Integration of a Physiologically Based Pharmacokinetic and Pharmacodynamic Model for Tegoprazan and Its Metabolite: Application for Predicting Food Effect and Intragastric pH Alterations

In Vitro Metabolism and Transport Characteristics of Zastaprazan

Zastaprazan (JP-1366), a novel potassium-competitive acid blocker, is a new drug for the treatment of erosive esophagitis. JP-1366 is highly metabolized in human, mouse, and dog hepatocytes but moderately metabolized in rat and monkey hepatocytes when estimated from the metabolic stability of this compound in hepatocyte suspension and when 18 phase I metabolites and 5 phase II metabolites [i.e., N-dearylation (M6), hydroxylation (M1, M19, M21), dihydroxylation (M7, M8, M14, M22), trihydroxylation (M13, M18), hydroxylation and reduction (M20), dihydroxylation and reduction (M9, M16), hydrolysis (M23), hydroxylation and glucuronidation (M11, M15), hydroxylation and sulfation (M17), dihydroxylation and sulfation (M10, M12), N-dearylation and hydroxylation (M3, M4), N-dearylation and dihydroxylation (M5), and N-dearylation and trihydroxylation (M2)] were identified from JP-1366 incubation with the hepatocytes from humans, mice, rats, dogs, and monkeys. Based on the cytochrome P450 (CYP) screening test and immune-inhibition analysis with CYP antibodies, CYP3A4 and CYP3A5 played major roles in the metabolism of JP-1366 to M1, M3, M4, M6, M8, M9, M13, M14, M16, M18, M19, M21, and M22. CYP1A2, 2C8, 2C9, 2C19, and 2D6 played minor roles in the metabolism of JP-1366. UDP-glucuronosyltransferase (UGT) 2B7 and UGT2B17 were responsible for the glucuronidation of M1 to M15. However, JP-1366 and active metabolite M1 were not substrates for drug transporters such as organic cation transporter (OCT) 1/2, organic anion transporter (OAT) 1/3, organic anion transporting polypeptide (OATP)1B1/1B3, multidrug and toxic compound extrusion (MATE)1/2K, P-glycoprotein (P-gp), and breast cancer-resistant protein (BCRP). Only M1 showed substrate specificity for P-gp. The findings indicated that drug-metabolizing enzymes, particularly CYP3A4/3A5, may have a significant role in determining the pharmacokinetics of zastaprazan while drug transporters may only have a small impact on the absorption, distribution, and excretion of this compound.

Assessment of food effects during clinical development

Food-drug interactions frequently hamper oral drug development due to various physicochemical, physiological and formulation-dependent mechanisms. This has stimulated the development of a range of promising biopharmaceutical assessment tools which, however, lack standardized settings and protocols. Hence, this manuscript aims to provide an overview of the general approach and the methodology used in food effect assessment and prediction. For in vitro dissolution-based predictions, the expected food effect mechanism should be carefully considered when selecting the level of complexity of the model, together with its drawbacks and advantages. Typically, in vitro dissolution profiles are then incorporated into physiologically based pharmacokinetic models, which can estimate the impact of food-drug interactions on bioavailability within 2-fold prediction error, at least. Positive food effects related to drug solubilization in the GI tract are easier to predict than negative food effects. Preclinical animal models also provide a good level of food effect prediction, with beagle dogs remaining the gold standard. When solubility-related food-drug interactions have large clinical impact, advanced formulation approaches can be used to improve fasted state pharmacokinetics, hence decreasing the fasted/fed difference in oral bioavailability. Finally, the knowledge from all studies should be combined to secure regulatory approval of the labelling instructions.

Prediction of the Drug–Drug Interaction Potential between Tegoprazan and Amoxicillin/Clarithromycin Using the Physiologically Based Pharmacokinetic and Pharmacodynamic Model

Tegoprazan is a novel potassium-competitive acid blocker. This study investigated the effect of drug–drug interaction on the pharmacokinetics and pharmacodynamics of tegoprazan co-administered with amoxicillin and clarithromycin, the first-line therapy for the eradication of Helicobacter pylori, using physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) modeling. The previously reported tegoprazan PBPK/PD model was modified and applied. The clarithromycin PBPK model was developed based on the model provided by the SimCYP® compound library. The amoxicillin model was constructed using the middle-out approach. All of the observed concentration–time profiles were covered well by the predicted profiles with the 5th and 95th percentiles. The mean ratios of predicted to observed PK parameters, including the area under the curve (AUC), maximum plasma drug concentration (Cmax), and clearance, were within the 30% intervals for the developed models. Two-fold ratios of predicted fold-changes of Cmax and AUC from time 0 to 24 h to observed data were satisfied. The predicted PD endpoints, including median intragastric pH and percentage holding rate at pH above 4 or 6 on day 1 and day 7, were close to the corresponding observed data. This investigation allows evaluation of the effects of CYP3A4 perpetrators on tegoprazan PK and PD changes, thus providing clinicians with the rationale for co-administration dosing adjustment.

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 C_max 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 C_max 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.