Integrating AI into pharmaceutical lifecycle: from R&D to manufacturing and safety

Abstract

Artificial Intelligence (AI) is transforming the pharmaceutical lifecycle —from discovery to manufacturing and safety oversight. In discovery, AI accelerates target identification, molecular design, and repurposing (e.g., baricitinib; rentosertib), while the DSP-1181 experience highlights the risk of structural conservatism. In clinical development, AI supports patient stratification, digital-twin-informed design, and automated analysis, improving statistical power and operational speed. In manufacturing, predictive analytics, computer vision, and knowledge-graph-assisted documentation enhance quality and throughput. Pharmacovigilance benefits from natural language processing (NLP) — enabled signal detection and workflow automation. Emerging uses in antibody engineering and personalized therapy broaden its scope, but real-world benefit hinges on data quality, rigorous validation, transparency, and regulatory alignment (including real-world evidence).

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