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Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib

PURPOSE: Fedratinib (INREBIC(®)), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/inductio...

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Autores principales: Wu, Fan, Krishna, Gopal, Surapaneni, Sekhar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515950/
https://www.ncbi.nlm.nih.gov/pubmed/32886148
http://dx.doi.org/10.1007/s00280-020-04131-y
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author Wu, Fan
Krishna, Gopal
Surapaneni, Sekhar
author_facet Wu, Fan
Krishna, Gopal
Surapaneni, Sekhar
author_sort Wu, Fan
collection PubMed
description PURPOSE: Fedratinib (INREBIC(®)), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios. METHODS: The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp(®) (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data. RESULTS: The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates. CONCLUSIONS: The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00280-020-04131-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-75159502020-10-07 Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib Wu, Fan Krishna, Gopal Surapaneni, Sekhar Cancer Chemother Pharmacol Original Article PURPOSE: Fedratinib (INREBIC(®)), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios. METHODS: The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp(®) (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data. RESULTS: The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates. CONCLUSIONS: The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00280-020-04131-y) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-09-04 2020 /pmc/articles/PMC7515950/ /pubmed/32886148 http://dx.doi.org/10.1007/s00280-020-04131-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Article
Wu, Fan
Krishna, Gopal
Surapaneni, Sekhar
Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib
title Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib
title_full Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib
title_fullStr Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib
title_full_unstemmed Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib
title_short Physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib
title_sort physiologically based pharmacokinetic modeling to assess metabolic drug–drug interaction risks and inform the drug label for fedratinib
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515950/
https://www.ncbi.nlm.nih.gov/pubmed/32886148
http://dx.doi.org/10.1007/s00280-020-04131-y
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