Physiologically Based Pharmacokinetic Modeling to Assess Perpetrator and Victim Cytochrome P450 2C Induction Risk

Area of Science:

• Pharmacology
• Drug Metabolism
• Toxicology

Background:

• CYP2C enzyme induction-mediated drug-drug interactions (DDIs) pose a significant challenge in drug development.
• Current preclinical models and limited clinical data hinder accurate DDI risk assessment for CYP2C substrates.

Purpose of the Study:

• To evaluate the utility of physiologically based pharmacokinetic (PBPK) modeling for assessing CYP2C induction-based DDIs.
• To compare the predictive performance of PBPK modeling against mechanistic static modeling (MSM).

Main Methods:

• An all-human hepatocyte triculture system was used to quantify CYP2C induction in vitro.
• In vitro induction parameters were integrated into a PBPK model to predict pharmacokinetics (PK) of CYP2C substrates.
• PBPK predictions were compared with MSM and clinical DDI outcomes.

Main Results:

• PBPK modeling demonstrated a reduced tendency to over- or underpredict CYP2C substrate exposure during DDIs.
• The PBPK approach showed excellent correlation with observed clinical DDI outcomes.
• MSM accurately predicted CYP3A4 induction DDIs but lacked precision for CYP2C induction.

Conclusions:

• PBPK modeling serves as a valuable complementary tool to MSM for evaluating CYP2C induction-based DDIs.
• This study highlights the first demonstration of PBPK modeling's utility in this context.
• Improved DDI risk assessment for CYP2C substrates can be achieved through PBPK modeling.