Simultaneous Physiologically Based Pharmacokinetic (PBPK) Modeling of Parent and Active Metabolites to Investigate Complex CYP3A4 Drug-Drug Interaction Potential: A Case Example of Midostaurin

Abstract

Midostaurin (PKC412) is being investigated for the treatment of acute myeloid leukemia (AML) and advanced systemic mastocytosis (advSM). It is extensively metabolized by CYP3A4 to form two major active metabolites, CGP52421 and CGP62221. In vitro and clinical drug-drug interaction (DDI) studies indicated that midostaurin and its metabolites are substrates, reversible and time-dependent inhibitors, and inducers of CYP3A4. A simultaneous pharmacokinetic model of parent and active metabolites was initially developed by incorporating data from in vitro, preclinical, and clinical pharmacokinetic studies in healthy volunteers and in patients with AML or advSM. The model reasonably predicted changes in midostaurin exposure after single-dose administration with ketoconazole (a 5.8-fold predicted versus 6.1-fold observed increase) and rifampicin (90% predicted versus 94% observed reduction) as well as changes in midazolam exposure (1.0 predicted versus 1.2 observed ratio) after daily dosing of midostaurin for 4 days. The qualified model was then applied to predict the DDI effect with other CYP3A4 inhibitors or inducers and the DDI potential with midazolam under steady-state conditions. The simulated midazolam area under the curve ratio of 0.54 and an accompanying observed 1.9-fold increase in the CYP3A4 activity of biomarker 4-hydroxycholesterol indicated a weak-to-moderate CYP3A4 induction by midostaurin and its metabolites at steady state in patients with advSM. In conclusion, a simultaneous parent-and-active-metabolite modeling approach allowed predictions under steady-state conditions that were not possible to achieve in healthy subjects. Furthermore, endogenous biomarker data enabled evaluation of the net effect of midostaurin and its metabolites on CYP3A4 activity at steady state and increased confidence in DDI predictions.

In Vitro Screening of Six Protein Kinase Inhibitors for Time‐Dependent Inhibition of CYP2C8 and CYP3A4: Possible Implications with regard to Drug–Drug Interactions

“…These clinical data indicate that midostaurin inhibits its own CYP3A4‐mediated metabolism during the first week of therapy, followed by strong auto‐induction surpassing the initial CYP3A4 inhibitory effects. Time‐dependent inhibition and induction of CYP3A4 caused by midostaurin and its two major metabolites are likely to explain the time‐dependent pharmacokinetics of midostaurin . In this light, our interaction predictions for midostaurin could be viewed as a possible worst‐case scenario that could occur during some of the first days of midostaurin treatment.…”

Changes in perampanel levels during de‐induction: Simulations following carbamazepine discontinuation

“…22 As a result, PBPK models are well suited for the evaluation of complex DDI cases that may be too difficult to study in a formal clinical trial or healthy volunteers. 23,24 Simulations were performed with the PBPK model to evaluate changes in perampanel steady-state plasma concentrations when co-administered with carbamazepine, and following discontinuation of carbamazepine. Carbamazepine was selected as it is a strong CYP3A inducer and was the most frequently co-administered EIASM in phase 2/3 clinical trials of perampanel.…”

Physiologically-Based Pharmacokinetic/Pharmacodynamic Model of MBQ-167 to Predict Tumor Growth Inhibition in Mice

“…Physiologically- based pharmacokinetic (PBPK) modelling represents a mathematical framework that integrates physicochemical, physiological, and biochemical information to predict the concentration-time course at target tissues for a wide range of exposure conditions in animals or humans [ 4 ]. In recent years, the use of PBPK models has clearly improved the model-informed drug discovery and development process of several drugs [ 5 , 6 , 7 ], which has facilitated its recognition by the main regulatory agencies (FDA and EMA) [ 8 , 9 ]. Currently, the main purposes of PBPK models are to qualitatively and quantitatively predict drug-drug interactions and to support initial dose selection in pediatric and first-in-human trials [ 8 ].…”