Dealing with the complex drug-drug interactions: towards mechanistic models

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Systems Biology and Modeling
• Drug Development and Safety

Background:

• Drug-drug interactions (DDIs) can cause severe adverse events, leading to drug withdrawal.
• Predicting complex DDIs involving multiple clearance pathways and metabolites remains a significant challenge.
• Physiologically based pharmacokinetic (PBPK) modeling is emerging as a key tool for DDI assessment.

Purpose of the Study:

• To review discussions from the 2013 AAPS symposium on mechanistic modeling for complex DDIs.
• To highlight the application and limitations of PBPK models in predicting DDIs.
• To explore the role of modeling in understanding pharmacokinetics in special populations.

Main Methods:

• Review of symposium discussions on mechanistic models for DDIs.
• Application of physiologically based pharmacokinetic (PBPK) models.
• Integration of in vitro-to-in vivo extrapolation for disposition.
• Consideration of CYP-mediated metabolism, transporter effects, and metabolite contributions.

Main Results:

• PBPK models successfully predict clinical pharmacokinetics and DDIs, particularly for CYP-mediated metabolism.
• Models can explain transporter-mediated and complex DDIs.
• Challenges persist in predicting DDIs involving complex transporter-enzyme interplay and metabolites.
• Regulatory guidelines recommend PBPK modeling for quantitative DDI evaluation.

Conclusions:

• Mechanistic modeling, especially PBPK, is valuable for predicting and rationalizing complex DDIs.
• PBPK models provide a basis for understanding pharmacokinetics in special populations (renal impairment, pediatrics, ethnic groups).
• Continued development is needed for improved mechanistic prediction of transporter-enzyme interactions and metabolite effects.