{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T20:11:18Z","timestamp":1792440678535,"version":"4.5.0"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"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:p><jats:bold>Introduction:<\/jats:bold> The analysis of pharmacovigilance databases is crucial for the safety profiling of new and repurposed drugs, especially in the COVID-19 era. Traditional pharmacovigilance analyses\u2013based on disproportionality approaches\u2013cannot usually account for the complexity of spontaneous reports often with multiple concomitant drugs and events. We propose a network-based approach on co-reported events to help assessing disproportionalities and to effectively and timely identify disease-, comorbidity- and drug-related syndromes, especially in a rapidly changing low-resources environment such as that of COVID-19.<\/jats:p><jats:p><jats:bold>Materials and Methods:<\/jats:bold> Reports on medications administered for COVID-19 were extracted from the FDA Adverse Event Reporting System quarterly data (January\u2013September 2020) and queried for disproportionalities (Reporting Odds Ratio corrected for multiple comparisons). A network (the Adversome) was estimated considering events as nodes and conditional co-reporting as links. Communities of significantly co-reported events were identified. All data and scripts employed are available in a public repository.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> Among the 7,082 COVID-19 reports extracted, the seven most frequently suspected drugs (remdesivir, hydroxychloroquine, azithromycin, tocilizumab, lopinavir\/ritonavir, sarilumab, and ethanol) have shown disproportionalities with 54 events. Of interest, myasthenia gravis with hydroxychloroquine, and cerebrovascular vein thrombosis with azithromycin. Automatic clustering identified 13 communities, including a methanol-related neurotoxicity associated with alcohol-based hand-sanitizers and a long QT\/hepatotoxicity cluster associated with azithromycin, hydroxychloroquine and lopinavir-ritonavir interactions.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Findings from the Adversome detect plausible new signals and iatrogenic syndromes. Our network approach complements traditional pharmacovigilance analyses, and may represent a more effective signal detection technique to guide clinical recommendations by regulators and specific follow-up confirmatory studies.<\/jats:p>","DOI":"10.3389\/fphar.2021.740707","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T06:13:10Z","timestamp":1638943990000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System"],"prefix":"10.3389","volume":"12","author":[{"given":"Michele","family":"Fusaroli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuel","family":"Raschi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milo","family":"Gatti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabrizio","family":"De Ponti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabetta","family":"Poluzzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s40264-015-0375-8","article-title":"A Method for the Minimization of Competition Bias in Signal Detection from Spontaneous Reporting Databases","volume":"39","author":"Arnaud","year":"2016","journal-title":"Drug Saf."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.5005\/jp-journals-10071-23695","article-title":"What Every Intensivist Should Know about Impairment of Cardiac Function and Arrhythmias in Liver Disease Patients: A Review","volume":"24","author":"Arya","year":"2020","journal-title":"Indian J. 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