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.

Fig. 1

A simplified schematic representation of the study design used. The top level represents ‘Sim,’ the overall simulation. Each subsequent level represents parameters and possible values for said parameter evaluated in this study. This pattern continues for all possible parameters represented by ‘Parameter X’. Each simulation result is generated from the various combinations of the different values for each parameter. Refer to Table 2 for every explored parameter and their individual values. BID twice daily, QD once daily

Fig. 2

A basic 3D plot used to show discrepancies between models. IMDR is the inter-model discrepancy ratio. An IMDR of > 1.25 shows up as red zones and represents patient risk, IMDR of 0.8–1.25 shows up as green zones signifying no discrepancies between models, and IMDR of < 0.8 shows up as yellow zones indicating sponsor risk. k_a (x-axis) and K_i (z-axis) of the inhibitor range from 0.05 to 6 h^–1 and from 0.015 to 7.68 µM, respectively.

Fig. 3

A 3D representation of the parameter discrepancy space between static (C_avg,ss) and dynamic models when the inhibitor is administered in the 200 mg BID, Inhibitor fu_Gut: 1, and Substrate F_h: 0.26 scenario. Each row and column represent different Inhibitor V_ss and Substrate F_g values, respectively. The F_g values increase from left to right, while V_ss values increase from bottom to top. The discrepancy metric (IMDR) values are shown on the individual plot y-axis, and are colour coded according to the legend. k_a (x-axis) and K_i (z-axis) of the inhibitor range from 0.05 to 6 h^–1 and from 0.015 to 7.68 µM, respectively, as shown on the central plot schematic. BID twice daily, F_g fraction escaping gut metabolism, fu_Gut unbound fraction of drug in enterocytes, F_h fraction escaping hepatic metabolism, IMDR inter-model discrepancy ratio, k_a absorption rate constant, k_i inhibition constant, V_ss volume of distribution at steady state

Fig. 4

A 3D representation of the parameter discrepancy space between static (C_avg,ss) and dynamic models when the inhibitor is administered in the 400 mg QD, Inhibitor fu_Gut: 0.06, and Substrate F_h: 0.26 scenario. Each row and column represent different Inhibitor V_ss and Substrate F_g values, respectively. The F_g values increase from left to right, while V_ss values increase from bottom to top. The discrepancy metric (IMDR) values are shown on the individual plot y-axis, and are colour coded according to the legend. k_a (x-axis) and K_i (z-axis) of the inhibitor range from 0.05 to 6 h^–1 and from 0.015 to 7.68 µM, respectively, as shown on the central plot schematic. F_g fraction escaping gut metabolism, fu_Gut unbound fraction of drug in enterocytes, F_h fraction escaping hepatic metabolism, IMDR inter-model discrepancy ratio, k_a absorption rate constant, k_i inhibition constant, QD once daily, V_ss volume of distribution at steady state

Fig. 5

A 3D representation of the parameter discrepancy space between static (C_avg,ss) and dynamic models when the inhibitor is administered in the 400 mg QD, Inhibitor fu_Gut: 1, and Substrate F_h: 0.26 scenario. Each row and column represent different Inhibitor V_ss and Substrate F_g values, respectively. The F_g values increase from left to right, while V_ss values increase from bottom to top. The discrepancy metric (IMDR) values are shown on the individual plot y-axis, and are colour coded according to the legend. k_a (x-axis) and K_i (z-axis) of the inhibitor range from 0.05 to 6 h^–1 and from 0.015 to 7.68 µM, respectively, as shown on the central plot schematic. F_g fraction escaping gut metabolism, fu_Gut unbound fraction of drug in enterocytes, F_h fraction escaping hepatic metabolism, IMDR inter-model discrepancy ratio, k_a absorption rate constant, k_i inhibition constant, QD once daily, V_ss volume of distribution at steady state

Fig. 6

A 3D representation of the parameter discrepancy space between static (C_avr,ss) and dynamic models using the PopRep AUCr (average individual) (A) and 95th AUCr percentile (vulnerable individual) (B), when the inhibitor is administered in the 200 mg BID, Inhibitor fu_Gut: 0.06, and Substrate F_h: 0.85 scenario. Each row and column represent different Inhibitor V_ss and Substrate F_g values, respectively. The F_g values increase from left to right, while V_ss values increase from bottom to top. The discrepancy metric (IMDR) values are shown on the individual plot y-axis, and are colour coded according to the legend. k_a (x-axis) and K_i (z-axis) of the inhibitor range from 0.05 to 6 h^–1 and from 0.015 to 7.68 µM, respectively, as shown on the central plot schematic. AUCr area under the plasma–concentration time curve ratio, BID twice daily, F_g fraction escaping gut metabolism, fu_Gut unbound fraction of drug in enterocytes, F_h fraction escaping hepatic metabolism, IMDR inter-model discrepancy ratio, k_a absorption rate constant, k_i inhibition constant, PopRep population representative, V_ss volume of distribution at steady state