FDA and EMA set 10 guiding principles for AI in drug development

The U.S. Food and Drug Administration and the European Medicines Agency on January 14 published a single, joint document laying out 10 high-level guiding principles for the use of artificial intelligence across the medicines lifecycle. The principles are framed as broad, risk-based guidance intended to promote safe, transparent and scientifically sound deployment of AI from discovery and trial design through manufacturing, evidence generation and post-market safety monitoring.

The agencies positioned the principles as cross-cutting rather than prescriptive technical rules. They emphasize a total product lifecycle approach to risk assessment and management when AI tools are used in drug development, underscoring that regulatory scrutiny should follow a product from early research to post-authorisation surveillance. The document ties core expectations to established software engineering practices, data quality controls and cybersecurity measures, reiterating longstanding themes from earlier Good Machine Learning Practice work by regulators in multiple jurisdictions and international bodies.

Ethics, transparency and scientific soundness are foregrounded. The principles call for clear documentation of methods and data provenance, robust validation of models against relevant clinical endpoints, and governance measures to mitigate bias and protect patient welfare. The agencies also identified the need for international collaboration on research, harmonized standards and capacity building, suggesting the principles are a foundation for future detailed guidance and consensus standards rather than an endpoint.

EMA officials linked the move to the agency’s broader European medicines agencies network strategy to 2028 and to an existing multiannual Data and AI workplan developed with national regulators. The release also follows sustained U.S.-EU regulatory engagement, including bilateral discussions in 2024 that sought greater alignment on emergent technologies in life sciences. The agencies explicitly framed the principles as a first step toward global convergence on AI topics in medicines development and as a basis for tailored national or regional guidance.

European Commissioner for Health and Food Safety Oliver Varhelyi hailed the initiative as “a first step of a renewed EU‑US cooperation... to preserve our leading role in the global innovation race, while ensuring the highest level of patient safety.” Industry lawyers and consultants said the document will help manufacturers and applicants begin to map regulatory expectations for AI-enabled processes, though they cautioned that concrete filing requirements remain undefined.

The publication arrives against a backdrop of rapid industry activity in AI-driven drug research and infrastructure. Pharma and technology firms are deepening investments in AI capabilities, including acquisitions of specialized startups and multi-billion dollar collaborations to build research labs and computational platforms. Regulators are also piloting internal uses of generative AI to improve review efficiency, reflecting parallel adoption inside agencies.

For developers and marketing-authorisation holders, the principles offer a high-level roadmap: prioritize lifecycle risk management, adhere to sound data and software practices, and be transparent about methods and limits. Regulatory officials signaled that standards bodies and public health partners will be invited to engage in research and harmonization work, and that more detailed guidance and jurisdiction-specific rules are likely to follow as technical consensus emerges.

The document does not provide technical checklists or filing templates, leaving many operational questions for future work. Still, by setting shared expectations across two major regulatory powers, the FDA and EMA have created a platform that could accelerate consistent oversight of AI in drug development while raising the bar for safety, reliability and ethical use.