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

Deok Yong YoonSeungHwan LeeIn-Jin JangMyeongjoong KimHeechan LeeSeokuee KimBongtae KimGeun Seog SongSu Jin Rhee
Published in: Pharmaceutics (2021)
This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of tegoprazan and to predict the drug-drug interaction (DDI) potential between tegoprazan and cytochrome P450 (CYP) 3A4 perpetrators. The PBPK model of tegoprazan was developed using SimCYP Simulator® and verified by comparing the model-predicted pharmacokinetics (PKs) of tegoprazan with the observed data from phase 1 clinical studies, including DDI studies. DDIs between tegoprazan and three CYP3A4 perpetrators were predicted by simulating the difference in tegoprazan exposure with and without perpetrators, after multiple dosing for a clinically used dose range. The final PBPK model adequately predicted the biphasic distribution profiles of tegoprazan and DDI between tegoprazan and clarithromycin. All ratios of the predicted-to-observed PK parameters were between 0.5 and 2.0. In DDI simulation, systemic exposure to tegoprazan was expected to increase about threefold when co-administered with the maximum recommended dose of clarithromycin or ketoconazole. Meanwhile, tegoprazan exposure was expected to decrease to ~30% when rifampicin was co-administered. Based on the simulation by the PBPK model, it is suggested that the DDI potential be considered when tegoprazan is used with CYP3A4 perpetrator, as the acid suppression effect of tegoprazan is known to be associated with systemic exposure.
Keyphrases
  • helicobacter pylori
  • emergency department
  • mycobacterium tuberculosis
  • helicobacter pylori infection
  • machine learning
  • big data
  • deep learning
  • case control