Transforming Drug Development with Artificial Intelligence: Reflections on Applications in Safety and Clinical Planning

Maria Camila Marenco

doi:10.14482/inde.44.01.813.443

Authors

Maria Camila Marenco Independent author (Massachusetts Institute of Technology) https://orcid.org/0009-0003-8094-7615

DOI:

https://doi.org/10.14482/inde.44.01.813.443

Keywords:

advanced Analytics, Drug Development, Artificial Intelligence, Optimization, Patient Safety, Pharmacovigilance, Clinical site allocation

Abstract

Pharmaceutical development faces mounting pressure from escalating costs, lengthy timelines, and stringent regulatory oversight. Artificial intelligence (AI) has emerged as a potential catalyst for redesigning this end?to?end process. This reflective article critically reviews the current state and future trajectory of AI in the pharmaceutical industry, illustrated by two projects led by the author: (i) an AI?empowered pharmacovigilance framework that forecasts adverse events before clinical manifestation and (ii) a dynamic optimization algorithm that redefines the strategy for selecting and activating clinical sites. Methodological challenges (data quality, interpretability, bias) and lessons learned for scalable adoption are discussed, as well as requirements for governance and regulatory collaboration. Evidence suggests that AI can reduce costs, compress timelines, and enhance patient safety; however, its ultimate value will depend on rigorous principles of transparency, validation, and ethical oversight.

References

Deloitte Centre for Health Solutions, Measuring the Return from Pharmaceutical Innovation. London, U.K.: Deloitte Touche Tohmatsu Ltd., 2022.

Biotechnology Innovation Organization, Clinical Development Success Rates?2006–2015. Washington, DC, USA: BIO, 2016.

U.S. Food and Drug Administration, Artificial Intelligence and Machine Learning in Drug Development, Discussion Paper. Silver Spring, MD, USA, Mar.?2023. [Online]. Available: https://www.fda.gov/media/167973/download (accessed?Jul.?26,?2025).

E.?J.?Topol, “High?performance medicine: The convergence of human and artificial intelligence,” Nat. Med, vol.?25, no.?1, pp.?44–56, Jan.?2019, doi: 10.1038/s41591?018?0300?7.

MITRE Corporation, Artificial Intelligence in Clinical Trials. McLean, VA, USA: MITRE, 2023. [Online]. Available: https://mitre.org (accessed?Jul.?26,?2025).

M. I. Miller, L. C. Shih, and V. B. Kolachalama, “Machine Learning in Clinical Trials: A Primer with Applications to Neurology,” PLoS Comput. Biol., vol. 18, no. 4, pp. e1010970, Apr. 2023. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228463/. [Accessed: Jul. 30, 2025].

U.S. Food and Drug Administration, “Artificial Intelligence in Drug Development,” U.S. Food and Drug Administration, Silver Spring, MD, USA. [Online]. Available: https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development. [Accessed: Jul. 30, 2025]

M.?C. Marenco, Predicting drug adverse events using AI, M.S. capstone project report, Massachusetts Inst. Technol., Cambridge, MA, USA, 2023.

M.?C. Marenco, An efficient strategy for clinical site allocation, M.S. capstone project report, Massachusetts Inst. Technol., Cambridge, MA, USA, 2022.

U.S. Food and Drug Administration, “FDA Proposes Framework to Advance Credibility of AI Models Used in Drug and Biological Product Submissions,” FDA News, May 2025. [Online]. Available: https://www.fda.gov/news-events/press-announcements/fda-proposes-framework-advance-credibility-ai-models-used-drug-and-biological-product-submissions. [Accessed: Jul. 30, 2025].

U.S. Food and Drug, “Responsive Regulation of Artificial Intelligence in Drug Development,” CDER Office of Medical Policy, May 29, 2024. [Online]. Available: https://www.fda.gov/media/184256/download. [Accessed: Jul. 30, 2025].