Semi-mechanistic physiologically-based pharmacokinetic modeling of clinical glibenclamide pharmacokinetics and drug-drug-interactions

Abstract

We studied if the clinical pharmacokinetics and drug-drug interactions (DDIs) of the sulfonylurea-derivative glibenclamide can be simulated via a physiologically-based pharmacokinetic modeling approach. To this end, a glibenclamide PBPK-model was build in Simcyp using in vitro physicochemical and biotransformation data of the drug, and was subsequently optimized using plasma disappearance data observed after i.v. administration. The model was validated against data observed after glibenclamide oral dosing, including DDIs. We found that glibenclamide pharmacokinetics could be adequately modeled if next to CYP metabolism an active hepatic uptake process was assumed. This hepatic uptake process was subsequently included in the model in a non-mechanistic manner. After an oral dose of 0.875 mg predicted Cmax and AUC were 39.7 (95% CI:37.0-42.7)ng/mL and 108 (95% CI: 96.9-120)ng/mLh, respectively, which is in line with observed values of 43.6 (95% CI: 37.7-49.5)ng/mL and 133 (95% CI: 107-159)ng/mLh. For a 1.75 mg oral dose, the predicted and observed values were 82.5 (95% CI:76.6-88.9)ng/mL vs 91.1 (95% CI: 67.9-115.9) for Cmax and 224 (95% CI: 202-248) vs 324 (95% CI: 197-451)ng/mLh for AUC, respectively. The model correctly predicted a decrease in exposure after rifampicin pre-treatment. An increase in glibenclamide exposure after clarithromycin co-treatment was predicted, but the magnitude of the effect was underestimated because part of this DDI is the result of an interaction at the transporter level. Finally, the effects of glibenclamide and fluconazol co-administration were simulated. Our simulations indicated that co-administration of this potent CYP450 inhibitor will profoundly increase glibenclamide exposure, which is in line with clinical observations linking the glibenclamide-fluconazol combination to an increased risk of hypoglycemia. In conclusion, glibenclamide pharmacokinetics and its CYP-mediated DDIs can be simulated via PBPK-modeling. In addition, our data underline the relevance of modeling transporters on a full mechanistic level to further improve pharmacokinetic and DDI predictions of this sulfonylurea-derivative.

Keywords: ADME; AUC; C(max); CYP450; Clinical pharmacokinetics; DDI; Drug metabolism; Drug transport; EMA; European medicines agency; FDA; Glibenclamide; Glyburide; IVIVE; IndC(50); K(app); K(i); K(m); Medication safety; PBPK-model; Physiologically-based pharmacokinetic modeling; SU; V(max); V(ss); absorption, distribution, metabolism, excretion; area under the plasma concentration time curve; concentration at which half-maximum biotransformation rate is reached; concentration of mechanism-based inhibitor associated with half maximal inactivation rate; concentration resulting in 50% inhibition; cytochrome P450; drug–drug interaction; f(a); f(u); food and drug administration; fraction absorbed.; fraction unbound; in vitro–in vivo extrapolation; inactivation rate of the enzyme (1/h); inducer concentration that supports half maximal induction. Ind(max), maximal fold induction over control; inducer concentration that supports half maximal induction. Ind(max), maximal fold induction over control. IVIVE, in vitro–in vivo extrapolation; k(inact); maximum enzymatic biotransformation rate; maximum plasma concentration; physiologically-based pharmacokinetic model; sulfonylurea; volume of distribution at steady state.