II. B. 6  Page 1 of 162 Appendix II. B. 6 Prepared by: U. S. Environmental Protection Agency II. B. 6  Page 2 of 162 Physiologically Based Pharmacokinetic Modeling for the N Methyl Carbamate Cumulative Risk Assessment Prepared by U. S. Environmental Protection Agency Office of Research and Development Human Exposure and Atmospheric Sciences Division Exposure and Dose Research Branch P. O. Box 93478 Las Vegas, NV 89193 3478 II. B. 6  Page 3 of 162 A. Introduction and Background The Food Quality Protection Act ( FQPA) of 1996 requires EPA to consider potential human health risks from all pathways of dietary and non dietary exposure to more than one pesticide acting through a common mechanism of toxicity. In 2001, EPA established the N methyl carbamate pesticide as a common mechanism group based on their structural characteristics and shared ability to inhibit acetylcholinesterase ( AChE) by carbamylation of the serine hydroxyl group located in the active site of the enzyme.

Physiologically based pharmacokinetic and pharmacodynamic ( PBPK/ PD) models are powerful tools to evaluate risk. The mathematical representation accounts for relevant anatomic structures and specific physiological and biochemical processes associated with chemical absorption, distribution, metabolism, excretion, ( ADME) and effects. All variables in the model are accessible as dose metrics, which can be investigated under diverse exposure scenarios and implementation of hypotheses about the biological processes that may affect ADME and the pharmacodynamics. Due to the absence of the specific biological pathways, simple mathematical models are limited in their ability to generate testable hypotheses and systematically address important issues, such as lifestage and species extrapolation, risk to susceptible subpopulations and mechanisms of chemical interactions.

A current limitation of applying PBPK/ PD models to risk assessment is in the model development process. The available data on the ADME and effects of the N methyl carbamates, and methods to evaluate model simulations against the data, are limited relative to the model complexity required to address the questions of interest. Active research to address these areas includes computational prediction of model parameters, including QSAR methods, extrapolation of in vitro experiments to an in vivo context, and development of preliminary models to illustrate their utility and investigate methods for model evaluation.

This appendix contains descriptions of two research activities in PBPK/ PD modeling relevant to the N methyl carbamate cumulative risk assessment. An abstract briefly describes a cumulative PBPK/ PD model that simulates the ADME and effects of a mixture of aldicarb, carbaryl, and propoxur ( Use of Exposure Related Dose Estimating Model ( ERDEM) for Assessment of Aggregate Exposure of Infants and Children to N Methyl Carbamate Insecticides"). The considerations and details associated with modeling each individual chemical is conceptually similar to that described in the comprehensive report for a PBPK/ PD model of carbaryl (" Assessment of Carbaryl Exposure Following Turf Application Using a Physiologically Based Pharmacokinetic Model"). The carbaryl PBPK/ PD model was presented to the FIFRA SAP in February, 2005. The panel was complimentary of EPA's effort and suggested some key revisions that included II. B. 6  Page 4 of 162 the application of quantitative methods to evaluate model sensitivity and simulation of data, reduction in the number of chemicals tracked, and enhancement of the pharmacodynamic modeling of cholinesterase inhibition. The panel also emphasized the importance of communicating and documenting the data considered in model development in a transparent and clear way. EPA has not made any revisions to the carbaryl PBPK/ PD model or the report since February, 2005. The report provided here for the carbaryl PBPK/ PD model is the same as that reviewed by the SAP. II. B. 6  Page 5 of 162 B. Use of Exposure Related Dose Estimating Model ( ERDEM) for Assessment of Aggregate Exposure of Infants and Children to N Methyl Carbamate Insecticides ( Abstract and poster presentation from SOT, 2005)

Abstract Presented at the 2005 Society of Toxicology Annual Meeting ( New Orleans, LA, March 6 10, 2005)

Power F. W., Okino M. S., Tornero Velez R., Blancato J. N. and Dary C. C.

U. S. Environmental Protection Agency Office of Research and Development Human Exposure and Atmospheric Sciences Division Exposure and Dose Research Branch P. O. Box 93478 Las Vegas, NV 89193 3478 A physiologically based pharmacokinetic ( PBPK) model was developed within the Exposure Relational Dose Estimating Model ( ERDEM) framework to investigate selected exposure inputs related to recognized exposure scenarios of infants and children to N methyl carbamate pesticides as specified under the Food Quality Protection Act ( FQPA). Assumptions underlying residential exposure and cumulative risk were examined as inputs for particular exposure scenarios using residential dermal transfer coefficients, ambient air concentrations, and dietary intake. Physiological, pharmacokinetic and pharmacodynamic parameters describing the fate and effects of carbaryl, aldicarb, and propoxur in rats were scaled to establish the model structure for exposure to humans. Adjustments were made for differences in metabolism and physiology between children and adults. Michaelis Menten kinetics were used to describe metabolism, where the chemical species compete for the catalytic enzymes. Bimolecular rate constants, ki ( pM 1 hr 1), were used to describe inhibition of acetylcholinesterase by the parent compounds, where the doses to the target tissue are assumed to be additive. The simulation results over a day reveal the effects of residential exposure on cholinesterase activity, and highlight the scenarios and biological pathways where chemical interactions are important. The interactions are a cause for differences in risk between cumulative and single chemical exposures. II. B. 6  Page 6 of 162 II. B. 6  Page 7 of 162 C. Assessment of Carbaryl Exposure Following Turf Application Using a Physiologically Based Pharmacokinetic Model Prepared by Miles S. Okino Frederick W. Power Rogelio Tornero Velez Jerry N. Blancato Curtis C. Dary U. S. Environmental Protection Agency Office of Research and Development Human Exposure and Atmospheric Sciences Division Exposure and Dose Research Branch P. O. Box 93478 Las Vegas, NV 89193 3478 Disclaimer: The information in this document has been funded in part by the United States Environmental Protection Agency under interagency agreement number DW47939443 to the General Services Administration for a work assignment to Anteon Corporation. It has been subjected to the Agency's peer and administrative review and has been approved for publication as an EPA document. II. B. 6  Page 8 of 162 Preface The document entitled " Assessment of carbaryl exposure following turf application using a physiologically based pharmacokinetic model" was developed by EPA's National Exposure Research Laboratory in collaboration with the Office of Pesticide Programs ( OPP). This effort represents one part of EPA's on going efforts to improve risk assessment methodologies and approaches. This document and the relevant attachments and models reflect research efforts only. All results contained in this draft are considered preliminary in nature and therefore, OPP does not anticipate using them as part of the either the risk assessment for carbaryl or the cumulative risk assessment for N methyl carbamates. EPA issued the Interim Reregistration Eligibility Decision for carbaryl on June 30, 2003. That assessment contains EPA's most recent aggregate risk assessment for carbaryl. The IRED is available to the public at http:// www. epa. gov/ oppsrrd1/ REDs/ carbaryl_ ired. pdf. Moreover, given the preliminary nature of the methodology, it is important to note that they do not reflect current OPP risk assessment policies regarding evaluation of inter and intra species extrapolation. Specific considerations mandated by the Food Quality Protection Act ( FQPA) of 1996 such as aggregation of multiple pathways of exposure and specific consideration of potential sensitivity of infants and children are not considered here. As this methodology matures, OPP will need to examine how it can be incorporated into the overall approach to the assessment of risks to human health. II. B. 6  Page 9 of 162 Abstract Carbaryl ( 1 naphthol N methylcarbamate, CAS# 63252) is a widely used neurotoxic insecticide in agriculture, residential applications, and professional turf management. A member of the N methylcarbamate class of pesticides, carbaryl is a reversible inhibitor of acetylcholinesterase ( AChE).

A physiologically based pharmacokinetic ( PBPK) model was used to evaluate the available pharmacokinetic data and perform necessary species to species and route to route extrapolations to evaluate the dose metrics relevant to risk. The National Exposure Research Laboratory ( NERL) has developed a PBPK modeling system that includes a comprehensive description of chemical absorption, distribution, metabolism and excretion ( ADME), as well as the flexibility to evaluate diverse chemical inputs corresponding to different exposure scenarios. This model, the Exposure Related Dose Estimating Model ( ERDEM) has been used in the current assessment as a tool for aiding in risk characterization of carbaryl following turf exposure. The approach illustrated by this assessment is representative of the investigations possible with PBPK models.

The PBPK model was first developed to be consistent with rat data from the literature and registrant, Bayer CropScience. The human scenarios were then implemented by substituting human physiologic parameters in the PBPK model. Oral hand to mouth and dermal exposure scenarios were implemented for children, and the degree of acetylcholinesterase inhibition and tissue concentrations were evaluated. The magnitude of exposure was constrained by a biomonitoring study conducted by Bayer CropScience that measured carbaryl metabolite mass in urine. The urine metabolite levels corresponded to absorbed doses of approximately 0.08 mg/ kg, relative to the no observed adverse effect level ( NOAEL) of 1 mg/ kg.

Acetylcholinesterase activity based on the assumptions in this analysis was predicted to remain > 99% of the baseline levels for the children's exposure scenarios. The simulated scenarios were intentionally designed to be conservative. All the excreted metabolite was associated with a single activity period, which results in high peak tissue concentrations. The expected variability in model parameters associated with brain concentration levels and acetylcholinesterase inhibition were investigated. Minimal changes were observed for the predicted acetylcholinesterase inhibition, but peak brain concentrations were found to be dependent on values used for the blood: brain partition coefficient and the metabolism rates. Additional data on these pathways would reduce the uncertainties in the assessment of carbaryl. ( Note: The results contained in this report are preliminary in nature and do not reflect current OPP and EPA policies regarding evaluation of inter and intra species extrapolation, aggregate risk, and application of the FQPA safety factor for potential sensitivity of infants and children). II. B. 6  Page 10 of 162 Table of Contents 1.0 INTRODUCTION..................................................................................... 11 1.1 Overview.................................................................................................... 11 1.2 Carbaryl ADME Characteristics............................................................... 11 1.2.1 Absorption.................................................................................................. 14 1.2.2 Distribution................................................................................................. 15 1.2.3 Metabolism and Excretion........................................................................ 16 1.2.4 Carbaryl Pharmacodynamics................................................................... 18 2.0 METHODS................................................................................................. 18 2.1 PBPK Models ............................................................................................ 19 2.1.1 Exposure Related Dose Estimating Model ( ERDEM) ........................... 19 2.1.2 Carbaryl Model Structure in ERDEM ...................................................... 21 2.2 PBPK/ PD Model Parameters ................................................................... 28 2.2.1 Physiological Parameters......................................................................... 28 2.2.2 Physiochemical Parameters .................................................................... 30 2.2.3 Biochemical Parameters .......................................................................... 32 2.2.4 Pharmacodynamic Parameters ............................................................... 34 2.3 Exposure Pathways Following Turf Application ..................................... 35 3.0 RESULTS................................................................................................ 37 3.1 Overview.................................................................................................... 37 3.2 Rat Model................................................................................................... 37 3.3 Oral Exposure by Hand to Mouth Activities ........................................... 45 3.3.1 Estimates of absorbed dose from oral exposure ................................... 46 3.3.2 Estimates of blood and tissue concentrations following oral exposure47 3.4 Dermal Exposure ...................................................................................... 48 3.4.1 Estimates of absorbed dose from dermal exposure.............................. 49 3.4.2 Estimates of blood and tissue concentrations following dermal exposure .................................................................................................... 50 4.0 DISCUSSION .......................................................................................... 52 5.0 REFERENCES........................................................................................ 58 Appendix A. Model ........................................................................................... 63 Appendix B. QA................................................................................................ 96 Appendix C. Biomonitoring Study Data Analysis........................................ 103 Appendix D. Cardiac Output and Partition Coefficients ............................. 110 Appendix E. Additional Model Simulation Results...................................... 134 II. B. 6  Page 11 of 162 1.0 INTRODUCTION 1.1 Overview Carbaryl ( 1 naphthol N methylcarbamate, CAS# 63252) is a widely used neurotoxic insecticide in agriculture, residential applications, and professional turf management. A member of the N methylcarbamate class of pesticides, carbaryl is a reversible inhibitor of acetylcholinesterase ( AChE). In response to the widespread use of this carbamate pesticide, the U. S. EPA evaluated the risks of carbaryl and, in June 2003, reached an Interim Reregistration Eligibility Decision ( IRED). The IRED identifies additional pharmacokinetic data as a means to refine post application risks from broadcast applications to turf lawns with liquid formulations.

At the present time, appropriate pharmacokinetic studies with blood or urine levels of carbaryl or its metabolites in humans exposed to carbaryl following turf application are not available. Biomonitoring studies, such as Bayer 2004b, provide information on the amount of chemical absorbed over the observation period, but it is not feasible to record or control all exposure activities. The relevant health effect, acetylcholinesterase inhibition, occurs over a much shorter time scale ( hours) than can be estimated from daily urine collection. However, physiologically based pharmacokinetic ( PBPK) models may be used to evaluate the available pharmacokinetic data and perform necessary species to species and route to route extrapolations to evaluate the dose metrics relevant to risk. PBPK models describe the time course disposition of chemicals and their metabolites. PBPK models are well suited to help assess risk from carbaryl following turf application. They provide a representation of the absorption, distribution, metabolism, and excretion ( ADME) of xenobiotics that are believed to contribute to the potential of inducing adverse human health responses.

The National Exposure Research Laboratory ( NERL) has developed a PBPK modeling system that includes a comprehensive description of chemical ADME, as well as the flexibility to evaluate diverse chemical inputs corresponding to different exposure scenarios. This model, ERDEM ( Exposure Related Dose Estimating Model) has been used in the current assessment as a tool for aiding in risk characterization of carbaryl following exposure to treated turf. The approach illustrated by this assessment is representative of the investigations possible for the cumulative assessment for the N methyl carbamate pesticides.

1.2 Carbaryl ADME Characteristics Pharmacokinetic models provide a framework in which to organize the available pharmacokinetic and pharmacodynamic ( PD) data. The laboratory studies in rats are suitable for characterizing various ADME and PD behaviors of carbaryl, dependent on the type of study and measurements collected. The studies used II. B. 6  Page 12 of 162 to characterize the behavior of carbaryl, and ultimately for development of the PBPK/ PD model are listed in Table 1. II. B. 6  Page 13 of 162 Table 1. Pharmacokinetic studies in rats for estimation of kinetic parameters Source Scenario Measurements Bayer 2004a Oral 1 mg/ kg Brain 14C, blood 14C Bayer 2004a Oral 10 mg/ kg Brain 14C, blood 14C, brain carbaryl, brain naphthol, fat 14C, liver 14C, plasma naphthyl sulfate, plasma naphthol, plasma carbaryl ( n. d.) Bayer 2004a IV 1 mg/ kg Brain 14C, blood 14C Bayer 2004a IV 10 mg/ kg Brain 14C, blood 14C, brain carbaryl, brain naphthol, fat 14C, liver 14C, plasma naphthyl sulfate, plasma naphthol, plasma carbaryl, fat carbaryl, fat naphthol, liver carbaryl, liver naphthol Bayer 2004a Oral 0.075 mg/ kg x2 with dermal 0.75 mg/ kg Brain 14C, blood 14C, brain carbaryl, brain naphthol Brooks and Broxup 1995a Oral 10, 50, 125 mg/ kg Blood, brain cholinesterase inhibition Brooks and Broxup 1995b Oral 10, 30, 90 mg/ kg Blood, brain cholinesterase inhibition Cheng 1994 Dermal 0.793 mg Urine 14C, total carcass 14C, blood 14C, skin carbaryl, skin surface carbaryl Knaak et al. 1984 Dermal 1.74 mg Urine 14C, fat 14C, liver 14C, rapidly perfused tissue 14C, slowly perfused tissue 14C, kidney 14C, blood 14C, skin carbaryl, skin surface carbaryl, evaporated carbaryl Knaak et al., 1965 Oral and IP doses of 20 mg/ kg Major urine metabolites: urine Naphthyl sulfate, urine naphthyl glucuronide, urine 4 OH carbaryl sulfate, urine 4 OH carbaryl glucuronide, urine 5,6 DIOH carbaryl glucuronide Marshall and Dorough 1979 Oral dose 0.01 mg/ kg to bile cannulated animals Urine 14C, bile 14C II. B. 6  Page 14 of 162 1.2.1 Absorption Important absorption pathways following turf application are dermal for direct contact with treated surfaces and oral resulting from hand to mouth activities. Several types of studies are considered to characterize absorption. The comparison of intravenous ( IV) studies to oral or dermal studies illustrates the kinetics of absorption. The completeness of the absorption is characterized by comparison to IV studies, and by total mass balance studies that account for the excretion pathways.

Oral absorption of carbaryl into the blood is rapid, where peak blood concentrations of 14C ( ring label) occur within 15 minutes in rats after dosing with 1 mg/ kg ( Figure 1). The fast kinetics from Bayer 2004a of oral absorption are consistent with other oral studies ( Casper et al. 1973, Ahdaya and Guthrie 1982).

0 0.2 0.4 0.6 0.8 1  5 0 5 10 15 20 25 Time ( hours) ppm 14C residue Figure 1. Blood 14C concentrations ( ring label) after 1 mg/ kg carbaryl oral dosing in rats. Data from Bayer 2004a, average of 4 animals at each sample time.

Oral absorption is essentially complete, where the peak concentration observed after an oral dose of 1 mg/ kg ( Figure 1) is similar to the peak observed after 1 mg/ kg IV dose ( Bayer 2004a). This observation is consistent with the high percent recovery of radiolabeled carbaryl in urine (> 85%) relative to feces (< 10%, Bayer 2004b, Struble 1994).

As expected, dermal absorption is slower, where peak concentrations in blood are observed 10 12 hours after initial application ( Figure 2). The slower kinetics of dermal absorption are consistent with other dermal absorption studies ( Cheng 1994, Shah et al. 1981). II. B. 6  Page 15 of 162 0 0.00002 0.00004 0.00006 0.00008 0.0001 0.00012 0 50 100 150 200 Time ( hours) Concentration ( mmol/ L)

Figure 2. Blood 14C concentrations ( ring label) after 1.74 mg ( 20 cm2) carbaryl dermal application on rats. Data from Knaak et al. 1984, average of 3 animals at each sample time.

1.2.2 Distribution Once a chemical enters the blood stream, its disposition in blood, other fluids ( e. g. bile), organs and tissues determines its access to the site or sites of action. Chemical disposition involves distribution from blood and fluids to tissues and organs, metabolism in liver and other organs of metabolism, and elimination in exhaled breath, fluids, e. g. milk, and excreta.

Measurements of each specific chemical, the parent compound and metabolites, in the tissues of interest are ideal to assess distribution. Most of the available data are for 14C ( Table 1), which are not sufficient for characterization since each chemical distributes to the tissues differently based on its physical and chemical properties.

Due to its high octanol: water partition coefficient, carbaryl is expected to favor sequestration in tissues, while its more water soluble metabolites will favor the blood. The distribution of carbaryl to tissues is limited by its rapid metabolism, although its metabolites are available for distribution in the body for longer time periods. At a high intravenous ( IV) dose in rats, carbaryl was detected in blood, brain, liver and fat ( Bayer 2004a), and the sharp peak in brain ( Figure 3) provides II. B. 6  Page 16 of 162 evidence for the fast metabolism. Similar profiles were observed in the other compartments.

0 2 4 6 8 10 12 14 0 2 4 6 8 10 Time ( hours) Concentration ( mg/ L)

Figure 3. Brain carbaryl concentration after 10 mg/ kg carbaryl IV dose in rats. Data from Bayer 2004a, average of 4 animals at each sample time.

Measurements of 14C for a variety of doses and exposures illustrates the distribution of the metabolites. In addition to transport by the blood, the glucuronidated species are recirculated through the bile. 14C residues were found in blood ( Figures 1 and 2), fat, brain, liver, bile, and rapidly and slowly perfused tissues ( Bayer 2004a, Knaak et al. 1984, Marshall and Dorough 1979). Few measurements have been taken for specific chemicals in the tissues, but their identity can be inferred from metabolism studies.

1.2.3 Metabolism and Excretion The metabolism of carbamate pesticides has been the subject of several reviews ( Roberts and Hutson 1999, Cool and Jankowski 1985, Kuhr and Dorough 1976). The pathways are shown in Figure 4. II. B. 6  Page 17 of 162 Figure 4. Metabolism pathways for carbaryl. Adapted from Ross and Driver 2002.

The metabolism of carbaryl in rats has been studied by Knaak et al. ( 1965), Hassan et al. ( 1966), Bend et al. ( 1971), and Sullivan et al. ( 1972). A comparison of the metabolite profiles in urine showed that the rat metabolism pathways were conserved in humans ( Knaak et al. 1965; Knaak et al. 1968).

The primary metabolites of carbaryl are 4 hydroxy carbaryl, 3,4 dihydroxy carbaryl, 5,6 dihydroxycarbaryl, and 1 naphthol. Based on the chemistry, 5 hydroxycarbaryl and hydroxymethylcarbaryl are possible products, but these have not been detected in in vivo studies in rats or humans. The carbaryl products can be further metabolized to the corresponding naphthol species, and II. B. 6  Page 18 of 162 all the metabolites are subject to conjugation with sulfate or glucuronide. Conjugation increases the aqueous solubility to facilitate urinary excretion. Minimal excretion occurs via the feces ( Struble 1994, Krishna and Casida 1966). Hydroxylation reactions are catalyzed by liver microsomal P450 enzymes ( Tang et al. 2002), while hydrolases, including carboxylesterase ( Sogorb et al. 2002), catalyze the hydrolysis reactions.

Following an intraperitoneal dose of 20 mg/ kg 14C carbaryl ( ring label) in rats, the major urinary metabolites identified after 24 hours were naphthyl sulfate, naphthyl glucuronide, 4 hydroxycarbaryl glucuronide, 4 hydroxycarbaryl sulfate, and 5,6 dihydroxycarbaryl glucuronide ( Knaak et al. 1965, Sullivan et al. 1972).

Measurements of total 14C in rat urine indicate that excretion of carbaryl and its metabolites after an oral dose occurs within 1 3 days ( Knaak et al. 1965, Benson and Dorough 1984). Excretion occurs over a longer time scale following dermal exposure due to the slower absorption rate that results in continuous absorption over a longer period and susceptibility of the carbaryl on the skin surface to other removal mechanisms, i. e., evaporation ( Knaak et al. 1984, Cheng 1994).

1.2.4 Carbaryl Pharmacodynamics Acetylcholinesterase ( AChE) inhibition in the blood and brain is a health effect of interest for the N methyl carbamates, including carbaryl. The inhibition occurs through carbamylation of the serine hydroxyl group located in the active site of the enzyme, and results in accumulation of acetylcholine at a nerve synapse or neuromuscular junction. Continued accumulation of the neurotransmitter acetylcholine may result in the overstimulation of cholinergic pathways in the central and peripheral nervous systems and possibly to the expression of cholinergic signs and symptoms such as nausea, gastrointestinal distress, vomiting, tremors, paralysis and depression of respiratory function.

Generally, cholinesterase ( ChE) inhibiting chemicals compete with the acetylcholine for binding to the enzyme ( AChE). As more ChE inhibiting chemical binds with the enzyme, the acetylcholine is subject to slower or less hydrolysis and its activity is prolonged. In rat studies, carbaryl was given orally at doses from 10 to 125 mg/ kg, and the blood and brain ChE activity was measured over time. The time scale of ChE recovery to baseline activity levels was observed to be less than a day ( Brooks and Broxup 1995a, b).

2.0 METHODS The objective of this study is to develop a structure to organize and evaluate the existing data on carbaryl. Physiologically based pharmacokinetic models ( PBPK), with integrated PK components, are suitable for this purpose since the known physiologic and biochemical processes that affect chemical ADME are explicitly represented. The deterministic framework of the model enables the II. B. 6  Page 19 of 162 extension of the model to other exposure scenarios ( route to route extrapolation), and provides the basis for systematic scaling among species and age groups.

An established PBPK modeling system was utilized, the Exposure Related Dose Estimating Model ( ERDEM). The system is adapted for the carbaryl scenarios by implementing parameters specific to the subjects and chemicals. The suitability of these parameter values is then evaluated by comparing the model results to the available data.

2.1 PBPK Models 2.1.1 Exposure Related Dose Estimating Model ( ERDEM)

ERDEM is an exposure and dose modeling system developed by ORD scientists. The heart of ERDEM is a physiologically based pharmacokinetic ( PBPK) model that simulates the absorption, distribution, metabolism, and elimination of chemicals in mammals. Simulated chemicals are introduced into the physiological system by any of several routes including injection, ingestion, inhalation, and/ or dermal absorption. The ERDEM system is complex and flexible, with over 30 physiological compartments such as arterial and venous blood, brain, derma, fat, intestine, kidney, liver, rapidly and slowly perfused tissue, and stomach ( Figure 5). Any subset of compartments can be included for the PBPK model of a specific chemical. II. B. 6  Page 20 of 162 LV Liver CR Carcass KD Kidney FT Fat SL Slowly Perfused RP Rapidly Perfused DR Dermis VB Venous AB Arterial Inputs Bolus Dose Ingestions Rate Ingestions Intraperitoneal Injection Kidney Metabolites...

Kidney Elimination Intramuscular injection Skin Surface Bolus Dose Injections Infusions Open Chamber Inhalation Open Chamber Exhalation CC Closed Chamber Inhalation Liver Metabolites Carcass Metabolites QBCR QBLV QBKD QBFT QBSL QBRP QBDR PU Lung ( Pulmonary)

Lung Metabolites QA QB GI Tract Lymph Flow Bile Flow Portal Blood Arterial Blood SP Spleen Fat Metabolites Slowly Perfused Metabolites Rapidly Perfused Metabolites QBSP Spleen Metabolites BR Brain QBBR Brain Metabolites There are N chemicals modeled. Most compartments have binding, elimination, and metabolism.

Enzyme inhibition is modeled in brain, blood, kidney, and liver.

There are up to K metabolites of up to N chemicals.

There is binding modeled in the blood.

Figure 5. ERDEM System Flow with Static Lung and Full GI Metabolism can occur in many of these compartments and multiple metabolites are tracked as they and the parent compound( s) circulate through the system. It is important to note that adjustments are made for differences in metabolism and physiology between children and adults. ERDEM is programmed in the Advanced Continuous Simulation Language ( ACSL). To execute ERDEM, the II. B. 6  Page 21 of 162 user enters physiological, biological, and pharmacodynamic modeling data specific to their chemical and/ or scenario of interest ( U. S. EPA, 2002).

Any PBPK model, including ERDEM, is made up of a series of the differential equations which describe the rates of inflow, distribution, metabolism, or outflow of a chemical and various metabolites in each separate biological compartment. Appendix A describes in detail the equations for each of the biological compartments contained in ERDEM.

2.1.2 Carbaryl Model Structure in ERDEM The structure of the carbaryl model is defined by the scenarios and metrics of interest, and available data for evaluation. Exposure following turf application is expected to occur through the dermal and oral routes. The relevant dose and effect metrics for carbaryl are the concentrations in blood and brain, and level of acetylcholinesterase inhibition in those compartments.

Based on the model requirements and available measurements, the model for carbaryl includes the following compartments: blood ( venous, arterial, portal), brain, liver, fat, rapidly and slowly perfused tissues, and bile. The liver is required as the primary site of metabolism, the brain as the target tissue, and kidney as the site of urinary elimination. Also, the skin, stomach and other GI tract components are modeled to account for the absorption routes of interest.

<IP_ADDRESS> Absorption Model in ERDEM Mathematical representations of oral and dermal absorption are included in ERDEM to simulate exposure following turf application. Dermal absorption occurs due to direct contact with treated surfaces, while oral absorption would occur through hand to mouth activities of young children.

Oral absorption is modeled as a first order kinetic process as transfer from the luminal volume to the walls of the GI tract. Chemical can be input as either a bolus dose or as a rate process into the stomach lumen. The stomach and other GI components ( duodenum, small and large intestine, colon) are modeled, with absorption in each compartment, and transfer from the stomach to the remainder of the GI tract. The equation for flow through the stomach lumen is below:

i i i i i i STL SW NL STL STL F BIG RIG STL A K C Q dt dA dt dA dt dA , ,     + =

where i STL A = amount of ith chemical in stomach lumen i STL C = concentration of ith chemical in stomach lumen II. B. 6  Page 22 of 162 dt dA i STL = the rate of change of the ith chemical in the stomach lumen STL F Q , = the volume rate of food flowing through the stomach lumen to the duodenum i SW NL K , = the rate constant for the amount of the ith chemical from the stomach lumen to the stomach wall dt dA i RIG = the rate of change of the amount of the ith chemical as a rate ingestion dt dA i BIG = the rate of change of the amount of the ith chemical as a bolus ingestion Complete equations for GI absorption are in Appendix A.

Two compartments are used for GI absorption ( stomach; and lumped duodenum, small and large intestines, colon) to account for the known biological processes that affect the ADME of carbaryl. The glucuronidated metabolites of carbaryl are known to circulate from the liver to the duodenum through the bile, so the lumped compartment is necessary.

In the dermal model, movement of carbaryl from the vehicle into the skin is determined by the concentration ( mg/ L) of carbaryl on the skin surface, the area of exposed skin ( cm2), and permeation coefficient ( cm/ hr) as follows:

dA sks dr i dt K sks dr prm i A sk C sks i ,

, , =

where C A V sks sks sk i i =

and A sk = area of skin exposed V sk = volume of skin exposed A sks i = amount of the ith chemical on the skin K sks dr prm i , , = the permeation coefficient for the ith chemical from the skin surface to dermis II. B. 6  Page 23 of 162 dA sks dr i dt , = the rate that the ith chemical moves from the skin surface into the dermis The mass of carbaryl absorbed into the skin over the exposure period slowly enters the vascular compartment ( Figure 2). Mass on the skin surface may also be removed by evaporation or washing. Only the mass absorbed into the skin contributed to the internal dose. The complete equations for dermal exposure are in Appendix A.

<IP_ADDRESS> Distribution Model in ERDEM Distribution of chemicals from the blood to tissues is modeled as perfusion limited, where it is dependent on the blood flow rate and equilibrium partition coefficient between the blood and tissue. The transport of the ith chemical to the brain ( BN) through the circulation is described below:

V dC dt Q C Q C R BN BN B BN AB B BN BN BN VB i i i i =   , , ,

where V BN = volume of the brain Q B BN , = blood flow to the brain C BNi = concentration of ith chemical in the brain C ABi = concentration of ith chemical in the arterial blood R BN VBi , = partition coefficient for the ith chemical between the brain and blood The chemical in tissues is also subject to changes due to metabolism and excretion. The complete equations are described in Appendix A.

<IP_ADDRESS> Metabolism Model in ERDEM For this assessment, metabolism is modeled to occur in the liver. Based on the metabolism studies ( Knaak et al. 1965), the pathways to the following metabolites are included in the model: naphthyl sulfate, naphthyl glucuronide, 4 hydroxycarbaryl glucuronide, 4 hydroxycarbaryl sulfate, and 5,6 dihydroxycarbaryl glucuronide. Other metabolites consistent with the carbaryl scheme account for the unidentified species in the studies. The modeled pathways are summarized in Table 2. II. B. 6  Page 24 of 162 Table 2. Metabolism Structure for Carbaryl in Rats and Humans Metabolic Reaction Enzyme Compartment Carbaryl to 4 OH carbaryl Cytochrome P450 isozymes liver Carbaryl to 3,4 diOH carbaryl Cytochrome P450 isozymes liver Carbaryl to 5,6 diOH carbaryl Cytochrome P450 isozymes liver Carbaryl to 1 naphthol hydrolases liver 4 OH carbaryl to 4 OH carbaryl glucuronide UDPGA transferase liver 4 OH carbaryl to 4 OH carbaryl sulfate sulfotransferase liver 3,4 diOH carbaryl to 3,4 diOH 1 naphthol hydrolases liver 3,4 diOH carbaryl to 3,4 diOH carbaryl glucuronide UDPGA transferase liver 3,4 diOH carbaryl to 3,4 diOH carbaryl sulfate sulfotransferase liver 5,6 diOH carbaryl to 5,6 diOH carbaryl glucuronide UDPGA transferase liver 1 Naphthol to 3,4 diOH 1 naphthol Cytochrome P450 isozymes liver 1 Naphthol to 1 naphthyl glucuronide UDPGA transferase liver 1 Naphthol to 1 naphthyl sulfate sulfotransferase liver 3,4 diOH 1 naphthol to 3,4 diOH 1 naphthyl glucuronide UDPGA transferase liver 3,4 diOH 1 naphthol to 3,4 diOH 1 naphthyl sulfate sulfotransferase liver A saturable Michaelis Menten form of metabolism was assumed:

dA dt V C K C LV M i j LV i j LV i m LV i j LV i , , , max, , , ,

, ,, , = +

where the formation rate of the jth metabolite from the ith chemical in the liver is determined from the maximum velocity of metabolism ( Vmax), the Michaelis Menten constant ( Km) and the concentration of the ith chemical in the liver. II. B. 6  Page 25 of 162 The parameters Vmax and Km, were determined using ERDEM by adjusting these values to fit to the available data ( see Results). The Vmax was scaled as to body weight at the 0.7 power for the different rat body weights. Vmax was further scaled from the rat to the human according to age ( 3 and 9 years of age).

V V V V LV i j LV US i j BW BW f max, , , max, , , , , Re . ( ) = 0 7 where the subscript US represents the unscaled quantity. The Vmax and Km values, obtained after a number of iterations of comparing model runs with multiple sets of experimental data for the rat, were then used in the modeling of the human. This is an important quality assurance step ( see Appendix B) for establishing model parameters based on experimental data.

The total metabolism rate for carbaryl is expected to scale from rat to human, but the ratio of products based on the relative rates of each specific pathway may not be representative of actual human metabolism. The few human studies show evidence of different ratios of enzyme activity, resulting in a different metabolite profile between the rat and man ( Knaak et al. 1965). The model would benefit from additional metabolism data in humans from which to determine appropriate values for rate constant ratios in man.

<IP_ADDRESS> Excretion Model in ERDEM The urine elimination rate for the ith chemical was modeled as saturable, determined from the concentration of the ith chemical in the kidney and the urine elimination rate constants ( Vm and KmM) for the ith chemical.

dA dt V C K C Urine i mUrine i KD i mM Urine i KD i ,

, , ,

, , , = +

Elimination rate constants were determined for the rat by fitting the available data ( see Results) to the model. The scaling for the rat Vmax was set by body weight to the 0.7 power.

V V V V mUrine i mUrine US i BW BW f , , , , , , Re . ( ) = 0 7 where the subscript US represents the unscaled quantity. The human values for the urine elimination rate constant specific to each age group were scaled from the rat to the human based on body weight. II. B. 6  Page 26 of 162 <IP_ADDRESS> Acetylcholinesterase Inhibition Model in ERDEM The following outlines the basic process of ChE inhibition for a single N methyl carbamate pesticide.

1. There is a certain amount of ChE in each tissue and a certain amount is synthesized to keep this level at a near physiological steady state ( Ks). This is a basic physiological process independent of any foreign chemicals entering the system.

2. A certain amount of enzyme is degraded ( Kd). This also reduces the amount of free enzyme available to perform its normal physiological function. When no inhibitor is present this degradation process is balanced by the synthesis described above. However in the presence of inhibitor, the formation of the complex can be thought of as another stress that reduces the amount of enzyme available for normal physiological function. This reduces the activity of the enzyme on its normal physiological substrate, acetylcholine at the neurological site.

3. Inhibitors, such as the N methyl carbamates, enter the system and reduce the amount of free enzyme by forming a complex with the enzyme. The enzyme that is complexed with the ChE inhibiting chemical is no longer available to perform its normal physiological activity leading to the build up of acetylcholine. ( Each N methyl carbamate pesticide has a unique rate constant for the formation of the complex with the enzyme, Ki).

4. The enzyme inhibitor complex in turn reacts to result in a break down of the ChE inhibiting chemical and a return or regeneration of free enzyme. This process is also governed by a chemical specific rate constant, Kr. The period of inhibition varies for different compounds and is generally dependent upon the rate of regeneration. Because the period of inhibition is often brief ( due to rapid regeneration), the whole process has been dubbed as ` reversible'.

Figure 6 summarizes this process. The " released metabolite" represents the Nmethyl carbamate that is broken down. Each carbamate has its own specific rate constants for the process. Any number of N methyl carbamates can interact at same time or at any time with the free AChE. II. B. 6  Page 27 of 162 Figure 6. Schematic diagram of N methyl carbamates binding to AChE.

The following differential equations represent the mass balance for the Figure 6:

j rj jx ij x xj j j rj jx j ij d x s x INce K C K Ace dt dINce INce K C K K Ace K dt dAce ×   × × = × +

      × + ×   =

where Acex is the amount of AChE ( µ mol) in compartment x, INcexj is the amount ( µ mol) complex of AChE and inhibitor j in compartment x, Ks is zero order rate of enzyme synthesis, Kd is the first order rate of enzyme degradation ( hr 1), Kij is the bimolecular rate of inhibition for jth inhibitor, Krj is the first order rate of regeneration for jth complex, subscript x indicates tissue compartment, and subscript j indicates the identity of the inhibiting chemical. II. B. 6  Page 28 of 162 Thus, the total amount of active enzyme is equal to the amount present in the system minus the amount degraded minus the amount forming a complex with the inhibitor plus the amount regenerated after the enzyme breaks down or metabolizes the inhibitor.

This assessment considers only a single carbamate, carbaryl. The PD model integrated with the PBPK model in the compartments where acetylcholinesterase inhibition occurs enables the evaluation of the relevant health effects. The carbaryl model provides a basis for the consideration of multiple compounds acting in combination at any of the steps outlined above. The simplest interaction would be simply adding the inhibition caused by each compound. In such cases, depending upon the specific rate constants, different chemical molecules would each contribute to enzyme inhibition. It might be possible however that interaction would involve competition between the various chemicals for binding with the enzyme. If data suggest that interactions between the N methyl carbamates other than dose additive ones are observed, these will be included in future modeling efforts.

2.2 PBPK/ PD Model Parameters Parameters for PBPK models include three distinct types of data: physiological, physiochemical, and biochemical. The physiological data are independent of the chemical being modeled and refer to such things as organ volumes and blood flows. Distribution within, between and among organs, tissues, and fluid is modeled according to compartmental volumes, blood flow rates, and blood tissue partitioning. Compartments are modeled in ERDEM based on the information available for the exposure to a particular chemical or chemicals and the metabolites. The compartments used for a metabolite may be a subset of those used for the parent chemical.

2.2.1 Physiological Parameters The body volume is determined for each species, where the rat values were measured in the experimental studies, and human values are based on age. The compartment volumes are expressed as a percentage of the body volume ( Table 3). These compartments are the components that are active in the carbaryl model in ERDEM ( Figure 5). II. B. 6  Page 29 of 162 Table 3. Volumes of Compartments for Humans by Percentage for PBPK Modeling with ERDEM rat human Ages adult 3 9 Volume of the Body ( kg) a as measured 15 20.4 Compartments (% of Volume of the Body)

Arterial Blood 1.75 1.75 1.75 Brain f 1.21 2.1 2.1 Dermis b 5.1 5.1 5.1 Fat c, g 6 11.5 11.5 GI Tract Walls h ( not including stomach) 1.7 1.7 1.7 Kidney b, h 0.9 0.4 0.4 Liver d, h 3.55 2.6 2.6 Portal Blood 0.5 0.5 0.5 Rapidly Perfused Tissue d 1.24 3 3 Slowly Perfused Tissue e 73.9 67.2 67.2 Stomach Wall h 0.4 0.4 0.4 Venous Blood 3.75 3.75 3.75 a. Assume density of 1 L/ kg b. Value from Corley et al. ( 1990) c. Boot et al., 1997, the age range is 4 21 years in both males and females d. Fisher et al. ( 1998) Adjusted based on fat volume percentage. e. Value estimated from the Fat content using Fisher et al. ( 1998) f. Estimated from many sources, Milner ( 1990) g. Fisher et al. 1991 h. ILSI 1994 II. B. 6  Page 30 of 162 Cardiac output is determined for each species and human demographic group. The compartment blood flows are specified as a percentage of the cardiac output. The compartments requiring blood flow input are the brain, liver, kidney, fat, dermis, slowly perfused tissue ( muscle), rapidly perfused tissue, and the walls of the GI tract. The same blood flow percentages are used for each age group ( Table 4).

Table 4. Blood Flows ( Percentage of Cardiac Output)

rat human Age adult 3 Years 9 Years Cardiac Output ( L/ hr) a, c 5.66 303 337 Compartment Blood Flow by Compartment Percentage of Cardiac Output Brain b, d 3 10 10 Dermis b 7.4 7.4 7.4 Fat b, c 9 5 5 GI Tract Wall e ( not including stomach) 9.4 9.4 9.4 Kidney b, d 17.4 13.5 13.5 Liver b, c 20 20.8 20.8 Rapidly Perfused Tissue b, c 17.7 17.8 17.8 Slowly Perfused Tissue b, c 15 15 15 Stomach Wall e 1.1 1.1 1.1 a. Agata et al ( 1994), Schmitz et al, ( 1998), See Appendix C b. Fisher et al. ( 1998). No age adjustment was made for human blood flow percentages. c. Fisher et al. 1991 d. Keys et al. 2003 e. ICRP 2002 2.2.2 Physiochemical Parameters The distribution of carbamate pesticides and their metabolites between body tissues and sub cellular organelles is largely dependent upon the manner in which they partition between water and lipids ( Poulin and Theil 2000). The partitioning behavior has been associated with the chemical structure, enabling parameter estimates from quantitative structure activity relationships ( QSAR). The derivation of carbaryl specific tissues to blood partition coefficients is II. B. 6  Page 31 of 162 examined in Appendix D. A summary of partition coefficients for each physiological compartment in relation to the blood flow are presented in Table 5.

Table 5. Partition Coefficients for Carbaryl and Metabolites Chemical Compartment to Venous Blood Carbaryl 4 OH carbaryl 3,4 DIOH carbaryl 5,6 DIOH carbaryl Naphthol 3,4 DIOH Naphthol Brain/ Blood b 1 1 1 0.1 1 0.3 Dermis/ Blood 5.45 a n/ a n/ a n/ a n/ a n/ a Fat/ Blood 5.5 b 4.81 a 5.07 a 0.17 a 10 b 0.31 a GI/ Blood c 4.11 2.12 2.18 0.84 1 0.9 Kidney/ Blood 4.11 a 2.12 a 2.18 a 0.84 a 1 b 0.9 a Liver/ Blood 4.41 a 2.2 a 2.27 a 0.78 a 1 b 0.84 a Rapidly Perfused Tissue/ Blood 1.5 b 2.12 c 2.18 c 0.84 c 1 b 0.9 c Slowly Perfused Tissue/ Blood 1 b 1.55 a 1.59 a 0.81 a 1 b 0.84 a a. Calculated using QSAR techniques of Poulin and Theil ( 2000), see Appendix 4. b. Based on consistency with pharmacokinetic data ( Table 5) and QSAR considerations c. Same as kidney ( other rapidly perfused tissue) n/ a. The Dermal compartment is not active for this chemical.

The tissue: blood partition coefficients for the other metabolites and tissues were assumed to be 1, except that the brain: blood value was set to a low value ( 0.1) for the glucuronides. The partition coefficients for the downstream metabolites have a minor impact on the simulation results relative to the 14C measurements.

The QSAR predictions based on tissue composition are typically within a factor of 3 of the in vitro measured value ( Poulin and Theil 2000, Payne and Kenny 2002). The values in Table 5 that are based on the pharmacokinetic data are within a factor of 3 of the QSAR predicted value, except for the brain: blood partition coefficients that are significantly less than the QSAR predicted values. Large disagreements between the actual and QSAR values have been attributed to active transport mechanisms at the blood: barrier ( Liu et al. 2004), but this phenomenon has not been studied for carbaryl or its metabolites. II. B. 6  Page 32 of 162 2.2.3 Biochemical Parameters The biochemical parameters include the rate constants that describe the rate at which chemical ADME occurs. These parameters have been fit to available pharmacokinetic studies in rats, and scaled to humans as described previously ( Section 2.1.2). The studies are listed in Table 1.

Chemical absorption parameters were estimated based on the rate of appearance of 14C or carbaryl in blood. The fast appearance after oral dosing ( Figure 1) indicates that a significant amount of the carbaryl dose is absorbed in the stomach. The data from bile cannulated rats ( Marshall and Dorough 1979) indicate that reabsorption of the glucuronides occurs more slowly than the parent compound. Dermal absorption occurs on a slower time scale ( Figure 2), and for modeling purposes, is assumed to occur through a depth of 1 cm of skin. The parameters associated with absorption are shown in Table 6.

Table 6. Carbaryl absorption parameters Parameter Value Units Carbaryl absorption rate ( stomach) 27.3 1/( h kg0.25)

Food flow, stomach to remaining GI 0.0436 L/( h kg0.74)

Carbaryl absorption rate ( GI) 10 1/( h kg0.25)

Glucuronide recirculation rate, liver to GI through bile 1.5 1/( h kg0.25)

Glucuronide absorption rate ( GI) 1 1/( h kg0.25)

Carbaryl dermal permeation coefficient 0.012 cm/ h Effective skin depth 1 cm The metabolism processes are defined by saturable Michaelis Menten kinetics. The parameters are the maximum velocity of metabolism ( Vmax) and the Michaelis Menten constant, Km.

The total metabolism rate of the parent compound was found to be constrained by the plasma 14C data, and carbaryl specific data where available. The metabolism to specific chemicals was constrained by the species specific data in tissues, blood, and urine. The reported values in Table 7 reasonably simulate the study results in Table 1. An emphasis was placed on the low dose studies since those are expected to be closer to the exposures resulting from the turf application. II. B. 6  Page 33 of 162 Table 7. Metabolism Rates for carbaryl in Rats and Humans Metabolic Reaction Enzyme Compartment Vmax mM/( hr kg 0.7) Km ( mM)

Carbaryl to 4 OH carbaryl Cytochrome P450 isozymes liver 0.021 0.05 Carbaryl to 3,4 diOH carbaryl Cytochrome P450 isozymes liver 0.034 0.04 Carbaryl to 5,6 diOH carbaryl Cytochrome P450 isozymes liver 0.016 0.04 Carbaryl to 1 naphthol hydrolases liver 0.071 0.048 4 OH carbaryl to 4 OH carbaryl glucuronide UDPGA transferase liver 0.012 0.01 4 OH carbaryl to 4 OH carbaryl sulfate sulfotransferase liver 0.002 0.01 3,4 diOH carbaryl to 3,4 diOH 1 naphthol hydrolases liver 0.009 0.01 3,4 diOH carbaryl to 3,4 diOH carbaryl glucuronide UDPGA transferase liver 0.009 0.01 3,4 diOH carbaryl to 3,4 diOH carbaryl sulfate sulfotransferase liver 0.007 0.01 5,6 diOH carbaryl to 5,6 diOH carbaryl glucuronide UDPGA transferase liver 0.02 0.01 1 Naphthol to 3,4 diOH 1 naphthol Cytochrome P450 isozymes liver 0.073 0.013 1 Naphthol to 1 naphthyl glucuronide UDPGA transferase liver 0.09 0.01 1 Naphthol to 1 naphthyl sulfate sulfotransferase liver 0.05 0.01 3,4 diOH 1 naphthol to 3,4 diOH 1 naphthyl glucuronide UDPGA transferase liver 0.01 0.01 3,4 diOH 1 naphthol to 3,4 diOH 1 naphthyl sulfate sulfotransferase liver 0.005 0.01 In vitro assays exist to estimate the metabolism rates ( Tang et al. 2002, Lipscomb et al. 1998), but the appropriate methods and models by which to scale up to the in vivo system require further research ( Lipscomb et al. 1998).

Urinary elimination is also modeled as a saturable process. The rate constants were initially set relative to the metabolism rates, so that the glucuronidated and sulfated conjugates would be rapidly eliminated after formation. The specific values were constrained by the 14C profiles in urine, as well as by the specific metabolite data in urine. Also, the elimination constant values are weakly constrained by the tissue and blood profiles. The elimination parameters for metabolites modeled by ERDEM are presented in Table 8. II. B. 6  Page 34 of 162 Table 8. Parameters for Urine Elimination Metabolite Vmax mM/( hr kg0.7) Km ( mM)

4 OH carbaryl 0.0001 0.005 3,4 DIOH carbaryl 0.0001 0.005 5,6 DIOH carbaryl 0.0001 0.005 Naphthol 0.0002 0.005 3,4 DIOH naphthol 0.001 0.005 Naphthyl sulfate 0.004 0.005 Naphthyl glucuronide 0.004 0.005 3,4 DIOH naphthyl sulfate 0.002 0.005 3,4 DIOH naphthyl glucuronide 0.002 0.005 4 OH carbaryl sulfate 0.002 0.005 4 OH carbaryl glucuronide 0.002 0.005 3,4 DIOH carbaryl sulfate 0.002 0.005 3,4 DIOH carbaryl glucuronide 0.002 0.005 5,6 DIOH carbaryl glucuronide 0.002 0.005 2.2.4 Pharmacodynamic Parameters The pharmacodynamic parameters for acetylcholinesterase inhibition are based on the kinetic profile of inhibition and recovery. Initial values were based on in vitro experiments ( Hetnarski and O'Brien 1975), and then refined based on the rat experiments ( Table 1). The parameter values must also be consistent with the concentration profiles in the compartments where inhibition occurs. The values for the acetylcholinesterase inhibition parameters ( Section <IP_ADDRESS>) are shown in Table 9. II. B. 6  Page 35 of 162 Table 9. Parameters for Acetylcholinesterase Inhibition Parameter Value Units Brain Baseline concentration 3.74 × 10 5 mM Inhibition Rate 250 1/( h mM) Regeneration Rate 1 1/ h Synthesis Rate 0.01 mM/ h Degradation Rate 0.001 1/ h Blood Baseline concentration 1.1 × 10 6 mM Inhibition Rate 200 1/( h mM) Regeneration Rate 1 1/ h Synthesis Rate 0.01 mM/ h Degradation Rate 0.001 1/ h 2.3 Exposure Pathways Following Turf Application Oral exposure through hand to mouth activities and dermal exposure were simulated as representative activities following turf application. Hand to mouth activities are characteristic of toddlers, and a 3 year old subject was implemented. An exposure frequency of 20 events/ hour over two hours was simulated, based on the Phase 5 ORE Risk Assessment ( U. S. EPA 2003). Hand to mouth activities are not expected to be significant for older children, where exposure is expected to occur through the dermal pathway. A 9 year old subject was implemented to be representative of older children. A skin area of 1000 cm2 was assumed to be exposed over 3.55 hours of contact time following turf application. The contact time is based on reported values from a biomonitoring study ( Bayer 2004c).

In the Bayer 2004 biomonitoring study, the potential absorbed doses of carbaryl to homeowners and residents were evaluated during and following the residential application of carbaryl by measuring urinary pesticide metabolite levels in the applicator, spouse, and children of representative families that use pesticides. Non professional adult and child volunteers were used to measure carbaryl absorbed doses in homeowners and their families during and after application of Sevin ® GardenTech Ready To Spray, a formulation of carbaryl. Sites were selected in Missouri and California. Ten families were monitored in Missouri and 13 families were monitored in California. The focus for this assessment was the exposure of the children.

The simulated absorbed doses in this assessment were based on the Bayer 2004 biomonitoring study. Based on the reported values of urinary 1 naphthol in the various children's age groups, distributions were created to establish likely ranges of metabolite excretion that would correspond to ranges of carbaryl absorbed dose ( Appendix C). The data analysis is based on the Missouri cohort II. B. 6  Page 36 of 162 since their application behavior was consistent with the label instructions of 4 lbs a. i./ acre. The Missouri group applied 2.3 6.5 lbs a. i./ acre, compared to 2.0 to 163 lbs a. i./ acre in California.

After 4 days, the excreted mass for the 99.9% ile of the 4 5 year old group is 1.8 mg carbaryl equivalents, and 2.5 mg carbaryl equivalents for the 9 12 year old group. The 4 5 year old group is assumed to be representative of toddler exposure, including age 3. For the simulations, carbaryl doses that resulted in similar excretion levels were applied.

The proposed simulations are intentionally conservative for the dose metric of brain cholinesterase inhibition. By assuming continuous turf exposure over the estimated duration, as opposed to distributing the exposure duration over several periods during the day, higher peak carbaryl concentrations are possible. The excretion data actually imply that the exposure is spread over the observation period since a plateau in metabolite levels is not clearly reached after 4 days ( Appendix C); the excretion of carbaryl from a single event is expected to occur within 1 3 days ( Knaak et al. 1965, Benson and Dorough 1984). Also, the source of all metabolites is assumed to be from exposure events associated with activities on the carbaryl treated turf, but non zero metabolite levels on day 0 ( pre treatment) indicate that other exposures, including to non carbaryl sources of 1 naphthol, may be occurring ( Appendix C). These other exposures would contribute to the total amount of carbaryl absorbed, but the peak carbaryl values in brain, and thus the maximum acetylcholinesterase inhibition, would be reduced. II. B. 6  Page 37 of 162 3.0 RESULTS 3.1 Overview A robust PBPK model enables the estimation of relevant dose metrics for a variety of exposure scenarios in different populations. For carbaryl, the brain and blood concentrations, and degree of acetylcholinesterase inhibition in those compartments are the metrics of interest. The PBPK model was designed to predict those values for scenarios associated with turf post application activities for children.

The PBPK model was first developed to be consistent with the rat data ( Table 1). The physiologic parameters were implemented for the rat, and the kinetic parameters were estimated based on the experimental data.

The human scenarios were then implemented by substituting human physiologic parameters in the PBPK model. The kinetic parameters were appropriately scaled as described in Section 2.1.2. The oral hand to mouth and dermal exposure scenarios were implemented for children, and the degree of acetylcholinesterase inhibition and tissue concentrations were evaluated.

3.2 Rat Model The PBPK model simulates results consistent with the available data. These data include studies published in the literature and those delivered to the U. S. EPA by registrants ( Table 1). Priority was given to the studies at lower doses, since exposures following turf application will likely be low relative to the laboratory setting.

Intravenous ( IV) dosing experiments enable the evaluation of the model distribution, metabolism, and excretion behaviors independent of the absorption characteristics. IV doses of approximately 1 and 10 mg/ kg of 14C carbaryl ( ring label) were administered to rats, and radioactive residues were measured in a variety of tissues ( Bayer 2004a).

Representative comparisons of the PBPK model to the experimental data are shown in Figures 7 and 8. The complete set of simulation charts is in Appendix E. II. B. 6  Page 38 of 162 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 2 4 6 8 10 Time ( hours) ppm 14C residue data PBPK model Figure 7. 14C concentration in brain after IV dose of 0.8 mg/ kg carbaryl in rats. Data from Bayer 2004a, average of 4 animals at each sample time.

0 0.5 1 1.5 2 2.5 3 0 2 4 6 8 10 Time ( hours) ppm 14C residue data PBPK model Figure 8. 14C concentration in blood after IV dose of 0.8 mg/ kg carbaryl in rats. Data from Bayer 2004a, average of 4 animals at each sample time.

The 14C data were reported in ppm ( Bayer 2004a). The PBPK model tracks 14C based on chemical species, resulting in units of mmol/ L. The model results were converted to ppm by multiplying by the molecular weight of carbaryl for comparison to the data.

The rat PBPK model shows similar consistency with oral studies, conducted near the NOAEL level of 1 mg/ kg ( U. S. EPA) and also at 8.45 mg/ kg. One exception is that brain 14C concentrations are over predicted ( Figure 9). II. B. 6  Page 39 of 162 0 0.1 0.2 0.3 0.4 0.5  5 0 5 10 15 20 25 Time ( hours) ppm 14C residue data PBPK model Figure 9. Brain 14C concentration after oral dose of 1 mg/ kg carbaryl in rats. Data from Bayer 2004a, average of 4 animals at each sample time.

By reducing the partition coefficient for carbaryl and its major metabolites to less than or equal to one ( Table 5), the low dose data were better simulated. The equal partitioning between blood and brain ( partition coefficient = 1) is inconsistent with the known physical properties of carbaryl, so the partition coefficient values were not reduced to less than 1.

Carbaryl was measured for the higher dose oral study, where brain concentrations of the parent compound were reasonably simulated ( Figure 10), while blood concentrations were predicted but not detected ( Figure 11).

0 0.5 1 1.5 2 0 2 4 6 8 10 Time ( hours) Concentration ( mg/ L)

data PBPK model Figure 10. Brain carbaryl concentration after oral dose of 8.45 mg/ kg carbaryl in rats. Data from Bayer 2004a, average of 4 animals at each sample time. II. B. 6  Page 40 of 162 0 0.5 1 1.5 0 2 4 6 8 10 Time ( hours) Concentration ( mg/ L)

PBPK model Figure 11. Blood carbaryl concentration after oral dose of 8.45 mg/ kg carbaryl in rats. Carbaryl was not detected in blood at 15 minutes, approximately the time of the PBPK model predicted peak ( Bayer 2004a).

Dermal exposure studies in rats of 1.74 mg/ 20 cm2 ( Knaak et al. 1984) and 0.793 mg/ 12.5 cm2 ( Cheng 1994) were evaluated to assess the dermal permeability of carbaryl. Representative comparisons to the data show that the model predicts the kinetics of absorption ( Figures 12 and 13). Similar fits are observed with data from other dermal studies and for other compartments. Complete comparisons are in Appendix E. II. B. 6  Page 41 of 162 0 0.00005 0.0001 0.00015 0.0002 0.00025 0 50 100 150 200 Time ( hours) Concentration ( mmol/ L)

data PBPK model Figure 12. Blood 14C concentrations following 1.74 mg/ 20 cm2 carbaryl dermal application on rats. Data from Knaak et al. 1984, average of 3 animals at each sample time.

0 0.0005 0.001 0.0015 0.002 0 50 100 150 200 Time ( hours) Cumulative residue ( mmol)

data PBPK data Figure 13. Cumulative 14C residues in urine following 1.74 mg/ 20 cm2 carbaryl dermal application on rats. Data from Knaak et al. 1984, pooled urine from 3 animals.

A metabolism study in rats measured concentrations of specific metabolites in urine after intraperitoneal ( IP) dosing of rats at 20 mg/ kg ( Knaak et al. 1965). The model simulates the results for the identified metabolites ( Figure 14). The excretion of glucuronides is slower than sulfates due to enterohepatic recirculation. II. B. 6  Page 42 of 162 0 2 4 6 8 10 12 14 16 18 0 5 10 15 20 25 30 Time ( hours) % dose in urine naphthol sulfate PBPK model naphthol glucuronide PBPK model 5,6 DIOH carbaryl glucuronide PBPK model 4 OH carbaryl glucuronide PBPK model 4 OH carbaryl sulfate PBPK model Figure 14. Excretion of metabolites following 20 mg/ kg IP dosing of carbaryl in rats. Data from Knaak et al 1965, pooled urine from 3 animals.

Oral studies in rats, separate from the ones discussed above, measured the degree of cholinesterase inhibition in blood and brain ( Brooks and Broxup 1995a, b). The lowest dose tested was 10 mg/ kg, which resulted in measurable inhibition followed by recovery within 8 hours. These dynamics were reproduced by the model ( Figures 15 and 16). The data were given by inhibition in each brain region, and were averaged for comparison to the PBPK model. II. B. 6  Page 43 of 162 0 20 40 60 80 100 120 0 5 10 Time ( hours) % control Brooks 1995a Brooks 1995b PBPK model Figure 15. Blood acetylcholinesterase inhibition after 10 mg/ kg carbaryl oral dose in rats. Data are average of 12 animals ( 6 male, 6 female) at each sample time.

0 20 40 60 80 100 120 0 5 10 Time ( hours) % control Brooks 1995a Brooks 1995b PBPK model Figure 16. Brain acetylcholinesterase inhibition after 10 mg/ kg carbaryl oral dose in rats. Data are average of 12 animals ( 6 male, 6 female) at each sample time.

A combined low dose oral and dermal exposure study in rats was conducted to simulate conditions corresponding to hypothesized repeated hand to mouth activities in toddlers ( Bayer 2004a). Two oral doses of 0.075 mg/ kg were given an hour apart, while a 0.75 mg/ kg dermal dose was applied for hours 0 to 2. The II. B. 6  Page 44 of 162 model simulations are consistent with the data, showing modest under prediction of the blood 14C data, and over prediction of the brain 14C and carbaryl concentrations ( Figures 17 19).

0 0.05 0.1 0.15 0.2 0 2 4 6 8 Time ( hours) ppm 14C residue data PBPK model Figure 17. Blood 14C concentration following dermal and repeated oral dosing of carbaryl in rats. Data from Bayer 2004a, average of 4 animals at each sample time.

0 0.01 0.02 0.03 0.04 0.05 0 2 4 6 8 Time ( hours) ppm 14C residue data PBPK model Figure 18. Brain 14C concentration following dermal and repeated oral dosing of carbaryl in rats. Data from Bayer 2004a, average of 4 animals at each sample time. II. B. 6  Page 45 of 162 0 0.002 0.004 0.006 0.008 0.01 0 2 4 6 8 10 Time ( hours) Concentration ( mg/ L)

data PBPK model Figure 19. Brain carbaryl concentration following dermal and repeated oral dosing of carbaryl in rats. Data from Bayer 2004a, average of 4 animals at each sample time.

Additional simulations ( not shown) without the dermal exposure showed results nearly identical to Figures 17 to 19. This indicates that in the hand to mouth exposure scenario, the oral route is the dominant route of exposure.

The rat model sufficiently captures the behavior of carbaryl, including the important dose metrics for evaluation of the post turf application exposure scenario. The simulations are consistent with the available data across studies and measurements. Additional comparisons to data may be found in Appendix E. Exact matches of simulations to data would not be expected based on the diverse studies and experimental and animal variability. One unexpected difference is the non detection of carbaryl in blood 15 minutes after an oral dose of 8.45 mg ( Figure 11). The non detection of carbaryl in the experimental study appears to be inconsistent with the corresponding brain carbaryl concentrations ( Figure 10), and expected blood acetylcholinesterase inhibition predicted by the PBPK model and observed in a separate study under similar dosing conditions ( Figure 16). Aside from that particular measurement, the model describes the absorption and distribution of carbaryl to the brain, where acetylcholinesterase inhibition   the dose metric of interest   occurs, and simulates the subsequent elimination of carbaryl and its metabolites.

3.3 Oral Exposure by Hand to Mouth Activities An oral exposure scenario by hand to mouth activities was simulated for a 3 year old child. The rat model was converted to an age specific human model by implementing the appropriate physiological parameters ( Section 2.2.1) and scaling the kinetic constants ( Section 2.2.3) for a 3 year old child. II. B. 6  Page 46 of 162 Starting at time 0, two hours of hand to mouth activity occurred at 20 events/ hour. The simulation duration was for 4 days from the beginning of the activity, over which the cumulative urine excretion and tissue concentrations were tracked.

3.3.1 Estimates of absorbed dose from oral exposure The absorbed dose associated with the oral hand to mouth exposure is based on the biomonitoring study ( Bayer 2004b, Appendix C). For the assumed scenario, a magnitude of exposure was chosen to be consistent with the observed excreted mass of carbaryl equivalents.

A total dose of 0.08 mg/ kg for the hand to mouth events resulted in urine metabolite levels consistent with the 99.9% ile of excreted mass of carbaryl equivalents ( Figure 20). Similar to the oral bolus studies, the urinary profiles of metabolites indicate significant elimination within a day.

0 0.5 1 1.5 2 0 20 40 60 80 100 Time ( hours) Cumulative mass ( mg)

data PBPK model Figure 20. Cumulative excretion of carbaryl equivalents in urine from 3 year olds following turf application. The error bars represent the 99.9% range. The PBPK model assumes oral exposure through hand to mouth activities.

The plateau in the metabolite levels is reached earlier than in the biomonitoring study ( Figure 20). As discussed in section 2.3, this simulation is a conservative scenario. By assuming that the entire absorbed dose occurs in a single 2 hour activity period, the peak tissue concentrations will be highest. II. B. 6  Page 47 of 162 3.3.2 Estimates of blood and tissue concentrations following oral exposure The concentration of carbaryl in blood increases over the exposure duration, then rapidly decreases, and a similar profile is observed in other tissues ( Figure 21).

0 0.002 0.004 0.006 0.008 0 5 10 15 20 25 Time ( hours) Concentration ( mg/ L)

PBPK model Figure 21. Carbaryl concentration in venous blood in 3 year olds following turf application. The PBPK model assumes oral exposure through hand to mouth activities.

The peak concentrations of carbaryl in brain and blood determine the magnitude of acetylcholinesterase inhibition ( Figure 22). The scale in Figure 22 only spans from 90 100% so that the dynamics of the slight change can be visualized. The level of inhibition for the oral hand to mouth scenario is minimal ( Table 10). II. B. 6  Page 48 of 162 90 95 100 0 2 4 6 8 10 Time ( hours) Activity (% baseline)

brain blood Figure 22. Acetylcholinesterase activity in brain and blood in 3 year olds following turf application. The PBPK model assumes oral exposure through hand to mouth activities.

Table 10. Blood and brain carbaryl concentrations and acetylcholinesterase activity. Compartment Peak carbaryl concentration ( mg/ L) Acetylcholinesterase activity (% baseline) Blood 0.0061 99.4 Brain 0.0061 99.5 3.4 Dermal Exposure A dermal exposure scenario was simulated for a 9 year old child. The rat model was converted to an age specific human model by implementing the appropriate physiological parameters ( Section 2.2.1) and scaling the kinetic constants ( Section 2.2.3) for a 9 year old child.

At time 0, a mass of carbaryl is applied on the exposed skin surface of 1000 cm2, and is washed off after 3.55 hours. This is intended to simulate the average of transfer and rub off activities to the lower arms and legs ( including hands and feet) over the duration of activity on the treated turf. The simulation duration was for 4 days from the beginning of the activity, over which the cumulative urine excretion and tissue concentrations were tracked.

The profile of the amount of carbaryl on skin over the exposure period shows slow absorption until it is washed off at the end of the activity ( Figure 23). Since the kinetics of absorption are slow, the representation of average dermal II. B. 6  Page 49 of 162 exposure through an initial application is a suitable approximation of the expected variable profile due to transfer and removal events.

0 10 20 30 40 50  1 1 3 5 7 9 Time ( hours) Mass ( mg)

Figure 23. Modeled carbaryl mass on skin over exposure period.

3.4.1 Estimates of absorbed dose from dermal exposure The absorbed dose associated with the dermal exposure scenario is based on the biomonitoring study ( Bayer 2004b, Appendix C). For the assumed scenario, a magnitude of exposure was chosen to be consistent with the observed excreted mass of carbaryl equivalents.

A dose of 2 mg/ kg was applied on the skin surface, and the total amount absorbed before wash off is 1.69 mg, or a dose of 0.083 mg/ kg. This dose resulted in urine metabolite levels consistent with the 99.9% ile of excreted mass of carbaryl equivalents ( Figure 24). Similar to the oral bolus studies, the urinary profiles of metabolites indicate significant elimination within a day. II. B. 6  Page 50 of 162 0 0.5 1 1.5 2 2.5 3 0 20 40 60 80 100 Time ( hours) Cumulative mass ( mg)

data PBPK model Figure 24. Cumulative excretion of carbaryl equivalents in urine from 9 year olds following turf application. The error bars represent the 99.9% range. The PBPK model assumes dermal exposure by contact with treated surfaces.

The plateau in the metabolite levels is reached earlier than in the biomonitoring study ( Figure 24). As discussed in section 2.3, this simulation is a conservative scenario. By assuming that all of the absorbed dose occurs in a single activity period, the peak tissue concentrations will be highest.

3.4.2 Estimates of blood and tissue concentrations following dermal exposure The concentration of carbaryl in blood increases over the exposure duration, then rapidly decreases, and a similar profile is observed in other tissues ( Figure 25). II. B. 6  Page 51 of 162 0 0.002 0.004 0.006 0.008 0.01 0 5 10 15 20 25 Time ( hours) Concentration ( mg/ L)

PBPK model Figure 25. Carbaryl concentration in venous blood in 9 year olds following turf application. The PBPK model assumes dermal exposure by contact with treated surfaces.

The peak concentrations of carbaryl in brain and blood determine the magnitude of acetylcholinesterase inhibition ( Figure 26). The scale in Figure 26 only spans from 90 100% so that the dynamics of the slight change can be visualized. The level of inhibition due to the dermal exposure scenario is minimal ( Table 11).

90 95 100 0 2 4 6 8 10 Time ( hours) Activity (% baseline)

brain blood Figure 26. Acetylcholinesterase activity in brain and blood in 9 year olds following turf application. The PBPK model assumes dermal exposure by contact with treated surfaces. II. B. 6  Page 52 of 162 Table 11. Blood and brain carbaryl concentrations and acetylcholinesterase activity. Compartment Peak carbaryl concentration ( mg/ L) Acetylcholinesterase activity (% baseline) Blood 0.0084 99.1 Brain 0.0084 99.2 4.0 DISCUSSION Note: The results contained in this report are preliminary in nature and do not reflect current OPP and EPA policies regarding evaluation of inter and intraspecies extrapolation, aggregate risk, and application of the FQPA safety factor for potential sensitivity of infants and children.

For the carbaryl exposure simulation, the risk metric of interest was brain acetylcholinesterase inhibition, which is dependent on the peak brain carbaryl concentration. The exposure scenarios were for children following turf application of carbaryl. For both the oral hand to mouth scenario, and dermal scenario, minimal cholinesterase inhibition was predicted. This is not surprising, based on the comparison of the peak brain concentrations after exposure relative to the peak brain concentration at the rat no observed adverse effect level ( NOAEL) of 1 mg/ kg oral ( Table 12).

Table 12. Comparison of peak concentrations after turf exposure scenarios to the rat NOAEL level Rat 1 mg/ kg oral Oral exposure estimate ( 3 yr old human) Dermal exposure estimate ( 9 yr old human) Peak brain concentration ( mg/ L) 0.18 0.0061 0.0084 The confidence in the model predictions, and applicability to the population in general is dependent on the behavior of the outcome metrics across model parameter ranges. The relevant range of values may span the uncertainty or variability space. The model parameters that affect the brain dose metrics are expected to be those associated with the kinetics of acetylcholinesterase inhibition, clearance of carbaryl, and transport to the brain. Parameters that fall into this category include the rate of carbaryl metabolism, blood: brain partition coefficient, blood flow rate to brain, and the rate of acetylcholinesterase inhibition. A formal sensitivity analysis was not performed and may reveal other important variables. II. B. 6  Page 53 of 162 The range of parameter values was based on the likely variability in the human population. Where statistical distributions for the parameters could be established from the literature data, the coefficient of variation was applied relative to the best fit value used in the exposure scenarios ( Sections 2.3 and 2.4) to calculate a low and high value. Otherwise, reported limits were used for the investigation. The investigated ranges of parameter values are listed in Table 13, followed by discussion of how each range was established.

Table 13. Investigated parameters and values Parameter Best fit low high Variability basis Blood flow to brain (% Cardiac Output) 10 6.7 12.9 Reference range Blood: brain partition coefficient 1 0.48 1.52 Observed variability for rat blood: brain, compared with human fat: blood and rat fat: blood Inhibition rate ( mmol/ hr) 250 120 530 Observed variability for rat acetylcholinesterase inhibition Hydrolysis rate ( Vmax, mmol/ hr/ kg) 0.071 0.022 0.22 Observed malathion variability for carboxylesterase Total oxidation rate ( Vmax, mmol/ hr/ kg) 0.071 0.043 0.12 Observed in vitro, and variability for 4 OH formation and 3A4 drugs For the blood flow to the brain, the range is based on cited limits reported for adults ( Altman and Dittmer 1974). The ratio of the high and low values relative to the mean was then applied to the mean values used in each of the child scenarios. For example, the high limit for adults was cited as 67 ml/ g/ min with a mean of 52 ml/ g/ min for a high/ mean ratio of 1.29. The average % cardiac output to brain for children is 10% ( Table 4), so the high investigated value was 12.9%. The additional blood flow was subtracted from the flow to rapidly perfused tissues so the blood flows to the various compartments add to 100%. The low value investigated was 6.7% of cardiac output.

Brain: blood partition coefficients have not been measured for carbaryl in humans or other species, so a distribution in humans is not available. Another chemical where similar data exists was used to estimate a realistic value for the coefficient of variation ( COV) in brain: blood partition coefficients. Sato et al. 1977 and Simmons et al. 2002 measured the brain: blood partition coefficients in rats for trichloroethylene, and reported the means and standard deviations. The COV was applied relative to the fit value used in the initial model ( Table 13) to II. B. 6  Page 54 of 162 establish the low and high values to be investigated. Although the data are not human, when the COV in the fat: blood partition coefficients for trichloroethylene are compared between rats ( Sato et al. 1977, Simmons et al. 2002, Koizumi 1989, Barton et al. 1995) and humans ( Sato et al. 1977), the rat COV is larger providing some evidence that the values based on the rat experiments may include the likely human values. The dissimilarity between trichloroethylene and carbaryl, and the low number of subjects in the partition coefficient studies ( N= 5 for the human study, up to 10 in the rat studies) lowers the confidence in this analysis. However, simulations of this initial range illustrate the importance of this value and related assumptions.

The kinetics of acetylcholinesterase inhibition in the brain were measured in rats by the hydrolysis of acetylthiocholine iodide ( Mortensen et al. 1998). They modeled the process as saturable ( Michaelis Menten) and reported the Vmax and Km values. Inhibition is modeled as a linear process for lower environmental exposures, so the variability in the quotient of Vmax/ Km was calculated as an estimate of the variability of the inhibition rate constant, ki.

The metabolism of carbaryl occurs through two pathways, hydrolysis and hydroxylation ( Table 7). Hydroxylation primarily occurs through P450 CYP3A4, and the kinetics were measured in vitro ( Tang et al. 2002). The standard deviation of the log distribution was applied to the model in vivo value ( Table 7) to establish the investigation range. This range is consistent with the metabolism rate ranges ( Hattis, and cited references) of pharmaceuticals known to be metabolized through the CYP3A4 pathway ( Flockhart), including alfentanil, diltiazem, fentanyl, haloperidol, midazolam, nifedipine, and triazolam. Carboxylesterase in involved in the hydrolysis pathway ( Sogorb et al. 2002), so kinetic data from the metabolism of malathion were used to estimate a reasonable range. The importance of carboxylesterase may be inferred from the metabolic interactions for mixtures of carbaryl and malathion ( Lechner and Abdel Rahman 1986). Talcott et al. 1977, and Sams and Mason 1999 reported the malathion metabolic activity from individual human liver and serum samples, respectively. The geometric standard deviation is approximately twice as large for metabolism by serum than by liver carboxylesterase ( 0.25 vs. 0.11), so that value was used to establish the range to be investigated for the hydrolysis pathway for carbaryl ( Table 13).

The dependence of the results on a particular parameter value can be evaluated by the change in a dose metric as that value is changed. Each parameter was changed from the best fit value to the low value or high value, while the other parameters were held at the best fit value (` low' and ` high' in Figures 27 30). The dose metrics of acetylcholinesterase inhibition and peak brain carbaryl concentrations were compared to the case where all the parameters were held at their best fit values (` all' in Figures 27 30). II. B. 6  Page 55 of 162 The metabolism rate and brain: blood barrier partition coefficient values impact the brain carbaryl peak concentration. The implemented changes to the brain blood flow and acetylcholinesterase inhibition rates do not affect the peak concentrations for either the hand to mouth or dermal exposure scenarios ( Figures 27 and 28).

0 0.005 0.01 0.015 all blood: brain brain blood flow met abolis m enzyme ki brain concentration ( mg/ L)

low high fit Figure 27. Brain concentrations for different parameter values in 3 year olds following turf application. The PBPK model assumes oral exposure due to handto mouth activity.

0 0.005 0.01 0.015 all blood: brain brain blood flow met abolis m enzyme ki brain concentration ( mg/ L)

low high fit Figure 28. Brain concentrations for different parameter values in 9 year olds following turf application. The PBPK model assumes dermal exposure due to contact with treated surfaces.

Although there are modest changes to the brain concentrations, the maximum brain acetylcholinesterase inhibition levels are not affected. Effectively no II. B. 6  Page 56 of 162 inhibition occurs for either exposure scenario when the various parameter values are applied ( Figures 29 and 30).

90 92 94 96 98 100 al l bloo d: brain brain blood fl ow metaboli s m en zy me ki % basal activity low high fit Figure 29. Maximum acetylcholinesterase inhibition for different parameter values in 3 year olds following turf application. The PBPK model assumes oral exposure due to hand to mouth activity.

90 92 94 96 98 100 al l bloo d: brain brain blood fl ow metaboli s m en zy me ki % basal activity low high fit Figure 30. Maximum acetylcholinesterase inhibition for different parameter values in 9 year olds following turf application. The PBPK model assumes dermal exposure due to contact with treated surfaces.

The parameter investigations illustrate the uncertainty in and variability captured by the model for the results in Section 3. The parameters that affect the metrics of interest also highlight important experimental measurements that would increase confidence in the model. Measurements and accurate representation of II. B. 6  Page 57 of 162 distribution of carbaryl to the brain, and of its metabolism in humans, and the associated variability, would reduce uncertainty in the model predictions.

Established methods exist for measuring tissue: blood partition coefficients for non volatile chemicals ( Jepson et al. 1994). However, if the active transport mechanisms of the blood brain barrier are the cause of the low effective partition coefficient value, the model structure would need to be expanded to include them; a perfusion limited description of distribution may not capture the important distribution behavior. The involvement of active transporters may also affect the assumptions for extrapolating the model to children. The partitioning behavior is assumed to be constant for children 3 years and older, but the development of the active transport systems of the blood: brain barrier would need to be considered.

Metabolism in humans remains difficult to estimate, although the in vitro studies with specific enzymes provide a basis ( Tang et al. 2002). The extrapolation to the human in vivo scenario remains an area of research ( Lipscomb et al. 1998). Metabolic studies in adults will likely be applicable, since the P450 metabolic enzyme profile, and metabolism of the few studied substrates, in children older than 1 year is similar to adults ( Ginsberg et al. 2004).

PBPK modeling techniques offer the promise of interpreting this type of exposure. However, the reliability of the output from such a model is dependent on the necessary input information. Continued development and testing of the model with quality data is necessary to refine the input parameters and values. In this regard, physiological and pharmacokinetic ( PK) data are gleaned from in vivo and in vitro studies as reported in the literature to establish key values for PK parameters and the time course for disposition of the compounds in the body. This is an iterative process. It is anticipated that further refinements will continue to improve and evolve as more reliable data become available. Therefore, the results reported herein reflect the current state of reliability with the understanding that further anticipated refinements may influence the conclusions. II. B. 6  Page 58 of 162 5.0 REFERENCES Agata Y, Hiraishi S, Misawa H, Hirota H, Nowatari M, Hiura K, Fujino N, Oguchi K, and Horiguchi Y. ( 1994) Regional Blood Flow Distribution and Left Ventricular Output during Early Neonatal Life: A Quantitative Ultrasonographic Assessment. Pediatric Research. Vol 36, pp. 805 810.

Ahdaya, S., Guthrie, F. E. Stomach absorption of intubated insecticides in fasted mice, Toxicol. 22, 311 317 ( 1982).

Altman, P. L, Dittmer, D. S. Biology Data Book, 2nd ed. Vol. III. Federation of American Societies for Experimental Biology, Washington, DC ( 1974).

Barton, H. A., J. R. Creech, C. S. Godin, G. M. Randall, C. S. Seckel ( 1995). " Chloroethylene mixtures: pharmacokinetic modeling and in vitro metabolism of vinyl chloride, trichloroethylene, and trans 1,2 dichloroethylene in rat." Toxicol. Appl. Pharmacol. 130: 237 247.

Bayer 2004a: Metabolism and Pharmacokinetics of [ 14C] Carbaryl in Rats. Bayer CropScience. Bayer reports 201025 and 201026.

Bayer 2004b: Application of carbaryl pharmacokinetic data in the estimation of potential post application health risk associated with broadcast lawn care products. Infoscientific, Inc.

Bayer 2004c. Measurement of Pesticide Exposure of Suburban Residents Associated with the Residential Use of Carbaryl. ABC Laboratories.

Benson, W. H., Dorough, H. W. Comparitive Ester Hydrolysis of Carbaryl and Ethiofencarb in Four Mammalian Species, Pesticide Biochem. Physiol. 21, 199 206 ( 1984).

Boot, AM, Bouquet, J, de Ridder MAJ, Krenning, EP, and de Muicnk Keizer Schrama, S. ( 1997). Determinants of body composition measured by dual energy X ray absorptiometry in Dutch children and adolescents. Am J Clin Nutr. 66: 232 8.

Brooks, W., and Broxup, B. ( 1995a). An acute study of the time course cholinesterase inhibition by orally administered carbaryl technical. MRID 438452 02.

Brooks, W., and Broxup, B. ( 1995b). A time of peak effects study of a single orally administered dose of carbaryl, technical grade, in rats. MRID 438452 03. II. B. 6  Page 59 of 162 Casper, H. H., Peaks, J. C., Dinusson, W. E. Gastric Absorption of a Pesticide ( 1 Naphthyl N Methylcarbamate) in the Fasted Rat, Pesticide Biochem Physiol. 2, 391 396 ( 1973).

Cheng, T. ( 1994). Dermal Absorption of 14C Carbaryl ( 80S) in Male Rats ( Preliminary and Definitite Phases). MRID 433297 01.

Cool, M., Jankowski, K. A Survey of the Metabolism of Carbamate Insecticides in Insecticides, Hutson, D. H, Roberts, T. R. Eds. John Wiley and Sons, Ltd., New York, NY, 1985.

Corley, RA, Mendrala, AL, Smith, FA, Staats, DA, Gargas, M. L., Conolly, R. B., Andersen, M. E., and Reitz, R. H. ( 1990) Development of a Physiologically Based Pharmacokinetic Model for Chloroform. Toxicol Appl Pharmacol. 103: 512 527 Fisher, J. W., M. L. Gargas, B. C. Allen and M. E. Andersen ( 1991). " Physiologically based pharmacokinetic modeling with trichloroethylene and its metabolite, trichloroacetic acid, in the rat and mouse." Toxicol. Appl. Pharmacol. 109: 183 195.

Fisher, JW, Mahle, D., and Abbas, R. 1998. A Human Physiologically Based Pharmacokinetic Model for Trichloroethylene and Its Metabolites, Trichloroacetic Acid and Free Trichloroethanol. Toxicol. Appl. Pharmacol. 152: 339 359.

Flockhart Table, Cytochrome P450 Drug Interaction Table, http:// medicine. iupui. edu/ flockhart/ table. htm Ginsberg, G., Hattis, D., Sonawane, B. Incorporating Pharmacokinetic Differences between Children and Adults in Assessing Children's Risk to Environmental Toxicants. Toxicol. Appl. Pharmacol. 198, 164 183 ( 2004).

Hassan, A., Zayed, S. M. A. D., Abdel Hamid, F. M. Metabolism of Carbamate Drugs I: Metabolism of 1 Naphthyl N Methyl Carbamate ( Sevin) in the Rat, Biochem. Pharamacol. 15, 2045 2055 ( 1966).

Hattis, D. Human Interindividual Variability in Parameters Related to Susceptibility for Toxic Effects, http:// www2. clarku. edu/ faculty/ dhattis/

Hetnarski, B.; O'Brien, R. D. Electron donor and affinity constants and their application to the inhibition of acetylcholinesterase by carbamates. Journal of Agricultural and Food Chemistry 1975, 23, 709 713.

International Commission on Radiological Protection ( ICRP) " Annals of the ICRP: Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values" Volume 32( 3 4), Publication 89. Elsevier Science Ltd. New York, NY ( 2002). II. B. 6  Page 60 of 162 International Life Science Institute, Risk Science Institute ( ILSI RSI) ( 1994) " Physiological Parameters for PBPK Models" Report. December.

Jepson, G. W., Hoover, D. K., Black, R. K., McCafferty, J. D., Mahle, D. A., Gearhart, J. M. A Partition Coefficient Determination Method for Nonvolatile Chemicals in Biological Tissues. Fund. Appl. Toxicol. 22, 519 524 ( 1994).

Keys, D. A., J. V. Bruckner, S. Muralidhara and J. W. Fisher ( 2003). " Tissue dosimetry expansion and cross validation of rat and mouse physiologically based pharmacokinetic models for trichloroethylene." Toxicol Sci 76( 1): 35 50.

Knaak, J. B., Tallant, M. J., Bartley, W. J., Sullivan, L. J. The Metabolism of Carbaryl in the Rat, Guinea Pig, and Man, J. Agr. Food Chem. 13, 537 543 ( 1965).

Knaak, J. B., Yee, K., Ackerman, C. R., Zweig, G., Fry, D. M., Wilson, B. W. Percutaneous Absorption and Dermal Dose Cholinesterase Response Studies with Parathion and Carbaryl in the Rat, Toxicol. Appl. Pharmacol. 76, 252 263 ( 1984).

Koizumi A. ( 1989) " Potential of physiologically based pharmacokinetics to amalgamate kinetic data of trichloroethylene and tetrachloroethylene obtained in rats and man." Br J Ind Med. 46( 4): 239 49.

Krishna, J. G., Casida, J. E. Fate in Rats of the Radiocarbon from Ten Variously Labeled Methyl and Dimethyl Carbamate C14 Insecticide Chemicals and Their Hydrolysis Products, J. Agr. Good Chem. 14, 98 105 ( 1966).

Kuhr, R. J., Dorough, H. W. Carbamate Insecticides: Chemistry, Biochemistry, and Toxicology. CRC Press, Cleveland, OH, 1976.

Lechner, D. W., Abdel Rahman, M. S. Kinetics of Carbaryl and Malathion in Combination in the Rat. J Toxicol. Environ. Health 18, 241 256 ( 1986).

Lipscomb, J. C., J. W. Fisher, P. D. Confer, J. Z. Byczkowski ( 1998). " In vitro to in vivo extrapolation for trichloroethylene metabolism in humans." Toxicol. Appl. Pharmacol. 152: 376 387.

Liu, X., Tu, M., Kelly, R. S., Chen, C., Smith, B. J. Development of a Computational Approach to Predict Blood Brain Barrier Permeability. Drug Metab. Dispos. 32: 132 139 ( 2004).

Marshall, T. C., Dorough, H. W. Biliary Excretion of Carbamate Insecticides in the Rat, Pesticide Biochem. Physiol. 11, 56 63 ( 1979). II. B. 6  Page 61 of 162 Milner, R., " Cranial Capacity." The Encyclopedia of Evolution: Humanity's Search For Its Origins. New York: Holt, 1990: 98 Mortensen, S. R., Hooper, M. J., Padilla, S. Rat Brain Acetylcholinesterase Activity: Developmental Profile and Maturational Sensitivity to Carbamate and Organophosphorous Inhibitors. Toxicol. 125: 13 19 ( 1998).

Payne, M. P., Kenny, L. C. Comparison of Models for the Estimation of Biological Partition Coefficients. J Toxicol. Environ. Health, Part A. 65: 897 931 ( 2002).

Poulin P, Theil FP. A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically based pharmacokinetic models in drug discovery. J Pharm Sci 89: 16 35 ( 2000).

Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism based prediction of volume of distribution. J Pharm Sci 91: 129 156 ( 2002).

Poulin P, Krishnan, K. A mechanistic algorithm for predicting blood: air partition coefficients of organic chemicals with the consideration of reversible binding in hemoglobin. Toxicol Appl Pharmacol 136: 131 137 ( 1996).

Roberts TR. Hutson DH ( 1999). Metabolic Pathways of Agrochemicals, Part 2: Insecticides and Fungicides. The Royal Society of Chemistry, 15 24.

Sams, C., Mason, H. J. Detoxification of Organophosphates by A Esterases in Human Serum. Human Experimental Toxicol. 18, 653 658 ( 1999).

Sato A., T. Nakajima, Y. Fujiwara, and N. Murayama ( 1977) " A pharmacokinetic model to study the excretion of trichloroethylene and its metabolites after an inhalation exposure." Br J Ind Med. 34( 1): 56 63.

Schmitz L, Koch H, Bein G, and Brockmeier K. ( 1998). Left Ventricular Diastolic Function in Infants, Children, and Adolescents. Reference Values and Analysis of Morphologic and Physiologic Determinants of Echocardiographic Doppler Flow Signals During Growth and Maturation. Journal of the American College of Cardiology. Vol 32, pp. 1441 1448 Shah, P. V., Monroe, R. J., Guthrie, F. E. Comparative Rates of Dermal Penetration of Insecticides in Mice, Toxicol. Appl. Pharmacol. 59, 414 423 ( 1981).

Simmons J. E., W. K. Boyes, P. J. Bushnell, J. H. Raymer, T. Limsakun, A. McDonald, Y. M. Sey, and M. V. Evans ( 2002). " A physiologically based pharmacokinetic model for trichloroethylene in the male long evans rat." Toxicol Sci. 69( 1): 3 15. II. B. 6  Page 62 of 162 Sogorb, M. A., Carrera, V., Benabent, M., Vilanova, E. Rabbit Serum Albumin Hydrolyzes the Carbamate Carbaryl, Chem. Res. Toxicol. 15, 520 526 ( 2002).

Sullivan, L. J., Eldridge, J. M., Knaak, J. B., Tallant, M. J. 5,6 Dihydro 5,6 dihidroxycarbaryl glucuronide as a significant metabolite of carbaryl in the rat, J. Agric. Food Chem. 20, 980 985 ( 1972).

Struble, C. ( 1994). Metabolism of 14C carbaryl in rats ( preliminary and definitive phases). MRID 43332101, Rhone Poulenc.

Talcott, R. E., Mallipudi, N. M., Fukuto, T. R. Malathion Carboxylesterase Titer and Its Relationship to Malathion Toxicity. Toxicol. Appl. Pharmacol. 50, 501 504 ( 1977).

Tang, J., Cao, Y., Rose, R. L., Hodgson, E. In vitro Metabolism of Carbaryl by Human Cytochrome P450 and its Inhibition by Chlorpyrifos, Chemico Bio. Interact. 141, 229 241 ( 2002).

U. S. EPA 2002. Exposure Related Dose Estimating Model ( ERDEM) for Assessing Human Exposure and Dose. EPA/ 600/ X 04/ 060.

U. S. EPA 2003. HED Phase 5 Occupational and Residential Exposure Assessment. II. B. 6  Page 63 of 162 Appendix A. Model 1.0 Descriptions of Exposure Exposure occurs at the boundary of the body or test system. It is of considerable interest to the U. S. EPA to limit, reduce and in specific instances eliminate exposure. Humans become exposed to chemical and biological substances, physical energy and radiation through the activities they perform routinely in everyday life or occupationally as part of certain policies, practices or procedures. Exposure can occur accidentally as a random event, or during an occupationally related task or as a result of a purposeful action such as a ( terrorist) attack.

Humans may become incidentally and unknowingly exposed. Exposure to particles and gasses in the air we breathe may be unavoidable. Dermal contact with surfaces residues may be unforseen and unrecognizable. Ingestion of particles and residues in food may be unintended and unsuspected. Under certain conditions, exposure can be limited or reduced through education, managerial oversight, regulatory responsiveness, and use of proper personal protection devices ( Ness, 1994).

When exposure is perceived as unavoidable, we may wish to describe exposure events in time and space under certain recognizable exposure scenarios. This may be accomplished more easily for occupationally related exposures where policies, practices and procedures have been established than for those that occur randomly or incidentally. However, regardless of the nature of the exposure, exposure follows along recognized pathways, e. g., inhalation, ingestion, and dermal, and routes, respiratory, oral, percutaneous.

ERDEM was designed to examine three pathways of exposure, inhalation, ingestion and dermal, and eight routes of entry into the in silico test system. Experimental pathways and routes of entry were included ( Appendix A Section 1.1) along with what might be perceived as naturally occurring unscheduled or not experimentally controlled pathways and routes ( Appendix A Section 1.2). This approach greatly enhanced the database to include laboratory animal and clinical studies in addition to environmental field studies. For example, enteral administration is represented by intraperitoneal injection ( IP) of chemical into the GI tract via the Portal Blood ( Liver for the Stomach/ Intestine Gastro Intestinal model).

1.1 Experimental Pathways and Routes of Entry 1.1.1 Intraperitoneal Injection Intraperitoneal Injections into the Portal Blood may be given for multiple chemicals for up to nine scenarios starting at time TINPj , and repeated at the II. B. 6  Page 64 of 162 interval TINP TTj , . The amount of chemical to be injected is calculated from the concentration of the chemical times the Body Volume. The amount injected decreases at an exponential rate. All injections start before the simulation start time ( T0). When the scheduled event occurs to start the injection, the amount is calculated as: A A C V INP J INP CUR J INP J B i j i j i j , , , ,,

, , , = +

where A INP CUR Ji j , , , is the amount remaining to be absorbed from the previous interval. The amount of the ith chemical from the jth exposure remaining to be absorbed is:

A A e INP CUR J INP J MIN K T K i j i j INP ABS Ji j INP LIMi , , , ( ,

. , , , , , )

=

where   T T T INP STL J j =   , , , T INP STL J j , , is the start time for the last IP Injection for the jth exposure, and MIN is the mean minimum of the two terms. The amount of the ith chemical remaining to be absorbed for all exposures is:

A A INP CUR INP CUR J j N i ij INP EXP , ,,, ,

= =

1 and the rate of change of the amount of chemical injected into the Portal Blood is given by:

dA dt K A INP INP ABS J INP CUR J j N i i j i j INP EXP = =

, , , , , , ,

1 At the start of the next injection interval, the IP amount remaining to be absorbed is accumulated for each chemical; the elapsed IP simulation time is reset to zero, and the next injection occurrence is scheduled.

1.1.2 Intramuscular Administration Parenteral administration is represented by intramuscular injection ( IM) in the muscle ( Slowly Perfused Tissue). Intramuscular Injections may be given for multiple chemicals and up to nine scenarios starting at time T INMj , and repeat at the interval T INM TTj , . The amount of chemical to be injected is calculated from the concentration of the chemical times the Body Volume. The amount injected decreases at an exponential rate. All injections that start before the simulation start time ( T0) and before the simulation end time are scheduled. When the scheduled event occurs, to start the injection the amount is calculated as: A A C V INM J INM CUR J INM J B i j i j i j , , , , . , , .

= +

where A INM CUR Ji j , , , is the amount remaining to be absorbed from the previous interval. The amount of the ith chemical from the jth exposure remaining to be absorbed is:

( ) A A e INM CUR Ji j INM J MIN K T K i j INM ABS Ji j INM LIMi , , , , ,

, , , , ,

=     II. B. 6  Page 65 of 162 where   T T T INM STL J j =   , , , T INM STL J j , , is the start time for the last IM Injection for the jth exposure, and MIN means find the minimum of the two terms. The amount of the ith chemical remaining to be absorbed for all exposures is:

A A INM CUR INM CUR J j N i ij INM EXP , ,,, ,

= =

1 and the rate of change of the amount of chemical injected into the Slowly Perfused Tissue ( muscle) is given by:

dA dt K A INM INM ABS J INM CUR J i N i i j i j INM EXP = =

, , , , , , ,

1 At the start of the next injection interval, the IM amount remaining to be absorbed for each chemical is reset to zero and scheduled for the next injection occurrence.

1.1.3 Intravascular Administration There are two forms of intravascular administration into the Venous Blood, Bolus Intravenous Injection or Infusion.

<IP_ADDRESS> Infusion into the Venous Blood Infusion is the direct insertion of chemical into the Venous Blood at time TINFj for a period of time, T INF Dj , , which can be repeated at the interval, T INF TTj , . There can be many chemicals in each infusion, each with its own concentration. The rate of change of the amount of the ith chemical infused into the Venous Blood versus time is given by: dA dt C Q INF VB INF j N INF i i j INF j ,

, . = =

1 The flow rate, Q INFj , is independent of the chemical. There is one flow rate for each exposure. However, the concentration of the ith chemical in the jth exposure, C INFi j , , can be different for each chemical. The total amount of the ith chemical passed to the Venous Blood by Infusion is:

A dA dt dt INF VB INF VB T t Ti i , , . =

0 <IP_ADDRESS> Bolus Intravenous Injections Bolus Dose Intravenous ( IV) Injections start at a given time, TBIVj , and may be repeated at an input interval, T RIG TTj , . Bolus Dose Intravenous Injections ( IVs)

injected before simulation start time are not modeled. Those that occur at simulation start time ( T0) are modeled as true Bolus Doses into the Venous II. B. 6  Page 66 of 162 Blood. The equation for the initial values for the amount of chemical in the Bolus IV Dose and in the Venous Blood are given by:

A A BIV BIV j N T i i j BIG 0 1 = =

,

A A VB BIV T i T i 0 0 = for the exposures that start at simulation start time. A Bolus Dose IV that occurs after the simulation start time is simulated with a rate input normally having a time duration of one quarter of a communication interval or one quarter of a maximum integration step, whichever is less. The equation for the jth exposure for the ith chemical then takes the form: dA dt A T T BIV BIV BIV BIV i j i j E B , ,

=   and for all exposures to the ith chemical at time t: dA dt dA dt BIV BIV j N i i j BIV = =

,

. 1 A dA dt dt A BIV BIV T t BIV Ti i T i = +

0 0 1.1.4 Inhalation Administration There are two types of inhalation, Open or Closed Chamber Inhalation. Open Chamber Inhalation is assumed.

The subjects in each simulation are in a closed chamber or an open chamber. They cannot be mixed. If the simulation uses an open chamber then:

A. If no exposure is defined then the simulation starts with an open chamber with no concentration of chemical. B. There is no change in the concentration of chemical in an open chamber due to exhaled air. C. If one or more open chamber exposures are defined and none are designated the starting exposure then exposure number one is the exposure starting the simulation. D. Any number of chemicals can have a concentration in an Open Chamber exposure. E. The simulation cannot switch in the middle from Open to Closed Chamber Inhalation.

If the simulation uses a closed chamber then:

F. There must be a Closed Chamber exposure defined to start the simulation. G. Only one Closed Chamber exposure can be active at once. II. B. 6  Page 67 of 162 H. Any number of chemicals can be assigned a concentration for a Closed Chamber exposure. I. The chemicals in the exhaled air change the concentration of each chemical in the closed chamber. J. If Closed Chamber Inhalation is chosen then the whole simulation will be with a closed chamber. K. Open Chamber Inhalation can be approximated with an extremely large closed chamber.

The input concentration for an Open Chamber is in units of parts per million. The input for Closed Chamber Inhalation can be the amount ( mass units), or the concentration ( units of parts per million). The Open Chamber concentration for the ith chemical in mass per unit volume is calculated from:

C C C INHi AIR J j N AIR PPM i j INH EXP i = =

, , , ,

1 1 ,

For Closed Chamber Inhalation the volume of the chamber is required. The volume of the air in the chamber is calculated by subtracting the volume of the number of subjects: V V N V CC GAS CC SBJ B j j , =

If the input into the Closed Chamber is a concentration then it is given in parts per million and converted to mass per unit volume units. But, if the input is an amount, then the amount is converted to concentration by:

C A V INH CC J CC GAS i i j j = ,

, ,

.

There are two types of lung included in ERDEM, Static Lung and Breathing Lung. The inhalation pathway involves entry through the Open or Closed Chamber Static Lung or the Breathing Lung. The lung equations are not included in this report since inhalation exposures were not considered. Details may be found in the ORD\ NERL APM report " Exposure Related Dose Estimating Model ( ERDEM) for Assessing Human Exposure and Dose" ( U. S. EPA 2002).

1.2 Implementation of the Exposure Time Histories for Rate Ingestion, Inhalation, and Skin Surface Exposures.

Exposure time histories have been implemented in ERDEM for rate ingestion, open chamber Inhalation, and skin surface exposure time histories. There can be up to nine time histories for each exposure type ( except for skin surface exposure which can have up to five exposures), but only one for each chemical. The time histories may be repeated periodically. Each time history will have a start time and duration interval. Any exposure can be expressed as an exposure time history.

Each exposure route has most of these variables: II. B. 6  Page 68 of 162 I. Concentration of chemical in a volume of food, water, or air; II. Volume of the food, water or air; III. Flow rate  volume per unit time; IV. Start time of exposure, duration of exposure, and interval between exposures.

If an exposure starts on or before the simulation start time, then the simulation starts with the exposure in effect. Otherwise, there is an event to start and one to terminate the exposure. There can be overlapping exposures of the same type in most cases ( not for Closed Chamber Inhalation exposures). If the exposure is an exposure time history, then only one chemical can be modeled and there can be only one exposure time history of a particular type in any one simulation.

1.2.1 Ingestion Into the Stomach and the Stomach Lumen Rate ingestion input is a time history of the time and the amount per unit time ( concentration times flow rate) of the chemical. Linear interpolation is used to obtain intermediate values.

<IP_ADDRESS> Bolus Dose Ingestion Bolus dose ingestion occurs when chemical is taken into the Gastro Intestinal tract very rapidly; for instance in one big bite or drink. Bolus dose inputs that occur at simulation start time ( T0) are modeled as true bolus doses. The initial value for the integration in the stomach or stomach lumen is the sum of all exposures that start at simulation start time. The equation is:

A A C V BIG BIG BIG BIG j N T i T i i j j Big 0 0 1 = + =

,

for all exposures to the ith chemical at time T0.

Bolus dose inputs with start time TBIGj that are greater than the simulation start time and before the simulation stop time are simulated by rate inputs that start at the scheduled bolus dose start time with a duration of 1/ 4 of a communication interval, or 1/ 4 of a maximum integration interval, whichever is less. The bolus dose for the jth exposure can be repeated at the input interval, T BIG TTj , . The approximation of a bolus dose input via a rate input of a relatively short duration produces results very similar to those achieved with an actual bolus dose while allowing a more accurate evaluation of amounts and concentrations via numerical integration. The equation for the jth exposure for the ith chemical then takes the form: dA dt C V T T BIG BIG BIG BIG BIG i, j i, j j E B =   and for all exposures to the ith chemical at time t: II. B. 6  Page 69 of 162 dA dt dA dt BIG BIG j N i i j BIG = =

,

. 1 The variable A BIGT i 0 is the initial value in the numerical integrations for the total amount of the ith chemical in the bolus dose ingestion, and the amount in the stomach or stomach lumen:

A dA dt dt A BIG BIG T t BIG Ti i T i = +

0 0 <IP_ADDRESS> Rate Ingestion The Rate Ingestion for each exposure starts at a given time, TRIGj , occurs over a duration of time, T RIG Dj , , and may be repeated at an input interval, T RIG TTj , . The concentration of the chemical in the food or drink and the flow rate are required inputs. The product results in the rate of change of chemical in the stomach or stomach lumen versus time. Overlapping exposures are allowed. The rate of change of the ith chemical in rate ingestion versus time is given by: dA dt C Q RIG RIG j N RIG i i j RIG j = =

, . 1 Thus there is one flow rate for each exposure. But the concentration of each chemical in the jth exposure may be different. The numerical integration to obtain the total amount of the ith chemical passed to the stomach by Rate Ingestion is:

A dA dt dt A RIG ST RIG T t RIG Ti i i , = +

0 0 1.2.2 Inhalation Exposure Inhalation exposure input follows a time history and concentration in Parts Per Million ( PPM) of chemical. Linear interpolation is used to obtain intermediate values. The inhalation pathway involves entry through the Open or Closed Chamber Static Lung or the Breathing Lung. The lung equations are not included in this report since inhalation exposures were not considered. Details may be found in the ORD\ NERL APM report " Exposure Related Dose Estimating Model ( ERDEM) for Assessing Human Exposure and Dose" ( U. S. EPA 2002).

1.2.3 Dermal Exposure There are two types of dermal exposure modeled in ERDEM, one for chemicals in an aqueous vehicle, most often a water based diluent, and chemicals as a dried residue or adsorbed onto particles as a dry source. II. B. 6  Page 70 of 162 <IP_ADDRESS> Skin Surface Exposure to a Chemical in an Aqueous Vehicle Skin Surface exposure input follows a time history where a surface area of the skin ( square centimeters) becomes exposed to a chemical in an aqueous vehicle as a concentration ( mass per centimeter cubed). This concentration and area of the skin are used to compute the rate of change of the amount of chemical absorbed. Linear interpolation is used to obtain intermediate values. The skin surface is exposed to chemical in an aqueous vehicle ( water) at time T SKWj for a period of time, T SKW Dj , , which can be repeated at the interval, T SKW TTj , . Skin surface ( water) exposures progress from a simulation start time and end at a scheduled termination time point. The concentration of the ith chemical at the skin surface is found from summing the concentrations from each of the up to five exposure scenarios:

C C SKS SKW J j N i ij SKW EXP = =

, , ,

1 The rate of change of chemical in the epidermis due to the concentrationC SKSi on the skin surface is given by: dA dt C K A SKW DR SKS SKS DR PRM SK i i i ,

, , = .

<IP_ADDRESS> Skin Surface Exposure to Transfer from a Dry Surface A chemical exists on a surface represented as a mass per unit area. It is transferred to the skin of a subject represented by a transfer coefficient. A short exposure period would represent a bolus.

The rate of change of chemical on the dermis due to a dry exposure is: dA sks ex i dt A surf i K sks rt i ,

, =

Integrating this equation gives the total applied dose.

The rate of loss of chemical from the skin surface due to evaporation is given by: dA sks ev i dt wof A sks i ev K sks ev i ev K sks ev i , ( . ) ( , , ) =   + 10 1 1 2 2       where   wof = 1 if a wash off is in progress, and zero otherwise,

  ev1 1 = if the first evaporation rate constant is active, and zero otherwise,

  ev2 1 = if the second evaporation rate constant is active and zero otherwise.

The rate that the ith chemical moves from the skin surface into the dermis is given by: II. B. 6  Page 71 of 162 dA sks dr i dt K sks dr prm i A sk C sks i ,

, , =

where C A V sks sks sk i i =

If no wash off is in progress, then the rate of change of the amount of the ith chemical on the skin is given by the rate of application minus the rate of chemical moving into the dermis minus the rate of loss due to evaporation: dA sks i dt dA sks ex i dt dA sks dr i dt dA sks ev i dt =     , ,

,.

If a wash off is in progress, then: dA sks i dt dA sks wof i dt =   ,

where the wash off is scheduled at time t wof for one time step,   t, to remove all chemical on the dermis: dA sks wof i dt t wof A sks i t t wof , ( ) ( ) . =

1.3 Variable Definitions Bolus Dose Ingestions:

A BIGi = The amount of the ith chemical in all of the Bolus Dose Ingestions at time t, A BIGT i 0 = The total amount of the ith chemical in the Bolus Dose at simulation start time, C BIGi j , = The concentration of the ith chemical in the jth Bolus Dose,

N BIG = The number of Bolus Dose Ingestion exposures,

T BIGB = The time that the Bolus Dose Ingestion starts,

T BIGE = The time that the Bolus Dose Ingestion ends, and V BIGj = The volume of the jth Bolus Dose.

Rate Ingestions:

A RIG i 0 = The initial value for the ith chemical in the Rate Ingestions,

A RIG STTi , = The total amount of the ith chemical passing from Rate Ingestions to the Stomach at time t, II. B. 6  Page 72 of 162 C RIGi j , = The concentration of the ith chemical in the jth Rate Ingestion exposure, dA dt RIGi = The rate of change of the ith chemical in the Rate Ingestions at time t,

N RIG = The number of Rate Ingestion exposures, and Q RIGj = The flow rate for the jth Rate Ingestion.

Infusions:

A INF VBTi , = The total amount of the ith chemical in Infusions to Venous Blood at time t, dA dt INF VBi , = The rate of change of the ith chemical in Infusions versus time at time t, C INFi j , = The concentration of the ith chemical in the jth Infusion,

Q INFj = The Infusion flow rate for the jth exposure.

Bolus Dose Intravenous Injection ( Bolus IV):

j i BIV A , = The amount of the ith chemical in the jth Bolus Dose IV,

A BIVT i 0 = The total amount of the ith chemical in the Bolus Dose IV at simulation start time, N BIV = The number of Bolus Dose IV exposures,

T BIVB = The time that the Bolus Dose IV starts,

T BIVE = The time that the Bolus Dose IV ends.

Intraperitoneal Injection:

A INP CUR Ji j , , , = The amount of the ith chemical remaining to be absorbed from the previous interval for the jth IP scenario,

A INP Ji j , , = The amount of the ith chemical currently in the IP Injection for the jth scenario,

A INPi = The amount of the ith chemical currently in the IP Injection,

C INP Ji j , , = The concentration of the ith chemical in the IP Injection for the jth scenario, II. B. 6  Page 73 of 162 dA dt INPi = The rate of change of the amount of the ith chemical in the IP Injection,

K INP ABS Ji j , , , = The first order absorption rate constant for the jth set of IP Injections of the ith chemical,

K INP LIMi , = The factor to limit the minimum amount of the ith chemical from IP Injections remaining to be absorbed.

V B = Volume of the Body of each subject.

Intramuscular Injection:

A INM CUR Ji j , , , = The amount of the ith chemical remaining to be absorbed from the previous interval for the jth IM Injection scenario,

A INM Ji j , , = The amount of the ith chemical currently in the IM Injection for the jth scenario,

A INMi = The amount of the ith chemical currently in the IM Injection,

C INM Ji j , , = The concentration of the ith chemical in the IM Injection for the jth scenario,

dA dt INMi = The rate of change of the amount of the ith chemical in the IM Injection,

K INM ABS Ji j , , , = The first order absorption rate constant for the jth set of IM Injections and the ith chemical,

K INM LIMi , = The factor to limit the minimum amount of the ith chemical from IM Injections remaining to be absorbed.

Skin Surface Exposure ( Water):

A SK = Area of the skin covered by the solution containing the chemical,

A SKW DR , = The amount of the ith chemical that has moved from the skin surface to the Dermis, II. B. 6  Page 74 of 162 dA dt SKW DRi , = The rate of change in the amount of the ith chemical moving from the skin surface to the Dermis,

C SKS = The concentration of the ith chemical on the skin surface due to all overlapping exposures,

C SKW Ji j , , = The concentration of the ith chemical for the jth exposure on the skin surface,

K SKS DR PRMi , , = The permeation coefficient for the ith chemical from Skin Surface to Dermis.

Inhalation:

A CC Ji j . , = The amount of the ith chemical in the jth Closed Chamber,

C AIR PPMi ,1 = The concentration of ith chemical in air for one part per million at one atmosphere and 25 º C. This is used to convert concentration in PPM to mass per unit volume.

C AIR Ji j , , = The concentration of the ith chemical in the jth exposure in parts per million.

C INH = The concentration of the ith chemical in inhaled air, units of mass per unit volume.

N SBJ = The number of subjects in the jth Closed Chamber,

V CCj = The volume of the Closed Chamber for the jth inhalation exposure,

V CC GASi , = The volume of the gas in the chamber adjusted for the volume of the subjects.

2.0 Chemical Disposition In Silico Absorption involves entry of a drug or chemical into the body. We have observed that a chemical may enter directly into the GI tract from intraperitoneal injection ( Appendix A Section 1.1.1) or more naturally from ingestion of food or from purposeful ( pica or geophagia) or accidental non dietary ingestion of filth and extraneous matter. II. B. 6  Page 75 of 162 Intravascular parenteral administration directly into the blood stream, intravenously or intra arterially, was considered as an exposure route because this route of administration is important for laboratory or clinical testing ( Appendix A Section 1.1.1). This approach was also developed for intramuscular injection ( Appendix A Section 1.1.2) as an avenue of comparison with other parenteral routes of exposure, especially dermal.

Once the drug or chemical enters the blood stream, its disposition in blood and other fluids, e. g. cerebrospinal fluid ( CSF), organs and tissues determines its access to the site or sites of action. Drug and chemical disposition involves distribution from blood and fluids to tissues and organs, metabolism in liver and other organs of metabolism, and elimination in exhaled breath, fluids, e. g. milk, and excreta.

2.1 Distribution of Chemical from Blood to Tissues, Organs and in Fluids <IP_ADDRESS> Binding in the Arterial Blood The binding in the arterial blood is of the Michaelis Menten form but is an equilibrium relationship so that the amount of the ith chemical that is bound is calculated rather than the rate. The equation is A K C V K ABSC AB B AB MxB AB AB AB DB AB i i i i i , , , ( ( )) = + <IP_ADDRESS> Calculation of Free Chemical in the Arterial Blood The free chemical in the arterial blood is calculated by subtracting the amount bound from the total amount as follows:

A A A AB F AB AB B i i i , , =

2.1.2 The Venous Blood The venous blood contains chemical output from the compartments and input to the static lung or the breathing lung. The chemical output to blood from the GI walls is passed to the portal blood.

<IP_ADDRESS> Binding in the Venous Blood The binding in the venous blood is of the Michaelis Menten form but an equilibrium relationship so that the amount of the ith chemical that is bound is calculated rather than the rate. The equation is:

A K C V K ABSC VB B VB MxB VB VB VB DB VB i i i i i , , , ( ( )) = + II. B. 6  Page 76 of 162 <IP_ADDRESS> Calculation of Free Chemical in the Venous Blood The free chemical in the venous blood is calculated by subtracting the amount bound from the total amount as follows:

A A A VB F VB VB B i i i , , =

2.1.3 Distribution in Tissues <IP_ADDRESS> Distribution in the Residual Carcass The rate of change of the ith chemical in the carcass is given by the rate that chemical enters from the arterial blood, and in the chylomicrons from the lymph pool ( when the four walled GI model is used), and exits via the venous blood. Elimination is modeled and the rate of elimination of the ith chemical is subtracted. Chemical may be metabolized and the rate of metabolism further reduces the rate of increase of the chemical in the carcass. Other metabolites may metabolize to the ith chemical and their rate of formation is added. The equation is:

V dC dt Q C K A Q C R dA dt dA dt dA dt CR CR B CR AB F LP CR LP B CR CR F CR VB CR E CR, M j= N CR M I i i i i i i i i i, j M j l m C l m = +

= , , , , ,

, ,

, ,

, , , 1 where the variable IC, l, m is the circulating compound that is the mth metabolite of the lth circulating compound. The equations for metabolism are presented in Section 2.2 of Appendix A. Binding and elimination equations are presented below.

<IP_ADDRESS>.1 Binding in the Carcass The binding in the carcass is of the Michaelis Menten form but is an equilibrium relationship so that the amount of the ith chemical that is bound is calculated rather than the rate. The equation is A K C V K ABSC CR Bi CR MxB CR CR CR DB CR i i i i , , , ( ( )) = +

<IP_ADDRESS>.3 Calculation of Free Chemical in the Carcass The free chemical in the carcass is calculated by subtracting the amount bound from the total amount as follows: II. B. 6  Page 77 of 162 A A A CR F CR CR B i i i , , =

<IP_ADDRESS>.4 Elimination in the Carcass There are two types of elimination currently implemented in ERDEM: a linear form in which the rate of elimination is proportional to the amount of the free ith chemical in the Carcass, and a saturable Michaelis Menten form. The linear form is dA dt K A CR E CR E CR F i i i ,

, , =

and the saturable form for elimination is:

dA dt V C K ABSC CR E m CR E CR F mM CR E CR F i i i i i ,

, , ,

, , , ( ( )) = +

<IP_ADDRESS> Distribution in Fat Tissue The rate of change of the ith chemical in the fat tissue is given by the rate that chemical enters from the arterial blood and the chylomicrons from the lymph pool and exits via the venous blood. Elimination is modeled and the rate of elimination of the ith chemical is subtracted. Chemical may be metabolized and the rate of metabolism further reduces the rate of increase of the chemical in the fat tissue. Other metabolites may metabolize to the ith chemical and their rate of formation is added. The equation is:

V dC dt Q C K A Q C R dA dt dA dt dA dt FT FT B FT AB F LP FT LP B FT FT F FT VB FT E FT, M j= N FT M I i i i i i i i i i, j M j l m C l m = +

= , , , , ,

, ,

, ,

, , , 1 where the variable IC, l, m is the circulating compound that is the mth metabolite of the lth circulating compound. The equations for metabolism are presented in Section 2.2 in Appendix A. Binding and elimination equations are presented below.

<IP_ADDRESS>.1 Binding in Fat Tissue The binding in the fat is of the Michaelis Menten form but is an equilibrium relationship so that the amount of the ith chemical that is bound is calculated rather than the rate. The equation is: II. B. 6  Page 78 of 162 A K C V K ABSC FT Bi FT MxB FT CR FT DB FT i i i i , , , ( ( )) = +

<IP_ADDRESS>.2 Calculation of Free Chemical in Fat Tissue The free chemical in the fat tissue is calculated by subtracting the amount bound from the total amount as follows:

A A A FT F FT FT B i i i , , =

<IP_ADDRESS>.3 Elimination in Fat Tissue There are two types of elimination currently implemented in ERDEM: a linear form in which the rate of elimination is proportional to the amount of the free ith chemical in the Fat, and a saturable Michaelis Menten form. The linear form is:

dA dt K A FT E FT E FT F i i i ,

, , =

and the saturable form for elimination is:

dA dt V C K ABSC FT E m FT E FT F mM FT E FT F i i i i i ,

, , ,

, , , ( ( )) = +

<IP_ADDRESS> Distribution in Slowly Perfused Tissue The rate of change of the ith chemical in the slowly perfused tissue is given by the rate that the chemical is input from intramuscular injections, from the lymph pool as chylomicrons and as input from the arterial blood that exits via the venous blood. Elimination is modeled and the rate of elimination of the ith chemical is subtracted. Chemical may be metabolized and the rate of metabolism further reduces the rate of increase of the chemical in the slowly perfused tissue. Other metabolites may metabolize to the ith chemical and their rate of formation is added. The equation is:

V dC dt Q C K A dA dt Q C R dA dt dA dt dA dt SL SL B SL AB F LP SL LP INM B SL SL F SL VB SL E SL, M j= N SL M I i i i i i i i i i i, j M j l m C l m = + +

= , , , , ,

,

, , ,

, , , 1 where the variable IC, l, m is the circulating compound that is the mth metabolite of the lth circulating compound. The equations for metabolism are presented in II. B. 6  Page 79 of 162 Section 2.2 of Appendix A. Binding and elimination equations are presented below.