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Poster at PAGE 2022: Development of a physiological-based pharmacokinetic model for tegoprazan: its application to prediction of drug-drug interactions with clarithromycin #384
Introductions: Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea). It exhibits its antisecretory effects by competitively and reversibly blocking the availability of K+ of the H+, K+-ATPase. Tegoprazan has been approved in South Korea for the treatment of gastroesophageal reflux disease (GERD) in 2019, and for the treatment of gastric ulcers and Helicobacter pylori infection in 20201. It has been reported that Tegoprazan is a potential substrate of cytochrome P450 (CYP) 3A4 enzyme 1,2. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, investigation of drug-drug interactions (DDI) between tegoprazan and other CYP3A4 perpetrators is imperative.
Objectives:
Develop a physiological-based pharmacokinetic (PBPK) model for tegoprazan using PKsim®.
Simulate DDI profile of tegoprazan and clarithromycin, a potent inhibitor of CYP3A4: tegoprazan 100mg and clarithromycin 500 mg twice daily for 6 days, then tegoprazan 100mg and clarithromycin 500 mg once daily for the 7th day.
Methods: The whole-body PBPK models of tegoprazan were developed and evaluated using a total of 16 clinical PK profiles of tegoprazan and its major metabolite, namely M1, following oral administration of tegoprazan in humans. These clinical PK profiles covered a wide tegoprazan dose range (from 50 to 400 mg) with both single- and multiple-dose profiles. The hepatic metabolism of tegoprazan by CYP3A4 was described by following the first-order kinetics. Physiological-dependent parameters from PK-Sim® at default values were used. Drug-dependent parameters (i.e., physicochemical and ADME properties) were extracted from the literature. Input parameters not available from the literature were optimized by fitting the model to the observed dataset. Finally, some model parameters were refined to get the best fit.
Results: The present study successfully developed a PBPK model for tegoprazan. For tegoprazan, the renal plasma clearance (1.31 L/h), solubility (45.3 mg/L), and the fraction unbound (12.4%) were fixed at the values picked up from the literature. The lipophilicity (logP 0.94), specific intestinal permeability (6.52 E-7 cm/s), and the specific CYP3A4 clearance (0.27 µM/min) were identified by fitting the model with the clinical dataset. For tegoprazan metabolite, M1, two parameters were optimized, including the fraction unbound (13.88%) and the total renal clearance (0.068L/h). The developed PBPK model describes the PK profiles of both tegoprazan and M1 well, with 98.3% of the predicted concentrations lying between the two-fold border of the observed concentrations. This model was then applied to predict DDI between tegoprazan and clarithromycin. The result was that clarithromycin significantly increased the exposure of tegoprazan. The Cmax and AUC of tegoprazan increased 1.7 folds (from 1074 ng/mL to 1826 ng/mL) and 2.2 folds (from 5237.1 ng/h/mL to 11590 ng/h/mL), respectively. The result was close consistent with the observed DDI profile (2.2 folds and 2.7 folds, respectively)3.
Conclusions: A PBPK model for tegoprazan was successfully developed from clinical PK profiles of both tegoprazan and its metabolite, M1, following oral administration of tegoprazan. The model was then applied to simulate DDI profile between tegoprazan and clarithromycin. The Cmax and AUC of tegoprazan increased 1.7 folds and 2.2 folds, respectively, when the drug was administered with clarithromycin. The result was consistent with the observed DDI profile (2.2 folds and 2.7 folds, respectively). This developed PBPK model can be applied to predict DDI between tegoprazan and other CYP3A4 perpetrators.
Lien Thi Ngo, Jaeyeon Lee, Quyen Thi Tran, Sungwoo Goo, Hwi-Yeol Yun, Jung-Woo Chae
https://www.page-meeting.org/default.asp?abstract=10160
Introductions: Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea). It exhibits its antisecretory effects by competitively and reversibly blocking the availability of K+ of the H+, K+-ATPase. Tegoprazan has been approved in South Korea for the treatment of gastroesophageal reflux disease (GERD) in 2019, and for the treatment of gastric ulcers and Helicobacter pylori infection in 20201. It has been reported that Tegoprazan is a potential substrate of cytochrome P450 (CYP) 3A4 enzyme 1,2. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, investigation of drug-drug interactions (DDI) between tegoprazan and other CYP3A4 perpetrators is imperative.
Objectives:
Methods: The whole-body PBPK models of tegoprazan were developed and evaluated using a total of 16 clinical PK profiles of tegoprazan and its major metabolite, namely M1, following oral administration of tegoprazan in humans. These clinical PK profiles covered a wide tegoprazan dose range (from 50 to 400 mg) with both single- and multiple-dose profiles. The hepatic metabolism of tegoprazan by CYP3A4 was described by following the first-order kinetics. Physiological-dependent parameters from PK-Sim® at default values were used. Drug-dependent parameters (i.e., physicochemical and ADME properties) were extracted from the literature. Input parameters not available from the literature were optimized by fitting the model to the observed dataset. Finally, some model parameters were refined to get the best fit.
Results: The present study successfully developed a PBPK model for tegoprazan. For tegoprazan, the renal plasma clearance (1.31 L/h), solubility (45.3 mg/L), and the fraction unbound (12.4%) were fixed at the values picked up from the literature. The lipophilicity (logP 0.94), specific intestinal permeability (6.52 E-7 cm/s), and the specific CYP3A4 clearance (0.27 µM/min) were identified by fitting the model with the clinical dataset. For tegoprazan metabolite, M1, two parameters were optimized, including the fraction unbound (13.88%) and the total renal clearance (0.068L/h). The developed PBPK model describes the PK profiles of both tegoprazan and M1 well, with 98.3% of the predicted concentrations lying between the two-fold border of the observed concentrations. This model was then applied to predict DDI between tegoprazan and clarithromycin. The result was that clarithromycin significantly increased the exposure of tegoprazan. The Cmax and AUC of tegoprazan increased 1.7 folds (from 1074 ng/mL to 1826 ng/mL) and 2.2 folds (from 5237.1 ng/h/mL to 11590 ng/h/mL), respectively. The result was close consistent with the observed DDI profile (2.2 folds and 2.7 folds, respectively)3.
Conclusions: A PBPK model for tegoprazan was successfully developed from clinical PK profiles of both tegoprazan and its metabolite, M1, following oral administration of tegoprazan. The model was then applied to simulate DDI profile between tegoprazan and clarithromycin. The Cmax and AUC of tegoprazan increased 1.7 folds and 2.2 folds, respectively, when the drug was administered with clarithromycin. The result was consistent with the observed DDI profile (2.2 folds and 2.7 folds, respectively). This developed PBPK model can be applied to predict DDI between tegoprazan and other CYP3A4 perpetrators.