Physiologically-Based Pharmacokinetic Modeling of Macitentan: Prediction of Drug–Drug Interactions
Autor: | Alexander Treiber, Stephan Buchmann, Jérôme Segrestaa, Stephane Delahaye, Christopher Kohl, Patricia N. Sidharta, Ruben de Kanter, Carmela Gnerre |
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Rok vydání: | 2015 |
Předmět: |
Adult
Male Physiologically based pharmacokinetic modelling Endothelin A Receptor Antagonists Pharmacology Bioinformatics Models Biological 030226 pharmacology & pharmacy Sildenafil Citrate 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Pharmacokinetics medicine Cytochrome P-450 CYP3A Humans Drug Interactions Pharmacology (medical) Active metabolite Macitentan Sulfonamides Endothelin receptor antagonist business.industry Endothelin B Receptor Antagonists Ketoconazole Pyrimidines chemistry 030220 oncology & carcinogenesis Cyclosporine Cytochrome P-450 CYP3A Inhibitors Ritonavir Warfarin Rifampin business Saquinavir medicine.drug |
Zdroj: | Clinical Pharmacokinetics. 55:369-380 |
ISSN: | 1179-1926 0312-5963 |
Popis: | 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. 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. 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. This example of the application of PBPK modeling to predict drug–drug interactions was used to support the labeling of macitentan (Opsumit). |
Databáze: | OpenAIRE |
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