Pharmacokinetic and Pharmacodynamic Interaction of Finerenone with Diltiazem, Fluconazole, and Ritonavir in Rats.

BACKGROUND AND OBJECTIVES: Finerenone, a novel selective non-steroidal mineralocorticoid receptor antagonist, has been indicated in chronic kidney disease associated with type 2 diabetes mellitus. Considering the potential complications of diabetes, finerenone can be co-administered with various drugs, including fluconazole, diltiazem, and ritonavir. Given that finerenone is a substrate of cytochrome P450 (CYP) 3A4, the concurrent administration of finerenone with CYP3A4 inhibitors (diltiazem or fluconazole or ritonavir) could potentially lead to drug interactions, which may cause adverse events such as hyperkalemia. No studies have investigated interactions between finerenone and diltiazem or fluconazole or ritonavir. Therefore, this study aims to investigate the pharmacokinetic interaction of finerenone with diltiazem or fluconazole or ritonavir and to evaluate the impact of fluconazole on the pharmacodynamics of finerenone. METHODS: The pharmacokinetic study included four rat groups (n = 8 rats/group), including a control group (finerenone alone) and test groups (finerenone pretreated with diltiazem or fluconazole or ritonavir) using both non-compartment analysis (NCA) and population pharmacokinetic (pop-PK) modeling. The pop-PK model was developed using non-linear mixed-effects modeling in NONMEM® (version 7.5.0). In the pharmacodynamic study, serum potassium (K+) levels were measured to assess the effects of fluconazole on finerenone-induced hyperkalemia. RESULTS: The NCA results indicated that the area under the plasma concentration-time curve (AUC) of finerenone increased by 1.86- and 1.95-fold when coadministered with fluconazole and ritonavir, respectively. In contrast, diltiazem did not affect the pharmacokinetics of finerenone. The pharmacokinetic profiles of finerenone were best described by a one-compartment disposition with first-order elimination and dual first-order absorption kinetics. The pop-PK modeling results demonstrated that the apparent clearance of finerenone decreased by 50.3% and 49.2% owing to the effects of fluconazole and ritonavir, respectively. Additionally, the slow absorption rate, which represents the absorption in the distal intestinal tract of finerenone, increased by 55.7% due to the effect of ritonavir. Simultaneously, a pharmacodynamic study revealed that finerenone in the presence of fluconazole caused a significant increase in K+ levels compared with finerenone alone. CONCLUSIONS: Coadministration of finerenone with fluconazole or ritonavir increased finerenone exposure in rats. Additionally, the administration of finerenone in the presence of fluconazole resulted in elevated K+ levels in rats. Further clinical studies are required to validate these findings.

Application of the Population Pharmacokinetics Model-Based Approach to the Prediction of Drug-Drug Interaction between Rivaroxaban and Carbamazepine in Humans.

Rivaroxaban (RIV) is one of the direct oral anticoagulants used to prevent and treat venous and arterial thromboembolic events. Considering the therapeutic indications, RIV is likely to be concomitantly administered with various other drugs. Among these is carbamazepine (CBZ), one of the recommended first-line options to control seizures and epilepsy. RIV is a strong substrate of cytochrome P450 (CYP) enzymes and Pgp/BCRP efflux transporters. Meanwhile, CBZ is well known as a strong inducer of these enzymes and transporters. Therefore, drug-drug interaction (DDI) between CBZ and RIV is expected. This study aimed to predict the DDI profile of CBZ and RIV in humans by using a population pharmacokinetics (PK) model-based approach. We previously investigated the population PK parameters of RIV administered alone or with CBZ in rats. In this study, those parameters were extrapolated from rats to humans by using simple allometry and liver blood flow scaling, and then applied to back-simulate the PK profiles of RIV in humans (20 mg RIV per day) used alone or with CBZ (900 mg CBZ per day). Results showed that CBZ significantly reduced RIV exposure. The AUCinf and Cmax of RIV decreased by 52.3% and 41.0%, respectively, following the first RIV dose, and by 68.5% and 49.8% at the steady state. Therefore, the co-administration of CBZ and RIV warrants caution. Further studies investigating the extent of DDIs between these drugs should be conducted in humans to fully understand their safety and effects.

Development of a Physiologically Based Pharmacokinetic Model for Tegoprazan: Application for the Prediction of Drug-Drug Interactions with CYP3A4 Perpetrators.

Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea) for the treatment of gastroesophageal reflux disease and helicobacter pylori infections. Tegoprazan is mainly metabolized by cytochrome P450 (CYP) 3A4. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, the investigation of drug-drug interactions (DDI) between tegoprazan and CYP3A4 perpetrators is imperative. In the present study, we first aimed to develop a physiologically based pharmacokinetic (PK) model for tegoprazan and its major metabolite, M1, using PK-Sim®. This model was applied to predict the DDI between tegoprazan and CYP3A4 perpetrators. Clarithromycin, a potent inhibitor of CYP3A4, and rifampicin, a strong inducer of CYP3A4, were selected as case studies. Our results show that clarithromycin significantly increased the exposure of tegoprazan. The area under the concentration-time curve (AUC) and Cmax of tegoprazan in the steady state increased up to 4.54- and 2.05-fold, respectively, when tegoprazan (50 mg, twice daily) was coadministered with clarithromycin (500 mg, three times daily). Rifampicin significantly reduced the exposure of tegoprazan. The AUC and Cmax of tegoprazan were reduced by 5.71- and 3.51-fold when tegoprazan was coadministered with rifampicin (600 mg, once daily). Due to the high DDI potential, the comedication of tegoprazan with CYP3A4 perpetrators should be controlled. The dosage adjustment for each individual is suggested.

Application of physiologically-based pharmacokinetic model approach to predict pharmacokinetics and drug-drug interaction of rivaroxaban: A case study of rivaroxaban and carbamazepine.

Rivaroxaban (RIV; Xarelto; Janssen Pharmaceuticals, Beerse, Belgium) is one of the direct oral anticoagulants. The drug is a strong substrate of cytochrome P450 (CYP) enzymes and efflux transporters. This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model for RIV. It contained three hepatic metabolizing enzyme reactions (CYP3A4, CYP2J2, and CYP-independent) and two active transporter-mediated transfers (P-gp and BCRP transporters). To illustrate the performance of the developed RIV PBPK model on the prediction of drug-drug interactions (DDIs), carbamazepine (CBZ) was selected as a case study due to the high DDI potential. Our study results showed that CBZ significantly reduces the exposure of RIV. The area under the concentration-time curve from zero to infinity (AUCinf ) of RIV was reduced by 35.2% (from 2221.3 to 1438.7 ng*h/ml) and by 25.5% (from 2467.3 to 1838.4 ng*h/ml) after the first dose and at the steady-state, respectively, whereas the maximum plasma concentration (Cmax ) of RIV was reduced by 37.7% (from 266.3 to 166.1 ng/ml) and 36.4% (from 282.3 to 179.5 ng/ml), respectively. The developed PBPK model of RIV could be paired with PBPK models of other interested perpetrators to predict DDI profiles. Further studies investigating the extent of DDI between CBZ and RIV should be conducted in humans to gain a full understanding of their safety and effects.

Effects of Carbamazepine and Phenytoin on Pharmacokinetics and Pharmacodynamics of Rivaroxaban.

Rivaroxaban (RIV) is commonly prescribed with carbamazepine or phenytoin (CBZ/PHT) in post-stroke seizure or post-stroke epilepsy patients. Although adverse events have been reported in several previous studies when they are coadministered, there are no studies of the interactions between these drugs. Therefore, our study was conducted to solve this lack of information. The potential effects of CBZ/PHT were investigated by comparing the pharmacokinetic (PK) and pharmacodynamic (PD) parameters of RIV between the control group (RIV alone) and the test groups (RIV administered with CBZ/PHT) in rats using the noncompartmental analysis (NCA) and the compartmental model approach. The NCA results indicate that AUCt of RIV decreased by 57.9% or 89.7% and Cmax of RIV decreased by 43.3% or 70.0% after administration of CBZ/PHT, respectively. In addition, both CBZ and PHT generally reduced the effects of RIV on the prothrombin times of the blood samples. PK profiles of RIV were most properly described by a two-compartment disposition model with a mixed first- and zero-order absorption kinetics and a first-order elimination kinetics. The compartmental model approach showed that a 211% or 1030% increase in CL/F of RIV and a 33.9% or 43.4% increase in D2 of RIV were observed in the test groups by the effects of CBZ/PHT, respectively. In conclusion, CBZ and PHT significantly reduced RIV exposure and therefore reduced the therapeutic effects of RIV. Consequently, this might result in adverse events due to insufficient RIV concentration to attain its therapeutic effects. Further studies are needed to validate this finding.

Clinical Evaluation of Acetaminophen-Galgeuntang Interaction Based on Population Approaches.

Galgeuntang (GGT), a traditional herbal medicine, is widely co-administered with acetaminophen (AAP) for treatment of the common cold, but this combination has not been the subject of investigation. Therefore, we investigated the herb-drug interaction between GGT and AAP by population pharmacokinetics (PKs) modeling and simulation studies. To quantify PK parameters and identify drug interactions, an open label, three-treatment, three-period, one-sequence (AAP alone, GGT alone, and AAP and GGT in combination) clinical trial involving 12 male healthy volunteers was conducted. Ephedrine (EPD), the only GGT component detected, was identified using a one-compartment model. The PKs of AAP were described well by a one-compartment model and exhibited two-phase absorption (rapid followed by slow) and first-order elimination. The model showed that EPD significantly influenced the PKs of AAP. The simulation results showed that at an AAP dose of 1000 mg × 4 times daily, the area under the concentration versus time curve of AAP increased by 16.4% in the presence of GGT compared to AAP only. In conclusion, the PKs of AAP were affected by co-administration of GGT. Therefore, when AAP is combined with GGT, adverse effects related to overdose of AAP could be induced possibly.