Prediction of mechanism -based inhibition of CYP3A by single and multiple inhibitors

Xin Zhang, Purdue University

Abstract

The prediction of the extent of drug-drug interactions (DDIs) between the mechanism-based inhibitors erythromycin (ERY), diltiazem (DTZ), and the cytochrome P450 3A4 (CYP3A4) substrate midazolam (MDZ) is confounded by the time and concentration-dependant clearance of the inhibitor. Predictive models of complex DDIs between multiple inhibitors and their metabolites have not been evaluated. The ultimate goal of this work is to predict mechanism-based inhibition (MBI) involving multiple inhibitors. The specific aims are to validate an interaction model for CYP3A4 that takes into consideration the simultaneous reversible and irreversible inhibition by multiple inhibitors; confirm the validity of the in vitro-in vivo correlation approach for MBI; and to develop a physiologically-based pharmacokinetic (PBPK) model for the prediction of clinical DDIs. In the first part of this work, ERY, DTZ, and their major metabolites, N-desmethyl erythromycin (nd-ERY) and N-desmethyl diltiazem (nd-DTZ), were chosen to evaluate the interaction model. kinact (rate constant for maximal inactivation), KI (inhibitor concentration at 50% maximal inactivation), and Ki (reversible inhibition constant) were estimated for ERY, DTZ, nd-ERY, and nd-DTZ, respectively, using cDNA-expressed CYP3A4 and human liver microsomes under optimal experimental conditions. To test the interaction model, combinations of drugs and metabolites were incubated at concentrations equal to KI, ½KI and 2K I of each inhibitor for specified durations in both systems. The model was further evaluated by the incubation of combinations of drugs with the substrate testosterone for ten minutes. The time course of CYP3A4 inactivation was also determined. CYP3A4 inhibition in the presence of drug mixtures was predicted from the inhibition parameters determined for each drug alone. The CYP3A4 activity in the presence of multiple inhibitors was well predicted by the interaction model incorporating the competition between the inhibitors (%mean error and %mean absolute error ranged from -0.15 to 0.14, and 0.09 to 0.18, respectively). In conclusion, the interaction model predicted the combined effect of multiple inhibitors on CYP3A4 inhibition in vitro . Moreover, simultaneous reversible and irreversible inhibition effects should be taken into account in a reaction mixture of substrate and multiple inhibitors of CYP3A4. In the second part, the interaction between ERY and MDZ was investigated and predicted using a chronically-cannulated rat model. Specifically, the change in the in vivo intrinsic clearance (CLint) of MDZ following single and multiple doses of ERY were estimated. The change in the CYP3A2 activity in vitro in the livers dissected from rats following single and multiple dose of ERY was also determined. Enzyme inhibition parameters (kinact, KI, and Ki) of ERY were estimated in vitro using rat liver microsomes made from the control rats. A physiologically-based interaction model was developed and the observed change in CLint of MDZ in vivo or the CYP3A2 activity in vitro was quantitatively predicted by the model with the in vitro-estimated enzyme inhibition parameters. The study demonstrated the utility and feasibility of the physiological approach for the prediction of in vivo DDIs involving MBI using in vitro-estimated enzyme inhibition parameters. In the third part, physiologically-based pharmacokinetic (PBPK) models were developed for individual drugs based on the reported pharmacokinetic (PK) and physiological parameters. A compartment for the major metabolite of DTZ, nd-DTZ, was also incorporated in the DTZ model. The in vitro -estimated enzyme kinetic parameters (kinact, KI and Ki) were used to model the time course of changes in the amount of CYP3A4 in liver and gut wall, which in turn, determined the nonlinear elimination of MDZ, ERY, and DTZ, and the corresponding DDIs. The robustness of the model prediction was assessed by comparing the results of the prediction to published PK data for ERY, DTZ, and MDZ and the ERY/MDZ and DTZ/MDZ interaction. The in vitro-validated additive model was also incorporated into the PBPK model to predict the inhibition by ERY and DTZ in combination. The PBPK model incorporating gut wall interaction and the effect of active metabolite successfully predicted the nonlinear disposition of ERY, DTZ and the interactions of ERY and DTZ, alone and in combination, with MDZ. Moreover, model simulation suggested that the values of the in vitro-estimated inhibition parameters and CYP3A4 turnover rate are critical for the prediction.

Degree

Ph.D.

Advisors

Tisdale, Purdue University.

Subject Area

Pharmaceuticals

COinS