Data-Driven Strategies for Rare Adverse Drug Reaction Detection: A Review of Modern Pharmacovigilance Tools and Techniques

Rare adverse drug reactions (ADRs), occurring in fewer than 1 in 10,000 patients, challenge pharmacovigilance due to their low incidence and limited detectability in premarket clinical trials. This review explores data-driven strategies and modern tools for identifying these elusive events during postmarketing surveillance. By integrating big data analytics, machine learning (ML), and real-world evidence (RWE), these approaches harness diverse data sources—electronic medical records (EMRs), spontaneous reporting systems (SRS) like FAERS and VigiBase, and social media platforms. Statistical methods, such as the proportional reporting ratio (PRR) and Bayesian techniques, establish a foundation for signal detection, complemented by ML tools like natural language processing (NLP) that enhance precision. RWE platforms, including VigiFlow, standardize global reporting and provide longitudinal insights. These advancements improve detection rates and accelerate response times across various ADR types, though challenges like underreporting and data complexity persist. The review delves into Bayesian theory’s role in refining rare signal detection and categorizes ADRs, highlighting their implications for pharmacovigilance strategies aimed at enhancing patient safety.