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

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) ne...

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Autores principales: Perrier, Jeremy, Gualano, Virginie, Helmer, Eric, Namour, Florence, Lukacova, Viera, Taneja, Amit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651654/
https://www.ncbi.nlm.nih.gov/pubmed/37667518
http://dx.doi.org/10.1111/cts.13622
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author Perrier, Jeremy
Gualano, Virginie
Helmer, Eric
Namour, Florence
Lukacova, Viera
Taneja, Amit
author_facet Perrier, Jeremy
Gualano, Virginie
Helmer, Eric
Namour, Florence
Lukacova, Viera
Taneja, Amit
author_sort Perrier, Jeremy
collection PubMed
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 (C (max)) 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.
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spelling pubmed-106516542023-09-28 Drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model Perrier, Jeremy Gualano, Virginie Helmer, Eric Namour, Florence Lukacova, Viera Taneja, Amit Clin Transl Sci Research 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 (C (max)) 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. John Wiley and Sons Inc. 2023-09-28 /pmc/articles/PMC10651654/ /pubmed/37667518 http://dx.doi.org/10.1111/cts.13622 Text en © 2023 Galapagos NV and The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research
Perrier, Jeremy
Gualano, Virginie
Helmer, Eric
Namour, Florence
Lukacova, Viera
Taneja, Amit
Drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
title Drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
title_full Drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
title_fullStr Drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
title_full_unstemmed Drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
title_short Drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
title_sort drug–drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651654/
https://www.ncbi.nlm.nih.gov/pubmed/37667518
http://dx.doi.org/10.1111/cts.13622
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