Regulatory Aspects in Drug Development with AI- Generated Tools

Abstract

This review delves into the multifaceted applications of Artificial Intelligence (AI) in drug development. It explores the utilization of AI, Deep learning and machine learning at different phases of drug development, emphasizing their roles in compound screening, drug design, target identification, and clinical trial planning. The regulatory environments in the USFDA and Europe (EMA) are discussed, reflecting the increasing integration of AI in drug development. The article highlights AI's impact on virtual screening, toxicity prediction, pharmacokinetic modeling, and clinical trial design, showcasing its transformative potential in enhancing efficiency and success rates. However, challenges such as human oversight, data quality, collaboration, transparency, and prioritizing patient safety are acknowledged. The opportunities presented by AI, coupled with human expertise, regulatory frameworks, and data quality, are crucial for revolutionizing drug development and ensuring safety and effectiveness.