title: Prediction of in vivo drug-drug interactions from in vitro data: impact of incorporating parallel pathways of drug elimination and inhibitor absorption rate constant.
abstract: OBJECTIVE
  Success of the quantitative prediction of drug-drug interactions via inhibition of CYP-mediated metabolism from the inhibitor concentration at the enzyme active site ([I]) and the in vitro inhibition constant (K(i)) is variable. The aim of this study was to examine the impact of the fraction of victim drug metabolized by a particular CYP (f(mCYP)) and the inhibitor absorption rate constant (k(a)) on prediction accuracy.
  
  
  METHODS
  Drug-drug interaction studies involving inhibition of CYP2C9, CYP2D6 and CYP3A4 (n = 115) were investigated. Data on f(mCYP) for the probe substrates of each enzyme and k(a) values for the inhibitors were incorporated into in vivo predictions, alone or in combination, using either the maximum hepatic input or the average systemic plasma concentration as a surrogate for [I]. The success of prediction (AUC ratio predicted within twofold of in vivo value) was compared using nominal values of f(mCYP) = 1 and k(a) = 0.1 min(-1).
  
  
  RESULTS
  The incorporation of f(mCYP) values into in vivo predictions using the hepatic input plasma concentration resulted in 84% of studies within twofold of in vivo value. The effect of k(a) values alone significantly reduced the number of over-predictions for CYP2D6 and CYP3A4; however, less precision was observed compared with the f(mCYP). The incorporation of both f(mCYP) and k(a) values resulted in 81% of studies within twofold of in vivo value.
  
  
  CONCLUSIONS
  The incorporation of substrate and inhibitor-related information, namely f(mCYP) and k(a), markedly improved prediction of 115 interaction studies with CYP2C9, CYP2D6 and CYP3A4 in comparison with [I]/K(i) ratio alone.
authors.affiliation: School of Pharmacy & Pharmaceutical Sciences, University of Manchester, Manchester M13 9PL, UK.
authors.forename: Hayley S
authors.initials: HS
authors.lastname: Brown
authors.forename: Kiyomi
authors.initials: K
authors.lastname: Ito
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Galetin
authors.forename: J Brian
authors.initials: JB
authors.lastname: Houston
mesh_terms: 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*; D004347:Drug Interactions; D004791:Enzyme Inhibitors / Q000378:metabolism*; D006801:Humans; D011237:Predictive Value of Tests
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; C104464:CYP3A protein, human; D019389:Cytochrome P-450 CYP2D6; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human