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Physiologically based kinetic models
PBK models are mathematical descriptions simulating the kinetics of chemicals in the body in relation to key physiological parameters (e.g. tissue blood flows and volumes), biochemical and physiochemical parameters (e.g. excretions rates and tissue/blood partition coefficients) (IPCS, 2010). These models are based on compartmental approaches, describing the body as compartments corresponding to realistic organs or tissues that reflects the determinants of the kinetics of the chemical to simulate concentration time-curves in blood or specific tissue (Bois et al., 2010; Clewell et al., 2008; Paini et al., 2019). They are traditionally used in order to perform extrapolations from route to route of exposure (e.g. intravenous to oral exposure), between different species or between sub-groups (e.g. healthy adults to patients or children). PBK models can also be used in a reverse way to estimate the exposure of a population to chemicals in comparison to biomonitoring data (Caldwell et al., 2012; Verner et al., 2009).
PBK models may present different degrees of complexity, considering the number of organs or tissues and whether they are described as homogenously well-mixed (perfusion-limited) compartments, the tissue barrier presents no barrier to distribution, or diffusion-limited compartments, a permeability coefficient is applied. However, it is generally recognised by risk assessors that the simplest model possible is preferred while complex models would be used when necessary and sufficient input data are available (Bois et al., 2010; Paini et al., 2019).
Human PBK models have been applied to address drug development, drug-drug interactions (or drug-food, drug-herbal product) and safety assessment of food, cosmetics and environmental contaminants (Madden et al., 2019). These applications of PBK models in food safety have been reviewed by the European Food Safety Authority (EFSA, 2014). In its notes of guidance for the testing of cosmetic ingredients and their safety evaluation, the Scientific Committee on Consumer Safety recognise the use of PBK models for quantitative risk assessment (SCCS, 2018). Recently, PBK models have also been used to assess the safety of nanomaterials (Lamon et al., 2019).
Hierarchical Bayesian models for the meta-analysis of kinetic data
Previous meta-analyses on human variability in kinetics for different metabolic pathways were based on weighted geometric means assuming fixed effect models with inverse variance weights. This approach allows to derive human variability in kinetic parameters, but it did not address the relative contribution of the variability across subgroups to the overall variability in the datasets, leading to uncertainty in the parameter estimates (Dorne et al., 2005). Recently, meta-analysis methods have been developed using Bayesian approaches in the health-care and risk assessment areas and allow for the quantification of variability and uncertainty (Rigaux et al., 2013; Sutton and Higgins, 2008). In a bayesian context, a prior distribution is set either based on expert knowledge or using evidence from the literature. These distributions are then updated taking into account available new data, leading to a posterior distribution (Figure 5) (Micallef et al., 2005). Bayesian estimation provides a distribution of credibility of the parameter values and a representation of parameter uncertainty that can be directly interpreted through the posterior distribution. Posterior distributions are estimated by generating a huge random sample of representative parameter values from the prior distribution using Markov chain Monte Carlo (MCMC) method. Consequently, it describes how uncertainty change when taking account new data (Kruschke and Vanpaemel, 2015). The Bayesian approach is ideally suited for multi-level models. Hierarchical models are used when the probability of a parameter is dependent on the value of another parameter leading a chain of dependencies among parameters (Kruschke and Vanpaemel, 2015). These models allow to account for different sample sizes of studies and their heterogeneity as well as inter-study variability so that strength can be borrowed from one study to another and are useful to quantify the variability among different populations (Shao et al., 2017). In the case of kinetic parameters, intra-substrate variability is dependant of the estimated inter-study variability, which supposed two levels, with prior information applied to the “substrate” level.
Enzymes and transporters involved in ADME processes
ADME processes describe the disposition of a chemical within the body and include inter-related processes namely absorption, distribution, metabolism and excretion (Figure 6). The toxicity of a chemical is dependent on its mode of action which includes kinetics ADME processes and dynamic processes (Meek et al., 2014). Chemicals can enter the human body via oral route, dermal contact or inhalation, etc. After absorption, the chemical enters the blood stream, where it may be distributed towards organs, including the target organ or tissue where it produces damage (Lehman-McKeeman, 2008). Xenobiotic metabolism or biotransformation is a series of enzymatic processes that transforms parent compounds into metabolites that are more hydrophilic that are easier to excrete through urinary or bile elimination. In many cases, the toxicity of a xenobiotic can be either mediated by the parent compound so that metabolism result in detoxification or through its reactive metabolites and metabolism results in bioactivation (e.g bioacativation of acrylamide to glycidamide by CYP2E1). While the intestine and the liver contain the highest enzyme concentrations, they are also widely distributed in other tissues such as kidneys which express several enzymes that actively eliminate xenobiotics into urine. Xenobiotic metabolising enzymes are classified as phase I and phase II enzymes according to their function such as hydrolysis, reduction, oxidation and conjugation respectively (Parkinson and Ogilive, 2008). Transporters of xenobiotics are involved either in uptake or efflux processes and are consequently classified as phase 0 or phase III respectively (Doring and Petzinger, 2014).
Needs for research and implementation of kinetic models in risk assessment
Human kinetic data provides a rich data source to integrate quantitative ADME data in hazard assessment particularly with regards to interindividual differences in phase I and phase II enzymes as well as transporters (EFSA, 2014). The use of human kinetic data to simulate plasma and tissue concentrations of chemicals has progressed mostly in the field of pharmaceuticals and still relatively limited in the food safety area (Punt, 2018). However, kinetic data is of considerable relevance and importance in other regulatory fields with the ban of animal testing for the safety assessment of cosmetic products (Regulation (EC) No 1223/2009) and the Commission Regulation (EU) No 283/2013 (2013) requires kinetic data for active substances of plant protection products and their metabolites.
Improvement of risk assessment methods includes new approaches methodologies (NAMs) including the refinement of UFs to determine safe level of exposure as well as in silico models incorporating kinetics, such as physiologically based kinetic (PBK) models and represent a challenge to regulatory agencies since they are complex to implement and require specialised training (IPCS, 2010; Paini et al., 2019). In order to move towards the integration of NAMs in chemical risk assessment for the food and feed safety area with regards to human health, animal health and environment, several projects have been launched by the European Food Safety Authority (EFSA). These projects involve the integration of kinetic data and the development of modelling tools, the modelling of population dynamics of aquatic and terrestrial organisms, and the modelling of human variability in kinetic and dynamic processes with physiologically based models. PBK models provide a quantitative approach to address ADME processes and are therefore very useful tools in hazard assessment (EFSA, 2014). Models are needed to enable in vitro data on toxicological effects to be transformed into in vivo data which is a necessary step to make them usable for risk assessment. Therefore, quantitative predictions of in vivo kinetics from in vitro assays (QIVIVE) using human cells offer great opportunities to reduce uncertainty in human risk assessments and will facilitate the future development and regulatory acceptance of alternatives to animal testing with respect to the 3Rs (Replacement, Reduction and Refinement of animal studies) (Bessems et al., 2015; OECD, 2010; Paini et al., 2017; Punt et al., 2017). However, in vitro assays usually provide mean values of kinetic parameters when extrapolated to in vivo parameters. Pathway-related UFs can be applied when human in vitro metabolism data are available for specific isoforms but no in vivo data, to implement variability on metabolism data to address the human population rather than a single individual. Previously published meta-analyses were based on weighted averages assuming fixed effect models with inverse variance weights and did not address the relative contribution of the variability across subgroups to the overall variability in the datasets.
Permethrin exposure data
In this work, exposure is defined as the amount of permethrin received by an organism up to its biological barriers (respiratory epithelium, digestive mucosa or dermis) without crossing them, related to the individual’s weight and duration of daily exposure (InVs, 2005).
Environmental exposure data
Environmental exposure of the adult French population to permethrin has already been assessed (Hermant et al., 2018). Inhalation, indirect dust ingestion and dermal exposure were calculated as chronic daily exposure. Table 2 presents the environmental exposure distribution according to the source of contamination and the gender of the sub-sample of 219 individuals for whom the concentration levels of urinary metabolites were quantified.
Table of contents :
1. GENERAL INTRODUCTION
1.1. HUMAN RISK ASSESSMENT OF CHEMICALS
1.2. UNCERTAINTY FACTORS IN CHEMICAL RISK ASSESSMENT
1.3. PHYSIOLOGICALLY BASED KINETIC MODELS
1.4. HIERARCHICAL BAYESIAN MODELS FOR THE META-ANALYSIS OF KINETIC DATA
1.5. ENZYMES AND TRANSPORTERS INVOLVED IN ADME PROCESSES
1.6. NEEDS FOR RESEARCH AND IMPLEMENTATION OF KINETIC MODELS IN RISK ASSESSMENT
1.7. SCOPE AND AIM OF THIS THESIS
2. AGGREGATE EXPOSURE OF THE ADULT FRENCH POPULATION TO PYRETHROIDS
ABSTRACT
2.1. INTRODUCTION
2.2. MATERIAL AND METHODS
2.2.1. Study population and biomonitoring data
2.2.2. Permethrin exposure data
2.2.3. Human aggregate PBPK model
2.2.4. Software
2.3. RESULTS
2.3.1. Model calibration
2.3.2. Simulated cis- and trans-DCCA urinary excretion
2.3.3. Contribution of sources and routes of exposure to simulated urinary concentrations of DCCA
2.4. DISCUSSION
ACKNOWLEDGMENTS
AUTHOR’S CONTRIBUTION
3. INTER-ETHNIC DIFFERENCES IN CYP3A4 METABOLISM: A BAYESIAN META-ANALYSIS FOR THE REFINEMENT OF UNCERTAINTY FACTORS IN CHEMICAL RISK ASSESSMENT
ABSTRACT
3.1. INTRODUCTION
3.2. MATERIAL AND METHODS
3.2.1. Extensive Literature Search and Data collection
3.2.2. Meta-analysis
3.2.3. Derivation of probabilistic CYP3A4-related uncertainty factors
3.2.4. Software
3.3. RESULTS
3.3.1. Overview of data collection
3.3.2. Inter-ethnic differences in CYP3A4 and CYP3A4-related uncertainty factors
3.3.3. Kinetic data for the elderly, children and neonates
3.4. DISCUSSION AND CONCLUSIONS
4. BAYESIAN META-ANALYSIS OF INTER-PHENOTYPIC DIFFERENCES IN HUMAN SERUM PARAOXONASE-1 ACTIVITY FOR CHEMICAL RISK ASSESSMENT
ABSTRACT
4.1. INTRODUCTION
4.2. MATERIAL AND METHODS
4.2.1. Extensive literature search and data collection
4.2.2. Meta-analysis
4.2.3. Software
4.3. RESULTS
4.3.1. Extensive literature searches and data collection
4.3.2. Inter-phenotypic differences in PON1 activity and related UFs
4.3.3. Inter-individual differences in PON1 activity and related UFs
4.1. DISCUSSION AND CONCLUSION
5. HUMAN VARIABILITY IN ISOFORM-SPECIFIC UDP-GLUCURONOSYLTRANSFERASES: MARKERS OF ACUTE AND CHRONIC EXPOSURE, POLYMORPHISMS AND UNCERTAINTY FACTORS
ABSTRACT
5.1. INTRODUCTION
5.2. MATERIALS AND METHODS
5.2.1. Extensive literature searches (ELS) and data collection
5.2.2. Data standardisation and meta-analyses
5.2.3. 2.3 Software
5.3. RESULTS AND DISCUSSION
5.3.1. Extensive literature searches and data collection
5.3.2. Interindividual differences for the kinetics of isoform-specific UGT probe substrates and related uncertainty factors in non-phenotyped adults
5.3.3. Frequencies of UGT isoform polymorphisms in world populations and impact on the pharmacokinetics of probe substrates in non-phenotyped subjects
5.4. CONCLUSIONS AND FUTURE PERSPECTIVES
AUTHORS’ CONTRIBUTION
6. HUMAN VARIABILITY IN INFLUX AND EFFLUX TRANSPORTERS IN RELATION TO UNCERTAINTY FACTORS FOR CHEMICAL RISK ASSESSMENT
ABSTRACT
6.1. INTRODUCTION
6.2. MATERIAL AND METHODS
6.2.1. Extensive literature search
6.2.2. Standardisation of datasets
6.2.3. Meta-analyses
6.2.4. Software
6.3. RESULTS
6.3.1. Data collection for P-gp, BCRP, and OAT1/3
6.3.2. P-glycoprotein
6.3.3. BCRP
6.3.4. Other efflux transporters
6.3.5. OAT1/3: Data analysis and polymorphisms
6.3.6. OATPs
6.3.7. OCTs
6.4. DISCUSSION
7. GENERAL DISCUSSION
7.1. AFTER ALMOST 20 YEARS, HERE WE ARE
7.1.1. Bayesian meta-analyse of kinetic data
7.1.2. Pathway-related variability and uncertainty factors for chemical risk assessment
7.2. TOWARDS NEXT GENERATION HUMAN RISK ASSESSMENT OF CHEMICALS
7.3. CONCLUSION AND RECOMMENDATIONS
8. REFERENCES
CURRICULUM VITAE / VALORISATIONS
LIST OF PUBLICATIONS
ORAL COMUNICATIONS
CONFERENCES
LIST OF COURSES
RESUME FRANÇAIS :
INTRODUCTION
Evaluation des risques chimiques pour l’Homme
Facteurs d’incertitude dans l’évaluation des risques des produits chimiques
Modèles bayésiens hiérarchiques pour la méta-analyse des données cinétiques
Objectifs de la thèse
MODELISATION PHYSIOLOGIQUES BASES SUR LA CINETIQUE
DIFFERENCES INTERETHNIQUES LIEES AU METABOLISME DE CYP3A4
VARIABILITE HUMAINE LIEE A LA PARAOXONASE-1
VARIABILITE HUMAINE DES UDP-GLUCURONOSYLTRANSFERASES
VARIABILITE HUMAINE LIEE AUX TRANSPORTEURS : ATP BINDING CASSETTE ET TRANSPORTEURS DE SOLUTES
CONCLUSION .
