dose response in typical individuals at 6 months, 3 and 30 years.

The LifeStage model was used to determine peak RBC inhibition in adults, 3 yearold children and 6 month-old infants following single dose exposures to CPF (Figure 26). 

The predictions of the LifeStage model suggest that for a single oral dose at or above 

0.5 mg/kg the inhibition of RBC and brain AChE is greater in infants than in adults. 

Values for children age three fell in between the values for adults and infants. Infants had 

inhibition rates approximately 2–fold higher than adults. However, at a single oral dose of 

0.5 mg/kg and below, the level of inhibition of RBC would not be experimentally 

quantifiable in any of the age groups. 

0.1 1 10

0.1

1

10

100

30 yr old

3 yr old

6 mo old

Chlorpyrifos Dose (mg/kg)

RBC ChE Inhibition

(Percent of Control)

0 12 24 36 48

50

60

70

80

90

100

6 mo old

3 yr old

30 yr old

0.5 mg/kg

Time (hr)

RBC ChE Inhibition

(Percent of Control)

0 12 24 36 48

0

10

20

30

40

50

60

70

80

90

100

110

30 yr old

3 yr old

6 mo old

1 mg/kg

Time (hr)

RBC ChE Inhibition

(Percent of Control)

Figure 26. Comparison of the LifeStage model’s predictions of RBC inhibition in adults, 3 

year-old children and infants. Top) shows a dose response curves for the for peak RBC 

inhibition for the three ages, the arrows in this plot indicate of 0.5 and 1 mg/kg. Bottom) 

LifeStage model predictions of time courses of inhibition and recover at 0.5 and 1 mg/kg for 

the three age groups. Note the different y-axes. 
65 

Section summary

This section documents the data and assumptions used in the creation of the 

LifeStage model. The LifeStage is built on the Typical Adult model which is an update of 

the original 2002 Timchalk model. The LifeStage model allows the simulation of agespecific dose response curves for humans of any age. The predictions of the LifeStage 

model reflect a number of improvements over the original 2002 model. In general, the 

LifeStage model predicts more inhibition in RBC and brain then the original 2002 model 

(Figure 26). The predictions of the LifeStage model were shown to be consistent with the 

data from human volunteer studies (Figures 21-24). The predictions of the LifeStage model 

suggest that for a single oral dose above 0.5 mg/kg, cholinesterase inhibition in the various 

age groups exhibits the following pattern: infants > children > adults. Infants had inhibition 

rates approximately 2–fold higher than adults at these doses. However, at a single oral dose 

of 0.5 mg/kg and below the level of inhibition of RBC would not be experimentally 

quantifiable in any of the age groups. More detailed comparisons in cholinesterase 

inhibition are described in Section 4 in conjunction with the development of the Variation 

PBPK/PD model. 
66 

4. Creation of the Variation PBPK/PD model  

Section overview

The LifeStage model (Section 3) focused on characterizing the response to CPF in 

typical individuals of differing age. Once the compartments have been described on a 

default physiological basis, adding inter-individual variability is the next logical step to 

determine physiological and enzymatic determinants of pharmacodynamic outputs (Bois, 

2010). This section describes the development of models of interindividual variation for 

each age. The process for deriving the Variation model consists of the following four steps. 

Step 1. Use a “sensitivity analysis” to identify parameters in the LifeStage model that drive 

variation in response; 

Step 2. Develop distributions of interindividual variation in values for the parameters that 

drive response; 

Step 3. Define how correlations between input values are addressed; 

Step 4. Compare prediction of variability to data from human volunteer studies. 

Once the model is created it is used to investigate the factors that drive the increased 

sensitivity to the effects of CPF. Finally, the Variation model is used to investigate the 

magnitude of the variation in response in populations of different ages at different doses. 

Endpoints investigated are measures of internal dose and RBC and brain AChE inhibition. 

4.1 Step 1. Determining the relative importance of model parameters using a

sensitivity analysis

The sensitivity analysis was conducted using tools provided in the acslXtreme 

software used to create the model. A local sensitivity analysis was performed using the 

LifeStage model. In a local sensitivity analysis each parameter was varied individually 

while the values of all other parameters were held fixed and the impact of the variation on 

the model’s output was determined. A single oral dose of 0.54 mg/kg, was selected since 
67 

the Typical Adult model predicted that this dose would cause a 10% RBC inhibition in a 

typical adult human. The individual was evaluated over time and peak inhibition of RBC 

and brain AChE were determined. 

For each model parameter, sensitivity is measured in terms of a sensitivity 

coefficient. The sensitivity coefficient of a parameter is defined by the change in peak 

RBC and brain AChE inhibition divided by the change in the parameter value. In this 

analysis a small change, 1%, was made in the parameter value and the impact on peak 

RBC and brain AChE inhibition was determined. A value of 1 indicates that the 1% 

change in parameter produces a 1% change in either the peak RBC or brain AChE 

inhibition. Values greater than 1 indicate a greater than 1% change in one of the two target 

effects. Values close to zero indicate that neither brain nor RBC AChE levels were affected 

by changes in the parameter. 

Model parameters that were varied are those listed in Tables 1 and 2. These 

parameters include: 

• Tissue volumes, blood flows and perfusion rates 

• Enzyme activity levels 

• Tissue partitioning coefficients 

• Binding of cholinesterases, recovery of cholinesterases, and regeneration (turnover) 

rates 

• Rates of transport of CPF from the stomach to the gut or uptake from the gut 

As Table 3 indicates, very few of the model parameters impacted the estimates of 

peak RBC or brain AChE inhibition. Only 8 parameters had sensitivity coefficients greater 

than 0.1. It is recognized that some parameters that vary widely across a population with 

coefficients smaller than 0.1 might also contribute to variation in response. However, using 

a cutoff criterion of 0.1 should capture the major drivers of variation in response. 
68 

Table 3. Sensitivity of the model parameters on RBC or brain ChE inhibition 

Parameter Normalized Sensitivity 

Coefficient 

RBC/Brain AChE Basis 

Tissue Volume 

Rapidly perfused tissues 0.09 Brain 

Slowly perfused tissues 0.20 Brain 

Liver 0.74 Brain 

Blood 1.77 RBC 

Fat 0.19 Brain 

Tissue Specific Blood Flows 

Rapidly perfused tissues <0.01 RBC 

Slowly perfused tissues <0.01 RBC 

Liver 0.30 RBC 

Fat <0.01 RBC 

Liver Metabolism Cytochrome CYP that converts CPF into: 

Oxon (activation) 0.98 RBC 

TCPy (detoxification) 0.92 RBC 

Brain Metabolism -Cytochrome CYP that converts CPF into: 

Oxon (activation) 0.32 Brain 

TCPy (detoxification) 0.10 Brain 

PON1 

Blood 0.26 RBC 

Liver 0.47 RBC 

Note: The more sensitive endpoint is reported. 

Based on the sensitivity analysis, the Variation model includes distributions for the 

following parameters:

• Brain and liver CYP metabolism and its effect on both the rate of formation of CPFoxon and the formation of TCPy 

• PON1 metabolism in blood and liver 

• Compartment volumes and blood flows5 

The remaining parameters use the mean age-specific values established by the 

LifeStage model. 

 

5 While only certain compartments were found to be statistically significant, the interrelationship of organ and 

compartment volumes requires that models of interindividual variation in compartment volume address all of the major 

compartments of an individual. 
69 

4.2 Step 2. Developing distributions for sensitive parameters

The critical factors identified in Step 1 can be divided into two categories: 

physiology (including blood flows) and enzyme activity. The following subsections 

describe the approach used to develop age-specific distributions for these categories. 

Physiology

Modeling variation in physiology is a challenge since physiological parameters are 

inherently correlated (larger body weight implies larger compartment volumes). Blood 

flows in an individual are related to organ sizes and variation in one organ affects the other 

organs. In order to capture these correlations, the modeling of physiology was performed 

in a systematic manner. The process can be described as follows. First, data on the 

variation of body weight is taken from the output of the exposure models. As described in 

Section 5, the exposure models generate estimates of each individual’s body weight based 

on the individual’s age and gender. Thus for each individual a body weight, age, and 

gender is provided as an input. 

Second, the volume of each compartment is estimated from the individual’s body 

weight using the equations in Table 2. As discussed above, these equations are polynomial 

functions that assign volumes to the individual’s different compartments and organs. Since 

each equation for an individual’s compartment is based on a common body weight, the 

volumes are consistent (see Figures 13-18). 

The one exception to this process is the method used to assign the volume of the 

body fat compartment. In the development of the LifeStage model, it was determined that 

the majority of the variability in the total individual was due to variability in the fat content 

(Figure 16). For the fat compartment, interindividual variability was added to the predicted 

compartment volume. The relative variability of height in the NHANES data was 

determined to be constant across body weight (data not shown). Since no other information 

was available about the individual variability, the relative variability for the fat 

compartment volume was thus assumed to be equal to the relative variability of the body 

height. The LifeStage model then calculates the fat compartment with the equations in the 

growth model and adjusts the calculated volume by randomly sampling from the normal 
70 

distribution of relative variability. The resulting variability in fat volume is shown in 

Figure 27. 

0 25 50 75 100 125 150 175

0

25

50

75

100

125

Male

Female

Body Weight (kg)

Fat Content (L)

Figure 27. LifeStage model output of fat volumes in simulated individuals (red triangles, blue 

squares, compared to published measurements (Lafortuna, 2005, Butte, 2000; or Valentin, 

2002) 

Total Blood flow (Cardiac Output)

As described above, blood flows were defined on a tissue-specific basis and summed 

to predict total cardiac output (See Figure 18). This approach provides an estimate of the 

cardiac output and organ-specific blood flow under resting conditions (see Fig 19). In the 

variability model, inter-individual differences in fat volume and workloads results in total 

cardiac outputs that have a similar range in values as seen for the volume of fat (data not 

shown). 

Effect of activity on total compartment specific blood flows

Blood flows are affected by an individual’s physical activity. Thus, the resting blood 

flows must be adjusted to reflect interindividual variation in activity levels. Data on 

interindividual variation in physical activity are available from sources such as the 
71 

databases of human activity (Xue, 2010). However, in this project we decided to first test 

the model to determine the importance of activity before expending the effort to 

incorporate actual data on human variability in activity levels. This was done by assigning 

individuals activity levels based on a uniform distribution. Under this approach, 

individuals were assigned a work level ranging from 0 to 5 Watts. This range of work 

levels corresponds to a range from a resting state to a level of light exercise (e.g., moderate 

walking rate). This level of activity is assumed to be constant over the duration of the 

modeling period. If the variation in activity could be shown to have little or no effect on 

the predictions of internal doses and impacts on AChE, then there would be no need to 

assess the impact of actual patterns of human activity. 

The role of activity on organ-specific blood flows is not the same for each organ. 

Increasing levels of activity change the total cardiac output and also redirect the output 

away from the digestive organs to the muscles and dermis. This redirection is important to 

capture since it has a critical effect on blood flow through the liver. Standard blood flows 

(Table 4) were adjusted based on a work to oxygen demand conversion proposed by 

Jonsson et al. (2001) in the following equation. 

Qi0 (L/hr) = Perfusion Ratei (L/hr/L tissue) * Tissue Volumei (L) + 

Work Changei (L/hr/W)*Work (W) 

Table 4. Impact of work on Organ-Specific Perfusion Rates 

Model Compartment Change in Perfusion rate /Change in work level a (L/hr/W)

Fat 0.00017 

Liver -0.0019 

Brain 0 

Slowly perfused b 0.075 

Richly perfused c -0.0019 a

 Adapted from work rates from (Jonsson et al. 2001) 

b

 Skin and Muscle 

c Red Marrow, GI Organs, Pancreas, Thyroid, Spleen, Kidney 

As Table 4 indicates, richly perfused tissues including the liver are relatively 

unchanged by increases in activity levels. In addition, the perfusion rates actually decrease 
72 

with activity. This suggests that individuals are most sensitive to CPF at lower activity 

levels. 

Modeling enzyme activity levels

The activities of CYP and PON1 in plasma and liver have been investigated using a 

number of substrates. The catalytic efficiency of hepatic and plasma PON1 is believed to 

be an important factor in modulating CPF neurotoxicity (Albers et al., 2010; Costa et al., 

2005). There are two polymorphisms of PON1 that have been discovered, a leucine (L) to 

methionine (M) substitution at position 55, and a glycine (Q) to arginine (R) substitution at 

position 192 (Furlong et al., 1988; Adkins et al., 1993; Costa et al., 2003; Richter et al., 

2009). The first polymorphism has little impact on the catalytic activity of PON1; 

however, the polymorphism at position 192 has been demonstrated to have a substratedependent functional impact on PON1 mediated esterase activity (Furlong et al., 1988; 

Adkins et al., 1993; Costa et al., 2003; Richter et al., 2009). These different phenotypes of 

PON1 have been reported to demonstrate different age-dependent maturation patterns as 

well (Huen et al., 2009; Huen et al., 2010). Genetic variation in the PON1 gene results in 

polymorphisms that alter the catalytic efficiency toward organophosphorous compounds 

(Albers et al., 2010; Costa et al., 2005; Furlong et al., 2000). The ability of each form of 

PON1 to hydrolyze different oxon substrates depends on the specific substrate (Davis et 

al., 2009; Richter and Furlong, 1999). To address this issue, variation in enzyme activity 

was determined in liver and plasma using CPF as a substrate (See Figures 8 and 10 in 

Section 2). 

As described in Section 2, plasma samples were obtained from individuals ranging 

from 3 days to 43 years of age. Rates of diazoxon and paraoxon hydrolysis were used to 

resolve the plasma samples into three functional genotypes. Frequencies of functional 

genotypes were 0.5, 0.4, and 0.1 for QQ, QR, and RR phenotypes, respectively (Figure 

28). The ability of these plasma samples to hydrolyze CPF-oxon was compared to their 

polymorphic identification. When CPF-oxon Vmax values were paired with paraoxonase 

activities, QQ and QR functional phenotypes were clearly resolved, but RR was not 
73 

resolved from the QR phenotypes (Figure 28). These results are consistent with those 

presented by Albers et al., (2010), who showed that changes in BuChE activity in workers 

following exposure to CPF are not dependent upon PON1 phenotype or CPF-oxonase 

activity. 

A 

B 

Figure 28. (A) PON1 phenotypes in human plasma samples from individuals aged 3 days to 

43 years. (B) PON1 phenotypes (QQ, QR, RR) comparing CPF-oxonase vs. Paraoxonase in 

human plasma samples from individuals aged 3 days to 43 years. 
74 

Modeling variation in enzyme activity in liver

As discussed above, there is no indication of age-related differences in metabolic 

activity in the liver on a per mg protein basis, suggesting that ontogeny of enzymes that 

metabolize CPF occurs at a young age. The variations in activity levels of the three 

enzymes are modest, varying by approximately an order of magnitude (See Figures 6 and 8 

in Section 2). The cumulative distribution of the observed levels of activity, presented as 

the Z-score of the Vmax for the three enzymes are given in Figure 29. The lognormal 

distributions used in the Variation model to characterize the enzymes are also presented in 

Figure 29 (the three solid lines). 

Figure 29. Distribution of in vitro enzyme liver activities for CYP (CPF to Oxon and TCPy) 

and PON1 (Oxon to TCPy). Data points represent measured in vitro values scaled to the 

standard adult. Lines represent the fitted lognormal distribution used in the Variation model. 

Note that the distribution is allowed to extend beyond the limits of the measured data. 

There were a limited number of microsomal samples available from younger 

individuals. To address this limitation, the distributions of rates are allowed to exceed the 

observed range. For computational implementation, the lognormal mean and standard 

distribution are entered into the distribution functions in acslXtreme and used to calculate 

the tissue-based Vmax for each individual. The values of Vmax are then multiplied by the 

tissue volumes (as is standard) to derive the in vivo rates (as previously described). 
75 

Modeling variation in plasma  

The variation in the plasma PON1 is handled slightly differently than metabolic 

variability in the liver and brain. Since the plasma PON1 is produced in the liver and 

released into the plasma, the variability in the plasma PON1 can reasonably be expected to 

be correlated with the variability in the liver PON1 (i.e., individuals with high or low liver 

PON1 will have similarly high or low plasma PON16

). For the Variation model, the value 

of PON1 in plasma was rank correlated with the value in plasma for each individual7; The 

end result is that in the Variation model no simulated individual will be assigned a low 

PON 1 activity in liver (or plasma) and high activity level in the plasma. Thus an 

individual with low plasma PON1 will not be masked by having a high liver activity. 

Figure 30 shows the range (mean ± 95% CI) for model output for plasma PON1 for 

three ages six months, three years, and 30 years. There three ranges are super-imposed 

over the PON1 data from the 20 plasma samples discussed in Section 2. The in vitro values 

in the original study (See Figure 10 in Section 2) have been converted to in vivo

equivalents by multiplying them times average age specific protein levels and plasma 

volumes. As Figure 30 shows, the current ranges of plasma PON1 are consistent with, or 

exceed the range of the measured data. 

 

6 While actual data on PON1 activity levels was measured in both plasma and liver the tissue samples did not 

come from the same individuals; therefore, it was not possible to directly determine correlation between the levels in 

the tissues. 

7 A small additional variability was included in the plasma PON1 to allow for some interindividual variation 

in liver/plasma ratios. This additional variation was set to 5% of the mean value for the individual's age. 
76 

0.01 0.1 1 10 100

1×100 3

1×100 4

1×100 5

1×100 6

1×100 7

Age (yr)

Vmax (μmol/hr)

Figure 30. Total Vmax of human enzymatic PON1 metabolism of CPF-oxon to TCPy in the 

plasma over various ages. Symbols represent in vivo rates extrapolated from the in vitro data 

(See Figure 10). Error bars show the 95% CI of Vmax values used in the model. 

Modeling variation in the brain

There are no experimental data available to estimate the enzymatic variation in the 

brain. The variation in brain CYP450 levels was therefore assumed to have the same size 

of distribution as the corresponding liver CYP450s. The liver and brain CYP450 levels are 

not, however, correlated to each other in the simulated individuals. 

Combination of enzyme and tissue volumes

As discussed in the LifeStage model development (Section 3), the final variability in 

the metabolic capacity of a simulated individual is a combination of the varied in vitro 

metabolic rate scaled with the individual's compartment volumes. In this manner, the 

ranges of the final metabolic capacity can be quite large (Figure 31). Infants (6 months old) 

have lower plasma PON1 than adults. In addition, the variation in metabolism in infants is 
77 

smaller than in adults (roughly 30-fold vs. 100-fold) due to the small interindividual 

variation in physiology (compartment volumes). 

-3 -2 -1 0 1 2 3

10-2

10-1

100

101

102

103

104

105

106

107

Liver PON

Blood PON

Liver CPF to TCP

Liver CPF to oxon

Brain CPF to TCP

Brain CPF to oxon

Z-Score

Vmax (

μmol/hrl)

-3 -2 -1 0 1 2 3

10-2

10-1

100

101

102

103

104

105

106

107

Liver PON

Blood PON

Liver CPF to TCP

Liver CPF to oxon

Brain CPF to TCP

Brain CPF to oxon

Z-Score

Vmax (

μmol/hrl)

Figure 31. Variation in vivo metabolic rates in children (top) and adults (bottom). The range 

of the variation in metabolism can be inferred by comparing the ranges of the curves for the 

various enzymes. 

Variation in Response to CPF and Ethnicity

Certain investigators have raised the issue of potential for ethnic variation in 

response (Davis et al., 2009; Ginsberg, 2010). This concern has been based on the 
78 

observation that the PON1 phenotypes exhibit differential distributions in different ethnic 

groups. The functional phenotype ratios reported for a Caucasian population was 0.6 for 

QQ, 0.31 for QR, and 0.07 for RR. The ratios for an African American population were; 

and 0.15 for QQ, 0.34 for QR, and 0.44 for RR (Davis et al., 2009). Ratios for Hispanics 

are 0.24 for QQ, 0.52 for QR, and 0.24 for RR (Huen et al., 2009; Huen et al., 2010). 

Although the distribution of each polymorphic expression may be different, this variation 

has limited impact on the variation in metabolism of CPF oxon. As shown in Figure 26 

individuals in each of the three phenotypes have similar abilities to metabolize CPF-oxon. 

This finding is compound specific. As shown by (Ginsberg, 2010), polymorphic 

expression may be important for other compounds. Thus the issue of ethnic variation in 

metabolism should be evaluated when constructing source-to-outcome models for other 

compounds. 

4.3   Step 3. Accounting for correlation

Based on body weight, the volume of each compartment is estimated using the 

equations in Table 2. As discussed above, these equations are polynomial functions that 

assign volumes to multiple compartments and organs. Since each equation for an 

individual is defined by a common body weight, the volumes are internally consistent (See 

Figures 13-18). This produces compartment volumes which are physiologically consistent. 

The potential for correlation is also addressed when assigning metabolism rates. As 

was discussed in Step 2, plasma PON1 originates in the liver so it is modeled as rank 

correlated with the levels in the liver. Variations in CYPs are assumed to be genetically 

determined and activities in the brain are likely correlated to the levels in the liver. There 

are no experimental data available to estimate the enzymatic variation in the brain. The 

variation in the brain CYP450 levels was therefore assumed to have the same size of 

distribution as the corresponding liver CYP450s. The liver and brain CYP450 levels are 

not, however, correlated to each other in the simulated individuals. 

Rates of CPF-oxon formation from CPF and its subsequent hydrolysis to TCPy were 

measured simultaneously from each donor liver sample. A correlation was observed 

between the metabolic rates of desulfurylation and oxon hydrolysis (Figure 9). However, 
79 

we could find neither a biological nor a literature basis for this correlation and thus 

conservatively varied the CYP-based desulfurylation and liver PON1 hydrolysis rates 

independently. This assumption is conservative because it allows combinations of enzyme 

levels that allow for high formation of the CPF-oxon and low removal rates. 

4.4   Step 4. Comparison of variation model’s predictions to observed variation in

human volunteer studies

As discussed in Section 3, the LifeStage model predictions were compared to the 

mean responses in human volunteer studies (Figure 24). In this section we compare the 

range in responses predicted using the Variation model to the range of values reported in 

the (Kisicki et al., 1999) study. 

In the Kisicki et al. study, groups of healthy 12 adult volunteers were given single 

dietary exposures of 2.0, 1.0 and 0.5 mg/kg of chlorpyrifos. Figure 32 presents the ranges 

of the measured values using the black triangles and error bars determined in (Timchalk et 

al., 2002a). 

The Variation model was used to predict the range of the values of the blood 

measurements that would occur in each of the individuals in each group. Variation in the 

test individuals was simulated by modeling each individual in each dose group separately 

using data on the individuals’ physiology and gender. To capture enzyme variability, each 

individual was simulated 100 times each time varying the levels of enzyme activities. The 

median values from each simulation for each time point over the five days are shown as the 

solid line in Figure 32 and the ranges of the 5-95th percentiles are presented in gray. As the 

figure shows, the simulations closely match the experimental data for blood levels of TCPy 

for all three doses. The model also predicts RBC AChE levels for doses of 0.5 and 1 

mg/kg. The model overestimates the impact of CPF on RBC AChE by a factor of 2 to 3 for 

individuals exposed to 2 mg/kg. 
80 

0.01

0.1

1

10

Blood TCP (nmol/ml)

0 24 48 72 96 120

Hours

0 24 48 72 96 120

Hours

0 24 48 72 96 120

50

60

70

80

90

100

110

Hours

RBC AChE (% basal)

0.5 mg/kg

0.5 mg/kg 1 mg/kg

1 mg/kg 2 mg/kg

2 mg/kg

Figure 32. AChE inhibition and TCPy in blood in volunteers (Kisicki et al., 1999) over time 

following a single oral dose of CPF. Triangles and error bars reflect actual data. Black line is 

mean predictions and gray area presents the 5th and 95 percentiles of predicted range of 

response for individuals over time from the Variation model. 

4.5 Description of the Variation model

The LifeStage model produces a single estimate of response to one dose at any given 

age. The Variation model produces a range of responses to a single dose that reflect 

differences in physiology, metabolism, and activity levels. The Variation model requires that 

the modeler provide a series of inputs for each individual including age of the individual, 

gender, body weight, and doses on consecutive days. These inputs are described in Sections 5

and 6. Based on the age and gender, values are selected for each of the parameters described 

in Table 2 using the distributions and procedures described above. The Variation model then 

predicts the time course of blood concentrations of CPF and CPF-oxon, the inhibition of RBC 

and brain AChE inhibition, and plasma BChE inhibition (other tissue concentrations may be 

simulated but are not discussed here). If the same inputs are repeated, different values and 

different estimates of the output values are generated reflecting the range of responses 

associated with individuals of that age, gender, and body weight. 

A copy of the Variation model is attached to this report (Attachment C). 
81 

4.6   Applying the Variation model to the assessment of the toxicity of CPF

In this section we have described the development of the Variation model and the 

evaluation of the model by comparison to the Kisicki et al. study. In the next two sections 

we use the model to investigate the toxicological properties of CPF in humans. This is 

done in two ways. First, the different sources of interindividual variation are systematically 

turned on and off, to identify those factors which drive variation in human sensitivity. 

Second, the Variation model is used to generate dose response relationships for different 

age groups that include human variation. The purpose of these assessments is to gain 

insight on human response to that would inform an actual risk assessment. 

4.7 Evaluating the contribution of the different sources of variation on human

response to CPF

One of the major interests in toxicology is human variation and its impact on the 

ability to tolerate chemical exposure. This section uses the Variation model to investigate 

the relationship between variation in physiology, enzymes, and level of physical activity. 

The goal is to determine which of these characteristics of humans has the greatest impact 

on the relative sensitivity of individuals. 

Methodology

The relative importance of each source of variation was investigated by running the 

Variation model for a population of individuals of a specific age exposed to a single dose 

of CPF. The result is an age-specific measure of variation in human sensitivity. This 

approach is used to investigate the impact of different sources of variation on different 

endpoints, different doses, and for individuals of different ages. The approach was to 

systematically introduce distributions for the various characteristics in populations of 

different ages. The sources of variation were grouped as follows: 

• Variation in level of physical activity; 

• Variation in physiology; 

• Variation in CYP enzymes in the liver and brain; and 

• Variation of PON1 in the liver and the blood. 
82 

Preliminary work demonstrated that the relative contribution of the four groups of 

characteristics to the variation in RBC AChE increased in the following order for the four 

groups: PON1>CYP>Physiology>Physical Activity. The following six combinations of 

sources of variation were run for populations of 1,000 individuals: 

1. Vary physical activity only 

2. Vary physical activity plus physiological differences 

3. Vary physical activity, physiology, and CYP enzymes 

4. Vary physical activity, physiology, and CYP enzymes and PON1 

Because PON1 was found to be the major source of variation in sensitivity for RBC 

inhibition, two additional analyses were performed; one where only liver PON1 was varied 

and a second where only liver and plasma PON1 are varied. 

5. Vary PON1 in liver only 

6. Vary PON1 in liver and plasma only 

These six assessments were run at a number of doses including 1 mg/kg, 0.5 mg/kg, 

0.1, and 0.001 mg/kg. Separate assessments were modeled for infants (six months), young 

children (ages 3 years) and adults (30 years). Variation in body weight and gender was 

determined by using data from the population generator in the CARES model (for 

additional information see the discussion in the section of this report on dietary modeling). 

When physiology was turned off (simulation run number 1) the mean body weight for 

females of each age group was used and the individual was assumed to be female. 

The results of the simulations were analyzed as follows. First, the levels of either 

RBC or brain inhibition in each of the 1,000 individuals in each of the six simulations were 

ranked from smallest to largest. Second, the rank of each individual was used to assign a 

Z-score for that individual. Third, the relative impact of the dose on each individual 

compared to the impact on a “typical” individual in their age group was determined by 

dividing each individual’s inhibition level by the inhibition observed in the median 

individual. These ratios represent a measure of relative sensitivity across the population. 

Finally, the cumulative distributions of the ratios were plotted against the Z-scores of the 

individuals. The results are six curves that show the impact of variation in different 
83 

characteristics on the relative sensitivity of a population of humans of a specific age to a 

specific dose of CPF. 

Factors that drive variation in RBC AChE inhibition

The relative contributions of the variation in the four characteristics on relative 

sensitivity to CPF as measured by peak inhibition of RBC for the three age groups were 

examined at the point of the largest interindividual variation, using an oral dose of 0.5 

mg/kg. Figure 33 present the curves for the six analyses. Note that the relative sensitivity is 

shown on log scale. At 0.5 mg/kg when all sources of variation are included, the range of 

peak inhibitions across the simulated individuals (from the most sensitive to the least 

sensitive of the 1,000 simulated adults) is about 100-fold. However, the most sensitive of 

the 1,000 individuals has an inhibition rate that is approximately 12-fold higher than the 

inhibition rate for the typical individual. 

The contributions to variation in sensitivity from the different characteristics of the 

individuals are not the same. Variation in physical activity does not significantly contribute 

to variation in RBC inhibition. This is shown by the fact that the curve for physical activity 

alone is virtually flat. Adding variations in physiology and CYPs explains about one third 

of the variation in sensitivity. The largest change in sensitivity comes from the introduction 

of variation in PON1 levels in liver and plasma. This can be seen when variation in PON1 

in liver and plasma are run alone (physiology, CYPs, and physical activity are held 

constant). As Figure 33 shows, the interindividual variation in RBC from the variation in 

PON1 in the liver and blood is almost as large as the variation observed when all sources 

are considered. 

Finally, variations in plasma PON1 levels do not appear to drive variation in RBC 

response. Evidence of this lack of impact is shown in the fact that the results for varying 

“liver PON1 enzyme levels” and “liver PON1 plus plasma PON1 enzyme levels” are 

virtually identical. 
84 

Figure 33. Six cumulative distributions of RBC Inhibition (relative to the median inhibition 

rate) in adults exposed to 0.5 mg/kg where variation in Physical Activity (PA), Physiology (P), 

liver and brain CYP Enzymes (CYP), and Liver and Plasma PON1 are “turned on” either 

alone or in combinations. 

A review of the two younger ages shows a similar pattern (Figures 34 and 35). CYPs 

appear to play a slightly larger role and physiological variation a smaller role than in 

adults. However, liver PON1 variation is still the dominant source of variation in response. 
85 

Figures 34. Six cumulative distributions of RBC Inhibition (relative to the median inhibition 

rate) in young children exposed to 0.5 mg/kg where variation in Physical Activity (PA), 

Physiology (P), liver and brain CYP Enzymes (CYP), and Liver and Plasma PON1 are 

“turned on” either alone or in combinations. 
86 

Figure 35. Six cumulative distributions of RBC Inhibition (relative to the median inhibition 

rate) in infants exposed to 0.5 mg/kg where variation in Physical Activity (PA), Physiology 

(P), liver and brain CYP Enzymes (CYP), and Liver and Plasma PON1 are “turned on” 

either alone or in combinations. 

The impact of variation in an individual’s characteristics at doses above 0.5 mg/kg 

were evaluated and found to be similar to those at 0.5 mg/kg (data not shown). 

The contribution of variation in individual’s characteristics on RBC AChE inhibition 

was investigated at lower doses, Figures 36, 37, and 38. Note these three figures are in 

arithmetic-scale (not-log scale as in Figures 33, 34, and 35). At a dose of 0.1 mg/kg per 

day, the level of variation was found to be significantly smaller than at 0.5 mg/kg. The 

total range in response (most sensitive to lease sensitive of the 1,000 individuals) when 

variation in all four groups of characteristics are considered dropped from 100 fold to 10 

fold. The difference between the typical and the most sensitive of the simulated individuals 

when all sources of variation are considered dropped from 12 fold to 2-fold. The driver for 

the variation at these lower doses was the same, PON1 levels in liver. The same pattern of 

variation was observed for all three age groups. Finally this pattern of sensitivity remained 

constant at all lower doses evaluated including doses as low as 0.0001 mg/kg (data not 

shown). 
87 

0

0.5

1

1.5

2

2.5

3

‐3 ‐2 ‐10 1 2 3

Ratio of%RBC Inhbition to RBC Inhibition in the Medina

individual

Z‐score

Relative variation in RBC AChE inhibition from a single

dose of 0.1 mg/kg dose of chlorpyrifosin adults age 30

Physical Activity (PA)

Liver and Plasma PON1

PA+ P+ CYP + Liver and Plasma PON1

Liver PON1

PA + Physiology (P)

PA+ P+ CYP Enzymes

Figure 36. Six cumulative distributions of RBC Inhibition (relative to the median inhibition 

rate) in adults exposed to 0.1 mg/kg where variation in Physical Activity (PA), Physiology (P), 

liver and brain CYP Enzymes (CYP), and Liver and Plasma PON1 are either “turned on” 

alone or in combinations. 
88 

Figure 37. Six cumulative distributions of RBC Inhibition (relative to the median inhibition 

rate) in young children exposed to 0.1 mg/kg where variation in Physical Activity (PA), 

Physiology (P), liver and brain CYP Enzymes (CYP), and Liver and Plasma PON1 are either 

“turned on” alone or in combinations. 
89 

Figure 38. Six cumulative distributions of RBC Inhibition (relative to the median inhibition 

rate) in infants exposed to 0.1 mg/kg where variation in Physical Activity (PA), Physiology 

(P), liver and brain CYP Enzymes (CYP), and Liver and Plasma PON1 are either “turned 

on” alone or in combinations. 

Evaluation of the factors that drive variation in the level of brain AChE

inhibition

For brain AChE inhibition, the same ages and grouping of sources of variation were 

used. In general, the range of variation was similar to that of RBC AChE inhibition at 0.5 

mg/kg, with the most sensitive individual having about an 8-fold increase in response 

compared to the median individual. However, the driver for the variation was different. 

The variation in brain inhibition across populations at all ages and at doses of 0.1, 0.5, and 

1.0 mg/kg doses was driven entirely by variation in CYPs in the liver and the brain (Figure 

39). Future work should focus on the relative impact of the different CYPs in the different 

organs. 
90 

Figure 39. Six cumulative distributions of brain inhibition (relative to the median inhibition 

rate) in adults where variation in Physical Activity (PA), Physiology (P), liver and brain CYP 

Enzymes (CYP), and Liver and Plasma PON1 are either “turned on” alone or in 

combinations. The figures for the other ages and doses are similar (data not shown). 

These analyses are not meant to be exhaustive investigations into CPF. Rather they 

demonstrate how the Variation model and future versions for other compounds could be 

used to identify those factors that are most important to investigate when modeling 

interindividual uncertainty in response. Based on these findings for CPF, interindividual 

variation in response to CPF is driven by variation in metabolism and is only slightly 

affected by differences in physiology and level of physical activity. Thus, future work on 

refining estimates of outcome should focus on the improvement of estimates of 

interindividual variation in metabolism rather than attempting to refine estimates of 

variation in physical activity levels or physiology. 

These finding can also be used to determine the reasonableness of the behavior of the 

Variation model. The impact of variation in physiology is predicted to be smaller for 
91 

infants than adults. This appears reasonable since adults have higher variations in weight 

than infants and thus physiology would be more important for adults. The fact that the 

level of physical activity had little or no effect on sensitivity appears to be reasonable 

given the finding that blood flow to the liver and brain are relatively unaffected by increase 

levels of physical activity (see Table 4). 

4.8 Interindividual variation in response­ predictions by the Variation model

The Variation model created using the previously described distributions can 

generate dose response predictions for any of the model outputs such as blood 

concentrations of CPF and CPF-oxon and RBC or brain AChE inhibition. Separate 

predictions can also be made for groups of various ages. Unlike the single curve produced 

by the Lifestage model, the Variation model produces a fuzzy “band” or “cloud” that 

reflects the interindividual variation in response to a given dose. The size of the variation at 

a specific dose is determined by a vertical distance across the cloud at that dose. 

To examine the model’s predictions of peak CPF and peak CPF-oxon blood 

concentrations, populations of 5,000 individual adults (30 years) and infants (6 months) 

with varying body weights and similar numbers of both genders were modeled as receiving 

a single oral dose where the dose rate ranged between 0.01 and 10 mg/kg (Figure 40)8. 

 

8 Data for children age three were also calculated and fell between the infants and adults. These data were 

not included in the figure for clarity. 
92 

Figure 40. Peak blood concentrations of CPF and CPF-oxon following single oral doses that 

range between 0.01 and 10 mg/kg (log-log plot). 5,000 individual adults (30 years) and infants 

(6 months) with varying body weights and similar numbers of both genders are modeled. 

The relationship between oral dose and the peak blood concentration of CPF 

increases in a consistent, but non-linear fashion over this range of doses9. The constant 

width of the cloud around CPF suggests that the processes that drive interindividual 

variation in levels of CPF in blood are not dose-dependent for this dose range. Finally, the 

predicted peak doses of CPF are slightly higher for adults than children as seen by 

comparing the moving averages for the two age groups (Figure 40). 

The results for the peak CPF-oxon are different from CPF. A visual inspection of 

Figure 40 indicates that at doses below 0.3 mg/kg, the oxon appears to be a consistent 

fraction of the CPF peak concentrations. Interindividual variations at doses below 0.3 

mg/kg have a consistent width that is similar to, but slightly larger than, the width of the 

CPF blood concentration cloud. Above 0.3 mg/kg, the ratio of peak concentrations to oral 

dose become more variable and the ratios increase by a factor of 20 for adults and 40 for 

 

9 The slope appears to be linear due to the log-log plot but actually indicates a sub-linear relationship 

between the peak levels and the dose. This can be seen from the approximately 4 orders of magnitude decline in 

peak levels of blood CPF over the 3 orders of magnitude change in dose. 
93 

infants. In addition, at doses above 0.3 mg/kg, infants have higher peak levels of oxon than 

adults at the same doses. 

This suggests that the balance of CPF metabolism changes above doses of 0.3 mg/kg 

for both adults and infants and that variation PON1 and CYP in the liver activity becomes 

more important above this dose. Additional evidence for this point is given in the 

discussions of the importance of PON1 variation on AChE inhibition in Section 2. 

Additionally, while the data are not shown, the pattern of interindividual variations in CPF 

and CPF-oxon between 0.3 and 0.01 mg/kg were found to apply to doses below 0.01 

mg/kg. This includes the range of doses that result from current dietary exposures (see 

Sections 5 and 9). Finally, the fraction of the oral dose of CPF converted to CPF-oxon is 

predicted to increase slightly in an age-related manner. 

A comparison of the dose-response predictions for peak RBC and brain AChE 

inhibition for adults and infants for the Variation and LifeStage models shows behavior 

similar to the predictions of peak levels of oxon in blood (Figure 41). The same increase in 

variability occurs at 0.3 mg/kg and infants become more sensitive at that dose. This is not 

surprising since the oxon directly complexes with RBC AChE. 
94 

Figure 41. Peak blood RBC AChE inhibition following single oral doses that range between 

0.01 and 10 mg/kg (log-log plot). 5,000 individual adults (30 years) and infants (6 months) 

with varying body weights and similar numbers of both genders are modeled with the 

Variation model. The lines represent the typical 30 year-old and 6 month-old from the 

LifeStage model. 

In contrast, the variation in prediction of the inhibition of brain AChE is similar to 

the predictions of peak CPF concentrations (Figure 42). There is no sharp change in 

interindividual variation at 0.3 mg/kg, with adults always appearing to be slightly more 

sensitive than infants. One difference is that width of the cloud is larger than the width of 

the peak CPF. 
95 

Figure 42. Peak blood brain AChE inhibition following single oral doses that range between 

0.01 and 10 mg/kg (log-log plot). 5,000 individual adults (30 years) and infants (6 months) 

with varying body weights and similar numbers of both genders are modeled with the 

Variation model. The lines represent the typical 30 year-old and 6 month-old from the 

LifeStage model. 

Section summary

This section has presented the development of the Variation model and its evaluation 

by comparison to the results of the human volunteer studies. The model’s predictions of 

interindividual variation were either consistent or over estimated the observed variations in 

small groups of healthy volunteers. 

This section provides two examples by which a Variation model of PBPK/PD can be 

used in the assessment of a pesticide and possible responses to exposure. The section 

demonstrated how a model can be used to identify the drivers of a populations’ relative 

sensitivity to a chemical’s effects. For example, RBC and brain AChE inhibition by CPF 

were driven by variation in metabolism and not physiology or levels of physical activity. 

Variation in RBC inhibition was driven by variation in liver but not plasma PON1 activity. 
96 

Variation in brain inhibition was not affected by PON1 but was affected by variation in 

CYP activities in the liver and the brain. Such findings could help researchers identify 

issues where additional research is warranted. The Variation model also indicates that 

variation in RBC inhibition is strongly dose dependent. Variation across individuals ranges 

from 100-fold at doses above 0.3 mg/kg but drops to 4-fold for dose at below 0.3 mg/kg. 

This finding allows a better understanding of differences in sensitivity at low doses. In 

contrast, the variation in brain inhibition was not dose dependent. 
97 

5. Modeling variation in dietary exposure for different age

groups

Section overview

This section presents a review of the current approaches to assess dietary exposures. 

The section describes the collection and organization of residue data and the software used 

to characterize dietary exposures. The issue of longitudinal dietary exposure is investigated 

and estimates of correlations between an individual’s doses of CPF on two consecutive 

days are determined using three different methods of simulating longitudinal dietary 

exposures. 

5.1 Dietary exposure assessments  

The Agency uses a probabilistic approach to characterize interindividual variation in 

dietary exposures. The dietary exposure software programs originally focused on 

estimating interindividual variation in doses that occurred on a single day. Over the last 10 

years, however, program developers have sought to assess longitudinal dietary exposures 

as well. There are a number of dietary exposure programs available or currently under 

development (CARES, LifeLine, DEEMTM, and SHEDS). All of these programs use 

similar approaches for estimating dietary exposures. Software comparison exercises have 

shown that dietary exposure software programs (CARES, LifeLine and DEEMTM (U.S. 

EPA, 2004) and DEEMTM and SHEDS (Xue, 2010)) produce similar estimates of the 

distribution of single-day doses in various age groups. 

This assessment uses the CARES software for the exposure portion of the source-tooutcome model. CARES is a software program designed to conduct complex exposure and 

risk assessments for pesticides. Scientific and technical contributions to the program’s 

development came from a broad team of experts, including scientists from Crop Life 

America member companies and staff, consulting companies, and the Environmental 

Protection Agency and Department of Agriculture. CARES is available without charge 
98 

from the ILSI Research Foundation.10 CARES, has been reviewed by the Science Advisory 

Panel (U.S. EPA 200211) and is used in regulatory decisions by the Agency. 

The CARES dietary software was used a number of ways in this project. The 

program was used to produce estimates of daily doses over multiple consecutive days that 

form the basis for the predictions of the impact of the current dietary exposures on levels of 

cholinesterase inhibition (Section 9). The program also was used to produce longer 

exposure histories (365 days) that are used in the analysis of the upper peak exposures in 

the general population (Section 8). Finally the program was used to assign gender and 

body weights to the populations of individuals assessed using the Variation PBPK/PD 

model (Section 4). 

Dietary exposure software programs depend on input estimates of residues of CPF 

on different foods as a result of crop protection. The values used in this demonstration 

were developed using general EPA policies for use of residue data in dietary risk 

assessments and regulation of pesticides. The input files were built up from EPA’s 

reregistration-era (1999 – 2002) dietary assessments for chlorpyrifos as well as the 

comprehensive listing of tolerances announced by EPA via the CFR Final Rule in 2008 

and include new monitoring data through 2008 as posted by USDA in late 2009 and 

updated percent crop treated (PCT) data. USFDA monitoring data for a few commodities 

are also incorporated. A parsing of the dietary exposure between imports and domestic 

observations was made for several key crops. The dietary files were built using the recent 

DEEM software version 2.16. Over 70 individual residue files (RDF) were built; most 

were created from the newer monitoring data, although a few were reused from the 

reregistration-era assessment conducted by EPA in 2000. Decisions for the inclusion or 

exclusion of specific information reflect general guidance for a higher tiered regulatory 

exposure assessment. Attachment D provides a detailed description of the data used in 

developing these inputs and the specific assumptions used to analyze the data. 

 

10 Electronic copies of the program and support documentation are publicly available at no charge at 

HTTP://www.ilsi.org/ResearchFoundation/Pages/CARES.aspx. 

11 http://www.epa.gov/oscpmont/sap/meetings/2002/april/agenda.htm 
99 

The residue data was entered into three dietary exposure software programs, 

CARES, DEEMTM version 2.16 (Durango Software, 2010) and LifeLine version 4.40 

(LifeLine, 2006). Electronic copies of the input files for the three software programs are 

provided in Attachment E. 

 

5.2 Results of the dietary exposure modeling prediction of daily doses

The cumulative distributions of daily doses of CPF created using CARES for 1,000 

adults, three year-old children, and six month-old infants are given in Figure 43. The 

dietary doses (on a body weight basis) are larger for the three years-olds than either adults 

or infants. The data in Figure 43 are plotted on a dose versus Z-score. The Z-scores reflect 

that portion of the population from the 3rd to 97th percentile. None of the individuals is 

predicted to have a one day dose greater than 0.0002 mg/kg. 

Figure 43. Predicted distribution of daily dietary doses of CPF in 1,000 adults, 3 year-old 

children, and six month old infants from the CARES program using the assessment of dietary 

residues given in Attachment D 
100 

The predictions of dietary exposure produced by CARES were compared with the 

predictions of DEEMTM version 2.16 (Durango Software, 2010) and LifeLine version 4.40 

(LifeLine, 2006)(Table 5). The predictions of the variation in daily doses in the top 10 

percent of the U.S. population of adults, children, and infants are similar for all three 

programs. The findings increase the confidence that the data have been entered into the 

CARES program correctly and indicate that the predictions reflect the current data on 

residues and U.S. diets. 

Table 5. Estimates of CPF Daily Dietary Intake for Three Percentiles of the Cumulative 

Distributions of Three age groups in the U.S. Population (mg/kg) 

 DEEM TM(Version 2.16) LifeLine (Version 4.1) CARES (Version 3.0) 

Adults 

90th Percentile 0.000010 0.000010 0.000017 

99th Percentile 0.000034 0.000026 0.000047 

99.9th Percentile 0.00015 0.000045 0.00012 

Children 3 year-olds 

90th Percentile 0.000033 0.000031 0.000055 

99th Percentile 0.000093 0.000083 0.000113 

99.9th Percentile 0.00022 0.00014 0.00021 

Infants (6 months) 

90th Percentile 0.000043 0.000027 0.000054 

99th Percentile 0.000086 0.000075 0.000081 

99.9th Percentile 0.00021 0.00011 0.00020 

CARES is also used to generate estimates of body weight for different ages. Table 6 

presents the values of body weight generated by the CARES and LifeLine dietary exposure 

programs. As the table indicates, both programs predict values that are consistent with the 

results from the NHANES 1999-2000 survey. 
101 

Table 6. Body weight predictions from dietary programs and national survey 

results 

 Infant Body weights 

Age 6 Month (kg) 

Children Body weights 

Age 3 yr (kg) 

Adults Body Weights 

Age 30 yr (kg) 

Percentile 10th 50th 90th 10th 50th 90th 10th 50th 90th

NHANES 

(1999-2000) 1

7 8 10 13 16 18 59 80 107 

LifeLine 7 8 9 13 15 17 44 62 91 

CARES 6 7 9 13 16 19 54 76 100 1

(McDowell, 2005) 

5.3 Simulating longitudinal exposures

Longitudinal dietary exposure is an issue for source-to-outcome models since the 

effects of one day’s exposures carry across to subsequent days. However, current surveys 

of dietary intake do not collect data on food consumption on contiguous days. As a result, 

dietary programs are required to make assumptions on how to simulate temporal 

relationships between dietary intakes in an individual. Multiple approaches have been 

proposed for constructing longitudinal dietary histories for individuals based on the 

available data (Crop Life America, 2002; LifeLine, 2006; Xue, 2010). There is no 

consensus on which of these approaches should be preferred or the level of uncertainty that 

is introduced by the use of such approaches. 

For this project we investigated the impact of the use of different simulation 

approaches for characterizing longitudinal exposures. Both the CARES and LifeLine 

dietary exposures software produce longitudinal estimates of dietary doses for individuals. 

LifeLine estimates an individual’s dietary doses for up to 5 sequential days using two 

options for simulating longitudinal exposures - a daily and a seasonal approach. The daily 

approach selects a separate dietary record from multiple individuals to create a longitudinal 

record of a person. The seasonal approach selects two records for each of the four seasons. 

The records are selected from individuals of a similar age and from the same season of the 

year. The records are repeated, the first one for 2 days (representing the week end days) 

and the second for 5 days (representing the week days). This pattern is repeated multiple 

times to complete a season, and new records are selected for the next season. This 

approach was later adopted by SHEDs as one of its options (the eight record approach) 

(Xue, 2010). The seasonal approach results in a smaller longitudinal variation in dose than 
102 

the LifeLine daily model since the same records are often repeated for multiple 

consecutive days. CARES produces sequential daily estimates for periods of up to one 

year. CARES uses a statistical technique for identifying demographic and anthropometric 

records that come from individuals with statistically similar characteristics. Using this 

technique, the creators of CARES constructed 365 day dietary histories for a set of 

simulated individuals (Crop Life America, 2002). 

Longitudinal dietary exposures were created for each of the three approaches. 

Estimates of doses on two consecutive days were determined for 1,000 adults. The 

correlation between the doses of CPF received on two consecutive days under each 

approach was evaluated. Figure 44 presents a scatter plot of individual’s doses on day one 

versus their dose on day two. The doses are plotted on a log-log scale. A simple trend line 

was created in Excel using a power function. The slope and R2 of the trend line are also 

given on the plots. 
103 

Figure. 44 Correlation between daily doses on consecutive days under A- LifeLine Daily 

approach, B- LifeLine Seasonal approach, and C- CARES approach. 

As Figure 44 indicates, the three approaches of longitudinal exposures produce 

different correlations between consecutive days. The Lifeline daily approach (A) and the 

CARES (C) approach essentially make each day’s dose independent of the prior day. The 

LifeLine seasonal approach produces estimates of dose with a moderate correlation 

between days (R2

 of 0.57). This correlation occurs because in the LifeLine seasonal 

approach, the majority of individuals are assumed to eat the same foods in the same 

amounts on consecutive days. Because none of the approaches correlate residues from one 

day to the next (the concentration of the residue on an apple consumed on one day is 

assumed to be independent of the residue on an apple consumed the following day), none 

of the approaches predict the occurrence of identical doses on consecutive days. The 

impacts of the three sets of longitudinal data are presented in Section 7 of this document. 

A B

C 
104 

Section summary

Dietary exposures for CPF are defined by the residue data and the software programs 

available for dietary exposure assessment. The estimates of daily dose predicted by 

multiple software programs produce similar results. However, different longitudinal 

approaches predict different correlations between doses on consecutive days. 
105 

6. Connection of exposure and PBPK/PD models

Section overview

Up to this point we have described the development of the two components of the 

source-to-outcome model, a dietary exposure software program (Section 5) and the 

Variation PBPK/PD model (Section 4). This section now addresses the linking of the two 

components. The linkage of the two components raises technical issues on the definition of 

the individual’s characteristics and the definition of dose. Specifically, the components 

need to have consistent definitions of the individual and establish a clear process for 

converting the oral doses from the dietary exposure model into the dose information used 

by the PBPK model (LifeLine, 2004). This process must account for the absorption and 

uptake process. 

6.1 Assigning characteristics to each individual

The approach used to link the two components’ definition of ‘individual’ was to use 

an individual’s demographic and anthropometric characteristics as defined by the CARES 

model and incorporate that data into the PBPK model. As discussed in Section 5, the 

CARES model defines the age, gender and bodyweight of each individual (ILSI, 2009). 

These data are taken from the 2000 Public Use Microdata Sample released by the U.S. 

Census Bureau (Crop Life America, 2002). The Variation model used this information to 

develop the detailed physiology required by the PBPK/PD simulations. This detailed 

physiology includes definition of the volumes of the key compartments, including liver and 

adipose tissues, compartment and organ specific blood flows, and total cardiac output (see 

Sections 3 and 4). This approach leverages the fact that the CARES model determines the 

individual’s characteristics by taking data from one individual. As shown in Section 5, the 

predictions are representative of the U.S. population on a national basis. This approach 

gives a statistically representative set of internally consistent physiological data for each of 

the simulated individuals. 

 
106 

6.2 Definition of dose in the source­to­outcome model

The output of the dietary models has historically been an estimate of the total daily 

dose received from all foods consumed in a day. The reason for this is that the modeling 

results were compared to measures of toxicity, such as Reference Doses, that are expressed 

in units of daily dose per kg body weight (mg/kg). The oral uptake portion of the PBPK 

model converts the daily dose per kg body weight into a rate of CPF uptake from the gut 

over time in units of mass per time (μmoles/sec, see Figure 11 in Section 3). The 

development of the oral uptake portion of the model was performed using the following 

assumptions. 

1. PBPK models have the ability to address doses that are consumed over the course of a 

day. Considerable effort has been made to capture this variation in dose over a day in 

dietary models. Recent versions of CARES12 and SHEDS (Xue, 2010) include the 

ability to assign times of the day for different eating events (eating occasions). The 

source-to-outcome model for CPF, however, has not attempted to account for the 

impact of the daily doses being spread over multiple eating events. Instead the sourceto-outcome model assumes that an entire day’s dose occurs during a single meal. The 

reasons for this assumption are threefold. 

First, we investigated the fraction of the daily dose that comes from various foods. 

This analysis suggests that daily exposures are not evenly spread across eating 

events (meals and snacks) of a day. Instead, most, or all, of a day’s intake of CPF 

occurs from the consumption of a specific type of food (e.g. a fruit or vegetable). 

Specifically, the analysis of the CARES model outputs suggest that on average 

43% of an individual’s daily dose comes from the consumption of a single food. 

For those individuals with daily doses in the top 2% of the population, this 

fraction increases to 75%. Thus, the assumption that the entire dose occurs at one 

meal does not appear to be unreasonable for CPF. This may not be true for other 

chemicals. 

 

12 http://www.ilsi.org/ResearchFoundation/Pages/CARES.aspx 
107 

Second, use of a single daily dose greatly simplifies size and complexity of the 

information that is passed to the PBPK/PD model. 

Finally the assumption is conservative. Assuming that the entire days’ dose occurs 

in one dose increases the estimate of peak blood concentrations of CPF and peak 

inhibition since the dose is not spread over multiple eating events in a day. 

2. Digestion of foods during a meal is a well-understood process. Solid foods consumed 

during a meal are held in the stomach for digestion. This digestion involves secretion 

of gastric fluids and the conversion of the food into a liquid (chyme) that then passes at 

a controlled rate (gastric emptying rate) into the gut on a continual basis until the 

stomach is empty. The gastric emptying rate decreases over time and has been 

described based on the “time to empty the first half” of the stomach’s content (t1/2). 

Values for t1/2 in individuals range from 30 to 120 minutes (Hellmig S, 2006). 

3. The CPF uptake parameters are determined by fitting data from human volunteers 

(Nolan et al., 1984) into the PBPK model. The parameterization is consistent with the 

gastric emptying rates identified in step 2. 

4. The gut compartment of the PBPK model also contains metabolism of CPF to both 

CPF-oxon and TCPy (via CYP) as discussed in Section 3 (see Table 1). 

5. As discussed in Section 4, the sensitivity analysis of model parameters evaluated the 

rate of CPF entering the gut. The peak levels of RBC and brain AChE inhibition were 

not sensitive to the gut parameters. The sensitivity coefficient for the first order rate 

uptake was less than 0.1.

Based on these assumptions, the daily dose of CPF entering the PBPK model is 

treated as a single event that occurs over several hours at the beginning of each day. The 

total mass of CPF entering the gut during this process is equal to the total daily dose as 

estimated by the dietary exposure models. The uptake parameterization is not agedependent. 
108 

6.3 Design of the source­to­outcome model

The final model consists of the following. The CARES (or other appropriate dietary 

software model) is used to convert a pesticide’s residue data into estimates of daily dietary 

exposures in populations of individuals of a specific age. The output from CARES is used 

to create a table containing each individual’s daily doses for the number of consecutive 

days of interest to the risk assessor. The format used for storing this table is “Commaseparated values” format (.csv). This format can be specified for programs running on SAS 

or can be created using Excel. 

The .csv file has the following format. Data on each individual is provided in one 

row. The values in the first columns 1 through 3 define the individual’s age, weight and 

gender. The daily doses for each day of each individual’s longitudinal dietary exposure 

history are placed in the columns 4 and above. Any number of days up to 50 days13 can be 

modeled by placing the daily doses in the appropriate columns. The following is an 

example of a table for two individuals having five consecutive days of exposure. 

Table 7. Example table for five days of exposure for two infants 

Age (y) 

Sex 

(0=male, 

1=female) 

Weight 

(kg) 

Dose 

Day 1 

(mg/kg) 

Dose 

Day 2 

(mg/kg) 

Dose 

Day 3 

(mg/kg) 

Dose 

Day 4 

(mg/kg) 

Dose Day 

5 (mg/kg) 

0.5 1 6.8 3.97E-06 4.39E-07 1.12E-05 1.04E-05 1.65E-06 

0.5 1 6.4 3.31E-06 9.61E-07 1.12E-05 6.78E-05 1.65E-06 

Copies of the .csv files used in the simulations describe in the following sections are 

included as Attachment F. 

As described in Section 3, the Variation model reads the .csv file and uses the age, 

sex and weight data to predict the physiology of the individual. The model assumes that 

the meals occur once every 24 hours. The gut uptake rate assumed in the model results in 

approximately one half of the dose entering the gut within the first 90 minutes and three 

quarters in the first 180 minutes. 

 

13 The model currently only accepts 50 daily doses but can easily be modified to accept more.. 
109 

In the final source-to-outcome model, the PBPK/PD model reports: 

• The blood concentration of CPF 

• The blood concentration of CPF-oxon 

• The inhibition of RBC AChE 

• The inhibition of brain AChE 

• The inhibition of plasma BuChE 

• The mass of TCPy removed from the blood stream 

The model provides a continual simulation of all modeled compartments, including 

the metrics above. By default, the model reports every 15 minutes for each 24 hour period 

following a dietary dose and saves an individual output file for each of the six metrics 

above. Attachment C provides additional information on the program. 

Section summary

This section has presented a description of the approaches used to link the output 

from the dietary model to the PBPK/PD Variation model. Data on the characteristics and 

the exposure history of the simulated individuals is prepared by the user from the output of 

one of the dietary exposure software programs. This file is read by the Variation model and 

the predictions of the time course of the outputs are produced. 
110 

7. Predictions of one and multiple day (longitudinal)

dietary exposures on internal doses and cholinesterase

inhibition

Section overview

This section presents the use of the source-to-outcome model to investigate and 

ultimately characterize the impacts of longitudinal dietary exposures to CPF on body 

burdens and inhibition of blood and brain AChE. Longitudinal exposures are of interest 

because neither the pharmacokinetic nor pharmacodynamic effects of a dose may return to 

normal 24 hours after an intake of CPF. The body retains CPF for periods of time longer 

than 24 hours. In addition, at high doses the effects on cholinesterase are not fully reversed 

within 24 hours. However, as discussed in Section 5, there is considerable uncertainty in 

modeling longitudinal exposures. Therefore in this section we will examine how the 

impacts of longitudinal exposures relate to the impact of a single day’s exposure. 

This section begins by using the Variation model to investigate the impact of 

longitudinal exposures to constant dietary doses on internal metrics of exposure and RBC 

and brain AChE inhibition. This is followed by an analysis of the impact of the variable 

dietary exposures over multiple days using the complete source-to-outcome model. The 

impacts of the three different models of longitudinal dietary exposures discussed in Section 

5 (CARES, LifeLine daily and LifeLine seasonal) on predictions of the impact of five days 

of dietary exposures are then investigated using the complete source-to-outcome model. 

Finally an analysis of responses at or above the 99.9th percentile of daily dietary exposures 

is presented. This analysis measures the impact of doses on the days prior or subsequent to 

the days when an individual receives a dose at or above the 99.9th percentile dose.

7.1 Model predictions of the impacts of identical daily exposures over time

The Variation model was used to investigate the cumulative effects of consistent 

dietary exposures to CPF. The model was used to predict the progressive impact of 

constant exposures in a typical adult and infant over a thirty day period. Parameters were 
111 

assessed include: blood concentrations of CPF and CPF-oxon, percent inhibition of RBC 

AChE, and percent inhibition of brain AChE. Five doses are modeled ranging from 0.0001 

to 0.5 mg/kg. The lowest of these doses, 0.0001 mg/kg falls in the upper end of the range 

of currently estimated acute dietary exposures (see Attachment D). 

Figure 45 presents the predicted time courses of the blood concentrations of CPF and 

CPF-oxon. These two plots only present data for the first 10 days. This was done to make 

the changes in the first few days clearer in the figure. (The patterns of daily changes in 

these two parameters do not vary for days 11 through 30.) The CPF plot indicates at all 

five doses there was little carryover of CPF from one day to the next for either adults or 

infants. The peak concentrations of the two compounds are only 5% higher on day 30 than 

day 1 for infants and 16% higher for adults. 

In contrast, the second plot indicates that CPF-oxon levels increase over time. At 

doses of 0.5 mg/kg the peak oxon level on day 30 was 18-fold (1,800 %) higher than on 

day 1 for infants and 9 (900 %) fold higher for adults. Most of this increase occurs in the 

first 5 days of exposure. However this increase is dose dependent and decreases with dose. 

At the lowest dose modeled, 0.0001 mg/kg, only a 1% increase in peak oxon levels was 

observed over the 30 day period. 
112 

Figure 45. Predicted time course of CPF and CPF-oxon in blood (umoles/l) for a typical adult 

and child exposed to 10 consecutive daily oral doses of CPF for one of five modeled dose rate 

ranging from 0.5 to 0.0001 mg/kg. 
113 

Figure 46 presents the time course of inhibition of RBC and brain AChE in a typical 

adult and child. The two plots indicate that at constant doses, the peak inhibitions increase 

over time in a tissue and dose-dependent manner. 

Figure 46. Prediction of RBC and brain inhibition from thirty days of constant exposures to 

five modeled doses in typical adults and infants. 
114 

As indicated in Figure 47, at all of the doses, the majority of the decreases in RBC 

AChE activity occur in the first 10 days. However, at lower doses the decreases occur 

sooner and 90% of 30 day inhibition is achieved by day 5. For RBC AChE in infants, there 

is a 20-fold difference between the day 1 peak inhibition and the day 30 peak inhibition at 

doses of 0.1 mg/kg. The difference between day 1 and day 10 levels of inhibition is smaller 

for adults (12-fold increase). At lower doses the difference between day 1 and day 30 is 

less for both adults and infants. At the lowest dose tested the difference for both adults and 

infants was 2.5-fold. 

The time required to reach steady state is less for brain inhibition and is not strongly 

dose dependent. At both ages, 80% of the inhibition occurs by day 5 and 90% by day 10. 

At the lowest dose tested, the increase over the thirty days is 3-fold for infants and 3.5-fold 

for adults.

7.2 Modeling the impacts of time varying dietary exposures –determining when a

population reaches quasi­steady state conditions

Dietary doses of pesticides vary from day to day. As a result, no individual ever 

reaches true steady state with respect to their dietary intakes. Rather the blood levels and 

levels of AChE inhibition constantly fluctuate over time. However, if the distribution of 

doses received by a population remains relatively constant, the distribution of peak levels of 

the CPF and CPF-oxon in the blood and AChE inhibition in RBC and brain across a 

population stabilizes over time. Under this quasi-steady state condition, the peak values can 

be specified for different fractions of the population. The time necessary for a population to 

reach quasi-steady state can be empirically measured by determining the cumulative 

distribution of peak levels across the population for each day in a model simulation. 

In this analysis the peak concentrations and peak inhibition levels that occur at any 

point in time during the day are determined for each individual in two age groups, adults 

age 30 years and infants age 6 months. The cumulative distributions of the peak values for 

each of the 1,000 individual in the age groups for each day can then be used to determine if 

and when the populations have reached a quasi-steady state.
115 

Figures 47 and 48 present the cumulative distribution of peak blood concentrations 

of CPF and CPF-oxon across the populations of adults and children for each of the five 

days. As the figures indicate, there is no change from day 1 to day 5. This suggests that 

peak blood concentrations of CPF and oxon across the population have reached quasisteady state by day 1. This finding is consistent with the results in Figure 46 that showed 

that at the doses currently received from diet, neither CPF nor CPF-oxon increased over 

time. This implies that dietary exposures from prior days have little or no effect on blood 

levels of CPF or CPF-oxon. 

Infants Adults

Figure 47. Cumulative distributions of peak CPF across populations of adults and infant on 

days 1 through 5 of a simulation of five days of dietary exposures. 
116 

Infants

Adults

Figure 48. Cumulative distributions of peak CPF-oxon across populations of adults and 

infant on days 1 through 5 of a simulation of five days of dietary exposures. 

Figures 49 and 50 present plots of the cumulative distributions of the peak levels of 

RBC and brain AChE for each of the 5 days. In contrast to the CPF and CPF-oxon, the 

plots in these two figures show that there is a small increase in the distribution of peak 

RBC and brain inhibition over the five days. However, the distributions for day 3 through 

day 5 are essentially identical. This suggests that by day 3 the population has reached 

quasi-steady state and that modeling individuals for longer durations of time would not 

result in different predictions of the distribution of body burdens or levels of AChE 

inhibition. In addition, for the highest percentiles the differences between day 1 and day 5 

are less than a factor of 2. However, with only a thousand individuals, estimates above the 

99th percentile (Z-score >2.3) are unstable (values are driven by the last few outliers). The 

data on infants at low Z-scores is affected by the number of individuals who are breast fed 

and whose intakes are not evaluated by the dietary exposure models. 
117 

Infants Adults

Figures 49. Cumulative distributions of peak RBC AChE inhibition on days 1 through 5 of a 

simulation of five days of dietary exposures to CPF in adults and infants. 

Infants Adults

Figures 50. Cumulative distributions of peak RBC AChE inhibition on days 1 through 5 of a 

simulation of five days of dietary exposures to CPF. 
118 

In summary, Figures 49 and 50 confirm that CPF and CPF-oxon do not accumulate 

over time. The inhibition of RBC and brain AChE do increase but the impact is minimal 

for individuals in the upper portion of the population. By day 2, the portion of the 

population with above average doses has reached quasi equilibrium and the distribution of 

values for this portion of the population do not appear to change over longer periods of 

time. 

7.3 Impact of three different longitudinal approaches of dietary exposure

The three longitudinal approaches of dietary exposure CARES, LifeLine daily, and 

LifeLine seasons were used in the source-to-outcome model. LifeLine is limited to 

modeling five consecutive days. Therefore, to be consistent, in this exercise the CARES 

program was also limited to 5 consecutive days. At the doses that currently occur from 

dietary exposure 80 to 90% of the cumulative impact on cholinesterases occurs in the first 

5 days. Two age groups were investigated, adults and three year-olds. One thousand 

individuals were investigated in each age group. 

Figure 51 presents the cumulative distribution of peak blood concentrations of CPF 

and CPF-oxon across populations of adults and children for each of the three approaches of 

longitudinal dietary exposures. The data presented are from the fifth day of the five-day 

simulation. These data reflect the contribution of day 5 and the prior days and thus should 

reflect the differences between the three longitudinal approaches. The data are plotted on a 

log dose versus Z-score. In general, the distributions of peak levels were found to be 

approximately log normal. This format has the advantage of expressing data that are lognormally distributed as straight lines. This facilitates the visual evaluation of the 

predictions of the three approaches. 
119 

As Figure 51 indicates, all three approaches produce very similar estimates of the 

distribution of peak levels of CPF and CPF-oxon. This suggests that any of the three 

approaches can be used to predict the impact of longitudinal dietary exposures on internal 

doses. 

Figure 51. Predictions of the distribution of blood levels of CPF and CPF-oxon AChE on day 

5 of a 5 day simulation in two age groups in three approaches of longitudinal exposures. 

In Figure 52, two plots present the distributions of the three approaches for RBC and 

brain AChE. In contrast to CPF and CPF-oxon, the choice of dietary software does have an 

impact on the predictions of cholinesterase inhibition. CARES and LifeLine daily 

approaches produce similar values. However, the LifeLine seasonal approach results in 

predictions of less inhibition for individuals in the lower half of the population. This 

finding is consistent with analysis of day to day correlation presented in the three plots that 

comprise Figure 45. Those plots indicates that LifeLine daily and the CARES approaches 

produced little or no correlation between doses of CPF on consecutive days and that 

LifeLine seasonal produced moderate levels of correlations. 

CARES

LifeLine Daily

            LifeLine Seasonal

-2 -1 0 1 2

10-4

10-3

10-2

10-1

100

101

Chlorpyrifos

Chlorpyrifos oxon

}3 Year Olds

}30 Year Olds

}3 Year Olds

}30 Year Olds

Z-Score (3-97th Percentiles)

Blood Concentration (ng/L)
120 

-2 -1 0 1 2 10 -6

10 -5

10 -4

10 -3 }3 Year Olds

}30 Year Olds

Brain AChE Inhibition (% basal)

Z-Score (3-97th Percentiles)

-2 -1 0 1 2 10 -5

10 -4

10 -3

10 -2

}3 Year Olds

}30 Year Olds

RBC AChE Inhibition (% basal)

Z-Score (3-97th Percentiles)

   CARES                  LifeLine Daily                     LifeLine Seasonal

Figure 52. Predictions of the distribution of blood RBC and brain AChE on day 5 of a 5 day 

simulation in two age groups in three approaches of longitudinal exposures. 

In summary, the three approaches to modeling longitudinal dietary exposures 

produced similar values for the blood levels of CPF and CPF-oxon. The three models 

predictions of AChE inhibitions were essentially identical for the portion of the population 

with the highest exposures. This suggests that any of the three approaches can be used to 

characterize the impacts of dietary exposure of CPF on individuals with above average 

exposures. Further, it suggests that predictions of internal doses and impacts on AChE 

from dietary exposures in the top half of the population are not substantively affected by 

the uncertainty introduced by the absence of longitudinal data on dietary intakes. Based on 

this finding, the CARES model of longitudinal variation is used in the remaining analyses 

in this report. 

7.4 Determining the impact of longitudinal exposures on the impacts of high level

dietary exposures (the 99.9th percentile dose) on RBC and brain AChE inhibition  

The above analyses of quasi-steady state and multiple longitudinal models provide 

evidence that the impact of prior exposures on blood levels and AChE inhibition is 

different for individuals with relatively low exposures than for individuals with high 

exposures. This difference causes the distributions on days 1 through 5 and the differences 

in the three longitudinal models to “fan out” at lower Z-score values (See Figures 49, 50, 
121 

and 52). The explanation for the “fanning” is the asymmetry of the effect of dose from 

prior days on the blood levels of CPF and CPF-oxon and the AChE inhibition. 

Specifically, high doses on one day affect the four parameters on subsequent low dose days 

but low doses do not affect subsequent days with high doses. For example, when day 1 

produces an exposure that is 100-fold larger than an exposure on day 2, it has a large 

impact on the four parameters on day 2 (and possibly later days). However, if day 2 

exposures are 100 times day 1 exposures, there is little or no effect on day 2 from day 1. 

Individuals with low Z-scores have received lower doses on a given day and thus are more 

sensitive to the impact of the prior day’s exposure. As a result, the values for the four 

parameters in this portion of the population are most affected by prior exposures. Thus this 

portion of the distribution of doses takes longer to reach equilibrium than individuals with 

above average exposures. 

This finding suggests that individuals receiving the highest exposures (>the 99.9th

percentile) in a population will be least affected by exposures from prior days. This 

hypothesis was investigated using the source-to-outcome model. 

In this analysis, the CARES dietary model was used to simulate year-long exposure 

histories for each of 4,000 adults. Figure 53 presents the year-long exposure history of 

three adults randomly selected from 4,000 individuals. The adults’ highest daily exposures 

for the year are identified by the ovals. 
122 

0.00E+00

5.00E‐05

1.00E‐04

1.50E‐04

2.00E‐04

2.50E‐04

0 50 100 150 200 250 300 350 400

0.00E+00

5.00E‐05

1.00E‐04

1.50E‐04

2.00E‐04

2.50E‐04

0 50 100 150 200 250 300 350 400

0.00E+00

5.00E‐05

1.00E‐04

1.50E‐04

2.00E‐04

2.50E‐04

0 50 100 150 200 250 300 350 400

Figure 53. Exposure history (dietary doses of CPF (mg/kg) for each of 365 days) for three 

adults. Ovals indicate the maximum daily dose for each adult. 

As indicated in Section 4, adults at the 99.9th percentile of the population are 

predicted by CARES to have a peak dose of 0.00012 mg/kg. A total of 499 individuals 

were identified who had one day during the 365-day period with an exposure equal to or 

slightly above the 99.9th percentile dose (0.00012 to 0.00024 mg/kg). For each of these 

individuals, the daily doses for the four days prior and four days subsequent to the high 

exposure day were identified (for a total of 9 days). Subsequent days were also included to 

investigate if the effect of the peak day could add to the effects of dose received on 

subsequent days. The result was the creation of 500 nine-day records where the fifth day 

had a dose at or slightly above the 99.9th percentile dose. The nine day exposure histories 

for the same three individuals are shown in Figure 54. 
123 

0.00E+00

5.00E‐05

1.00E‐04

1.50E‐04

2.00E‐04

2.50E‐04

0 1 2 3 4 5 6 7 8 9 10

0.00E+00

5.00E‐05

1.00E‐04

1.50E‐04

2.00E‐04

2.50E‐04

0 1 2 3 4 5 6 7 8 9 10

0.00E+00

5.00E‐05

1.00E‐04

1.50E‐04

2.00E‐04

2.50E‐04

0 1 2 3 4 5 6 7 8 9 10

Figure 54. Dietary doses in mg/kg for the high exposure day and the four prior and 

subsequent days (same three adults as figure 53). 

The 9-day exposure histories for the 499 adults were run through the source-tooutcome model and the peak RBC AChE inhibition levels were determined. In addition, 

the high days were also run through the source-to-outcome model as single day exposures 

and values of peak RBC AChE inhibition were determined. Figure 55 presents the 

cumulative distributions of the Peak AChE inhibition for both measures of exposure data. 

As the figure indicates, the impacts on RBC AChE from the high day plus the surrounding 

(+/- 4) days are very similar to the high day alone. The average impact of considering the 

contribution from the prior to subsequent days for the 500 individuals was a 9% increase in 

the estimate of the peak inhibition. This finding implies that for individuals receiving a 

large dose (relative to other individuals) the contributions from prior and subsequent days 

are minimal. A similar analysis for brain AChE showed an 11% average increase. 
124 

0.001

0.01

0.1

‐3 ‐2 ‐10 1 2 3

Peak RBC inhibtion (Percent)

z‐Score

High exposure day plus 4 prior

and  4 subsequent days

High exposure  day only

Figure 55. Cumulative distributions of 1) peak RBC AChE inhibition over the 9 day period in 

500 adults where day five is the high exposure day (dose at or above the 99.9th percentile 

dose) and 2) peak RBC AChE inhibition for the high exposure day alone. 

Section summary

This section has presented data from analyses where the source-to-outcome model 

and the Variation PBPK/PD model are used to investigate the issue of longitudinal 

exposure and its impact on four outcomes, CPF and CPF-oxon levels in blood and peak 

inhibition of RBC and brain AChE. The Variation model predicts that the effect of daily 

oral exposure to identical doses of CPF at current dietary results in effects that modestly 

accumulate over time (i.e., levels of RBC and brain inhibition increases by roughly a factor 

of three if an oral dose is repeated over multiple days). At the doses that are predicted from 

current dietary exposures (<0.0002 mg/kg), 90% of the increase in inhibition occurs over 

the first five days of exposure. In contrast, levels of CPF and CPF oxon in blood do not 

accumulate over time. 

Three dietary exposure models were found to produce similar estimates of blood 

levels of CPF and CPF-oxon and similar estimates of peak inhibition in RBC and brain 

AChE. An evaluation of the predictions of the cumulative impact of dietary exposures over 

five days suggests that estimates of the upper portions of the distribution of the four 

parameters do not change if the duration of dietary simulations are extended beyond five 
125 

days. Finally, the model indicates for individuals at the extreme tail of the exposed 

population (the 99.9th percentile of dose) RBC inhibition is largely driven by the dose from 

a single day. 

Based on these findings, we conclude that while repeated doses of equal size can 

result in higher inhibition rates over time, when there is large variation in day-to-day levels 

of dietary doses the impact of longitudinal exposures are minimized. This minimization is 

most apparent in the estimates of the individuals most exposed. As a result, if a person has 

a high level of exposure on a given day (in the top 0.1%), the impact on the four 

parameters are driven by the dose from that day. 

These findings also suggests that the use of the five-day exposure histories produced 

by CARES or other dietary exposure software provide a reasonable basis for the evaluation 

of the impacts of longitudinal exposures to current dietary residue levels of CPF. The 

confidence in the predictions is higher for the upper percentiles of the exposed populations 

since these exposures are less dependent on longitudinal exposures (i.e., are more driven 

by single day above average exposures). Finally, these results explain why the three 

models of longitudinal exposures provide similar predictions of AChE inhibition at higher 

percentiles. The individuals at the higher percentiles were simply not affected by doses 

from prior days and differences in correlation between consecutive days would not affect 

the predicted blood levels or effects on AChE. 
126 

8. Evaluation in the uncertainty in the predictions of

source­to­outcome model by comparison to measured values

Section overview

The predictions of the source-to-outcome model are subject to uncertainty that 

occurs from a number of factors including limitations in the fidelity of the model, 

uncertainty in the model inputs, and the relevance of the input data. Thus, the model is 

evaluated to determine the level of confidence in its output. The evaluation of uncertainty 

in the model is also a challenge because the model addresses multiple and diverse 

processes including variation in residues on food, food consumption rates, the conversion 

of oral doses to time histories of internal concentrations, and interactions with 

cholinesterase in blood and the brain. This section presents examples of how measured 

data from surveys can be used to evaluate the model predictions. 

8.1 Finding data for model evaluations

The source-to-outcome model ideally should be evaluated by data that matches 

dietary levels of exposure to inhibition of AChE and BuChE levels. Such an exercise is not 

possible since any impact of current levels of CPF would not be detectable against the dayto-day variation in blood AChE or plasma BuChE. Without such data, the evaluation must 

rely on studies that evaluate the components of the model. 

In the case of CPF, the monitoring data available for the evaluation of a source-tooutcome model are limited. While there are multiple sources of data that can be used, each 

of the sources has its particular limitations. In this section we discuss how to most 

effectively use the available data to evaluate the source-to-outcome and Variation models. 

In general, this is achieved when the model is used to mimic the conditions of the survey 

data. 

To address this issue of model evaluation, we believe that multiple comparisons must 

be made that address specific steps in the source-to-outcome process. Figure 56 presents a 
127 

description of the major steps of the source-to-outcome model and the data that can be 

used to evaluate each of the steps. 

Data on many of these steps have already been presented in this report. The process 

of modeling daily doses has been the subject of a number of reviews. The available 

software programs developed by multiple groups and using different methods for dose 

calculation have been shown to produce similar estimates of dietary exposure for pesticides 

(U.S. EPA, 2004; Xue, 2010). In this report we have shown that three of the current 

models produce similar estimates of the distribution of oral intakes of CPF in adults, 

children and infant (Table 5, Section 5). 

The consistency indicates that the residue data were correctly entered and the models 

were run correctly. It also confirms that the use of the current models does not introduce 

modeling uncertainty (bias due to choice of dietary model in the analysis). 
128 

PBPK

Modeling

PBPD

Modeling

AChE

Prediction

s

Daily

Dietary

Exposures

PBPK

Modeling

PBPD

Modeling

Model of Human

Variance (Adults)

Model of Human

Variance (Children)

Longitudin

al

Exposures

AChE

Predictions

Comparisons of

estimates of

dietary intakes in

three dietary

studies (Model‐to‐

Dietary

Residu

e Data

Comparisons of

blood levels across

three models of

longitudinal

exposure (Model‐

Comparison of Blood levels of CPF and Urine levels of

TCPy (Model‐to‐Measurements)

Comparison of TCPy and effects in CPF

manufacturing workers (Model‐to‐Measurement)

Variation in human volunteer

studies (Model‐to‐Measurement)

Comparisons of model

predictions of variation

in physiology to

literature values

(Model‐to‐

Measurement)

Figure 56. Steps in the Source-to-outcome model and analyses that provide a basis for the 

evaluation of the model output. 

The second source of uncertainty, the development of estimates of longitudinal 

exposures, has been shown not to be an issue for dietary exposures to CPF. The impacts of 

repeated oral exposures on blood levels of CPF and CPF-oxon at dietary levels do not 

accumulate over time. The impact of repeated daily exposures on inhibition rates has been 

shown to be minimal for individuals with above average dietary exposures (see Section 7). 

In Sections 3 and 4 of this report we have used the LifeStage model to predict the 

typical response in the groups (Figures 20-23) and the Variation model to predict the range 

of response observed across the individuals in Kisicki et al. (Figure 32). The predictions of 

the LifeStage and Variation PBPK/PD models are consistent with the observed levels 

BuChE inhibition in the Nolan et al. human volunteer study (Nolan et al., 1984) and the 

TCPy blood levels and RBC AChE inhibition reported in the Kisicki et al. study (Kisicki 

et al., 1999). 
129 

The LifeStage and Variation models have also demonstrated that their predictions of 

age related changes and interindividual variation in physiological parameters are also 

consistent with published data (Sections 3 and 4). 

The following sections present a series of additional comparisons that provide further 

perspective on the predictions of the source-to-outcome and Variation models. In these 

comparisons the models are used to make predictions that can be directly compared to 

values reported in surveys of the public. 

8.2 Using biomonitoring data of CPF and its metabolites to evaluate the dietary and

PBPK portions of the source­to­outcome model

We use the source-to-outcome model to predict 1) blood levels of CPF in adults, 2) 

TCPy levels in urine in adults and children. These predictions are then compared to survey 

results. In addition, the model is used to investigate data on BuChE inhibition in workers. 

Surveys of levels of pesticide metabolites in individuals are an important source of 

data for validation of dietary models. NHANES has been collecting blood samples for 

analysis of CPF levels in the 2007-2008 and 2009-2010 waves of sampling; however, these 

data have not been released as of this time. 

There are two smaller studies that have measured levels of CPF in sera of pregnant 

women in New York City (Whyatt et al., 2003; Whyatt et al., 2009) and New Jersey (Barr, 

2010). Table 7 presents the reported results of two surveys of blood levels in adult women 

at the time they gave birth (Barr, 2010; Whyatt et al., 2009). Data were taken from both the 

mothers and from the cord blood. These survey data were taken from periods (after 2000) 

when CPF exposures were largely limited to dietary intake of food residues. The data on 

the Whyatt cohort are limited to values published in the peer review literature. The 

investigators of this cohort have not made their data available. Data on the Barr cohort 

were provided by Dr. Dana Barr. 

The strategy used in performing this comparison has been to use the source-tooutcome model to produce estimates of the range of blood concentrations that would be 

found if a blood sample was taken any time during a 24 hour period after a dietary 
130 

exposure event. This was done by taking the values that occur at various points in time 

over a 24-hour period for 1,000 adults. The source-to-outcome model produces 96 

estimates per 24-hour period (one every 15 minutes). The 96,000 values were ranked and 

the various percentiles, min, max, and geometric mean were determined. The 24 hours of 

data were taken from day 5 of a 5-day model simulation performed in a population of 30 

year-old adults. If the last exposure event occurred no more than 8 hr before the time the 

blood was sampled, the predicted concentrations would be approximately 50% higher (data 

not shown). 

The Variation model does not consider the impact of pregnancy on blood levels of 

CPF. Lowe et al., 2009 investigated the impact of pregnancy on the ADME of CPF and 

found that blood levels would be increased by a factor 1.3 due to changes in the lipid levels 

during pregnancy. Therefore the predictions of CPF in blood from the source-to-outcome 

model were increased by a factor of 1.3. 
131 

Table 7. Comparison of measured blood CPF levels (ng/L) from selected biomonitoring 

studies with predictions from source-to-outcome model 

  

Source-tooutcome 

model1

Eaton et al. 

(2008)2 Whyatt et al. (2009)2

 Barr et.al (2010) 

Exposure Date3 2002-2010 2001-2002 2001-2004 2003-2004 

Measurement MB4 MB and CB5

 CB MB CB MB 

Number of 

Individuals5 1000 76 65 92 148 138

Range                  

Min 0.007 <LD7 <LD <LD <LD <LD

Max 7 16 -8

 - 1.8 10.1 

Geometric 

Mean 0.2 0.5 - - 1.5 4.2 

Percentile                  

10 0.05 - <LD <LD <LD <LD 

25 0.09 - <LD <LD <LD <LD 

50 0.2 - <LD <LD <LD <LD 

75 0.4 - <LD <LD 1.3 <LD 

90 0.7 - 2.3 1.5 1.7 <LD 

95 1.0 - 2.5 2.5 1.8 <LD 1

 Model simulations were conducted using the adult data. Values based on randomly selected data points on 

day 5. Blood levels were adjusted up by 1.3 to reflect impact of pregnancy on CPF levels in blood as 

described by Lowe et al., (2009). 2

 Summary data from the Whyatt studies were reported by both Eaton et al., 2008 and Whyatt et al. 2009. 

Data for 2001 and 2002 are reflected in both sets of values. 

3

 Date of that samples were taken for measured data or date of residue data used in dietary simulation 

4

 Maternal Blood 

5

 Cord Blood 

6

 Number of simulated blood levels or number of measured values. 

7

 Less than level of detection. Level of detection was reported by authors to range from 0.5 to 1.0 ng/L 

8

 Indicates data not available 

Table 7 indicates that that model predictions are relatively consistent with the limited 

monitoring data in pregnant women. This is encouraging since women of child-bearing age 

are one of the subpopulations of concern. However, because the surveys are limited in size 

and come from two adjacent states (New York and New Jersey) they are not ideal for 

evaluating the ability of the source-to-outcome model’s to predict the distribution for other 

age groups and in other regions. In addition, the majority of the samples are below the 
132 

detection limit for the analytical methodology. When additional data become available they 

should be used to confirm the model predictions. 

The second data set that can be used to evaluate the model’s prediction are the data 

on urinary levels of TCPy in NHANES and other smaller biomonitoring studies. The use 

of TCPy in blood or urine as a direct measure of CPF is complicated by the fact that TCPy 

itself is present in the environment, and there are dietary residues of TCPy in many 

foods14. As a result, researchers have concluded that only 5 to 20% of TCPy observed in 

urine is from dietary intake of CPF residues (Barr et al., 2005; Eaton et al., 2008; Wilson 

et al., 2003). 

Figure 57 presents the predicted distribution of TCPy levels in the U.S. population in 

2002 as determined by the NHANES survey of individuals over the age of 12 year. The 

data were below the level of quantification for 29% of the population and ranged from 0.43 

to 27.5 ug/l. Using the estimates of 5-20% as the fraction of TCPy in adults that occurs 

from metabolism of CPF, a range of plausible TCPy values is specified. The predicted 

levels of TCPy in adult urine were determined based on the amount of TCPy removed on 

the fifth day of a 5 day simulation of dietary exposures in adults. The daily urine 

production for each individual was estimated using the individuals’ body weights and 

assuming a daily urine production rate of 0.02 l/kg body weight (Brehrman RE, 2000; 

Snyder, 1975). The prediction of the distribution of TCPy in urine that occurs from dietary 

intake of CPF is presented in Figure 57 as the dashed line. The model predictions are 

consistent with the biomonitoring data for the median individuals, but tend to overestimate 

values for individuals with low TCPy urine levels and underestimate values for individuals 

with high TCPy levels. Current levels of dietary exposure of CPF are expected to be lower 

than levels in 2002 because of the changes in the regulation of the compound. This may 

explain why the predicted levels of TCPy from the source-to-outcome model are below the 

range suggested in the literature for the top of the cumulative distributions. However, any 

 

14 These level of TCPy result from degradation of the parent CPF (and the related pesticide CPF-methyl in 

stored grain) after application but prior to consumption of food Barr, D. B., Angerer, J., 2006. Potential uses of 

biomonitoring data: A case study using the organophosphorus pesticides chlorpyrifos and malathion. Environmental 

Health Perspectives. 114, 1763-1769, Eaton, D. L., et al., 2008. Review of the toxicology of chlorpyrifos with an 

emphasis on human exposure and neurodevelopment. Crit Rev Toxicol. 38 Suppl 2, 1-125.. 
133 

strong conclusions are prevented by the uncertainty in the fraction of the total TCPy in 

urine that comes from the metabolism of dietary intake of CPF. 

-3 -2 -1 0 1 2 3

0.1

1

10

100

Distribution of total TCPy

for portion if the population

with detectable levels

Predicted distribution from

Source-to-outcome model

}

Range of predictions of

TCPY from metabolism of

chlorpyrifos (5-20% of

total)

Portion of the

population with non

detectable levels of

TCPy

Z-Score

TCPy Concentration

in Urine (μg/L)

Figure 57. Cumulative distributions of predictions of current urinary levels of TCPy from the 

source-to-outcome model for adults, observed levels in the U.S. Population in the 2002 

NHANES survey (30% of population have levels below the NHANES level of quantification 

and are not shown), and estimates of the portions of NHANES levels that come from CPF 

dietary exposures. 

Table 8 compares the predicted levels of TCPy from the source-to-outcome model 

and the observed levels from two smaller studies (Curwin et al., 2007; Lu et al., 2008). 

The modeled predictions of TCPy range from 2% (adults) and 3-10% (children) of the 

observed values and are thus reasonably consistent with published findings of the fraction 

of TCPy that occurs from exposure to CPF. 
134 

Table 8. Comparison of selected measured urinary TCPy concentrations (μg/L) from selected 

biomonitoring studies with source-to-outcome model predictions 

Source-to-outcome 

model1 Curwin et al. (2007) Lu et al. (2008) 

Median (90% CI) Geo. Mean (±Std. Dev.) Mean (Range) 

Date2 2008-2009 2001 2003-2004 

N 1000 Varies2

 701 

Men - 12 (3.8-47) - 

Women - 11 (1.8-35) - 

Women & Men 0.2 (0.08-0.3) - - 

Children 0.5 (0.3-1.0) 16 (5.4-54) 5.1 (0-32) 1

 Source-to-outcome model simulations were conducted using the CARES model data. Day 5 values are 

presented. 

2

 Date of sampling for measured data or date of exposure data simulated for source-to-outcome model 

3

 24 adult women, 24 adult men and 51 children 

4 Data not reported in published study

The results of the comparison of TCPy provide confirmation that the dietary and 

pharmacokinetic portions of the source-to-outcome model are producing estimates for 

adults that are reasonably consistent with the reported blood concentrations of CPF and 

urinary TCPy concentrations in adults and children. These results provide considerable 

support that the dietary and PBPK portions of the model are likely to be at the right order 

of magnitude in their predictions of body burdens of CPF and its metabolites. 

8.3 PBPK/PD model predictions of the relationship between TCPy levels in urine and

butyryl cholinesterase in plasma in occupationally exposed adults

In many instances data are available that are only partially relevant to a model’s 

prediction. This subsection presents an example of how biomonitoring data from a study of 

workers could be used to evaluate the Variation model and the limitations of such a 

comparison. 

Garabrant et al. (2009) published an analysis of data on RBC AChE and plasma 

BuChE cholinesterase collected from male and female workers in a plant manufacturing 

CPF as part of a regular industrial hygiene monitoring program. Unlike the general 

population, the majority of TCPy in the urine of the workers occupationally exposed to 
135 

CPF is the result of the workers’ occupational exposure and not dietary residues of TCPy. 

In this group, TCPy can provide direct information on occupational CPF exposures 

(Garabrant et al., 2009). 

Figure 58, taken from the Garabrant et al. (2009) publication, depicts the observed 

BuChE levels for each worker (expressed as a percentage of each worker’s baseline level) 

plotted against the worker’s urinary levels of TCPy (normalized to creatinine). The TCPy 

urine levels in workers and controls range over three orders of magnitude and for workers 

with elevated levels are correlated with increased inhibition of plasma butyryl but not RBC 

acetyl cholinesterase. 

The Variation PBPK/PD model was used to simulate the relation of TCPy in urine 

and BuChE in adults. The model was run for a group of 2,000 adults. The model assumed 

that the individuals were exposed for 5 days to the same bolus oral doses. The doses were 

varied across the adults in a log uniform distribution with doses ranging from 10 mg/kg to 

0.001 mg/kg. The amounts of TCPy removed by the kidney on the fifth day were 

determined for each of the 2,000 adults. These data were used to estimate the concentration 

of TCPy in urine on a mg TCPy/gram creatinine basis. These estimates assume a daily 

urine production of 0.02 l/kg and creatinine levels in urine of 5-15 mg/l for adults. This 

estimate was plotted against BuChE activity. The model predictions are presented in 

Figure 58 as an overlay of the data reported by Garabrant et al. (2009). 
136 

0.1 1 10 100 1000

0

20

40

60

80

100

120

140

160

180

  

 

 





    

    

  

 

      

      

  



   

  

 





 

          



   

      



  



      



    





 



     

 

  







 



       





  



    





    



 



  







 









 



























   







   



 



 



 







 









 







 







 





     





 











 



 



























 



 

 







   

























 

 





 







 

















 







 



 









































 



 



































 



 







 



































 



















 















  































 















 





 



 

















  































 

 



 





 

 













 



 

 



 



















  

























 

   

  



























 

    



 













 







 









  



   

    



 

 











 

  





    

 



 

 







   

 





  



      

  





 

   

 



 

  

   

 



 



       





       



         





  

     

      

       

    

                    

 

              

            

                





  

      

                                                                                                                        

  



Garabrant

Typical Lifestage

Variability Model 

TCPy urine (μg/g Cr)

Percent Activity

Figure 58. Observed relationships of cholinesterase inhibition and TCPy in urine for workers 

in a CPF manufacturing plant and for control workers. Predicted relationship (assuming 

major route of exposure is oral) using Variation PBPK/PD model. 

As Figure 58 indicates, the Variation model’s predictions of the relationship between 

BuChE inhibition in workers and elevated levels of TCPy in urine are a slight overestimate 

of the observed relationship in the Garabrant et al. study. The reason for the overestimation 

may be due to differences in the route or timing of the dose. 

Since the study involved occupational exposures and not dietary exposures, route 

and timing of these exposures was not clearly defined. The Variation model predicts the 

response assuming a bolus oral dose. The workers’ exposures are more likely to spread out 

over a workday and could occur by the dermal and/or inhalation routes. Such differences 

could change the relationship between BuChE and TCPy by reducing the peak 

concentration of CPF and CPF-oxon in blood by spreading the doses received over a 

longer period of time (e.g., the 8-hr workday). 
137 

Section summary

In this section and in Sections 3 and 4 we have reviewed the available human studies 

and biomonitoring data on CPF and its metabolites in the published literature. These data 

allow evaluation of each of the four steps in the total source-to-outcome process. These 

are: 

1. Development of oral doses estimate for one day based on dietary residues 

2. Characterizing longitudinal doses 

3. Modeling the impact of oral doses on blood levels of CPF and CPF-oxon and TCPy 

levels in urine 

4. Modeling the impacts on BuChE 

The analyses presented in Sections 5 and 7 indicate that dietary modeling uncertainty 

and modeling longitudinal exposures are not major sources of uncertainty. The PBPK/PD 

modeling provided good fits to data in human volunteers, and the model’s predictions are 

not inconsistent with data in workers (although the route of exposure for such workers is 

unclear). The data on biomonitoring evaluates the quality of the residue data and both the 

dietary and PBPK portion of the source-to-outcome model. The predictions are consistent 

with current studies of CPF in blood and TCPy in urine. These findings suggest that the 

model is predicting values that are of the right order of magnitude and could be within a 

factor of 2 or 3 of actual values. However, because of the limitations in the biomonitoring 

data, this consistency cannot be taken as proof that the source-to-outcome model is making 

predictions of impacts on cholinesterases that are less than an order of magnitude (+/- 3-

fold) for the general population. Additional biomonitoring data will be necessary to 

determine if the source-to-outcome model’s predictions are more precise. 
138 

9. Model predictions of dietary dose impact on brain and

RBC inhibition.

Section overview

This section focuses on the predictions of the source-to-outcome model and their 

implications for public health assessment. 

9.1 Model predictions

To evaluate the public health relevance of dietary intake on the general population, 

the source-to-outcome model was used to generate dietary estimates for three age groups: 

six month-olds, three year-olds, and thirty year-olds. These ages were selected to 

demonstrate the capabilities of the model. A complete risk assessment would address other 

ages as well. One thousand individuals were modeled for each age group with dietary 

doses determined over a 5-day period for each individual (from CARES). The peak values 

of blood CPF, CPF-oxon, RBC AChE inhibition and brain AChE inhibition were 

determined for day five of the assessment (Figures 59 and 60). 
139 

Figure 59. Predicted blood concentrations of CPF and CPF-oxon on the 5th day of simulated 

daily dietary exposures in 1,000 adults (30 years), 1,000 children (3 years), and 1,000 infants 

(6 months). 
140 

Figure 60. Predicted RBC and brain AChE inhibition on the 5th day of simulated daily 

dietary exposures in 1,000 adults (30 years), 1,000 children (3 years), and 1,000 infants (6 

months). 
141 

As discussed in Section 7 of this report, by day five of a simulation, the model 

predictions should reflect the impact of an individual’s current day's dose and all of the 

individual’s prior days’ exposures (quasi steady-state). Thus these predictions should be a 

reflection of the current impact of dietary residues. The results of the modeling suggest that 

the current dietary exposures are resulting in blood levels of CPF that range from 10-7 to 

10-4 μmoles/l in infants or 10 -0.01 ng/l (ppt). The range of levels is about five fold lower 

in adults or 2 - 0.002 ng/l (ppt). As indicated in Section 8, these levels are consistent with 

the limited monitoring data currently available. The RBC and brain inhibition on day 5 are 

less than 0.01% for all of the simulated individuals. 

The vertically aligned clusters in the estimates for the infants are the result of the 

dietary exposure model predicting very similar doses for multiple infants. This may reflect 

dietary records indicating the consumption of standard amounts of a highly processed 

blended food (such a one jar of a specific type of baby food). The impact of this can also 

be seen in the “stair step” shape of the dose distribution for infants shown in Figure 43. 

Figures 61 and 62 describe the relationship between the predictions of the impacts of 

dietary exposures in infants in the context of the dose response “clouds” presented in 

Section 4. Figure 61 overlays data on CPF and CPF-oxon presented in Figure 59 onto 

Figure 40. Figure 62 over lays data from figure 60 on to the data presented in Figures 41 

and 42). 
142 

Figures 61. Predicted blood concentrations of CPF and CPF-oxon RBC predicted to occur on 

day 5 of a 5-day simulation of dietary exposures (yellow) and after a single exposure to a wide 

range of doses (blue) in infants (6 months). 
143 

Figures 62. Predicted RBC and brain AChE inhibition predicted to occur on day 5 of a 5 day 

simulation of dietary exposures (yellow) and after a single exposure to a wide range of doses 

(blue) in infants (6 months). 

The comparisons presented in Figures 61 and 62 place the predicted impacts of diet 

into the framework of predictions of human response and show the impact of longitudinal 

exposures on the response. The predictions of the blood levels of CPF and CPF-oxon look 

very similar to the levels predicted by a single dose (Figure 61). In contrast, the RBC and 

brain inhibition in Figure 62 are often higher than expected from a single day’s dose. This 
144 

increase is due to residual inhibition predicted to carry over from prior days. As discussed 

in Section 7, this impact is limited to lower dietary doses. At dietary doses at the top end of 

the range, the observed responses on day 5 are similar to the responses from a single day’s 

dose. While not shown, similar patterns were observed for children and adults. 

Section summary

The most straightforward approach for the use of the source-to-outcome model is to 

use the model to assess the impacts of current dietary residue levels on public health. This 

can be done by using the relevant data on dietary residues and predicting the impact on 

AChE for different populations. Such an evaluation would consider factors such as the 

relationship between various levels of inhibition of RBC and brain AChE and the 

frequency of the occurrence of apical effects in populations of different ages. In addition, 

the confidence in the predictions of the source-to-outcome model needs to be considered. 

 In this section, we have attempted to use the source-to-outcome model in a fashion that 

would support this use. Specifically, 

1. The model has been used to determine the distribution of impacts of current exposures 

in three age populations, adults, children, and infants. 

2. The model was run for a sufficient duration such that the predictions for the population 

reflects quasi-steady state conditions. 

3. Data were generated on multiple key events that could be used to evaluate the potential 

for the occurrence of apical effects from inhibition of cholinesterase. 

4. Supporting information on CPF and CPF-oxon pharmacokinetics are provided. 

5. Data are provided in a format that discloses the variation in response as well as the 

variation in dose. 

This source to outcome model has enabled us to simulate effects from exposures well 

below outcomes that can be visualized experimentally. AChE activity is sufficiently 

variable that it is difficult to demonstrate treatment-related inhibition less than 10 or 20%. 

Therefore, the model was used to estimate the effects the might occur following much 
145 

lower exposures. Finally, the value of the source-to-outcome model is accentuated when 

the model is used as a tool for exploring the processes that drive the source-to-outcome 

pathway rather than just as a risk “calculator” where the only thing of interest is the 

“answer”. In this paper we have used the source-to-outcome model to explore several 

issues of importance for the evaluation of CPF. These include the role that longitudinal 

exposure to time-varying doses plays in estimating upper bounds to risk (Section 7), the 

evidence that variations in metabolism rather than physiology or level of physical activity 

are important drivers of sensitivity to CPF’s effects on cholinesterases (Section 4), and the 

finding that interindividual variation in RBC inhibition is dose-dependent. 
146 

10 Using the source­to­outcome model to estimate

compound and age­specific uncertainty factors

Section overview

The source-to-outcome model was also used to derive values for the interspecies 

(UFA) and interindividual15 (UFH) uncertainty factors for two age groups. EPA and other 

organizations have developed guidance for the replacement of all or part of the values of 

the interindividual UFH and UFA using chemical specific data and PBPK models 

(Lipscomb, 2006; WHO, 2006). This guidance indicates that each uncertainty factor can be 

divided into two sub-factors, one for kinetic and a second for the dynamic components of 

the uncertainty factor. Unlike PBPK models, the source-to-outcome model predicts both 

measures of internal dose (CPF and CPF-oxon in blood) and inhibition of RBC and brain 

AChE. Thus the source-to-outcome model addresses both the kinetic and dynamic portions 

of the uncertainty factors. As a result, values are proposed for the total (kinetic plus 

dynamic portions) of UFH and UFA. 

In this section we explore how the source-to-outcome model could be used to 

determine the compound and age-specific UFH and UFA. We make a number of assumptions 

that have regulatory decision-making implications, although such considerations are outside 

the remit of this paper and its focus. These assumptions are made for the purpose of this 

illustration only. For example, we use the key event of RBC AChE inhibition as the basis 

for the point of departure (POD). The aPAD for CPF is currently set using three uncertainty 

factors, the 10 for the UFA, 10 for the UFH, and for infants, children, and women of 

childbearing age a value of 10 for the FQPA factor (Figure 63). 

 

15 Also referred to as the intraspecies uncertainty factor. 
147 

Animal 

POD

No adverse 

effects in 

typical 

human

No adverse effect 

in sensitive 

humans (aRfD)

aPAD for children and 

women of child bearing years 

UFA

UFH

FQPA factor

Figure 63. The relationship between the POD, aRfD, and aPAD 

The equation used to set the values for the aRfD and aPAD are: 

UFA UFH

PoD aRfD

× =

FQPA factor

RfD aPAD _ =

Historically, the interindividual uncertainty factor also considered sensitive 

subpopulations including subpopulations based on age. The source-to-outcome model 

allows the development of age-specific uncertainty factors that extrapolate from agespecific animal data to humans of a corresponding age. In this project we develop agespecific values for two age groups; adults 30 years old and infants six months of age. 

Because the model allows calculation of uncertainty factors at specific ages, there is no 

need to consider extrapolation from adults to children as part of the determination of the 

interindividual uncertainty factor. While not explored here, this approach envisions the 

future development of values of UFA and UFH for other ages (e.g., the elderly). 
148 

Example development of interspecies (UFA) uncertainty factor for adults

and infants

For the purpose of illustrating how the results from a source-to-outcome model can 

be used to derive interspecies factors, this project investigates the values for UFA based on 

the premise that a that the POD is based on RBC AChE inhibition data from the recent 

study by Marty and Andrus (2010). Attachment G provides a copy of the study summary 

for this effort. 

The recent study by Marty and Andrus (2010) measured blood and brain AChE 

inhibition in rats. The study included measurements with adults and pups (PND 11). In this 

analysis we focus on the results of the one day study (acute exposure). In this study adult 

rats were administered a single dose of the compound using corn oil as vehicle. As 

discussed above, the effects from dietary exposures can be treated as individual acute 

exposures if the focus of the assessment is to characterize risks from the highest exposed 

individuals. This suggests that the use of single dose studies is appropriate for evaluating 

cholinesterase inhibition associated with CPF. 

In the Marty and Andrus study, initial scoping studies identified the time to peak 

inhibition for the animals. The animals were sacrificed at the time of peak inhibition and 

RBC and brain AChE inhibition levels were determined. BMD and BMDL doses for RBC 

and brain AChE inhibition were developed using these data (Reiss et al., 2010). A copy of 

this report is provided in Attachment H. 

The acute RBC AChE inhibition BMDL10 is assumed to be the basis for the POD for 

both adults and pups. Separate analyses were performed on male and female pups; the 

lower of the two gender specific values of BMDL10 (males) reported by (Reiss et al., 2010) 

is used as the basis of this analysis. The final values of the POD for adults and pups are 

0.52 mg/kg and 0.36 mg/kg respectively. 

The adult rat and pup data (large red squares) are overlaid on the dose response 

“clouds” of adults and infants (originally presented in Figures 41 and 42 in Section 4) to 

show the relationship between the animal data and the predicted responses in humans 

(Figure 64). The animal data reflects the findings in dose groups where the animals were 
149 

statistically different from controls. At lower doses, RBC AChE inhibition was not 

statistically significant. The simulated dose response in adult and infant humans and the 

observed responses in the test animals are similar and fall within the range of variation in 

human response. The typical infant appears to be more sensitive than the rat pup. The 

typical human adult is also more sensitive than the adult rat. 
150 

Infants 

Adults 

Figure 64. Comparison of test animal data (Marty and Andrus, 2010) and simulations of 

variation in human response. The blue cloud is repeated from earlier figures and presents the 

outline of CPF response in populations of infants (6 months) and adults (30 years) exposed to 

a range of doses. Test animal data in adult rats and PND 11 rat pups indicated by large red 

squares. The data suggest that animals are less sensitive than most, but not all humans. 
151 

In this analysis, the value of the interspecies uncertainty factor UFA is determined by 

matching the simulation of humans to the specific dose regime, metric of response, and 

subsequent data analysis used to derive the POD values. This approach allows a direct 

comparison of the dose associated with the POD as measured in the test animal to the dose 

causing a corresponding effect in humans. The data on PND 11 pups are used to estimate a 

value for UFH for infants and the data on adult rats are used to estimate UFH values for 

adults. 

In this illustration the source-to-outcome models are used to match the animal data 

by making the following decisions: 

1. Since the animal study is a single bolus dose in adults and pups, the source-to-outcome 

model assumes a single dose. 

2. The study design attempted to identify the peak levels of inhibition in the animals; 

therefore, the simulation models are used to determine peak levels of inhibition in each 

simulated individual. 

3. The study design included eight test animals per dose group. The mean AChE activity 

levels in the group were compared to AChE activities in controls. Therefore, the 

simulation model is used to create sets of eight humans and the mean inhibition for the 

group is determined. 

4. The values of BMD10 and BMDL10 for RBC AChE inhibition were derived using the 

general dose response model developed by EPA for the organophosphate cumulative 

risk assessment (Reiss et al. 2010). Therefore, the source-to-outcome model is used to 

determine doses that correspond to the BMD and BMDL values. Specifically, 

a. 4,000 groups of 8 individuals are created and their response of each individual to a 

specific dose is modeled using the Variation PBPK/PD model. The mean inhibition 

levels for each group of humans is then determined. The results are a distribution of 

group mean responses. 

b. This process is repeated for multiple doses (Di). 
152 

c. The median value of the distribution of the group means, (Mi) for dose Di is 

assumed to be equivalent to the BMDMi. 

d. Values of BMD10 are determined by interpolation from values of Mi greater and 

less than 10%. 

e. A similar approach is used for deriving the equivalent to BMDL10. In the case of 

BMDL the upper 95 percentile of the distribution of group means is used instead of 

the median estimate. 

The results of this exercise are dose estimates that directly correspond to the BMD 

and BMDL. Assuming that the value of BMDL10 is used as the hypothetical POD, the 

value of UFA is given by: 

H

A PoD

PoD UF =

where, POD is the BMDL10 in either adult rats or pups and POD10 is the equivalent

BMDL10 in simulated output of the source-to-outcome models for adults and infants. 

The simulations for infants are overlaid on the one day dose response cloud for RBC 

inhibition (Figure 65). The Variation model was run for groups of 5,000 infants at doses of 

0.1, 0.2, 0.5, and 1.0 mg/kg. The ranges of RBC inhibition at each dose are presented in the 

figure as a series of vertical lines, one at each of the four doses. A series of 5,000 groups of 

8 individuals were created by sampling from the 5,000 individuals (with replacement). The 

means for the 5,000 groups are plotted for each dose. These appear as sets of black circles 

at the four doses. Note that the ranges of responses in the distribution of the 8-person group 

means are smaller than the range of responses of the individuals. The reason for this is that 

the variation in mean responses in groups of 8 is less than for single individuals (regression 

to the mean). 
153 

Figure 65.The range of % RBC inhibition in different simulated populations of infants 

exposed to a single daily dose of CPF. The blue cloud is repeated from earlier figures and 

presents the outline of CPF dose response in populations of infants exposed to a range of 

doses. The ranges of predicted responses for 5,000 simulated infants at four dose rates (0.1, 

0.2, 0.5, and 1.0 mg/kg) are given by the vertical light blue lines. The black circles show the 

range of mean responses in groups of eight infants (corresponding to the groups of eight test 

animals used in the Marty and Andrus, 2010). 

The values of the human equivalent of BMDL10 are determined by interpolation. In 

Figure 66 the same data on the distributions of the individual and mean values for groups 

of 8 are plotted. This plot is not in log-log space as is the prior plot. The 95th percentiles of 

the distributions of the 8-person group mean responses at each dose are identified and a 

solid line is created that connects the values. The human equivalents of the BMDL10 is 

determined to be 0.33 mg/kg, based on interpolation of the 95th percentile values of the 
154 

distribution of the observed at 0.2 and 0.5 mg/kg to determine the dose that causes a 10% 

inhibition in 95 % of the groups. The value is marked by a thick red circle on the figure. 

Figure 66. Derivation of human equivalent dose to an animal BMDL10. The mean responses 

in groups of eight infants (corresponding to the groups of eight test animals used in the Marty 

and Andrus, 2010) are designated by the black circles with the corresponding ranges of 

predicted responses for the simulated infants given by the vertical lines (blue). The 95th

percentile values of group means (shown by the red Xs and connected by the solid black line) 

were used to calculate the BMDL10 in the test animals (0.33 mg/kg, marked with the red 

circle). 

Using the reported BMDL in rats and the value of the human equivalent, the value of 

UFA for infants is: 
155 

UFA (Infants) = 0.36 (mg/kg/d) / 0.33 (mg/kg/d) 

UFA (Infants) = 1.1 

While not shown, the value of UFA for adults derived using this method is 1.7. 

Example development of interindividual (UFH) uncertainty factor for

adults and infants

The interindividual uncertainty factor is intended to address differences in the 

relative sensitivity of individuals to CPF for the different age groups. This factor is defined 

using the source-to-outcome model’s ability to assess variation in sensitivity in different 

age groups at different doses. (Lipscomb, 2006) and (WHO, 2006) recommend that the 

value of the UFA should be determined based on the distribution of the ratios of the 

administered doses that produce the same internal dose at the target organ across 

individuals. Separate ratios should be determined for a variety of percentiles of the 

populations. This illustration uses the 95th and 99th percentiles. 

The determination of the UFH also requires an assumption of a level of RBC 

inhibition is acceptable in the general population. In this assessment a value of 10% is 

used. Other values could be used. 

The values for UFH for the two age groups are calculated based on two endpoints. 

The first is the human equivalent of the BMDL10. This endpoint was established in the 

definition of UFA in the prior subsection. The second measure is the dose that does not 

result in a peak RBC AChE inhibition of more than 10%. In this assessment such a dose is 

considered to be a POD for sensitive humans (PODsensitiveH). The value of UFA is then 

defined asthe ratio of the human equivalent of the BMDL10 to a dose where there is a 95% 

(or 99%) confidence that an individual has less than a 10% peak RBC AChE inhibition 

(POD for the sensitive human or PODsensitiveH). 

SenstiveH

H

H PoD

PoD UF =
156 

The values of PODsensitiveH were determined by calculating the level of inhibition for 

the 95th and 99th percentiles of the peak RBC AChE inhibition levels for the 5,000 

individuals. The method used to determine the values of PODsensitiveH was also used to 

calculated the levels of inhibition for the 95 and 99th percentiles of the population at 

different doses and then to interpolate to find the doses where 95 and 99% of the 

population would have less than a 10% peak inhibition.

Figure 67 shows how this interpolation was performed. The data from Figure 62 is 

again presented; however, the 95th and 99th percentiles of the values of the response for the 

individuals are determined and connected by dotted lines. The doses that occur where these 

lines cross the 10% inhibition level are the two values of PODsensitiveH. 
157 

PODsenstiveH for 95 and

99 percent of

population

Figure 67. Determination of PODsensitiveH by interpolation of values of the 95 and 99th 

percentiles of the responses in simulated infants at various doses. The ranges of individuals’ 

responses at four dose rates (0.1, 0.2, 0.5, and 1.0 mg/kg) are given by the vertical light blue 

lines. The black circles show the range of mean responses in groups of eight infants (see the 

derivation of UFA above). The thick red circles indicate the single daily doses that are 

predicted to have peak RBC inhibitions of l0% or less for 95 and 99% of the population (0.24 

and 0.22 mg/kg respectively) . 

The values of PODsensitiveH for the 95th and 99th percentiles for both infants and 

adults (calculations not shown) are given in Table 9. The values of the PODH were 

developed using the approach described in the previous portion of this section and are 0.30 

mg/kg/d for adults and 0.33 mg/kg/d for the infants. Using the above equation and these 

doses, the values of UFH were determined for the 95th and 99th percentiles of the population 

and for each age group and are given in Table 9. The values of UFH for the 95th and 99th
158 

percentiles are very similar. This occurs because the variation in response at doses of 0.1 to 

0.3 mg/kg in both adults and infants are small (See Figures 42 and 43 in Section 4). 

Table 9. Values of POD for sensitive humans and UFH values for adults and infants 

Adults (30 years) 

PODSenstiveH (95%) PODSenstiveH (99%) 

0.26 mg/kg 0.23 mg/kg 

Infants (6 Months) 

PODSenstiveH (95%) PODSenstiveH (99%) 

0.24 mg/kg 0.22 mg/kg 

 

Adults (30 years) 

UFH (95%) UFH (99%) 

1.3 1.4 

Infants (6 Months) 

UFH (95%) UFH (99%) 

1.3 1.5 

Section summary

In summary, the source-to-outcome models indicate that the two age groups of 

humans (adults and infants) have RBC AChE inhibition responses that are similar to the 

test animals. In fact, the animal data fall within the range of human variation in response. 

In addition, the level of variation in response across the populations of infants and adults 

are modest at doses that cause a 10% RBC inhibition level is small. As a result the 

predicted values for both UFA and UFH for both adults and infants are less than 2.
159 

11 Summary

In this paper we have sought to describe the methodologies and data used to create a 

model that captures the events in the pathway that begins with data on residue levels and 

ends with predictions of cholinesterase inhibition across the U.S. population, a source-tooutcome model. As indicated in the introduction this is not a complete risk assessment for 

CPF and as such it does not purport to address all of the toxicological issues for the 

compound. The focus of the case study is on the use of linked exposure and PBPK/PD 

models to address the potential for the occurrence of apical effects that are produced by a 

specific mechanism, by a specific route of exposure, from a specific source. 

The source-to-outcome model described in this case study consists of two 

commercially available software programs. The first component is a dietary software 

program. The dietary exposure software used here is CARES but any of the existing 

programs that can produce estimates of longitudinal dietary exposures could be used. The 

second program is the Variation model a program created to run on acslXtreme a 

publically available software platform for PBPK modeling. 

The process for creation of the source-to-outcome model has been outlined in detail 

in the report and this process can be applied to other chemicals with other endpoints. This 

process begins by understanding the mode of action by which a chemical exerts its 

toxicity. Key events in the toxicity pathway are identified that are as close to the initial 

interaction between the initial biological targets/molecules in the body and the chemical or 

its active metabolite. Once the key events are determined, a PBPK/PD model is developed 

and used to predict the dose response for each key event (see Section 3). 

With a PBPK/PD model of the dose to key event process in place, a sensitivity 

analysis is performed that identifies the factors that drive variation in response to a given 

dose of a chemical (see Section 4). These driving factors require data on interindividual 

variation across the population of interest. These data and data on correlation between 
160 

values in individuals are identified from the published literature or are created using in 

vitro measurements. 

With these data, simulation models of the variation in response in the key event can 

be created (see Section 4). These models define dose and the individual in very specific 

ways. The exposure portions of the source-to-outcome models need to be matched to the 

specific needs of the PBPK/PD models of variation. In this project an existing dietary 

model was used to characterize exposure (see Section 5). The linkage between the two 

models was achieved by a careful consideration of physiology of the exposure event 

(Section 6) and data in human studies. 

In the development of this project, one concept that has been repeatedly used is the 

need to tailor the level of complexity to the importance of a process. For example, earlier 

efforts to link diet and PBPK models have expended considerable resources on modeling 

the timing of exposures within a day (Xue, 2010). Our analysis of CPF indicated that the 

assumption that an entire dose on a single day occurred at one eating event was a 

reasonable first assumption. This allowed the project to avoid the complexity of modeling 

intakes over multiple eating events. In a similar fashion, we found that the level of activity 

had little impact on variation in metabolism or increased sensitivity to CPF’s effects on 

AChE. Thus the modeling efforts did not include any attempt to model actual patterns of 

human activity using data such as activity diaries. 

The evaluation of the uncertainty in a source-to-outcome model’s predictions and the 

determination of the confidence that can be placed in the predictions is a challenge. The 

levels of the predictions of the model vary with the age, endpoint, and questions that the 

model attempts to answer. In Sections 3, 4, 5 and 8 we have investigated the ability of the 

model to address predictions of the change in AChE inhibition and interim findings such as 

distribution of daily dose, blood concentrations of parent and metabolites, and predictions 

of variation in physiology. These predictions can be evaluated using other modeled and 

measured data. In general, we have demonstrated that the various predictions of the sourceto-outcome model are consistent with measured data in the literature. However, we also 

found that the model-to-measurement comparisons were limited by the relevance of the 
161 

measurement data. Data often came from small surveys from specific locations and limited 

age ranges (women of child bearing age), had significant confounding factors (other 

sources of TCPy in diet), or reflected exposures that differed from diet (e.g., worker 

studies). Because of this, we conclude that the final predictions of impacts of AChE in the 

general population are within an order of magnitude but additional representative data are 

required in order to determine if the models are in fact more precise. 

Once the source-to-outcome model is completed, it can be used in a variety of ways. 

We believe that the value of a source-to-outcome model is fully realized when the model is 

used as a tool for exploring the processes that determine the source-to-outcome pathway 

rather than just as a risk “calculator.” In this project the source-to-outcome model has been 

used in a number of ways and has demonstrated a number of novel and potentially valuable 

findings. 

• The variation portion of the model was used to investigate the role of different factors 

in affecting adult and child sensitivity to CPF (see Section 4). The models found that 

variation in metabolism but not variation in physiology or level of physical activity 

were important for infants. For adults, variation in physiology had some effect but was 

still minor compared to metabolism. 

• The source-to-outcome allowed the investigation of the impact of repeated doses of 

CPF on blood levels of CPF and CPF–oxon and the impact on cholinesterases (see 

Section 7). This work indicated that neither CPF nor CPF-oxon accumulated over time. 

The cumulative impact of RBC inhibition (inhibition seen at 1 day of dosing versus 30 

days) was significant at doses of 0.5 mg/kg with as much as a 20-fold increase in 

response. But at the dose levels that currently occur from exposure to dietary residues 

the impact was much more modest (i.e., a 3-fold). In contrast increases in brain AChE 

inhibition were at most 3-fold across all doses. 

• When day-to-day variation in dietary exposure was considered, the increase in impact 

from one to multiple days was found to be very small for dietary exposures (Section 7). 

The population reached quasi-steady state within 3 days. More importantly the impact 

of multi-day exposures was much smaller for individuals with high exposures. The 
162 

impact of dietary exposures on the RBC and brain AChE inhibition for these 

individuals was dominated (90% of the impact) by the dose received from a single day. 

• Interspecies variation in response was investigated by comparing the predicted 

responses humans to the observed responses in rats. The findings suggest that rats are 

only slightly less sensitive than humans. 

• Interindividual variation in response to CPF was explored using the Variation model. 

The impact on RBC AChE inhibition was found to be dose dependent. At dietary 

levels the difference between the responses of the typical and sensitive individual 

(resulting from differences in metabolism, physiology, and activity) was less than 2. 

The source-to-outcome model was used to evaluate the impact of the current dietary 

exposure on RBC and brain AChE inhibition. This was done in two ways. First the model 

investigated the impacts in three age groups after quasi-steady state conditions were 

obtained (see Section 9). The peak inhibitions did not exceed 0.01% inhibition for either 

RBC or brain inhibition. The second way the models were used was to determine agespecific values for the interspecies and interindividual uncertainty factors (Section 10). 

This assessment found that models prediction of variation in response overlapped with the 

responses seen in recent animal studies (Attachment G). In addition, human variation in 

response was found to be modest (less than a factor of 2 between the responses in a typical 

adult or infant and a sensitive adult or infant) for doses that cause 10% or less RBC AChE 

inhibition in humans. As a result, the values of UFA and UFH calculated were small and 

ranged between 1 and 2. 

The exercise in this paper has achieved the goals set out in the introduction. We have 

identified a key event, AChE inhibition, and constructed models of interindividual 

variation in both dose and response. The models have been evaluated and have been used 

to make quantitative predictions of the AChE inhibition in the exposed population using an 

example pesticide. In addition, the modeling has operated as a framework that has allowed 

diverse information from in vitro studies of metabolism, biomonitoring studies in humans, 

test animal data, and statistical tools to come together to provide quantitative guidance on 

the impacts posed by a chemical to the general U.S. population. 
163 

Acknowledgements

Early efforts on development of this project occurred as a result of an American Chemistry 

Council LRI project 'Demonstrating Scientific Advances in Human Health Risk 

Assessment Practice' (MTH-07-01), and thanks for the support of the ACC are warmly 

offered. 

The authors acknowledge contributions to the experimental, modeling, and reporting 

phases of this project. For experimental contributions, we thank Dr. Susan Marty. For 

modeling contributions, we thank Dr. Carl Schnelle. 
164 

Attachments

Attachment A: 

Report on in vitro human metabolism results, in draft manuscript format 

Attachment B: 

Philpott, P. (2009) "June 24-25, 2009 EPA Human Studies Review Board 

Meeting Report." Memo and final report (40 pages). 

Attachment C: 

Copy of Model code for: acslXtreme Lifestage model and associated m files 

Attachment D: 

Cleveland, C.B. (2010) "2010 Update of the Acute Dietary Risk Assessment for 

Chlorpyrifos." Dow AgroSciences. Final report 101760 (140 pages). 

Attachment E: 

Copies of inputs files used in dietary models 

Attachment F: 

Copies of input files for PBPK/PD model runs 

Attachment G: 

Marty, M.S. and Andrus, A.K. (2010) "Comparison of Cholinesterase (ChE) 

Inhibition in Young Adult and Preweanling CD Rats after Acute and Repeated 

Chlorpyrifos or Chlorpyrifos-oxon Exposures." Dow AgroSciences. Public 

release summary (7 pages). 

Attachment H: 

Reiss, R., et al. (2010) "Benchmark Dose Modeling for Cholinesterase Inhibition 

from Exposure to Chlorpyrifos and Chlorpyrifos-Oxon." Dow AgroSciences. 

Public release summary (4 pages). 
165 

References

Akaike, H., 1974. A New Look at the Statistical Model Identification. I.E.E.E. Transactions on 

Automatic Control. AC 19, 716-723. 

Akaike, H., 1976. Canonical Correlation Analysis of Time Series and the Use of an Information 

Criterion. in: Lainotis, R. K. M. a. D. G., (Ed.), System Identification: Advances and 

Case Studies. Academic Press, New York, pp. 52-107. 

Albers, J. W., et al., 2010. Paraoxonase status and plasma butyrylcholinesterase activity in 

chlorpyrifos manufacturing workers. Journal of Exposure Science and Environmental 

Epidemiology. 20, 79-100. 

Alcorn, J., et al., 2007. Evaluation of the assumptions of an ontogeny model of rat hepatic 

cytochrome P450 activity. Drug Metab Dispos. 35, 2225-31. 

Alcorn, J., McNamara, P. J., 2002. Ontogeny of hepatic and renal systemic clearance pathways 

in infants - Part I. Clinical Pharmacokinetics. 41, 959-998. 

Amitai, G., et al., 1998. Inhibition of acetylcholinesterase and butyrylcholinesterase by 

chlorpyrifos-oxon. Biochemical Pharmacology. 56, 293-299 %U 

http://www.ncbi.nlm.nih.gov/pubmed/9744565. 

Andersen, M. E., 1995. Development of physiologically based pharmacokinetic and 

physiologically based pharmacodynamic models for applications in toxicology and risk 

assessment. Toxicology Letters. 79, 35-44. 

Andersen, M. E., 2003. Toxicokinetic modeling and its applications in chemical risk assessment. 

Toxicology Letters. 138, 9-27. 

Andersen, M. E., et al., 1994. Gas uptake studies of deuterium isotope effects on 

dichloromethane metabolism in female B6C3F1 mice in vivo. Toxicology and Applied 

Pharmacology. 128, 158-165. 

Bakke, J. E., Price, C. E., 1976. Metabolism of O,O-dimethyl-O-(3,5,6-trichloro-2-pyridyl) 

phosphorothioate in sheep and rats and of 3,5,6-trichloro-2-pyridinol in sheep. Journal of 

Environmental Science and Health. Part. B, Pesticides, Food Contaminants, and 

Agricultural Wastes. 11, 9-22 %U http://www.ncbi.nlm.nih.gov/pubmed/58885. 

Ballantyne, B., Marrs, T. C., 1992. Clinical and experimental toxicology of organophosphates 

and carbamates. Butterworth Heinemann %@ 9780750602716. 

Barr, D., Ananth CV, Yan X, Lashley S, Smulian JC, Ledoux TA, Hore P, Robson MG., 2010. 

Pesticide concentrations in maternal and umbilical cord sera and their relation to birth 

outcomes in a population of pregnant women and newborns in New Jersey. Science of 

The Total Environment. 408, 790-795. 

Barr, D. B., et al., 2005. Concentrations of selective metabolites of organophosphorus pesticides 

in the United States population. Environmental Research. 99, 314-326. 

Barr, D. B., Angerer, J., 2006. Potential uses of biomonitoring data: A case study using the 

organophosphorus pesticides chlorpyrifos and malathion. Environmental Health 

Perspectives. 114, 1763-1769. 

Barter, Z. E., et al., 2008. Covariation of Human Microsomal Protein Per Gram of Liver with 

Age: Absence of Influence of Operator and Sample Storage May Justify Interlaboratory 

Data Pooling. Drug Metabolism and Disposition. 36, 2405-2409. 
166 

Bjorkman, S., 2005. Prediction of drug disposition in infants and children by means of 

physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam 

as model drugs. Br J Clin Pharmacol. 59, 691-704. 

Blesch, K. S., et al., 2003. Clinical pharmacokinetic/pharmacodynamic and physiologically 

based pharmacokinetic modeling in new drug development: the capecitabine experience. 

Invest New Drugs. 21, 195-223. 

Bois, F., Jamei M, Clewell H, 2010. PBPK modelling of inter-individual variability in the 

pharmacokinetics of environmental chemicals. Toxicology. 

doi:10.1016/j.tox.2010.06.007. 

Bois, F. Y., 1999. Analysis of PBPK models for risk characterization. Ann.N.Y.Acad.Sci. 895,

317-337. 

Brehrman RE, K. R., Jenson HB,, 2000. Nelson Textbook of Pediatrics 16th Edition. W.B. 

Saunders, Philadelphia. 

Brown DM, D. M., Lindsted SL, Rhomber LR, Belites RP, 1997. Physiological Parameter 

Values for Physiologically Based Pharmacokinetic Models,. Toxicology and Industrial 

Health, . 13, 77. 

Busby-Hjerpe, A. L., et al., 2010. Comparative pharmacokinetics of chlorpyrifos versus its major 

metabolites following oral administration in the rat. Toxicology. 268, 55-63. 

Carr, R. L., Nail, C. A., 2008. Effect of different administration paradigms on cholinesterase 

inhibition following repeated chlorpyrifos exposure in late preweanling rats. 

Toxicological Sciences. 106, 186-192. 

Chambers, J. E., Chambers, H. W., 1989. Oxidative desulfuration of chlorpyrifos, chlorpyrifosmethyl, and leptophos by rat brain and liver. Journal of Biochemical Toxicology. 4, 201-

203 %U http://www.ncbi.nlm.nih.gov/pubmed/2481746. 

Chambers, J. E., Levi, P. E., 1992. Organophosphates: chemistry, fate, and effects. Academic 

Press %@ 9780121673451. 

Chambers, J. E., et al., 1994. Role of detoxication pathways in acute toxicity levels of 

phosphorothionate insecticides in the rat. Life Sciences. 54, 1357-1364 %U 

http://www.ncbi.nlm.nih.gov/pubmed/7514706. 

Chiu, W. A., et al., 2007. Evaluation of physiologically based pharmacokinetic models for use in 

risk assessment. Journal of Applied Toxicology. 27, 218-237. 

Choi, K., et al., 2006. Metabolism of chlorpyrifos and chlorpyrifos oxon by human hepatocytes. 

Journal of Biochemical and Molecular Toxicology. 20, 279-291 %U 

http://www.ncbi.nlm.nih.gov/pubmed/17163483. 

Clewell, H. J., et al., 2005. Evaluation of physiologically based pharmacokinetic models in risk 

assessment: An example with perchloroethylene. Critical Reviews in Toxicology. 35,

413-433. 

Cole, T. B., et al., 2005. Toxicity of chlorpyrifos and chlorpyrifos oxon in a transgenic mouse 

model of the human paraoxonase (PON1) Q192R polymorphism. 

Pharmacogenet.Genomics. 15, 589-598. 

Conolly, R. B., et al., 2004. Human respiratory tract cancer risks of inhaled formaldehyde: Doseresponse predictions derived from biologically-motivated computational modeling of a 

combined rodent and human dataset. Toxicological Sciences. 82, 279-296. 

Corley, R. A., et al., 2005. DEVELOPMENT OF A PHYSIOLOGICALLY BASED 

PHARMACOKINETIC MODEL FOR ETHYLENE GLYCOL AND ITS 
167 

METABOLITE, GLYCOLIC ACID, IN RATS AND HUMANS. Toxicological 

Sciences. 

Costa, L. G., et al., 2005. Modulation of paraoxonase (PON1) activity. Biochem Pharmacol. 69,

541-50. 

Croom, E. L., et al., 2010. Human variation in CYP-specific chlorpyrifos metabolism. 

Toxicology. 276, 184-91. 

Crop Life America, 2002. Cumulative and Aggregate Risk Evaluation System, Technical 

Manual. 

Curwin, B. D., et al., 2007. Urinary pesticide concentrations among children, mothers and fathers 

living in farm and non-farm households in iowa. Ann Occup Hyg. 51, 53-65. 

Davis, K. A., et al., 2009. Racial Differences in Paraoxonase-1 (PON1): A Factor in the Health 

of Southerners? Environmental Health Perspectives. 117, 1226-1231. 

DeWoskin, R. S., Thompson, C. M., 2008. Renal clearance parameters for PBPK model analysis 

of early lifestage differences in the disposition of environmental toxicants. Regulatory 

Toxicology and Pharmacology. 51, 66-86. 

Dixit, R., et al., 2003. Toxicokinetics and physiologically based toxicokinetics in toxicology and 

risk assessment. Journal of Toxicology and Environmental Health-Part B-Critical 

Reviews. 6, 1-40. 

Durango Software, DEEM (TM) dietary exposure analysis system Vol. 2010, 2010, pp. Web 

page of Durango software. 

Eaton, D. L., et al., 2008. Review of the toxicology of chlorpyrifos with an emphasis on human 

exposure and neurodevelopment. Crit Rev Toxicol. 38 Suppl 2, 1-125. 

el Masri, H. A., et al., 1995. Physiologically based pharmacokinetic/pharmacodynamic modeling 

of chemical mixtures and possible applications in risk assessment. Toxicology. 105, 275-

282. 

Foxenberg, R. J., et al., 2007. Human hepatic cytochrome P450-specific metabolism of parathion 

and chlorpyrifos. Drug Metabolism and Disposition. 35, 189-193. 

Furlong, C. E., et al., 2005. Role of paraoxonase (PON1) status in pesticide sensitivity: genetic 

and temporal determinants. Neurotoxicology. 26, 651-659. 

Furlong, C. E., et al., 2000. The PON1 gene and detoxication. Neurotoxicology. 21, 581-587. 

Garabrant, D. H., et al., 2009. Cholinesterase inhibition in chlorpyrifos workers: Characterization 

of biomarkers of exposure and response in relation to urinary TCPy. J Expo Sci Environ 

Epidemiol. 19, 634-42. 

Gearhart, J. M., et al., 1993. Variability of physiologically based pharmacokinetic (PBPK) model 

parameters and their effects on PBPK model predictions in a risk assessment for 

perchloroethylene (PCE). Toxicology Letters. 68, 131-144. 

Ginsberg, G., Neafsey P, Hattis D, Guyton KZ, Johns DO, Sonawane B., 2010. Genetic 

polymorphism in paraoxonase 1 (PON1): Population distribution of PON1 activity. 

Journal Toxicol Environ Health B Crit Rev. . 12, 34. 

Griffin, P., et al., 1999. Oral and dermal absorption of chlorpyrifos: a human volunteer study. 

Occupational and Environmental Medicine. 56, 10-13 %U 

http://www.ncbi.nlm.nih.gov/pubmed/10341740. 

Grigoryan, H., et al., 2009. Mass spectral characterization of organophosphate-labeled lysine in 

peptides. Analytical Biochemistry. 394, 92-100 %U 

http://www.ncbi.nlm.nih.gov/pubmed/19596251. 
168 

Grigoryan, H., et al., 2008. Mass spectrometry identifies covalent binding of soman, sarin, 

chlorpyrifos oxon, diisopropyl fluorophosphate, and FP-biotin to tyrosines on tubulin: a 

potential mechanism of long term toxicity by organophosphorus agents. ChemicoBiological Interactions. 175, 180-186 %U 

http://www.ncbi.nlm.nih.gov/pubmed/18502412. 

Hellmig S, V. S. F., Gadow C, Katsoulis S, Hedderich J, Fölsch UR, Stüber E, 2006. Gastric 

emptying time of fluids and solids in healthy subjects determined by 13C breath tests: 

influence of age, sex and body mass index. Journal of Gastroenterology and Hepatology 

21, 6. 

ICRP, 2003. Basic Anatomical and Physiological Data for Use in Radiological Protection: 

Reference Values, 89. 

ILSI, Cumulative and Aggregate Risk Evaluation System (CARES)TM. Vol. 2009, 2009. 

Janssen, I., et al., 2000. Skeletal muscle mass and distribution in 468 men and women aged 18-

88 yr. J Appl Physiol. 89, 81-8. 

Johnson, T. N., et al., 2006. Prediction of the clearance of eleven drugs and associated variability 

in neonates, infants and children. Clinical Pharmacokinetics. 45, 931-956. 

Kaushik, R., et al., 2007. Concentration-dependent interactions of the organophosphates 

chlorpyrifos oxon and methyl paraoxon with human recombinant acetylcholinesterase. 

Toxicology and Applied Pharmacology. 221, 243-250. 

Kisicki, J. C., et al., A Rising Dose toxicology Study to Determine the No-Observable-EffectLevels (NOEL) for Erythrocyte Acetylcholinesterase (AChE Inhibitino and Cholinergic 

Signs and Symptoms of Chlorpyrifos at Three Dose levels. The Toxicology Research 

Laboratory: Health and Environmental Research Laboratories, The DOW Chemical 

Company, 1999. 

Koukouritaki, S. B., et al., 2004. Developmental expression of human hepatic CYP2C9 and 

CYP2C19. Journal of Pharmacology and Experimental Therapeutics. 308, 965-974. 

Kousba, A., et al., 2004. In vitro interactions between organophosporous pesticides and 

consequences of pre-treatment with nicotine and alcohol on in vitro pesticide metabolism 

in rats. Drug Metabolism Reviews. 36, 268-268. 

Kousba, A. A., et al., 2007. Age-related brain cholinesterase inhibition kinetics following in vitro 

incubation with chlorpyrifos-oxon and diazinon-oxon. Toxicological Sciences. 95, 147-

155. 

Krishnan, K., Johanson, G., 2005. Physiologically-based pharmacokinetic and toxicokinetic 

models in cancer risk assessment. J.Environ.Sci.Health 

C.Environ.Carcinog.Ecotoxicol.Rev. 23, 31-53. 

Lafortuna, C. L., et al., 2005. Gender variations of body composition, muscle strength and power 

output in morbid obesity. Int J Obes (Lond). 29, 833-41. 

Lassiter, T. L., et al., 1999. Gestational exposure to chlorpyrifos: dose response profiles for 

cholinesterase and carboxylesterase activity. Toxicological Sciences. 52, 92-100. 

Lee, S., et al., 2009. Effects of nicotine exposure on in vitro metabolism of chlorpyrifos in male 

Sprague-Dawley rats. Journal of toxicology and environmental health. Part A. 72, 74-82 

%U http://www.ncbi.nlm.nih.gov/pubmed/19034796. 

Licata, A. C., et al., 2001. A physiologically based pharmacokinetic model for methyl tert-butyl 

ether in humans: implementing sensitivity and variability analyses. Toxicological 

Sciences. 62, 191-204. 
169 

LifeLine, Designing Exposure Models that Support PBPK/PBPD Models of Cumulative Risk. 

2004. 

LifeLine, Technical Manual LifeLineTM Version 4.3 Software, The LifeLine Group Inc., 2006. 

Lipscomb, J. C., Use of Physiologically Based Pharmacokinetic Models to Quantify the Impact 

of Human Age and Interindividual Differences in Physiology and Biochemistry Pertinent 

to Risk. In: Office of Research and Development, N CEA, (Ed.), 2006. 

Lowe, E. R., et al., 2009. The Effect of Plasma Lipids on the Pharmacokinetics of Chlorpyrifos 

and the Impact on Interpretation of Blood Biomonitoring Data. Toxicological Sciences. 

108, 258-272. 

Lu, C., et al., 2008. Dietary intake and its contribution to longitudinal organophosphorus 

pesticide exposure in urban/suburban children. Environ Health Perspect. 116, 537-42. 

Lu, C., et al., 2010. The implications of using a physiologically based pharmacokinetic (PBPK) 

model for pesticide risk assessment. Environ Health Perspect. 118, 125-30. 

Luecke, R. H., et al., 2007. Postnatal growth considerations for PBPK modeling. Journal of 

Toxicology and Environmental Health-Part a-Current Issues. 70, 1027-1037. 

Marty, M. S., et al., 2007. The effect of route, vehicle, and divided doses on the 

pharmacokinetics of chlorpyrifos and its metabolite trichloropyridinol in neonatal 

Sprague-Dawley rats. Toxicological Sciences. 100, 360-373. 

Mattsson, J. L., et al., 2000. Lack of differential sensitivity to cholinesterase inhibition in fetuses 

and neonates compared to dams treated perinatally with chlorpyrifos. Toxicological 

Sciences. 53, 438-446. 

McDowell, M., Fryar, CD, Hirsch, R, Ogden CL, Anthropometric Reference Data for Children 

and Adults: U.S. Population, 1999–2002. In: CDC, N. D., (Ed.), 2005, pp. 32. 

Medinsky, M. A., 1995. The application of physiologically based 

pharmacokinetic/pharmacodynamic (PBPK/PD) modeling to understanding the 

mechanism of action of hazardous substances. Toxicology Letters. 79, 185-191. 

Mendrala, A. L., Brzak, K. A., Chlorpyrifos: Part A-- Concentration-Time Course of 

Chlorpyrifos and Chlorpyrifos-Oxon in Blood (in Rats): Lab Project Number: 971187A. 

Unpublished study prepared by The Dow Chemical Company. 63 p. (MRID 44648102). 

The Dow Chemical Company, 1998. 

Moreira, E. G., et al., 2010. Toxicogenomic profiling in maternal and fetal rodent brains 

following gestational exposure to chlorpyrifos. Toxicol Appl Pharmacol. 245, 310-25. 

Mutch, E., Williams, F. M., 2006. Diazinon, chlorpyrifos and parathion are metabolised by 

multiple cytochromes P450 in human liver. Toxicology. 224, 22-32. 

National Research Council, 2007. Toxicity Testing in the 21st Centryu a Vision and a Strategy. 

National Academies Press, Washington, DC. 

National Research Council, 2008. Science and Decisions: Advancing Risk Assessment. 

Committee on Improving Risk Analysis Approaches Used by the U.S. EPA. National 

Research Council, Washington, DC. 

Nigg, H. N., Knaak, J. B., 2000. Blood cholinesterases as human biomarkers of 

organophosphorus pesticide exposure. Reviews of Environmental Contamination and 

Toxicology. 163, 29-111. 

Nolan, R. J., et al., Chlorpyrifos: Tissue Distribution and Metabolism of Orally Administered 

Carbon 14-Labeled Chlorpyrifos in Fischer 344 Rats: Laboratory Project Study ID: K044793-(76). Unpublished study prepared by The Dow Chemical Company. 41 p. [MRID 

40458901]. The Dow Chemical Company, 1987. 
170 

Nolan, R. J., et al., 1984. Chlorpyrifos: pharmacokinetics in human volunteers. Toxicology and 

Applied Pharmacology. 73, 8-15. 

Nomura, D. K., Casida, J. E., 2010. Activity-Based Protein Profiling of Organophosphorus and 

Thiocarbamate Pesticides Reveals Multiple Serine Hydrolase Targets in Mouse Brain†. 

Journal of Agricultural and Food Chemistry. null-null. 

Noort, D., et al., 2009. Covalent binding of organophosphorothioates to albumin: a new 

perspective for OP-pesticide biomonitoring? Archives of Toxicology. 83, 1031-1036 %U 

http://www.ncbi.nlm.nih.gov/pubmed/19575182. 

Peters, S. A., Hultin, L., 2008. Early identification of drug-induced impairment of gastric 

emptying through physiologically based pharmacokinetic (PBPK) simulation of plasma 

concentration-time profiles in rat. Journal of Pharmacokinetics and Pharmacodynamics. 

35, 1-30. 

Poet, K. S., et al., 2002. Physiologically based pharmacokinetic/pharmacodynamic modeling of 

organophosphate pesticides for biologically based risk assessments. Epidemiology. 13,

S240-S240. 

Poet, T. S., et al., 2003. In vitro rat hepatic and intestinal metabolism of the organophosphate 

pesticides chlorpyrifos and diazinon. Toxicological Sciences: An Official Journal of the 

Society of Toxicology. 72, 193-200 %U http://www.ncbi.nlm.nih.gov/pubmed/12655035. 

Ray, A., et al., 2010. Dose-related gene expression changes in forebrain following acute, lowlevel chlorpyrifos exposure in neonatal rats. Toxicology and Applied Pharmacology. 248,

144-155 %U http://www.ncbi.nlm.nih.gov/pubmed/20691718. 

Reddy, M. B., et al., 2005. Physiologically Based Pharmacokinetic Modeling: Science and 

Applicaitons. John Wiley and Sons, Hoboken, NJ. 

Reitz, R. H., et al., 1988. Physiologically based pharmacokinetic modeling with 

methylchloroform: Implications for interspecies, high dose/low dose, and dose route 

extrapolations. Toxicology and Applied Pharmacology. 95, 185-199. 

Richter, R. J., Furlong, C. E., 1999. Determination of paraoxonase (PON1) status requires more 

than genotyping. Pharmacogenetics. 9, 745-53. 

Sams, C., et al., 2004. Biotransformation of chlorpyrifos and diazinon by human liver 

microsomes and recombinant human cytochrome P450s (CYP). Xenobiotica. 34, 861-73. 

Sangster, J., LOGKOW Databank. A databank of evaluated octanol-water partition coefficients 

(Log P) on microcomputer diskette. Montreal, Quebec, Canada: Sangster Research 

Laboratories (as referenced in the PhysProp Database of US EPA KowWin program 

(v1.67a)). 1994. 

Schwartz, G., 1978. Estimating the Dimension of a Model. Annals of Statistics. 6, 461-464. 

Slikker, W., et al., 2005. Improving predictive modeling in pediatric drug development: 

Pharmacokinetics, pharmacodynamics, and mechanistic modeling. Neuroprotective 

Agents. 1053, 505-518. 

Smith, G. N., et al., 1967. METABOLISM Investigations on Dursban Insecticide. Metabolism of 

[ 36Cl]0,O-Diethyl 0-3,5,6-Trichloro-2-pyridyl Phosphorothioate in Rats. Journal of 

Agricultural and Food Chemistry. 15, 132-138 %U 

http://dx.doi.org/10.1021/jf60149a017. 

Snyder, W., 1975. Report of the Task Group on Reference Man. Elsevier Science Limited, 

Oxford, UK. 

Sultatos, L. G., 1994. Mammalian toxicology of organophosphorus pesticides. Journal of 

Toxicology and Environmental Health. 43, 271-289. 
171 

Sunaga, M., et al., 1989. [Metabolism and urinary excretion of chlorpyrifos in rats]. Nippon 

Eiseigaku Zasshi. Japanese Journal of Hygiene. 43, 1124-1129 %U 

http://www.ncbi.nlm.nih.gov/pubmed/2473231. 

Tan, Y. M., et al., 2006. Reverse dosimetry: interpreting trihalomethanes biomonitoring data 

using physiologically based pharmacokinetic modeling. J.Expo.Sci.Environ.Epidemiol. 

Tang, J., et al., 2001. Metabolism of chlorpyrifos by human cytochrome P450 isoforms and 

human, mouse, and rat liver microsomes. Drug Metab Dispos. 29, 1201-1204. 

Tardif, R., et al., 2002. Impact of human variability on the biological monitoring of exposure to 

toluene: I. Physiologically based toxicokinetic modelling. Toxicology Letters. 134, 155-

163. 

Tateishi, T., et al., 1997. A comparison of hepatic cytochrome P450 protein expression between 

infancy and postinfancy. Life Sciences. 61, 2567-2574. 

Teeguarden, J. G., Barton, H. A., 2004. Computational modeling of serum-binding proteins and 

clearance in extrapolations across life stages and species for endocrine active compounds. 

Risk Analysis. 24, 751-770. 

Thompson, C. M., et al., 2010. Mass spectrometric analyses of organophosphate insecticide oxon 

protein adducts. Environmental Health Perspectives. 118, 11-19 %U 

http://www.ncbi.nlm.nih.gov/pubmed/20056576. 

Timchalk, C., et al., 2002a. Monte Carlo analysis of the human chlorpyrifos-oxonase (PON1) 

polymorphism using a physiologically based pharmacokinetic and pharmacodynamic 

(PBPK/PD) model. Toxicology Letters. 135, 51. 

Timchalk, C., et al., 2002b. A Physiologically based pharmacokinetic and pharmacodynamic 

(PBPK/PD) model for the organophosphate insecticide chlorpyrifos in rats and humans. 

Toxicological Sciences. 66, 34-53. 

Timchalk, C., Poet, T. S., 2008. Development of a physiologically based pharmacokinetic and 

pharmacodynamic model to determine dosimetry and cholinesterase inhibition for a 

binary mixture of chlorpyrifos and diazinon in the rat. Neurotoxicology. 29, 428-443. 

Timchalk, C., et al., 2005. Pharmacokinetic and pharmacodynamic interaction for a binary 

mixture of chlorpyrifos and diazinon in the rat. Toxicology and Applied Pharmacology. 

205, 31-42. 

Timchalk, C., et al., 2006. Age-dependent pharmacokinetic and pharmacodynamic response in 

preweanling rats following oral exposure to the organophosphorus insecticide 

chlorpyrifos. Toxicology. 220, 13-25. 

Treluyer, J. M., et al., 1997. Developmental expression of CYP2C and CYP2C-dependent 

activities in the human liver: in-vivo/in-vitro correlation and inducibility. 

Pharmacogenetics. 7, 441-452. 

U.S. EPA, A Model Comparison: Dietary (Food and Water) Exposure in DEEM/Calendex, 

CARES, and LifeLine. 2004. 

U.S. EPA, Guidelines for Carcinogen Risk Assesment. In: Forum, R. A., (Ed.), 2005. 

Welsch, F., et al., 1995. Physiologically based pharmacokinetic models applicable to 

organogenesis: extrapolation between species and potential use in prenatal toxicity risk 

assessments. Toxicology Letters. 82-83, 539-547. 

WHO, CHEMICAL-SPECIFIC ADJUSTMENT FACTORS FOR INTERSPECIES 

DIFFERENCES AND HUMAN VARIABILITY: 

GUIDANCE DOCUMENT FOR USE OF DATA IN DOSE/CONCENTRATION–RESPONSE 

ASSESSMENT. 2006, pp. 100. 
172 

Whyatt, R. M., et al., 2003. Contemporary-use pesticides in personal air samples during 

pregnancy and blood samples at delivery among urban minority mothers and newborns. 

Environ Health Perspect. 111, 749-56. 

Whyatt, R. M., et al., 2009. A biomarker validation study of prenatal chlorpyrifos exposure 

within an inner-city cohort during pregnancy. Environ Health Perspect. 117, 559-67. 

Wilson, N. K., et al., 2003. Aggregate exposures of nine preschool children to persistent organic 

pollutants at day care and at home. Journal of Exposure Analysis and Environmental 

Epidemiology. 13, 187-202. 

Xue, J., Zartarian V., and Nako S., The Stochastic Human Exposure and Dose Simulation Model 

for Multimedia, Multipathway Chemicals (SHEDS-Multimedia):Dietary Module 

SHEDS-Dietary version 1 Draft Technical Manual June 16, 2010. In: US Environmental 

Protection Agency, O. o. R. a. D., National Exposure Research, US Environmental 

Protection Agency, Office of Pesticide Programs, Laboratory, Eds.), 2010. 

Yang, D., et al., 2008. Chlorpyrifos and chlorpyrifos-oxon inhibit axonal growth by interfering 

with the morphogenic activity of acetylcholinesterase. Toxicol Appl Pharmacol. 228, 32-

41. 

Yang, R. S., et al., 1995. The use of physiologically-based pharmacokinetic/pharmacodynamic 

dosimetry models for chemical mixtures. Toxicology Letters. 82-83, 497-504. 

Young, J. F., et al., 2009. Human Organ/Tissue Growth Algorithms that Include Obese 

Individuals and Black/White Population Organ Weight Similarities from Autopsy Data. 

Journal of Toxicology and Environmental Health-Part a-Current Issues. 72, 527-540. 

Zemke, A. C., et al., 2009. Molecular Staging of Epithelial Maturation Using Secretory CellSpecific Genes as Markers. American Journal of Respiratory Cell and Molecular 

Biology. 40, 340-348.