Prediction of time-dependent CYP3A4 drug-drug interactions: impact of enzyme degradation, parallel elimination pathways, and intestinal inhibition.
Time-dependent inhibition of CYP3A4 often results in clinically significant drug-drug interactions. In the current study, 37 in vivo cases of irreversible inhibition were collated, focusing on macrolides (erythromycin, clarithromycin, and azithromycin) and diltiazem as inhibitors. The interactions included 17 different CYP3A substrates showing up to a 7-fold increase in AUC (13.5% of studies were in the range of potent inhibition). A systematic analysis of the impact of CYP3A4 degradation half-life (mean t1/2deg = 3 days, ranging from 1 to 6 days) on the prediction of the extent of interaction for compounds with a differential contribution from CYP3A4 to the overall elimination (defined by fmCYP3A4) was performed. Although the prediction accuracy was very sensitive to the CYP3A4 degradation rate for substrates mainly eliminated by this enzyme fm(CYP3A4 >or= 0.9), minimal effects are observed when CYP3A4 contributes less than 50% to the overall elimination in cases when the parallel elimination pathway is not subject to inhibition. Use of the mean CYP3A4 t1/2deg (3 days), average unbound systemic plasma concentration of the inhibitor, and the corresponding fm(CYP3A4) resulted in 89% of studies predicted within 2-fold of the in vivo value. The impact of the interaction in the gut wall was assessed by assuming maximal intestinal inhibition of CYP3A4. Although a reduced number of false-negative predictions was observed, there was an increased number of overpredictions, and generally, a loss of prediction accuracy was observed. The impact of the possible interplay between CYP3A4 and efflux transporters on the intestinal interaction requires further evaluation.
Galetin
Aleksandra
A
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. Aleksandra.Galetin@manchester.ac.uk
Burt
Howard
H
Gibbons
Laura
L
Houston
J Brian
JB
D000465:Algorithms; D019540:Area Under Curve; D017963:Azithromycin / Q000378:metabolism / Q000493:pharmacokinetics / Q000494:pharmacology; D001692:Biological Transport; D017291:Clarithromycin / Q000378:metabolism / Q000493:pharmacokinetics / Q000494:pharmacology; D051544:Cytochrome P-450 CYP3A; D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism*; D004110:Diltiazem / Q000378:metabolism / Q000493:pharmacokinetics / Q000494:pharmacology; D004347:Drug Interactions; D004791:Enzyme Inhibitors / Q000378:metabolism* / Q000493:pharmacokinetics* / Q000494:pharmacology; D004917:Erythromycin / Q000378:metabolism / Q000493:pharmacokinetics / Q000494:pharmacology; D006801:Humans; D007413:Intestinal Mucosa / Q000378:metabolism*; D007422:Intestines / Q000201:enzymology; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D013997:Time Factors
D065607:Cytochrome P-450 Enzyme Inhibitors; D004791:Enzyme Inhibitors; D004917:Erythromycin; D017963:Azithromycin; D003577:Cytochrome P-450 Enzyme System; C104464:CYP3A protein, human; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human; D004110:Diltiazem; D017291:Clarithromycin