DO NOT CITE OR QUOTE 11 12 13 14 15 16 Principles of ...1 This report contains the collective views...

This report contains the collective views of an international group of experts and does not 1 necessarily represent the decisions or the stated policy of the World Health Organization, 2 the International Labour Organization or the United Nations Environment Programme. 3 4 5 6 7 8 Harmonization Project DRAFT Document for Public and Peer Review: 9

DO NOT CITE OR QUOTE 10 11 12 13 14 15 16

Principles of characterizing and applying 17

physiologically-based pharmacokinetic and 18

toxicokinetic models in risk assessment 19 20 21 22 23 24 25 26

This project was conducted within the WHO/IPCS project on the Harmonization of 27 Approaches to the Assessment of Risk from Exposure to Chemicals. 28 29 30 31 32 33 DRAFT published under the joint sponsorship of the World Health Organization, the 34 International Labour Organization and the United Nations Environment Programme, and 35 produced within the framework of the Inter-Organization Programme for the Sound 36 Management of Chemicals. 37 38 39 40 41 42 43 44 © World Health Organization 2008, Permissions pending 45

Principles of characterizing and applying physiologically-based pharmacokinetic and toxicokinetic models in risk assessment - DRAFT FOR PUBLIC REVIEW: DO NOT CITE OR QUOTE

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TABLE OF CONTENTS 1 2

ACRONYMS AND ABBREVIATIONS 3 3 4 PREFACE 5 5 6 1. INTRODUCTION 7 7 8

1.1. CONTEXT 7 9 1.2. OBJECTIVES 8 10 1.3. ORGANIZATION 8 11

12 2. TISSUE DOSIMETRY: CONCEPTS AND APPLICATION IN RISK ASSESSMENT 9 13 14 3. CHARACTERIZATION AND DOCUMENTATION OF PBPK MODELS 16 15

16 3.1. INTRODUCTION 16 17 3.2. SCOPE AND MODEL PURPOSE 17 18 3.3. MODEL STRUCTURE AND BIOLOGICAL CHARACTERIZATION 17 19 3.4. MATHEMATICAL DESCRIPTION OF ADME 18 20 3.5. COMPUTER IMPLEMENTATION AND VERIFICATION 20 21 3.6. PARAMETER ESTIMATION AND ANALYSIS 20 22 3.7. MODEL VALIDATION AND EVALUATION 21 23

3.7.1. Characterizing the level of confidence in PBPK models 22 24 3.7.1.1 Comparison of model simulations with data 22 25 3.7.1.2 Model robustness 23 26 3.7.1.3 Plausibility 24 27 3.7.1.4 Extrapolation uncertainty 24 28

3.7.2. Purpose-specific model evaluation 26 29 3.7.2.1. Interspecies extrapolation 26 30 3.7.2.2. Interindividual variability 32 31 3.7.2.3. High dose to low dose extrapolation 34 32 3.7.2.4. Route-to-route extrapolation 35 33

3.8. DOCUMENTATION 38 34 35 4. APPLICATION OF PBPK MODELS IN RISK ASSESSMENT 39 36

37 4.1. CHOICE OF CRITICAL STUDIES 39 38 4.2. SELECTION OF PBPK MODELS 39 39 4.3. EVALUATION OF DOSE METRICS 42 40 4.4. DETERMINATION OF HUMAN EXPOSURES 44 41

42 5. PROCESS CONSIDERATIONS 47 43

44 5.1. EXPERTISE 48 45 5.2. TRAINING 48 46 5.3. COMMUNICATION 49 47

48 6. CONCLUDING REMARKS 53 49 50 REFERENCES 55 51 52 APPENDIX 1: GLOSSARY OF TERMS 66 53 54 APPENDIX 2: FREQUENTLY ASKED QUESTIONS 71 55 56 APPENDIX 3: CASE STUDY 1: CHARACTERIZING AND APPLYING PBPK 57 MODEL IN THE RISK ASSESSMENT OF CHEMICAL XA 75 58 59 APPENDIX 4: CASE STUDY 2: CHARACTERIZING AND APPLYING PBPK 60 MODEL IN THE RISK ASSESSMENT OF CHEMICAL XB 84 61 62 APPENDIX 5: CASE STUDY 3: CHARACTERIZING AND APPLYING PBPK 63 MODEL IN THE RISK ASSESSMENT OF CHEMICAL XC 91 64


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1 ACRONYMS AND ABBREVIATIONS 2 3 ADAF chemical-specific adjustment factor for interspecies difference in 4

toxicodynamics 5 ADI acceptable daily intake 6 ADME absorption, distribution, metabolism and excretion 7 ADUF default uncertainty factor for interspecies differences in toxicodynamics 8 AF assessment factor (or uncertainty factor) 9 AKAF chemical-specific adjustment factor for interspecies differences in 10

toxicokinetics 11 AKUF default uncertainty factor for interspecies differences in toxicokinetics 12 AUC area under the concentration versus time curve 13 BMC benchmark concentration 14 BMD benchmark dose 15 BMDL lower confidence limit of the dose associated with a pre-determined 16

response level (e.g., 5%) 17 BW body weight 18 CF composite uncertainty factor 19 Cmax maximal concentration 20 CSAF chemical-specific adjustment factor 21 CSF cancer slope factor 22 CYP cytochrome P-450 23 GI gastrointestinal 24 GSH glutathione 25 GST glutathione S-transferase 26 HDAF chemical-specific adjustment factor for human variability in 27

toxicodynamics 28 HDUF default uncertainty factor for human variability in toxicodynamics 29 HEC human equivalent concentration 30 HED human equivalent dose 31 HKAF chemical-specific adjustment factor for human variability in toxicokinetics 32 HKUF default uncertainty factor for human variability in toxicokinetics 33 IPCS International Programme on Chemical Safety 34 IV intravenous 35 Km Michaelis-Menten constant 36 LOAEC lowest-observed-adverse-effect concentration 37 LOAEL lowest-observed-adverse-effect level 38 MCMC Markov Chain Monte Carlo 39 MOA mode of action 40 MOE margin of exposure 41 MOS margin of safety 42 NOAEC no-observed-adverse-effect concentration 43 NOAEL no-observed-adverse-effect level 44 PBPK physiologically-based pharmacokinetic 45 PBTK physiologically-based toxicokinetic 46


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PD pharmacodynamic 1 PK pharmacokinetic 2 POD point of departure 3 QSAR quantitative structure-activity relationship 4 RfC reference concentration 5 RfD reference dose 6 SCE sister chromatid exchange 7 TD toxicodynamics 8 TDI tolerable daily intake 9 TK toxicokinetic 10 UCL upper confidence limit 11 UF uncertainty factor 12 Vmax maximal rate of metabolism 13 Vmaxc maximal rate of metabolism normalized to a fractional power of BW 14 XA chemical XA 15 XA-M metabolite of chemical XA 16 XB chemical XB 17 XB-M1 metabolite M1 of chemical XB 18 XB-M2 metabolite M2 of chemical XB 19 XC chemical XC 20 XC-GSH glutathione metabolite of chemical XC 21 XC-M metabolite of chemical XC 22 23 24 25


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PREFACE 1 2 This document was prepared through a project on PBPK modeling under the auspices of 3 the WHO/IPCS Project on the Harmonization of Approaches to the Assessment of Risk 4 from Exposure to Chemicals. The first draft was prepared by Dr. Kannan Krishnan, 5 Université de Montréal, Canada under the guidance of the WHO/IPCS Planning Group 6 on PBPK modeling in risk assessment: 7

8 Bette Meek, Planning Group Chair 9 Safe Environments Programme 10 Health Canada, Ottawa 11 Canada 12 13 Hugh Barton 14 National Center for Computational Toxicology 15 U.S. EPA 16 Research Triangle Park, NC 17 U.S.A. 18 19 Jos Bessems 20 Centre for Substances and Integrated Risk Assessment 21 RIVM 22 Bilthoven 23 The Netherlands 24

25 Harvey Clewell III 26 The Hamner Institutes for Health Sciences 27 Research Triangle Park, N.C. 28 U.S.A. 29 30 Michel Bouvier-d’Yvoire 31 European Centre for the Validation of Alternative Methods 32 Joint Research Centre of the European Commission 33 Ispra 34 Italy 35

36 Harrie Buist 37 Department of Toxicological Risk Assessment 38 TNO Nutrition and Food Research 39 Zeist 40 The Netherlands 41 42 Ursula Gundert-Remy 43 Safety of Substances and Products 44 Bundesinstitut fuer Risikobewertung 45


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Berlin 1 Germany 2 3 John Lipscomb 4 ORD/NCEA 5 US EPA 6 Cincinnati, OH 7 U.S.A. 8 9 George Loizou 10 Computational Toxicology Section 11 Health and Safety Laboratory 12 Buxton 13 United Kingdom 14 15 David Moir 16 Health Canada 17 Ottawa 18 Canada 19 20 Martin Spendiff 21 Computational Toxicology Section 22 Health and Safety Laboratory 23 Buxton 24 United Kingdom 25

26 EC Representative 27 George Fotakis 28 Joint Research Centre - Ispra 29 Institute for Health and Consumer Protection European Chemicals Bureau 30 Ispra 31 Italy 32 33 Secretariat 34 Ms Carolyn Vickers 35 International Programme on Chemical Safety 36 World Health Organization 37 Geneva 38 Switzerland 39 40 41 Expert review and contribution to earlier drafts by Woody Setzer and John Wambaugh of 42 National Center for Computational Toxicology, U.S. EPA, Research Triangle Park, NC, 43 U.S.A. are greatfully acknowledged. 44


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1. Introduction 1 2

1.1. Context 3 4 This document provides high-level guidance on the characterization and application of 5 physiologically-based pharmacokinetic (PBPK or physiologically-based toxicokinetic 6 (PBTK))1,2 models in risk assessment. The PBPK models are part of the broader 7 continuum of increasingly data-informed approaches ranging from the default based on 8 external dose to more biologically-realistic dose response models. By facilitating the 9 incorporation of internal dose measures of relevance to the mode of action (MOA) of 10 chemicals, the PBPK models facilitate scientifically-sound extrapolations across studies, 11 species, routes, and dose levels. They are also fundamental to the development of 12 biologically-based dose-response models focusing to address uncertainty and variability 13 related to toxicokinetics and toxicodynamics. 14 15 This document represents the product of an initiative undertaken as part of the 16 International Programme on Chemical Safety (IPCS) Project on Harmonization of 17 Approaches to Evaluate Risk from Exposure to Chemicals. This document complements 18 outputs of other initiatives of the IPCS Project on Harmonization, particularly, those 19 relating to the weight of evidence for MOA of chemicals, human exposure models and 20 chemical-specific adjustment factors (WHO 1999, 2001, 2004a,b, 2005, 2006; Sonich-21 Mullin et al. 2001; Meek et al. 2003b; Boobis et al. 2006, 2008). A specific focus of this 22 document is on promoting best practices in characterizing and applying PBPK models, so 23 as to facilitate greater uptake of these tools by risk assessors. 24 25 This IPCS document draws upon the output of two international workshops that 26 addressed specific technical issues in PBPK modeling relevant to enhancing their uptake 27 in risk assessment: 28

• International Workshop on Uncertainty and Variability in PBPK Models held at 29 Research Triangle Park, North Carolina, U.S.A. (31 October–2 November 2006; 30 Sponsors: U.S. Environmental Protection Agency (Office of Research and 31 Development, National Center for Environmental Assessment, National Center 32 for Computational Toxicology, and National Health Effects and Environmental 33 Research Laboratory) & National Institute of Environmental Health Sciences, 34 National Institutes of Health, U.S.A.) 35

• International Workshop on the Development of Good Modelling Practice for 36 PBPK models at the Mediterranean Agronomic Institute of Chania, Crete, Greece 37 sponsored by The UK Health and Safety Executive (HSE), the UK Health and 38 Safety Laboratory (HSL), Health Canada, The European Chemical Industry 39

1In this document, the terms “pharmacokinetic” and “toxicokinetic” are considered to have the same meaning. Therefore, “physiologically-based pharamacokinetic (PBPK) model” is equivalent to “physiologically-based toxicokinetic (PBTK) model” (WHO 2005). 2 PBPK models are quantitative descriptions of absorption, distribution, metabolism and excretion of chemicals in biota based on interrelationships among key physiological, biochemical and physicochemical determinants of these processes.


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Council (CEFIC) and The European Centre for the Validation of Alternative 1 Methods (ECVAM) (April 26 – April 28, 2007). 2

3 Additionally, capitalizing on recent, separate initiatives of U.S. EPA, Health Canada and 4 EU, this IPCS document provides: (i) an international perspective that seems to be 5 lacking so far, (ii) a basis for international endorsement of the best practices for 6 characterizing and applying PBPK models in risk assessment, and (iii) a means of raising 7 international level of awareness on the applicability and value of PBPK modeling in risk 8 assessment. 9 10

1.2. Objectives 11 12 The primary goal of this report is to document the key principles and best practices for 13 characterizing and applying PBPK models in risk assessment. This report presents these 14 principles both for risk assessors who need to evaluate PBPK models for use in risk 15 assessment as well as for PBPK modelers who are interested in developing models 16 capable of risk assessment applications. A particular emphasis is made in the report 17 regarding the need for effective and transparent communication to facilitate greater 18 uptake of PBPK models by the risk assessment community. In this regard, case studies 19 dealing with the characterization and application of PBPK models in risk assessment are 20 presented and discussed throughout this document. 21 22

1.3. Organization 23 24 The main text of this document is organized in five sections. Section 1 presents a brief 25 historical account of the process that went into the development of this document, as well 26 as contextual reference of this guidance to other initiatives of the IPCS Project on the 27 Harmonization of Approaches to the Assessment of Risk from Exposure to Chemicals. 28 Section 2 addresses the critical role of toxicokinetics and dosimetry modeling in risk 29 assessment, whereas Section 3 summarizes the principles for characterizing and 30 documenting PBPK models intended for use in risk assessments. Section 4 discusses the 31 principles and practice of PBPK model application in health risk assessment with 32 particular reference to high dose to low dose, interspecies, interindividual as well as 33 route-to-route extrapolations. Section 5 focuses on process considerations regarding the 34 incorporation of PBPK models within risk assessment; specific aspects discussed in this 35 section include training, expertise and communication issues to overcome the 36 impediments to the use of PBPK models in risk assessment. Finally, a glossary of 37 technical terms (Appendix 1), frequently asked questions (FAQs) regarding application of 38 PBPK models in risk assessment (Appendix 2) as well as illustrative case studies of 39 PBPK model characterization and application in risk assessment (Appendices 3-5) are 40 presented. 41 42 43


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1 2. Tissue dosimetry: concepts and application in risk assessment 2 3 Health risk assessments are conducted on the basis of problem formulation, hazard 4 identification, dose-response assessment, exposure assessment and risk characterization 5 (NRC 1983, WHO 1999, 2005, 2006). The dose-response assessment frequently involves 6 the identification of a point of departure (POD) for deriving the acceptable exposure 7 concentration or tolerable daily dose for humans, including sensitive individuals. In the 8 case of threshold toxicants, the POD (e.g., NOAEL, NOAEC, LOAEL, LOAEC, BMC, 9 BMDL) is divided by numerical factor(s) referred to as assessment factor(s) to determine 10 the tolerable daily intake (TDI), acceptable daily intake (ADI), reference dose (RfD), 11 reference concentration (RfC), margin of exposure (MOE) or margin of safety (MOS) 12 (WHO 1987, 1994, 1999, 2004a). The assessment factors are also referred to as 13 uncertainty factors, safety factors, extrapolation factors or adjustment factors by various 14 jurisdictions (WHO 2004a; Ritter et al. 2007; Konietzka et al. 2008). 15 16 For nonthreshold toxicants, particularly for those substances that interact with genetic 17 material, one of the following approaches is used (Younes et al. 1998; WHO 2001, 18 2005): 19

• Estimation of risk at low doses based on linear extrapolation of response observed 20 at an effective dose 21

• Expression of the dose-response relationship near the experimental range in terms 22 of potency estimates 23

• Comparison of the effective dose with the exposure dose to compute a margin of 24 exposure, and 25

• Advice to control the exposure levels to the maximum extent possible so as to 26 reduce risk to humans 27

28 The risk assessment process has evolved over the years to effectively accommodate 29 increasingly data-informed approaches, particularly for chemicals with a critical 30 exposure-response characteristic. In such cases, the tendency is to move away from the 31 conduct of “external dose vs response” analysis, and incorporate relevant data on MOA, 32 toxicokinetics and toxicodynamics (Figure 1). In this context, MOA refers to the 33 description of the processes leading to the induction of relevant toxicity end-point(s) for 34 which weight of evidence supports plausibility (WHO 2001). For a given chemical, the 35 MOA reflects the current state of knowledge regarding the biological basis of the 36 response and the relevant ‘dose metric’ (i.e., dose measure that is causally related to the 37 toxic outcome) (Andersen et al. 1987; Clewell et al. 2002; Andersen 2003). 38 39 The use of external dose (or environmental concentration) to relate to adverse response 40 does not reflect the current understanding of MOA of a chemical and is essentially 41 uninformative as it does not facilitate scientifically-sound extrapolations to other 42 scenarios, species, individuals or exposure routes (Clewell et al. 2002). The availability 43 of data on categories or groups of chemicals might facilitate the development of 44 “categorical” approaches as they relate to interspecies extrapolation, interindividual 45


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variability and route-to-route extrapolations (e.g., Jarabek 1994; Naumann et al. 2001). 1 The IPCS described approaches for incorporating chemical-specific data on kinetic and 2 dynamic aspects to characterize interspecies differences and human variability in dose-3 response assessment (WHO 2001, 2005). The IPCS framework for chemical-specific 4 adjustment factors (CSAFs) enables the risk assessor to consider and incorporate data on 5 MOA, toxicokinetics and toxicodynamics, with the incentive of using a more appropriate 6 assessment factor in risk assessments instead of the default values. In the broader 7 continuum of increasingly data-informed approaches which range from default (i.e., 8 presumed health-protective) to more biologically-realistic dose response models (Figure 9 1), the PBPK models represent higher level tools. The PBPK models facilitate the 10 estimation of dose to target tissue by taking into account the rate of absorption into the 11 body, distribution and storage in tissues, metabolism, and excretion on the basis of 12 interplay among critical physiological, physicochemical, and biochemical determinants. 13 The motivation in using PBPK models and tissue dosimetry concepts in risk assessment 14 is essentially to provide a better representation of "biologically effective dose” and a 15 stronger biological basis for conducting extrapolations across studies, species, routes, and 16 dose levels as discussed below. 17 18

19 20 Figure 1. The relationship between external dose and toxic response for specific 21 compounds. Based on Renwick et al. (2001) and WHO (2001). 22 23 Interspecies extrapolation: The extrapolation of exposure dose corresponding to POD 24 from one species to another (typically from test animal species to humans) has been 25 addressed with the use of a factor of 10, body surface area scaling or body weight to a 26 fractional power (BW0.67 to BW0.75) in data-poor situations (Andersen et al. 1995b; 27

EXTERNAL DOSE

INTERNAL DOSE

TARGET ORGAN DOSE

TARGET ORGAN

METABOLISM

TARGET ORGAN

RESPONSES TOXIC

RESPONSE

EXTERNAL DOSE

INTERNAL DOSE

TARGET ORGAN DOSE

TOXIC RESPONSE

EXTERNAL DOSE

INTERNAL DOSE

TOXIC RESPONSE

EXTERNAL DOSE

TOXIC RESPONSE

EXTERNAL DOSE

INTERNAL DOSE

TARGET ORGAN DOSE

TARGET ORGAN

METABOLISM TOXIC

RESPONSE

Kinetic parameters

Physiologically-based pharmacokinetic (PBPK) models

PBPK models with target organ metabolism

Biologically-based dose-response model

Basic data set


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Dourson et al. 1996; Jacobs 2007). These approaches are assumed to provide 1 toxicokinetically-equivalent doses in test species and humans, and to adjust for 2 toxicodynamic differences for certain chronic effects under the following conditions: (i) 3 the biologically-active form is the parent chemical, (ii) the clearance and toxicodynamic 4 processes are proportional to body surface area in both test animal species and humans, 5 and (iii) the area-under-the blood-concentration vs time curve is the appropriate measure 6 of dose to target tissue. When one or more of these assumptions do not hold, these 7 default approaches may not be sufficiently health-protective or may be overly 8 conservative; but in reality, the adequacy of these approaches to account for interspecies 9 differences in target tissue dose and tissue response for specific chemicals is not certain 10 (Andersen et al. 1991, 1995b). 11 12 When sufficient data to inform about toxicokinetics, toxicodynamics and MOA are 13 available, WHO (2001, 2005) has suggested the derivation of CSAFs for toxicokinetic 14 and toxicodynamic differences between animals and humans (Figure 2). Accordingly, 15 ADUF of 2.5 (equal to 100.4) and AKUF of 4 (based on 100.6) were proposed on the basis of 16 observations and data reviewed by Renwick and co-workers (1993, 1999). An even split 17 between the two components was not favoured based on the greater potential for 18 differences in tissue dose than in tissue response between animals and humans (Renwick, 19 1993; Walton et al. 2001a,b,c, 2004). The suggested default values of 4 and 2.5 for the 20 toxicokinetic and toxicodynamic components, however, are reflective of rats as the test 21 animal species. As such, when relevant chemical-specific data are available, they can be 22 used to replace one or both factors, i.e., AKUF and ADUF (Health Canada 1994; Renwick 23 1999; Gundert-Remy and Sonich-Mullin 2002; Meek et al. 2003a; WHO 2005). More 24 importantly, AKUF can be replaced with the use of PBPK models which facilitate the 25 conduct of interspecies (i.e., test species to human) extrapolation of exposure doses on 26 the basis of target organ dose or another dose metric closely related to the toxic response 27 (Rowland 1985; Andersen et al. 1995b; Jarabek 1995). 28 29 30

31 32 33 Figure 2. Subdivision of the composite uncertainty factor (CF). Adopted from 34 WHO (2001). 35

UNCERTAINTY FACTOR – 100

INTERSPECIES DIFFERENCES

10

TOXICO-KINETIC

AKUF 100.6

4.0

TOXICO-DYNAMIC

ADUF 100.4

2.5

TOXICO-KINETIC

HKUF 100.5

3.16

INTERINDIVIDUAL DIFFERENCES

10

TOXICO-DYNAMIC

HDUF 100.5

3.16


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Interindividual variability: The application of an assessment factor to account for human 1 variability is based on the following assumptions (Dourson et al. 1996; Price et al. 1999): 2 3

o (i) variability in target organ dose and toxicological response in a 4 population is greater than that observed in a finite study sample; 5

o (ii) certain individuals in the human population are more sensitive than the 6 average individual due to differences in target tissue dose and/or tissue 7 response. 8

9 Several jurisdictions have used a factor ranging from 1 to 10 (reviewed in Dourson et al. 10 1996; Vermeire et al. 1999), implying that the exposure dose associated with a certain 11 level of response (or the no-observed-adverse-effect level) could vary between the 12 “average” individual and “sensitive” individuals by as much as an order of magnitude. 13 The applicability of the default value of 10 for all chemicals, regardless of the extent of 14 available information on the population variability in toxicokinetics, toxicodynamics or 15 MOA lacks a clearly defined scientific basis. It is uncertain then as to whether the default 16 value is adequate, inadequate or overly conservative for specific chemicals. In this 17 regard, when relevant data on toxicokinetics and toxicodynamics are available, the IPCS 18 framework facilitates a rational development of CSAF, thus allowing deviation from the 19 default approach. Based on a strategy similar to interspecies considerations, IPCS 20 suggested a default value of 3.16 for HKUF and HDUF, which can in turn be modified 21 according to the data availability for toxicokinetic and toxicodynamic aspects (WHO 22 2001, 2005). With the availability of PBPK models, more relevant data, i.e., the dose 23 metrics in the population including specific lifestages and subgroups, can be incorporated 24 into risk assessments for a scientifically-sound characterization of human variability in 25 toxicokinetics (Barton et al. 1996; Clewell et al. 1999; Haber et al. 2002; Lipscomb and 26 Ohanian 2007). 27

28 Route-to-route extrapolation: Often the toxicity data or POD for a given chemical might 29 be available for one exposure route (e.g., oral), but other exposure routes (e.g., dermal, 30 inhalation) might also be relevant for risk assessment. In such cases, route-to-route 31 extrapolation is conducted if the toxicity of the compound is systemic and not local (i.e., 32 portal of entry) in nature (ECETOC 2003, IGHRC 2005). Depending upon the extent of 33 available data, such extrapolations have been conducted on the basis of equivalent 34 exposure dose or absorbed dose (e.g., Gerrity and Henry 1990; Pauluhn 2003). These 35 approaches, however, do not facilitate the derivation of route-specific exposure doses on 36 the basis of equivalent tissue exposure or tissue response, because they do not account for 37 route-to-route differences in first-pass effect, toxicokinetic processes or dose metrics of 38 relevance to MOA in the test animal species and humans (ECETOC 2003, IGHRC 2005). 39 Such considerations to reduce the uncertainty related to route-to-route extrapolation are 40 facilitated by PBPK models (Clewell and Andersen 1987; Gerrity and Henry 1990). 41 42 High dose to low dose extrapolation: The extrapolation of tissue dose and tissue 43 response from high dose to low dose is particularly important when toxicity data are 44 collected in laboratory animals. The dose-response relationship, based on exposure dose, 45 is frequently complex due to nonlinearity in the toxicokinetic processes (Slikker et al. 46


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2004). For example, the target tissue dose of the toxic moiety is not likely to be 1 proportional to the doses administered in animal bioassays when saturable metabolic 2 pathways are operative in that dose range (e.g., Gehring et al. 1978). In such cases, the 3 derivation of cancer risk or POD based on appropriate measure of internal dose – as 4 estimated by PBPK models - would be scientifically-sound and more closely related to 5 tissue response than the exposure dose (e.g., Andersen et al. 1987). 6 7 Dosimetry data and Dose-response analysis: The application of increasingly data-8 informed approaches effectively addresses the scientific uncertainties by taking into 9 account the critical determinants of MOA, toxicokinetics and toxicodynamics as they 10 relate to interspecies, route-to-route and high dose to low dose extrapolations as well as 11 interindividual variability. In this regard, the use of PBPK models in dose-response 12 assessment is to facilitate an analysis based on internal dose, target tissue dose or 13 cellular/subcellular dose (Figure 3) in lieu of exposure dose, thus providing a more 14 accurate extrapolation to human exposure conditions. Implicit in such an application is 15 the assumption that the toxic effects in the target tissue are more closely related to a 16 measure of an active form of the substance, consistent with the understanding of the 17 MOA underlying the adverse response (Clewell et al. 2002). 18 19 Even though it may be possible in certain cases to access the target site and ethically 20 characterize the time-course of the concentration of the toxic moiety during windows of 21 susceptibility, realistically such data in both test species and humans would not become 22 routinely available for all chemicals and exposure scenarios of interest. Even in animal 23 studies, it is only practical to measure systemic or urinary concentrations of chemicals 24 and their metabolites at specific time points rather than the complete time-course of the 25 actual toxic moiety in the target tissue. Toxicokinetic models (i.e., mathematical 26 descriptions of the rate and magnitude of absorption, distribution, metabolism and 27 excretion (ADME)) are therefore central to the estimation of the time-course of tissue or 28 blood concentrations of toxic substances for various exposure scenarios, routes, species, 29 and doses (U.S. EPA 2006). 30 31

32 33 Figure 3. Schematic of the processes involved in the expression of toxic responses 34 placed in a risk assessment context. 35 36

Observable effect

Cellular/ subcellular Interaction

Early response

Late response

Dynamics

Kinetics Cellular/ subcellular

dose Internal dose/ exposure

Tissue dose/ exposure

External dose/ exposure


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Among the compartmental toxicokinetic models, PBPK models are the most appropriate 1 and useful tools for conducting extrapolations needed for dose-response analysis because 2 they incorporate relevant information on the physiological and biological processes 3 underlying toxicokinetics (Clewell and Andersen 1985, 1987; Clewell et al. 2002). The 4 chief advantage of a PBPK model over an empirical compartmental description is its 5 greater extrapolation power. Since fundamental biochemical processes are described, 6 extrapolation over dose ranges where saturation of metabolism occurs is possible 7 (Ramsey and Andersen, 1984; Clewell and Andersen 1985, 1987). Similarly, as the input 8 parameters relate to mechanistic determinants, the PBPK models are instrumental in 9 facilitating the prediction of interindividual, interspecies and interroute differences in 10 dose metrics. For example, interspecies extrapolation is conducted either by replacing 11 the input parameter values specific to the species of interest (e.g., Arms and Travis 1988; 12 Brown et al. 1997; Davies and Morris 1993), or by calculating them on the basis of body 13 surface or body weight differences (Adolph, 1949; Dedrick et al., 1973; Dedrick and 14 Bischoff, 1980). 15 16 PBPK models are essentially intended to estimate target tissue dose in species and under 17 exposure conditions for which little or no data exist. Thus, if a complete toxicokinetic 18 data set is available for the exposure scenarios and species of interest, then there would 19 be no need to develop a PBPK model. Such an optimal toxicokinetic data set for risk 20 assessment would consist of the time-course data on the most appropriate dose metric 21 associated with exposure scenarios and doses used in the critical studies chosen for the 22 assessment (e.g., animal bioassays or human clinical and epidemiological studies) and 23 relevant human exposure conditions. In the absence of such information, PBPK models 24 are instrumental in providing simulations of dose metrics for conducting interspecies, 25 human variability (including different developmental stages), route-to-route and high 26 dose to low dose extrapolations of chemicals present singly or as mixtures (Clewell and 27 Andersen 1985; NRC 1987; Gerrity and Henry 1990; Krishnan et al. 2002). 28 29 The determination of whether a particular PBPK model is of use in a given risk 30 assessment depends upon the: 31 32

• Model credibility 33 • Model reliability and 34 • Model applicability 35

36 To be credible, a PBPK model should possess certain characteristics in terms of its 37 structure and parameters as well as documented evaluation of these aspects. A credible 38 model may, however, not be reliable or applicable for risk assessment. To be reliable for 39 risk assessment application, the model should be able to simulate the dose metrics of 40 relevance to the MOA of the chemical. Finally, to be applicable in a risk assessment 41 (i.e., fit for purpose), it should have the essential features (i.e., in terms of structure and 42 parameters) consistent with the intended use in risk assessment (e.g., high dose to low 43 dose extrapolation). The key characteristics of credible PBPK models are discussed in 44


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Section 3, whereas aspects relating to reliability and applicability of PBPK models in risk 1 assessment are considered in section 4. 2


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3. Characterization and documentation of PBPK models 1 2

3.1. Introduction 3 4 PBPK models accommodate quantitative descriptions of ADME of chemicals in biota 5 based on the interrelationships among physiological, biochemical and physicochemical 6 determinants of these processes (Teorell et al. 1937a,b; Dedrick et al. 1972, Fiserova 7 Bergerova 1983; NRC 1987; Andersen 2003; Reddy et al. 2005; Lipscomb and Ohanian 8 2007). Figure 4 presents the key steps involved in the development of PBPK models. 9 Typically, the development of PBPK models intended for risk assessment application 10 would begin with a critical evaluation of the peer-reviewed literature to assemble the 11 following chemical-specific or more generic (i.e., species-specific) information: 12

the nature of the critical effect(s) 13 the dose-response relationship for the critical effect(s) 14 MOA, including nature of the toxic moiety (i.e., whether the parent chemical, 15

a stable metabolite, or a reactive intermediate produced during metabolism is 16 responsible for the toxicity), 17

the pathways and rates of ADME, and 18 the physiology (i.e., tissue weights and blood flow rates) of the species of 19

interest. 20 21

22 23

24 Figure 4. Schematic of the steps involved in the development of PBPK models for 25 application in risk assessment. 26 27 Several authors have discussed good PBPK modeling principles and practices in terms of 28 the following aspects (Andersen et al. 1995a; Clark et al. 2004; Gentry et al. 2004; Kohn 29 1995; Barton et al. 2007; Chiu et al. 2007; Clewell and Clewell 2008; Loizou et al. 30 2008): 31

Model refinement

Problem formulation and data evaluation

Parameter estimation and analysis

Simulation of toxicokinetics

Model structure and characterization

Mathematical and computational implementation

"Purpose-specific" model validation and

evaluation


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1 • Scope and model purpose 2 • Model structure and biological characterization 3 • Mathematical descriptions of ADME 4 • Computer implementation and verification 5 • Parameter estimation and analysis 6 • Model validation and evaluation 7

8 Building upon these principles, the following sections highlight the key aspects of 9 characterization and documentation of PBPK models intended for use in risk assessment. 10 11

3.2. Scope and model purpose 12 13 The scope for the use of PBPK models in a particular risk assessment essentially 14 determines the intended model capability and the extent of model evaluation (Box 1). 15 Therefore, it is critical to clearly identify the type of risk assessment it is intended to 16 support (e.g., screening or full; cancer or non-cancer; acute or chronic), the aspects of the 17 assessment it is designed to facilitate (e.g., cross-route or cross-species dosimetry), as 18 well as the mode-of-action hypotheses underlying the model structure (e.g., toxicity from 19 a reactive metabolite vs receptor binding). In each case then, a strategy for incorporating 20 relevant data (i.e., PBPK model results) to address/resolve the sources of uncertainty in 21 risk assessment would determine the extent of the modeling effort. For example, if the 22 uncertainty in a current or proposed risk assessment for a given chemical relates to lack 23 of knowledge regarding interspecies differences in tissue dosimetry, a PBPK model 24 capable of providing human-equivalent doses would be sought. A single PBPK model 25 may not be sufficient to address all areas of uncertainty (e.g., a PBPK model in an adult 26 animal may not be adequate to simulate kinetics during specific windows of susceptibility 27 during gestation). Therefore, the model purpose and capability of PBPK models should 28 be characterized in terms of the species, lifestage, exposure routes/windows and dose 29 metrics that are central to their application in risk assessment (Box 1). 30 31

3.3. Model structure and biological characterization 32 33 The structure of a PBPK model should be characterized in the form of boxes and arrows; 34 the organs and organ systems are represented by the boxes and the specific 35 physiological/clearance processes are identified by arrows (e.g., Figure 5). The model 36 structure should reflect a balance between the principles of parsimony (i.e., minimal but 37 essential elements characterizing a system) and plausibility (i.e., reflective of 38 physiological reality and consistency with current state of knowledge) in order to 39 simulate dose metrics of relevance to risk assessment. For a hydrophilic chemical whose 40 concentrations are fairly similar across compartments, a one compartmental model might 41 be sufficient. However, for chemicals which are metabolized or accumulated to different 42 extents in various tissues leading to characteristic time-course profiles, a multi-43 compartmental description is required. Typically, tissues exhibiting similar 44 “concentration vs time” course behavior are lumped together (e.g., richly perfused tissues, 45


18

poorly perfused tissues). The need to represent a particular organ or tissue as a separate 1 compartment (either by lumping or splitting) is determined based on its relevance to 2 target organ toxicity, MOA, toxicokinetic mechanisms and portal of entry (exposure 3 route) of the chemical under study (Krishnan and Andersen 2007). However, model 4 complexity and the number of compartments should not be equated with accuracy and 5 usefulness of the model description; oftentimes model complexity yields a multitude of 6 parameters to be estimated and greater uncertainty in the resulting PBPK model. In this 7 regard, the initial version of a PBPK model for organic chemicals has often based on a 8 so-called “generic” model (e.g., Ramsey and Andersen 1984; Brightman et al., 2006) 9 consisting of descriptions of the major ADME processes and mechanisms. Deviations 10 from such generic structures are then characterized on the basis of specific mechanisms 11 of ADME of a chemical. 12 13 Box 1: Scope and purpose of PBPK model application in risk assessment (adopted 14 from Clark et al. 2004) 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

3.4. Mathematical descriptions of ADME 39 40 The equations employed in a PBPK model should be consistent with the knowledge on 41 the mechanisms of ADME for the particular chemical. In this regard, the influx and 42 efflux clearance of chemicals are frequently described according to perfusion-limitation 43 (i.e., compartment is considered homogenous or well-mixed) or diffusion-limitation (i.e., 44 compartment is heterogeneous and therefore represented as sub-compartments of cellular 45 matrix and tissue blood) principles (Himmelstein and Lutz 1979; Gerlowski and Jain 46

RISK ASSESSMENT NEEDS • Dose-response analysis

o Estimating internal dose measures o Extrapolation across species o Evaluating human TK variability

• Exposure issues o Route-to-route extrapolation o Extrapolation across exposure patterns

• Cumulative risk assessment • Cross-chemical evaluation (e.g., parent & metabolite)

GENERIC ISSUES

• What chemical(s)? • What ages, lifestages and physical activity? • What species? • What target organs and potential critical effects? • What exposure routes, matrices, and regimens in risk assessment, potential critical

studies and toxicokinetic studies? • What dose metric and acceptable level of prediction, given the purpose of the

assessment ?


19

1983); any departure from such principles should be characterized on the basis of 1 adequate reasoning. Similarly, the type of rate equation used (i.e., first-order, second-2 order, or saturable) should be consistent with biochemical evidence and first principles 3 (Krishnan and Andersen 2007). A pragmatic approach is to characterize mathematical 4 descriptions that are either ifferent from published PBPK models or that cannot be readily 5 and unequivocally derived from the flow diagrams. 6 7

8 9 Figure 5. Illustration of the structure of an inhalation PBPK model for a volatile 10 organic chemical. 11 12 PBPK models, like any other quantitative models, are simplified descriptions of reality. 13 Accordingly, they are based on some general assumptions regarding the mechanisms of 14 ADME (Rideout 1991): 15

o (i) the mixing of the chemical in the effluent blood from the tissues is 16 instantaneous and complete 17

o (ii) blood flow is unidirectional, constant, and non-pulsatile, and 18 o (iii) the presence of chemicals in the blood does not alter the blood 19

flow rate. 20 Any deviations from such general assumptions of PBPK models should be characterized 21 and documented. 22

Liver

Richly perfused tissues

Poorly perfused tissues

Lungs

Adipose tissue

A R T E R I A L

B L O O D

V E N O U S

B L O O D

Inhaled Exhaled

Metabolism


20

3.5. Computer implementation and verification 1 2 The set of equations constituting the PBPK model along with the input parameters are 3 written as a program for conducting simulations. Simulation, in the present context, 4 refers to the system behavior (i.e., kinetic profiles in blood and tissues) predicted by 5 solving the differential and algebraic equations constituting the PBPK model. Since 6 PBPK models contain differential equations (i.e., equations calculating the differential in 7 a dependent variable (e.g., concentration) with respect to the independent variable (i.e., 8 time)) as well as non-linear descriptions (e.g., saturable metabolism), algorithms are used 9 to solve all equations simultaneously. The algorithms (i.e., fixed step-by-step procedure) 10 used for PBPK modeling should be characterized for: (i) their theoretical basis, (ii) self-11 starting capability, (iii) automatic control of step size and method order, (iv) ability to 12 deal with both stiff and nonstiff problems and to detect stiffness automatically, as well as 13 (iv) stability of solutions (Rideout 1991). When newer or non-standard algorithms are 14 used, they should be characterized with respect to the above criteria. However, if an 15 algorithm has previously been shown to work for PBPK modeling, then such a detailed 16 characterization is usually not needed. 17

18 The accuracy of mathematical and computational implementations of PBPK models 19 should be verified systematically. Model verification essentially focuses on making sure 20 that the model is built right (Balci 1997). In the context of PBPK modeling, 21 “verification” would involve a systematic review to ensure that: 22

• the model codes (i.e., equations and parameter values) are devoid of 23 syntax/mathematical errors, 24

• the units of input parameters and variables are accurate, 25 • the chemical mass balance as well as blood flow balance within the model 26

are respected at all times, and 27 • there is no solver or numerical error. 28

The confidence in the computer implementation of a PBPK model would be increased if 29 the model is coded and run in a different computer language/program. 30

31 3.6. Parameter estimation and analysis 32

33 The methods for estimation and analysis of chemical-specific parameters as well as 34 biological input data for PBPK models have been reviewed in the literature (e.g., 35 Krishnan and Andersen 2007; Lipscomb and Ohanian 2007). In the present document, 36 emphasis is placed on characterization of the input parameters of PBPK models intended 37 for risk assessment application, as discussed below: 38 39

The input parameters should reflect biological or mechanistic determinants of 40 ADME of the chemical being modeled. 41

42 The values (i.e., point estimates or distributions) used for physiological 43

parameters in the model (e.g., alveolar ventilation rate, cardiac output, tissue 44 volumes, rates of tissue blood flow, glomerular filtration rate) should be within 45


21

the documented range for the particular species and lifestage (e.g., ICRP 1975; 1 Arms and Travis 1988; Davies and Morris 1993; Brown et al. 1997; Price et al. 2 2003; Gentry et al. 2004). 3

4 The sum total of the volumes of compartments should be equal to or within the 5

body weight of the organism being modeled. 6 7

The cardiac output specified in the model should be equal to the sum of blood 8 flow rates to the tissue compartments such that the mass balance is maintained in 9 the PBPK model at all times. 10

11 The ventilation:perfusion ratio should be within the physiological range for the 12

species, age group and activity level. 13 14

Physicochemical parameters (e.g., blood:air, tissue:plasma and tissue:blood 15 partition coefficients) should have preferably been obtained on the basis of 16 independent measurements (in vitro, in vivo) or using algorithms in the valid 17 domain of application (e.g., QSARs, tissue composition-based algorithms) 18 (Béliveau et al. 2005; Krishnan and Andersen 2007; Rodgers and Rowland 2007; 19 Schmitt 2008). 20

21 Biochemical parameters estimated by fitting model simulations to time-course 22

data obtained under in vivo conditions should be characterized on the basis of 23 their sensitivity to the in vivo data (Krishnan and Andersen 2007). If in vitro 24 approaches are used, the validity of the in vitro system to adequately predict the 25 kinetics of chemicals in vivo should be ensured. Processes and parameters 26 operative in vivo should be accounted for while scaling in vitro rates to the whole 27 animal (Lipscomb et al. 1998; Barter et al. 2007; Lipscomb and Poet 2008). 28

29 Allometric scaling of model parameters, if done, should be justified or explained 30

as to the basis and appropriateness (Adolph, 1949; Dedrick et al., 1973; Dedrick 31 and Bischoff, 1980). 32 33

3.7. Model validation and evaluation 34 35

All models of complex real-world systems have potentially two types of built-in 36 errors varying in magnitude and importance: (i) structural uncertainty/error and (ii) 37 parameter uncertainty/error (WHO 2004b). The adequacy of model structure as well as 38 parameter values are often inferred by comparing model predictions with experimental 39 data that had not been used for estimating parameters. This process is referred to as 40 “validation”, “strong validation” or “external validation” (Cobelli et al. 1984; Leijnse and 41 Hassanizadeh 1994; WHO 2004b). Validation is defined as the process by which 42 reliability and relevance of a particular approach (or model, in this context) is established 43 for a defined purpose (WHO 2004b). In the context of PBPK and such simulation 44 models in risk assessment, we should really be concerned about purpose-specific 45 “evaluation” rather than generic “validation” (Barton et al. 2007). Model evaluation is 46


22

the process of establishing confidence in the model on the basis of scientific principles, 1 quality of input parameters as well as ability of the model to reproduce independent 2 empirical data (Oreskes 1998). 3 4

Regardless of the terminology (i.e., model validation vs model evaluation), the overall 5 intent here is the same, i.e., to assess appropriateness (whether the major determinants of 6 the system behavior have been adequately captured by the model) and predictive ability 7 (whether the model with its set of input parameters can adequately simulate the behavior 8 of a chemical for specific use purposes). In this regard, it is important to realize that the 9 simulations of more than one model may agree with a limited set of data (Oreskes 1998). 10 Further, no model can be proven to be universally valid i.e., demonstrate that it is capable 11 of providing simulations that correspond to reality or experimental data for all relevant 12 dose metrics in test species and humans exposed by all exposure scenarios, doses, and 13 routes of interest; such a goal, in fact, is neither pertinent nor feasible (Cobelli et al. 1984; 14 WHO 2004b). If dose metric data were available for every chemical, exposure scenario 15 and species, then we would not opt for the use of PBPK models in risk assessment. The 16 advantage of modeling is really to simulate dose metrics that cannot otherwise be 17 measured because it is neither feasible nor ethical. The issue then really becomes: what 18 level of confidence is required for a model to be used for a specific risk assessment 19 application and what other alternative to PBPK model is available for effectively 20 addressing the uncertainty associated with the specific risk assessment issue/requirement. 21 Section 3.7.1 discusses the characterization of the level of confidence in PBPK models 22 whereas section 3.7.2 places it in the context of use in risk assessment. 23 24

3.7.1. Characterizing the level of confidence in PBPK models 25 26 The level of confidence in PBPK models intended for specific end-use in risk assessment 27 should be characterized on the basis of: (i) comparison of model simulations with 28 experimental data, (ii) model robustness, (iii) plausibility and (iv) extrapolation 29 uncertainty. The confidence level, consistent with the specific application (e.g., high 30 dose to low dose extrapolation), should be determined based on consideration of all these 31 aspects. 32 33

3.7.1.1. Comparison of model simulations with data 34 35 The comparison of PBPK models with experimental data may be done using one of 36 several methods: visual inspection, statistical tests, and discrepancy indices (Chiu et al. 37 2007). Statistical hypothesis tests and discrepancy tests, however, are of limited use in the 38 context of PBPK modeling. Because the model is an approximation of the actual system, 39 a null hypothesis that the system and the model are the same is clearly false. However, 40 plots of residuals against the fitted values, time, dose, etc. would be useful in identifying 41 systematic errors in the model. 42 43 Visual inspection, the most frequently used approach, focuses on the qualitative ability of 44 the model to reproduce the shape of the time-course of chemical concentrations in 45 biological matrices. Accordingly, if the model consistently reproduces the general trend 46


23

of the data (peaks, bumps and valleys, saturation of metabolism, etc.) then there will be 1 greater confidence in the model than if it fits only a portion of the data (e.g., absorption 2 phase) perfectly (Chiu et al. 2007). 3 4 While attempting to compare model simulations and experimental data, it is important to 5 remember that both sides are subject to uncertainty, and it should be considered in order 6 to provide a fair comparison between the two (Marcus and Elias 1998; WHO 2006). The 7 experimental data, frequently obtained in a few animals or human subjects, may 8 constitute a biased sample which may not reflect the entire range of values or the central 9 tendency of the values found in reality. It must be kept in mind that all deterministic 10 models predict mean concentrations whereas a single experimental observation 11 constitutes only one sample from the hypothetically infinite range of observations from 12 that identical experiment (Veerkamp and Wolff 1996). 13 14 Frequently in PBPK modeling, predictions that are, on average, within a factor of two of 15 the experimental data have been considered adequate. When the training (or parameter 16 estimation) data set and evaluation data set are obtained in different animals/subjects as in 17 most PBPK modeling activities, the resulting simulations are not anticipated to perfectly 18 fit the TK data at all time points. Therefore, due to the lack of precise knowledge, i.e., 19 uncertainty, of parameter values for the animals (or humans) used in estimating 20 parameters vs those used for generating the TK data for model evaluation, some level of 21 discordance is to be expected. Confidence in a PBPK model would be greater if it 22 simulated adequately the TK data that were used during calibration as well as those that 23 were not part of the calibration data set; but the value of the model should not be judged 24 on the basis of closeness to data alone (Rescigno and Beck 1987); rather consideration of 25 model robustness, plausibility and extrapolation uncertainty should be an integral part of 26 this process. 27 28

3.7.1.2. Model robustness 29 30 Confidence in a PBPK model would be high if it reproduced a variety of data with a 31 single set of equations and parameters. When the structure and parameters are kept the 32 same across simulations of experiments (e.g., routes, doses), it limits the ability of the 33 model to provide optimal fit to any one particular experimental dataset (Gentry et al. 34 2004). Similarly, if the model can not reproduce TK profiles with any realistic parameter 35 values, or it can do so only by using values that are inconsistent with current state of 36 knowledge, then one can reasonably conclude that the model structure and/or the 37 parameters is/are inadequate. In this regard, model robustness can be inferred by 38 evaluating the change in dose metric associated with a change in parameter values (i.e., 39 sensitivity analysis) (Loizou et al. 2008). If a small change in a parameter value (i.e., less 40 than typical variability in its measurement) leads to changes in predictions of a dose 41 metric that are less than variation expected from its experimental measurements – then 42 the model is considered to be robust (Kohn 1995). Conversely, if the dose metrics 43 simulated by a model were not sensitive to large variations or errors in key input 44 parameters, then the model is not robust; confidence in such a model would be low, 45 implying that it can not be relied upon for risk assessment applications. 46


24

1 3.7.1.3. Plausibility 2

3 The level of confidence in a PBPK model that reproduces all available experimental data 4 on TK and dose metrics would be particularly high if its structure is consistent with 5 accepted physiological and biochemical theories. Regardless of how well a PBPK model 6 matches the data or exhibits robustness, it should not violate what is known about the 7 physiology of the modeled organism. Basically, the model assumptions, processes, 8 parameters and structure should be biologically plausible and consistent with the 9 available data on the TK and TD of the chemical being modeled. Thus, if the optimized 10 parameter values are biologically implausible, then either the model is wrong, the prior 11 information about the parameter(s) is wrong or the data are wrong. In the case of 12 Chemical XC (Appendix 5), for example, the confidence in the use of a model to perform 13 route-to-route extrapolation is based more on the extent to which the model reflects 14 correct physiology (e.g., the anatomical relationships of tissues and blood flows) and 15 biochemical principles (e.g., the Michaelis Menten equation) than on the extent of 16 supportive empirical data. However, when the plausibility of one or more model 17 parameters, assumptions, processes or structural elements is questionable, the confidence 18 in such a PBPK model will be low, thus impeding its uptake in risk assessment for 19 making reliable extrapolations. 20 21

3.7.1.4. Extrapolation uncertainty 22 23 The degree of extrapolation uncertainty in a PBPK model intended for application in risk 24 assessment depends upon the extent to which the model predictions have been tested 25 against real data. The extrapolation uncertainty would be low if the PBPK model has 26 been evaluated using data for the species, lifestage, exposure route, dose range (linear vs 27 saturable doses), activity levels (physical activity vs resting condition) and dose metric of 28 relevance to the risk assessment. The required level of confidence relating to 29 extrapolation uncertainty then is determined largely by the risk assessment application 30 and the nature of the dose metric. 31 32 With regard to dose metrics, the extrapolation uncertainty would be low if the PBPK 33 model was tested against dose metric data collected in species and conditions relevant to 34 risk assessment. In such cases, the use of PBPK model would be essentially to make 35 interpolations and only limited extrapolations (e.g., to somewhat lower doses or a 36 different physical activity level). In the case of Chemical XA (Appendix 3), for example, 37 the data on the metabolite AUC was only reported for the post-exposure period in the 38 critical toxicological study. Therefore, PBPK model was used to calculate the total AUC 39 (i.e., during exposure + post exposure) in test animals for the purpose of computing 40 AKAF. Further, for this chemical, the published data on AUC in humans were collected 41 under exercising conditions, and these might not be reflective of the kinetics under 42 resting conditions. Therefore, limited extrapolation using PBPK model was conducted, 43 in order to estimate the AUC during resting conditions on the basis of appropriate 44 physiological parameters (Table 1). 45 46


25

The extrapolation uncertainty regarding dose metrics would be high if the dose metric is 1 not directly measurable and less well determined by available calibration methods. An 2 example might be a chemical for which metabolite concentrations in specific cell types in 3 nose or lung is relevant, but which is practically not measurable with currently avaialble 4 methods or which is poorly determined by in vivo calibration data (e.g., because they 5 represent a very small fraction of the overall TK). This situation is illustrated with 6 Chemical XB (Appendix 4), for which the toxicity and carcinogenicity are hypothesized 7 to be associated with a reduction in intracellular pH caused by one of its metabolites. 8 Since it is not possible to make measurements of the time course of pH changes in nasal 9 tissues of exposed animals and humans, the PBPK model was used for making 10 predictions. Confidence in this PBPK model was established by comparing simulations 11 with data on nasal flow rates, deposition in upper airways as well as metabolite 12 production and release via the nasal cavity, for various exposure concentrations in the rat. 13 Also, the model consistently simulated data on concentrations of XB and its metabolite 14 (XB-M2) in the airstream of nasopharyngeal cavity during controlled human exposures to 15 low concentrations (1 to 10 ppm XB). Thus, the confidence in this model would be 16 characterized as medium/low in consideration of the extrapolation uncertainty (Table 2). 17 18 In the context of risk assessment application, the confidence in a PBPK model intended 19 for deriving AKAF would be high if the simulations compared well with data on chemical 20 concentration in the appropriate biological matrix (relevant to the dose metric chosen for 21 risk assessment) (i) in adult animals exposed by the same route and dose range as the 22 critical toxicity study(ies), and (ii) in adult humans for the exposure route, dose level 23 (usually in the linear range), and physical activity relevant to the risk assessment. 24 Similarly, the confidence level in a PBPK model intended for route-to-route extrapolation 25 would be high when the model is tested against dose metric data obtained for the two 26 exposure routes of relevance (e.g., oral and inhalation) in test species and/or humans at 27 relevant dose levels. Regarding high dose to low dose extrapolation of dose metric in test 28 animals, the confidence in a PBPK model would be high if it is tested for its ability to 29 simulate TK and dose metrics associated with doses covering the linear and saturable 30 range in animals exposed by the same exposure route as the critical study(ies). 31 32 However, if the simulations of a PBPK model are only compared with data on parent 33 chemical across dose levels, routes and species, then the level of confidence in the 34 predictions of dose metric by such a model would be low, particularly when the 35 metabolite is the toxic moiety. Because the use of parent chemical data to test the model 36 estimates of metabolism in the organism can frequently be misleading because the 37 metabolism parameters may have little impact on parent chemical concentrations. In this 38 regard, the confidence level associated with the PBPK model may be improved by time-39 dependent sensitivity analysis to verify whether, in fact, the metabolic parameter values 40 are identifiable from the available data sets. Ideally, a PBPK model should be compared 41 with data that are informative regarding the parameters to which the dose metric 42 predictions are sensitive. This pre-supposes the use of sensitivity and uncertainty 43 analyses to identify the parameters of concern (i.e., those that are least certain but have 44 the most influence on the dose metric). 45 46


26

Whereas the uncertainty analysis focuses to evaluate the impact of the lack of precise 1 knowledge of parameter values and model structure on dose metric simulations, the 2 sensitivity analysis focuses to provide a quantitative evaluation of how parameters in 3 input functions influence the dose metrics or other model output of relevance to risk 4 assessment (Iman and Helton 1988; Farrar et al. 1989; Krewski et al. 1995; Campolongo 5 et al. 1997; Nestorov et al. 2001; Gueorguieva et al. 2006b; Chiu et al. 2007; Loizou et 6 al. 2008). The benefit of uncertainty/sensitivity analysis might be limited for a chemical 7 such as XC, for which the PBPK model was evaluated using extensive data sets 8 representing dose range, exposure routes and species (Appendix 5). Conversely, the 9 uncertainty analysis is likely to be useful when a PBPK model does not adequately 10 simulate the experimental data, or when it has only been evaluated with limited datasets. 11 For example, in the case of chemical XB (Appendix 4), the simulations of dose metric by 12 the PBPK model (i.e., change in intracellular pH) cannot be evaluated with experimental 13 data in animals or humans for the exposure scenarios of interest to risk assessors 14 (technical feasibility issue). In such cases then, only some other aspects of the model 15 could be evaluated (e.g., nasal outflow) such that uncertainty and sensitivity analyses 16 would help address concerns of model reliability (Hattis et al. 1993; Clewell et al. 1994). 17 18 Overall, comparison of simulations with TK data is not the only basis for developing 19 confidence in a PBPK model; equally important are aspects relating to plausibility, 20 robustness and extrapolation uncertainty, supplemented with appropriate analyses of 21 variability, uncertainty and sensitivity. 22 23

3.7.2. Purpose-specific model evaluation 24 25

The extent and nature of model evaluation would depend on the specific end-use 26 in the context of risk assessment. In this regard, the key questions are: 27

28 (i) are we sufficiently confident in the simulations of dose metrics 29

obtained with a PBPK model for a specific risk assessment application? 30 and 31

(ii) what are the relative uncertainties associated with alternative(s) to 32 PBPK models ? 33

34 The following sections examine these questions, as a function of the model evaluation 35

principles for each specific application in risk assessment (i.e., interspecies, 36 interindividual variability, high dose to low dose and route-to-route extrapolations) 37 (sections 3.7.2.1.-3.7.2.4.). 38 39

3.7.2.1 Interspecies extrapolation 40 41 For interspecies extrapolation or in the context of AKAF, the PBPK model is used to 42 calculate how the dose metric compares between an animal and a human. The confidence 43 in a PBPK model intended for use in interspecies extrapolation to humans would be high 44 if it were tested against TK data in different species, including humans (Figure 6), in 45 addition to satisfactory evaluation in terms of model robustness, plausibility and 46


27

extrapolation uncertainty. If no TK data are available in humans or if only parent 1 chemical data are available in humans (and not the relevant dose metric, i.e., metabolite 2 in this case), then the extrapolation uncertainty associated with the PBPK model would 3 be high - for the purpose of interspecies extrapolation. In such cases, however, the 4 correspondence of metabolite predictions with data in several animal species could be 5 used as a surrogate, but this deficiency should be carefully considered when applying the 6 model to predict human metabolism, as done with Chemical XC (Appendix 5). 7 8 Given that the interspecies extrapolation aspect focuses on the evaluation of the central 9 tendency of the animal to human difference in kinetics, deterministic PBPK models have 10 been used for this purpose (U.S. EPA 2006). Regarding the uncertainty in the “average” 11 values of parameters of a PBPK model used for interspecies extrapolation, a relevant 12 question is: 13

What is the impact of this parameter uncertainty on the simulations of dose 14 metrics relative to the uncertainty associated with the use of the availabe 15 alternative approach (e.g., the default)? 16

The uncertainty related to the available alternative, i.e., default AKUF, arises from the 17 conceptual model (based on non-specific empirical observations), parameters (e.g., same 18 for all chemicals and species; often based on average body weight of 0.25 kg for the rat 19 and 70 kg for humans) as well as the toxic moiety (i.e., unknown) (Figure 7). In this 20 regard, PBPK models offer an opportunity to incorporate more data to inform the 21 adequacy of or reduce the uncertainty associated with the default approaches, by 22 simulating chemical-specific dose metrics on the basis of physiological, biochemical and 23 physicochemical determinants in both test species and humans. In the case study 24 involving chemical XA, the overall confidence in the use of PBPK model for interspecies 25 extrapolation was high based on considerations of performance, robustness, plausibility 26 and extrapolation uncertainty (Table 1). For this chemical, data on the toxic moiety 27 reflective of the dose metric (i.e., blood concentrations of XA-M) were available both in 28 rats and humans and were used in model evaluation, such that the extrapolation 29 uncertainty was low. 30 31 On the contrary, in the case of chemical XB, PBPK model predictions of the relevant 32 dose metric (i.e., pH changes in nasal tissues of rats or humans) were not evaluated such 33 that the confidence in this model based on extrapolation uncertainty is low (Table 2). 34 However, the model adequately simulated TK in upper respiratory tract in both rats and 35 humans, and a sensitivity analysis of the dose metric of relevance to risk assessment was 36 conducted to provide additional support (Appendix 4). 37


28

1

Model is unable to reproduce the shape (i.e., bumps, valleys) of the TK time-course data in test species and humans

Model reproduces some TK data or the shape of part but not all of the time-course curves in test species and/or humans.

Model reproduces consistently all TK data, including the shape of time-course profiles in test animal species and humans.

Model reproduces TK profiles observed in several experiments using a single set of input parameters for each species

The model parameters, structure and assumptions are biologically plausible and consistent with available data in test animal species and humans.

Model simulations are compared with dose metrics measured in relevant biological matrix for the species, lifestage, exposure route, and scenario of relevance to toxicity studies or risk assessment

Model is capable of reproducing some but not all available datasets, with a single set of input parameters for each species

The plausibility of some model parameters, structural elements or assumptions is questionable.

Model is unable to reproduce TK profiles with a single set of parameter values for any one species.

The model parameters, structure or assumptions are neither consistent with the biology nor the current state of knowledge regarding the TK or TD of the chemical.

Model simulations not evaluated against TK data in the tissue, species, lifestage, exposure route or scenario of relevance to critical toxicity studies or risk assessment.

Model simulations are not compared with data on relevant dose metric; but comparisons are made with another form of chemical that is measurable or in another biological matrix that is accessible.

HIGH NONE LEVEL OF CONFIDENCE

Model vs

data

Model robustness

Extrapolation uncertainty

Plausibility

Figure 6. An illustrative scale of confidence level in PBPK models intended for use in interspecies dose extrapolation based on central tendencies.


29

1 2

3 4 Figure 7. Degree of uncertainty associated with the use of increasingly data-5 informed approaches in interspecies extrapolation. 6 7 8

HIGH DEGREE OF UNCERTAINTY

Based on non-specific empirical observations

LOW "Default" "Categorical" "CSAF/PBPK"

Identified/hypothesized based on information on mode of action and plausibility

Unknown

Based on some but not all relevant biological determinants of tissue dose (e.g., dose normalization based on body surface area)

Chemical-specific; applicable to dose, species, exposure route and lifestage of relevance to risk assessment

Biologically-based; consistent with mode of action

Specific for each chemical class, TK mechanism and/or species combinations

Steady-state concentration of parent chemical

Same for all chemicals and test species

Conceptual model

Model parameters

Dose metric


30

Table 1. Application for interspecies extrapolation: characterizing the level of 1 confidence in PBPK model for Chemical XA 2 3 Aspect Level of

confidence Description

Performance (data vs model) High

The model consistently simulated the TK profile of XA and its metabolite (XA-M) following inhalation exposure (the route of relevance to the risk assessment) in rats and humans

Model robustness High

The model, with the same set of chemical-specific and species-specific parameters, simulated TK data for various exposure routes (inhalation, oral, IV) in the rat, and for inhalation in humans.

Plausibility High

The model parameters, structure and assumptions are biologically plausible and consistent with available data

Extrapolation uncertainty High

The model simules the toxic moiety reflective of the dose metric (i.e., blood concentrations of XA-M) (i) in rats exposed by inhalation to atmospheric concentrations in the range of the dose-response study (31 – 250 ppm) as well as (ii) in humans exposed to a low concentration (i.e., first order range) of relevance to risk assessment.

4


31

Table 2. Application for interspecies extrapolation: characterizing the level of 1 confidence in PBPK model for Chemical XB 2 3 4 Aspect Level of


Performance (data vs model)

High

Model, with the consideration of high affinity carboxylesterase pathway, appropriately reproduces the steep concentration gradient of XB through the mucosa as well as other TK data.


The PBPK model, with a single set of species-specific parameters, simulated in vivo data on uptake and metabolism of XB in the upper respiratory tract of rats exposed to atmospheric concentrations ranging from 73 to 2190 ppm; and human data on XB concentrations and XB-M2 wash-out in the airstream of nasopharyngeal cavity during controlled exposures of 1 to 10 ppm.

Plausibility High

The five compartmental nasal PBPK model of the rat nasal cavity and the four compartmental PBPK model for human nasal cavity are consistent with current knowledge of upper airways; the model parameters and assumptions are biologically plausible.

Extrapolation uncertainty Low

The model simulations were not compard with pH changes in nasal tissues of rats or humans; however the model adequately simulated TK in upper respiratory tract in both rats and humans; conduct of sensitivity analysis on the dose metric of relevance to risk assessment to provide additional support

5 6 7 8 9 10


32

3.7.2.2. Interindividual variability 1 2 For assessing human variability or HKAF, a PBPK model is initially evaluated with TK 3 data from several individuals or a population subgroup, and subsequently expanded to 4 include estimates of distributions of input parameters. It is impossible and irrelevant to 5 expect an evaluation of the human PBPK model based on comparison with data on dose 6 metrics in the whole population. Rather, the goal would be to obtain an estimate of the 7 distribution of key (or sensitive) parameters at the population level and incorporate them 8 within PBPK models to characterize dose metric distributions (Figure 8). Quantifying 9 and separating mean uncertainty from true, biologically-based interindividual variability 10 by using hierarchial and model parameter structures can be very useful when sufficient 11 data are available (Barton et al. 2007). Several investigators would argue that, instead of 12 using any human data for model evaluation, they should all be used to improve the 13 parameter estimates, so that no data are “wasted” toward that end. Such an iterative 14 approach to model evaluation and calibration maximizes the use of the available human 15 data. In this regard, Bayesian analysis utilizing Markov chain Monte Carlo (MCMC) 16 simulation is being increasingly implemented in PBPK models, to refine parameter 17 values on the basis of information contained in additional data (e.g., Gelman et al. 1996; 18 Bois 2000a,b; Bernillon and Bois 2000; Jonsson and Johanson 2001, 2002; Gueorguieva 19 2006a). Here, an impediment to the uptake of PBPK model in risk assessment might be 20 related to the concern that the resulting parameter distributions do not adequately 21 characterize variability in the whole population. 22 23 Again, this issue should be viewed relative to the uncertainty associated with the use of 24 the available alternatives (e.g., default approach) for characterizing human variability. 25 The uncertainty related to the default HKUF arises from the conceptual models (i.e., 26 sensitive population model, finite sample model) (Price et al. 1999) and lack of 27 consideration of toxic moiety as well as other chemical-specific or population-specific 28 determinants. The PBPK models offer an opportunity to incorporate relevant data to 29 inform the adequacy of (or reduce the uncertainty associated with) the default approach 30 (i.e., use of HKUF of 3.16 for all chemicals to account for the difference in dose metric 31 between the median and sensitive individual in a population); specifically they facilitate 32 the simulation of dose metrics of relevance to MOA on the basis of heterogeneity of 33 physiological, biochemical and physicochemical determinants in human populations 34 (e.g., Haber et al. 2002). In this regard, the effect of variability of sensitive parameters in 35 the population would be characterized for their influence on the dose metric, when the 36 PBPK model is intended for estimating HKAF (e.g., Allen et al. 1996). For example, data 37 on enzyme polymorphism and distributions of physiological parameters have been 38 integrated within a human PBPK model for parathion to facilitate the characterization of 39

40


33

1 2

Figure 8. An illustrative scale of confidence level in PBPK models intended for evaluation of human variability in dose 3 metrics4

HIGH

Model not tested against any human data; no analysis of parameter or structural sensitivity/uncertainty

Model reproduces available TK data from limited human studies and subgroups.

Model reproduces consistently all available (extensive) human TK data.

Model uses a single set of population-level input parameters to reproduce several TK data sets.

The populations distributions of all input of PBPK model are biologically plausible and consistent with available data.

Model simulations are compared with dose metrics measured in relevant biological matrix in several sub-groups of the population.

Model is probabilistic and the input parameter distributions are consistent with available but limited human data.

The plausibility of distribution of some model parameters not established (due to lack of data).

Model is deterministic in nature and unable to simulate population distribution of dose metric.

The model parameters do neither reflect the biology nor population variability of the determinants of TK or TD of the chemical.

Model simulations are evaluated against TK data in a rodent species only.

Model simulations are compared with available data on a measurable form of chemical in several subjects (observational, accidental or intentional exposure data).

NONE

Plausibility

Extrapolation uncertainty

LEVEL OF CONFIDENCE

Model vs

data

Model robustness


34

population distribution of dose metrics (Haber et al. 2002). If one were using the 1 alternative (default) approaches, then these highly relevant data could not otherwise be 2 used judiciously for characterizing human variability (Figure 9). 3 4

3.7.2.3. High dose to low dose extrapolation 5 6 For high dose to low dose extrapolation, the evaluation of model performance should be 7 undertaken in the dose range of relevance (e.g., linear and saturable dose ranges). Here, 8 the confidence in a PBPK model would be high if it simulated the time-course profile in 9 terms of bumps and valleys both for low and high doses using a single set of parameter 10 values. The confidence in a model would be undermined, if it used different values for a 11 given parameter (e.g., maximal velocity) in order to provide adequate simulations of 12 kinetic profiles at each dose level. Such a model would be relatively uninformative for 13 high dose to low dose extrapolation or other risk assessment applications. In the absence 14 of a PBPK model or another similar construct for conducting high dose to low 15 extrapolation, the historical alternative has been the analysis of dose-response data on the 16 basis of exposure doses. In the case of Chemical XC, the confidence in the application of 17 PBPK model for the conduct of high dose to low dose extrapolation would be high based 18 on considerations of model performance, robustness, plausibility, and extrapolation 19 uncertainty (Table 3). Here, the default linear extrapolation based on exposure dose 20 would be highly uncertain, compared to a PBPK approach that uses dose metric based on 21 MOA and accounts for nonlinearity of the relevant processes. This aspect has been 22 illustrated with dichloromethane, for which the PBPK model facilitated high dose to low 23 dose extrapolation of the dose metric by accounting for the saturation of CYP2E1-24 mediated metabolism and disproportionate increase in GST metabolism with increasing 25 doses (Andersen et al. 1987; Bos et al. 2006). 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Figure 9. Degree of uncertainty associated with increasingly data-informed 45 approaches in characterizing human variability in risk assessment. 46

LOW HIGH DEGREE OF UNCERTAINTY

Based on sensitive population or finite sample model

"Default" "Categorical" "CSAF/PBPK"

Identified/hypothesized based on information on mode of action and plausibility

Unknown

Based on some but not all relevant biological determinants of tissue dose (e.g., enzyme polymorphism)

Chemical-specific; applicable to sub-groups of populations of relevance to risk assessment

Biologically-based; consistent with MOA and reflective of factors responsible for human variability in TK.

Specific for each chemical class, pathways and/or population subgroups (e.g., elderly)

Blood concentration of parent chemical (AUC or concentration)

Assumed to be the same for all chemicals, toxic endpoints and populations

Conceptual model

Model parameters

Dose metric


35

These examples emphasise the critical role and benefit of the uptake of PBPK models in 1 the risk assessment to address uncertainty associated with empirical approaches for high 2 dose to low dose extrapolation. 3 4

3.7.2.4. Route-to-route extrapolation 5 6 The confidence in the use of PBPK models for route-to-route extrapolation would be high 7 when evaluation of kinetics and/or dose metric is conducted for both routes in one or 8 more species. Qualitatively, the model should be able to simulate the kinetic profile 9 observed for each specific exposure route, whereas on the quantitative front the model 10 should facilitate the simulation of the concentration-time profile in an acceptable manner 11 (see Section 3.7.1). A PBPK model that provides simulations of dose metrics based on 12 the consideration of physiology, MOA and first pass effects is clearly more relevant than 13 an alternative (i.e., default) approach that assumes 100% absorption for the exposure 14 routes. The uncertainty associated with route-to-route extrapolation could be quite large, 15 e.g., the absorbed dose might actually be anywhere between 0 and 100 %. The degree of 16 uncertainty is reduced progressively along the continuum of increasingly data-informed 17 approaches. For example, with the dermal absorption route, the extrapolation uncertainty 18 diminishes with the use of the available information/data, in the following order 19 (Kephalopoulos 2005): 20 21

• No data; assumption of 100% absorption 22 • Calculation/modeling of maximal flux 23 • Prediction based on in vitro data using human or pig skin 24 • Prediction based on in vitro test using rodent skin 25 • Simulation with PBPK model based on rat data 26 • Simulation with PBPK model based on human data 27

28 The relative uncertainty in the route-to-route extrapolation of exposure dose is due to the 29 conceptual model, parameters used as well as the dose metrics for the default approach vs 30 PBPK modeling approach. The PBPK models can be used to extrapolate POD from one 31 exposure route to another (Appendix 5) or to estimate the percent absorption for each 32 exposure route (Krishnan and Carrier 2008), depending upon the risk assessment need. 33 As illustrated with Chemical XC (Table 4), the confidence in the use of PBPK model to 34 perform route-to-route extrapolation relies essentially upon its ability to accout for route-35 specific phenomena, physiology (e.g., the anatomical relationships of tissues and blood 36 flows) and biochemical principles (e.g., the Michaelis Menten equation). Since PBPK 37 models provide predictions of internal dose associated with both exposure routes in the 38 same animal (or human), it might be that variability or uncertainty analysis would not 39 change the conclusions based on average parameter values. In this regard, the 40 conventional route-to-route extrapolations are only based on average values, without 41 taking into account the distributions of exposure dose, animal physiology, route-specific 42 absorption or contact rates. 43 44 45


36

Table 3. Application for high dose to low dose extrapolation: characterizing the level 1 of confidence in PBPK model for Chemical XC 2 3 4 Aspect Level of


Performance (data vs model) High

Model simulates consistently the respiratory uptake of chemical XC, amount metabolized as well as hepatic GSH concentrations following inhalation exposure


Model, with a single set of input parameters, reproduces TK data for various inhalation exposure concentrations (covering the linear and saturable range)

Plausibility High

The model parameters, structure and assumptions are biologically plausible and consistent with available data

Extrapolation uncertainty Medium

Dose metric not directly measurable (re: reactive metabolite), but well estimated by the calibration methods used in the study; model simulations of total amount metabolized and GSH depletion were compared with experimental data

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22


37

Table 4. Application for route-to-route extrapolation: characterizing the level of 1 confidence in PBPK model for Chemical XC. 2 3 4 Aspect Level of


Performance (data vs model)

High Model simulates consistently the respiratory uptake of chemical XC, amount metabolized as well as hepatic GSH concentrations following inhalation exposure

Model robustness Medium Model, with a single set of input parameters, reproduces TK data for one of the routes, i.e., inhalation (for exposure concentrations covering the linear and saturable range)

Plausibility High The model parameters, structure and assumptions are biologically plausible and consistent with available data

Extrapolation uncertainty Low/Medium Dose metric not directly measurable (re: reactive metabolite), but well estimated by the calibration methods used in the study; model simulations of total amount metabolized and GSH depletion were compared with experimental data for the inhalation route but lack of such data for the oral route

5


38

3.8. Documentation 1 2 The documentation of a PBPK model intended for use in risk assessment requires the 3 inclusion of sufficient information about the model and its parameters so that an 4 experienced modeler can accurately reproduce and evaluate its performance. In order to 5 facilitate transparency, reproducibility and credibility, the developer should 6 systematically document the characteristics of a PBPK model. Even though the extent of 7 documentation might depend upon the end-use, the modelers generally should provide 8 enough details to facilitate a clear understanding of the input-output relationships (and 9 not just a flow diagram plus a table of parameters). Accordingly, the documentation 10 should be sufficient to facilitate an experienced modeler, expert reviewer or an interested 11 end-user to evaluate the model and reproduce the input-output relationships for the dose 12 metric of relevance to MOA. Overall, PBPK model documentation should address the 13 following broad topics: 14 15

• Scope and model purpose 16 • Model structure and biological characterization 17 • Mathematical descriptions of ADME 18 • Computer implementation and verification 19 • Parameter estimation and analysis 20 • Model validation and evaluation 21 • Evaluation/justification of dose metrics 22 • Specialized analysis, if any. 23

24 The above aspects can be captured in summary form for the risk assessment audience but 25 in greater depth for specialists, as is typically done in technical publications. In the latter 26 case, it is particularly important to identify clearly the datasets that were used to evaluate 27 the model along with the rationale for excluding datasets, if any, during model 28 development. Similarly, the alternative model structures considered, the range of values 29 assigned for each of the input parameters, as well as the exposure conditions for 30 sensitivity, uncertainty and variability analyses, should be presented along with the 31 rationale. For risk assessment application, the original model code is essential and should 32 be provided to the regulatory scientist(s) for independent evaluation and reproduction of 33 any simulations that form the basis of dose metrics used in risk assessment. In this 34 regard, justification of the dose metric on the basis of plausibility and consistency with 35 available information on MOA as well as dose-response information for the critical effect 36 should also be presented (see Section 4.3. for further discussion; also see WHO (2001, 37 2005)). 38 39


39

4. Application of PBPK models in risk assessment 1 2 The PBPK models shift the focus from the administered dose to internal dose which is 3 more closely associated with the toxic response. The enhanced scientific basis of PBPK-4 based risk assessment is due to the mechanistic basis of these models as well as their 5 ability to predict dose metrics of relevance to the MOA of chemicals. Such predictions 6 can not only be obtained for POD (e.g., NOAEL) but also for a variety of study doses for 7 enhancing the modeling of the dose-response data. So it is critical that a PBPK model 8 intended for use in risk assessment be able to adequately simulate the dose metrics 9 (potentially or causally related to toxic responses) for the exposure route, dose ranges, 10 lifestage and species used in the critical studies as well as for anticipated human 11 exposures. The guiding principles for PBPK-based risk assessments are discussed in 12 sections 4.1 – 4.4 in terms of the following aspects: 13 14

Choice of critical studies 15 Selection of PBPK models 16 Evaluation of dose metrics 17 Determination of human exposures 18

19 20

4.1. Choice of critical studies 21 22 The starting point for performing a risk assessment using PBPK models is essentially the 23 same as the conventional approach, i.e., evaluate the available toxicological, 24 epidemiological and mechanistic data for the chemical and identify potentially useful 25 critical studies. In an assessment based on PBPK models, like in any other systematic 26 assessment of toxicological and epidemiological data, more than one critical study (i.e., a 27 study that predicts effects at the lowest dose) may be retained for identifying the point of 28 departure (POD). This is a consequence of the fact that dose-equivalence across species 29 for different toxicological end points can not be predicted a priori and would depend 30 upon the dose metrics (consistent with the MOA) to be simulated eventually with the use 31 of PBPK models (Gentry et al. 2004). Once the critical studies are identified, it would be 32 clear as to the required capability of PBPK models in terms of the species, lifestages, 33 dose ranges, exposure routes and endpoints (dose metrics and target tissues). 34 35

4.2. Selection of PBPK models 36 37 PBPK model(s) is(are) selected to be consistent with the critical studies identified in 38 section 4.1. as well as the objectives of the risk assessment. Box 2 presents a checklist of 39 key aspects – based on credibility, reliability and applicability - to be considered in 40 selecting chemical-specific PBPK models for a given risk assessment. 41


40

Box 2. Check list of characteristics for selecting PBPK models for use in risk 1 assessment 2

CREDIBILITY • Are the major sites/processes of absorption, storage, transformation and clearance

included in the model? • Are the mathematical equations of ADME based on sound theoretical or biological

basis? • Are the input parameters related to the characteristics of the host, chemical or

environment? • Is the sum total of compartment volumes equal to or within 100% of the body weight? • Is the sum total of the tissue blood flow rates equal to the cardiac output? • Is the ventilation:perfusion ratio specified in the model within physiological limits ? • Is the volume of each tissue compartment within known physiological limits? • Is the approach used to establish partition coefficients within the domain of valid

application? • Is the method used for estimating biochemical parameters adequate? • Is the allometric scaling of parameters, if applicable, done appropriately? • Is the integration algorithm used in the study known for solving differential equations

and dealing with stiff and non-stiff conditions? • Has the model been verified for syntax errors and the accuracy of units (i.e.,

dimensional consistency)? • Has the model been evaluated for its ability to predict kinetics under various

conditions, consistent with its intended application? RELIABILITY

• Is the model capable of providing predictions of the concentration time-course of the candidate dose metrics in the target organ or a suitable surrogate compartment (e.g., blood)?

• Are the proposed MOA and dose metric adequately justified? • Has the uncertainty in model predictions of dose metric been assessed for the relevant

exposure conditions? • Is the sensitivity of the dose metric to change in numerical values of input parameters

characterized for relevant exposures? APPLICABILITY

• Has the model been developed and evaluated in the species and lifestage of relevance to risk assessment?

• Do the exposure routes in the model correspond to that of anticipated human exposures as well as that of the critical studies chosen for the assessment ?

• Has the model been tested for the exposure doses and duration of relevance to the intended extrapolations?

• Does the model contain point estimates (or distributions) of parameters, consistent with the purpose of application?


41

A model that is credible, i.e., whose scientific validity has been established through peer-1 review, and is capable of providing reliable dose metric simulations may not be 2 applicable in a risk assessment if it is not ‘fit for the purpose’. The intended application 3 in risk assessment (e.g., evaluating interspecies differences, characterizing population 4 distributions, route-to-route extrapolation) essentially determines the desired model 5 characteristics. For example, if a critical study was in adult animals, then for conducting 6 interspecies extrapolation, the PBPK model would have been developed and calibrated 7 for: 8 9

Adult test species and adult humans 10 Exposure route(s) used in the critical toxicological studies as well as the 11

exposure matrix and route of relevance to risk assessment (e.g., air: 12 inhalation), and 13

Providing simulations of not only parent chemical but also metabolites and 14 adducts (if applicable) in target tissue(s) or a surrogate compartment (e.g., 15 blood) 16

17 Here the models may be deterministic in nature, based on average parameter values for 18 the test animals and humans, as with chemicals XA, XB and XC presented in Appendices 19 3-5. 20 21 On the other hand, if the model is intended for use in quantifying human variability in 22 dose metrics, it would have been developed and calibrated for: 23 24

Human populations (i.e., characterized by distributions of parameters) 25 Exposure route(s) of relevance to risk assessment (e.g., air: inhalation), 26

and 27 Simulating population distributions of not only parent chemical but also 28

metabolites and adducts (if applicable) in target tissue(s) or a surrogate 29 compartment (e.g., blood) 30

31 In this regard, a Monte Carlo simulation approach, alone or in conjunction with the 32 Markov Chain Monte Carlo method, may be used to generate population distributions of 33 dose metrics (Bernillon and Bois 2000; Jonsson and Johanson 2001, 2002) (Figure 10). 34 35 For conducting route-to-route extrapolations with PBPK models, one of the two 36 approaches are used: (i) an animal model is used to extrapolate a POD for one route to 37 another on the basis of equivalent dose metric, and (ii) animal and human PBPK models 38 for a chemical are used to determine PODhuman for one route from the available PODanimal 39 for another route on the basis of equivalent dose metric (Chiu et al. 2007). The 40 extrapolation of the dose metric of a chemical from one exposure route to another is 41 performed by including appropriate equations to represent each exposure pathway 42 (Figure 11). In this context then, PBPK models would have been developed and 43 calibrated for: 44

• Adult animal and/or human (or another relevant lifestage) 45


42

• Simulating the kinetics of chemicals for the two routes of relevance 1 (i.e., the exposure route for which POD is available and the route for 2 which extrapolation is intended), and 3

• Providing simulations of not only parent chemical but also metabolites 4 and adducts (if applicable) in target tissue(s) or a surrogate 5 compartment (e.g., blood) both routes of exposure 6

7 Here the models may be deterministic in nature, based on average parameter values for 8 the test animals and humans, as with chemical XC (Appendix 5; Figure 11). 9 10 When there is more than one model that conforms to the above critera, it then becomes a 11 question of which among the alternative models would be the most appropriate for use. 12 In this regard, it is important to assess how well the particular models have been 13 evaluated for their reliability in predicting the candidate dose metrics. Statistical 14 comparisons of PBPK model structures in relation to the data could be carried out; 15 however, the goal should be to minimize the uncertainty in prediction of the dose metric 16 rather than the best fit to all available data. Based on considerations of the conformity of 17 the model predictions to experimental data, their ability to predict dose metrics of 18 relevance to MOA, biological plausibility and robustness, the appropriate model is 19 chosen for risk assessment application. 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Figure 10. Illustration of the PBPK model predictions of dose metric based on 39 population distributions of input parameters and Monte Carlo simulation. 40 41

4.3. Evaluation of dose metrics 42 43 The ”dose metric” required for the conduct of PBPK-based risk assessments relates to the 44 form of chemical (parent chemical, metabolite or adducts), its level (concentration or 45

Physiological

Physicochemical

Biochemical

Monte Carlo simulation

Dose metric

Input parameter distributions

Prob

abili

ty

Physiological

PhysicochemicalPhysicochemical

Biochemical

Monte Carlo simulation

Dose metricDose metric

Input parameter distributions

Prob

abili

ty


43

amount), duration (instantaneous, daily, lifetime, or a specific developmental period) 1 intensity (peak, average or integral) as well as the biological matrix (e.g., blood, target 2 tissue) that is consistent with the MOA of the chemical. The circulating level of the active 3 form of chemical is useful as a basis for TK extrapolations and comparisons (WHO 2001, 4 2005). More specifically, for non-carcinogenic effects, the AUC in target tissue or 5 surrogate compartment (e.g., blood), detemined as the daily average, has often been used 6 as the dose metric (Collins, 1987; Voisin et al., 1990; Clewell et al., 2002). For 7 carcinogens producing reactive intermediates, the amount of metabolite produced per unit 8 time and the amount of metabolite in target tissue over a period of time (e.g., mg 9 metabolite/L tissue during 24 hr) have been used as dose metrics (Andersen et al., 1987; 10 Andersen and Dennison, 2001). For developmental effects, the dose metric is defined in 11 the context of the window of exposure for a particular gestational event (e.g., Welsch et 12 al., 1995). Other metrics of tissue exposure may also be appropriate for risk assessment 13 purposes, and determined on the basis of current state of knowledge of MOA of 14 chemicals (WHO 2001, 2005). 15 16 17

0

0.004

0 4.5 18 19 20 21 22 23

0.0001000000

0.0014000000

0 24 24 25 26 27 28 29 30 31 32 33 Figure 11. Illustration of the conceptual approach for the application of PBPK 34 models in the conduct of route to route (oral to dermal) extrapolation. 35 36

Con

c.C

onc.

Exhaled

STRUCTURE SIMULATIONS ROUTE-SPECIFIC PARAMETERS

Lungs

Skin

Poorly perfused tissues

Dermal contact

Oral dose

Gut

Richly perfused tissues

Liver

Adipose tissue

Metabolism

Kidney

Excretion

DERMAL EXPOSURE

-Chemical concentration in medium contacting skin -Area of skin exposed -Skin permeability coefficient -Duration of contact

ORAL INGESTION

-Oral dose -Bioavailability -Absorption rate constant

Time

Time


44

1 Typically, the PBPK model is used to calculate several potentially plausible dose metrics 2 for each critical study and endpoint selected. The specific nature of the dose metric 3 calculations to be performed with PBPK models would depend upon the nature of the 4 assessment. For example, in the case of a non-cancer assessment based on NOAEL, the 5 dose metric would be calculated only for the NOAEL for a particular critical study and 6 endpoint. On the other hand, if the assessment uses the BMD approach, then dose metrics 7 associated with each of the treatment groups would be calculated (Gentry et al. 2004). It 8 is important to use the appropriate simulation period to characterize the dose metrics 9 associated with animal and human exposures, consistent with the knowledge on the MOA 10 of the chemical. For example, if toxicological measurements (e.g., neurobehavioral 11 measures) are done at a specific time following exposure, the dose metric should only 12 reflect internal exposures up to the time of that measurement. In this case, if 13 measurements were done at several times during a day, the peak concentration might be 14 the same for all of them but the AUC would increase with later measurements. The dose 15 metric associated with human exposures should be calculated in just the same way as the 16 dose metric for the critical toxicity study. For example, if the dose metric in the animal 17 toxicity study was expressed in terms of area under concentration vs time curve during 24 18 hr, then the dose metric for human exposure should also correspond to AUC24 hr. 19 Alternatively, if it is the peak concentration during a 24-hr period, then the same measure 20 should be obtained in both species. 21 22 When there are several candidate dose metrics, the approriate one for use in risk 23 assessment should be chosen on the basis of plausibility. The plausibility of a particular 24 dose metric is determined by its consistency with available information on the MOA as 25 well as dose-response information for the endpoint of concern. In this regard, the chosen 26 dose metric would facilitate linearization of the dose-response relationship within a given 27 study (internal consistency), and show a consistent quantitative relationship regardless of 28 differences in exposure scenario, route, and species (e.g., lower levels at lower doses and 29 higher levels at higher doses) (external consistency) (Clewell et al. 2002). If the analyses 30 fail to identify the most appropriate dose metric, the results can then be used to 31 characterize the range of uncertainty associated with the output of the analysis, where 32 feasible (Gentry et al. 2004). The last resort, when any one dose metric among the 33 possible ones cannot be selected (on the basis of resolution of differences and 34 inconsistencies across studies and dose groups), might involve the use of the dose metric 35 yielding the highest risk estimate or lowest acceptable human exposure level. 36 37 The dose metrics generated using the PBPK models are then used in the dose-response 38 model in lieu of exposure concentrations or exposure doses, to conduct various 39 extrapolations for deriving the human exposure values (Andersen et al. 1987; Clewell et 40 al. 2002; Thompson et al. 2008). 41 42

4.4. Determination of human exposures 43 44

Conventionally, in risk assessments, the human-equivalent dose is determined by 45 dividing the PODanimal by the assessment factors (or CSAFs). To the extent that PBPK 46


45

models are viewed as tools for estimating CSAFs, the model can be used to estimate the 1 dose metric associated with POD in both the animal species and humans, and then divide 2 one by the other to determine the magnitude of CSAF. However, in a PBPK-based risk 3 assessment, it is only appropriate to divide the dose metrics obtained from the toxicity 4 studies by the assessment factors rather than applying them on the exposure 5 concentrations or doses. The application of assessment factors to the exposure dose in 6 test species might lead to erroneous or questionable human doses particularly if the 7 relationship between the internal dose and applied dose is non-linear in the range of doses 8 used in the toxicology studies. Therefore, the dose metrics should be adjusted to assure 9 that the biologically effective dose is reduced to the extent desired, before deriving the 10 human doses. Accordingly, ADUF applied corresponds to 2.5, accounting for the 11 remaining uncertainty in toxicodynamic differences across species (Figure 12); however, 12 in specific cases, the actual value may be lower or greater than 2.5 based on other 13 available data on the relative sensitivity of humans compared to the test animal species 14 (WHO 2001, 2005). The process of implementation of PBPK models in the 15 determination of human exposure concentrations or doses from animal data can be 16 summarized as follows (Clewell et al. 2002; Gentry et al. 2004; Thompson et al. 2008): 17

18 A PBPK model is selected (section 4.2) and used to simulate dose metrics 19

associated with the dose(s) used for characterizing the POD or the dose-response 20 relationship (section 4.3) in the test species 21

22 The dose metric associated with the POD then is divided by the appropriate 23

assessment factors (AFs), and 24 25

The PBPK model, following replacement of parameters with those corresponding 26 to a typical human, is used to determine the safe exposure concentration or dose 27 associated with the target dose metric identified as above. In this processs, the 28 PBPK model is run repeatedly by varying the human exposure concentration or 29 dose until the target dose metric value is obtained. 30

31 It is relevant to note that PBPK model can be used with both NOAEL and BMDL 32 approaches to translate the study doses to dose metrics to which dose-response modeling 33 may then be applied. Appendices 3-5 present three case studies in which human 34 exposures or guideline values were derived with the use of PBPK models. Additional 35 examples of PBPK application in the risk assessment of carcinogens and systemic 36 toxicants have been summarized by DeWoskin et al. (2007). Two distinct characteristics 37 regarding the application of AFs in PBPK-based risk assessments are as follows (Gentry 38 et al. 2004): 39 40

1. The AFs are applied to the dose metric and not to the exposure concentration or 41 applied dose 42

2. Only a portion of the AF that corresponds to the pharmacodynamic uncertainty 43 (ADUF, HDUF) is applied. 44

45 46


46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Figure 12. The impact of kinetic data and PBPK models on the application of 28 uncertainty factors in the derivation of reference values. The ‘remaining UF/AF’ 29 refers to the interspecies toxicodynamic uncertainty as well as human variability in 30 toxicokinetics and toxicodynamics 31 32

Guideline

value

CSAF

Animal POD as

applied dose

TK default is 4x

Human equivalent

concentration or dose

Human equivalent

concentration or dose

PBPK analysis Dose metric for animal

POD

PBPK analysis

Remaining UF/AF

components

Remaining UF/AF components

Target dose metric in humans

Remaining UF/AF components


47

5. Process considerations 1 2 The application of PBPK models in the risk assessment could be enhanced by stimulating 3 the dialogue between modelers and risk assesors, without compromising the integrity of 4 the process. Optimally, the development of a model for application in risk assessment 5 would involve upfront consultation between the modeler and the risk assessor. 6 Particularly the definition of the scope for model use and the capacibility of the model 7 can be determined by the consideration of MOA and pathways of exposure, with the 8 involvement of the risk assessor. The development of PBPK models motivated by 9 curiosity-driven research or academic interest has not typically involved such two-way 10 interactions to maximize the relevance and applicability of the model for risk assessment 11 purposes. In order to facilitate the development of PBPK models consistent with the end-12 use (i.e., risk assessment), the following paradigms are useful (Loizou et al. 2008): 13 14

Continuous involvement: Here, the risk assessor is involved right from the 15 beginning in the model formulation and model evaluation processes. In this 16 regard, the modeler and risk assessor might jointly consider the data on MOA and 17 identify dose metrics of relevance. The interaction between the risk assessor and 18 modeler might continue through the evaluation of the dose-response data. Such 19 an on-going feedback mechanism would enable the modeler to consider and 20 address issues of relevance to the MOA and risk assessment – at every step of the 21 process, such that the resulting model is useful for addressing the key issues and 22 uncertainties in an assessment. 23

24 Iterative involvement: The risk assessor provides input at specific stages of model 25

development and evaluation. Depending upon the stage of the involvement, the 26 risk assessor may or may not have an impact on the end-product or its relevance. 27 In this regard, the interaction between the risk assessor and the modelder can be 28 helpful in identifying datagaps and prioritization of avenues for additional 29 research, including aspects of model development, particularly when an existing 30 model fails to address issue(s) at hand. 31

32 To the extent possible, the development of PBPK models intended for use in risk 33 assessment would be undertaken with continuous involvement of a risk assessor from the 34 problem formulation stage. This value-added, feedback mechanism involving the risk 35 assessor in the process, however, should in no way compromise the PBPK model 36 development or evaluation as an “independent” process. 37 38 The application and acceptance of PBPK models in risk assessment would depend upon 39 access to PBPK expertise within the regulatory environment such that adequate 40 evaluation of PBPK models and their output can be carried out. The following sections 41 identify some key aspects regarding the expertise, training and communication efforts 42 required to achieve the goals related to the optimal use of PBPK models in risk 43 assessment. 44 45


48

5.1. Expertise 1 2 Based on the scope and the identified need of a PBPK model in an assessment, the 3 implementation process would require personnel with expertise to perform the following 4 tasks: 5 6

1. Review and selection of critical toxicological studies 7 2. Review and selection of a PBPK model for the chemical in question 8 3. Evaluation of dose metrics, based on plausibility and mode of action 9

considerations, and 10 4. Determination of guideline values 11 12

Whereas the risk assessor would play a leading role in step 1 and additionally involved in 13 each of the other steps, the modeler as well as a statistician, would play the critical role in 14 steps 2 thur’ 4. Specifically, step 2 would benefit form peer engagement for ensuring the 15 integrity of PBPK model and its technical robustness. In this regard, an international 16 standing committee might be constituted to provide a support structure for providing 17 continuous feedback on robust considerations of PBPK models. 18 19 Risk assessors have traditionally received graduate training in pharmacology, toxicology 20 and related biomedical sciences, and therefore possess broad knowledge in a number of 21 areas including toxicokinetics, xenobiotic metabolism, physiology, target organ 22 toxicology and dose-response modeling. While expertise in these areas are 23 fundamentally useful for the understanding of the biological and mechanistic basis of 24 PBPK models, additional expertise in applied mathematics, statistics and computational 25 methods would be required in order to properly evaluate and apply these models in risk 26 assessment. The latter expertise, of course, would also facilitate a better understanding of 27 the more sophisticated mathematical models of human exposure, environmental fate and 28 risk characterization. In this regard, development of training materials and hiring of 29 personnel with appropriate expertise will be essential to augument the implementation of 30 MOA- and PBPK-based risk assessment by regulatory agencies. 31

32 5.2. Training 33

34 Training in biological and life sciences is evolving significantly with an orientation 35 toward quantitative analysis and modeling, as opposed to the historical focus on 36 qualitative aspects. A number of public and private institutions as well as professional 37 societies in Europe and North America have periodically been conducting hands-on 38 training sessions in PBPK modeling. Such focussed training, offered in the form of 39 continuing education courses, workshops or short courses, would be relevant to end-users 40 by providing an unique opportunity: 41 42

To understand how the toxicology and mode-of-action information are integrated 43 within the model development process; 44

To see how the equations are written, compiled and solved using commercially 45 available software; 46


49

To learn from case studies of application of PBPK models in risk assessment; and 1 To learn about current or newer methodologies for characterizing uncertainty and 2

variability in PBPK models. 3 4 In this regard, a number of software packages (e.g., MEGen, Berkeley Madonna) would 5 be useful as they permit the development of generic PBPK models for demonstrating to 6 students how physiological and biochemical information are integrated to address issues 7 and uncertainty related to specific toxicology and risk assessment problems. Such 8 generic models as well as step-by-step instructions may be assembled to develop a 9 focused, web-based training (and re-training) package that is easily accessible by risk 10 assessors and other interested audiences. 11 12 Even though such short-term or web-based training materials in PBPK modeling and its 13 applications in risk assessment can help prepare individuals lacking quantitative skills 14 and expertise, the longer term goal should be to include a more quantitative, 15 computationally-based study of toxicology in university curricula. 16 17

5.3. Communication 18 19 Efficient communication is centrally important in facilitating practical application of 20 PBPK models in risk assessment. The issues related to communication between modelers 21 and risk assessors are somewhat different from those encountered in the context of 22 communication within the risk assessment community or within the modeling 23 community. Effective communication needs to be accomplished to facilitate a more 24 efficient knowledge transfer and uptake of PBPK models in risk assessment. Some key 25 aspects that require consideration in this regard are as follows: 26 27

• Development of model descriptions in a standard format to effectively transfer 28 information about the model, the data supporting it, and the appropriateness of its 29 application under a range of conditions. In this regard, a template for PBPK 30 model description is presented in Box 3; several case studies are presented in 31 Appendices 3-5 to illustrate the effective use of this template by modelers to 32 communicate the essential elements to the risk assessors. 33

34 • Presentation of model parameters along with the methodologies used is critical to 35

a better communication and understanding of the model development elements by 36 the assessors (Tables A3.1, A3.2, A4.1, & A5.1). In this regard, it also important 37 to identify which of the parameters were developed based on fitting to TK 38 datasets, and whether the data used for model evaluation were different from 39 those used for model calibration (i.e., fitting). In cases where the fitting or re-40 estimation of model parameters is undertaken for simulating the kinetics in 41 various age groups, genders and exposure conditions, then it is more useful to 42 illustrate the overall process using a flow diagram or another such pictorial (e.g., 43 Clewell et al. 2008). Figure 13 illustrates the process of parameter estimation for 44 the inhalation PBPK model of chemical XA in the male rats (Appendix 3). 45


50

1 Box 3. Template for describing the characterization and application of PBPK 2 models in risk assessment 3

4 5 1. Problem formulation

2. Scope for PBPK model application

3. Model capability and selection

4. Model structure and Biological characterization

5. Parameter estimation and analysis

6. Purpose-specific model evaluation

7. Evaluation of dose metrics

8. Model application in risk assessment

9. Model documentation


51

1 2

Figure 13. Illustration of the processes of estimation, incorporation and 3 refinement of parameters in the inhalation PBPK model for chemical XA in 4 male rats. 5

Re-evaluation of protein binding

parameters

Literature data on physiological parameters, blood:air partition coefficients, as well as protein

binding

Fitted parameters: Vmax and Km for both metabolic

pathways based on inhalation TK data

Literature search

Decision on Basic Model Structure

Evaluation of model predictions of IV and

inhalation of XA (short term exposures)

Incorporation of equations to compute BW change with time; subsequent calculation of physiological and metabolic (Vmax) parameters

Evaluation of model predictions of XA and XA-M kinetics in rats exposed repeatedly

by inhalation for up to 18 months

Experimental data on

tissue:saline partition

coefficients


52

Such illustrations, combined with the parameter tables, would be instrumental in 1 communicating the model development aspects, with particular emphasis on the 2 parameter estimation processes. 3

4 • Communicative pieces illustrating the critical importance of ‘purpose-specific’ 5

model evaluation should be developed. A common impediment to the use of 6 PBPK models in risk assessment relates to the criticism/confusion regarding the 7 extent of validation or evaluation required. As illustrated with Chemicals XA, 8 XB and XC (Appendices 3-5), the focus should be on ‘fit for purpose’. 9 Accordingly, it is important to present clearly the level of confidence in a model 10 based on its performance, plausibility, robustness and extrapolation uncertainty as 11 they relate to a specific purpose or application in risk assessment. 12

13 • Establishment of standard abbreviations or parameter nomenclature as well as a 14

glossary for PBPK modeling is central to facilitate communication not only 15 between risk assessors and modelers but also among modelers themselves. In this 16 regard, a library of generic model structures, descriptions of departures from the 17 generic structure as well as lessons learnt regarding the evaluation of dose metrics 18 for specific MOAs might be envisaged.19


53

6. Concluding remarks 1 2

• The PBPK models provide a documentable and scientifically defensible means of 3 bridging the gap between critical toxicity studies and human risk estimates by 4 facilitating interspecies, interindividual, high dose to low dose and route to route 5 extrapolations. In particular, they shift the focus in risk assessments from external 6 dose to internal dose that is more closely associated with the tissue responses. 7 The PBPK models, however, will not remove all of the uncertainty associated 8 with the risk assessment process; specifically, in most cases, they would not 9 address toxicodynamic uncertainty (i.e., HDAF, ADAF). 10

11 • The complexity of PBPK models should be no more than is required for the task 12

at hand. The increased complexity and data demands of PBPK models must be 13 counter-balanced by the increased accuracy, biological plausibility and scientific 14 justifiability of the risk assessment using them. While complex PBPK models 15 may be relevant to chemicals for which margin between exposure and effect is 16 small, simpler models might be adequate for preliminary assessments to inform 17 additional steps. 18

19 • For PBPK models intended for application in risk assessment, the focus should be 20

on purpose-specific “evaluation” rather than generic “validation”. Accordingly, 21 comparison of model predictions with TK data is not the only way of establishing 22 confidence in a PBPK model; equally important are aspects relating to 23 plausibility, robustness and extrapolation uncertainty, supplemented with 24 appropriate analyses of variability, uncertainty and sensitivity. 25

26 • In order to facilitate transparency, reproducibility and credibility, the PBPK 27

models should be systematically characterized and documented. The 28 documentation should be sufficient to facilitate an experienced modeler, expert 29 reviewer or an interested end-user to evaluate a PBPK model and reproduce the 30 input-output relationships for the dose metric of relevance to risk assessment. In 31 this regard, improved transparency through dedicated repositories for data, 32 models, and their detailed documentation could be envisaged. 33

34 • Communication between the modeler and the risk assessor is of prime importance 35

in developing PBPK models applicable for risk assessment. The continuous 36 involvement of a risk assessor right from the problem formulation stage would be 37 key in facilitating the modeler consider and address issues of relevance to MOA 38 and risk assessment. 39

40 • Mechanism for adequate peer engagement at the international level for evaluating 41

PBPK models in the context of their suitability for specific applications in risk 42 assessment would be essential. This, in conjunction with enhanced access to 43 modeling expertise through recruitement, training or re-training, would facilitate 44


54

greater uptake and optimal use of PBPK models by the risk assessment 1 community. 2


55

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Thompson CM, Sonawane B, Barton HA, DeWoskin RS, Lipscomb JC, Schlosser P, 1 Chiu WA, Krishnan K (2008) Approaches for applications of physiologically based 2 pharmacokinetic models in risk assessment. Journal of Toxicology and Environmental 3 Health B Crit Rev, 11(7):519-547. 4 5 US.EPA (2006) Approaches for the Application of Physiologically Based 6 Pharmacokinetic (PBPK) Models and Supporting Data in Risk Assessment (Final 7 Report). Washington, D.C, US Environmental Protection Agency EPA/600/R-05/043A. 8 9 Veerkamp W, Wolff C (1996) Fate and exposure models in relation to risk assessment. 10 Environmental Science and Pollution Research, 3(2):91-95. 11 12 Vermeire T, Stevenson H, Pieters MN, Rennen M, Slob W, and Hakkert BC (1999) 13 Assessment factors for human health risk assessment: a discussion paper. Critical 14 Reviews in Toxicology, 29(5): 439-490. 15 16 Voisin EM, Ruthsatz M, Collins JM, Hoyles PC (1990) Extrapolation of animal toxicity 17 to humans : interspecies comparisons in drug development. Regulatory Toxicology and 18 Pharmacology, 12:107-116. 19 20 Walton K, Dorne JL, Renwick AG (2001a). Uncertainty factors for chemical risk 21 assessment: interspecies differences in glucuronidation. Food and Chemical Toxicology 22 39:1175-90. 23 24 Walton K, Dorne JL, Renwick AG (2001b). Uncertainty factors for chemical risk 25 assessment: interspecies differences in the in vivo pharmacokinetics and metabolism of 26 human CYP1A2 substrates. Food and Chemical Toxicology 39: 667-80. 27 28 Walton K, Dorne JL, and Renwick AG (2001c). Categorical default factors for 29 interspecies differences in the major routes of xenobiotic elimination. Human and 30 Ecological Risk Assessment 7: 181-201. 31 32 Walton K, Dorne JL, Renwick AG (2004). Species-specific uncertainty factors for 33 compounds eliminated principally by renal excretion in humans. Food and Chemical 34 Toxicology 42(2):261-74. 35 36 Welsch F, Blumenthal GM, Conolly RB (1995) Physiologically based pharmacokinetic 37 models applicable to organogenesis: extrapolation between species and potential use in 38 prenatal toxicity risk assessments. Toxicology Letters, 82-83:539-547. 39 40 WHO. (1987) Principles for the safety assessment of food additives and contaminants in 41 food.Geneva, World Health Organization, International Programme on Chemical Safety 42 (Environmental Health Criteria 70). 43 44 WHO (1994) Assessing human health risks of chemicals: Derivation of guidance values for 45 health-based exposure limits. Geneva, World Health Organization, International Programme 46


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on Chemical Safety (Environmental Health Criteria 170). 1 2 WHO. (1999) Principles for the assessment of risks to human health from exposure to 3 chemicals. Geneva, World Health Organization EHC 210. pp 110. 4 5 WHO (2001) Guidance document for the use of data in development of chemical-specific 6 adjustment factors (CSAFs) for interspecies differences and human variability in 7 dose/concentration response assessment. Geneva, World Health Organization, 8 International Programme on Chemical Safety. 9 10 WHO (2004a) IPCS risk assessment terminology. Geneva, World Health Organization. 11 12 WHO (2004b) Principles of characterizing and applying human exposure models. 13 Geneva, World Health Organization, International Programme on Chemical Safety. 14 15 WHO (2005) Chemical-specific adjustment factors for interspecies differences and 16 human variability: guidance document for use of data in dose/concentration-response 17 assessment. Geneva, World Health Organization. 18 19 WHO (2006) Draft Guidance document on characterizing and communicating 20 uncertainty in exposure assessment. Geneva, World Health Organization. 21 22 Younes M, Sonich-Mullin C, Meek ME (1998) Risk: Assessment and management. In: 23 Herzstein JA, Bunn WB, Fleming LE, eds. International occupational and environmental 24 medicine. St Louis, MO, Mosby, pp.62-74. 25 26


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APPENDIX 1: GLOSSARY OF TERMS 1 2 Absorbed dose: The amount of chemical that, after contact with the exchange boundary 3 (skin, lungs, gut), actually penetrates the exchange boundary and enters the circulatory 4 system. The amount may be the same or less than the applied dose. 5 6 Adverse effect : The change in morphology, physiology, growth, development or life 7 span of an organism which results in impairment of functional capacity or impairment of 8 capacity to compensate for additional stress or increase in susceptibility to the harmful 9 effects of other environmental influences. 10 11 Algorithm: A fixed step-by-step mathematical procedure. 12 13 Area under the curve (AUC): Area under the concentration versus time curve. The 14 AUC is a summary measure that integrates serial assessments of internal dose over the 15 duration of a study. 16 17 Assessment factor: See Uncertainty factor 18 19 Biologically effective dose: The amount of the chemical available for interaction with 20 any particular organ, cell or macromolecular target. 21 22 Chemical-Specific Adjustment factor: a factor based on quantitative chemical-specific 23 toxicokinetic or toxicodynamic data, which replaces the default uncertainty factor. 24 25 Benchmark dose/concentration: the lower confidence limit of the dose/concentration 26 calculated to be associated with a given incidence (e.g., 5 or 10% incidence) of effect 27 estimated from all toxicity data on that effect within that study. 28 29 Cancer slope factor (CSF): An estimate of the increased cancer risk from a lifetime 30 exposure to an agent. This estimate, usually expressed in units of proportion (of a 31 population) affected, is generally reserved for use in the low-dose region of the dose-32 response relationship. It is often an upper bound, approximating 95% confidence limit. 33 34 Critical effect: The first adverse effect, or its known precursor, that occurs in the 35 dose/concentration scale. 36 37 Default value: Pragmatic, fixed or standard value used in the absence of relevant data. 38 39 Delivered dose: The amount of a substance available for biologically significant 40 interactions in the target organ. 41 42 Diffusion-limited uptake: The diffusion of chemical (typically high molecular weight 43 and those with significant protein binding) across the membrane is the rate-limiting 44 process of tissue uptake. 45


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1 Dose: A stated quantity or concentration of a substance to which an organism is exposed 2 over a continuous or intermittent duration of exposure. It is most commonly expressed as 3 the amount of test substance per unit weight of test animal (e.g. mg/kg body weight). 4 5 Dose metric: The target tissue dose that is closely related to ensuing adverse responses. 6 Dose metrics used for risk assessment applications should reflect the biologically active 7 form of chemical, its level, duration of internal exposure, as well as intensity. 8 9 Dose-response assessment: The process of determining the relationship between the 10 magnitude of administered, applied, or internal doses and biological responses. Response 11 can be expressed as measured or observed incidence or hange in level of response, 12 percent response in groups of subjects (or population), or the probability of occurrence or 13 change in level of response within a population. 14 15 Evaluation: In the conext of modeling, “evaluation” refers to the process of establishing 16 confidence in a model on the basis of scientific principles, quality of input parameters and 17 ability to reproduce independent empirical data (Oreskes 1998). 18 19 Exposure: Contact between an agent and a target. 20 21 Exposure dose: Amount of an agent presented to an absorption barrier and available for 22 absorption, i.e., the amount ingested, inhaled, or applied to the skin. This amount may be 23 the same or greater than the absorbed dose. Also referred to as applied dose or potential 24 dose. 25 26 First-order process: A linear process whose output is strictly proportional to the dose or 27 concentration. 28 29 First-pass effects: Metabolism that occurs before a compound can enter the general 30 circulation. For example, an orally administered compoud may undergo metabolism in 31 the intestines and/or liver prior to systemic distribution. 32 33 Fitting: Process of optimizing model output to experimental data, for the estimation of 34 parameter(s) adequately identifiable from the data (Carson et al., 1983). 35 36 Flow-limited uptake: The chemical diffuses readily between blood and tissue 37 compartments and exchange is limited primarily by blood flow. 38 39 Human equivalent concentration or dose (HEC, HED): The human concentration (for 40 inhalation exposure) or dose (for oral exposure) of an agent that is believed to induce the 41 same magnitude of toxic effect as the exposure concentration or dose in experimental 42 animal species. This adjustment may incorporate toxicokinetic information on the 43 particular agent, if available, or use a default procedure. 44 45


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Internal dose: A more general term denoting the amount absorbed or the concentration 1 of a chemical (or its metaboiltes and adducts) in biological matrices.. 2 3 Lowest-observed-adverse-effect level/concentration (LOAEL/LOAEC): The lowest 4 concentration or amount of a substance, found by experiment or observation, that causes 5 an adverse alteration of morphology, functional capacity, growth, development or life 6 span of the target organisms distinguishable from normal (control) organisms of the same 7 species and strain under the same defined conditions of exposure. 8 9 Markov Chain Monte Carlo: A simulation approach that considers a model’s 10 parameters as random variables with a probability distribution for describing each 11 parameter. The distribution based only on prior information and assumptions is called the 12 prior distribution. Analysis of new data yields a posterior distribution of parameters that 13 reconciles the prior information and assumptions with the new data. 14 15 Mechanism of action: A detailed description of the precise chain of events from the 16 molecular level to gross macroscopic or histopathological toxicity. 17 18 Mode of action: A series of events that may lead to induction of the relevant end-point of 19 toxicity for which the weight of evidence supports plausibility. 20 21 Monte Carlo simulation: Repeated random sampling from the 22 distribution of input parameters to derive a distribution of output in the population. 23 Monte Carlo simulation with PBPK models can provide population distributions of dose 24 metric of relevance to risk assessment. 25 26 No-observed-adverse-effect level/concentration (NOAEL/NOAEC): the highest 27 concentration or amount of a substance, found by experiment or observation, that causes 28 no detectable adverse alteration of morphology, functional capacity, growth, development 29 or life span of the target organisms under defined conditions of exposure. 30 31 Pharmacodynamic models: Mathematical descriptions simulating the relationship 32 between a biologically effective dose and the occurrence of a tissue response over time. 33 34 Pharmacodynamics: see Toxicodynamics 35 36 Pharmacokinetic models: Mathematical descriptions simulating the relationship 37 between external exposure level and chemical concentration in biological matrices over 38 time. Pharmacokinetic models take into account absorption, distribution, metabolism,and 39 elimination of the administered chemical and its metabolites. 40 41 Pharmacokinetics: see Toxicokinetics 42 43 Physiologically based pharmacokinetic (PBPK) model: A model that estimates the 44 dose to target tissue by taking into account the rate of absorption into the body, 45


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distribution and storage in tissues, metabolism, and excretion on the basis of interplay 1 among critical physiological, physicochemical, and biochemical determinants. 2 3 Point of departure (POD): The dose-response point that marks the beginning of a low-4 dose extrapolation. This point can be the lower bound on dose for an estimated incidence 5 or a change in response level from a dose-response model (BMD, BMC), or a no-6 observed-adverse-effect level or lowest-observed-adverse-effect level for an observed 7 incidence or change in level or response. 8 9 Sensitivity analysis: Quantitative evaluation of how input parameters influence the 10 model output (e.g., dose metrics) 11 12 Simulation: System behaviour (e.g., blood kinetic profile in exposed organism) predicted 13 by solving the differential and algebraic equations constituting a model. 14 15 Steady state: A variable is said to have attained steady state when its value stays constant 16 in a given interval of time, i.e., when its derivative is zero. 17 18 Target organ: The biological organ(s) most adversely affected by exposure to a 19 chemical or physical agent. 20 21 Toxicodynamics: the process of interaction of chemical substances with target sites and 22 the subsequent reactions leading to adverse effects. The term has essentially the same 23 meaning as pharmacokinetics, but the latter term is frequently used in reference to 24 pharmaceutical substances. 25 26 Toxicokinetics: the process of the uptake of potentially toxic substances by the body, the 27 biotransformation they undergo, the distribution of the substances and their metabolites in 28 the tissues, and the elimination of the substances and their metabolites from the body. 29 Both the amounts and the concentrations of the substances and their metabolites are 30 studied. The term has essentially the same meaning as pharmacokinetics, but the latter 31 term is frequently used in reference to pharmaceutical substances. 32 33 Uncertainty: Uncertainty occurs because of lack of knowledge. Uncertainty can often be 34 reduced with greater knowledge of the system or by collecting more and better 35 experimental or simulation data. 36 37 Uncertainty factor: A product of several single factors by which the NOAEL or LOAEL 38 of the critical effect is divided to derive a tolerable intake. These factors account for 39 adequacy of the pivotal study, interspecies extrapolation, interindividual variability in 40 humans, adequacy of the overall database and nature of toxicity. 41 42 Validation: Process by which reliability and relevance of a particular approach (or 43 model) is established for a defined purpose. 44 45


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Variability: Variability refers to true heterogeneity or diversity. Differences among 1 individuals in a population are referred to as interindividual variability; differences for 2 one individual over time are referred to as intraindividual variability. 3 4 Verification: In the context of PBPK modeling, verification refers to the process of 5 examining the model structure, parameters, units, equations and model codes to ensure 6 accuracy 7


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APPENDIX 2: FREQUENTLY ASKED QUESTIONS 1 2 Which specific uncertainty factors can be refined or replaced with the simulations of 3 PBPK models? 4 5

• AKAF, i.e., toxicokinetic component of the interspecies uncertainty factor (central 6 estimate) 7

• HKAF, i.e., toxicokinetic component of the interindividual uncertainty factor 8 (population variability) 9

10 Will the use of PBPK model always result in a reduction of uncertainty factors 11 compared to the default values ? 12 13 No. The assessment factors derived on the basis of PBPK model simulations of dose 14 metrics may be equal to, greater than or less than the default value. 15 16 What is the relationship between PBPK model and CSAFs ? 17 18 CSAFs refer to chemical-specific adjustment factors derived on the basis of toxicokinetic 19 data relevant to the mode of action of chemicals. If relevant experimental data useful for 20 deriving CSAF are not available, PBPK models may be used to simulate the dose metrics 21 required for calculating the kinetic components of CSAF, i.e., AKAF, HKAF. 22 23 When PBPK models are not available for the species or exposure route relevant to 24 the critical toxicological study, how do I use the PBPK model in a risk assessment ? 25 26 It will only be of limited use, particularly for informing about linearity of internal dose vs 27 external dose relationship in a given species as well as for exploring the appropriate dose 28 metrics of relevance to the mode of action of the chemical. 29 30 Will PBPK modeling always suggest non-linearity in dose-response relationship ? 31 32 No. If all metabolic and physiological processes in a given species are linear in the dose 33 range of interest, then the dose-response analysis conducted with the PBPK model will 34 indicate no departure from linearity. 35 36 Are PBPK models available for all chemicals ? 37 38 No. There are compilations and websites that list PBPK models published to-date for 39 specific chemicals and species (Corley et al. 2003; Reddy et al. 2006; Krishnan and 40 Andersen 2007; www.pbpk.org). 41 42 What kind of factor is applied to account for remaining uncertainty (i.e., 43 toxicodynamic differences), once a PBPK model is applied to account for 44 interspecies differences? 45


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1 A default factor of 2.5 is applied to account for remaining uncertainty (WHO 2001, 2 2005). However, chemical-specific information on the interspecies differences in 3 toxicodynamics may lead to a further reduction or increase of this default value. 4 5 What kind of factor is applied to account for remaining uncertainty (i.e., 6 toxicodynamic differences), once a PBPK model is applied to account for 7 interindividual differences? 8 9 A default factor of 3 is applied to account for remaining uncertainty (WHO 2001, 2005). 10 However, chemical-specific information on the human variability in toxicodynamics may 11 lead to a further reduction or increase of this default value. 12 13 Do we always need a PBPK model to account for interspecies or interindividual 14 toxicokinetic differences in risk assessments? 15 16 No. If experimental data on relevant dose metrics are available for the exposure scenarios 17 and species of interest, then those data can be used to calculate CSAFs. 18 19 Would a multicompartmental PBPK model be always superior to a one 20 compartmental model ? 21 22 No. For example, for chemicals whose concentration vs time profiles are identical 23 throughout the body (e.g., certain hydrophilic chemicals), the use of one compartmental 24 models would be sufficient. 25 26 Are all PBPK models useful in quantifying the population variability of dose metric 27 and reponse ? 28 29 No. Only those PBPK models that contain information on the variability of input 30 parameters for the population of interest would be useful in providing information on the 31 distribution of dose metrics. 32 33 Does the use of PBPK model in risk assessment render the latter process less or 34 more conservative – compared to the conventional approach ? 35 36 It can go either way or end up giving the same results as the conventional appraoch. In 37 any case, the PBPK models contribute to augument the scientific basis of risk 38 assessments by basing them on internal dose (that is more closely related to tissue 39 response) rather than external dose of chemicals. 40 41 Do all PBPK models account for physiological and metabolic alterations, if any, to 42 facilitate the prediction of dose metrics during chronic exposures ? 43 44 No. Not all PBPK models account for dose-related patho-physiological or biochemical 45 changes that may occur during repeated exposure to high doses of chemicals. 46


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If there are multiple PBPK models for a given chemical, how do I proceed ? 1 2 Choose the model that: (i) has the least uncertainty in predictions of the dose metrics of 3 relevance to the mode of action, and (ii) has been calibrated and evaluated for the 4 exposure routes, dose ranges, species and lifestage of relevance to the assessment. 5 6 Are the PBPK models useful in replacing the subchronic to chronic, LOAEL to 7 NOAEL or modifying factors used in non-cancer risk assessements? 8 9 No. PBPK models cannot be used in isolation for assessing or suggesting the appropriate 10 magnitude of these factors. Therefore, the toxicological data, dose-response relationship 11 as well as the mode of action information would continue to be the critical pieces of 12 information supporting or refuting the default values for these UFs. 13 14 What constitutes adequate ‘validation’ in the case of PBPK models? 15 16 Validation and evaluation of PBPK models are context-specific, and should be conducted 17 on the basis of (i) comparison of simulations with data, (2) model robustness, (iii) 18 plausibility and (iv) extrapolation uncertainty. Supplementary analyses of variability, 19 uncertainty and sensitivity might be important depending upon the end-use and extent of 20 comparison with real-life data. 21 22 Are more complex PBPK models better than the simpler ones for application in risk 23 assessment? 24 25 Not necessarily. In principle, the credibility and usefulness of models should not be 26 equated to model complexity. 27 28 What constitutes adequate ‘simulation’ in PBPK modeling? 29 30 In PBPK modeling, simulations that are on average within a factor of two of the 31 experimental data have frequently been considered adequate. 32 33 Is variability analysis required for all PBPK models intended for risk assessment 34 application? 35 36 No. The focus of a variability analysis is to evaluate the range of values that a parameter 37 is expected to have in a population and its impact on the variability of the dose metric. 38 Such an analysis is fundamental to the use of PBPK models in estimating HKAF, i.e., 39 toxicokinetic component of the interindividual uncertainty factor. However, variability 40 analysis is not a prerequisite for a PBPK model intended for use in interspecies, high dose 41 to low dose or route-to-route extrapolations. 42 43 44


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What is uncertainty analysis and is it required for all PBPK models intended for 1 risk assessment application? 2 3 Uncertainty analysis focuses to evaluate the impact of the lack of precise knowledge of 4 parameter values and model structure on dose metric simulations; it would be 5 essential/beneficial for those PBPK models that do not adequately simulate the 6 experimental TK data, or that have have only been evaluated with limited datasets. 7 8 What is sensitivity analysis and is it required for all PBPK models intended for risk 9 assessment application? 10 11 In the context of PBPK modeling, sensitivity analysis focuses to provide a quantitative 12 evaluation of how parameters in input functions influence the dose metrics or other model 13 output of relevance to risk assessment. Though not usually required for all PBPK 14 models, it is critically useful in identifying the key parameters of the model for which 15 variability or uncertainty should be analyzed. 16


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APPENDIX 3: Case study 1: Characterizing and applying PBPK model 1 in the risk assessment of chemical XA 2 3

1. Problem formulation 4

XA is a volatile chemical for which the risk assessement focuses to establish a guideline 5 value (e.g., threshold concentration for humans) in air. XA is absorbed and distributed to 6 tissues following inhalation, oral and dermal exposures. It is primarily metabolized to 7 XA-M by alcohol/aldehyde dehydrogenase in the liver. Elimination is by exhalation of 8 the unchanged form or as XA-M in urine, with lesser amounts of glucuronide and sulfate 9 conjugates of XA. The major pathways of metabolism and disposition of XA are 10 qualitatively similar in rats, mice and humans. 11

XA causes hematological effects (e.g., reduction in red blood cell count, hemoglobin 12 level, hematocrit values) following short-term or long term exposure via inhalation, 13 ingestion or dermal contact in rats, mice, rabbits, dogs and monkeys. Effects on other 14 organs (spleen, liver, and kidneys) have also been observed following exposure to XA, 15 but at much higher doses than those associated with hematological effects. In vitro and in 16 vivo studies indicate that XA-M is the causative agent of XA-induced hemolytic effects. 17 The mode of action involves the initial interaction between XA-M and cellular 18 molecule(s) in erythrocytes, leading to erythrocyte swelling followed by cell lysis and 19 reduction in red blood cell count, hemoglobin level as well as hematocrit values. Rats 20 appear to be the most sensitive species towards the hemotological effects of XA. A 21 critical review of the literature indicates that sensitivity to XA-induced hematotoxicity is 22 correlated with variations in production and clearance of XA-M. 23

Experimental data on candidate dose metrics of relevance to MOA of XA, both in rats 24 and humans, essential for computing the magnitude of AKAF (i.e., the assessment factor 25 reflective of the uncertainty in rat to human difference in toxicokinetics) for risk 26 assessment purposes are not available. Such data, if obtained with PBPK models, could 27 be useful in deriving AKAF for chemical XA. 28

2. Scope for PBPK model application 29 30

The critical studies indicate that hematological effects were observed in rats exposed to 31 XA for upto 30 days at concentrations of 86 ppm or more, but not at 50 ppm or less. The 32 maximal concentration (Cmax) and area under the blood concentration vs time curve 33 (AUC) of XA-M in each of these studies were either not known or not established 34 appropriately. So, the PBPK models in the rat would be used to simulate the dose metrics 35 of relevance to the mode of action of XA (i.e., Cmax, AUC) for the various dose levels 36 used in the critical studies. Of relevance is that, in one critical toxicological study, the 37 AUCXA-M was reported only for the post-exposure period. In this case, the PBPK model 38 would be useful for calculating the total AUC (i.e., during exposure + post exposure) in 39 the test animals, required for computing AKAF. 40


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1 There is also a limitation regarding the available human data for XA. The literature data 2 on human AUCXA-M required for calculating AKAF were collected under exercising 3 conditions, and these might not be reflective of the kinetics under resting conditions, 4 because for compounds like XA the respiratory uptake is linearly related to the 5 ventilation rate. Therefore, the correction of AUC value in the available human study on 6 the basis of physiological conditions during working vs resting conditions would be 7 conducted using a human PBPK model. Thus, the use of a PBPK model would facilitate 8 the incorporation of better and more relevant data for calculation of AKAF in the risk 9 assessment of XA. 10

11 3. Model capability and selection 12

13 The critical studies for the risk assessment of XA were conducted in rats exposed by the 14 inhalation route. Also, the risk assessment focuses to derive the guideline value 15 associated with human exposure to XA in the air. Therefore, PBPK models capable of 16 simulating potential dose metrics, particularly XA and XA-M in blood, in both adult rats 17 (test animals) and adult humans exposed to this chemical by inhalation were selected. 18

19 4. Model structure and Biological characterization 20 21

The PBPK model consisted of two sub-models, one for the parent chemical XA and 22 another for the putative toxic metabolite XA-M. Each sub-model consisted of the 23 following compartments: lung, GI tract and liver, skin, muscle, fat, rapidly perfused 24 tissues and other slowly perfused tissues, interconnected by blood circulation. Kidney 25 was represented additionally as a separate compartment in the metabolite model. The 26 input of XA into the system was through IV infusion, oral dosing (gavage or drinking 27 water) or dermal contact, whereas the input for the XA-M model was the flux through the 28 metabolic pathway producing XA-M in the liver. Metabolism of XA in liver, clearance 29 of XA via exhalation and XA-M elimination via urine were represented as shown in the 30 rat and human models in Figure A3.1. 31

32 5. Parameter estimation and analysis 33 34

The physiological parameters for the rat and human PBPK models were obtained from 35 published literature. Rat and human blood:saline partition coefficients as well as rat 36 tissue:saline partition coefficients (for muscle, stomach, small intestine, cecum, lung, 37 liver, skin, kidney and fat) for XA and XA-M were determined by ultrafiltration. The 38 tissue:blood partition coefficients of XA and XA-M were computed by dividing the 39 tissue:saline partition coefficients by the blood:saline partition coefficients. 40

41 Vmax and Km to describe the saturable metabolism of XA (mediated by 42 alcohol/aldehyde dehydrogenase) were obtained from perfused liver experiments in the 43 rat and Vmax was scaled to humans on the basis of body weight0.7. A second saturable 44 but minor pathway was also included to account for metabolism of XA to other 45 metabolites. The rate constants for this pathway were estimated by fitting model 46


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simulations to data on the total amount of XA metabolized. For the XA-M submodel, the 1 urinary elimination rate was estimated by fitting the model simulations to urinary 2 metabolite data. The protein binding constants were arbitrarily set equal to the molar 3 equivalent values for phenolsulfophthalein and considered to be species-invariant. The 4 urinary elimination constants were scaled on the basis of body weight0.74. The values as 5 well as the estimation methods or sources of input parameters of the rat and human XA 6 PBPK models are listed in Tables A3.1 & A3.2. 7

8 6. Purpose-specific model evaluation 9

10 The evaluation of the XA PBPK model focuses on the extent to which it is ‘fit for 11 purpose’, i.e., for use in simulating blood concentrations of XA and XA-M in rats and 12 humans exposed by inhalation. These data would help identify Cmax and AUC in rats 13 and humans for computing AKAF. The purpose-specific evaluation of XA PBPK model 14 is based on the consideration of its performance, robustness, plausibility and 15 extrapolation uncertainty as follows: 16 17

• Performance (data vs model) : High 18 The model consistently simulated the TK profile (i.e., bumps and valleys) of XA and its 19 metabolite (XA-M) following inhalation exposure (the route of relevance to the risk 20 assessment) in male rats and humans. The rat model reproduced the blood concentrations 21 of XA and XA-M following iv doses ranging from 31.25 to 125 mg/kg and oral doses of 22 8.6 to 500 mg/kg. The rat PBPK model also reproduced the respiratory uptake of XA and 23 urinary levels of XA-M following a 6-hr inhalation exposure to 4.2 - 438 ppm XA as well 24 as XA-M blood and urinary concentrations after inhalation exposure of rats for 12 days to 25 20 – 100 ppm XA. The human model reproduced the uptake and blood concentrations of 26 XA following 2-hr inhalation exposure to 20 ppm in exercising individuals. Additionally, 27 kinetics of XA and XA-M in blood as well as cumulative excretion of XA-M in humans 28 exposed by inhalation to 50 ppm for 2 hr or by dermal route to 50 ppm XA for 2 hr was 29 simulated using the PBPK model. 30 31

• Model robustness : High 32 The rat model and the human model, with the same set of chemical-specific and species-33 specific parameters (Tables A3.1 & A3.2), simulated TK data from various experiments. 34 35

• Plausibility : High 36 The model parameters, structure and assumptions are biologically plausible and 37 consistent with available data. 38 39

• Extrapolation uncertainty : High 40 The model simulates the toxic moiety reflective of the dose metric (i.e., blood 41 concentrations of XA-M) (i) in rats exposed by inhalation to atmospheric concentrations 42 in the range of the dose-response study (31 – 250 ppm) as well as (ii) in humans exposed 43 to a low concentration (i.e., first order range) of relevance to risk assessment. 44 45


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Overall, the confidence in the XA PBPK models for the specified purpose (i.e., rat to 1 human extrapolation of TK) is high, given their performance, robustness, plausibility and 2 extrapolation uncertainty. 3 4

7. Evaluation of dose metrics 5 6 The maximal concentration (Cmax) and area-under-the-concentration vs time curve 7 (AUC) of XA-M were investigated as relevant dose metrics for hematological effects 8 induced by XA. Since several studies showed that XA itself is unlikely to induce the 9 observed efects and that XA-M, the major metabolite, is capable of inducing these 10 hematological effects in vitro, a dose metric based on the metabolite was deemed 11 appropriate. Even though both dose metrics based on XA-M are potentially relevant, 12 blood AUCXA-M was chosen as the relevant dose metric for the PBPK-based assessment. 13 Cmax is responsive to dose-rate (consistent with the hemolysis associated with gavage vs 14 drinking water exposure to XA); however, AUC has a time component which is critical to 15 the development and progression of hemolysis (which is not an instantaneous effect) and 16 it is also more conservative. 17

18 8. Model application in risk assessment 19 20

For the purpose of deriving AKAF, the PBPK model was used to compare how the dose 21 metric compares between an average animal and an average human for a given exposure 22 condition. The PBPK model was used to calculate the total AUC (i.e., during exposure + 23 post exposure) in the test animals, as well as the AUC in humans under resting 24 conditions, required for computing AKAF. These calculations of dose metrics would 25 otherwise not have been possible. The PBPK model-simulated ratio of dose metric 26 (AUCXA-M) was 0.5, compared to the default AKUF of 4. The uncertainty related to the 27 “default” approach, in this case, arises from the conceptual model, parameters and the 28 toxic moiety, which are all essentially assumed to be “unknown” despite the availability 29 of relevant TK data to inform upon these aspects. In this regard, the use of PBPK models 30 allowed to incorporate data to reduce the uncertainty in the risk assessment, by simulating 31 AUCXA-M on the basis of physiological, biochemical and physicochemical determinants 32 in both test species and humans. 33

34 9. Model documentation 35 36

The model was written and solved in Simusolv®. Parameter optimization was also 37 conducted using this software. The model equations, that are different from previously 38 published PBPK models, are listed in Appendix of the publications (Corley et al. 1994; 39 Lee et al. 1998). All parameter values, for several age groups, sex and species are listed 40 in tabular form. However, the details related to the parameter estimation methods are not 41 summarized in the Tables; they are provided in the Methods section of the publications. 42

43


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Figure A3-1. Structure of the PBPK model for XA and its major metabolite, XA-M 31 in rats and humans. Reproduced from Corley et al. (1994) permissions pending. 32

Oral dose Other metabolism

Lungs and arterial blood

Richly PerfusedTissues

Poorly PerfusedTissues

Muscle

Gastrointestinaltract

Kidney

(XA-M)

Liver

Fat

Skin




Muscle


Liver

Fat

Skin

Inhaled XA

IV infusion

Der

mal

Vapo

r/Liq

uid

Urine

Exhaled XA

Oral dose Other metabolism




Muscle


Kidney

(XA-M)

Liver

Fat

Skin




Muscle


Liver

Fat

Skin

Inhaled XA

IV infusion

Der

mal

Vapo

r/Liq

uid

Urine

Exhaled XA


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Table A.3.1. Parameters of the rat PBPK model for chemical XA 1 Parameter Rat Source

Surface area (cm2) 261 Scaled to body weight 0.74 Body Weights (kg) 0.213 Literature Tissue volumes (ml) Lung 1.18 Literature Liver 9.88 Literature Kidney (XA-M model) 2.0 Literature Fat 14.9 Literature GI tract 11.3 Literature Skin 21.3 Literature Spleen 0.56 Literature Rapidly perfused tissues 12.4 Literature Slowly perfused tissues 124 Literature Flows (Liter/hr) Alveolar ventilation 5.73 Scaled to body weight 0.74 Cardiac output 5.73 Scaled to body weight 0.74 Tissue blood flow (Liter/hr) Liver 1.43 Literature Kidney 1.43 Literature Fat 0.29 Literature GI tract 1.20 Literature Skin 0.29 Literature Spleen 0.046 Literature Rapidly perfused tissues 1.43 Literature Slowly perfused tissues 0.75 Literature Partition coefficients (PC) for XA Blood:air 7965 Lung:blood 11.3 Liver:blood 1.46 Kidney:blood 1.83 Fat:blood 2.03

Literature (data obtained in vitro by ultrafiltration method)

GI tract:blood 4.33 Assumed to be equal to the average of stomach, small

intestine and caecum:blood PC Skin:air 7965 Assumed to be equal to blood:air

PC Spleen:blood 1.46 Assumed to be equal to rapidly

perfused tissues:blood PC Rapidly perfused:blood 1.46 Assumed to be equal to

liver:blood PC


81

Slowly perfused:blood 0.64 Assumed to be equal to muscle:blood PC

Partition coefficients for XA-M Lung:blood 1.58 Liver:blood 1.30 Kidney:blood 1.07

Literature

Fat:blood 0.77 Assumed to be equal to the average of stomach, small

intestine and caecum:blood PC GI tract:blood 0.78 Data obtained In vitro

(ultrafiltration) Skin:blood 1.21 Assumed to be equal to blood:air

PC Spleen:blood 1.30 Assumed to be equal to rapidly

perfused tissues:blood PC Rapidly perfused tissues:blood 1.30 Assumed to be equal to

liver:blood PC Slowly perfused tisssues:blood 1.31 Assumed to be equal to

muscle:blood PC Metabolic Constants XA to XA-M VmaxC (mg/hr/kg) K ml (mg/liter)

875 170

Fitting to XA urinary data obtained following administration of XA (inhalation:31.2 ;62.5 and 125ppm for 104 weeks (6h/day;

5days/week)) to rats. XA to others Vmax2C (mg/hr/kg) K m2 (mg/liter) Vmax3C (mg/hr/kg) K m3 (mg/liter)

20 23.2 122.6 472

Fitting to experimental in vitro data where sum of Vmax of each

metabolite is equal to experimental total Vmax obtained

in literature Protein binding P (binding sites: μmol/liter) Kd (dissoc.const: μmol /liter)

1860 348

Adjusted according to plasma albumin concentration reported in

literature Literature

Elimination rate constant for XA-M in urine VmaxC (μmo/hr/kg) KmE (μmol/liter)

154.3 165.7

Fitting to XA-M blood kinetic data obtained following IV dose of XA in rats (31.25-125mg/kg)

Oral absorption rate constant K2S (hr-1) for XA in water

1.0

Fitting to blood and urinary kinetic data on XA and XA-M following oral dosing (8.6 and

126mg/kg)


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Table A.3.2. Parameters of the human PBPK model for chemical XA 1 Parameters Human Source/Method

Surface area (cm2) 19000 Literature Body Weight (kg) 70 Literature Tissue volumes (% body weight) Liver 3.14 Literature Kidney (XA-M model) 0.44 Literature Lung 1.15 Literature Fat 23.1 Literature Muscle 42.0 Literature GI tract 3.4 Literature Skin 5.1 Literature Rapidly perfused tissues 3.71 Literature (XA-M model) 3.27 Literature Slowly perfused tissues 9.4 Calculated as 91% BW- sum

of other tissue weights Flows (Liter/hr) Alveolar ventilation 347.9 Literature Cardiac output 347.9 Literature Tissue blood flow (% cardiac output) Liver (including portal flow) 25.0 Literature GI tract 21.0 Literature Kidney (XA-M model) 25.0 Literature Fat 5.0 Literature Muscle 15.0 Literature Skin 3.0 Literature Rapidly perfused tissues (XA-M model)

50.0 25.0

Calculated as Cardiac output-sum of other tissue flows

Slowly perfused tissues 2 Literature Partition coefficients (PC) for XA Blood/air 7965 Literature data obtained in

vitro (vial equilibration) Liver/blood 1.46 In vitro (ultrafiltration) Kidney/blood 1.83 In vitro (ultrafiltration) Lung/blood 11.3 In vitro (ultrafiltration) Fat/blood 2.03 In vitro (ultrafiltration) Muscle/blood 0.64 In vitro (ultrafiltration) GI tract/blood 4.33 Assumed to be equal to the

average of stomach, small intestine and caecum:blood PC

Skin/air 7965 Assumed to be equal to blood:air PC

Rapidly perfused tissues/blood 1.46 Assumed to be equal to liver:blood PC


83

Slowly perfused tissues/blood 0.64 Assumed to be equal to muscle:blood PC

Partition coefficients for XA-M Liver/blood 1.30 In vitro (ultrafiltration) Kidney/blood 1.07 In vitro (ultrafiltration) Lung/blood 1.58 In vitro (ultrafiltration) Fat/blood 0.77 In vitro (ultrafiltration) Muscle/blood 1.31 In vitro (ultrafiltration) GI tract/blood 0.78 Assumed to be equal to the

average of stomach, small intestine and caecum:blood PC

Skin/blood 1.21 Assumed to be equal to blood:air PC

Rapidly perfused tissues /blood 1.30 Assumed to be equal to liver:blood PC

Slowly perfused tissues /blood 1.31 Assumed to be equal to muscle:blood PC

Metabolic Constants XA to XA-M VmaxC (mg/hr/kg) K ml (mg/liter)

375 26.9

Scaling of rate constant from perfused liver experiments (rats) based on body weight 0.7

Protein binding P (binding sites: mg/liter) 164 Assumed to be equal to the

molar equivalent value for phenolsulfophthalein

Kd (dissoc.const:mg/liter) 46 Elimination rate constant for XA-M in urine VmaxC (mg/hr/kg) KmE (mg/liter)

20.4 0.5

Fitting to XA-M blood kinetic data obtained following IV dose of XA in rats (31.25-125 mg/kg)

Oral absorption rate constant K2S (hr-1) for XA in water

1.0

Fitting to blood and urinary kinetic data on XA and XA-M following oral dosing (8.6 and 126 mg/kg) in rats

1


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APPENDIX 4: Case study 2: Characterizing and applying PBPK model 1 in the risk assessment of chemical XB 2 3 1. Problem formulation 4

XB is a gas for which the risk assessment process focuses to establish a guideline value 5 (e.g., threshold concentration for humans) in air based on effects in the extrathoracic 6 region. Chronic exposure of rats and mice to high concentrations of XB (200 ppm or 600 7 ppm, 6 hr/day, 5/wk for 2 years) results in degeneration of olfactory epithelium. 8 Neoplastic lesions were observed in nasal cavity of rats, but not mice, exposed to XB. 9 10 Nasal dosimetry studies in the rat indicated that the extraction of XB in the upper airways 11 is exposure concentration-dependent. The fractional deposition of XB from the nasal 12 cavity ranged from 36 to 94% with greatest relative uptake at lowest exposure 13 concentration. XB is metabolized in nasal tissues by carboxylesterases and aldehyde 14 dehydrogenases forming an acid and aldehyde metabolite (XB-M1 and XB-M2). These 15 metabolites are are cytotoxic and clastogenic, respectively. The formation of the 16 cytotoxic metabolite of XB (XB-M1), mediated by carboxylesterases present in nasal 17 tissues (particularly two epithelial cell types – respirtory epithelium and olfactory 18 epithelium), has been demonstrated in a number of species. XB-M1 is ionized at 19 physiological pH, releasing protons that cause intracellular acidification of nasal 20 epithelial cells. Low pH has been shown to cause chromosomal aberrations and SCEs. 21 Drop in pH, associated with excessive XB-M1 in nasal tissues, could be the earliest of the 22 PD processes that contribute to the toxicity expressed in the nasal tissue. Intracellular 23 acidification, or low pH alone, can lead to clastogenicity, topoisomerase II-mediated 24 DNA strand breaks, as well as cell proliferation in nasal epithelium. 25 26 Studies have also shown that the genotoxicity of XB requires activation by 27 carboxylesterases (to form XB-M1). Conversely, the inhibition of carboxylesterases 28 blocked the cytotoxic effects of XB. XB-M2 is not cytotoxic but does induce induce 29 chromosomal aberrations in human lymphocytes and sister chromatid exchanges in 30 Chinese Hamster Ovary cells, similar to XB. Evidence of DNA-protein crosslinks 31 resulting from the aldehyde metabolite (XB-M2), although unstable at physiological pH 32 and temperature, raise concern about the pertinence of the contribution of this metabolite 33 to the nasal tumors seens in rats upon exposure to XB. 34 35 The use of data or model simulations of a dose metric of relevance to its mode of action 36 (XB, XB-M1 or XB-M2) would permit a scientifically-sound determination of the 37 human equivalent concentration from the rat NOAEL for XB. 38 39

2. Scope for PBPK model application 40

The toxicity and carcinogenicity of XB would appear to be due to a reduction in pH 41 caused by XB-M1. It is not possible to make measurements of the time course of change 42 in pH in nasal tissues of exposed animals and humans. Therefore, the use of a TK/TD 43


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model that enables the prediction of pH, with the consideration of biological buffers 1 would be particularly relevant. In this case then, the PBPK models can improve upon the 2 default approach by adjusting for interspecies differences in dose metric based on MOA. 3 Further, the inhalation exposures during bioassays for XB were conducted for 6 hr/day, 5 4 days/ week whereas the the human exposure scenario (for risk assessment considerations) 5 is 24 hr/day, 7 days per week. Duration extrapolation of exposure concentrations based 6 on dose metric of relevance to mode of action (simulated with PBPK models), would be 7 preferred over the default approach based on the assumption of the constancy of the 8 concentration-time product. 9

3. Model capability and selection 10 11 The PBPK models selected for use in the risk assessment of XB were developed 12

and parameterized for adult humans and rats, and were capable of simulating various 13 dose metrics (i.e., concentration of XB-M1 in target sites, alterations in pH in target 14 tissues associated with XB metabolism, proton concentrations in target sites, release of 15 aldehyde (XB-M2) into expired air) following inhalation exposures. 16 17

4. Model structure and Biological characterization 18 19

A 5-compartmental nasal PBPK model of the rat nasal cavity and a 4-compartmental 20 PBPK model for human nasal cavity were developed on the basis of existing upper 21 airway models. These models describe the splitting of the inspired air in two directions: 22 (i) dorsal-medial path and (ii) lateral-ventral path. The airstreams then pass over the 23 tissue compartments as shown in Figure A4-1. Specifically, the dorsal airstream passes 24 sequentially over a small portion of respiratory mucosa, an anterior olfactory mucosal 25 compartment, and finally a posterior olfactory compartment before combining with the 26 ventral airstream at the nasopharynx. The ventral airstream passes over two 27 compartments representing the respiratory mucosa. 28


Mass transfer coefficients were obtained literature. Blood flow rates to nasal tissues were 32 calculated from cardiac output and region-specific surface area. Distribution of enzymes 33 in specific nasal compartments was based on histochemical localization. Vmax and Km 34 for carboxylesterase and aldehyde dehydrogenase-mediated metabolism were obtained 35 using whole tissues, in vitro or gas uptake technique. Vmax for each compartment was 36 scaled on the basis of tissue surface areas. For rats, parameters for the high affinity 37 carboxylesterase pathway were estimated by fitting to XB vapor deposition data in vivo. 38 These values were adopted for humans, on the basis of differences in tissue amount 39 between rats and humans (Table A4.1). 40 41

6. Purpose-specific model evaluation 42 43


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The evaluation of the XB PBPK model focuses on the extent to which it is ‘fit for 1 purpose’, i.e., for use in simulating dose metric for XB in rats and humans exposed by 2 inhalation. These data would facilitate the compution of AKAF. The purpose-specific 3 evaluation of XB PBPK model is based on the consideration of its performance, 4 robustness, plausibility and extrapolation uncertainty as follows: 5

6 • Performance (data vs model): High 7

Model, with the consideration of high affinity carboxylesterase pathway, appropriately 8 reproduces the steep concentration gradient of XB through the mucosa as well as other 9 TK data. The PBPK model was applied to simulate in vivo experiments designed to 10 measure uptake and metabolism of XB in the upper respiratory tract of rats exposed to 11 atmospheric concentrations ranging from 73 to 2190 ppm for 1 hr. Model predicted an 12 exposure concentration-dependent increase in tissue concentrations of XB, XB-M1 and 13 XB-M2 in respiratory mucosa and olfactory epithelial mucosa (dosimeters given at XB 14 exposure concentrations of 50, 200, 600 and 1000 ppm). Overall, predictions of the XB 15 PBPK model were compared against several data sets that included multiple 16 concentrations, nasal flow rates, deposition in upper airways and XB-M2 production and 17 release via the nasal cavity. The model also reproduced data with regard to XB 18 concentrations and XB-M2 wash-out in the airstream of nasopharyngeal cavity during 19 controlled human exposures of 1 to 10 ppm. 20 21

• Model robustness: High 22 The PBPK model, with a single set of species-specific parameters (Table A4.1), 23 simulated in vivo data on uptake and metabolism of XB in the upper respiratory tract of 24 rats exposed to atmospheric concentrations ranging from 73 to 2190 ppm; and human 25 data on XB concentrations and XB-M2 wash-out in the airstream of nasopharyngeal 26 cavity during controlled exposures of 1 to 10 ppm. 27 28

• Plausibility: High 29 The five compartmental nasal PBPK model of the rat nasal cavity and the four 30 compartmental PBPK model for human nasal cavity are consistent with current 31 knowledge of upper airways; the model parameters and assumptions are biologically 32 plausible. 33 34

• Extrapolation uncertainty: Low 35 The confidence in the model due to extrapolation uncertainty was low since the model 36 simulations were not compared with pH changes in nasal tissues of rats or humans; 37 however the model reproduced TK in upper respiratory tract of both rats and humans. 38 The local sensitivity analysis indicated that free hydrogen ion concentration at the end of 39 inhalation exposure in the rat nose was most affected by: olfactory epithelial tissue 40 volume and surface area, the proton antiport system, the high affinity esterase pathway 41 and apportionment of flow to the dorsal airstreams. The sensitivity coefficients 42 associated with these parameters varied as a function of exposure concentrations, 43 indicative of nonlinear behavior in the concentration range investigated (200 – 600 ppm) 44 in the rat. 45


87

Overall, the confidence in the XB PBPK model for the specified purpose is medium, 1 given their performance, robustness, plausibility and extrapolation uncertainty. 2 3

7. Evaluation of dose metrics 4 5

Dose metrics related to XB-M1 and XB-M2 are potentially relevant. Whereas XB-M2 6 can contribute to genotoxicity, XB-M1 would appear to be primarily responsible for 7 cytotoxicity-induced cellular proliferation; however, the authors hypothesize that the 8 decrement in pH is the most appropriate dose metric based on the reasoning that 9 protection of nasal tissues from XB exposures that result in adverse decrements in pHi 10 should protect the tissue from all subsequent adverse effects (e.g., cell proliferation and 11 tumors). PBPK model analysis indicated that a 6-hr exposure to 50 ppm (NOAEL) in the 12 rat is not expected to cause a significant drop in pH (less than 0.1 unit); whereas a 13 disproportionate drop in pH would occur with increasing exposure concentratrions (0.25 14 units for 200 pm; 0.49 units for 600 ppm) consistent with the non-linearity observed with 15 XB toxicity and carcinogenicity. 16


The basis of the choice of one or combinations of the candidate dose metrics is critical to 20 the assessment. Considering the MOA of XB, XB-M2 can contribute to genotoxicity 21 whereas XB-M1 would appear to be primarily responsible for cytotoxicity. In the PBPK 22 study, based on simulation of pH changes (related to XB-M1), an exposure concentration 23 of 8.7 ppm in humans is equivalent to 10 ppm in rats. With regard to the interspecies 24 extrapolation of the exposure concentration of XB, the use of conventional approach of 25 calculating deposition in the nose, as a function of the ventilation rate in the extrathoracic 26 region divided by the surface area, would be highly uncertain. In this regard, the use of 27 PBPK model addresses a number of sources of uncertainty related to interspecies 28 extrapolation: air phase mass-transfer characteristics, rates of metabolism and production 29 of toxic metabolites, compartmental segregation of respiratory and olfactory tissues, 30 mode action, as well as XB-specific considerations. 31

32 9. Model documentation 33 34

The model structure, parameters and equations are described in the publication 35 (Bogdanffy et al. 1999). The parameter estimation methods are not summarized in the 36 table but provided either as foot-note or described in detail in the Methods section of the 37 publication. The PBPK model was implemented using ACSL® for Windows (Advanced 38 Continuous Simulation Language, Mitchell and Gauthier Associates, Concord, MA). The 39 authors have made the code available upon request. 40 41 42 43 44 45 46


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Figure A4-1. Schematic of the 5-compartmental PBPK model for XB. Reproduced 34 from Bogdanffy et al. (1999), permissions pending. 35

XB

Air Comp. 1 Air Comp. 2 Air Comp. 3

Mucus

Epithelial cell layer

Basal cell layer

Submucosa

Respiratory 1 Olfactory 1 Olfactory 2

Respiratory 2 Respiratory 3

Air Comp. 4 Air Comp. 5

Convective transport

Convective transport

Arterial blood Venous blood

In

XB

Out


89

Table A.4.1. Parameters of the rat and human PBPK model for chemical XB. 1 Parameter Human Rat Source

Anatomic/Physiologic

Body weight (kg) 70.0 0.250 Literature

Minute volume (L/min) 7.5 0.197 Literature

Surface area (cm2) Respiratory 1 Respiratory 2 Respiratory 3 Olfactory 1 Olfactory 2

10.1 42.1 72.3 13.2 -

0.2 1.8 4.5 0.42 6.33

Literature Literature Literature Literature Literature

Blood flow (ml/hr)

Respiratory 1 Respiratory 2 Respiratory 3 Olfactory 1 Olfactory 2

72.3 301 517 94 -

0.2 1.8 4.5 0.4 6.2

Total nasal perfusion assumed equal to 3% of cardiac output (CO); Scaled to species based on CO and surface nasal region

Fraction of airstream, dorsal 0.07 0.14 Literature

Fraction of airstream, ventral 0.93 0.86 Literature

Lumen volumes Respiratory 1 (cm3) Respiratory 2 (cm3) Respiratory 3 (cm3) Olfactory 1 (cm3) Olfactory 2 (cm3)

0.74 3.50 5.16 0.56 -

0.004 0.9 0.9 0.012 0.054


Mass transfer coefficients Respiratory 1 (cm/h) Respiratory 2 (cm/h) Respiratory 3 (cm/h) Olfactory 1 (cm/h) Olfactory 2 (cm/h)

2.32*104 1.36*104

1.87*104 4.21*105

-

4.8*105

2.0*106

1.1*102 4.3*105

1.6*106


Metabolic Carboxylesterase Vmax respiratory high affinity pathway (mg/hr) Vmax olfactory high affinity pathway (mg/hr) Km resp and olf. High affinity pathway (mg/mL) Vmax respiratory epithelium (mg/hr) Km respiratory epithelium (mg/L) Vmax olfactory epithelium (mg/hr) Vmax olfactory submucosa (mg/hr) Km olfactory epithelium (mg/hr)

44.6 4.74 4.7*10-3 441 0.050 45.0 45.0 0.050

1.3 1.3 4.7*10-3 63.0 0.040 45.4 - 0.050

In vitro data obtained using gas uptake technique (whole tissue); further fitting to experimental data on nasal extraction (inspiration flow rate = 100 ml/min). Scaled to humans based on tissue surface area.


90

Aldehyde dehydrogenase Vmax respiratory epithelium (mg/hr) Km respiratory epithelium (mg/mL) Vmax olfactory epithelium (mg/hr) Km olfactory epithelium (mg/hr) Acetyl CoA synthetase Vmax respiratory epithelium (mg/hr) Km respiratory epithelium (mg/mL) Vmax olfactory epithelium (mg/hr) Km olfactory epithelium (mg/mL)

88.2 1.10 2.31 1.10 2.05 0.018 0.218 0.018

1.35 0.8 2.7 0.800 0.053 0.018 0.067 0.018

In vitro data obtained using gas uptake technique (whole tissue). Scaled to humans based on tissue surface area. Liver acetyl Co A synthetase activity obtained from the literature was assumed to be the same for nasal tissues. Scaled to humans based on tissue surface area.

Na+/H+ antiport Vmax (mmoles/L/min) KH (mmoles/L) Initial pHi

63.4 2.6*10-4 7.4

63.4 2.9*10-4 7.4

Literature Literature Estimated; scaled to humans based on surface area

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


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APPENDIX 5: Case study 3: Characterizing and applying PBPK model 1 in the risk assessment of chemical XC 2 3

1. Problem formulation 4 5 XC is a volatile chemical for which the risk assessment focuses to derive guideline values 6 for oral and inhalation routes based on cancer risk. XC is absorbed and distributed 7 rapidly following inhalation and oral exposures. Its storage in tissues is limited by its 8 rapid metabolism and excretion. XC is metabolized via two saturable pathways, one 9 representing low capacity-high affinity oxidation by CYP2E1 and the other representing 10 higher capacity-lower affinity oxidation by other isozymes of CYP. The reactive, short-11 lived haloepoxide metabolite, XC-M, subsequently conjugates with GSH or yields a 12 reactive halocarbonyl compound. 13

14 Several epidemiological and animal studies demonstrated the association between 15 exposure to XC and cancer of the liver – both angiosarcoma and non-angiosarcoma. 16 Other tumor sites included the skin, bones, kidney, lung, brain, mammary glands, and 17 Zymbal glands even though a clear evidence of causal association to XC exposure did not 18 emerge from these studies. The lowest exposure concentrations at which XC-induced 19 angiosarcomas were 10 ppm (4 hr/d, 5 d/wk, 52 wk), 50 ppm (4 hr/d, 5 d/wk, 30 wk), and 20 500 ppm (4 hr/d, 5 d/wk, 30 wk), respectively, in rats, mice and hamsters. The 21 mutagenicity of XC was increased in most in vitro sytems following metabolic activation, 22 which was further augumented by the induction of liver CYP enzymes. Moreover, in 23 vivo and in vitro studies demonstrated the formation of etheno-DNA adducts (minor) and 24 guanine-DNA adducts (major), as well as mutations in the p53 tumor suppressor gene 25 and the ras proto-oncogene in liver tumors from rats and humans. Overall, the available 26 literature indicates that (i) the reactive metabolite XC-M is the plausible toxic moiety and 27 (ii) the relationship between the outcome (e.g., binding to macromolecules, liver effects, 28 tumors) and exposure concentrations of XC is not linear over the dose range investigated 29 in the various studies. 30

31 The scientific basis of high dose to low dose, route-to-route, and interspecies 32 extrapolations required in the context of cancer risk assessment for XC can be enhanced 33 with the use of a dose metric related to the putative toxic moiety (i.e., XC-M) that is more 34 closely related to the cancer response rather than the administered dose. The quantitative 35 cancer estimates from epidemiological studies can be compared with those derived from 36 animal bioassays, if they can be based on a common dose metric relevant to the MOA of 37 XC. 38

39 2. Scope for PBPK model application 40

The toxicity and carcinogenicity of XC is mediated by the production of the haloepoxide 41 metabolite XC-M. Since this metabolite is reactive and short-lived, its concentrations are 42 too low to be measured at the target organ. Therefore, the biologically plausible estimate 43 of the effective dose, in terms of the production or concentration of reactive metabolites 44


92

at the target tissue, can not be obtained experimentally. It is further complicated by the 1 fact that the ratio of this biologically effective dose to exposure concentration or 2 administered dose is not uniform across routes and species. Therefore, cancer risk 3 estimate based of administered dose of XC would be highly uncertain. In this context, 4 the PBPK model would be instrumental in providing simulations of dose metrics related 5 to XC-M to facilitate scientifically-sound conduct of high dose to low dose, route-to-6 route, and interspecies extrapolations required for the cancer risk assessment of XC. 7

3. Model capability and selection 8 9 Given the cancer risk assessment for XC focuses on oral and inhalation routes of 10 exposure, the PBPK models selected should be able to simulate potential dose metrics 11 related to XC-M (e.g., total amount metabolized/g liver, amount conjugated with GSH/g 12 liver, amount metabolized but not conjugated with GSH/g liver) in both adult rats (test 13 animals) and adult humans exposed by these exposure routes. Since additional TK data 14 are available in another species (mice), the capability of the PBPK model to simulate 15 these data would help further evaluate the biological basis of the model as well as its 16 robustness. 17 18

4. Model structure and Biological characterization 19 20

The PBPK model for XC consisted of four tissue compartments: liver, adipose tissue, 21 richly perfused tissue compartment (which includes all of the organs except the liver), 22 and poorly perfused tissue compartment which represents muscles and skin (Figure 23 A5.1). The absorption by the pulmonary and oral routes was described. Distribution of 24 XC from blood to tissues was described as a function of tissue volumes, tissue:blood 25 partition coefficients and tissue blood flows. Metabolism was characterized in liver by 26 two saturable pathways: 1. high affinity and low capacity (CYP2E1) and 2. low affinity 27 and high capacity (possibly representing one or more CYP2C isozymes). Exhalation of 28 XC as a function of its blood:air partition coefficient is described. The PBPK model 29 describes the production of the haloepoxide, its subsequent metabolism to CO2, and its 30 reaction with glutathione. It also contains a GSH module that allows the simulation of 31 the depletion of GSH following exposure to XC. 32


The animal physiological parameters were computed on the basis of the study-specific 36 body weight information whereas the human physiological values corresponded to U.S. 37 EPA’s reference values. Species-specific blood:air values and rat tissue:air partition 38 coefficients for XC, determined in vitro, were obtained from the literature. The 39 tissue:blood partition coefficients were calculated by dividing the rat tissue:air partition 40 coefficients with the species-specific blood:air values (Table A5.1). 41 42 The affinity (KM1) for the CYP2E1 pathway in the rat and mouse was set to 0.1 mg/L on 43 the basis of studies of the competitive interactions between CYP2E1 substrates in the rat. 44


93

The affinity used for the non-2E1 pathway (KM2) in the mouse and rat was set based on 1 the iterative fitting of the rat total metabolism, glutathione depletion, and rate of 2 metabolism data. The capacity parameters for the two oxidative pathways in the mouse 3 and rat were estimated by fitting the PBPK model to data from closed-chamber 4 exposures, holding all other parameters constant. 5

6 Initial estimates for the subsequent metabolism of the reactive metabolites and for the 7 glutathione submodel in the rat were taken from another published model for a related 8 chemical. These parameter estimates, along with the estimates for the low affinity 9 pathway, were then refined using an iterative fitting process on the basis of the closed 10 chamber data for the Sprague Dawley and Wistar rats as well as data on glutathione 11 depletion, total metabolism, and CO2 elimination. Since this last data set was obtained 12 for oral dosing of XC in corn oil, the rate constant for oral absorption, was estimated 13 from fitting of separate data on blood concentrations following dosing of rats with XC in 14 corn oil. The parameters obtained for the rat were used for the other species with 15 appropriate allometric scaling (BW-0.25). 16

17 The ratio of Vmax/Km for the high affinity pathway in humans was estimated by fitting 18 the model to data from closed chamber studies with two human subjects, in a manner 19 analogous to the method used for the animal closed chamber analysis. The Vmaxc 20 (mg/kg/hr) for this pathway in humans was considered to be similar to that of rodents, 21 based on supporting data from non-human primates and in vitro studies with human liver 22 microsomes. The flux through the low affinity pathway was considerd to be negligible 23 based on results of occupational kinetic studies for another chemical that has some 24 similarity in terms of metabolic pathways and metabolizing enzymes. 25

26 6. Purpose-specific model evaluation 27

28 The evaluation of the XC PBPK model focuses on the ability of the model to be used in 29 simulating candidate dose metrics for conducting (i) interspecies extrapolation, (ii) high 30 dose to low dose extrapolation and (iii) route-to-route extrapolation. 31 32

6.1. Evaluation of inhalation PBPK model for interspecies and high dose to low dose 33 extrapolations of XC 34

35 The level of confidence in XC PBPK model for the conduct of interspecies extrapolation 36 (rat to human) is considered high based on the consideration of its performance, 37 robustness, plausibility and extrapolation uncertainty as follows: 38 39

• Performance (data vs model) : High 40 Model simulates consistently the respiratory uptake of chemical XC, amount 41 metabolized, hepatic GSH concentrations as well as TK profile following inhalation 42 exposure. Specifically, the model reproduced the following data: 43

- Closed chamber concentrations of XC in rats following starting concentrations 44 ranging from 240 – 1400 ppm 45


94

- Glutathione concentrations at 0, 22 and 44 hr obtained in rats exposed to 15, 50, 1 150, 500 and 1500 mg/m3 for 4 hr 2

- Glutathione concentrations in rats following inhalation exposures to 1.5 to 5000 3 ppm 4

- Total amount metabolized in rats exposured for 6 hr to 1.5 to 4000 ppm 5 - Exhaled air concentration during and following inhalation exposure of human 6

volunteers to 2.5 ppm 7 - Chamber concentration vs time profile in two human volunteers exposed in 8

recirculating chamber with starting concentration of 10 ppm 9 10

• Model robustness : High 11 Model, with a single set of input parameters, reproduces TK data for various inhalation 12 exposure concentrations (covering the linear and saturable range; 1.5 to 5000 ppm). In 13 this regard, a sensitivity analysis of the parameters of the model showed convincingly 14 that there was no amplification of error from inputs to outputs. A Monte Carlo 15 uncertainty/variability analysis (4 realizations, 500 simulations/realizations) was 16 conducted to evaluate the impact of parameter uncertainty and variability on risk 17 prediction. The 95th percentile of the distribution of the upper confidence limit (UCL) 18 risk was approximately a factor of 2 of the mean UCL risk, indicating the robustness of 19 the model structure and parameters. 20 21

• Plausibility: High 22 The model parameters, structure and assumptions are biologically plausible and 23 consistent with available data. 24 25

• Extrapolation uncertainty : Medium 26 Dose metric not directly measurable (re: reactive metabolite), but well estimated by the 27 calibration methods used in the study; model simulations of total amount metabolized and 28 GSH depletion were compared with experimental data; since metabolite data were not 29 available in humans, the correspondence of metabolite predictions with data in several 30 animal species was used as a surrogate. The extrapolation uncertainty was medium since 31 the model simulations were compared with a variety of TK data, but not the actual 32 concentrations of XC-M (which cannot be reliably measured). 33 34

6.2. Evaluation of XC PBPK model intended for route-to-route extrapolation 35 36 The level of confidence in XC PBPK model for the conduct of route-to-route (inhalation 37 to oral) extrapolation is considered medium based on the consideration of its 38 performance, robustness, plausibility and extrapolation uncertainty as follows: 39 40

• Performance (data vs model): High 41 The rat PBPK model for XC simulated consistently its respiratory uptake, amount 42 metabolized as well as hepatic GSH concentrations following inhalation exposure to a 43 variety of exposure concentrations, including those used in the rodent cancer bioassay 44 (see section 6.1). Model parameters for deriving dose metrics via the oral route were 45


95

established such that the dose metrics generated would be "conservative," that is, 1 predictive of higher human risk from animal results, to compensate for the lack for oral 2 TK data for model evaluation. 3 4

• Model robustness: Medium 5 Model, with a single set of input parameters, reproduces a variety of data (kinetics of XC, 6 amount metabolized and GSH depletion) following inhalation exposures (for exposure 7 concentrations covering the linear and saturable range; 1.5 to 5000 ppm) and amount 8 metabolized following oral doses (0.05 – 100 mg/kg) (see section 6.1). 9 10

• Plausibility: High 11 The model parameters, structure and assumptions are biologically plausible and 12 consistent with available data. 13 14

• Extrapolation uncertainty : Low/Medium 15 Dose metric not directly measurable (re: reactive metabolite), but well estimated by the 16 calibration methods used in the study; model simulations of total amount metabolized and 17 GSH depletion were compared with experimental data for the inhalation route but not for 18 the oral route (see section 6.1). 19 20

7. Evaluation of dose metrics 21 22

The data on the clearance and lack of reactivity of XC, in conjunction with the 23 observations that (i) the mutagenicity of XC was increased in vitro following metabolic 24 activation and (ii) mutatgenic effects were further exacerbated following the induction of 25 liver CYP enzymes, indicate that the tissue dose of reactive metabolites, rather than the 26 parent chemical, would be more closely related to the tumor response. Given the short-27 lived nature and reactivity of the metabolite(s), a measure of their concentration in the 28 target tissue (liver, the site of their formation) was deemed the appropriate dose metric. 29 Three candidate dose metrics were evaluated: 30

1.total amount of XC metabolized normalized to the volume of the tissue in 31 which it is produced (i.e., liver) 32

2.total amount of XC metabolism not detoxified by reaction with glutathione, 33 divided by the volume of liver 34

3.total amount of XC-GSH conjugate produced divided by the volume of the 35 liver 36

It was not possible to select one dose metric over another on the basis of their description 37 of dose-response data or results of the goodness of fit analysis. However, the second 38 dose metric was considered to be the most biologically plausible. Risk estimates were 39 found to be insensitive to the choice of dose metric. 40


For chemical XC, since the tumor response is related to the rate of reactive metabolite 44 produced via a saturable pathway, the use of default extrapolations (high dose to low 45


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dose, route-to-route, interspecies) based on exposure concentration would clearly be 1 erroneous and introduce uncertainty in the process. For example, the liver tumor 2 incidence for the oral route predicted using PBPK dose metric to extrapolate from the 3 inhalation potency agreed well with the observed tumor incidence in the oral bioassays in 4 the rat; on the contrary, the external dose based analysis suggested an apparently higher 5 potency of XC by the oral route when comparisons were made on the basis of mg/kg/d 6 with the inhalation route. The use of PBPK models, by facilitating the incorporation of 7 relevant dose metric data into risk assessment, reduces the uncertainty associated with the 8 extrapolations based on external dose. As a result, when expressed in terms of XC-M, 9 the cancer potency of XC is essentially the same across routes (oral, inhalation) and 10 species (rat, mouse, human). 11 12

9. Model documentation 13 The model for XC was implemented in ACSL®; model code is available from the 14 corresponding author. Study-specific body weight and other parameters (fat volume and 15 metabolic parameters) are provided along with distributions of parameters used for 16 variability/uncertainty analysis; however, the sources or estimation methods for the 17 parameters were not included in the Table but provided in the Methods section of the 18 publication (Clewell et al. 2001). 19 20


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1 2

3 Figure A5-1. Structure of the PBPK model for XC in rats, mice and humans. 4 Adopted from Clewell et al. (2001), permissions pending. 5 6 7

High affinity low capacity metabolism

Low affinityhigh

capacity metabolism

Inhaled XC Exhaled XC

Fat

Richly Perfused Tissues

Liver

Poorly Perfused Tissues

Lungs

XC-M

Further metabolism

Oral dose


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Table A.5.1. PBPK model parameters for chemical XC. 1 1 Mouse

(CV%) Rat

(CV-%) Human (CV-%)

Source

Body Weight (kg) (11) 11 70.0 (30) Literature or study values specific

Alveolar Ventilation (L/hr, 1 kg animal)

30.0 (58) 21.0 (58) 24.0 (16) Scaled to BW

Cardiac Output (L/hr, 1 kg animal)

18.0 ( 9) 18.0 ( 9) 16.5 ( 9) Scaled to BW

Tissue blood flows (fraction of Cardiac Output) Flow to Rapidly Perfused Tissues

0.51 (50) 0.51 (50) 0.5 (20) Literature

Flow to Fat 0.09 (60) 0.09 (60) 0.05 (30) Literature

Flow to Slowly Perfused Tissues

0.15 (40) 0.15 (40) 0.19 (15) Literature

Flow to Liver 0.25 (96) 0.25 (96) 0.26 (35) Literature

Tissue volumes (fraction of Body weight) Volume of Slowly Perfused Tissues

0.77 (30) 0.75 (30) 0.63 (30) Literature

Volume of Fat 30 30 0.19 (30) Literature

Volume of Richly Perfused Tissues

0.035 (30) 0.05 (30) 0.064 (10) Literature

Volume of Liver 0.055 ( 6) 0.04 ( 6) 0.026 ( 5) Literature

Partition Coefficients (PC)

Blood/Air 2.26 (15) 2.4 (15) 1.16 (10) In vitro (vial equilibration) data obtained from literature


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Fat/Blood 10.62 (30) 10.0 (30) 20.7 (30)

Slowly Perfused Tissue/Blood

0.42 (20) 0.4 (20) 0.83 (20)

Richly Perfused Tissue/Blood

0.74 (20) 0.7 (20) 1.45 (20)

Liver/Blood 0.74 (20) 0.7 (20) 1.45 (20)

Calculated by dividing tissue:air PC by blood:air PC obtained from literature (rats) ; For mice and humans, tissue:blood PCs were calculated by dividing rat tissue:air PC by species-specific blood:air obtained in vitro

Metabolic parameters

Maximum Velocity of First Saturable Pathway (mg/hr, 1 kg animal)

20 20 4.0 (30)

Fitting to in vivo data from closed chamber exposures (rats; mice);Vmax scaled to human as BW raised to the 3/4 power

Affinity of First Saturable Pathway (mg/L) 0.1 (30) 0.1 (30) 1.0 (50)

Literature on competitive interactions among CYP2E1 substrates in the rat and mouse

Maximum Velocity of Second Saturable Pathway (mg/hr, 1 kg animal)

20 20 0.1 ( 0) Low value in humans based on lack of evidence for this metabolic pathway

Affinity of Second Saturable Pathway (mg/L) 10.0 (30) 10.0 (30) 10.0 (50)

Fitting to data on total metabolism, gluthatione depletion and rate of metabolism

GSH-pathways First Order XC-M Breakdown to CO2

1.6 (20) 1.6 (20) 1.6 (20)

Conjugated Rate Constant with Metabolite

0.13 (20) 0.13 (20) 0.13 (20)

Conjugated Rate Constant with Non-GSH

35.0 (20) 35.0 (20) 35.0 (20)

Initial GSH Concentration 5800.0 (20) 5800.0 (20) 5800.0 (20)

First Order Rate Constant for GSH Breakdown

0.12 (20) 0.12 (20) 0.12 (20)

Constant Controlling Resynthesis

2000.0 (20) 2000.0 (20) 2000.0 (20)

Zero Order Production of GSH 28.5 (20) 28.5 (20) 28.5 (20)

Initial estimates for the rat obtained from literature;

refined by interative fitting to closer chamber data and glutathione depletion data

Dosing parameters


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Oral Uptake Rate (/hr) 3.0 (50) 3.0 (50) 3.0 (50)

Fitting to data on blood concentrations following dosing of rats with XC in corn oil; Scaled as BW -1/4 for other species

1 (1) CV-%: Coefficient of Variation = 100 * Standard Deviation / Mean 2 3 4 5 6 7 8 9 10

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