Quantitative Prediction of Mechanism-Based Inhibition Caused by Mibefradil in Rats

Abstract

ABSTRACT:It was previously demonstrated that mibefradil, which shows mechanism-based inhibition in humans, also caused drug-drug interactions (DDIs) with midazolam (MDZ) in rats. In this study, we aimed to quantitatively predict the DDIs observed in rats using a physiologically based pharmacokinetic (PBPK) model from in vitro inactivation parameters. For more precise predictions, contribution ratios of cytochrome P450 (P450) isozymes involved in MDZ metabolism and inactivation parameters of mibefradil against each isozyme were incorporated in the predictive model. The evaluation of metabolic rate using recombinant P450s suggested that CYP3A2 and CYP2C11 contributed to 89 and 11% of MDZ metabolism, respectively. Inactivation studies of mibefradil against the two isozymes showed that the maximal inactivation rate constants (k inact ) were considerable in both isozymes (0.231-0.565 min ؊1 ), whereas the inhibitor concentration producing half the k inact (K I, app ) of CYP3A2 (0.263-0.410 M) was a good deal lower than that for CYP2C11 (6.82-11.4 M). As a result of predicting the DDIs using the PBPK model, predicted increases in areas under the concentration-time curve of MDZ with coadministration of mibefradil (284 and 510% at 6 and 12 mg/kg mibefradil, respectively) closely corresponded to the observed values (226 and 545%, respectively). From those results, it was thought that the construction of a predictive model for DDIs using the PBPK model in detail would enable us to quantitatively predict in vivo DDIs from in vitro data. This approach to predict DDIs on the basis of the contributing isozymes would be important for predicting clinical DDIs of drugs metabolized by multiple enzymes.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

“…Dynamic modeling takes into consideration the victim as well as inactivator concentrationtime profile upon a specified dosing regimen, as well as the change in enzyme levels with time. Some literature reports suggest that dynamic models may be better than static models for DDI predictions Iga & Kirayama, 2017;Sekiguchi, et al, 2011;.…”

Prediction of Human Nonlinear Pharmacokinetics of a New Bcl-2 Inhibitor Using PBPK Modeling and Interspecies Extrapolation Strategy

“…HLMs were prewarmed with S 55746 (0, 0.25, 0.5, 1, 2, 3, 10, and 30 mM) for 5 minutes at 37°C. The preincubation was initiated by the addition of NADPH or buffer, and at different preincubation times (0, 2, 4, 6, 8, 12, and 14 minutes) an aliquot of the preincubate was collected and added to a vial containing midazolam (Sekiguchi et al, 2011). After an incubation of 7 minutes, reactions were terminated by the addition of methanol.…”

The National Institutes of Health Microphysiological Systems Program focuses on a critical challenge in the drug discovery pipeline

“…Understanding the limitations of these model systems will also be important. For instance, in cases where recognition of toxicity in the human population has been delayed - such as seen in the marked hypo tension with clozapine, the pulmonary fibrosis and marrow toxicity with tocainide, the valvulopathy associated with fenfluramine hydrochloride and the subarachnoid hemorrhage associated with phenyl propanol-amine [13] - the application of relatively short-term exposures of drugs in the MPS may limit the predictive value of the system for delayed toxicities.…”