Mapping the BCPNN Learning Rule to a Memristor Model

Data mining and safety analysis of dual orexin receptor antagonists (DORAs): a real-world pharmacovigilance study based on the FAERS database

Objective

Using the Food and Drug Administration Adverse Event Reporting System (FAERS) database, four signal detection methods were applied to mine adverse drug events (ADEs) related to use of dual orexin receptor antagonists (DORAs) to provide reference for safe clinical use.

Research design and Methods

Data collected from Q3rd 2014 to Q4th 2023 were obtained from the FAERS database. According to the preferred terminology (PT) and systematic organ classification (SOC) of MedDRA v.26.0, the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN) were used to detect ADE signals.

Results

A total of 11,857 DORAs-related adverse reactions were detected, reported with suvorexant, lemborexant, and daridorexant as the main suspected drugs was 8717584, and 2556, respectively. A higher proportion of females than males were reported (57.27% vs. 33.04%). The top 20 positive PT signals from three DORAs showed that "sleep paralysis" ranked first. "Brain fog" was stronger following daridorexant but was not detected for the other two drugs, and "sleep sex" and "dyssomnia" were stronger in suvorexant but not in the other two drugs. Additionally, some PTs occurred that were not included in drug instructions, such as "hangover" and "hypnagogic hallucination."

Conclusion

In this study, four algorithms (ROR, PRR, BCPNN, and MGPS) were used to mine the safety signals of DORAs. We identified some potential ADE signals that can promote the rational use of DORAs and improve their safety.