Adverse drug events in the prevention and treatment of COVID-19: A data mining study on the FDA adverse event reporting system - Guo, Qiang; Duan, Shaojun; Liu, Yaxi; Yuan, Yinxia (Frontiers in Pharmacology, 2022)
Background: In the emergent situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients more adverse drug event (ADE) risk. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs to help health professionals and patients get rid of the risk. Objective: This pharmacovigilance study aimed to investigate the ADEs of some attractive drugs (i.e., “Hot Drugs” in this paper) in COVID-19 prevention and treatment based on the data of the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). Methods: The FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2022 were retrieved with “Hot Drugs” and frequent ADEs recognized. A combination of support, lower bound of 95% confidence interval (CI) of proportional reporting ratio (PRR) was applied to detect significant “Hot Drug” & ADE signals by Python programming language on Jupyter notebook. Results: 66,879 COVID-19 cases were retrieved with 22 “Hot Drugs” and 1,108 frequent ADEs on “Preferred Term” (PT) level. The algorithm finally produced 598 significant ADE signals on PT level among which unexpected signals such as “hypofibrinogenaemia” of tocilizumab, “disease recurrence” of nirmatrelvir\ritonavir stood out. A picture of signals on “System Organ Class” (SOC) level was also provided for a comprehensive understanding of these ADEs. Conclusions: Data mining is a promising and efficient way to assist pharmacovigilance work and the result of this paper could help timely recognize ADEs in the prevention and treatment of COVID-19.