Adverse Drug Events in the Prevention and Treatment of COVID-19: A Data Mining Study on FDA Adverse Event Reporting System (FAERS) (Preprint)

BACKGROUND In the emergency situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs so as to help health professionals and patients get rid of these risks. OBJECTIVE This pharmacovigilance study aimed to investigate the ADEs of “Hot Drugs” in COVID-19 prevention and treatment based on the data of the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). METHODS FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2021 were retrieved with “Hot Drugs” and frequent ADEs recognized. A combination of support, proportional reporting ratio (PRR) and Chi-square (2) test was applied to detect significant “Hot Drug” & ADE signals by Python programming language on Jupyter notebook. RESULTS 13,178 COVID-19 cases were retrieved with 18 “Hot Drugs” and 312 frequent ADEs on “Preferred Term” (PT) level. 18  312 = 5,616 “Drug & ADE” candidates were formed for further data mining. The algorithm finally produced 219 significant ADE signals associated with 17 “Hot Drugs”and 124 ADEs.Some unexpected ADE signals were observed for chloroquine, ritonavir, tocilizumab, Oxford/AstraZeneca COVID-19 Vaccine and Moderna COVID-19 Vaccine. 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.

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Systematic analysis of safety profile for darunavir and its boosted agents using data mining in the FDA Adverse Event Reporting System database

Scientific Reports ◽

10.1038/s41598-021-91549-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiaojiang Tian ◽

Yao Yao ◽

Guanglin He ◽

Yuntao Jia ◽

Kejing Wang ◽

...

Keyword(s):

Data Mining ◽

Adverse Event ◽

Reporting System ◽

Proportional Reporting Ratio ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Reporting Odds Ratio ◽

Systematic Analysis ◽

Event Reporting ◽

Exfoliative Dermatitis

AbstractThis current investigation was aimed to generate signals for adverse events (AEs) of darunavir-containing agents by data mining using the US Food and Drug Administration Adverse Event Reporting System (FAERS). All AE reports for darunavir, darunavir/ritonavir, or darunavir/cobicistat between July 2006 and December 2019 were identified. The reporting Odds Ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) were used to detect the risk signals. A suspicious signal was generated only if the results of the three algorithms were all positive. A total of 10,756 reports were identified commonly observed in hepatobiliary, endocrine, cardiovascular, musculoskeletal, gastrointestinal, metabolic, and nutrition system. 40 suspicious signals were generated, and therein 20 signals were not included in the label. Severe high signals (i.e. progressive extraocular muscle paralysis, acute pancreatitis, exfoliative dermatitis, acquired lipodystrophy and mitochondrial toxicity) were identified. In pregnant women, umbilical cord abnormality, fetal growth restriction, low birth weight, stillbirth, premature rupture of membranes, premature birth and spontaneous abortion showed positive signals. Darunavir and its boosted agents induced AEs in various organs/tissues, and were shown to be possibly associated with multiple adverse pregnant conditions. This study highlighted some novel and severe AEs of darunavir which need to be monitored prospectively.

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Real-world Safety Profile of Darunavir and its Boosted Agents: An Analysis of FDA Adverse Event Reporting System Database

10.21203/rs.3.rs-225287/v1 ◽

2021 ◽

Author(s):

Xiaojiang Tian ◽

Yao Yao ◽

Guanglin He ◽

Yuntao Jia ◽

Kejing Wang ◽

...

Keyword(s):

Adverse Event ◽

Adverse Drug Events ◽

Reporting System ◽

Proportional Reporting Ratio ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Reporting Odds Ratio ◽

Event Reporting ◽

Exfoliative Dermatitis ◽

Adverse Pregnancy

Abstract This current investigation was aimed to generate signals for adverse drug events of darunavir-containing agents by data mining using the US Food and Drug Administration Adverse Event Reporting System (FAERS). All adverse event (AE) reports for darunavir, darunavir/ritonavir, or darunavir/cobicistat between July 2006 and December 2019 were identified. The reporting Odds Ratio (ROR), proportional reporting ratio(PRR), Bayesian confidence propagation neural network(BCPNN) were used to detect the risk signals. A suspicious signal was generated only if the results of the three algorithms were all positive. A total of 10756 AE reports were identified commonly observed in cardiovascular, endocrine, musculoskeletal, gastrointestinal, hepatobiliary, metabolic, and nutrition system. 40 suspicious signals were generated, and therein 20 signals were not included in the label. Severe high signals (i.e. progressive extraocular muscle paralysis, acute pancreatitis, exfoliative dermatitis, acquired lipodystrophy and mitochondrial toxicity) were identified. In pregnant women, umbilical cord abnormality, fetal growth restriction, low birth weight, stillbirth, premature rupture of membranes, premature birth and spontaneous abortion showed positive signals. Darunavir and its boosted agents induced AEs in various organs/tissues, and were shown to be associated with multiple adverse pregnancy conditions. This study highlighted some novel and severe AEs of darunavir which need to be monitored prospectively.

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