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

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...

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Autores principales: Ji, Tao, Chen, Xuejun, Yeleswaram, Swamy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286713/
https://www.ncbi.nlm.nih.gov/pubmed/35506332
http://dx.doi.org/10.1002/psp4.12805
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author Ji, Tao
Chen, Xuejun
Yeleswaram, Swamy
author_facet Ji, Tao
Chen, Xuejun
Yeleswaram, Swamy
author_sort Ji, Tao
collection PubMed
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 [C(max)] increase). The model underpredicted rifampin induction by 100% (approximate 6.7‐fold decrease in AUC and approximate 2.6‐fold decrease in C(max) 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.
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spelling pubmed-92867132022-07-19 Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling Ji, Tao Chen, Xuejun Yeleswaram, Swamy CPT Pharmacometrics Syst Pharmacol Research 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 [C(max)] increase). The model underpredicted rifampin induction by 100% (approximate 6.7‐fold decrease in AUC and approximate 2.6‐fold decrease in C(max) 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. John Wiley and Sons Inc. 2022-05-23 2022-07 /pmc/articles/PMC9286713/ /pubmed/35506332 http://dx.doi.org/10.1002/psp4.12805 Text en © 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research
Ji, Tao
Chen, Xuejun
Yeleswaram, Swamy
Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling
title Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling
title_full Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling
title_fullStr Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling
title_full_unstemmed Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling
title_short Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling
title_sort evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286713/
https://www.ncbi.nlm.nih.gov/pubmed/35506332
http://dx.doi.org/10.1002/psp4.12805
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