Physiologically‐Based Pharmacokinetic Modeling for the Prediction of a Drug–Drug Interaction of Combined Effects on P‐glycoprotein and Cytochrome P450 3A

Abstract

Direct oral anticoagulants, like apixaban and rivaroxaban, are important for the treatment and prophylaxis of venous thromboembolism and to reduce the risk of stroke and systemic embolism in patients with nonvalvular atrial fibrillation. Since apixaban and rivaroxaban are predominantly eliminated by CYP3A and P-glycoprotein (P-gp), concomitant use of combined P-gp and strong CYP3A4 inhibitors and inducers should be avoided. Physiologically-based pharmacokinetic (PBPK) models for apixaban and rivaroxaban were developed to estimate the net effect of CYP3A induction, P-gp inhibition and P-gp induction by rifampicin. The disposition of rivaroxaban is more complex compared to apixaban since both hepatic and renal P-gp is considered to contribute to rivaroxaban elimination. Furthermore, organic anion transporter-3 (OAT3), a renal uptake transporter, may also contribute the elimination of rivaroxaban from systemic circulation. The models were verified with observed clinical drug-drug interactions with CYP3A and P-gp inhibitors. With the developed models, the predicted AUC and C max ratios were 0.43 and 0.48, respectively, for apixaban, and 0.50 to 0.52 and 0.72 to 0.73, respectively, for rivaroxaban when co-administered with 600 mg multiple dose of rifampicin and which were very close to observed data. The impact of each of the elimination pathways was assessed for rivaroxaban and inhibition of CYP3A led to a larger impact over intestinal and hepatic P-gp. Inhibition of renal OAT3 or P-gp led to an overall modest interaction. The developed apixaban and rivaroxaban models can be further applied to the investigation of interaction with other P-gp and/or CYP3A4 inhibitors and inducers.

Physiologically‐based pharmacokinetic modeling to evaluate in vitro‐to‐in vivo extrapolation for intestinal P‐glycoprotein inhibition

“…20 Subsequently, the vendor-verified verapamil and norverapamil models from Simcyp (Certara UK Ltd., Simcyp Division, Sheffield, UK) have been applied to the DDI prediction with bosutinib, dabigatran etexilate, and rivaroxaban with or without modifications. [27][28][29][30] Pgp K i values used in these models were 0.10 to 0.16 μM for verapamil and 0.04 to 0.3 μM for norverapamil, whereas PBPK models often utilized the lower end of the reported in vitro K i values as the worst-case scenario. Overall, in these reports, only one Pgp substrate was used for the model verification and/or application, resulting in no comparisons of the predictive model performance among the different Pgp substrates.…”

“…PBPK models for verapamil and norverapamil were reported to account for the effects of both Pgp and CYP3A inhibition on DDIs with digoxin and midazolam, respectively 20 . Subsequently, the vendor‐verified verapamil and norverapamil models from Simcyp (Certara UK Ltd., Simcyp Division, Sheffield, UK) have been applied to the DDI prediction with bosutinib, dabigatran etexilate, and rivaroxaban with or without modifications 27–30 . Pgp K i values used in these models were 0.10 to 0.16 μM for verapamil and 0.04 to 0.3 μM for norverapamil, whereas PBPK models often utilized the lower end of the reported in vitro K i values as the worst‐case scenario.…”

Application of a physiologically based pharmacokinetic model of rivaroxaban to prospective simulations of drug–drug–disease interactions with protein kinase inhibitors in cancer‐associated venous thromboembolism

“…For instance, Grillo et al estimated the fractional CYP3A4 and CYP2J2 metabolism in the liver to be 0.37 and 0.29 respectively in their PBPK analysis, and utilizing similar estimates, we previously demonstrated how the enzyme-mediated DDI between rivaroxaban and ketoconazole (CYP3A4/2J2 inhibitor) was adequately recapitulated. 7,8 Conversely, Otsuka et al adjusted the fractional CYP3A4/2J2 metabolism to recapitulate the extent of DDI between rivaroxaban and fluconazole, 59 and neither OAT3 interaction nor simulation of the reduced CL R of rivaroxaban due to the clinical DDI with fluconazole was considered. 44 As our preliminary data demonstrated that fluconazole is an inhibitor of OAT3-mediated uptake of rivaroxaban (Figure S4), future PBPK modelling of this DDI should be performed with the consideration of OAT3 interaction.…”

Application of physiologically-based pharmacokinetic modeling to predict drug-drug interactions of dronedarone as a perpetrator with oral anti-coagulants

“…The apixaban and rivaroxaban PBPK models were based on the study by Otsuka et al and were kindly provided by the authors [20]. In the study by Otsuka et al, the rivaroxaban model was validated with the DDI study results with ketoconazole, ritonavir, clarithromycin, erythromycin, verapamil, and rifampicin.…”