Involving Quality and Regulatory Early in AI Proof of Concept Projects Involving Quality and Regulatory Early in AI Proof of Concept Projects The introduction of Artificial Intelligence (AI) and Machine Learning (ML) into Good Practice (GxP) quality systems represents a significant evolution in the pharmaceutical and biotech industries. Given the complexities involved, regulatory professionals must understand the expectations set forth by regulatory agencies such as the FDA, EMA, and MHRA. Early involvement of quality and regulatory affairs in AI proof of concept projects is critical to ensuring compliance and addressing potential deficiencies proactively. Context In GxP settings, AI systems can…

Designing Internal Policies for Responsible AI in GMP Quality Operations Designing Internal Policies for Responsible AI in GMP Quality Operations Introduction As the biopharmaceutical industry increasingly adopts Artificial Intelligence (AI) and Machine Learning (ML) technologies, understanding regulatory expectations is critical for maintaining compliance within Good Manufacturing Practices (GMP) quality systems. This article provides a comprehensive guide on the FDA’s expectations regarding the use of AI in GxP operations, drawing connections to relevant regulations, guidelines, and best practices. Regulatory Context for AI in GxP Quality Systems The integration of AI/ML technologies into GxP operations necessitates a thorough understanding of the regulatory…

Global convergence of FDA, EMA and MHRA positions on AI in GxP systems Global convergence of FDA, EMA and MHRA positions on AI in GxP systems Artificial Intelligence (AI) and Machine Learning (ML) are changing the landscape of Quality Systems (GxP) within the pharmaceutical and biotech industries. Regulatory expectations set forth by the FDA, EMA, and MHRA provide essential guidelines to ensure that AI and ML implementations maintain the integrity, safety, and efficacy of drug products. This regulatory explainer manual delves into the FDA’s expectations for AI in GxP quality systems, elucidating the relevant regulations and guidelines, decision-making points, and…

Training QA and RA teams on emerging FDA thinking for AI and ML Training QA and RA teams on emerging FDA thinking for AI and ML The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Good Practice (GxP) quality systems has transformed various dimensions of pharmaceuticals and biotech industries. Regulatory Affairs (RA) professionals must remain at the forefront of these advancements, particularly concerning the expectations set forth by the U.S. Food and Drug Administration (FDA). This article serves as a comprehensive guide for training Quality Assurance (QA) and RA teams on FDA expectations for AI and ML in…

KPIs to track compliance and value of AI adoption in quality systems KPIs to Track Compliance and Value of AI Adoption in Quality Systems The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies in the pharmaceutical and biotech industries is transforming Quality Systems (QS) within Good Practices (GxP) ecosystems. Understanding the FDA expectations regarding AI use in GxP quality systems is crucial for compliance and ensuring the integration of responsible AI is effective. This manual provides an in-depth overview of key regulatory considerations, essential documentation, review processes, and the common deficiencies that may arise during regulatory inspections. Regulatory…