title: Prediction of in vivo drug-drug interactions from in vitro data : factors affecting prototypic drug-drug interactions involving CYP2C9, CYP2D6 and CYP3A4.
abstract: BACKGROUND
  Quantitative predictions of in vivo drug-drug interactions (DDIs) resulting from metabolic inhibition are commonly made based upon the inhibitor concentration at the enzyme active site [I] and the in vitro inhibition constant (K(i)). Previous studies have involved the use of various plasma inhibitor concentrations as surrogates for [I] along with K(i) values obtained from published literature. Although this approach has resulted in a high proportion of successful predictions, a number of falsely predicted interactions are also observed.
  
  
  OBJECTIVE
  To focus on three issues that may influence the predictive value of the [I]/K(i) ratio approach: (i) the use of unbound K(i) (K(i,u)) values generated from standardised in vitro experiments compared with literature values; (ii) the selection of an appropriate [I]; and (iii) incorporation of the impact of intestinal metabolic inhibition for cytochrome P450 (CYP) 3A4 predictions. To this end we have selected eight inhibitors of CYP2C9, CYP2D6 and CYP3A4 and 18 victim drugs from a previous database analysis to allow prediction of 45 clinical DDI studies.
  
  
  METHODS
  In vitro kinetic and inhibition studies were performed in human liver microsomes using prototypic probe substrates of CYP2C9 and CYP2D6, with various inhibitors (miconazole, sulfaphenazole, fluconazole, ketoconazole, quinidine, fluoxetine, fluvoxamine). The K(i) estimates obtained were corrected for non-specific microsomal binding, and the K(i,u) was incorporated into in vivo predictions using various [I] values. Predictions for CYP3A4 were based upon in vitro data obtained from a previous publication within our laboratory, and an assessment of the impact of the interaction in the gut wall is included. Predictions were validated against 45 in vivo studies and those within 2-fold of the in vivo ratio of area under the plasma concentration-time curve of the substrate, in the presence and absence of the inhibitor (AUC(i)/AUC) were considered successful.
  
  
  RESULTS
  Predictions based upon the average systemic total plasma drug concentration ([I](av)) [incorporating the effects of parallel drug elimination pathways] and the K(i,u) value resulted in 91% of studies predicted to within 2-fold of the in vivo AUC(i)/AUC. This represents a 35% improvement in prediction accuracy compared with predictions based upon total K(i) values obtained from various published literature sources. A corresponding reduction in bias and an increase in precision were also observed compared with the use of other [I] surrogates (e.g. the total and new unbound maximum hepatic input plasma concentrations). No significant improvement in prediction accuracy was observed by incorporating consideration of gut wall inhibition for CYP3A4.
  
  
  CONCLUSIONS
  DDI predictions based upon the use of K(i,u) data obtained under a set of optimal standardised conditions were significantly improved compared with predictions using in vitro data collated from various sources. The use of [I](av) as the [I] surrogate generated the most successful predictions as judged by several criteria. Incorporation of either plasma protein binding of inhibitor or gut wall CYP3A4 inhibition did not result in a general improvement of DDI predictions.
authors.affiliation: School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester, UK.
authors.forename: Hayley S
authors.initials: HS
authors.lastname: Brown
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Galetin
authors.forename: David
authors.initials: D
authors.lastname: Hallifax
authors.forename: J Brian
authors.initials: JB
authors.lastname: Houston
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001189:Aryl Hydrocarbon Hydroxylases / Q000037:antagonists & inhibitors / Q000378:metabolism*; D065729:Cytochrome P-450 CYP2C9; D019389:Cytochrome P-450 CYP2D6 / Q000378:metabolism*; D065690:Cytochrome P-450 CYP2D6 Inhibitors; D051544:Cytochrome P-450 CYP3A; D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism*; D016208:Databases, Factual; D004347:Drug Interactions; D004791:Enzyme Inhibitors / Q000494:pharmacology; D005544:Forecasting; D006801:Humans; D066298:In Vitro Techniques; D007700:Kinetics; D008861:Microsomes / Q000378:metabolism
chemical_list: D065690:Cytochrome P-450 CYP2D6 Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D004791:Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System; C450260:CYP2C9 protein, human; D065729:Cytochrome P-450 CYP2C9; D001189:Aryl Hydrocarbon Hydroxylases; D019389:Cytochrome P-450 CYP2D6; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human