Physiologically-Based Pharmacokinetic Modeling of Macitentan: Prediction of Drug–Drug Interactions

• Published in: Clinical pharmacokinetics 2016-03, Vol.55 (3), p.369-380
• Main Authors: de Kanter, Ruben, Sidharta, Patricia N., Delahaye, Stéphane, Gnerre, Carmela, Segrestaa, Jerome, Buchmann, Stephan, Kohl, Christopher, Treiber, Alexander
• Format: Article
• Language: English
• Subjects: Adult; Cyclosporine - pharmacology; Cytochrome P-450 CYP3A - metabolism; Cytochrome P-450 CYP3A Inhibitors - pharmacology; Drug Interactions; Endothelin A Receptor Antagonists - pharmacokinetics; Endothelin B Receptor Antagonists - pharmacokinetics; Humans; Internal Medicine; Ketoconazole - pharmacology; Male; Medicine; Medicine & Public Health; Models, Biological; Original Research Article; Pharmacology/Toxicology; Pharmacotherapy; Pyrimidines - blood; Pyrimidines - pharmacokinetics; Rifampin - pharmacology; Sildenafil Citrate - pharmacology; Sulfonamides - blood; Sulfonamides - pharmacokinetics; Warfarin - pharmacology
• ISSN: 0312-5963; 1179-1926
• DOI: 10.1007/s40262-015-0322-y

Introduction Macitentan is a novel dual endothelin receptor antagonist for the treatment of pulmonary arterial hypertension (PAH). It is metabolized by cytochrome P450 (CYP) enzymes, mainly CYP3A4, to its active metabolite ACT-132577. Methods A physiological-based pharmacokinetic (PBPK) model was developed by combining observations from clinical studies and physicochemical parameters as well as absorption, distribution, metabolism and excretion parameters determined in vitro. Results The model predicted the observed pharmacokinetics of macitentan and its active metabolite ACT-132577 after single and multiple dosing. It performed well in recovering the observed effect of the CYP3A4 inhibitors ketoconazole and cyclosporine, and the CYP3A4 inducer rifampicin, as well as in predicting interactions with S-warfarin and sildenafil. The model was robust enough to allow prospective predictions of macitentan–drug combinations not studied, including an alternative dosing regimen of ketoconazole and nine other CYP3A4-interacting drugs. Among these were the HIV drugs ritonavir and saquinavir, which were included because HIV infection is a known risk factor for the development of PAH. Conclusion This example of the application of PBPK modeling to predict drug–drug interactions was used to support the labeling of macitentan (Opsumit).