Physiologically‐based pharmacokinetic modeling of quinidine to establish a CYP3A4, P‐gp, and CYP2D6 drug–drug–gene interaction network

Author:

Feick Denise1,Rüdesheim Simeon12ORCID,Marok Fatima Zahra1,Selzer Dominik1,Loer Helena Leonie Hanae1,Teutonico Donato3,Frechen Sebastian4,van der Lee Maaike5,Moes Dirk Jan A. R.5,Swen Jesse J.5,Schwab Matthias267ORCID,Lehr Thorsten1ORCID

Affiliation:

1. Clinical Pharmacy Saarland University Saarbrücken Germany

2. Dr. Margarete Fischer‐Bosch‐Institute of Clinical Pharmacology Stuttgart Germany

3. Translational Medicine & Early Development Sanofi‐Aventis R&D Chilly‐Mazarin France

4. Bayer AG, Pharmaceuticals, Research & Development Systems Pharmacology & Medicine Leverkusen Germany

5. Department of Clinical Pharmacy & Toxicology Leiden University Medical Center Leiden The Netherlands

6. Departments of Clinical Pharmacology, Pharmacy and Biochemistry University of Tübingen Tübingen Germany

7. Cluster of Excellence iFIT (EXC2180) “Image‐guided and Functionally Instructed Tumor Therapies” University of Tübingen Tübingen Germany

Abstract

AbstractThe antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P‐glycoprotein (P‐gp) and is therefore recommended for use in clinical drug–drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P‐gp, it is susceptible to DDIs involving these proteins. Physiologically‐based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug–drug(–gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P‐gp perpetrators as well as CYP2D6 and P‐gp victims. The quinidine parent‐metabolite model including 3‐hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1–600 mg). The model covers efflux transport via P‐gp and metabolic transformation to either 3‐hydroxyquinidine or unspecified metabolites via CYP3A4. The 3‐hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two‐fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two‐fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

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