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# Source-to-Outcome Modelin g Physiologically Based Pharmacokinetic/Pharmacodynamic (PBPK/PD) Model Linked to a Dietary Exposure Model: Chlo rpyrifos as a Case Study Science Issues Paper for SAP Meeting on Chlorpyrifos Physiologically Based P harmacokinetic/Pharmacodynamic (PBPK/PD) Model linked to the Cumulative and Aggreg ate 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

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# Table of Contents Page Terminology.................................................................................................................... ........... 5 Project Contributors ........................................................................................................... ........ 8 Executive Summary .............................................................................................................. ..... 9 1. Introduction.................................................................................................................. 1 3 1.1 Report objectives..................................................................................................... 13 1.2 Document organization ........................................................................................... 15 1.3 The use of a source-to-outcome model a nd 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 indi viduals 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 import ance 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) 

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3 Effect of activity on total comp artment 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 a ssessment of the toxicity of CPF ................ 81 4.7 Evaluating the contribution of the diffe rent sources of variation on human response to CPF....................................................................................................... 81 Methodology................................................................................................................... 8 1 Factors that drive variati on in RBC AChE inhibition .................................................... 83 Evaluation of the factors that drive va riation 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 expos ure for different age groups ................................ 97 Section overview ............................................................................................................ 97 5.1 Dietary exposure assessments ................................................................................. 97 5.2 Results of the dietary exposure mo deling 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 longitudina l approaches of dietary exposure................. 118 7.4 Determining the impact of longitudinal exposures on the impacts of high level dietary exposures (the 99.9 th 

# percentile dose) on RBC and brain AChE inhibition ............................................................................................................... 120 Section summary .......................................................................................................... 124 8. Evaluation in the uncertainty in the pr edictions of source-to-outcome model by comparison to measured values ................................................................................. 126

# TABLE OF CONTENTS (continued) 

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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 cholineste rase in plasma in occupa tionally exposed adults...... 134 Section summary .......................................................................................................... 137 9. Model predictions of dietary dose im pact 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 (UF A 

# ) uncertainty factor for adults and infants ............................................................................................................... 148 Example development of interindividual (UF H 

# ) uncertainty factor for adults and infants ............................................................................................................... 155 Section summary .......................................................................................................... 158 11 Summary .................................................................................................................... 159 Acknowledgements............................................................................................................... . 163 Attachments .................................................................................................................... ....... 164 References..................................................................................................................... ......... 165

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# Terminology AChE – acetylcholinesterase ADME – adsorption, distribution, metabolism, and excretion BuChE – butyrylcholinesterase CARES: Cumulative and Aggregate Risk Evaluati on 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, DEEM TM 

# 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 ch ange in one variable and another in a model, and b) A measure of the toxico logical 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 potenti al 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 desc ribes the pharmacokinetics and pharmacodynamics of CPF, its major metabolites, and cholinesterase for a typical adult from oral doses of CPF, based on the model fi rst described by Timchalk et al, (2002). LifeStage model : PBPK/PD program expanded to incl ude 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 inte rindividual variation in response to oral doses of CPF in populations of individua ls of specified ages, an expansion of the LifeStage model. Variability : Aleatory uncertainty, aris ing from actual differences in dose received or response to those doses due to differences in individuals. Z-score: A method of describing variation in term s 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 pe rcentiles 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 distribut ion against their Z-score produces a straight line if the data are normally distributed. Plotting the logs of data against Z-sc ores produces a straight line when the data are log-normally distributed.

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# Project Contributors A variety of scientists made important c ontributions 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

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# 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 uncertain ty represents advancement in knowledge. Specifically, this project links data, computer simulation models, and new analyses to improve risk assessment for a chemical, chlorpyr ifos (CPF), which has as database that is sufficiently robust to support th ese new modeling techniques. In this paper we describe the methodologies and data used to cr eate 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 consis ts of two publically available software programs. The first program can be any of the dietary exposure software programs that produce estimates of longitudinal dietary expos ures (the specific model used here is CARES). The second program is the Variat ion 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-ou tcome 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 be tween 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/pharm acodynamic (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 pe rformed that identifies the factors that drive variation in response (see Section 4 ). Information on the interi ndividual variation in these factors is obtained from published sources a nd is used to create simulation models of interindividual variation in the relati onship between dose and the key event.

# 10 The two models define dose and the indivi dual 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 th e two models was achieved by a careful considerat ion 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 ar e 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 complete d, 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 sour ce-to-outcome pathway rather than just as a risk “calculator.” In th is project the source-to-outcome model has been used in a number of ways and has demonstrat ed 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 infant s. 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 invest igation 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 incr eased 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 e xposure 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 mu ch smaller for individuals with high exposures. The impact of dietary exposures on the RBC and brain AChE inhibition for these indivi duals was dominated (90% of the impact) by the dose received from a single day. • At current dietary levels of exposure the di fference between the res ponses of the typical and sensitive individual (resu lting from differences in metabolism, physiology, and activity) was less than 2 ( Section 10 ). The source-to-outcome model was used to ev aluate 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 st ate 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 an d interindividual uncertainty factors ( Section 10 ). This assessment found that models prediction of variation in response overlapped with the responses seen in re cent animal studies ( Attachment G ). In addition, human variation in response was found to be modest (less than a fa ctor of 2 between the responses in a typical adult or infant and a sensitive adult or infa nt) for doses that cause 10% or less RBC AChE inhibition in humans. As a result , the values of age-specific interspecies a nd interindividual uncertainty factors were sma ll and ranged between 1 and 2.

# 12 The case study achieved its goals. A key ev ent, AChE inhibition, was identified and models of interindividual variation in both dose and re sponse were constructed. The combined models form a complete source-to-o utcome model. The fi nal source-to-outcome model’s quantitative predictions of the AChE inhibition and inte rnal concentrations of CPF and its metabolites were evaluated. The final source-to-outco me 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 leve raged 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 thinki ng brought forward in this project will allow multiple stakeholders to utilize these and sim ilar approaches for advancing risk assessment and aiding regulatory decision-mak ing, goals that align with and that have been embraced by the broader scientific community.

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# 1. Introduction 1.1 Report objectives Risk assessments of dietary exposures to pesticides have used detailed characterization of variation in age-specific di etary 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 n ecessarily reflect specific knowledge of a chemical’s mechanism of action, or our increased ability to quantitati vely predict internal doses and responses in humans. This paper summarizes the development of a source-to-outcome m odel that provides a single consistent analysis of both toxicity and exposure and specifi cally includes data on mechanism of action. The source-to-outcome model provides a quant itative 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/pharmacodynami c 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 uncertain ty in the model’s predictions. The sourceto-outcome model is composed of any of the existing dietar y exposure software that can produce estimates of longitudinal expos ure and a pharmacokinetic/pharmacodynamic model of response. Together these programs re late 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’ ‘ 

# Variation model ’ 

# Dietary exposure software Relates data on pesticide residue on

consumed foods (CARES, LifeLine, Calendex TM 

# , SHEDS) 

# Source ‐ to ‐ Outcome Model + Linking Data 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 inve stigate of the impact of current dietary exposures on three different age groups (thirt y 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 re sidues 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 de velopment of chemical- and age-specific uncertainty factors. The analysis in this document focuses on m easures 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 i nhibition 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 mechanisms 1 

# , 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 th e relevant information on the absorption, distribution, metabolism, and excretion ( ADME) and mechanism of action for CPF ( Section 2 ). We next describe th e 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 dos e to the inhibition of cholinesterase. The development of this component is presented in a series of thr ee steps (Figure 2).

Step 1 ( Section 3 ): a PBPK/PD model of the relati onship between oral doses and 1) internal concentrations of CPF and its metabo lites 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-re sponse relationships in childr en and infants is described. This expanded model is calle d the “LifeStage” model. Step 3 ( Section 4 ): the LifeStage model is expanded to address interindiv idual variability in dose response that occurs from diffe rences in physical activity, physiology, and metabolism in specific age groups. This final ve rsion is called the “Variation” model, and is used as the PBPK/PD component of the final s ource-to-outcome model. 1 This project has determined internal concentrations of CPF and CPF oxon that are measur es 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 Variatio n 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 se ries of model-to-measu rement exercises that demonstrate the LifeStage and Variation’s mode ls’ ability to predict age-related variation in physiology and the effects of CPF inhuman volunteer studies. Finally, in Section 4 the Variation model is used to investigate the inte rindividual variation of the dose response of RBC and brain AChE. Section 5 presents the dietary exposure compone nt of the source-to-outcome model. The modeling of CPF is described along with th e data on residues used in this analysis. The issue of the uncertainty in characteri zing longitudinal exposures is addressed and multiple approaches of characterizing longitudinal exposure are investigated. Section 6 presents the process used to link the di etary 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 a nd future source-to-outcome models. The document then describes the use of th e complete source-to-outcome model to characterize the impacts of di etary exposures using CPF as a case study. This description begins in Section 8 with the use of the s ource-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 dietar y exposures on AChE in different age groups ( Section 9 ). Finally, the source-to-outcome mode l 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 source ­ to ­ outcome model and the future of risk assessment Risk assessments of chemicals are undergoing a transi tion. New tools and approaches are enabling assessors to directly characterize the impact of factors such as extrapolation from toxicity in animals to hum ans, age-related differe nces, 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 es timates 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 discusse d in two recent National Academies Reports (National Research Council, 2007; National Research Coun cil, 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 21 st 

# Century, toxicology is challenged to understand toxi city 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 toxicologica l investigations of a chemical should focus on the early events in th e 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 fitt ing of response data. This may be achieved by constructing quantitative dose response relationships for those perturbations us ing PBPK/PD models. The concept of a perturbation in a cellular res ponse 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 nece ssary 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 num ber of advantages. First, ea rly key events provide more tractable targets for building pharmacodynamic (P D) 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 organophosphor us insecticide, CPF, as an example to illustrate the methodology of the source-to -outcome model. Similar to numerous organophosphorous pesticides, high dos es 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 pertur bation of the cholinergic system that leads to these apical effects is the inhibition of AChE in the centr al nervous system and in other tissues. Thus, this change is an example of the “obligat ory 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 inhi bition of these blood enzymes likely occurs presystemically in the liver dur ing 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 deta iled 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 in clude processes in the toxicity pathway beyond the key event. The PBPD model does ma ke quantitative predictions of when the key event occurs in an individual based on th e mechanisms of metabolism and interaction with cholinesterase. Thus the PBPD model fulf ills the goal of extrapolation as stated on page 9 of the National Acad emies of Sciences report Toxicity Testing in the 21 st 

# Century , to provide a quantitative, mechanistic unders tanding of the dose-re sponse relationship.

Figure 3. Source-to-outcome model for the key ev ents of CPF and the subsequent portions of the toxicity pathway. Portions enclosed in sh aded 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 expresse d 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 qua ntitative 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 predic t when cholinergic effects may or may not occur. A second issue raised in Science and Deci sions: Advancing Risk Assessment, is the importance of considering populat ion variability in humans. As discussed in detail in Sections 3 and 4 of this paper, the source-to-outco me model allows for a systematic examination of the factors that affect varia tion in dose, ADME, and responses in the key events. These models allow the direct inves tigation of sensitive subpopulations defined by age, variation in enzyme ac tivity, or other factors. Finally, Science and Decisions: Advancing Risk Assessment, raises the issue of default assumptions in risk assessment and the le vel of theory and evidence that is required to deviate from a default. In Section 10 of this paper, we presen t an illustration of how the source-to-outcome models can be used for sett ing values of interspecies and interindividual uncertainty factors that differ from the defau lt values. This presenta tion occurs at the end of the paper after the technical basis for the model and its pr edictions 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 “transpare ncy, 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 de velopment of quantitative models of the key event is an example of th e 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 detoxificat ion to trichloropyri dinol (TCPy) and dialkylphosphate metabolites (diethylphosphate (DEP) and di ethylthiophosphate (DETP)) (Figure 4). Since the concentration of CPF-oxon is the crucial dose me tric 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 dos e- or species-differences in activation and detoxification of CPF. In vitro studies of metabolism of C PF were performed to better understand interindividual and age related vari ation. 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 phosphothi oate functionality, two ethoxy moieties, and a trichloropyridinol leaving group. C PF is fairly lipophilic with a log K ow 

# value of 4.96 (Sangster, 1994), whereas the CPF-oxon and hydrolysis metabolites TCPy, DETP and DEP are substantially more polar. The pare nt compound; containing the P=S bond is more chemically stable than the reactiv e 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 (Balla ntyne and Marrs, 1992; Chambers and Levi, 1992). As a result, toxicological effects are ge nerally ascribed to the more reactive CPFoxon than the parent co mpound (Sultatos, 1994).

# 22 Toxicological effects arising from fo rmation of the CPF-oxon metabolite are primarily via inhibition of acetylcholinestera se (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 O H 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 C H 3 

### C H 3 

### P O O O N Chlorpyrifos (CPF)

O Cl Cl Cl C H 3 

### C H 3 

### P O O O N O C H 3 

### C H 3 

### P O O OH S C H 3 

### C H 3 

### P O O OH S Cl Cl Cl C H 3 

### C H 3 

### 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 amount s of TCPy-sulfate and free TCPy (Nolan et al. , 1987). Sunaga, et al. (1989) report similar findi ngs, following inter-peritoneal administration of CPF to rats, showing urinary TCPy accounted for >85% of the administered dose of CPF, with lower per centages of the DETP and DEP metabolites recovered. A subsequent study by Mendrala and Brzak was conduc ted to evaluate the timecourse of blood metabolites following or al administration of 0.5-100 mg CPF/kg body weight. The authors found 99% of blood meta bolites as TCPy with only 1% as parent compound. Trace levels of CPF-oxon levels we re 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-pa ss 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 oral ly 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 concen trations observed. These CPF-oxon results are quite consistent with the rat pharmacokineti c results of Mendrala, discussed above, and indicate that CPF undergoes high first-pass me tabolism in humans as well as animals.

# 24 Any minor variations in the hepatic or extr ahepatic formation a nd/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 hydr olysis 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 ri ng and glucuronide phe nolic conjugates of TCPy have been seen in human liver S9 a nd 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 iden tified 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 resu lts 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, re spectively, remained unchanged. These data indicate that the majority of these CPF hydrolysis products are excreted without further Phase I metabolism. In a more recent Comp arative 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 additi on, 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, th ese results indicate that CPF-oxon also

# 25 undergoes high first-pass metabolism in the ad ult and neonatal rat. A more thorough overview of these animal and human metabolis m/kinetic studies can be found in recent reviews of CPF metabolis m (US EPA, 2008b; Eaton et al. , 2008). 

# Dose ­ dependent kinetics Since the first steps in CPF metabolism i nvolve CYP oxidation, it would be expected that the rate of metabolis m of this compound could be dependent on concentration. A comparison of the ratio of blood TCPy/CPF c oncentrations from various toxicokinetic studies in rats (Mendrala et al. , 1998; Timchalk et al. , 2006; Marty et al. , 2010) was therefore conducted to evalua te 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 studi es evaluated. Overall, the PND-5 to PND-12 rats show lower metabolism rates than the P ND-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 me tabolite ratio above 1 mg/kg do se levels. One would expect that a similar phenomenon of CYP-based meta bolic saturation would occur in humans. Therefore, an important validation check for th e 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 Chambe rs, 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 th e various esterases in blood that can hydrolyze CPF-oxon (Pond et al. , 1995; Jokanovi ć , 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, resp ectively. Many investigators have studied both the rates of microsomal me tabolism 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 (C room 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 3A 4/5 apparently have significant capacity to metabolize CPF. Overall, these investigat ors 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 metabolis m. What is important to population modeling, however, is the inter-subject variabilit y 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 Vari ation model, Foxenberg notes th at some variability seen in metabolic rates between different sources may be due to artifacts in microsomal viability because of varying tissue isolation an d microsomal preparation conditions. The degradation of CPF-oxon occurs primarily via esterase activity, in the liver, blood and various tissues. Some authors report micr osomal-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 este rase 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. 

# Age ­ dependent 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 th e young, affording higher levels of CPF and/or CPF-oxon. Researchers studying the ontogeny of metabolic systems have reported that individual CYP enzymes matu rate 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 ta ke 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 Life Stage 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 age ­ dependent metabolic rates As discussed above, age-dependent change s 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 characteri zation of enzymatic act ivity (i.e. MichaelisMenten enzymatic kinetics) with regards to age in humans. Therefore, a new study was conducted to quantify age-dependent en zymatic 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 samp les were obtained from XenoTech, LLC (Lenexa, KS, USA). Microsomes were select ed 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 ra nge of hepatic microsome donors was 13 d-75 y. Both oxidative desulfurylation (CPF to C PF-oxon) and dearylation (CPF to TCPy) pathways of CPF metabolism were measur ed by quantifying product formation from in vitro incubations of hepatic microsomes with CPF. Microsomes (0.5 mg protein) were incubated in 50 mM HEPES buffe r (7.4 pH) containing 15 mM MgCl 2 

# •6H 2 

# O (Poet et al. ,

# 29 2003; Lee et al. , 2009) and 1 mM ethylenediaminetetraac etic 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 th e 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 meas ured 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 plas ma (2.5 μL) samples were incubated in 0.1 M Tris-HCl buffer (8.5 pH) contai ning 2 M NaCl and 2 mM CaCl 2 

# (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 addi tion of substrate (CPF-oxon) at 3 min, and terminated with the addition of 0.25 ml of 3 M HC l saturated with Na Cl 5 min after the addition of CPF-oxon. Incubation volume was 0 .5 ml. Blanks, containing microsomes or plasma inactivated by additi on of HCl solution prior to incubation, were included to quantify non-enzymatic product formation. Meta bolite 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 V max 

# values, K m 

# values, pseudo first-order rates (V max 

# /K m 

# ), protein concentrations in plasma, and marker substrate activities, was evaluated for age dependen ce 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 V max 

# and K m 

# were 0.73 ± 0.38 nmol/min/mg microsomal proteins and 81 ± 73 μM, respectively. Neither V max 

# nor

# 30 K m 

# demonstrated age dependence ( p = 0.60 and 0.17 respectively) . The desulfurylation V max 

# and K m 

# were 0.35 ± 0.21 nmol/min/mg microsomal protein and 96 ± 62 μM, respectively, and again neith er were age dependent ( p = 0.90 and 0.16, respectively). Pseudo first-order rates were estimated by dividing the V max 

# by the respective K m 

# . 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 compar able with those published previously by other groups. Previously reported CPF dearylation V max 

# 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 K m



# 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 V max 

# rates from pooled human microsomes of 0.40 and 0.49 nmol/min/mg micr osomal protein, respectively, and Sams et al. , (2004) reported a mean V max 

# of 0.35 nmol/min/mg microsom al protein using individual human microsomes. All three values are consis tent with the mean value determined here (0.35 nmol/min/mg microsomal protein). Reported K m



# 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 ra tes 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 we re 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 Fi gure 7. Although there is some variability, a fairly good correlation is seen between desulf urylation and dearyla tion 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 metabo lism 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 TC Py are shown in Figure 8. The mean (± SD) V max 

# and K m 

# 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 V max 

# , and K m 

# , 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 V max 

# of 233 nmol/min/mg microsomal protei n, somewhat higher than the value reported here (78 nmol/min/mg microsomal pr otein). This difference could potentially be

# 33 explained by differi ng incubation buffers. Sams et al. , (2004) used a phosphate buffer (pH 7.4), while a high salt Tris-HCl buffer was uti lized 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 obser ved in CPF-oxon metabolism to TCPy, the overall mean values are used in the LifeStag e 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 fr om each donor liver sample. A correlation was observed between the metabolic rates of desulfurylation a nd 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 desu lfurylation 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 V max 

# rates on a per ml plasma basis ( p = 0.03, Figure 10). Mean (± SD) V max 

# rates were 1901 ± 663 nmol/min/ml in children < 6 months of age (n=5) and 6828 ± 1614 nmol/min/ml protei n in adults (indivi duals > 12 years of age; n=5). Plasma protein concentration also significantly increased with age ( p = 0.005). Overall, CPF-oxon metabolism V max 

# 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, K m 

# constants did not demonstrate any age-dependence ( p = 0.87); however, 4 out of the 5 lowest K m



# values were measured in children < 6 months of age (data not shown). These age-based changes in plasma PON1 ac tivity 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 interval s (dashed lines) around the data displayed. Confidence intervals were determined usin g GraphPad Prism (version 5.03 for Windows, GraphPad Software, San Diego Calif ornia USA, www.graphpad.com) 

# Section summary The metabolism of CPF and its relationshi p to the active metabolite CPF-oxon have been the subject of a number of studies. Da ta 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 s ection, 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 consiste nt 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 th e 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 th e LifeStage model, and concludes with an evaluation of the model and its predicti ons for adults, child ren and infants. The section begins with a descriptio n 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 fo r 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 Adu lt model), the modeling of typical individuals at di fferent 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 developm ent of the PBPK/PD model as it pertains to the Typical Adult and the Life Stage capabilities. A descript ion 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 a nd 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 co mpared to published data on physiology. The predictions of internal dose and cholinestera ses 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 CP F 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 extens ively 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 extr apolation (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 variab ility and polymorphisms (Licata et al. , 2001; Tardif et al. , 2002; Timchalk et al. , 2002a). Robust PBPK models that ar e capable of exploiting larg e experimentally-derived data for model parameterization and validation offer an elegant solution to determine dosimetry and investigate potential mechan isms 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 resear ch results, test hypothe ses, 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 include s a complete description of the

# 38 pharmacokinetics and pharmacodynamics of CP F-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 firs t-order urinary elimination of total urinary TCPy. In the case of CPF and other organophos phorus pesticides, the AChE inhibiting mechanism of action is well understood and biom arkers of exposure, inhibition of plasma cholinesterase, and biomarkers of early e ffects 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 incl uded in blood (plasma and RBC), diaphragm, brain, and liver compartments. Th e PBPD model for CPF is a di rect extension of the PBPK model and relates the prediction of the fo rmation 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 ye ars 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).39Figure 11 PBPK/PD model (Typical Adult model) structure. The model structure has the same foundation as originally published in2002 (Timchalket al., 2002b). The shaded compartments denote tissues which contain B-esterases (bottom panel). Tissue volumesand 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. cholinestera se inhibition rates and CYP metabolism rates) and has had additional co mplexity added (i.e. metabolism in the intestines). Specific modifications include: • Improvement of the modeling of extrahep atic 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 metabol ism to TCPy or CPF-oxon and PON1 metabolism of CPF-oxon to TC Py 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 betw een plasma and vegetable oil (a fat surrogate) to determine blood:tissue part itioning using a QSAR approach (Lowe et al. , 2009) • Verification of the bimolecular inhibition ra te 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 ac ross 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 gr owth (ontogeny). To address this issue, gender-specific human growth was incorporat ed into the LifeStage version of the PBPK/PD model, based on the e quations 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 eval uation of the predictions using data on age specific body weights and weight - tissue volum es relationships reported in the published literature. The development of the LifeStage model be gins 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 compartmen ts based on specific ontogeny of each tissue. Once the age-related physiology is define d, 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 metabo lism from birth to adulthood. Cholinesterase (A- and B-) are scaled with age-specific tissue vol ume. Blood flows are likewise scaled based on tissu e 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 gende r-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.

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### B. Male Age (yr)Body Weight (kg) 0 10 20 30 40 50 60 70 80 0 50 100 150 200 250

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## 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 we ight 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 kidne y, spleen, lung, gastroin testinal tract (GI), and pancreas volumes, and the slowly perfus ed compartment consists of a summation of muscle, skin, bone marrow, and non-fat adipose tissue volumes. Figure 13 presents a summary of the co mpartments 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×BW 1.117 

## 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 1.17x10 7 

## Maxwell et al. , 1987, Timchalk et al. , 2002b Acetyl 3.66x10 6 

## Maxwell et al. , 1987, Timchalk et al. , 2002b Carboxyl 1.086x10 5 

## 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 additi onal data including children and larger individuals were used to re -fit the polynomials for the LifeStage model. Compartments existing within the LifeStage model are equiva lent to the Typical Adult model (Timchalk et al. , 2002b), discussed previously) and include br ain, blood, diaphragm, fat, liver, rapid, and slow compartments (Figure 13). The rapidly pe rfused compartment consists of a summation of kidney, spleen, lung, gastroin testinal tract (GI), and panc reas volumes; and the slowly perfused compartment consists of a summati on of muscle, skin, bone marrow, and non-fat adipose tissue volumes. Modified equations incl ude fat (both male and female), adipose, brain, liver, and muscle and were refit with a dditional data or extrapolations to achieve adequate fits up to a 170 kg individual. For all other compartments, previously published polynomials or constant values were us ed to define compartmental volumes ( Table 2 ).46Table 2. Compartmental Growth.CompartmentFraction of Body Weight Equation1Source for LifeStage ParametersEqn. FormatV0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6(BW: Body Weight in g)V0 V1 V2 V3 V4 V5 V6Leucke 9.15e-2-8.59e-71.25e-11-6.46e-17- - -Young 8.97e-2-3.50e-76.54e-13BloodLifeStage 8.970e-2-3.500e-76.540e-13- - - -Younget al.2009Leucke 1.19e-1-3.51e-64.28 e-11-1.82 e-16- -Young 1.41e-1-5.54e-69.30 e-11-6.83e e-161.80e-21Brain (Figure14)LifeStage 1.216e-1-3.465e-64.354e-112.463e-165.132e-22Fit to Valentinet al.2002(bwt≤70) & Younget al.2009data (body weight>70 kg) andextrapolated values based onTable 4A (Younget al.2009)Leucke 3.000e-4- - - - - -Young NA - - - - - -DiaphragmLifeStage 3.000e-4- - - - - -Lueckeet al.2007Fat*Leucke 5.91e-21.20e-5-5.80e-101.12e-14-6.36e-20- -Young 1.84e-2-6.86e-62.46e-10-2.11e-157.58e-21-9.94e-27-Female(Figure 16)LifeStage 9.217e-021.401e-05-6.787e-101.540e-14-1.558e-197.249e-251.273e-30Fit to Valentinet al.2002 &Lafortunaet al.2005 data andextrapolated data based onequation in Fig 1B (Lafortunaetal.2005)47CompartmentFraction of Body Weight Equation1Source for LifeStage ParametersEqn. FormatV0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6(BW: Body Weight in g)V0 V1 V2 V3 V4 V5 V6Leucke 3.95e-21.59e-5-6.99e-101.09e-14-5.26e-20- -Young 1.61e-2-3.59e-6-8.28e-11-3.57e-164.73e-22- -Male(Figure 16)LifeStage 3.484e-22.803e-5-1.422e-92.892e-14-2.718e-191.203e-24-2.036e-30Fit to Valentinet al.2002 &Lafortunaet al.2005 data andextrapolated data based onequation in Fig 1B (Lafortunaetal.2005)Leucke 3.49e-2-3.23e-72.13e-12- - - -Young 4.25e-2-1.01e-61.99e-11-1.66e-164.83e-22- -Liver(Figure 15)LifeStage 3.917e-2-6.789e-71.082e-11-7.393e-171.701e-22- -Fit to Valentinet al.2002 (bodyweight≤70) & Younget al.2009extrapolated values based onTable 4ARapidLeucke 1.67e-2-9.96e-8-1.09e-131.13e-17- - -Young 1.860e-2-4.550e-8- - - - -LungLifeStage 1.860e-2-4.550e-8- - - - -Younget al.2009Leucke 7.31e-3-8.29e-82.13e-12- - - -Young 7.26e-3-6.69e-83.33e-13- - - -KidneyLifeStage 7.260e-3-6.690e-83.330e-13- - - -Younget al.2009Leucke 1.17e-3-1.18e-81.81e-13- - - -Young 1.48e-3- - - - - -PancreasLifeStage 1.480e-3- - - - - -Brownet al.1997 and Youngetal.2009Leucke 3.05e-3-2.09e-81.24e-13- - - -Young 3.12e-35.57e-9- - - - -SpleenLifeStage 3.120e-35.570e-9- - - - -Younget al.200948CompartmentFraction of Body Weight Equation1Source for LifeStage ParametersEqn. FormatV0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6(BW: Body Weight in g)V0 V1 V2 V3 V4 V5 V6Leucke 1.93e-2-4.42e-79.28e-12-4.88e-17- - -Young NA - - - - - -GILifeStage 1.650e-2- - - - - -Brownet al.1997SlowLeucke NA NA NA NA NA NA NAYoung NA NA NA NA NA NA NANon-Fatadipose*LifeStage 2.044e-12.617e-5-1.542e-93.268e-14-3.116e-191.387e-24-2.35e-30Fit to Valentinet al.2002 &Lafortunaet al.2005 fat data/0.8(Valentin 2002) data andextrapolated data based onequation in Fig 1A (Lafortunaetal.2005)Leucke 9.61e-2-4.88e-63.05e-10-3.62e-151.22e-20- -Young 9.68e-1-3.32e-61.83e-10-1.24e-15- - -MuscleLifeStage 1.251e-11.458e-5-2.927e-102.114e-15-5.250e-21- -Fit to Valentinet al.2002 &Janssenet al.2000 data andextrapolated values based on Fig2A (Janssenet al.2000)Leucke 1.07e-1-3.26e-66.11e-11-5.43e-161.83e-21- -Young 1.03e-1-2.56e-63.68e-11-2.580e-168.620e-22-1.100e-27-SkinLifeStage 1.030e-1-2.560e-63.680e-11-2.580e-168.620e-22-1.100e-27-Younget al.2009Leucke 5.19e-28.06e-7-1.96e-107.63e-15-1.08e-195.14e-25NAYoung NA NA NA NA NA NA NABone MarrowLifeStage 2.100e-2- - - - - -Brownet al.1997 (red only,yellow is in adipose)

# 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 langua ge 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 th at 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 criterion 2 

# . When selecting data for fitting the polynomia ls 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 diffe rences in anatomical and physiological characteristics of reference individuals. Th is source was used for data on body weights from birth to ~70 kg, while other references we re used for compartmental data at higher body weights. Data for some compartments we re 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 quantitat ively select the best fit model by balancing goodness-of-fit (by maximum likelihood) and parsimon y (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 L n 

## () 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. c n 

## (k)=-2×ln(L n 

## (k))/n+k× φ (n)/n

# 50 The following sections describe the modeli ng 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. Extrapolati ons of brain volume were made using Table 4A in Young et al. , (2009) for both the body weights presente d in that table and extrapolated for 150-175 kg body weights. The resulting fit was a 4 th 

# 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 orde r 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 th e 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 Va lentin, 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 do tted 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 re fitting 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 equa tions with undesirably high variation across narrow body weight ranges. Fitt ing the function to the refere nce values for liver from Table 4A in Young et al. (2009) resulted in a 4 th 

# order polynomial that was a much smoother fit (Figure 15, pres ented 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 Va lentin, 2002 (blue), or Young et al. , 2009 (black). BTW is body weight in kg. 

# Adipose and fat The ICRP Publication 89 (ICRP, 2003) descri bes adipose tissue as a combination of fat and connective tissue. Reference fat conten t of adipose tissue is 40% at birth and increases to ~80% in adults (ICRP, 2003). Fa ilure to account for these differences could lead to inaccurate compartmental volume estimat es of fat or fat-partitioning coefficients which are estimated based on chemical make up of tissue. These potential inaccuracies could have substantial implication on overall pharmacokinetics for lipophilic compounds, like CPF. In the LifeStage model, the fat comp artment is defined as the lipid content of adipose tissue. The non-fat adipose tissue (connective tissue and the adip ose compartment subtracted from the fat compartment) is include d 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 assump tion 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 da ta obtained from Figure 4 A of ICRP Publication 89 (ICRP, 2003) and from Janssen et al. , (2000). The final function was a 4 th 

# order equation that is capable of predic ting the muscle compartment upto 170 kg. The volume of the muscle is used in the estim ate 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 compartmen ts are shown in Figur e 17. The benefit of describing tissue volumes correctly as a functi on of age and weight, ra ther 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 infa nts 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 volum es predicted for at typical male using the original Typical Adult model, and default ti ssue 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 BRAIN6 mo3 yr30 yr 

# Body Weight (kg)Tissue Volume (L)



# 0 50 100 150 0.1

1 10 100 

# BLOOD SLOW RAPID6 mo3 yr30 yr 

# Body Weight (kg)Tissue Volume (L)

# 55 

# Blood flows Once the volumes of the organs and tissu es 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 metabolis m 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 V max 

# 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). 

# tissue] [L tissue] protein/L [mg protein] mg [μmole/hr/ [μmole/hr] me organ volu * organ in level Protein * in tissue levels Enzyme = individual in activity Enzyme

# 56 Total V max 

# 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 enzy matic capacity of the individual varies with age. Fi gure 19 showing the impact of th e age-related changes in liver volume. The values in this figure were gene rated by multiplying the observed Vmax times the age-specific liver volume.



# 0.01 0.1 1 10 100 1 × 

# 10 0 1 

# 1 × 

# 10 0 2 

# 1 × 

# 10 0 3 

# 1 × 

# 10 0 4 

# 1 × 

# 10 0 5 

# 1 × 

# 10 0 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 V max 

# is on a logarithmic scale. 3 There is limited evidence that microsomal protein cont ent may change with age Bart er, 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 Po oling. 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 ra nge in protein content suggested by these data was accounted for in the variability model to address any age- related di fferences in microsomal prot ein 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 ba sis, as described previously ( Section 2 ). Therefore, the age-specific V max 

# was scaled to age-depe ndent 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 × 

# 10 0 3 

# 1 × 

# 10 0 4 

# 1 × 

# 10 0 5 

# 1 × 

# 10 0 6 

# 1 × 

# 10 0 7 

# Age (yr)Vmax (μmol/hr)


# Figure 20. Total V max 

# 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 V max used in the model for standard individuals. 

# Metabolism of CPF in the brain was estim ated based on an extrapolation from limited studies in rats. Incuba tion 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 assessme nt, 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 th ese studies was 5.2 mg/g brain and 35 mg/g liver, so the final in vivo metabolic rate constants were es timated to be approximately 760- fold higher in whole liver than whole brain. Because brain size changes much less than

# 58 liver size over ontogeny, th e difference in predicted V max 

# for the bioactivation of CPF to CPF-oxon in the liver is only 3.7-fold le ss 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 c holinesterase 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 c ode includes within it the Typi cal Adult model (See Figure 2) and is designed such that ei ther a body weight or an age ca n be put into the model. For these examples, the LifeStage model was em ployed 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 st udy 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 bot h scientific and ethical cons iderations by the EPA Human Studies Review Board (See Attachme nt 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 cholines terase 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 re sults 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 th e second study, oral abso rption 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 ar e 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 dos e in both studies (Figures 22 and 23). The predictions of the LifeStage PBPK/PD mode l for BuChE in plasma from the Noland et al. study were also very good with the peak inhi bition accurately predicted (Figure 21). The LifeStage model’s predictions of RBC AChE inhi bition are consistent with the finding of Kisicki et al. (Figure 24). In this study no statis tically significant differences were observed in RBC AChE activity in the volunteers, regardless of dose. The LifeStage predicts a peak inhibition of approximate ly 7% for the 2 mg/kg exposure and lower inhibition levels for the other groups. This level would not ha ve been detectable given the relatively high day-to-day varia tion 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 be low 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 inhi bition was measured after 0.5, 1, or 2 mg/kg dosing: the model predicts about 7 percent inhibi tion 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 m odel is based on the model published in 2002 (Timchalk et al. , 2002b), with modifications as describe d 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 extrap olated from rat data. Thus, our confidence in the parameterization of th e human model and its predictiv e 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 th e LifeStage model while the earlier model results are given using dotted lines. While the predictions fo r plasma BuChE inhibition in the two models are very similar, the predic tions of RBC and brai n AChE inhibition are approximately 2-3 fold lower. The RBC and br ain 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 followi ng 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 th e values for adults and infants. Infants had inhibition rates approximately 2–f old higher than adults. However, at a single oral dose of 0.5 mg/kg and below, the level of inhibi tion 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) show s a dose response curves for the for peak RBC inhibition for the three ages, the arrows in th is 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 a ssumptions 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 Life Stage 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 th e 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, cholinestera se inhibition in the various age groups exhibits the following pattern: infa nts > children > adults. Infants had inhibition rates approximately 2–fold higher than adults at these doses. Ho wever, at a single oral dose of 0.5 mg/kg and below the level of inhibi tion 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, addi ng inter-individual va riability is the next logical step to determine physiological and enzymatic determ inants of pharmacodynamic outputs (Bois, 2010). This section describes the development of models of interindi vidual 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 va lues for the parameters that drive response; Step 3. Define how correlations be tween 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, th e Variation model is used to investigate the magnitude of the variation in response in populat ions of different ages at different doses. Endpoints investigated are measures of intern al 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 analys is 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 eval uated 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 ch ange 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 determine d. 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 great er than 1% change in one of the two target effects. Values close to zero indicate that ne ither 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 stom ach to the gut or uptake from the gut As Table 3 indicates, very few of the mode l parameters impacted the estimates of peak RBC or brain AChE inhibition. Only 8 para meters had sensitivity coefficients greater than 0.1. It is recognized that some parameters that vary widely acr oss a population with coefficients smaller than 0.1 might also contri bute to variation in response. However, using a cutoff criterion of 0.1 should capture the ma jor drivers of varia tion 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 Vari ation model includes di stributions 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 flows 5 

# 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 volu me 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 enzy me activity. The following subsections describe the approach used to develop age-sp ecific distributions for these categories. 

# Physiology Modeling variation in physiology is a chal lenge since physiological parameters are inherently correlated (larger body weight im plies larger compartment volumes). Blood flows in an individual are relate d to organ sizes and variation in one organ affects the other organs. In order to capture these correlat ions, 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 out put of the exposure models. As described in Section 5 , the exposure models generate estimate s 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 co mpartments and organs. Since each equation for an individual’s compartm ent 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 th e 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, interindi vidual 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 indi vidual variability, the relative variability for the fat compartment volume was thus assumed to be eq ual to the relative variability of the body height. The LifeStage model then calculates th e fat compartment with the equations in the growth model and adjusts th e calculated volume by randomly sampling from the normal

# 70 distribution of relative variab ility. 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 defi ned on a tissue-specifi c 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 und er resting conditions (see Fig 19). In the variability model, inter-indivi dual differences in fat volume a nd workloads results in total cardiac outputs that have a similar range in va lues 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 interindi vidual 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 distribu tion. 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 stat e to a level of light exercise (e.g., moderate walking rate). This level of activity is assu med 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 impact s on AChE, then there would be no need to assess the impact of actual pa tterns of human activity. The role of activity on organ-specific bl ood flows is not the same for each organ. Increasing levels of activity change the tota l 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 th e liver. Standard blood flows (Table 4) were adjusted based on a wo rk to oxygen demand conversion proposed by Jonsson et al. (2001) in the following equation. Q i 

# 0 (L/hr) = Perfusion Rate i 

# (L/hr/L tissue) * Tissue Volume i 

# (L) + Work Change i 

# (L/hr/W)*Work (W) Table 4. Impact of work on Organ-Specific Perfusion Rates Model Compartment Change in Perfu sion 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 ti ssues 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 catalyt ic efficiency of hepatic and plasma PON1 is believed to be an important factor in modul ating 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 im pact on the catalytic activity of PON1; however, the polymorphism at position 192 has b een demonstrated to have a substratedependent functional impact on PON1 mediated esterase activity (Furlong et al., 1988; Adkins et al., 1993; Costa et al., 2003; Rich ter et al., 2009). These different phenotypes of PON1 have been reported to demonstrate di fferent 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 effi ciency 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 th is 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 obtai ned 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 func tional 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 V max 

# values were paired with paraoxonase activities, QQ and QR func tional 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 depende nt upon PON1 phenot ype 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 vari ations 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 obs erved levels of activity, presented as the Z-score of the V max 

# for the three enzymes are given in Figure 29. The lognormal distributions used in the Vari ation 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 lognorma l distribution used in the Variation model. Note that the distribution is allowed to ex tend beyond the limits of the measured data. 

# There were a limited number of micros omal samples available from younger individuals. To address this lim itation, the distributions of ra tes are allowed to exceed the observed range. For computati onal implementation, the logn ormal mean and standard distribution are entered into th e distribution functions in acsl Xtreme and used to calculate the tissue-based V max 

# 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 produ ced in the liver and released into the plasma, the variability in th e plasma PON1 can reasonably be expected to be correlated with the variability in the live r PON1 (i.e., individuals with high or low liver PON1 will have similarly high or low plasma PON1 6 

# ). For the Variation model, the value of PON1 in plasma was rank correlated w ith the value in plasma for each individual 7 

# ; The end result is that in the Variation model no simulated individual wi ll be assigned a low PON 1 activity in liver (or plasma) and hi gh 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, th e current ranges of plasma P ON1 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 possibl e 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 × 

# 10 0 3 

# 1 × 

# 10 0 4 

# 1 × 

# 10 0 5 

# 1 × 

# 10 0 6 

# 1 × 

# 10 0 7 

# Age (yr)Vmax (μmol/hr)


# Figure 30. Total V max 

# 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 CY P450 levels was therefore assumed to have the same size of distribution as the corres ponding liver CYP450s. The liver and brain CYP450 levels are not, however, correlated to each othe r 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 indi vidual 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 qui te large (Figure 31). Infants (6 months old) have lower plasma PON1 than adults. In additi on, 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 

# 10 0 

# 10 1 

# 10 2 

# 10 3 

# 10 4 

# 10 5 

# 10 6 

# 10 7 

# Liver PON Blood PON Liver CPF to TCP Liver CPF to oxon Brain CPF to TCP Brain CPF to oxon Z-ScoreVmax (μmol/hrl)



# -3 -2 -1 0 1 2 3 10 -2 

# 10 -1 

# 10 0 

# 10 1 

# 10 2 

# 10 3 

# 10 4 

# 10 5 

# 10 6 

# 10 7 

# Liver PON Blood PON Liver CPF to TCP Liver CPF to oxon Brain CPF to TCP Brain CPF to oxon Z-ScoreVmax (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 phenot ypes exhibit differential distri butions in different ethnic groups. The functional phenotype ratios repo rted 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 (D avis et al., 2009). Ratios for Hispanics are 0.24 for QQ, 0.52 for QR, and 0.24 for RR (H uen et al., 2009; Huen et al., 2010). Although the distribution of each polymorphic e xpression may be different, this variation has limited impact on the variation in meta bolism of CPF oxon. As shown in Figure 26 individuals in each of the three phenotypes ha ve similar abilities to metabolize CPF-oxon. This finding is compound specific. As shown by (Ginsberg, 2010), polymorphic expression may be important for other compou nds. Thus the issue of ethnic variation in metabolism should be evaluated when constr ucting 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 equati ons are polynomial functions that assign volumes to multiple compartments and organs. Since each equation for an individual is defined by a co mmon body weight, the volumes ar e internally consistent (See Figures 13-18). This produces compartment vol umes which are physiolo gically consistent. The potential for correlation is also addr essed when assigning metabolism rates. As was discussed in Step 2, plasma PON1 origin ates in the liver so it is modeled as rank correlated with the levels in the liver. Variat ions 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 estimat e the enzymatic variation in the brain. The variation in the brain CYP450 levels was ther efore assumed to have the same size of distribution as the correspondi ng liver CYP450s. The liver and brain CYP450 levels are not, however, correlated to each othe r 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 desulfuryla tion and oxon hydrolysis (Figure 9). However,

# 79 we could find neither a biological nor a lite rature basis for this correlation and thus conservatively varied the CYP-based desulf urylation and liver P ON1 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 (F igure 24). In this section we compare the range in responses predicted usi ng 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 adu lt volunteers were given single dietary exposures of 2.0, 1.0 and 0.5 mg/kg of ch lorpyrifos. Figure 32 pr esents the ranges of the measured values using the black triangl es and error bars determined in (Timchalk et al. , 2002a). The Variation model was used to predict the range of the va lues of the blood measurements that would occur in each of the individuals in each group. Variation in the test individuals was simulated by modeling e ach individual in each dose group separately using data on the individuals’ physiology and gender. To capture enzy me variability, each individual was simulated 100 times each time vary ing the levels of enzyme activities. The median values from each simulation for each tim e point over the five days are shown as the solid line in Figure 32 an d the ranges of the 5-95 th 

# percentiles are presen ted 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 predic ts 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 10Blood 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
HoursRBC 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 activ ity 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 consecuti ve days. These inputs are described in Sections 5 and 6 . Based on the age and gender, values are sel ected 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 m odel 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 de velopment 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 toxicolo gical properties of CPF in humans. This is done in two ways. First, the different sources of interindividual variat ion are systematically turned on and off, to identify those factors which drive variation in human sensitivity. Second, the Variation model is used to genera te dose response relati onships for different age groups that include human variation. The purpose of these assessments is to gain insight on human response to that woul d 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 vari ation and its impact on the ability to tolerate chemical exposure. This se ction uses the Variation model to investigate the relationship between varia tion in physiology, enzymes, and level of physical activity. The goal is to determine which of these char acteristics 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 ag e-specific measure of variati on 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 characteristic s in populations of different ages. The sources of vari ation 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 re lative 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 Activ ity. 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 perfor med; 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 nu mber 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 year s). Variation in body weight and gender was determined by using data from the populat ion generator in th e CARES model (for additional information see the discussion in th e 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 indivi duals 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 rela tive impact of the dose on each individual compared to the impact on a “typical” indi vidual in their age group was determined by dividing each individual’s i nhibition level by the inhibiti on observed in the median individual. These ratios repres ent a measure of relative se nsitivity across the population. Finally, the cumulative distributi ons of the ratios were plotte d 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 variati on in the four characteristics on relative sensitivity to CPF as measured by peak inhi bition of RBC for the three age groups were examined at the point of the largest interi ndividual variation, usi ng an oral dose of 0.5 mg/kg. Figure 33 present the curves for the six an alyses. Note that the relative sensitivity is shown on log scale. At 0.5 mg/kg when all sour ces of variation are in cluded, the range of peak inhibitions across the si mulated individuals (from the most sensitive to the least sensitive of the 1,000 simulated adults) is abou t 100-fold. However, the most sensitive of the 1,000 individuals has an inhi bition rate that is approxima tely 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. Va riation 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 expl ains about one third of the variation in sensitivity. The largest chan ge in sensitivity comes from the introduction of variation in PON1 levels in liver and plas ma. This can be seen when variation in PON1 in liver and plasma are run alone (physi ology, CYPs, and physical activity are held constant). As Figure 33 shows, the interindivi dual variation in RBC from the variation in PON1 in the liver and blood is almost as larg e 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 P ON1 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 variati on in Physical Activity (PA), Physiology (P), liver and brain CYP Enzymes (CYP), and Liv er 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 la rger role and physiological vari ation a smaller role than in adults. However, liver PON1 variation is stil l 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 var iation in Physical Activity (PA), Physiology (P), liver and brain CYP Enzymes (CYP), a nd 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 individua l’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 scal e 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 th an 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 ar e 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 dr opped 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 incl uding 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 3Ratioof%RBCInhbitiontoRBCInhibitionintheMedinaindividual


## Z ‐ score



# Relative variation in RBC AChE inhibition from a single dose of 0.1 mg/kg dose of chlorpyrifos in 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 variati on 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 var iation 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 ha ving about an 8-fold increase in response compared to the median individual. However, the driver for the variation was different. The variation in brain inhibi tion 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 imp act 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 PO N1 are either “turned on” alone or in combinations. The figures for the other ag es and doses are similar (data not shown). 

# These analyses are not meant to be exhausti ve investigations into CPF. Rather they demonstrate how the Variation model and futu re versions for other compounds could be used to identify those factors that are mo st important to investigate when modeling interindividual uncertainty in response. Base d 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 leve l 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 determin e 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 app ears 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 sensitiv ity appears to be reasonable given the finding that blood flow to the liver and brain are relativel y unaffected by increase levels of physical activity (see Table 4). 

# 4.8 Interindividual variation in response ­ predictions by the Variation model The Variation model create d using the previously de scribed distributions can generate dose response predictions for a ny of the model outputs such as blood concentrations of CPF and CPF-oxon and RBC or brain AChE i nhibition. 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 variat ion 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 ra nged 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 si milar 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 doses 9 

# . The constant width of the cloud around CPF suggests that the processes that drive interindividual variation in levels of CPF in blood are not dos e-dependent for this dose range. Finally, the predicted peak doses of CPF are slightly hi gher 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 differe nt 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. In terindividual 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 incr ease 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 me tabolism changes above doses of 0.3 mg/kg for both adults and infants and that variati on 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 P ON1 variation on AChE inhibition in Section 2 . Additionally, while the data are not shown, the pattern of interindividua l 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 pred ictions for peak RBC and brain AChE inhibition for adults and infants for the Vari ation 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 beco me more sensitive at that dose. This is not surprising since the oxon direc tly 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 num bers of both genders are modeled with the Variation model. The lines represent the typica l 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 adu lts always appearing to be slightly more sensitive than infants. One difference is that wi dth of the cloud is larg er 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 num bers of both genders are modeled with the Variation model. The lines represent the typica l 30 year-old and 6 month-old from the LifeStage model. 

# Section summary This section has presented th e development of the Variat ion model and its evaluation by comparison to the results of the human vol unteer studies. The m odel’s predictions of interindividual variation were ei ther consistent or over estimate d the observed variations in small groups of healthy volunteers. This section provides two examples by whic h 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 id entify 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 inhi bition was not affected by PON1 but was affected by variation in CYP activities in the liver and the brain. Su ch findings could help researchers identify issues where additional research is warrante d. The Variation model also indicates that variation in RBC inhibition is strongly dose dependent. Varia tion across individuals ranges from 100-fold at doses above 0.3 mg/kg but dr ops 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 curre nt approaches to as sess dietary exposures. The section describes the collect ion 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 i ndividual’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 th at occurred on a single day. Over the last 10 years, however, program developers have soug ht to assess longitudinal dietary exposures as well. There are a number of dietary e xposure programs available or currently under development (CARES, LifeLine, DEEM TM 

# , and SHEDS). All of these programs use similar approaches for estimating dietary e xposures. Software comparison exercises have shown that dietary exposure software programs (CARES, LifeLine and DEEM TM 

# (U.S. EPA, 2004) and DEEM TM 

# and SHEDS (Xue, 2010)) produce similar estimates of the distribution of single-day dos es in various age groups. This assessment uses the CARES software for the exposure portion of the source-tooutcome model. CARES is a software progr am designed to conduct complex exposure and risk assessments for pesticides. Scientific and technical contributi ons to the program’s development came from a broad team of expe rts, including scientists from Crop Life America member companies and staff, consulting companies, and the Environmental Protection Agency and Department of Agricu lture. CARES is ava ilable without charge

# 98 from the ILSI Research Foundation. 10 

# CARES, has been reviewed by the Science Advisory Panel (U.S. EPA 2002 11 

# ) and is used in regulator y 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 uppe r peak exposures in the general population ( Section 8 ). Finally the program was used to assign gender and body weights to the populations of individua ls assessed using th e Variation PBPK/PD model ( Section 4 ). Dietary exposure software programs depe nd 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 fo r 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 ch lorpyrifos as well as the comprehensive listing of to lerances announced by EPA via the CFR Final Rule in 2008 and include new monitoring data thro ugh 2008 as posted by USDA in late 2009 and updated percent crop treated (PCT) data. USF DA monitoring data for a few commodities are also incorporated. A parsing of the di etary exposure between imports and domestic observations was made for several key crops. Th e dietary files were bui lt 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. Deci sions for the inclusion or exclusion of specific information reflect ge neral guidance for a hi gher 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/ResearchFo undation/Pages/CARES.aspx. 11 http://www.epa.gov/oscpmont/sap/meetings/2002/april/agenda.htm

# 99 The residue data was ente red into three dietary ex posure software programs, CARES, DEEMTM version 2.16 (Durango Soft ware, 2010) and LifeLine version 4.40 (LifeLine, 2006). Electronic copi es of the input files for th e 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 dos es of CPF created using CARES for 1,000 adults, three year-old children, and six m onth-old infants are given in Figure 43. The dietary doses (on a body weight ba sis) are larger for the three y ears-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 3 rd 

# to 97 th 

# 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 diet ary 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 produ ced by CARES were compared with the predictions of DEEMTM versi on 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, chil dren, and infants are similar for all three programs. The findings increase the confidence th at the data have been entered into the CARES program correctly and indicate that th e predictions reflect the current data on residues and U.S. diets. Table 5. Estimates of CPF Daily Dietary Inta ke 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 90 th 

## Percentile 0.000010 0.000010 0.000017 99 th 

## Percentile 0.000034 0.000026 0.000047 99.9 th 

## Percentile 0.00015 0.000045 0.00012 Children 3 year-olds 90 th 

## Percentile 0.000033 0.000031 0.000055 99 th 

## Percentile 0.000093 0.000083 0.000113 99.9 th 

## Percentile 0.00022 0.00014 0.00021 Infants (6 months) 90 th 

## Percentile 0.000043 0.000027 0.000054 99 th 

## Percentile 0.000086 0.000075 0.000081 99.9 th 

## 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 generate d by the CARES and LifeLine dietary exposure programs. As the table indicates, both programs pr edict 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 10 th 

## 50 th 

## 90 th 

## 10 th 

## 50 th 

## 90 th 

## 10 th 

## 50 th 

## 90 th 

## 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 issu e for source-to-outcome models since the effects of one day’s exposures carry across to subsequent days. Howe ver, current surveys of dietary intake do not colle ct 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 in takes in an individual. Mul tiple approaches have been proposed for constructing longitudinal dietar y histories for individuals based on the available data (Crop Life America, 2 002; LifeLine, 2006; Xue, 2010). There is no consensus on which of these approaches should be preferred or the leve l of uncertainty that is introduced by the use of such approaches. For this project we investigated the imp act 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 seasona l approach. The daily approach selects a separate dietary record from multiple individuals to create a longitudinal record of a person. The seasonal approach sel ects 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 (repres enting the week days). This pattern is repeated multiple times to complete a season, and new record s are selected for the next season. This approach was later adopted by SHEDs as one of its options (the ei ght record approach) (Xue, 2010). The seasonal approach results in a smaller longitudi nal variation in dose than

# 102 the LifeLine daily model since the same records are often repeated for multiple consecutive days. CARES produ ces sequential daily estimates for periods of up to one year. CARES uses a statistical technique for identifying de mographic and anthropometric records that come from individuals with sta tistically similar characteristics. Using this technique, the creators of CARES construc ted 365 day dietary histories for a set of simulated individuals (Crop Life America, 2002). Longitudinal dietary exposures were crea ted 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 rece ived 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 plo tted on a log-log scale. A simple trend line was created in Excel using a power function. The slope and R 2 

# 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 appr oaches of longitudinal exposures produce different correlations between consecutive da ys. The Lifeline daily approach (A) and the CARES (C) approach essentially make each da y’s dose independent of the prior day. The LifeLine seasonal approach produces estimat es of dose with a moderate correlation between days (R 2 

# of 0.57). This correlation occurs because in the LifeLine seasonal approach, the majority of individuals are a ssumed to eat the same foods in the same amounts on consecutive days. Because none of th e 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 l ongitudinal data are presented in Section 7 of this document. A B
C

# 104 

# Section summary Dietary exposures for CPF are defined by th e residue data and the software programs available for dietary exposure assessment . The estimates of daily dose predicted by multiple software programs produce similar re sults. However, different longitudinal approaches predict different correlati ons 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 diet ary exposure software program ( Section 5 ) and the Variation PBPK/PD model ( Section 4 ). This section now addre sses the linking of the two components. The linkage of the two components raises technical issues on the definition of the individual’s characterist ics 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 e xposure model into the dose information used by the PBPK model (LifeLine, 2004). This pr ocess must account for the absorption and uptake process. 

# 6.1 Assigning characteristics to each individual The approach used to link the two compone nts’ definition of ‘individual’ was to use an individual’s demographic and anthropometri c 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 i ndividual (ILSI, 2009). These data are taken from the 2000 Public Us e 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 specifi c blood flows, and tota l cardiac output (see Sections 3 and 4 ). This approach leverages the fact that the CARES model determines the individual’s characteristic s 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 representativ e set of internally consistent physiological data for each of the simulated individuals.

# 106 

# 6.2 Definition of dose in the source ­ to ­ outcome 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 toxicit y, 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 ti me (μmoles/sec, see Figure 11 in Section 3 ). The development of the oral uptake portion of th e model was performed using the following assumptions. 1. PBPK models have the ability to address dos es 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 CARES 12 

# and SHEDS (Xue, 2010) include the ability to assign times of the day for diffe rent eating events (eating occasions). The source-to-outcome model for CPF, howeve r, has not attempted to account for theimpact 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 exposu res 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 CARE S 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 assump tion that the entire dose occurs at one meal does not appear to be unreasonable fo r CPF. This may not be true for other chemicals. 12 http://www.ilsi.or g/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. Assu ming 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 we ll-understood process. Solid foods consumed during a meal are held in the stomach for digestion. This diges tion involves secretion of gastric fluids and the conversion of the f ood 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 (t 1/2 

# ). Values for t 1/2 

# 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 para meterization is consistent with the gastric emptying rates identified in step 2. 4. The gut compartment of the PBPK model al so 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 leve ls of RBC and brain AChE inhibition were not sensitive to the gut parameters. The sens itivity coefficient for the first order rate uptake was less than 0.1. Based on these assumptions, the daily dos e 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 source ­ to ­ outcome model The final model consists of the following. The CARES (or other appropriate dietary software model) is used to c onvert a pesticide’s residue data into estimates of daily dietary exposures in populations of indi viduals 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 fo rmat 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. Da ta on each individual is provided in one row. The values in the first columns 1 thr ough 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 days 13 

# can be modeled by placing the daily doses in the a ppropriate columns. The following is an example of a table for two individuals havi ng 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 simula tions 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 physiol ogy of the individual. The model assumes that the meals occur once every 24 hours. The gut upt ake rate assumed in the model results in approximately one half of the dose entering th e gut within the first 90 minutes and three quarters in the first 180 minutes. 13 The model currently only accepts 50 daily do ses but can easily be modified to accept more..

# 109 In the final source-to-outcome m odel, 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 comp artments, including the metrics above. By default, the model re ports 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 additi onal 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 Varia tion model. Data on the characteristics and the exposure history of the simulated individual s is prepared by the user from the output of one of the dietary exposure soft ware programs. This file is read by the Variation model and the predictions of the time cour se 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 so urce-to-outcome model to investigate and ultimately characterize the impacts of l ongitudinal dietary exposures to CPF on body burdens and inhibition of blood and brain AChE. Longitudinal exposures are of interest because neither the pharmacokinetic nor pharm acodynamic effects of a dose may return to normal 24 hours after an intake of CPF. Th e body retains CPF for periods of time longer than 24 hours. In addition, at high doses the effe cts 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 Variati on model to investigate the impact of longitudinal exposures to consta nt 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 Li feLine seasonal) on predictions of the impact of five days of dietary exposures are then investigated using the complete sour ce-to-outcome model. Finally an analysis of re sponses at or above the 99.9 th 

# 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 re ceives a dose at or above the 99.9 th 

# 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 us ed 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 uppe r end of the range of currently estimated acute dietar y exposures (see Attachment D). Figure 45 presents the predicted time course s 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 pattern s of daily changes in these two parameters do not vary for days 11 through 30.) The CPF pl ot indicates at all five doses there was little carr yover of CPF from one day to th e next for either adults or infants. The peak concentrati ons of the two compounds are on ly 5% higher on day 30 than day 1 for infants and 16% higher for adults. In contrast, the second plot indicates th at 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 a dults. Most of this increase occurs in the first 5 days of exposure. However this increas e 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 do ses 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 inhibi tion of RBC and brain AChE in a typical adult and child. The two plots in dicate 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. Ho wever, at lower doses the decreases occur sooner and 90% of 30 day inhi bition 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 le ss for brain inhibition and is not strongly dose dependent. At both ages, 80% of the i nhibition occurs by day 5 and 90% by day 10. At the lowest dose tested, the increase over the th irty 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 quasi ­ steady state conditions Dietary doses of pesticides vary from da y 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 cons tantly fluctuate over time. Ho wever, 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 quas i-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 popul ation for each day in a model simulation. In this analysis the peak concentrations a nd peak inhibition levels that occur at any point in time during the day are determined fo r each individual in two age groups, adults age 30 years and infants age 6 months. The cumula tive 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 di stribution of peak blood concentrations of CPF and CPF-oxon across the po pulations of adults and children for each of the five days. As the figures indicate, there is no cha nge 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 result s 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. Adults Infants
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 cumula tive 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 sugge sts that by day 3 the population has reached quasi-steady state and that modeling individu als for longer durations of time would not result in different predictions of the dist ribution of body burdens or levels of AChE inhibition. In addition, for the highest percentiles the differe nces between day 1 and day 5 are less than a factor of 2. However, with only a thousand individuals, estimates above the 99 th 

# percentile (Z-score >2.3) are unstable (val ues are driven by the la st 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 evaluate d 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 ex posures 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 AC hE do increase but the impact is minimal for individuals in the uppe r portion of the population. By day 2, the portion of the population with above average doses has reache d quasi equilibrium and the distribution of values for this portion of the population do not appear to change ove r longer periods of time. 

# 7.3 Impact of three different longitudinal approaches of dietary exposure The three longitudinal approaches of diet ary exposure CARES, LifeLine daily, and LifeLine seasons were used in the source-t o-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 cholines terases occurs in the first 5 days. Two age groups were investigated, adu lts and three year-olds. One thousand individuals were investig ated in each age group. Figure 51 presents the cumulative distributi on of peak blood concentrations of CPF and CPF-oxon across populations of adults and ch ildren for each of the three approaches of longitudinal dietary exposures. The data presen ted are from the fifth day of the five-day simulation. These data reflect th e contribution of day 5 and the prior days and thus should reflect the differences between the three long itudinal approaches. The data are plotted on a log dose versus Z-score. In general, the dist ributions of peak levels were found to be approximately log normal. This format has th e advantage of expressi ng data that are lognormally distributed as straig ht lines. This facilitates th e visual evaluation of the predictions of the three approaches.

# 119 As Figure 51 indicates, all three approach es produce very similar estimates of the distribution of peak levels of CPF and C PF-oxon. This suggests that any of the three approaches can be used to predict the impact of longitudinal dietar y 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 distribu tions of the three approaches for RBC and brain AChE. In contrast to CPF and CPF-oxon, the choice of di etary software does have an impact on the predictions of cholinestera se 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 analys is of day to day correlation pres ented in the three plots that comprise Figure 45. Those plots indicates that LifeLine daily and the CARES approaches produced little or no correlation between dos es of CPF on consecutive days and that LifeLine seasonal produced modera te levels of correlations. CARES LifeLine Daily LifeLine Seasonal 

# -2 -1 0 1 2 10 -4 

# 10 -3 

# 10 -2 

# 10 -1 

# 10 0 

# 10 1 

# 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 Z-Score (3-97th Percentiles)Brain AChE Inhibition (% basal)
-2 -1 0 1 2 10 -5 10 -4 10 -3 10 -2 

## } 3 Year Olds 

## } 30 Year Olds Z-Score (3-97th Percentiles)RBC AChE Inhibition (% basal)



### 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 leve ls 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 di etary exposure of CPF on indi viduals with above average exposures. Further, it suggests that predicti ons of internal doses and impacts on AChE from dietary exposures in the top half of th e population are not subs tantively affected by the uncertainty introduced by the absence of lo ngitudinal data on dietary intakes. Based on this finding, the CARES model of longitudinal va riation 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 99.9 th 

# 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 exposu res on blood levels and AChE inhibition is different for individuals with relatively low exposures th an for individuals with high exposures. This difference causes the distribut ions on days 1 through 5 and the differences in the three longitudinal models to “fan out” at lower Z-sc ore 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 C PF and CPF-oxon and the AChE inhibition. Specifically, high doses on one day affect the f our 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 e xposure. 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 99.9 th 

# percentile) in a population will be least aff ected 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 indi viduals. 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 maximu m daily dose for each adult. 

# As indicated in Section 4 , adults at the 99.9 th 

# 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 99.9 th 

# 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 wa s the creation of 500 nine-day records where the fifth day had a dose at or slightly above the 99.9 th 

# 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 hi gh 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 inhibi tion levels were determined. In addition, the high days were also run through the sour ce-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 i nhibition for both measures of exposure data. As the figure indicates, the impacts on RBC AC hE from the high day plus the surrounding (+/- 4) days are very simila r to the high day alone. The aver age 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 fi nding implies that for individuals receiving a large dose (relative to other individuals) the contributions from prio r 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 3PeakRBCinhibtion(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 99.9 th 

# percentile dose) and 2) peak RBC AChE inhibition for the high exposure day alone. 

# Section summary This section has presented data from an alyses where the source-to-outcome model and the Variation PBPK/PD model are used to investigate the i ssue 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 curre nt dietary results in effects that modestly accumulate over time (i.e., levels of RBC and br ain inhibition increa ses 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 contra st, levels of CPF and CPF oxon in blood do not accumulate over time. Three dietary exposure models were f ound to produce similar estimates of blood levels of CPF and CPF-oxon and similar estima tes of peak inhibition in RBC and brain AChE. An evaluation of the predictions of th e cumulative impact of dietary exposures over five days suggests that estimates of the uppe r portions of the dist ribution of the four parameters do not change if the duration of dietary simulations are extended beyond five

# 125 days. Finally, the model indicates for indivi duals at the extreme tail of the exposed population (the 99.9 th 

# 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 mi nimized. This minimization is most apparent in the estimates of the individu als most exposed. As a result, if a person has a high level of exposure on a given day (i n 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 expos ure software provide a reasona ble basis for the evaluation of the impacts of longitudinal exposures to curren t dietary residue levels of CPF. The confidence in the predictions is higher for th e upper percentiles of the exposed populations since these exposures are less dependent on l ongitudinal exposures (i.e., are more driven by single day above average exposures). Fina lly, these results explain why the three models of longitudinal exposures provide sim ilar predictions of AChE inhibition at higher percentiles. The individuals at the higher per centiles were simply not affected by doses from prior days and differences in correlati on between consecutive days would not affect the predicted blood levels or effects on AChE.

# 126 

# 8. Evaluation in the uncertainty in the predictions of source ­ to ­ outcome 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 fact ors including limitations in the fidelity of the model, uncertainty in the model inputs, and the rele vance of the input data. Thus, the model is evaluated to determine the level of confiden ce in its output. The eval uation 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 intern al 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 shoul d be evaluated by data that matches dietary levels of exposure to in hibition of AChE and BuChE leve ls. Such an exercise is not possible since any impact of current levels of CPF would not be det ectable against the dayto-day variation in blood AChE or plasma Bu ChE. 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 s ource-to-outcome process. Figure 56 presents a

# 127 description of the major steps of the source-to-outcome mode l and the data that can be used to evaluate each of the steps. Data on many of these steps have already b een presented in this report. The process of modeling daily doses has been the subjec t 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 es timates 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 dist ribution of oral intakes of CPF in adults, children and infant (Table 5, Section 5 ). The consistency indicates that the residue da ta were correctly entered and the models were run correctly. It also confirms that th e 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 E xposures 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 ex p osure ( 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 mode l and analyses that provide a basis for the evaluation of the model output. 

# The second source of uncertainty, the deve lopment 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 w ith 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 (F igures 20-23) and the Variati on model to predict the range of response observed across th e 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 inhi bition 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 vari ation 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 pred ictions 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 source ­ to ­ outcome 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 i nvestigate 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. NHA NES has been collecting blood samples for analysis of CPF levels in the 2007-2008 a nd 2009-2010 waves of sampling; however, these data have not been released as of this time. There are two smaller studies that have meas ured 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 publis hed 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 co mparison has been to use the source-tooutcome model to produce estimates of the ra nge of blood concentrations that would be found if a blood sample was taken any tim e during a 24 hour period after a dietary

# 130 exposure event. This was done by taking the va lues that occur at va rious points in time over a 24-hour period for 1,000 adults. Th e source-to-outcome model produces 96 estimates per 24-hour period (one every 15 mi nutes). The 96,000 values were ranked and the various percentiles, min, max, and geomet ric mean were determined. The 24 hours of data were taken from day 5 of a 5-day m odel simulation performed in a population of 30 year-old adults. If the last exposure event occurred no more th an 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 f actor 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 modelSource-tooutcome model 1 

# Eaton et al. (2008) 2 

# Whyatt et al. (2009) 2 

# Barr et.al (2010) Exposure Date 3 

## 2002-2010 2001-2002 2001-2004 2003-2004 

# Measurement MB 4 

## MB and CB 5 

## CB MB CB MB 

# Number of Individuals 5 

## 1000 76 65 92 148 

# 138 Range Min 0.007 <LD 7 

## <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 wome n of child-bearing age are one of the subpopulations of concern. Howe ver, because the surveys are limited in size and come from two adjacent states (New Yo rk 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. Wh en additional data become available they should be used to confirm the model predictions. The second data set that can be used to ev aluate 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 th ere are dietary residu es of TCPy in many foods 14 

# . As a result, researchers have concluded that only 5 to 20% of TCPy observed in urine is from dietary inta ke 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 quantificati on for 29% of the population and ranged from 0.43 to 27.5 ug/l. Using the estimates of 5-20% as th e fraction of TCPy in adults that occurs from metabolism of CPF, a range of plausibl e 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 diet ary 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 th e distribution of TCPy in urin e 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, buttend to overestimate values for individuals with low TCPy urine le vels and underestimate values for individuals with high TCPy levels. Current levels of diet ary 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 fr om the source-to-outcome model are below the range suggested in the literatur e 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 uncertain ty in the fraction of the total TCPy in urine that comes from the metabolis m 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-ScoreTCPy Concentrationin Urine (μg/L)


# Figure 57. Cumulative distributions of prediction s of current urinary levels of TCPy from the source-to-outcome model for a dults, observed levels in the U.S. Population in the 2002 NHANES survey (30% of population have leve ls 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 sour ce-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 consis tent with published findings of the fraction of TCPy that occurs from exposure to CPF.

# 134 Table 8. Comparison of selected measured urinary TCPy concentrations ( μ g/L) from selected biomonitoring studies with source-to-outcome model predictions Source-to-outcome model 1 

# Curwin et al. (2007) Lu et al. (2008) Median (90% CI) Geo. Mean (±Std. Dev.) Mean (Range) Date 2 

## 2008-2009 2001 2003-2004 N 1000 Varies 2 

## 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 pr ovide 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 lik ely 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 rele vant to a model’s prediction. This subsection presents an exampl e of how biomonitoring data from a study of workers could be used to evaluate the Va riation 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 urin e of the workers occupationally exposed to

# 135 CPF is the result of the workers’ occupationa l exposure and not diet ary 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, de picts 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 ove r three orders of magnitude and for workers with elevated levels are corre lated 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 fo r 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 creatin ine basis. These estimates assume a daily urine production of 0.02 l/kg and creatinine leve ls 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 th e 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 cholinestera se inhibition and TCPy in urine for workers in a CPF manufacturing plant and for contro l 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 el evated levels of TCPy in ur ine are a slight overestimate of the observed relations hip in the Garabrant et al. study. The reason for the overestimation may be due to differences in th e route or timing of the dose. Since the study involved occupational expos ures and not dietary exposures, route and timing of these exposures was not clearl y defined. The Variation model predicts the response assuming a bolus oral dose. The worker s’ 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 bl ood by spreading the dos es 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 huma n volunteers, and the m odel’s predictions are not inconsistent with data in workers (alt hough the route of exposure for such workers is unclear). The data on biomonitori ng evaluates the quality of th e residue data and both the dietary and PBPK portion of the source-to-outco me model. The predictions are consistent with current studies of CPF in blood and TCPy in urine. These findi ngs suggest that the model is predicting values that are of the ri ght 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-ou tcome 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 m odel’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-outco me 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 gene rate 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 assessmen t would address other ages as well. One thousand individuals we re 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 brai n 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 5 th 

# day of simulated daily dietary exposures in 1,000 adults (30 yea rs), 1,000 children (3 years), and 1,000 infants (6 months).

# 140 Figure 60. Predicted RBC and brain AChE inhibition on the 5 th 

# 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 im pact of an individual’s current day's dose and all of the individual’s prior days’ exposur es (quasi steady-state). Thus these predictions should be a reflection of the current impact of dietary resi dues. 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 availa ble. The RBC and brain inhibition on day 5 are less than 0.01% for all of th e simulated individuals. The vertically aligned clusters in the estim ates 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 dos e distribution for infants shown in Figure 43. Figures 61 and 62 describe the relationship be tween 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 a nd 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 a fter a single exposure to a wide range of doses (blue) in infants (6 months). 

# The comparisons presented in Figures 61 a nd 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 pr edicted by a single dose (Figure 61). In contrast, the RBC and brain inhibition in Figure 62 ar e often higher than expected from a single day’s dose. This

# 144 increase is due to residual i nhibition 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 di etary residues and predicting the impact on AChE for different populations. Such an eval uation would consider factors such as the relationship between various levels of i nhibition 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 m odel in a fashion that would support this use. Specifically, 1. The model has been used to determine the di stribution of impacts of current exposures in three age populations, adults, children, and infants. 2. The model was run for a sufficient duration su ch 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 disclose s the variation in res ponse as well as the variation in dose. This source to outcome model has enabled us to simulate effect s from exposures well below outcomes that can be visualized expe rimentally. AChE activity is sufficiently variable that it is difficult to demonstrate tr eatment-related inhibiti on 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 th e processes that drive the source-to-outcome pathway rather than just as a risk “calculat or” where the only thing of interest is the “answer”. In this paper we have used the source-to-outcome mode l to explore several issues of importance for the evaluation of C PF. These include the role that longitudinal exposure to time-varying doses plays in esti mating upper bounds to ri sk (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 source ­ to ­ outcome model to estimate compound and age ­ specific uncertainty factors Section overview The source-to-outcome model was also used to derive values for the interspecies (UF A 

# ) and interindividual 15 

# (UF H 

# ) uncertainty factors for two age groups. EPA and other organizations have developed guidance for the re placement of all or part of the values of the interindividual UF H 

# and UF A 

# using chemical specific data and PBPK models (Lipscomb, 2006; WHO, 2006). This guidance indicat es that each uncertainty factor can be divided into two sub-factors, one for kine tic 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 a ddresses both the kineti c and dynamic portions of the uncertainty factors. As a result, valu es are proposed for the total (kinetic plus dynamic portions) of UF H 

# and UF A 

# . In this section we explor e how the source-to-outcome model could be used to determine the compound and age-specific UF H 

# and UF A 

# . We make a number of assumptions that have regulatory decision-making implicatio ns, 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 ke y 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 UF A 

# , 10 for the UF H 

# , and for infants, children, and women of childbearing age a value of 10 fo r the FQPA fact or (Figure 63).
15 Also referred to as the in traspecies uncertainty factor.

# 147 Animal POD No adverse effects in typical human No adverse effect in sensitive humans ( aRfD ) a PAD for children and women of child bearing years 

# UF A 

# U F H 

# F Q PA factor Figure 63. The relationship between the POD, aRfD, and aPAD 

# The equation used to set the values for the aRfD and aPAD are: H A 

# UF UF PoD aRfD × = 

# factor FQPA RfD aPAD

_ = 

# Historically, the interindividual uncertain ty factor also considered sensitive subpopulations including subpopulations base d on age. The source-to-outcome model allows the development of age-specific uncer tainty factors that extrapolate from agespecific animal data to humans of a corres ponding 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 uncerta inty factors at specific ages, there is no need to consider extrapolation from adults to children as part of the determination of the interindividual uncertainty fact or. While not explored here, this approach envisions the future development of values of UF A 

# and UF H 

# for other ages (e.g., the elderly).

# 148 

# Example development of interspecies (UF A



# ) 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 UF A 

# 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 in cluded measurements with adu lts and pups (PND 11). In this analysis we focus on the results of the one da y study (acute exposure). In this study adult rats were administered a single dose of th e compound using corn oil as vehicle. As discussed above, the effects from dietary e xposures can be treate d as individual acute exposures if the focus of the assessment is to characterize ri sks 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 scopi ng studies identified the time to peak inhibition for the animals. The animals were sa crificed 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 de veloped using these data (Reiss et al. , 2010). A copy of this report is provided in Attachment H. The acute RBC AChE inhibition BMDL 10 

# is assumed to be the basis for the POD for both adults and pups. Separate analyses we re performed on male and female pups; the lower of the two gender specific values of BMDL 10 

# (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 s quares) are overlaid on the dose response “clouds” of adults and infa nts (originally presented in Figures 41 and 42 in Section 4 ) to show the relationship between the animal da ta and the predicted responses in humans (Figure 64). The animal data reflects the fi ndings 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 re sponse in adult and infant humans and the observed responses in the test animals are sim ilar 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 l ess sensitive than most, but not all humans.

# 151 In this analysis, the value of the interspecies uncertainty factor UF A 

# is determined by matching the simulation of humans to the spec ific dose regime, metric of response, and subsequent data analysis used to derive th e 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 UF H 

# for infants and the data on adu lt rats are used to estimate UF H 

# values for adults. In this illustration the source-to-outcome m odels are used to match the animal data by making the following decisions: 1. Since the animal study is a si ngle bolus dose in adults and pups, the source-to-outcome model assumes a single dose. 2. The study design attempted to identify the p eak 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 gr oup. 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 BMD 10 

# and BMDL 10 

# 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-o utcome model is used to determine doses that correspond to the BMD and BMDL values. Specifically, a. 4,000 groups of 8 individuals are created a nd their response of each individual to a specific dose is modeled using the Varia tion PBPK/PD model. The mean inhibition levels for each group of humans is then de termined. The results are a distribution of group mean responses. b. This process is repeated for multiple doses (D i 

# ).

# 152 c. The median value of the dist ribution of the group means, (M i 

# ) for dose D i 

# is assumed to be equivalent to the BMD Mi 

# . d. Values of BMD 10 

# are determined by interpolation from values of M i 

# greater and less than 10%. e. A similar approach is used for deriving the equivalent to BMDL 10 

# . In the case of BMDL the upper 95 percentile of the distri bution of group means is used instead of the median estimate. The results of this exercise are dose estim ates that directly correspond to the BMD and BMDL. Assuming that the value of BMDL 10 

# is used as the hypothetical POD, the value of UF A 

# is given by: H A 

# PoD PoD UF = 

# where, POD is the BMDL 10 

# in either adult rats or pups and POD 10 

# is the equivalent BMDL 10 

# in simulated output of the source-to-out come 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 inhi bition at each dose are presented in the figure as a series of vertical lines, one at ea ch 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 do se. These appear as sets of black circles at the four doses. Note that the ranges of re sponses in the distributi on 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 singl e 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 bl ue cloud is repeated from earlier figures and presents the outline of CPF dose response in popu lations 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 infa nts (corresponding to the groups of eight test animals used in the Marty and Andrus, 2010). 

# The values of the human equivalent of BMDL 10 

# 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 lo g-log space as is th e prior plot. The 95 th 

# percentiles of the distributions of the 8-person group mean responses at each dos e are identified and a solid line is created that connects the values. The human equivalents of the BMDL 10 

# is determined to be 0.33 mg/kg, based on interpolation of the 95 th 

# percentile values of the

# 154 distribution of the observed at 0.2 and 0.5 mg/ kg to determine the dos e 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 bl ack circles with the corresponding ranges of predicted responses for the simulated infants given by the vertical lines (blue). The 95 th 

# percentile values of group means (shown by the red Xs and connected by the solid black line) were used to calculate the BMDL 10 

# in the test animals (0.33 mg/kg, marked with the red circle). 

# Using the reported BMDL in rats and the va lue of the human equivalent, the value of UF A 

# for infants is:

# 155 UF A 

# (Infants) = 0.36 (mg/kg/d) / 0.33 (mg/kg/d) UF A 

# (Infants) = 1.1 While not shown, the value of UF A 

# for adults derived using this method is 1.7. 

# Example development of interindividual (UF H



# ) uncertainty factor for adults and infants The interindividual uncertainty factor is intended to address differences in the relative sensitivity of individuals to CPF for th e 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. (Lipscom b, 2006) and (WHO, 2006) recommend that the value of the UF A 

# 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 95 th 

# and 99 th 

# percentiles. The determination of the UF H 

# also requires an assumption of a level of RBC inhibition is acceptable in the general population. In this a ssessment a value of 10% is used. Other values could be used. The values for UF H 

# for the two age groups are calculated based on two endpoints. The first is the human equivalent of the BMDL 10 

# . This endpoint was established in the definition of UF A 

# 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 (POD sensitiveH 

# ). The value of UF A 

# is then defined as the ratio of the human equivalent of the BMDL 10 

# to a dose where there is a 95% (or 99%) confidence that an individual ha s less than a 10% peak RBC AChE inhibition (POD for the sensitive human or POD sensitiveH 

# ). SenstiveH H H 

# PoD PoD UF =

# 156 The values of POD sensitiveH 

# were determined by calculating the level of inhibition for the 95 th 

# and 99 th 

# percentiles of the peak RBC AC hE inhibition levels for the 5,000 individuals. The method used to determine the values of POD sensitiveH 

# was also used to calculated the levels of inhibition for the 95 and 99 th 

# percentiles of the population at different doses and then to interpolate to find the do ses where 95 and 99% of the population would have less than a 10% peak inhibition. Figure 67 shows how this interpolation wa s performed. The data from Figure 62 is again presented; however, the 95 th 

# and 99 th 

# percentiles of the values of the response for the individuals are determined a nd connected by dotted lines. The doses that occur where these lines cross the 10% inhibition le vel are the two values of POD sensitiveH.

# 157 POD senstiveH 

## for 95 and 99 percent of

population



# Figure 67. Determination of POD sensitiveH 

# 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 UF A 

# 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 POD sensitiveH 

# for the 95 th 

# and 99th percentiles for both infants and adults (calculations not shown) are gi ven in Table 9. The values of the POD H 

# were developed using the approach described in th e previous portion of th is 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 UF H 

# were determined for the 95 th 

# and 99 th 

# percentiles of the population and for each age group and are given in Table 9. The values of UF H 

# for the 95 th 

# and 99 th

# 158 percentiles are very similar. This occurs because the variation in respons e at doses of 0.1 to 0.3 mg/kg in both adults and infants ar e small (See Figures 42 and 43 in Section 4 ). Table 9. Values of POD for sensitive humans and UF H 

# values for adults and infants Adults (30 years) POD SenstiveH 

# (95%) POD SenstiveH 

# (99%) 0.26 mg/kg 0.23 mg/kg Infants (6 Months) POD SenstiveH 

# (95%) POD SenstiveH 

# (99%) 0.24 mg/kg 0.22 mg/kg Adults (30 years) UF H 

# (95%) UF H 

# (99%) 1.3 1.4 Infants (6 Months) UF H 

# (95%) UF H 

# (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 w ithin the range of human variation in response. In addition, the level of varia tion in response across the popula tions of infants and adults are modest at doses that cause a 10% RBC i nhibition level is small. As a result the predicted values for both UF A 

# and UF H 

# 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 i nhibition 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 addre ss 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 occurren ce 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. Th e 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 longit udinal 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 ar e 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 ke y event process in place, a sensitivity analysis is performed that iden tifies the factors that drive va riation in response to a given dose of a chemical (see Section 4 ). These driving factors requ ire data on interindividual variation across the population of interest. Th ese 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-out come models need to be matched to the specific needs of the PBPK/PD models of vari ation. 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 consider ation of physiology of the exposure event ( Section 6 ) and data in human studies. In the development of this project, one con cept 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 pr oject to avoid the complexity of modeling intakes over multiple eating events. In a simila r 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 mode l actual patterns of human activity using data su ch as activity diaries. The evaluation of the uncertain ty in a source-to-outcome model’s predictions and the determination of the confidence that can be pl aced in the predictions is a challenge. The levels of the predictions of the model vary w ith 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 an d metabolites, and predictions of variation in physiology. These predictions can be evaluated us ing other modeled and measured data. In general, we have demonstrat ed that the various pred ictions of the sourceto-outcome model are consistent with measured data in the literatur e. However, we also found that the model-to-measurement comparis ons 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 e xposures that differed fro m diet (e.g., worker studies). Because of this, we c onclude that the final prediction s 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 complete d, 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 sour ce-to-outcome pathway rather than just as a risk “calculator.” In th is project the source-to-outcome model has been used in a number of ways and has demonstrat ed 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, vari ation in physiology had some effect but was still minor compared to metabolism. • The source-to-outcome allowed the investigation of the impa ct of repeated doses of CPF on blood levels of CPF and CPF–oxon a nd 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 (inhib ition 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 curren tly occur from exposure to dietary residues the impact was much more modest (i.e., a 3-fo ld). In contrast incr eases in brain AChE inhibition were at most 3-fold across all doses. • When day-to-day variation in dietary exposur e 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 with in 3 days. More importantly the impact of multi-day exposures was much smaller fo r individuals with high exposures. The

# 162 impact of dietary exposures on the R BC and brain AChE inhibition for these individuals was dominated (90% of the impact) by the dose re ceived from a single day. • Interspecies variation in response was investigated by comparing the predicted responses humans to the observed responses in rats. The findings s uggest that rats are only slightly less sensitive than humans. • Interindividual variation in response to C PF was explored using the Variation model. The impact on RBC AChE inhibition was f ound to be dose dependent. At dietary levels the difference between the responses of the typical and sensitive individual (resulting from differences in metabolis m, physiology, and activity) was less than 2.

The source-to-outcome model was used to ev aluate 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 st ate 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 an d interindividual uncertainty factors ( Section 10 ). This assessment found that models prediction of variation in response overlapped with the responses seen in recent anim al studies (Attachment G). In addition, human variation in response was found to be modest (less than a fa ctor of 2 between the responses in a typical adult or infant and a sensitive adult or infa nt) for doses that cause 10% or less RBC AChE inhibition in humans. As a result, the values of UF A 

# and UF H 

# 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, a nd 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 expos ed population using an example pesticide. In additi on, 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 co me 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 o ccurred as a result of an American Chemistry Council LRI project 'Demonstr ating 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 cont ributions to the experime ntal, 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 Life stage 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) "C omparison of Cholinesterase (ChE) Inhibition in Young Adult a nd Preweanling CD Rats after Acute and Repeated Chlorpyrifos or Chlorpyrifos-oxon E xposures." 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 

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