TY - JOUR
T1 - Physiological pharmacokinetic analysis using population modeling and informative prior distributions
JF - Journal of the American Statistical Association
Y1 - 1996/12//
SP - 1400
EP - 1412
A1 - Andrew Gelman
A1 - Frédéric Y. Bois
A1 - Jiming Jiang
KW - bayesian methods
KW - hierarchical models
KW - informative prior distributions
KW - markov chain simulation
KW - pharmacokinetics
KW - posterior predictive checks
KW - sensitivity analysis
KW - tetrachloroethylene
KW - toxicokinetics
AB - 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.
VL - 91
U1 - 7.1

DO - 10.2307/2291566
ER -
TY - JOUR
T1 - Population toxicokinetics of tetrachloroethylene
JF - Archives of Toxicology
Y1 - 1996/
SP - 347
EP - 355
A1 - Frédéric Y. Bois
A1 - Andrew Gelman
A1 - Jiming Jiang
A1 - Don Maszle
A1 - Lauren Zeise
A1 - George Alexeeff
KW - human metabolism
KW - pharmacokinetics
KW - population toxicokinetics
KW - tetrachloroethylene
AB - In 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.
VL - 70
U1 - 7.1

ER -