Data Mining and Analysis for Adverse Drug Event Signals of Vericiguat

OBJECTIVE To explore the potential adverse reactions of vericiguat by mining the relevant data of FDA Adverse Event Reporting System(FAERS) to provide rationales for clinical safe drug dosing.

METHODS Reporting odds ratio(ROR) and Bayesian confidence propagation neural network(BCPNN ) were utilized for simultaneously detecting the adverse event signals of vericiguat in the database of FAERS. The search time limit was from the first quarter of 2021 when the drug was launched to the fourth quarter of 2023. The results were analyzed for 12 quarters.

RESULTS Four hundred and seventy-four cases of adverse drug event(ADE) positive signals related to vericiguat were obtained, including adverse reactions such as hypotension, dizziness, and anemia as recorded in the instructions, as well as new suspicious drug warnings not recorded in the instructions, including syncope, breathing difficulties, gastrointestinal bleeding, peripheral edema, cerebral infarction, and abdominal discomfort.

CONCLUSION By mining FAERS data, the adverse reactions of vericiguat after marketing can be comprehensively and deeply analyzed. In addition to the adverse reactions recorded in the instructions, it is also necessary to strengthen monitoring, pay attention to new adverse reactions, and promote the improvement of the drug instructions.