Artificial intelligence (AI) and machine learning (ML) hold tremendous promise for accelerating pharmaceutical development, from drug discovery to manufacturing and quality control. However, the implementation of these technologies faces key challenges, including data quality, model interpretability, reproducibility, and algorithmic bias—issues that can impact reliability, trust, and regulatory acceptance. Additionally, the lack of standardized frameworks for model validation and compliance introduces uncertainty when applying AI/ML to CMC processes and decision-making in highly regulated environments.
This talk will explore the current limitations of AI/ML in pharmaceutical development, addressing practical risks and organizational considerations. Key discussion points include ensuring data integrity, reducing bias, improving model transparency, and strategies for building robust, compliant systems under evolving regulatory expectations. We will also look ahead to emerging solutions and collaborative approaches that can enable responsible, scalable integration of AI/ML across drug development, manufacturing, and quality systems.
Learning Objectives:
Identify key limitations of AI/ML in pharma, including issues with data quality, bias, interpretability, and reproducibility.
Understand the implications of algorithmic bias and lack of transparency on decision-making in regulated environments.
Evaluate current regulatory expectations and the challenges of integrating AI/ML within GxP frameworks.
Explore emerging strategies and collaborative approaches for responsible, scalable adoption of AI/ML across drug development and manufacturing.
