In vitro-in vivo correlation of the inhibition potency of sodium-glucose cotransporter inhibitors in rat: a pharmacokinetic and pharmacodynamic modeling approach.

To evaluate the relationship between the in vitro and in vivo potency of sodium-glucose cotransporter (SGLT) inhibitors, a pharmacokinetic and pharmacodynamic (PK-PD) study was performed using normal rats. A highly selective SGLT2 inhibitor, tofogliflozin, and four other inhibitors with different in vitro inhibition potency to SGLT2 and selectivity toward SGLT2, versus SGLT1 were used as test compounds, and the time courses for urinary glucose excretion (UGE) and the plasma glucose and compound concentrations were monitored after administration of the compounds. A PK-PD analysis of the UGE caused by SGLT inhibition was performed on the basis of a nonlinear parallel tube model that took into consideration the consecutive reabsorption by different glucose transport properties of SGLT2 and SGLT1. The model adequately captured the time course of cumulative UGE caused by SGLT inhibition; then, the in vivo inhibition constants (Ki) of inhibitors for both SGLT1 and SGLT2 were estimated. The in vivo selectivity toward SGLT2 showed a good correlation with the in vitro data (r = 0.985; P < 0.05), with in vivo Ki values for SGLT2 in the range of 0.3-3.4-fold the in vitro data. This suggests that in vitro inhibition potency to both SGLT2 and SGLT1 is reflected in vivo. Furthermore, the complementary role of SGLT1 to SGLT2 and how selectivity toward SGLT2 affects the inhibitory potency for renal glucose reabsorption were discussed using the PK-PD model.

Quantitative prediction of mechanism-based inhibition caused by mibefradil in rats.

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 µM) was a good deal lower than that for CYP2C11 (6.82-11.4 µM). 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.