PHYSIOLOGICAL PHARMACOKINETIC ANALYSIS USING POPULATION MODELING AND INFORMATIVE PRIOR DISTRIBUTIONS

We describe a general approach using Bayesian analysis for the estimat ion of parameters in physiological pharmacokinetic models. The chief s tatistical difficulty in estimation with these models is that any phys iological model that is even approximately realistic will have a large number of-parameters, often comparable to the number of observations in a typical pharmacokinetic experiment (e.g., 28 measurements and 15 parameters for each subject). In addition, the parameters are generall y poorly identified, akin to the well-known ill-conditioned problem of estimating a mixture of declining exponentials. Our modeling includes (a) hierarchical population modeling, which allows partial pooling of information among different experimental subjects; (b) a pharmacokine tic model including compartments for well-perfused tissues, poorly per fused tissues, fat, and the liver; and (c) informative prior distribut ions for population parameters, which is possible because the paramete rs represent real physiological variables. We discuss how to estimate the models using Bayesian posterior simulation, a method that automati cally includes the uncertainty inherent in estimating such a large num ber of parameters. We also discuss how to check model fit and sensitiv ity to the prior distribution using posterior predictive simulation. W e illustrate the application to the toxicokinetics of tetrachloroethyl ene (perchloroethylene [PERC]), the problem that motivated this work.