Prediction of Drug-Drug Interaction Potential of Tegoprazan Using Physiologically Based Pharmacokinetic Modeling and Simulation

Phase I Study to Evaluate the Effect of Hepatic Impairment on Pharmacokinetics and Safety of Tegoprazan, a Potassium Competitive Acid Blocker

INTRODUCTION

Tegoprazan is a potassium-competitive acid blocker, and its systemic exposure is presumably affected by hepatic clearance and bioavailability. This study aimed to investigate the effect of hepatic impairment (HI) on the safety and pharmacokinetics of tegoprazan and metabolite.

METHODS

An open-label, multicenter, parallel-group study was conducted in patients with mild (n = 8), moderate (n = 8) and severe (n = 1) HI according to the Child-Pugh classification as well as controls. Healthy subjects (n = 8) were matched to patients with the moderate category based on age, body mass index and sex. Blood and urine samples were obtained to evaluate the concentrations of tegoprazan and metabolite (M1) until 48 h after a single oral administration of 50 mg of tegoprazan.

RESULTS

The geometric mean ratio with a 90% confidence interval of maximum plasma concentration and area under the plasma concentration-time curve for tegoprazan in patients with impaired hepatic function compared to controls were 0.8228 (0.4997-1.3550) and 1.2264 (0.7447-2.0197) in the mild category, 1.0332 (0.6274-1.7015) and 1.7676 (1.0733-2.9109) in the moderate category, and 1.0699 (0.3713-3.0823) and 1.9567 (0.6792-5.6377) in the severe category. The half-life, apparent clearance, renal clearance, and fraction unbound to plasma protein were comparable across study groups. The plasma concentration of M1 increased and decreased faster in the normal group. Tegoprazan was generally well tolerated in patients with HI.

CONCLUSIONS

Systemic exposure to tegoprazan tended to be increased in subjects with HI. The difference between patients with mild HI and the controls was deemed not to require dose adjustment for tegoprazan.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov identifiers: NCT04494269 (31 Jul 2020).

Efficacy of Potassium-Competitive Acid Blockers Versus Proton Pump Inhibitors for Gastric Ulcers: Bayesian and Frequentist Network Meta-Analysis With Cross-Inference Through a Quality management System

Purpose

Proton pump inhibitors (PPIs) have been the mainstay treatment for gastric ulcer (GU) for over 30 years. However, since the discovery of a new class of acid suppressants, potassium-competitive acid blockers (P-CABs), the desire for a therapeutic agent has continued and the clinical trials on P-CABs have been conducted. In this regard, we aimed to assess whether P-CABs are noninferior to PPIs in patients with GU in terms of efficacy.

Methods

We performed a systematic review and network meta-analysis (NMA) based on randomized controlled trials (RCTs). Additionally, we used a new methodology of inference concept with the purpose of grouping between P-CABs and PPIs. Moreover, our quality management system was integrated throughout the research to ensure data accuracy.

Findings

We initially screened 438 studies and extracted 10 homogeneous GU RCTs with 6315 participants. The odds ratios (ORs) for the 4-week cure rate in Bayesian + frequentist NMA, tegoprazan 100 mg (OR = 4.14, 95% credible interval [CI] 0.56-26.3) and pantoprazole 40 mg (OR = 4.12, 95% CI 1.90-8.88) were the largest, respectively. The ORs for the 8-week cure rate in Bayesian + frequentist NMA, lansoprazole 30 mg (OR = 8.77, 95% credible interval [CI] 0.95-78.9) and lansoprazole 30 mg (OR = 7.91, 95% CI 2.60-24.03) was the largest, respectively.

Conclusions

The results of the NMA reveal that the cure rates of P-CABs in cases of GU were not inferior to those of PPIs. As the inference by grouping PPIs and P-CABs, the results showed similar trends in terms of effectiveness between the two therapeutic classes.

Prediction of combination effect of quinidine on the pharmacokinetics of tipepidine using a physiologically based pharmacokinetic model

Tipepidine, an antitussive drug, has been reported to have central pharmacological effects and can be expected to be safely repositioned as treatment for psychiatric disorders. Since tipepidine requires three doses per day, development of a once-daily medication would be highly beneficial. Previously, we reported that combination use with quinidine, a CYP2D6 inhibitor, prolongs the half-life of tipepidine in chimeric mice with humanised liver.In this study, to predict this combination effect in humans, a physiologically based pharmacokinetic (PBPK) model was developed, and quantitative simulation was conducted. The simulation results indicated that concomitant administration of tipepidine with quinidine increased the predicted Cmax, AUC, and t1/2 of tipepidine in the Japanese population by 3.4-, 6.6-, and 2.4-fold, respectively.Furthermore, to compare with another approach that aims to prolong the half-life, the PK profile of tipepidine administered in hypothetical extended-release form was simulated. Extended-release form was predicted to be more influenced by CYP2D6 genotype than combination with quinidine, and the predicted plasma exposure was markedly increased in poor metabolizers, potentially leading to adverse effects.In conclusion, quantitative simulation using the PBPK model suggests the feasibility of the safe repositioning of tipepidine as a once-daily medication in combination with quinidine.

Efficacy and Tolerability of 14-Day Tegoprazan- versus Rabeprazole-Based Triple Therapy for Eradication of Helicobacter pylori: A Real-World Evidence Study

Background/Aims

Tegoprazan, a new, fast, and strong potassium-competitive acid blocker, has been approved for the treatment of gastric acid-related diseases in Korea. However, real-world clinical data regarding this drug are scarce. We aimed to compare the Helicobacter pylori eradication rates of tegoprazan- and rabeprazole-based triple therapy.

Methods

We retrospectively reviewed data from patients who received first-line treatment for H. pylori infection using tegoprazan- or rabeprazole-based triple therapy for 2 weeks (50 mg tegoprazan or 20 mg rabeprazole+1,000 mg amoxicillin+500 mg clarithromycin twice daily). The primary endpoint was the eradication rate as determined by intention-to-treat analysis.

Results

Of the 677 patients included in our study, 344 and 333 received tegoprazan-based and rabeprazole-based triple therapy, respectively. The eradication rate from intention-to-treat analysis was 76.7% (95% confidence interval [CI], 72.1% to 81.0%) for tegoprazan-based triple therapy and 75.4% (95% CI, 70.5% to 79.8%) for rabeprazole-based triple therapy. There was no significant difference in the eradication rates between the two groups (p>0.999). Per-protocol analysis also revealed no significant difference between the eradication rates of the two groups (tegoprazan 83.4% [95% CI, 79.0% to 87.2%] vs rabeprazole 83.5% [79.0% to 87.4%], p>0.999). Furthermore, there was no significant difference in adverse event rates between the two groups (tegoprazan, 27.6%; rabeprazole, 25.8%; p=0.604).

Conclusions

The eradication rate of tegoprazan-based triple therapy was similar to that of rabeprazole-based triple therapy. Further studies on the dose-escalation effect of tegoprazan for H. pylori eradication and the efficacy of tegoprazan in regimens other than conventional triple therapy are needed.

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.

Pharmacokinetic Interactions Between Tegoprazan and Naproxen, Aceclofenac, and Celecoxib in Healthy Korean Male Subjects

PURPOSE

Tegoprazan is a potassium-competitive acid blocker used for gastric acid suppression and may be used with NSAIDs to reduce gastrointestinal adverse effects. The aim of this study was to evaluate the pharmacokinetic interaction between tegoprazan and commonly used NSAIDS, namely, naproxen, aceclofenac, and celecoxib.

METHODS

An open-label, 3-cohort, randomized, multiple-dose, 3-way crossover study was conducted in healthy male subjects. In cohort 1, tegoprazan (50-mg tablet, once daily) and naproxen (500-mg tablet, twice daily) were administered separately or concurrently for 7 days in each period. In cohort 2, tegoprazan and aceclofenac (100-mg tablet, twice daily) were administered separately or concurrently for 7 days in each period. In cohort 3, tegoprazan and celecoxib (200-mg capsule, twice daily) were administered separately or concurrently for 7 days in each period. Pharmacokinetic blood samples were collected up to 24 hours after the last dose.

FINDINGS

Seventeen subjects from cohort 1, sixteen subjects from cohort 2, and thirteen subjects from cohort 3 were included in the pharmacokinetic analysis. In cohort 1, the geometric least squares mean ratios (90% CIs) for AUC_τ (AUC profiles over the dosing interval) and C_ss,max (C_max at steady state) were 1.01 (0.91-1.12) and 0.99 (0.83-1.17) for tegoprazan, and 1.00 (0.97-1.03) and 1.04 (0.99-1.09) for naproxen, respectively. The values in cohort 2 were 1.03 (0.93-1.13) and 0.94 (0.86-1.04) for tegoprazan, and 1.06 (1.00-1.12) and 1.31 (1.08-1.60) for aceclofenac. The values in cohort 3 were 1.01 (0.86-1.18) and 1.02 (0.87-1.19) for tegoprazan, and 1.08 (0.96-1.22) and 1.18 (0.97-1.43) for celecoxib.

IMPLICATIONS

Changes in the maximum aceclofenac or celecoxib concentrations were detected after concurrent administration with tegoprazan, which were considered mainly due to the pharmacodynamic effect of tegoprazan. Because systemic drug exposure (shown as AUC_τ) was unchanged after concurrent administration of any 3 NSAIDs with tegoprazan, the increase in aceclofenac or celecoxib C_ss,max when administered with tegoprazan would not be clinically significant in practice.

CLINICALTRIALS

gov Identifier: NCT04639804.

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

A physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model for tegoprazan and its major metabolite M1 was developed to predict PK and PD profiles under various scenarios. The PBPK model for tegoprazan and M1 was developed and predicted using the SimCYP^® simulator and verified using clinical study data obtained after a single administration of tegoprazan. The established PBPK/PD model was used to predict PK profiles after repeated administrations of tegoprazan, postprandial PK profiles, and intragastric pH changes. The predicted tegoprazan and M1 concentration-time profiles fit the observed profiles well. The arithmetic mean ratios (95% confidence intervals) of the predicted to observed values for the area under the curve (AUC_{0-24 h}), maximum plasma drug concentration (C_max), and clearance (CL) for tegoprazan and M1 were within a 30% interval. Delayed time of maximum concentration (T_max) and decreased C_max were predicted in the postprandial PK profiles compared with the fasted state. This PBPK/PD model may be used to predict PK profiles after repeated tegoprazan administrations and to predict differences in physiological factors in the gastrointestinal tract or changes in gastric acid pH after tegoprazan administration.