title: Physiologically based pharmacokinetic modeling to predict drug-drug interactions involving inhibitory metabolite: a case study of amiodarone.
abstract: Evaluation of drug-drug interaction (DDI) involving circulating inhibitory metabolites of perpetrator drugs has recently drawn more attention from regulatory agencies and pharmaceutical companies. Here, using amiodarone (AMIO) as an example, we demonstrate the use of physiologically based pharmacokinetic (PBPK) modeling to assess how a potential inhibitory metabolite can contribute to clinically significant DDIs. Amiodarone was reported to increase the exposure of simvastatin, dextromethorphan, and warfarin by 1.2- to 2-fold, which was not expected based on its weak inhibition observed in vitro. The major circulating metabolite, mono-desethyl-amiodarone (MDEA), was later identified to have a more potent inhibitory effect. Using a combined "bottom-up" and "top-down" approach, a PBPK model was built to successfully simulate the pharmacokinetic profile of AMIO and MDEA, particularly their accumulation in plasma and liver after a long-term treatment. The clinical AMIO DDIs were predicted using the verified PBPK model with incorporation of cytochrome P450 inhibition from both AMIO and MDEA. The closest prediction was obtained for CYP3A (simvastatin) DDI when the competitive inhibition from both AMIO and MDEA was considered, for CYP2D6 (dextromethorphan) DDI when the competitive inhibition from AMIO and the competitive plus time-dependent inhibition from MDEA were incorporated, and for CYP2C9 (warfarin) DDI when the competitive plus time-dependent inhibition from AMIO and the competitive inhibition from MDEA were considered. The PBPK model with the ability to simulate DDI by considering dynamic change and accumulation of inhibitor (parent and metabolite) concentration in plasma and liver provides advantages in understanding the possible mechanism of clinical DDIs involving inhibitory metabolites.
authors.affiliation: Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, California chen.yuan@gene.com.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, California.
authors.forename: Jialin
authors.initials: J
authors.lastname: Mao
authors.affiliation: Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, California.
authors.forename: Cornelis E C A
authors.initials: CE
authors.lastname: Hop
mesh_terms: D000284:Administration, Oral; D000328:Adult; D000638:Amiodarone / Q000008:administration & dosage / Q000031:analogs & derivatives / Q000037:antagonists & inhibitors / Q000097:blood / Q000378:metabolism / Q000493:pharmacokinetics*; D000889:Anti-Arrhythmia Agents / Q000008:administration & dosage / Q000737:chemistry / Q000378:metabolism / Q000493:pharmacokinetics*; D001711:Biotransformation / Q000187:drug effects; D003198:Computer Simulation; D065688:Cytochrome P-450 CYP2C9 Inhibitors / Q000097:blood / Q000737:chemistry / Q000378:metabolism / Q000493:pharmacokinetics; D065690:Cytochrome P-450 CYP2D6 Inhibitors / Q000097:blood / Q000737:chemistry / Q000378:metabolism / Q000493:pharmacokinetics; D065692:Cytochrome P-450 CYP3A Inhibitors / Q000097:blood / Q000737:chemistry / Q000378:metabolism / Q000493:pharmacokinetics; D004305:Dose-Response Relationship, Drug; D004347:Drug Interactions; D005103:Expert Systems; D005260:Female; D006801:Humans; D007262:Infusions, Intravenous; D008099:Liver / Q000187:drug effects / Q000201:enzymology / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015203:Reproducibility of Results; D055815:Young Adult
chemical_list: D000889:Anti-Arrhythmia Agents; D065688:Cytochrome P-450 CYP2C9 Inhibitors; D065690:Cytochrome P-450 CYP2D6 Inhibitors; D065692:Cytochrome P-450 CYP3A Inhibitors; C440780:mono-N-desethylamiodarone; D000638:Amiodarone