Signal Detection in Pharmacovigilance: Emerging Methodologies and Clinical Implications

Abstract

Pharmacovigilance plays a pivotal role in the global healthcare landscape, offering crucial insights to maintain and improve the safety and efficacy of medicines by continuously detecting, assessing, understanding and preventing adverse drug reactions (ADRs). Signal detection, an integral aspect of pharmacovigilance, refers to the detection of previously unrecognized or partially documented safety issues early in the life of a drug. Historically, spontaneous reporting systems, coupled with disproportionate analysis techniques, were indispensable to drug safety surveillance, but suffered from major drawbacks such as underreporting, reporting biases, and lack of sufficient information. However, the use of Artificial Intelligence (AI), machine learning, natural language processing, big data analysis, and real-world evidence (RWE) has provided unprecedented capabilities for safety signal discovery and verification, thus paving the way for efficient identification and verification of potentially harmful drugs. Subsequent to the detection of an initial safety signal, thorough validation, risk assessment, and subsequent regulatory review of its clinical relevance are mandatory to ensure adequate regulatory action and decision-making based on scientific evidence. This article discusses the basic principles, novel approaches, regulations, and prospects of signal detection and its contribution to global health and patient safety.