Stochastic population and Bayesian toxicokinetic modeling for improving exposure and dose assessment

This thesis demonstrates the usage of Physiologically Based Toxicokinetic (PBTK) modeling to improve approaches for exposure and dose assessment. The objective of this thesis is to develop and test a framework that incorporates the variability of exposure, anatomical, and toxicokinetic parameters, within a population, into the development of a "population PBTK model".; Population PBTK modeling is implemented via the following two approaches: (1) stochastic incorporation of inter-individual variability into the estimation of microenvironmental exposure profiles and physiological parameters for PBTK modeling, and (2) explicit characterization of the variability of physiological and biochemical parameters for each individual member of a sample population participating in a controlled laboratory study.; The first approach is suited to large-scale exposure studies where only limited information for individuals is available. When a detailed dosing history is available, the second approach, which employs the Bayesian method, can be used to refine PBTK model parameters for the population studied.; Two case studies are presented to demonstrate the implementations of the above two approaches.; The first case study focuses on simulating the multimedia/multipathway exposures of the general population in Franklin County, OH, utilizing data from the National Human Exposure Assessment Survey for USEPA Region V as well as from a variety of other available databases. This case study demonstrates the application of source-to-dose modeling techniques, with emphasis on PBTK modeling, to multi-route, multi-pathway exposures to arsenic, TCE, and chlorpyrifos. The results of the population-based simulations are compared to biomarker measurements.; The second case study demonstrates the optimization of population PBTK model parameters using Bayesian methods. A Bayesian hierarchical model was developed to estimate uncertainty and variability of PBTK parameters from data on multiple route exposure to chloroform. Inter-individual variability and uncertainty of PBTK parameters were decreased from the prior distributions through Bayesian inference.; The present work demonstrates the usage of stochastic population source-to-dose and of Bayesian approaches, involving the characterization of inter-individual variability of PBTK model parameters among the population for a large-scale exposure assessment and for a controlled laboratory study respectively. It further demonstrates the utilization of PBTK modeling in providing internal target tissue dose estimation for exposure assessment, and its potential to improve evaluation of health risks associated with chemical exposures.