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Drug–drug interaction prediction: a Bayesian meta‐analysis approach
Author(s) -
Li Lang,
Yu Menggang,
Chin Raymond,
Lucksiri Aroonrut,
Flockhart David A.,
Hall Stephen D.
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2837
Subject(s) - bayesian probability , markov chain monte carlo , prior probability , statistics , drug drug interaction , confidence interval , meta analysis , bayesian hierarchical modeling , sample size determination , computer science , bayesian inference , drug , econometrics , mathematics , pharmacology , medicine
In drug–drug interaction (DDI) research, a two drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject‐specific drug concentration data from clinical PK studies on inhibitor/inducer or substrate's PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. In this paper, an innovative DDI prediction method based on a three‐level hierarchical Bayesian meta‐analysis model is developed. The first level model is a study‐specific sample mean model; the second level model is a random effect model connecting different PK studies; and all priors of PK parameters are specified in the third level model. A Monte Carlo Markov chain (MCMC) PK parameter estimation procedure is developed, and DDI prediction for a future study is conducted based on the PK models of two drugs and posterior distributions of the PK parameters. The performance of Bayesian meta‐analysis in DDI prediction is demonstrated through a ketoconazole–midazolam example. The biases of DDI prediction are evaluated through statistical simulation studies. The DDI marker, ratio of area under the concentration curves, is predicted with little bias (less than 5 per cent), and its 90 per cent credible interval coverage rate is close to the nominal level. Sensitivity analysis is conducted to justify prior distribution selections. Copyright © 2007 John Wiley & Sons, Ltd.