Adoption of artificial-intelligence (AI) and machine-learning (ML) tools offers great promise to the future of biomanufacturing. But regulatory guidances must still evolve in order for such novel technologies to reach their full potential without impacting product quality and safety. The foundations for those guidelines already exist; regulatory bodies and biomanufacturers must continue to work together to adapt AI tools while maintaining compliance.
In this eBook, experts discuss practical standards for AI tools and software in biomanufacturing. First, Sivakumar Kalidoss discusses the inefficiencies of risk-management frameworks for AI-driven computer systems that only incorporate qualification protocols. He explains how organizations can close that validation gap within existing regulatory guidance. Then, Sun Chau Siu and Kaisong Zhou examine how the governance of AI systems is evolving from models centered on one-time validation toward life-cycle–based paradigms. They share operational applications of such systems in biomanufacturing, including a successful use case of AI tools that predicted a protein A chromatography breakthrough. Finally, James Colley of BioPhorum reveals how AI adoption throughout the industry has been halted by data readiness. Organizations must spend years cleaning and connecting fragmented, inaccessible datasets before realizing the full potential of AI technologies.