Building a validation master plan for AI and ML applications in QA Building a Validation Master Plan for AI and ML Applications in QA Context As artificial intelligence (AI) and machine learning (ML) technologies become increasingly integrated into quality assurance (QA) processes within pharmaceutical and biotech industries, regulatory affairs professionals must ensure these technologies comply with established regulations. Particularly, the guidance surrounding data governance, validation, and compliance with 21 CFR Part 11 is critical to ensure data integrity and reliability. This article will serve as a regulatory explainer manual that details the necessary framework for developing a robust validation master…

Case Studies of Data Governance Gaps that Undermined AI Initiatives Case Studies of Data Governance Gaps that Undermined AI Initiatives In today’s regulatory landscape, the integration of Artificial Intelligence (AI) systems in the pharmaceutical and biotechnology sectors presents unique challenges. Effective data governance is essential for ensuring compliance with regulatory mandates, particularly with respect to 21 CFR Part 11 in the United States, EU Annex 11, and device software regulations in the UK. This article outlines the critical link between data governance, AI validation, and compliance, illustrating case studies where lapses have undermined AI initiatives. Context The adoption of AI…

Designing Metadata Standards and Data Catalogs for AI Ready Quality Data Designing Metadata Standards and Data Catalogs for AI Ready Quality Data This regulatory explainer manual provides a comprehensive guide on the intersection of artificial intelligence (AI), data governance, and regulatory compliance specifically within the frameworks of 21 CFR Part 11 in the US, and similar regulations in the EU and UK. Designed for regulatory affairs professionals, this article elaborates on the necessary components for establishing robust metadata standards and data catalogs that ensure high-quality, compliant data in AI enabled environments. Regulatory Affairs Context As the pharmaceutical and biotechnology industries…

Managing Training, Test and Production Datasets Under Part 11 Controls Managing Training, Test and Production Datasets Under Part 11 Controls Regulatory Context In the rapidly evolving landscape of pharmaceuticals and biotechnology, artificial intelligence (AI) plays a pivotal role in quality systems. The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in the EU have established stringent regulations and guidelines on data governance, particularly concerning electronic records and signatures as outlined in 21 CFR Part 11. Compliance with these regulations is critical for ensuring the integrity and reliability of AI systems used throughout the…

How to document AI models and validation in CSV deliverables How to document AI models and validation in CSV deliverables Context As artificial intelligence (AI) technologies continue to permeate the pharmaceutical and biotech industries, the need for robust data governance has never been more critical. The integration of AI into Quality Systems mandates compliance with various regulatory standards, most notably the U.S. Food and Drug Administration (FDA) guidelines outlined in 21 CFR Part 11, the European Medicines Agency (EMA) requirements, and the UK’s Medicines and Healthcare products Regulatory Agency (MHRA) directives. This article aims to serve as a regulatory explainer…

Supplier assessments and audits focused on AI data integrity controls Supplier assessments and audits focused on AI data integrity controls Regulatory Affairs Context In the evolving landscape of pharmaceutical and biotech sectors, the integration of Artificial Intelligence (AI) into quality systems presents unique challenges and opportunities. The regulatory framework governing these technologies, particularly in relation to data governance, validation, and compliance under 21 CFR Part 11, is critical for safeguarding data integrity and ensuring patient safety. Regulatory agencies such as the FDA, EMA, and MHRA have established guidelines that must be adhered to when integrating AI into quality assurance (QA)…

Change control processes for retraining and updating AI models Change Control Processes for Retraining and Updating AI Models Context In the evolving landscape of pharmaceutical and biotechnology industries, artificial intelligence (AI) is increasingly deployed for a variety of functions including data analysis, predictive modeling, and quality control. The integration of AI systems necessitates vigilant adherence to regulatory frameworks to ensure their suitability for compliant use within regulated environments. Particularly regarding data governance, the 21 CFR Part 11 compliance requirements hold significant implications for AI validation and the management of data integrity. Legal/Regulatory Basis The regulatory foundation for AI in pharmaceutical…

Regulatory considerations for explainability and transparency in AI Regulatory considerations for explainability and transparency in AI As Artificial Intelligence (AI) continues to proliferate in the pharmaceutical and biotechnology sectors, understanding regulatory considerations surrounding data governance, transparency, and explainability is crucial. This is particularly pertinent under relevant regulations and guidelines such as 21 CFR Part 11 for the United States, EU regulations, and the guidelines established by the ICH and MHRA. This article serves as a comprehensive manual for Kharma and regulatory professionals aiming to navigate these complex regulatory waters effectively. Regulatory Context for AI in Quality Systems The incorporation of…

Cybersecurity and Access Controls for AI Enabled Quality Systems Cybersecurity and Access Controls for AI Enabled Quality Systems Regulatory Affairs Context The increasing adoption of Artificial Intelligence (AI) in Quality Systems within the pharmaceutical and biotechnology industries raises significant regulatory considerations. Regulatory Affairs (RA) professionals must ensure compliance with various regulatory frameworks, notably 21 CFR Part 11 in the United States, Annex 11 in the European Union, and related guidelines from agencies such as the FDA, EMA, and MHRA. This article serves as a detailed explainer manual outlining the regulations, best practices for data governance, validation, and compliance when integrating…

Global perspectives on data governance for AI across FDA, EMA and MHRA Global perspectives on data governance for AI across FDA, EMA and MHRA As artificial intelligence (AI) technologies evolve within the pharmaceutical and biotechnology sectors, regulatory frameworks are being adapted to ensure proper governance of these technologies. Data governance, especially within the context of 21 CFR Part 11, has become critical in maintaining compliance while leveraging the transformative potential of AI. Regulatory Affairs Context Data governance refers to the overall management of data availability, usability, integrity, and security in an organization. In the context of AI, it encompasses various…