We describe a general approach using Bayesian analysis for the estimation of parameters in physiological pharmacokinetic models. The chief statistical difficulty in estimation with these models is that any physiological 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 generally poorly identified, akin to the wellknown 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 pharmacokinetic model including compartments for well-perfused tissues, poorly perfused tissues, fat, and the liver; and (c) informative prior distributions for population parameters, which is possible because the parameters represent real physiological variables. We discuss how to estimate the models using Bayesian posterior simulation, a method that automatically includes the uncertainty inherent in estimating such a large number of parameters. We also discuss how to check model fit and sensitivity to the prior distribution using posterior predictive simulationY We illustrate the application to the toxicokinetics of tetrachloroethylene (perchloroethylene [PERC]), the problem that motivated this work.

10abayesian methods10ahierarchical models10ainformative prior distributions10amarkov chain simulation10apharmacokinetics10aposterior predictive checks10asensitivity analysis10atetrachloroethylene10atoxicokinetics1 aGelman, Andrew1 aBois, Frédéric, Y.1 aJiang, Jiming uhttps://indoor.lbl.gov/publications/physiological-pharmacokinetic02121nas a2200229 4500008004100000245005300041210005300094300001200147490000700159520144600166653002101612653002101633653003001654653002401684100002501708700001901733700001801752700001601770700001801786700002101804856006601825 1996 eng d00aPopulation toxicokinetics of tetrachloroethylene0 aPopulation toxicokinetics of tetrachloroethylene a347-3550 v703 aIn assessing the distribution and metabolism of toxic compounds in the body, measurements are not always feasible for ethical or technical reasons. Computer modeling oﬀers a reasonable alternative, but the variability and complexity of biological systems pose unique challenges in model building and adjustment. Recent tools from population pharmacokinetics, Bayesian statistical inference, and physiological modeling can be brought together to solve these problems. As an example, we modeled the distribution and metabolism of tetrachloroethylene (PERC) in humans. We derive statistical distributions for the parameters of a physiological model of PERC, on the basis of data from Monster et al. (1979). The model adequately ﬁts both prior physiological information and experimental data. An estimate of the relationship between PERC exposure and fraction metabolized is obtained. Our median population estimate for the fraction of inhaled tetrachloroethylene that is metabolized, at exposure levels exceeding current occupational standards, is 1.5% [95% conﬁdence interval (0.52%, 4.1%)]. At levels approaching ambient inhalation exposure (0.001 ppm), the median estimate of the fraction metabolized is much higher, at 36% [95% conﬁdence interval (15%, 58%)]. This disproportionality should be taken into account when deriving safe exposure limits for tetrachloroethylene and deserves to be veriﬁed by further experiments.

10ahuman metabolism10apharmacokinetics10apopulation toxicokinetics10atetrachloroethylene1 aBois, Frédéric, Y.1 aGelman, Andrew1 aJiang, Jiming1 aMaszle, Don1 aZeise, Lauren1 aAlexeeff, George uhttps://indoor.lbl.gov/publications/population-toxicokinetics