Artificial intelligence creates new opportunities for data analysis, clinical trial optimization and improving manufacturing processes, potentially transforming pharmaceutical development and production. Both the United States and the European Union have recently advanced their regulatory frameworks to address the unique challenges and risks posed by AI in the medicinal product lifecycle.

Reflecting a harmonized approach to AI oversight in drug development, FDA has collaborated with EMA to articulate ten guiding principles. Released in January 2026, the "Guiding Principles of Good AI Practice in Drug Development" are designed to ensure AI use remains ethical, transparent, and reliable across the drug lifecycle – from research to post-marketing. These principles emphasize human-centric design, risk-based oversight, adherence to standards, robust data governance, and clear communication. They also call for multidisciplinary expertise, rigorous model development, and continuous performance monitoring. By fostering international collaboration and harmonized standards, these guidelines aim to advance responsible AI adoption while safeguarding patient health and regulatory integrity, complementing the steps that the EMA and FDA are each taking to foster and regulate AI-based innovation.

Blockchain, meanwhile, offers the ability to store and transmit data in a decentralized and real-time manner. Blockchain could help resolve many ATMP supply chain challenges due to its inherent characteristics of: (i) consensus; (ii) provenance and immutability; (iii) finality; (iv) security and reliability; and (v) decentralization. Blockchain transactions are secure, authenticated and verifiable. This is relevant to ATMPs because:

U.S. FDA's Risk-Based Guidance

FDA's 2025 draft guidance on the use of AI in regulatory decision-making for drugs and biologics introduces a comprehensive, risk-based framework for sponsors, emphasizing model credibility, transparency, and lifecycle management. Key elements include a seven‑step process covering AI model risk and credibility assessments, required credibility assessment plans and reports, and continuous oversight through lifecycle maintenance plans. Notably, FDA's guidance applies to nonclinical, clinical, postmarketing, and manufacturing phases, but excludes AI used solely for drug discovery or operational efficiencies that do not impact patient safety or data reliability. Early FDA engagement is strongly encouraged.

In parallel, FDA has embraced AI as an internal agency tool under the current Commissioner, announcing its adoption of agentic AI for premarket review, review validation, post-market surveillance, meeting management, and for inspections and compliance purposes, as well as administrative functions.