{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T22:11:45Z","timestamp":1793052705681,"version":"4.5.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"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>Azacitidine is used to treat myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). It acts as a cytosine analog and DNA methyltransferase inhibitor, inducing DNA hypomethylation to reverse epigenetic modifications and restore normal gene expression. However, adverse events (AEs) associated with azacitidine are mainly reported in clinical trials, with limited real-world evidence. This study aims to assess the AE profile of azacitidine by utilizing data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and WHO-VigiAccess databases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We extracted adverse event (AE) reports related to azacitidine from the FAERS and WHO-VigiAccess databases, covering the period from the drug\u2019s market introduction to the third quarter of 2024. We used statistical methods including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM) to analyze the association between azacitidine and documented AEs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The investigation unveiled 16,056 azacitidine-related adverse event (AE) reports from FAERS and 19,867 reports from WHO-VigiAccess. The median duration for the occurrence of these AEs during the observation period was 36\u00a0days, with an interquartile range (IQR) spanning from 11 to 126\u00a0days. Our statistical analysis identified 27 organ systems associated with AEs induced by azacitidine. Among these, the notable System Organ Classes (SOCs) that met four specific criteria included: infections and infestations, blood and lymphatic system disorders, and neoplasms benign, malignant, and unspecified (including cysts and polyps). Four algorithms identified 443 significant disproportionality preferred terms (PTs), including previously unreported AEs such as death, sepsis, septic shock, respiratory failure, cardiac failure, tumor lysis syndrome, bone marrow failure, interstitial lung disease, and pericarditis. Analysis from the WHO-VigiAccess database showed a ROR of 3.65 and a PRR of 3.30 for the SOC of infections and infestations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This research not only confirms the widely acknowledged AEs linked to azacitidine but also uncovers several potentially new safety concerns noted in actual clinical practice. These results may offer important vigilance information for clinicians and pharmacists when addressing safety issues associated with azacitidine.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1555838","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T07:10:14Z","timestamp":1742368214000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["The real-world analysis of adverse events with azacitidine: a pharmacovigilance study based on the FAERS and WHO-VigiAccess databases"],"prefix":"10.3389","volume":"16","author":[{"given":"Zhaorui","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youfu","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baiquan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"B1","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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