A Bayesian Approach for Population Pharmacokinetic Modeling of Alcohol in Japanese Individuals

• Main Authors: Asuka Nemoto, MPharm, ME, Matsuura Masaaki, PhD, Kazue Yamaoka, PhD
• Format: Article
• Language: English
• Published: Elsevier 2017-01-01
• Series: Current Therapeutic Research
• Subjects: Population pharmacokinetics; pharmacogenomics; aldehyde dehydrogenase 2; MCMC Bayesian estimation; NONMEM
• Online Access: http://www.sciencedirect.com/science/article/pii/S0011393X1630131X
• ISSN: 0011-393X; 1879-0313

Background: Blood alcohol concentration data that were previously obtained from 34 healthy Japanese subjects with limited sampling times were reanalyzed. Characteristics of the data were that the concentrations were obtained from only the early part of the time-concentration curve. Objective: To explore significant covariates for the population pharmacokinetic analysis of alcohol by incorporating external data using a Bayesian method, and to estimate effects of the covariates. Methods: The data were analyzed using a Markov chain Monte Carlo Bayesian estimation with NONMEM 7.3 (ICON Clinical Research LLC, North Wales, Pennsylvania). Informative priors were obtained from the external study. Results: A 1-compartment model with Michaelis-Menten elimination was used. The typical value for the apparent volume of distribution was 49.3 L at the age of 29.4 years. Volume of distribution was estimated to be 20.4 L smaller in subjects with the ALDH2*1/*2 genotype than in subjects with the ALDH2*1/*1 genotype. Conclusions: A population pharmacokinetic model for alcohol was updated. A Bayesian approach allowed interpretation of significant covariate relationships, even if the current dataset is not informative about all parameters. This is the first study reporting an estimate of the effect of the ALDH2 genotype in a PPK model.