Bayesian refinement of a physiologically based pharmacokinetic model for ethylbenzene pharmacokinetics in mice, rats, and humans.

Abstract

Although several physiologically based pharmacokinetic (PBPK) models exist for ethylbenzene (EB), a systematic evaluation of variability and uncertainty across species is still missing. This study aims to develop and validate a universal, population-based Bayesian PBPK model to study EB inhalation kinetics for mice, rats, and humans using a Markov Chain Monte Carlo (MCMC) approach to enhance model parameterization and its predictions. A comprehensive database was used for calibration and evaluation. This refined model demonstrates a superior or comparable fit to the data when contrasted with earlier published PBPK models for EB. Except for mouse fat and lung tissues, the concentrations of EB in tissues and its metabolites were generally within residual errors of 3-fold across species. Specifically, urinary concentrations of mandelic acid, the primary downstream metabolite of EB, are generally well predicted in both rats and humans. Our approach offers a better characterization of pharmacokinetic variability and uncertainty than previous EB models, with strong agreement between predictions and experimental data. This supports efforts to adopt PBPK modeling for data extrapolation from animal studies to inform human health assessments, thereby greatly promoting public health. The confidence in applying the current refined PBPK model could be increased by confirming the predictions made by our analysis with additional targeted data collection. Impact Statement: This study presents a refined Bayesian PBPK model that captures EB pharmacokinetics across species. It outperforms previous EB models and improves interspecies extrapolation for human health risk assessment.