title: Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: predictions of compounds as precipitants of interaction.
abstract: Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for "steady-state" and "time-based" approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.
authors.affiliation: Pfizer Global Research and Development, Groton, CT 06340, UA. odette.a.fahmi@pfizer.com
authors.forename: Odette A
authors.initials: OA
authors.lastname: Fahmi
authors.forename: Susan
authors.initials: S
authors.lastname: Hurst
authors.forename: David
authors.initials: D
authors.lastname: Plowchalk
authors.forename: Jack
authors.initials: J
authors.lastname: Cook
authors.forename: Feng
authors.initials: F
authors.lastname: Guo
authors.forename: Kuresh
authors.initials: K
authors.lastname: Youdim
authors.forename: Maurice
authors.initials: M
authors.lastname: Dickins
authors.forename: Alex
authors.initials: A
authors.lastname: Phipps
authors.forename: Amanda
authors.initials: A
authors.lastname: Darekar
authors.forename: Ruth
authors.initials: R
authors.lastname: Hyland
authors.forename: R Scott
authors.initials: RS
authors.lastname: Obach
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D051544:Cytochrome P-450 CYP3A / Q000096:biosynthesis*; D065692:Cytochrome P-450 CYP3A Inhibitors; D004347:Drug Interactions; D004790:Enzyme Induction; D004791:Enzyme Inhibitors / Q000009:adverse effects / Q000494:pharmacology*; D006801:Humans; D007422:Intestines / Q000187:drug effects / Q000201:enzymology; D008099:Liver / Q000187:drug effects / Q000201:enzymology; D008874:Midazolam / Q000493:pharmacokinetics*; D008954:Models, Biological; D015394:Molecular Structure; D015203:Reproducibility of Results; D018570:Risk Assessment; D013329:Structure-Activity Relationship; D013379:Substrate Specificity
chemical_list: D065692:Cytochrome P-450 CYP3A Inhibitors; D004791:Enzyme Inhibitors; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human; D008874:Midazolam