{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T21:15:50Z","timestamp":1784754950373,"version":"3.55.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Tyrosine kinase inhibitors (TKIs) are the preferred targeted therapy for advanced gastrointestinal stromal tumors (GIST). Ripretinib, the first tyrosine kinase switch control inhibitor, has not yet been extensively studied for long-term safety in large populations. This study evaluates Ripretinib-related adverse events (AEs) in real-world applications by analyzing data from the FDA\u2019s Adverse Event Reporting System (FAERS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To quantify signals of AEs, we employed several disproportionality analyses: the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the FAERS database, out of 7,064,646 reports, 3,161 were identified as related to Ripretinib AEs, with 438 significant disproportionality in preferred terms. The most common adverse reactions were tiredness, hair loss, nausea, constipation, diarrhea, loss of appetite, palmar-plantar erythrodysesthesia syndrome, and vomiting. These reactions align with the medication instructions and reports from corresponding clinical trials. Notably, the label includes unexpected and significant AEs such as \u201chepatic neoplasm\u201d, \u201chair texture abnormal\u201d, \u201cmetastases to liver\u201d and \u201cred blood cell count decreased\u201d. The median onset time for Ripretinib-related AEs was 99\u00a0days, with an interquartile range of 27\u2013245\u00a0days. Most cases (26.74%, n = 165) occurred within the first month of Ripretinib administration.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our findings align with clinical observations. We identified novel and unexpected AEs signatures of Ripretinib, indicating that prospective clinical studies are necessary to confirm these findings and clarify their implications. These results could provide valuable evidence to guide further safety studies on Ripretinib.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1469597","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T07:05:03Z","timestamp":1742281503000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A real-world disproportionality analysis of ripretinib data mining of the public version of FDA adverse event reporting system"],"prefix":"10.3389","volume":"16","author":[{"given":"Yingkai","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Fa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"B1","unstructured":"Targeted therapy\n          \n          \n          2024"},{"key":"B2","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s002280050466","article-title":"A Bayesian neural network method for adverse drug reaction signal generation","volume":"54","author":"Bate","year":"1998","journal-title":"Eur. 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