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Aug 8, 2025
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Abstract

The integration of Artificial Intelligence (AI) into drug discovery and development (DDD) has revolutionized pharmaceutical research by accelerating timelines, reducing costs, and improving success rates. However, this rapid advancement presents significant regulatory challenges, including algorithmic transparency, data privacy, bias mitigation, and validation reproducibility. This review examines AI's role across key stages of DDD, evaluates global regulatory frameworks (FDA, EMA, PMDA, CDSCO), and analyzes case studies of AI-driven drug approvals. We highlight critical gaps in AI governance and propose harmonized guidelines, risk management strategies, and collaborative approaches to ensure safe and equitable AI adoption. Recommendations include standardized validation protocols, adaptive licensing pathways, and global adverse event monitoring. The study underscores the need for regulatory agility and international cooperation to harness AI's full potential while safeguarding patient safety and public trust.

Keywords

Artificial Intelligence, Machine Learning, Deep Learning, International Council for Harmonisation, Adverse Drug Reactions

Article Details

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