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

Clin Pharmacokinet

Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland.

Published: March 2016

AI Article Synopsis

  • Macitentan is a new drug designed to treat pulmonary arterial hypertension (PAH) by blocking endothelin receptors and is primarily metabolized by the CYP3A4 enzyme to its active form, ACT-132577.
  • A pharmacokinetic model was developed using clinical data and laboratory measurements to understand how the drug behaves in the body, including how it gets absorbed, distributed, metabolized, and excreted.
  • The model successfully predicted the drug's interactions with other medications and was instrumental in providing information for the labeling of macitentan, highlighting its potential interactions with drugs like ketoconazole and HIV treatments.

Article Abstract

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).

Download full-text PDF

Source
http://dx.doi.org/10.1007/s40262-015-0322-yDOI Listing

Publication Analysis

Top Keywords

drug-drug interactions
8
active metabolite
8
physiologically-based pharmacokinetic
4
pharmacokinetic modeling
4
macitentan
4
modeling macitentan
4
macitentan prediction
4
prediction drug-drug
4
interactions introduction
4
introduction macitentan
4

Similar Publications

Background: Recent clinical case reports have generated controversy concerning the adverse events (AEs) of amputation linked to sodium-glucose co-transporter 2 inhibitors (SGLT2i). We assessed the risk of osteomyelitis AE reporting linked to SGLT2i or SGLT2i-metformin co-medication.

Research Design And Methods: Investigated the FDA Adverse Event Reporting System for osteomyelitis-related AEs associated with SGLT2i or SGLT2i-metformin co-medication from 2013q2 to 2023q1.

View Article and Find Full Text PDF

Background: Paxlovid® (nirmatrelvir and ritonavir) is the only licensed oral antiviral for COVID-19. Ritonavir is a potent inhibitor of cytochrome P450 enzymes causing numerous drug-drug interactions (DDIs).

Aim: To describe the frequency, type, and severity of detected drug related problems (DRPs) associated with Paxlovid®.

View Article and Find Full Text PDF

Background: Kidney transplant recipients with severe acute respiratory syndrome-coronavirus-2 infection have an increased risk of severe disease and mortality. Nirmaltrevir/ritonavir (Paxlovid) is an effective oral disease-modifying therapy that has been shown to reduce risk of progression to severe disease in high-risk, nonhospitalized adults. However, owing to the potential for serious drug-drug interactions owing to ritonavir-induced inhibition of the CYP3A enzyme, this drug is not suitable option for transplant recipients with mild-moderate severe acute respiratory syndrome-coronavirus-2 infection.

View Article and Find Full Text PDF

Background: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing attention as a potential resource for DDI detection. However, a substantial portion of adverse drug reaction (ADR) information is hidden in unstructured narrative text, which has yet to be efficiently harnessed, thereby introducing bias into the research.

View Article and Find Full Text PDF

Objectives: We aimed to assess the characteristics of adverse drug reactions (ADRs) collected in a university hospital.

Methods: A retrospective analysis of ADRs spontaneously reported in the Hospital Pharmacovigilance Program database (RutiRAM) over a 13-year period was conducted. The analysis included a description of ADRs [System Organ Class (SOC)] and their seriousness, the drugs involved [level 1 of the Anatomical Therapeutic Chemical (ATC) Classification System], drug-drug interactions, medication errors, drugs 'under additional monitoring', positive rechallenge, and the 'pharmacovigilance interest' of ADRs.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!