Director / Principle Scientist ARD Veranova Acton, Massachusetts
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being integrated across the pharmaceutical product lifecycle to accelerate drug discovery, optimize clinical development, enhance manufacturing efficiency, and strengthen pharmacovigilance. As these technologies become more prevalent in regulated environments, pharmaceutical scientists must understand the evolving regulatory expectations governing their use. This presentation reviews the U.S. Food and Drug Administration (FDA) draft guidance on Artificial Intelligence in Drug and Biological Product Development and discusses practical approaches for implementing risk-based validation, lifecycle management, human oversight, and governance frameworks. Attendees will gain a clear understanding of how to deploy AI responsibly and compliantly to improve decision-making while maintaining data integrity, product quality, and patient safety.
Learning Objectives:
Describe key principles and regulatory expectations outlined in the FDA draft guidance for AI and ML in drug and biological product development.
Identify opportunities to apply AI across discovery, clinical development, manufacturing, quality, and pharmacovigilance.
Explain risk-based approaches for validating, monitoring, and governing AI models in regulated pharmaceutical settings.
Evaluate strategies to mitigate bias, model drift, and other risks associated with AI implementation.
Apply governance and lifecycle management practices to support compliant and trustworthy AI adoption in pharmaceutical development.
