Abstract
Drug safety is a critical aspect of public health, yet traditional detection methods may miss rare or long-term side effects. Recently, machine learning (ML) techniques have shown promise in predicting drug-related side effects earlier in the development pipeline. The objective of this policy brief was to propose evidence-based policy options for using ML techniques to predict drug-related side effects. This policy brief was developed upon a previously published scoping review of relevant studies. A secondary analysis synthesized key barriers and opportunities relevant to policy development. Key findings revealed some challenges in data standardization, interpretability, and regulatory alignment. Moreover, the results highlighted the potential of explainable ML and cross-sector collaboration to improve prediction accuracy and fairness. Five policy recommendations were proposed: (1) establishing standardized data collection and secure protocol sharing; (2) funding ML model development and rigorous validation; (3) integrating ML into drug development pipelines; (4) increasing public awareness through targeted education; and (5) implementing fairness regulations to address bias. These recommendations require joint efforts from governments, regulatory bodies, pharmaceutical firms, and academia to be implemented in practice. While ML offers transformative potential for drug safety, its real-world implementation faces ethical, regulatory, and technical hurdles. Policies must ensure model transparency, promote equity, and support infrastructure for ML adoption. Through interdisciplinary coordination and evidence-based policymaking, stakeholders can responsibly advance ML use in drug development to enhance patient outcomes.