The Combination of Population Pharmacokinetic Studies

Pharmacokinetic data consist of drug concentrations with associated known sampling times and are collected following the administration of known dosage regimens. Population pharmacokinetic data consist of such data on a number of individuals, possibly along with individual-specific characteristics. During drug development, a number of population pharmacokinetic studies are typically carried out and the combination of such studies is of great importance for characterizing the drug and, in particular, for the design of future studies. In this paper, we describe a model that may be used to combine population pharmacokinetic data. The model is illustrated using six phase I studies of the antiasthmatic drug fluticasone propionate. Our approach is Bayesian and computation is carried out using Markov chain Monte Carlo. We provide a number of simplifications to the model that may be made in order to ease simulation from the posterior distribution.

[1]  Adrian F. M. Smith,et al.  Bayesian Analysis of Linear and Non‐Linear Population Models by Using the Gibbs Sampler , 1994 .

[2]  Jon Wakefield,et al.  Statistical methods for population pharmacokinetic modelling , 1998, Statistical methods in medical research.

[3]  A Racine-Poon,et al.  A Bayesian approach to nonlinear random effects models. , 1985, Biometrics.

[4]  David J. Lunn,et al.  Markov Chain Monte Carlo Techniques for Studying Interoccasion and Intersubject Variability: Application to Pharmacokinetic Data , 1997 .

[5]  J. Wakefield The Bayesian Analysis of Population Pharmacokinetic Models , 1996 .

[6]  Murray Aitkin,et al.  A general maximum likelihood analysis of overdispersion in generalized linear models , 1996, Stat. Comput..

[7]  K Abrams,et al.  Approximate Bayesian inference for random effects meta-analysis. , 1998, Statistics in medicine.

[8]  Adrian F. M. Smith,et al.  Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus , 1993 .

[9]  A. Mallet A maximum likelihood estimation method for random coefficient regression models , 1986 .

[10]  A. Bye,et al.  Pharmacokinetics of intravenous fluticasone propionate in healthy subjects. , 2003, British journal of clinical pharmacology.

[11]  Nicholas H. G. Holford,et al.  The Population Approach: Rationale, Methods, and Applications in Clinical Pharmacology and Drug Development , 1994 .

[12]  J. Swarbrick Drugs and the pharmaceutical sciences , 1975 .

[13]  Alain Mallet,et al.  Optimal design in random-effects regression models , 1997 .