Unveiling Aducanumab's safety profile: A comprehensive pharmacovigilance analysis

Aducanumab, a monoclonal antibody targeting amyloid-beta plaques, has been introduced as a pivotal therapeutic agent for Alzheimer's disease (AD). Although it offers promising benefits in the treatment of early-stage Alzheimer's disease, a thorough evaluation of its safety profile and potential adverse events (AEs) is essential to ensure patient safety.

This retrospective pharmacovigilance study analyzed data from the FDA Adverse Event Reporting System (FAERS) database to evaluate AEs associated with Aducanumab. Employing a case/non-case methodology, the study utilized signal detection algorithms, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS), to identify AEs signals related to Aducanumab use.

The study encompassed a total of 11517459 reports, with 431 specifically citing Aducanumab. A substantial number of AEs were identified, particularly among the elderly population and those with pre-existing neurological conditions. The most frequently reported AEs were related to the nervous system, including "amyloid-related imaging abnormalities" such as edema/effusion and microhemorrhages. Other affected system organ classes (SOCs) included psychiatric disorders and general disorders and administration site conditions. Specific preferred terms (PTs) linked with Aducanumab included "confusional state," "disorientation," and "cerebral microhemorrhage." Unexpected AEs such as "subdural hematoma" and "head injury" were also noted, indicating a broader safety profile that requires further investigation.

The study's findings underscore the necessity for close monitoring of Aducanumab use, especially in elderly patients with AD. The identification of both expected and unexpected AEs emphasizes the need for ongoing pharmacovigilance and additional research to fully understand the safety profile of Aducanumab in clinical practice.

Strength: Utilized multiple signal detection algorithms (ROR, PRR, BCPNN, MGPS) to enhance robustness of pharmacovigilance findings. Limitation: Reliance on spontaneous FAERS reports, which are prone to underreporting, overreporting, and reporting bias.