A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators

Author:

Marok Fatima Zahra1,Wojtyniak Jan-Georg12,Fuhr Laura Maria1,Selzer Dominik1,Schwab Matthias234,Weiss Johanna56ORCID,Haefeli Walter Emil56ORCID,Lehr Thorsten1ORCID

Affiliation:

1. Clinical Pharmacy, Saarland University, 66123 Saarbruecken, Germany

2. Dr. Margarete Fischer-Bosch-Institut of Clinical Pharmacology, 70376 Stuttgart, Germany

3. Departments of Clinical Pharmacology, and of Biochemistry and Pharmacy, University Hospital Tuebingen, 72076 Tuebingen, Germany

4. Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University Tuebingen, 72076 Tuebingen, Germany

5. Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, 72076 Tuebingen, Germany

6. German Center for Infection Research (DZIF), Heidelberg Partner Site, 69120 Heidelberg, Germany

Abstract

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing’s syndrome, is prone to drug–food interactions (DFIs) and is well known for its strong drug–drug interaction (DDI) potential. Some of ketoconazole’s potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole’s metabolites on its DDI potential. The parent–metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits.

Funder

Robert Bosch Stiftung

a European Commission Horizon 2020 UPGx grant

German Federal Ministry of Education and Research

Deutsche Forschungsgemeinschaft

Publisher

MDPI AG

Subject

Pharmaceutical Science

Reference63 articles.

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