Static Versus Dynamic Model Predictions of Competitive Inhibitory Metabolic Drug-Drug Interactions via Cytochromes...

Area of Science:

• Pharmacology
• Drug Metabolism
• Computational Chemistry

Background:

• Predicting metabolic drug-drug interactions (DDIs) involving cytochrome P450 enzymes (CYP) is critical in pharmaceutical development.
• Current debate exists on the suitability of in vitro-in vivo extrapolation (IVIVE) static models versus dynamic models for regulatory submissions.
• This study addresses the equivalence of static and dynamic models for quantitative DDI prediction.

Purpose of the Study:

• To evaluate the equivalence of static and dynamic models for predicting metabolic DDIs caused by competitive CYP inhibition.
• To compare quantitative predictions from dynamic simulations against static calculations across varied drug parameter spaces.

Main Methods:

• Simulated 30,000 DDIs for CYP3A4 using hypothetical substrates and inhibitors.
• Compared area under the plasma concentration-time profile ratios (AUCr) from dynamic simulations (Simcyp V21) and static models.
• Calculated inter-model discrepancy ratios (IMDR) using 'population' and 'vulnerable patient' scenarios with different inhibitor driver concentrations (Cmax, Cavg,ss).

Main Results:

• Significant discrepancies (IMDR outside 0.8-1.25) were observed between static and dynamic models.
• The 'population' representative showed high discrepancy rates (up to 85.9% for IMDR <0.8) when using Cavg,ss.
• The 'vulnerable patient' representative exhibited a notable rate of discrepancies (37.8% for IMDR >1.25).

Conclusions:

• Static models are not equivalent to dynamic models for predicting metabolic DDIs from competitive CYP inhibition.
• Dynamic models provide more reliable predictions, particularly for vulnerable patient groups.
• Caution is advised when relying solely on static IVIVE approaches for metabolic DDI risk assessment in drug development.