Integrating in vitro kinetic data from compounds exhibiting induction, reversible inhibition and mechanism-based inactivation: in vitro study design.

ABSTRACT

Drugdrug interactions continue to be an obstacle for the pharmaceutical industry in the development of potential drug candidates. Considering the number of compounds that have been withdrawn from the market due to drugdrug interactions (e.g. cisapride, terfenadine and mibefradil), more pressure is placed on the pharmaceutical industry to investigate potential interactions prior to regulatory submission. In particular, induction and inhibition of drug metabolizing enzymes can profoundly alter the pharmacological and toxicological effects observed during monotherapy. However, due to differences in the expression and regulation of both metabolic enzymes and nuclear receptors responsible for induction, in vivo studies with pre-clinical species are not predictive of the human clinical situation. Although in vitro kinetic data also have limitations when extrapolating in vivo, in vitro testing has become more commonplace due to reduced cost and higher throughput. However, in the in vitro setting, complex enzyme kinetics can alter the estimation of kinetic parameters. Time-dependent or non-Michaelis-Menten kinetics can alter parameter estimates if experimental conditions are not optimal, and can therefore confound clinical predictions. Furthermore, mechanism-based inactivation (MBI) will reduce the active enzyme pool, both in vitro and in vivo, and thus complicate any parameter estimates. To further complicate matters, some compounds (e.g., ritonavir) inhibit, induce, as well as cause mechanism-based enzyme inactivation. For compounds such as ritonavir, the accurate estimation of kinetic parameters requires optimal experimental design at a minimum. This review will highlight the challenges in estimating enzyme kinetic parameters when both inhibition and induction are present, and will offer experimental viewpoints for the optimization of the experimental conditions.