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

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.

Physiologically based pharmacokinetic modeling of imatinib and N‐desmethyl imatinib for drug–drug interaction predictions

“…Following the model development process, a DDI network centered around imatinib acting as both a victim and perpetrator drug was successfully established by coupling the final imatinib model with previously published models of the perpetrator drugs rifampicin, ketoconazole, and gemfibrozil, as well as of the victim drugs simvastatin and metoprolol 27–31 . Good overall predictive performance was attained for the modeled DDI scenarios, reflected by 24/24 predicted AUC last and C max ratios being within twofold of observed ratios.…”

“…To investigate the role of imatinib and NDMI acting as either victims or perpetrators in DDI scenarios, the developed model was coupled with previously published PBPK models of rifampicin, ketoconazole, gemfibrozil, simvastatin, and metoprolol. 27 , 28 , 29 , 30 , 31 Relevant interaction types, including induction, competitive inhibition, non‐competitive inhibition, and mechanism‐based inactivation, were incorporated as described in the Open Systems Pharmacology Suite manual, 32 with the corresponding interaction parameters adopted from the literature.…”

A physiologically‐based pharmacokinetic precision dosing approach to manage dasatinib drug–drug interactions

“…Goodness of fit plots were generated to compare predicted and observed AUC from the first to the last timepoint of measurement (AUC last ), maximum plasma concentration ( C max ) values and plasma concentrations, respectively. As quantitative measures, the mean relative deviation (MRD) of predicted plasma concentrations and the geometric mean fold error (GMFE) of predicted AUC last and C max values were calculated as previously described 29 . Additionally, a local sensitivity analysis was performed as described in Section S2.4.1.…”

“…For the enzyme‐mediated DDIs, the dasatinib PBPK model was coupled with previously published PBPK models of ketoconazole, 29 rifampicin 30 and simvastatin 31 . Here, inhibition and induction processes were implemented as described in the Open Systems Pharmacology Suite manual, 32 using interaction parameters sourced from published PBPK models for each perpetrator drug 29,30 . For the pH‐dependent DDIs, the reduced gastric solubility due to intake of the ARAs rabeprazole, 4 famotidine 5 and Maalox® 5 was captured by increasing the gastric pH as previously performed 33 and adjusting the gastric emptying time for rabeprazole and Maalox®.…”

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions

“…In a similar fashion, reversible and time-dependent inhibition experiments, together with enzyme induction experiments, are used to assess the potential for a drug to alter the metabolic capacity of a given metabolic pathway (Foti et al, 2010). In addition to assessment of the DDI potential of the parent drug, metabolites can also contribute to DDI and hence, should be evaluated when appropriate (Isoherranen et al, 2009;Lutz et al, 2010;VandenBrink and Isoherranen, 2010;Yeung et al, 2011;Zamek-Gliszczynski et al, 2014;Prieto Garcia et al, 2018;Marok et al, 2021;Marok et al, 2023). Given their significant role in the metabolism of the majority of marketed drugs, cytochrome P450 enzymes are often the primary focus when conducting an initial DDI assessment, though additional drug metabolizing enzymes such as UDP-glucuronosyltransferases (UGT) and drug transporters such as P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP), need to be evaluated for a thorough assessment of DDI (Cerny, 2016;Foti and Dalvie, 2016;Nishiya et al, 2020).…”