Bayesian Analysis of a Lipid-Based Physiologically Based Toxicokinetic Model for a Mixture of PCBs in Rats

Alan F Sasso,Kannan Krishnan,Panos G Georgopoulos,Sastry S Isukapalli

doi:10.1155/2012/895391

Abstract

A lipid-based physiologically based toxicokinetic (PBTK) model has been developed for a mixture of six polychlorinated biphenyls (PCBs) in rats. The aim of this study was to apply population Bayesian analysis to a lipid PBTK model, while incorporating an internal exposure-response model linking enzyme induction and metabolic rate. Lipid-based physiologically based toxicokinetic models are a subset of PBTK models that can simulate concentrations of highly lipophilic compounds in tissue lipids, without the need for partition coefficients. A hierarchical treatment of population metabolic parameters and a CYP450 induction model were incorporated into the lipid-based PBTK framework, and Markov-Chain Monte Carlo was applied to in vivo data. A mass balance of CYP1A and CYP2B in the liver was necessary to model PCB metabolism at high doses. The linked PBTK/induction model remained on a lipid basis and was capable of modeling PCB concentrations in multiple tissues for all dose levels and dose profiles.

Highlights

Polychlorinated biphenyls (PCBs) are industrial chemicals that have persisted in the environment despite widespread international bans beginning in the 1970s [1]
A lipid-based physiologically based toxicokinetic (PBTK) model has been developed for a mixture of six polychlorinated biphenyls (PCBs) in rats
Since distributions for basal metabolic rate of all 6 PCBs were relatively similar, an additional Markov-Chain Monte Carlo (MCMC) analysis was performed for step 1 assuming a single population distribution for all PCB clearances v0all

Summary

Introduction

Polychlorinated biphenyls (PCBs) are industrial chemicals that have persisted in the environment despite widespread international bans beginning in the 1970s [1]. Mixtures of PCBs are commonly detected in blood samples of the human population, with estimated elimination half-lives of up to 10–15 years [3]. Assessing risks from these mixtures is complicated by the significant variability of toxicological properties of individual PCBs, the time-varying changes in the composition of PCB mixtures in the environment [4], and the metabolic interactions among individual PCBs in the body [5,6,7]. Approaches that minimize the number of parameters in mixture PBTK models while still capturing the major interactions can help reduce such data burdens

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