title: Prediction of time-dependent CYP3A4 drug-drug interactions by physiologically based pharmacokinetic modelling: impact of inactivation parameters and enzyme turnover.
abstract: Predicting the magnitude of time-dependent metabolic drug-drug (mDDIs) interactions involving cytochrome P-450 3A4 (CYP3A4) from in vitro data requires accurate knowledge of the inactivation parameters of the inhibitor (K(I), k(inact)) and of the turnover of the enzyme (k(deg)) in both the gut and the liver. We have predicted the magnitude of mDDIs observed in 29 in vivo studies involving six CYP3A4 probe substrates and five mechanism based inhibitors of CYP3A4 of variable potency (azithromycin, clarithromycin, diltiazem, erythromycin and verapamil). Inactivation parameters determined anew in a single laboratory under standardised conditions together with data from substrate and inhibitor files within the Simcyp Simulator (Version 9.3) were used to determine a value of the hepatic k(deg) (0.0193 or 0.0077h(-1)) most appropriate for the prediction of mDDIs involving time-dependent inhibition of CYP3A4. The higher value resulted in decreased bias (geometric mean fold error - 1.05 versus 1.30) and increased precision (root mean squared error - 1.29 versus 2.30) of predictions of mean ratios of AUC in the absence and presence of inhibitor. Depending on the k(deg) value used (0.0193 versus 0.0077h(-1)), predicted mean ratios of AUC were within 2-fold of the observed values for all (100%) and 27 (93%) of the 29 studies, respectively and within 1.5-fold for 24 (83%) and 17 (59%) of the 29 studies, respectively. Comprehensive PBPK models were applied for accurate assessment of the potential for mDDIs involving time-dependent inhibition of CYP3A4 using a hepatic k(deg) value of 0.0193h(-1) in conjunction with inactivation parameters determined by the conventional experimental approach.
authors.affiliation: Simcyp Limited, Blades Enterprise Centre, John Street, Sheffield S2 4SU, UK. alfredoq@fcq.unc.edu.ar
authors.forename: K
authors.initials: K
authors.lastname: Rowland Yeo
authors.forename: R L
authors.initials: RL
authors.lastname: Walsky
authors.forename: M
authors.initials: M
authors.lastname: Jamei
authors.forename: A
authors.initials: A
authors.lastname: Rostami-Hodjegan
authors.forename: G T
authors.initials: GT
authors.lastname: Tucker
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D051544:Cytochrome P-450 CYP3A / Q000378:metabolism; D065692:Cytochrome P-450 CYP3A Inhibitors; D016208:Databases, Factual; D004347:Drug Interactions / Q000502:physiology*; D004791:Enzyme Inhibitors / Q000493:pharmacokinetics*; D005260:Female; D006801:Humans; D007700:Kinetics; D008297:Male; D008862:Microsomes, Liver / Q000378:metabolism; D008875:Middle Aged; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000097:blood*
chemical_list: D065692:Cytochrome P-450 CYP3A Inhibitors; D004791:Enzyme Inhibitors; D004364:Pharmaceutical Preparations; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human