Artificial Intelligence-driven prediction of Drug–Herb Interactions: A critical need in India’s pluralistic health culture and Pharmacovigilance framework

ABSTRACT: The Health landscape of India is pluralistic, with traditional medical systems termed as Ayush, i.e., Ayurveda, Yoga and Naturopathy, Siddha, Sowa-Rigpa, Unani, and Homeopathy, coexisting with modern medicine. There is no structured oversight or formal guidelines followed in the integration of these medical systems, except that they are patient-driven. This unsupervised interface initiates a crucial safety challenge: Drug–Herb Interactions (DHIs). The polyherbal and phytochemical cocktail profile of traditional medicines/formulations creates a platform for interactions with modern medicines that are unpredictable. The numerous traditional formulations belonging to multiple medical systems, as well as modern drugs and formulations, make the experimental testing of DHI very exhaustive and infeasible. This paper recommends Artificial Intelligence (AI) as a data-driven and scalable approach for identifying, predicting, and validating potential DHIs. Making use of the available phytochemical and pharmacodynamic data of herbs, AI can generate predictive maps of DHI. Through progressive validation – in silico, in vitro, in vivo , and clinical studies – the accuracy can be improved. This predictive framework can be incorporated into India’s Pharmacovigilance (PV) Programme for ASU and H Drugs, thus shifting safety monitoring to a proactive mode from a basic reactive mode. Early prediction and identification of DHIs through AI-driven PV could revolutionize integrative health care, improving decision-making and safe concurrent use of traditional and modern medicines. This blending of digital intelligence and traditional wisdom affirms Ayurveda’s epistemic ideal of Yukti – rational synthesis. This promises to make India a global leader in evidence-based integrative medicine that is safe and efficient.