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Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., et al. (2015) Hidden Technical Debt in Machine Learning Systems. Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, 7-12 December 2015, 1-9.

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• TITLE: Frameworks for the Future: Implementing FDA Guidance on Artificial Intelligence in Biopharma

  AUTHORS: Kishore Raju Vatsavai, Jayaram Kamma, Anil Kumar Ramgiri, Kishore Kumar Hotha

  KEYWORDS: Artificial intelligence (AI), Machine Learning (ML), Drug Development, FDA Guidance, Biopharmaceutical Regulation, Clinical Trials, Pharmaceutical Manufacturing, Pharmacovigilance

  JOURNAL NAME: American Journal of Analytical Chemistry, Vol.17 No.3, March 30, 2026

  ABSTRACT: Artificial Intelligence (AI) is transforming the pharmaceutical industry, from early drug discovery to post-market surveillance. The recent Food and Drug Administration (FDA) draft guidance “Artificial Intelligence in Drug and Biological Product Development”—outlines regulatory expectations for AI/ML in drug development, emphasizing transparency, reliability, and human oversight. This article compiles insights from regulatory discussions, technical literature, and real-world applications to examine how AI speeds up innovation while introducing new challenges. It highlights key opportunities in R&D, clinical development, manufacturing, and quality assurance, and discusses the future path for pharma and biotech organizations striving to responsibly incorporate AI into their operations.