Computational Approaches for Developing Informative Prior Distributions for Bayesian Calibration of PBPK Models

摘要

Using Bayesian statistical methods to quantify uncertainty and variability in human physiologically-based pharmacokinetic (PBPK.) model predictions for use in risk assessments requires prior distributions (priors), which characterize what is known or believed about parameters' values before observing in vivo data. Experimental in vivo data can then be used in Bayesian calibration of PBPK models to refine priors when it exist. However, when little or no in vivo data are available for calibration efforts, parameter estimates and uncertainties can be obtained from priors. In this chapter, we present approaches for specifying informative priors for chemical-specific PBPK model parameters based on information obtained from chemical structures and in vitro assays. Means and standard deviations (or coefficients of variation) for priors are derived from comparisons of predicted values from computational (e.g., QSAR) methods or in vitro assays and experimentally-determined chemical-specific values for a data set of chemicals.