Reinventing Drug Development and Regulatory Affairs through Artificial Intelligence

Drug discovery, development, and regulatory approval are traditionally lengthy, costly, and high-risk processes within the pharmaceutical industry. The integration of Artificial Intelligence (AI) offers a transformative approach to overcoming these challenges by enabling data-driven, efficient, and predictive decision-making across the drug development lifecycle. This thesis explores the role of AI technologies, including machine learning, deep learning, and natural language processing, in revolutionizing early drug discovery, preclinical and clinical development, and regulatory affairs. AI-driven methods enhance target identification, lead optimization, clinical trial design, and safety monitoring while reducing development timelines and costs. Furthermore, the study examines the growing application of AI in regulatory processes such as automated dossier review, risk assessment, and pharmacovigilance. Overall, this work highlights AI as a key enabler in modernizing pharmaceutical innovation and regulatory frameworks, ultimately improving the delivery of safe and effective therapies to patients.

The rapid advancement of Artificial Intelligence is fundamentally transforming drug development and regulatory affairs by addressing long-standing challenges related to time, cost, and high failure rates. AI-driven approaches enable more accurate target identification, efficient lead optimization, improved preclinical predictions, and smarter clinical trial design, ultimately enhancing decision-making across the pharmaceutical value chain. In parallel, the adoption of AI in regulatory affairs is modernizing submission processes, risk assessment, and post-marketing surveillance, leading to improved compliance, transparency, and patient safety.