¹Exposure Science Division, Environmental and Occupational Health Sciences Institute (EOHSI), Joint Institute of UMDNJ—Robert Wood Johnson Medical School and Rutgers University, 170 Frelinghuysen Road, Piscataway, NJ 08854, USA

Correspondence: Dr. Y. Yang, The Hamner Institutes for Health Sciences, Center for Human Health Assessment, Six Davis Drive, PO Box 12137, Research Triangle Park, NC 27709-2137, USA. Tel.: +919 558 1310. Fax: +919 558 1300. E-mail: YYang@TheHamner.org

^a2. Present address: The Hamner Institutes for Health Sciences, Center for Human Health Assessment, 6 Davis Drive, Research Triangle Park, NC 27709, USA

^b3. Present address: Global Clinical Pharmacokinetics & Clinical Pharmacology, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Raritan, NJ 08869, USA

Received 15 July 2008; Revised 17 April 2009; Accepted 19 April 2009; Published online 27 May 2009.

Abstract

A Bayesian hierarchical model was developed to estimate the parameters in a physiologically based pharmacokinetic (PBPK) model for chloroform using prior information and biomarker data from different exposure pathways. In particular, the model provides a quantitative description of the changes in physiological parameters associated with hot-water bath and showering scenarios. Through Bayesian inference, uncertainties in the PBPK parameters were reduced from the prior distributions. Prediction of biomarker data with the calibrated PBPK model was improved by the calibration. The posterior results indicate that blood flow rates varied under two different exposure scenarios, with a two-fold increase of the skin's blood flow rate predicted in the hot-bath scenario. This result highlights the importance of considering scenario-specific parameters in PBPK modeling. To demonstrate the application of a probability approach in toxicological assessment, results from the posterior distributions from this calibrated model were used to predict target tissue dose based on the rate of chloroform metabolized in liver. This study demonstrates the use of the Bayesian approach to optimize PBPK model parameters for typical household exposure scenarios.