Stochastic Prediction of Cyp3a-Mediated Inhibition of Midazolam Clearance by Ketoconazole

Abstract

ABSTRACT:Conventional methods to forecast CYP3A-mediated drug-drug interactions have not employed stochastic approaches that integrate pharmacokinetic (PK) variability and relevant covariates to predict inhibition in terms of probability and uncertainty. Empirical approaches to predict the extent of inhibition may not account for nonlinear or non-steady-state conditions, such as first-pass effects or accumulation of inhibitor concentration with multiple dosing. A physiologically based PK model was developed to predict the inhibition of CYP3A by ketoconazole (KTZ), using midazolam (MDZ) as the substrate. The model integrated PK models of MDZ and KTZ, in vitro inhibition kinetics of KTZ, and the variability and uncertainty associated with these parameters. This model predicted the time-and dose-dependent inhibitory effect of KTZ on MDZ oral clearance. The predictive performance of the model was validated using the results of five published KTZ-MDZ studies. The model improves the accuracy of predicting the inhibitory effect of increasing KTZ dosing on MDZ PK by incorporating a saturable KTZ efflux from the site of enzyme inhibition in the liver. The results of simulations using the model supported the KTZ dose of 400 mg once daily as the optimal regimen to achieve maximum inhibition by KTZ. Sensitivity analyses revealed that the most influential variable on the prediction of inhibition was the fractional clearance of MDZ mediated by CYP3A. The model may be used prospectively to improve the quantitative prediction of CYP3A inhibition and aid the optimization of study designs for CYP3A-mediated drug-drug interaction studies in drug development.Metabolism-based pharmacokinetic (PK) interactions are well recognized as a source of clinically significant adverse drug reactions Huang and Lesko, 2004). The early forecast of clinically significant metabolism-based pharmacokinetic drug-drug interactions is an increasingly important aspect of drug development. The assessment of drug-drug interaction potential before the conduct of a clinical trial often involves simple algebraic calculations. One such calculation is the ratio of the expected clinical exposure to the in vitro inhibition constant (K i ) of a new drug entity for a specific cytochrome P450 (Sahajwalla et al., 1999;Bjornsson et al., 2003). The primary sources of quantitative errors associated with this prediction approach are attributed to the experimental procedures used to determine the in vitro parameters, the variability in the intrinsic factors associated with PK properties of the inhibitor (e.g., absorption kinetics, plasma protein binding, and tissue partition coefficients), or the substrate (i.e., the fraction of total clearance attributed to the elimination pathway of interest), the extrinsic factors associated with the clinical study designs (e.g., dosing scheme, sampling design, and population demographics), and, most notably, the uncertainty in the effective concentration of the inhibitor at the enzyme site. In addition, complex drug disposition proper...

A Bayesian Meta-Analysis on Published Sample Mean and Variance Pharmacokinetic Data with Application to Drug–Drug Interaction Prediction

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“…Assuming a simultaneous oral dose KETO and an IV dose for MDZ, the interaction PK models follow equations (1) and (2) connected by (3). The inhibition constant Ki is assumed to take value of (0.0037, 0.01, 0.18) μM to assess their influence on DDI prediction.…”

“…The predicted logtransformed drug concentrations for KETO are calculated from the differential equation model (1) and the predicted log-transformed drug concentrations for MDZ are calculated from the differential equation model (2). Based on (known) DDI parameters, (Km I , Km S ,fu I , Ki,MW I ), the DDI outcome, AUCR, is then predicted by models (1) and (2) linked by (3). .…”

Towards an In Silico Approach to Personalized Pharmacokinetics

“…Michaelis-Menten equations are used for all enzymatic reactions. A competitive Michaelis-Menten inhibition model is used for this simulation as used for midazolam and ketoconazole inhibition by (Chien et al, 2006). In order to increase the predictive performance, a simplified pathway is used for the generation of simulation models from the viewpoint of the trade-off between model complexity and data availability.…”

In Vitro Evaluation of Reversible and Irreversible Cytochrome P450 Inhibition: Current Status on Methodologies and their Utility for Predicting Drug–Drug Interactions

“…The inhibitor concentration, [I], is often based on plasma concentrations because concentrations at the site of inhibition cannot be measured (47,44). Because of this, various inhibitor concentrations including plasma C max , plasma free C max , portal vein C max (inlet liver concentration) and portal vein free C max have been explored for prediction accuracy (44,(47)(48)(49). In some cases, the use of portal vein C max is fairly accurate in DDI prediction and for other compounds the portal vein free C max or plasma C max is reasonable as well.…”