Strengthening Signal Detection in Pharmacovigilance by Using International Nonproprietary Name (INN) Stems.

'Stems', which mark pharmacological relationships between substances, form the backbone of the International Nonproprietary Name (INN) system, developed by the WHO in the 1950s. In this paper, we propose using the INN stems to enhance pharmacovigilance signal detection. After analysis of historical cases and current pharmacovigilance practices, we discuss how stem-based classification could facilitate understanding of the adverse-effects profile of each stem, to be used as a benchmark for early identification of adverse drug reactions that deviate from expected class effects, in other words signals associated with newly marketed medicines or different uses of well-known medicines. We propose a potential framework for integrating stem-based analysis into existing pharmacovigilance databases, supplemented by artificial intelligence approaches, such as machine learning. While acknowledging limitations, such as stem variability and reporting bias, we suggest that this approach offers potential advantages for regulatory authorities and healthcare professionals in post-marketing surveillance. Implementation of stem-based post-marketing surveillance could enhance signal-detection efficiency and contribute to improved patient safety through earlier identification of unexpected adverse effects and adverse reactions.