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 The recent Food and Drug Administration (FDA) draft guidance “Artificial Intelligence in Drug and Biological Product Development”—outlines regulatory expectations for AI/ML in drug development, emphasizing transparency,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.
References
[1]
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., et al. (2019) Applications of Machine Learning in Drug Discovery and Development. NatureReviewsDrugDiscovery, 18, 463-477. https://doi.org/10.1038/s41573-019-0024-5
[2]
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P. and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. MolecularPharmaceutics, 13, 2524-2530. https://doi.org/10.1021/acs.molpharmaceut.6b00248
[3]
Mak, K. and Pichika, M.R. (2019) Artificial Intelligence in Drug Development: Present Status and Future Prospects. DrugDiscoveryToday, 24, 773-780. https://doi.org/10.1016/j.drudis.2018.11.014
[4]
U.S. Food and Drug Administration (2023) Artificial Intelligence in Drug and Bio-logical Product Development: Draft Guidance for Industry. https://www.regulations.gov/document/FDA-2023-N-0743-0001
[5]
U.S. Food and Drug Administration (2024) Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products. Discussion Paper and Request for Feedback. https://www.fda.gov/media/167973/download
[6]
U.S. Food and Drug Administration (2022) Real-World Evidence Program: Framework. https://www.fda.gov/media/120060/download
[7]
U.S. Food and Drug Administration (2021) Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device Action Plan. https://www.fda.gov/media/145022/download
[8]
Harrer, S., Shah, P., Antony, B. and Hu, J. (2025) Artificial Intelligence in Clinical Trials: Opportunities, Challenges, and Future Directions. DrugDiscoveryToday, 30, Article ID: 103470.
[9]
Amann, J., Blasimme, A., Vayena, E., Frey, D. and Madai, V.I. (2020) Explainability for Artificial Intelligence in Healthcare: A Multidisciplinary Perspective. BMCMedicalInformaticsandDecisionMaking, 20, Article No. 310. https://doi.org/10.1186/s12911-020-01332-6
[10]
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.
[11]
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J. and Zhang, G. (2025) Learning under Concept Drift: A Review on Data Drift Detection and Adaptation in Machine Learning for Healthcare. JournalofBiomedicalInformatics, 151, Article ID: 104654.
[12]
Sendak, M.P., Gao, M., Nichols, M., Lin, A., Balu, S. and Barbian, H. (2024) Machine Learning Operations (MLOps) in Healthcare: A Scoping Review of Post-Deployment Monitoring and Governance. NPJDigitalMedicine, 7, Article No. 64.
[13]
Rabanser, S., Günnemann, S. and Lipton, Z.C. (2024) Failing Loudly: An Empirical Study of Dataset Shift Detection in Real-World Healthcare Data. NatureCommunications, 15, Article No. 2314.
[14]
Vamathevan, J., Dunham, I. and Ferran, E. (2024) Artificial Intelligence across the Pharmaceutical Product Lifecycle. ComputersinBiologyandMedicine, 169, Article ID: 107684.
[15]
Horne, B.D., Manzi, S. and Mentz, R.J. (2025) Artificial Intelligence for Clinical Trial Risk Assessment and Adaptive Trial Design: A Scoping Review. NPJDigitalMedicine, 8, Article No. 112.
[16]
Bhardwaj, A. and Wanjari, M.M. (2023) Integration of Artificial Intelligence in Pharmaceutical Manufacturing for Quality Control and Supply Chain Management. InternationalJournalofPharmaceuticalSciencesandResearch, 14, 612-620.
[17]
Baranwal, Y., Singh, R. and Ierapetritou, M. (2025) Machine Learning Applications in Pharmaceutical Manufacturing of Oral Solid Dosage Forms: A Review. InternationalJournalofPharmaceutics, 654, Article ID: 123982.
[18]
Brunner, R., Gasser, R. and Korakianiti, E. (2025) Regulatory Perspectives on Artificial Intelligence and Machine Learning in Pharmaceutical Manufacturing and Quality Systems. PharmaceuticalResearch, 42, 1-15.
[19]
International Council for Harmonization (2022) ICH Q2(R2): Validation of Analytical Procedures. https://www.ich.org/page/quality-guidelines
[20]
Pfohl, S.R., Daniels, J. and Shah, N.H. (2025) Fairness Drift: Monitoring Algorithmic Bias over Time in Clinical Machine Learning Systems. Journal of the American Medical Informatics Association, 32, 845-854.
[21]
Choudhury, A. and Asan, O. (2020) Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIRMedicalInformatics, 8, e18599. https://doi.org/10.2196/18599
[22]
Ghassemi, M., Naumann, T., Schulam, P., Beam, A.L., Chen, I.Y. and Ranganath, R. (2021) A Review of Challenges and Opportunities in Machine Learning for Health. AMIASummitsonTranslationalScienceProceedings, San Francisco, 8-12 March 2021, 191-200.
[23]
Grote, T., Berens, P. and Jäger, A. (2025) The Responsibility Vacuum in Post-Deployment Monitoring of Medical AI Systems. AI&Society, 40, 1-15.
[24]
Collins, G.S., Dhiman, P., Andaur Navarro, C.L., et al. (2024) TRIPOD+AI Statement: Updated Reporting Guidance for Diagnostic and Prognostic Prediction Models That Use Artificial Intelligence. BMJ, 385, e078378.
[25]
Liu, X., Glocker, B., McCradden, M.M., et al. (2025) Guidelines and Standards for Artificial Intelligence in Healthcare: A Systematic Review of Reporting, Validation, and Governance Frameworks. JAMIA Open, 8, ooae155.
[26]
European Medicines Agency (2023) Artificial Intelligence in the Medicinal Product Lifecycle. https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/artificial-intelligence?utm_source=chatgpt.com
[27]
International Council for Harmonization (2023) ICH Q13: Continuous Manufacturing of Drug Substances and Drug Products. https://www.ich.org/page/quality-guidelines
[28]
Organization for Economic Co-operation and Development (2019) OECD Principles on Artificial Intelligence. https://oecd.ai/en/ai-principles
[29]
Topol, E.J. (2019) High-Performance Medicine: The Convergence of Human and Artificial Intelligence. NatureMedicine, 25, 44-56. https://doi.org/10.1038/s41591-018-0300-7
