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Bayesian Uncertainty Analysis of PBPK Model Predictions for Permethrin in Rats
Citation:
DAVIS, J., R. TORNERO-VELEZ, J. F. WAMBAUGH, AND R. W. SETZER. Bayesian Uncertainty Analysis of PBPK Model Predictions for Permethrin in Rats. Presented at Joint Statistical Meetings, 2011, Miami Beach, FL, July 30 - August 04, 2011.
Impact/Purpose:
Bayesian methods can then be used to combine prior knowledge with in vivo data to quantify the uncertainty associated with estimated parameters. We present some approaches for constructing informative priors for PBPK parameters by comparing data sets of measured values to predicted values from computational or in vitro methods.
Description:
Uncertainty analysis of human physiologically-based pharmacokinetic (PBPK) model predictions can pose a significant challenge due to data limitations. As a result of these limitations, human models are often derived from extrapolated animal PBPK models, for which there is usually more data for model development and validation. However, the paucity of in vivo data can still make it difficult to assign values and uncertainties to some animal model parameters. When there is little to no in vivo data, parameter estimates as well as parameter and model uncertainties can be determined from prior knowledge, using chemical properties and data from in vitro assays. Bayesian methods can then be used to combine prior knowledge with in vivo data to quantify the uncertainty associated with estimated parameters. We present some approaches for constructing informative priors for PBPK parameters by comparing data sets of measured values to predicted values from computational or in vitro methods. We illustrate our approaches in a hierarchical Bayesian analysis example with a rat permethrin PBPK model and in vivo pharmacokinetic data. [This abstract does not necessarily reflect U.S. EPA policy].