title: The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions.
abstract: The accuracy of in vitro inhibition parameters in scaling to in vivo drug-drug interactions (DDI) was examined for over 40 drugs using seven human P450-selective marker activities in pooled human liver microsomes. These data were combined with other parameters (systemic C(max), estimated hepatic inlet C(max), fraction unbound, and fraction of the probe drug cleared by the inhibited enzyme) to predict increases in exposure to probe drugs, and the predictions were compared with in vivo DDI gathered from clinical studies reported in the scientific literature. For drugs that had been tested as precipitants of drug interactions for more than one P450 in vivo, the order of inhibitory potencies in vitro generally aligned with the magnitude of the in vivo interactions. With the exception of many drugs known to be mechanism-based inactivators, the use of in vitro IC(50), the fraction of the affected drug metabolized by the target enzyme [f(m(CYP))] and an estimate of free hepatic inlet C(max), was generally successful in identifying those drugs that cause at least a 2-fold increase in the exposure to P450 marker substrate drugs. For CYP3A, incorporation of inhibition of both hepatic and intestinal metabolism was needed for the prediction of DDI. Many CYP3A inhibitors showed a different inhibitory potency for three different CYP3A marker activities; however, these differences generally did not alter the conclusions regarding whether a drug would cause a CYP3A DDI in vivo. Overall, these findings support the conclusion that P450 in vitro inhibition data are valuable in designing clinical DDI study strategies and can be used to predict the magnitudes of DDI.
authors.affiliation: Pfizer Global Research and Development, Groton Laboratories, MS4088, Groton, CT 06340, USA. r.scott.obach@pfizer.com
authors.forename: R Scott
authors.initials: RS
authors.lastname: Obach
authors.forename: Robert L
authors.initials: RL
authors.lastname: Walsky
authors.forename: Karthik
authors.initials: K
authors.lastname: Venkatakrishnan
authors.forename: Emily A
authors.initials: EA
authors.lastname: Gaman
authors.forename: J Brian
authors.initials: JB
authors.lastname: Houston
authors.forename: Larry M
authors.initials: LM
authors.lastname: Tremaine
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D051544:Cytochrome P-450 CYP3A / Q000378:metabolism; D065692:Cytochrome P-450 CYP3A Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism; D004347:Drug Interactions; D004791:Enzyme Inhibitors / Q000494:pharmacology*; D006801:Humans; D066298:In Vitro Techniques; D007413:Intestinal Mucosa / Q000378:metabolism; D007422:Intestines / Q000201:enzymology; D007527:Isoenzymes / Q000037:antagonists & inhibitors; D008099:Liver / Q000201:enzymology / Q000378:metabolism; D008862:Microsomes, Liver / Q000201:enzymology; D011237:Predictive Value of Tests
chemical_list: D065692:Cytochrome P-450 CYP3A Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D004791:Enzyme Inhibitors; D007527:Isoenzymes; D003577:Cytochrome P-450 Enzyme System; D051544:Cytochrome P-450 CYP3A