Drug-drug interaction prediction: a Bayesian meta-analysis approach.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Li L,Yu M,Chin R,Lucksiri A,Flockhart DA,Hall SD

doi

10.1002/sim.2837

subject

Has Abstract

pub_date

2007-09-10 00:00:00

pages

3700-21

issue

20

eissn

0277-6715

issn

1097-0258

journal_volume

26

pub_type

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