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A new probabilistic rule for drug–dug interaction prediction
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A new probabilistic rule for drug–dug interaction prediction
Jihao Zhou1, Zhaohui Qin1, Sara K. Quinney2, Seongho Kim2, Zhiping Wang2, Menggang Yu2, Jenny Y. Chien3, Aroonrut Lucksiri4, Stephen D. Hall4 and Lang Li2, 5 
| (1) |
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA |
| (2) |
Division of Biostatistics, Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN 46032, USA |
| (3) |
Clinical Pharmacology, Eli Lilly Inc., Indianapolis, IN 46202, USA |
| (4) |
Division of Clinical Pharmacology, Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN 46032, USA |
| (5) |
410 West 10th Street, Suite 3000, Indianapolis, IN 46202, USA |
Received: 11 February 2007 Accepted: 3 December 2008 Published online: 21 January 2009
Abstract An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This
rule is coupled with a hierarchical Bayesian model approach to summarize substrate/inhibitor’s PK models from multiple published
resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict
both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant
inhibitions are decided by the probabilities of predicted AUCR falling into three intervals, (−∞, 1.25), (1.25, 2), and (2,
∞). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that
the probabilistic rule considers the sample variability, and the decision is independent of sampling variation; while deterministic
rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the
situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically
significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as
an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater
variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily
true when considering between-subject variability. Additional simulation studies suggest that predicted AUCRs highly depend
on the interaction constant K
i
and dose combinations.
Keywords Area under the concentration curve ratio (AUCR) - Bayesian model - Drug–drug interaction (DDI) - Pharmacokinetics - Prediction
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