Static Versus Dynamic Model Predictions of Competitive Inhibitory Metabolic Drug-Drug Interactions via Cytochromes P450: One Step Forward and Two Steps Backwards.

Abstract

Background: Predicting metabolic drug-drug interactions (DDIs) via cytochrome P450 enzymes (CYP) is essential in drug development, but controversy has reemerged recently about whether in vitro-in vivo extrapolation (IVIVE) using static models can replace dynamic models for some regulatory filings and label recommendations.

Objective: The aim of this study was to determine if static and dynamic models are equivalent for the quantitative prediction of metabolic DDIs arising from competitive CYP inhibition.

Methods: Drug parameter spaces were varied to simulate 30,000 DDIs between hypothetical substrates and inhibitors of CYP3A4. Predicted area under the plasma concentration-time profile ratios for substrates (AUCr = AUC_{(presence of precipitant)}/AUC_{(absence of precipitant)}) were compared between dynamic simulations (Simcyp^® V21) and corresponding static calculations, giving an inter-model discrepancy ratio (IMDR = AUCr_dynamic/AUCr_static). Dynamic simulations were conducted using a 'population' representative and a 'vulnerable patient' representative with maximal concentration (C_max) or average steady-state concentration (C_avg,ss) as the inhibitor driver concentrations. IMDRs outside the interval 0.8-1.25 were defined as discrepancy between models.

Results: The highest rate of IMDR <0.8 and IMDR >1.25 discrepancies in the 'population' representative was 85.9% and 3.1%, respectively, when using C_avg,ss as the inhibitor driver concentration. Using the 'vulnerable patient' representative showed the highest rate of IMDR >1.25 discrepancies at 37.8%.

Conclusion: Static models are not equivalent to dynamic models for predicting metabolic DDIs via competitive CYP inhibition across diverse drug parameter spaces, particularly for vulnerable patients. Caution is warranted in drug development if static IVIVE approaches are used alone to evaluate metabolic DDI risks.