Objective  To study the safety of amlodipine post-marketing and to mine the potential adverse drug event (ADE) signals, and to construct an intelligent query platform for ADE signals that can be popularized and applied to a variety of drugs.

Methods  The data from the first quarter of 2004 to the fourth quarter of 2024 were retrieved from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. A variety of disproportionality methods were used, including the reporting odds ratio (ROR) method, the United Kingdom Medicines and Healthcare products Regulatory Agency (MHRA) comprehensive standard method, Bayesian confidence propagation neural network (BCPNN) method, and the multi-item Gamma-Poisson shrinker (MGPS) method, to mine the signals of ADEs related to amlodipine. At the same time, a general query platform for mining ADE signals was developed based on the DeepSeek AI model to achieve the monitoring and analysis of the safety of various drugs.

Results  A total of 51,166 ADE reports were obtained, in which amlodipine was the primary suspected drug. Through the combined screening of the four algorithms, multiple potential ADE signals which had not been mentioned in the existing package inserts were found, including diseases of the ear and labyrinth (such as sensorineural hearing loss), diseases of the respiratory system, thorax and mediastinum (such as non-cardiogenic pulmonary edema), mental disorders (such as completed suicide), etc. The constructed digital intelligent platform had achieved the automated processing and monitoring of ADE data, providing a rapid access for clinicians and regulatory authorities to obtain drug safety information.

Conclusion  This study reveals potential safety risks associated with the use of amlodipine through real-world data mining. A risk assessment of patients' medication should be carried out before clinical use. In addition to paying attention to known ADEs, newly discovered potential risk signals should also be closely monitored. The construction of the digital intelligent platform provides an efficient tool for pharmacovigilance work. It can be popularized and applied to the safety monitoring of a variety of drugs, which is of great significance for improving the level of pharmacovigilance and the safety of medication.

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