Statistical Analysis based on Physiologically-based Pharmacokinetic Models

Abstract

Physiologically-based pharmacokinetic (PBPK) modeling has reached considerable sophistication in its application in the pharmacological and environmental health areas. Yet, mature methodologies for making statistical inferences have not been routinely incorporated in these applications except in a few data-rich cases. In this dissertation we look at two important applications of PBPK modeling for which we will study and conduct a rigorous statistical analysis. Both frequentist and Bayesian statistical methods of analysis are explored. In the first application, we work with a previously developed PBPK model for the formation and disposition of DNA-protein cross-links formed by inhaled formaldehyde in the nasal lining of rats and rhesus monkeys and provide improved statistical inference on estimated model parameters. We purposefully choose this model because it is based on sparse time course data. The second application considers a PBPK model developed for inhalation and metabolism of dichloromethane (DCM, methylene chloride). In this application we work with time course data that exhibit serial correlation.