Browse
Submit Data
Databases
API
Help

Dataset Information

30 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT: 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.

SUBMITTER: Yoon DY

PROVIDER: S-EPMC8464955 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Simulation and Prediction of the Drug-Drug Interaction Potential of Naloxegol by Physiologically Based Pharmacokinetic Modeling.

Project description:Naloxegol, a peripherally acting μ-opioid receptor antagonist for the treatment of opioid-induced constipation, is a substrate for cytochrome P450 (CYP) 3A4/3A5 and the P-glycoprotein (P-gp) transporter. By integrating in silico, preclinical, and clinical pharmacokinetic (PK) findings, minimal and full physiologically based pharmacokinetic (PBPK) models were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models reasonably predicted the observed changes in naloxegol exposure with ketoconazole (increase of 13.1-fold predicted vs. 12.9-fold observed), diltiazem (increase of 2.8-fold predicted vs. 3.4-fold observed), rifampin (reduction of 76% predicted vs. 89% observed), and quinidine (increase of 1.2-fold predicted vs. 1.4-fold observed). The moderate CYP3A4 inducer efavirenz was predicted to reduce naloxegol exposure by ∼50%, whereas weak CYP3A inhibitors were predicted to minimally affect exposure. In summary, the PBPK models reasonably estimated interactions with various CYP3A modulators and can be used to guide dosing in clinical practice when naloxegol is coadministered with such agents.

| S-EPMC4879473 | biostudies-literature

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

Project description: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.

| S-EPMC10054914 | biostudies-literature

Evaluation of drug-drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling.

Project description:Pemigatinib is a potent inhibitor of fibroblast growth factor receptor being developed for oncology indications. It is primarily metabolized by cytochrome P450 (CYP) 3A4, and the ratio of estimated concentration over concentration required for 50% inhibition ratio for pemigatinib as an inhibitor of P-glycoprotein (P-gp), organic cation transporter-2 (OCT2), and multidrug and toxin extrusion protein-1 (MATE1) exceeds the cutoff values established in regulatory guidance. A Simcyp minimal physiologically based pharmacokinetic (PBPK) with advanced dissolution, absorption, and metabolism absorption model for pemigatinib was developed and validated using observed clinical pharmacokinetic (PK) data and itraconazole/rifampin drug-drug interaction (DDI) data. The model accurately predicted itraconazole DDI (approximate 90% area under the plasma drug concentration-time curve [AUC] and approximate 20% maximum plasma drug concentration [Cmax ] increase). The model underpredicted rifampin induction by 100% (approximate 6.7-fold decrease in AUC and approximate 2.6-fold decrease in Cmax in the DDI study), presumably reflecting non-CYP3A4 mechanisms being impacted. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors/inducers on pemigatinib PK and pemigatinib as an inhibitor of P-gp or OCT2/MATE1 substrates. The worst-case scenario DDI simulation for pemigatinib as an inhibitor of P-gp or OCT2/MATE1 substrates showed only a modest DDI effect. The recommendation based on this simulation and clinical data is to reduce pemigatinib dose for coadministration with strong and moderate CYP3A4 inhibitors. No dose adjustment is required for weak CYP3A4 inhibitors. The coadministration of strong and moderate CYP3A4 inducers with pemigatinib should be avoided. PBPK modeling suggested no dose adjustment with P-gp or OCT2/MATE1 substrates.

| S-EPMC9286713 | biostudies-literature

Prediction of Drug-Drug Interactions Between Opioids and Overdosed Benzodiazepines Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation.

Project description:BackgroundResearchers have long been interested in the potential drug-drug interactions (DDIs) between opioids and benzodiazepines. However, much remains unknown concerning the interactions between these two drug classes. The objective of this work is to study the mechanism underlying the DDIs between opioids and benzodiazepines from the perspective of their pharmacokinetic (PK) interactions. A PK interaction occurs when two drugs are metabolized by the same cytochrome P450 enzymes and is one of the most common reasons for DDIs.MethodsWe quantitatively predicted the DDIs between three opioids (fentanyl, oxycodone and buprenorphine) and four benzodiazepines (alprazolam, diazepam, midazolam and triazolam) using a physiologically based pharmacokinetic (PBPK) modeling approach. A set of PBPK models was first constructed for these common opioids and benzodiazepines using SimCYP software, and the DDIs between them were then explored at various dosages.ResultsOur simulation results suggested there were no PK interactions between normal doses of opioids and benzodiazepines; but weak interactions can be expected with the combination of opioids and overdosed benzodiazepines. Particular attention should be given to the combination of fentanyl and overdosed alprazolam since a PK interaction can be observed between them.ConclusionOur results appear to indicate that pharmacodynamics may play a more important role than PKs in causing DDIs between opioids and benzodiazepines. This study also demonstrated that molecular modeling can be a very useful tool to mitigate the problem of "missing metabolic reaction parameters" in PK modeling and simulation.

| S-EPMC6738369 | biostudies-literature

Physiologically based pharmacokinetic modeling framework for quantitative prediction of an herb-drug interaction.

Project description:Herb-drug interaction predictions remain challenging. Physiologically based pharmacokinetic (PBPK) modeling was used to improve prediction accuracy of potential herb-drug interactions using the semipurified milk thistle preparation, silibinin, as an exemplar herbal product. Interactions between silibinin constituents and the probe substrates warfarin (CYP2C9) and midazolam (CYP3A) were simulated. A low silibinin dose (160?mg/day × 14 days) was predicted to increase midazolam area under the curve (AUC) by 1%, which was corroborated with external data; a higher dose (1,650?mg/day × 7 days) was predicted to increase midazolam and (S)-warfarin AUC by 5% and 4%, respectively. A proof-of-concept clinical study confirmed minimal interaction between high-dose silibinin and both midazolam and (S)-warfarin (9 and 13% increase in AUC, respectively). Unexpectedly, (R)-warfarin AUC decreased (by 15%), but this is unlikely to be clinically important. Application of this PBPK modeling framework to other herb-drug interactions could facilitate development of guidelines for quantitative prediction of clinically relevant interactions.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e107; doi:10.1038/psp.2013.69; advance online publication 26 March 2014.

| S-EPMC4042458 | biostudies-literature

Potential drug-drug interaction of olverembatinib (HQP1351) using physiologically based pharmacokinetic models.

Project description:Olverembatinib (HQP1351) is a third-generation BCR-ABL tyrosine kinase inhibitor for the treatment of chronic myeloid leukemia (CML) (including T315I-mutant disease), exhibits drug-drug interaction (DDI) potential through cytochrome P450 (CYP) enzymes CYP3A4, CYP2C9, CYP2C19, CYP1A2, and CYP2B6. A physiologically-based pharmacokinetic (PBPK) model was constructed based on physicochemical and in vitro parameters, as well as clinical data to predict 1) potential DDIs between olverembatinib and CYP3A4 and CYP2C9 inhibitors or inducers 2), effects of olverembatinib on the exposure of CYP1A2, CYP2B6, CYP2C9, CYP2C19, and CYP3A4 substrates, and 3) pharmacokinetics in patients with liver function injury. The PBPK model successfully described observed plasma concentrations of olverembatinib from healthy subjects and patients with CML after a single administration, and predicted olverembatinib exposure increases when co-administered with itraconazole (strong CYP3A4 inhibitor) and decreases with rifampicin (strong CYP3A4 inducer), which were validated by observed data. The predicted results suggest that 1) strong, moderate, and mild CYP3A4 inhibitors (which have some overlap with CYP2C9 inhibitors) may increase olverembatinib exposure by approximately 2.39-, 1.80- to 2.39-, and 1.08-fold, respectively; strong, and moderate CYP3A4 inducers may decrease olverembatinib exposure by approximately 0.29-, and 0.35- to 0.56-fold, respectively 2); olverembatinib, as a "perpetrator," would have no or limited impact on CYP1A2, CYP2B6, CYP2C9, CYP2C19, and CYP3A4 enzyme activity 3); systemic exposure of olverembatinib in liver function injury with Child-Pugh A, B, C may increase by 1.22-, 1.79-, and 2.13-fold, respectively. These simulations inform DDI risk for olverembatinib as either a "victim" or "perpetrator".

| S-EPMC9792776 | biostudies-literature

Physiologically based pharmacokinetic modeling and simulation in pediatric drug development.

Project description:Increased regulatory demands for pediatric drug development research have fostered interest in the use of modeling and simulation among industry and academia. Physiologically based pharmacokinetic (PBPK) modeling offers a unique modality to incorporate multiple levels of information to estimate age-specific pharmacokinetics. This tutorial will serve to provide the reader with a basic understanding of the procedural steps to developing a pediatric PBPK model and facilitate a discussion of the advantages and limitations of this modeling technique.

| S-EPMC4260000 | biostudies-literature

Drug-drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model.

Project description:Ziritaxestat, an autotaxin inhibitor, was under development for the treatment of idiopathic pulmonary fibrosis. It is a substrate of cytochrome P450 3A4 (CYP3A4) and P-glycoprotein and a weak inhibitor of the CYP3A4 and OATP1B1 pathways. We developed a physiologically based pharmacokinetic (PBPK) network interaction model for ziritaxestat that incorporated its metabolic and transporter pathways, enabling prediction of its potential as a victim or perpetrator of drug-drug interactions (DDIs). Concurrently, we evaluated CYP3A4 autoinhibition, including time-dependent inhibition. In vitro information and clinical data from healthy volunteer studies were used for model building and validation. DDIs with rifampin, itraconazole, voriconazole, pravastatin, and rosuvastatin were predicted, followed by validation against a test dataset. DDIs of ziritaxestat as a victim or perpetrator were simulated using the final model. Predicted-to-observed DDI ratios for the maximum plasma concentration (Cmax ) and the area under the plasma concentration-time curve (AUC) were within a two-fold ratio for both the metabolic and transporter-mediated simulated DDIs. The predicted impact of autoinhibition/autoinduction or time-dependent inhibition of CYP3A4 was a 12% decrease in exposure. Model-based predictions for ziritaxestat as a victim of DDIs with a moderate CYP3A4 inhibitor (fluconazole) suggested a 2.6-fold increase in the AUC of ziritaxestat, while multiple doses of a strong inhibitor (voriconazole) would increase the AUC by 15-fold. Efavirenz would yield a three-fold decrease in the AUC of ziritaxestat. As a perpetrator, ziritaxestat was predicted to increase the AUC of the CYP3A4 index substrate midazolam by 2.7-fold. An overarching PBPK model was developed that could predict DDI liability of ziritaxestat for both CYP3A4 and the transporter pathways.

| S-EPMC10651654 | biostudies-literature

Quantitative prediction of breast cancer resistant protein mediated drug-drug interactions using physiologically-based pharmacokinetic modeling.

Project description:Quantitative assessment of drug-drug interactions (DDIs) involving breast cancer resistance protein (BCRP) inhibition is challenged by overlapping substrate/inhibitor specificity. This study used physiologically-based pharmacokinetic (PBPK) modeling to delineate the effects of inhibitor drugs on BCRP- and organic anion transporting polypeptide (OATP)1B-mediated disposition of rosuvastatin, which is a recommended BCRP clinical probe. Initial static model analysis using in vitro inhibition data suggested BCRP/OATP1B DDI risk while considering regulatory cutoff criteria for a majority of inhibitors assessed (25 of 27), which increased rosuvastatin plasma exposure to varying degree (~ 0-600%). However, rosuvastatin area under plasma concentration-time curve (AUC) was minimally impacted by BCRP inhibitors with calculated G-value (= gut concentration/inhibition potency) below 100. A comprehensive PBPK model accounting for intestinal (OATP2B1 and BCRP), hepatic (OATP1B, BCRP, and MRP4), and renal (OAT3) transport mechanisms was developed for rosuvastatin. Adopting in vitro inhibition data, rosuvastatin plasma AUC changes were predicted within 25% error for 9 of 12 inhibitors evaluated via PBPK modeling. This study illustrates the adequacy and utility of a mechanistic model-informed approach in quantitatively assessing BCRP-mediated DDIs.

| S-EPMC8452302 | biostudies-literature

Physiologically Based Pharmacokinetic Modeling Suggests Limited Drug-Drug Interaction Between Clopidogrel and Dasabuvir.

Project description:Dasabuvir, a nonnucleoside NS5B polymerase inhibitor, is a sensitive substrate of cytochrome P450 (CYP) 2C8 with a potential for drug-drug interaction (DDI) with clopidogrel. A physiologically based pharmacokinetic (PBPK) model was developed for dasabuvir to evaluate the DDI potential with clopidogrel, the acyl-β-D glucuronide metabolite of which has been reported as a strong mechanism-based inhibitor of CYP2C8 based on an interaction with repaglinide. In addition, the PBPK model for clopidogrel and its metabolite were updated with additional in vitro data. Sensitivity analyses using these PBPK models suggested that CYP2C8 inhibition by clopidogrel acyl-β-D glucuronide may not be as potent as previously suggested. The dasabuvir and updated clopidogrel PBPK models predict a moderate increase of 1.5-1.9-fold for Cmax and 1.9-2.8-fold for AUC of dasabuvir when coadministered with clopidogrel. While the PBPK results suggest there is a potential for DDI between dasabuvir and clopidogrel, the magnitude is not expected to be clinically relevant.

| S-EPMC5599937 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data