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Computational Approaches for Developing Informative Prior Distributions for Bayesian Calibration of PBPK Models
Citation:
DAVIS, J. L., R. TORNERO-VELEZ, AND R. W. SETZER. Computational Approaches for Developing Informative Prior Distributions for Bayesian Calibration of PBPK Models . Presented at 242nd ACS NATIONAL MEETING & EXPOSITION (AGRO), Denver, CO, August 28 - September 01, 2011.
Impact/Purpose:
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.
Description:
Using Bayesian statistical methods to quantify uncertainty and variability in human 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 exists. However, when little or no in vivo data is available for calibration efforts, parameter estimates and uncertainties can be obtained from priors. 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. This abstract does not necessarily reflect U.S. EPA policy.