title: A new probabilistic rule for drug-dug interaction prediction.
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, (-infinity, 1.25), (1.25, 2), and (2, infinity). 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.
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Jihao
authors.initials: J
authors.lastname: Zhou
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Qin
authors.forename: Sara K
authors.initials: SK
authors.lastname: Quinney
authors.forename: Seongho
authors.initials: S
authors.lastname: Kim
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Wang
authors.forename: Menggang
authors.initials: M
authors.lastname: Yu
authors.forename: Jenny Y
authors.initials: JY
authors.lastname: Chien
authors.forename: Aroonrut
authors.initials: A
authors.lastname: Lucksiri
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Hall
authors.forename: Lang
authors.initials: L
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D003198:Computer Simulation; D065692:Cytochrome P-450 CYP3A Inhibitors; D004347:Drug Interactions; D005215:Fasting; D005260:Female; D006801:Humans; D007654:Ketoconazole / Q000008:administration & dosage / Q000493:pharmacokinetics; D008297:Male; D015201:Meta-Analysis as Topic; D008874:Midazolam / Q000008:administration & dosage / Q000493:pharmacokinetics; D015233:Models, Statistical; D009010:Monte Carlo Method; D010599:Pharmacokinetics; D011336:Probability
chemical_list: D065692:Cytochrome P-450 CYP3A Inhibitors; D008874:Midazolam; D007654:Ketoconazole