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…

KPIs that Link Strong Data Governance to AI Compliance Success KPIs that Link Strong Data Governance to AI Compliance Success Context In the rapidly evolving landscape of pharmaceutical and biotechnology industries, the integration of Artificial Intelligence (AI) into Quality Systems necessitates a robust data governance framework, particularly concerning 21 CFR Part 11 compliance. This regulatory framework establishes the standards for electronic records and electronic signatures, ensuring that they are trustworthy, reliable, and equivalent to traditional paper records. As AI systems increasingly dominate operational landscapes, the need for effective data governance becomes imperative to ensure compliance, maintain data integrity, and facilitate…