Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin.
The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3Aas in the case of clarithromycin. Previous attempts to predict the effects of clarithromycin on CYP3A substrates, e.g., midazolam, failed to account for nonlinear metabolism of clarithromycin. A semiphysiologically based pharmacokinetic model was developed for clarithromycin and midazolam metabolism, incorporating hepatic and intestinal metabolism by CYP3A and non-CYP3A mechanisms. CYP3A inactivation by clarithromycin occurred at both sites. K(I) and k(inact) values for clarithromycin obtained from in vitro sources were unable to accurately predict the clinical effect of clarithromycin on CYP3A activity. An iterative approach determined the optimum values to predict in vivo effects of clarithromycin on midazolam to be 5.3 microM for K(i) and 0.4 and 4 h(-1) for k(inact) in the liver and intestines, respectively. The incorporation of CYP3A-dependent metabolism of clarithromycin enabled prediction of its nonlinear pharmacokinetics. The predicted 2.6-fold change in intravenous midazolam area under the plasma concentration-time curve (AUC) after 500 mg of clarithromycin orally twice daily was consistent with clinical observations. Although the mean predicted 5.3-fold change in the AUC of oral midazolam was lower than mean observed values, it was within the range of observations. Intestinal CYP3A activity was less sensitive to changes in K(I), k(inact), and CYP3A half-life than hepatic CYP3A. This semiphysiologically based pharmacokinetic model incorporating CYP3A inactivation in the intestine and liver accurately predicts the nonlinear pharmacokinetics of clarithromycin and the DDI observed between clarithromycin and midazolam. Furthermore, this model framework can be applied to other mechanism-based inhibitors.
Quinney
Sara K
SK
Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Zhang
Xin
X
Lucksiri
Aroonrut
A
Gorski
J Christopher
JC
Li
Lang
L
Hall
Stephen D
SD
D000777:Anesthetics / Q000008:administration & dosage / Q000493:pharmacokinetics; D000900:Anti-Bacterial Agents / Q000493:pharmacokinetics* / Q000494:pharmacology*; D017291:Clarithromycin / Q000008:administration & dosage / Q000097:blood / Q000493:pharmacokinetics* / Q000494:pharmacology*; D003198:Computer Simulation; D051544:Cytochrome P-450 CYP3A / Q000378:metabolism; D065692:Cytochrome P-450 CYP3A Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism; D004347:Drug Interactions; D004791:Enzyme Inhibitors / Q000493:pharmacokinetics / Q000494:pharmacology*; D005260:Female; D006801:Humans; D007413:Intestinal Mucosa / Q000187:drug effects / Q000201:enzymology / Q000378:metabolism; D008099:Liver / Q000187:drug effects / Q000201:enzymology / Q000378:metabolism; D008297:Male; D008657:Metabolic Clearance Rate; D008874:Midazolam / Q000008:administration & dosage / Q000493:pharmacokinetics; D008954:Models, Biological; D009928:Organ Specificity / Q000187:drug effects
D000777:Anesthetics; D000900:Anti-Bacterial Agents; D065692:Cytochrome P-450 CYP3A Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; D004791:Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System; C104464:CYP3A protein, human; D051544:Cytochrome P-450 CYP3A; D017291:Clarithromycin; D008874:Midazolam