A Review On: Artificial Intelligence in Preclinical and Clinical Trials
TL;DR
Challenges such as data quality, algorithmic bias, lack of transparency, privacy, cybersecurity, regulatory uncertainty and the need for human oversight remain important.
Abstract
Artificial Intelligence (AI) is rapidly transforming pharmaceutical research and drug development. The traditional process of discovering and developing new medicines is time-consuming, expensive and associated with a high rate of failure. AI, including machine learning, deep learning, natural language processing and generative AI, can analyses large and complex datasets and assist researchers in making predictions and decisions. In preclinical research, AI can support target identification, drug discovery, molecular screening, toxicity prediction, pharmacokinetic and pharmacodynamics modelling, and analysis of animal and laboratory data. In clinical trials, AI can assist with protocol design, patient recruitment, eligibility screening, clinical data management, monitoring, endpoint assessment, adverse-event detection and prediction of trial outcomes. The U.S. Food and Drug Administration (FDA) reports increasing use of AI across nonclinical, clinical, post-marketing and manufacturing stages of drug development. Despite these benefits, challenges such as data quality, algorithmic bias, lack of transparency, privacy, cybersecurity, regulatory uncertainty and the need for human oversight remain important. Therefore, appropriate validation, monitoring and risk-based regulatory approaches are necessary for the safe and effective use of AI in pharmaceutical development.
