{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:51:41Z","timestamp":1777657901414,"version":"3.51.4"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Chemother Pharmacol"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Fedratinib (INREBIC<jats:sup>\u00ae<\/jats:sup>), 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\u2013drug interaction (DDI) potentials for fedratinib under clinical scenarios.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp<jats:sup>\u00ae<\/jats:sup> (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>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\u00a0mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400\u00a0mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00280-020-04131-y","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T15:03:49Z","timestamp":1599231829000},"page":"461-473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Physiologically based pharmacokinetic modeling to assess metabolic drug\u2013drug interaction risks and inform the drug label for fedratinib"],"prefix":"10.1007","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8339-4049","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gopal","family":"Krishna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sekhar","family":"Surapaneni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"4131_CR1","unstructured":"FDA (2019) INREBIC\u00ae (fedratinib) capsules, for oral use (USA drug label)"},{"key":"4131_CR2","unstructured":"FDA (2019) NDA212327: Cross-Discipline Team Leader Review"},{"issue":"11","key":"4131_CR3","doi-asserted-by":"publisher","first-page":"1823","DOI":"10.1124\/dmd.115.065920","volume":"43","author":"JE Sager","year":"2015","unstructured":"Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N (2015) Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. 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