{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T11:21:52Z","timestamp":1789730512339,"version":"4.0.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"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>Amiodarone and dronedarone are both class III antiarrhythmic medications used to treat arrhythmias. The objective of this study was to enhance the current understanding of adverse drug reaction (ADR) associated with amiodarone and dronedarone by employing data mining methods on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS), and providing a reference for safe and reasonable clinical use.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The ADR records were selected by searching the FAERS database from 2011 Q3 to 2023 Q3. The disproportionality analysis algorithms, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM), were used to detect signals of amiodarone-related and dronedarone-related ADRs. The ADR profiles of amiodarone and dronedarone categorized by organ toxicity were compared through the Z-test and the Fisher exact test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>9,295 reports specifically mentioned the use of amiodarone and 2,485 reports mentioned the use of dronedarone among 9,972,109 reports, with the majority of ADRs occurring in males over 60\u00a0years old. The United States was responsible for the highest proportion of reported ADRs. Significant system organ classes (SOC) for both included Cardiac disorders, Respiratory, thoracic and mediastinal disorders, and Investigations, etc. At the preferred terms (PTs) level, the more frequent ADR signals for amiodarone were drug interaction (n = 856), hyperthyroidism (n = 758), and dyspnoea (n = 607), while dronedarone were atrial fibrillation (n = 371), dyspnoea (n = 204), and blood creatinine increased (n = 123). Notably, unexpected ADRs, including electrocardiogram T wave alternans (n = 16; EBGM05 = 231.27), accessory cardiac pathway (n = 11; EBGM05 = 140), thyroiditis (n = 178; EBGM05 = 125.91) for amiodarone, and cardiac ablation (n = 11; EBGM05 = 31.86), cardioversion (n = 7; EBGM05 = 22.69), and dysphagia (n = 47; EBGM05 = 3.6) for dronedarone, were uncovered in the instructions. The analysis also revealed significant differences in the ADR profiles of amiodarone and dronedarone, with dronedarone showing higher proportions of cardiac toxicity but lower thyroid toxicity compared to amiodarone.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>These findings underscore the significance of vigilantly monitoring and comprehending the potential risks linked to the use of amiodarone and dronedarone. New ADRs discovered and clear ADR profiles of amiodarone and dronedarone enhance a thorough understanding of these drugs, which is essential for clinicians to ensure safe use of amiodarone and dronedarone.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2024.1438292","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T04:51:03Z","timestamp":1729572663000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Adverse drug reaction signals mining comparison of amiodarone and dronedarone: a pharmacovigilance study based on FAERS"],"prefix":"10.3389","volume":"15","author":[{"given":"Ye","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"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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