PS52: AI-Powered Breakthroughs: Innovating Clinical Study Design and Conduct with Machine Learning

The motivation for integrating machine learning (ML) into clinical study design and conduct stems from the need to enhance efficiency and quality, and to reduce cost and timeline in clinical trials. Traditional methods often struggle with the complexities of integrating information and making adaptations, and the higher standards of agility needed in modern clinical development. ML offers the ability to combine data cleverly, identify patterns, and make data-driven predictions that can optimize study designs, maintain data privacy, and improve clinical data monitoring, etc. By leveraging ML, researchers can design more adaptive and responsive trials, ultimately accelerating the development of new therapies and improving patient outcomes. From a regulatory standpoint, the use of machine learning in clinical studies is evolving but still faces significant scrutiny. Regulatory bodies like the Food and Drug Administration (FDA) and European Medicines Agency (EMA) are increasingly recognizing the potential of ML but emphasize the importance of transparency, reproducibility, and validation of ML models. Guidelines are being developed to ensure that ML applications in clinical trials meet rigorous standards for patient safety and data integrity. In May 2023, FDA published a discussion paper on "Using AI & ML in the Development of Drug & Biological Products", describing the current and potential use of AI/ML in various stages of drug development. Meanwhile, EMA released a multi-annual AI workplan 2023-2028 and later a reflection paper on the use of AI in medicinal product cycle, providing the European medicines regulatory network's vision on AI. This regulatory landscape is crucial for fostering trust and ensuring that ML-driven innovations are both effective and compliant with existing frameworks. Recent advancements in machine learning have significantly impacted clinical study design and conduct. Innovations such as deep learning algorithms, natural language processing, and real-time data analytics are being applied to various aspects of clinical trials. For instance, ML models are now capable of identify abnormality, predicting patient responses to treatments, identifying potential adverse events earlier, and optimizing dosing regimens. These advancements not only enhance the precision and efficiency of clinical trials but also pave the way for more personalized medicine approaches. As the field continues to evolve, the integration of ML in clinical research promises to revolutionize how studies are designed, conducted, and analyzed, leading to more effective and safer therapeutic interventions. This session will invite speakers from regulatory agency and industry to share case studies and novel methodologies utilizing ML, to answer questions such as: - How to measure the value added by using ML compared to traditional methods? - What areas in clinical development have great potential for the innovative use of ML? - What are the practical challenges for using ML for study design and conduct? This session is intended for audiences who are interested in practical considerations and the recent advancement in methodologies of ML in clinical development space. Speaker 1: Maria Kudela, Pfizer [placeholder for title] [placeholder for abstract] Speaker 2: Rachael Liu, Takeda [placeholder for title] [placeholder for abstract] Speaker 3: Elena Sizikova, FDA [placeholder for title] [placeholder for abstract]