EMA and FDA Collaborate on Framework for AI Use in Drug Development

The European Medicines Agency (EMA) and FDA, in a significant step in the regulatory oversight of computational tools in the pharmaceutical industry, have established ten core principles intended to support the safe and ethical application of AI throughout the entire path of a medicine, from early laboratory research to the manufacturing floor and eventual post-market oversight (1,2). This alignment provides a necessary framework to manage the complexity and dynamic nature of these tools, ensuring that the evidence produced is both accurate and reliable.

What are the ten core principles established for pharmaceutical AI?

The joint initiative identifies specific areas where pharmaceutical developers and technical standards organizations can align their practices (2). These principles are as follows:

1. Human-centric by design: The development and use of AI technologies align with ethical and human-centric values (2,3).

2. Risk-based approach: The development and use of AI technologies follow a risk-based approach with proportionate validation, risk mitigation, and oversight based on the context of use and determined model risk (2,3).

3. Adherence to standards: AI technologies adhere to relevant legal, ethical, technical, scientific, cybersecurity, and regulatory standards, including Good Practices (GxP) (2,3).

4. Clear context of use: AI technologies have a well-defined context of use (role and scope for why it is being used) (2,3).

5. Multidisciplinary expertise: Multidisciplinary expertise covering both the AI technology and its context of use are integrated throughout the technology’s life cycle (2,3).

6. Data governance and documentation: Data source provenance, processing steps, and analytical decisions are documented in a detailed, traceable, and verifiable manner, in line with GxP requirements (2,3).

7. Model design and development practices: The development of AI technologies follows best practices in model and system design and software engineering and leverages data that is fit-for-use, considering interpretability, explainability, and predictive performance (2,3).

8. Risk-based performance assessment: Risk-based performance assessments evaluate the complete system including human-AI interactions, using fit-for-use data and metrics appropriate for the intended context of use, supported by validation of predictive performance through appropriately designed testing and evaluation methods (2,3).

9. Life cycle management: Risk-based quality management systems are implemented throughout the AI technologies’ life cycles, including to support capturing, assessing, and addressing issues (2,3).

10. Clear, essential information: Plain language is used to present clear, accessible, and contextually relevant information to the intended audience, including users and patients, regarding the AI technology’s context of use, performance, limitations, underlying data, updates, and interpretability or explainability (2,3).

How will these standards impact drug development and manufacturing?

The implementation of these principles is expected to facilitate more efficient pathways for both traditional medicines and biological products, as the term drug in this context encompasses both categories across various jurisdictions (1-3). In a message to American Association of Pharmaceutical Scientists members (4), Mark Arnold, PhD, owner and principal, Bioanalytical Solution Integration, wrote "Today, after several months of collaboration, the FDA and the European Medicines Agency… are releasing a common set of 10 guiding principles to inform, enhance, and promote the use of [AI] generating evidence across all phases of the drug product life cycle. The integration of AI in drug development has the potential to transform the way drugs are developed and evaluated, ultimately improving health care. AI technologies are anticipated to help promote innovation, reduce time-to-market, strengthen regulatory excellence and pharmacovigilance, and decrease reliance on animal testing by improving the prediction of toxicity and efficacy in humans."

The joint principles mean that future regulatory submissions involving AI will likely require more rigorous multidisciplinary integration and documented data provenance to meet good manufacturing practices expectations. As noted by Arnold, "As an advisor to an AI company, and a user of AI software, these are 10 short and clear guides." By adhering to these standards, companies can better prepare for future jurisdictional guidelines while contributing to a global innovation environment that prioritizes patient safety. European Commissioner for Health and Animal Welfare Olivér Várhelyi stated in an EMA news release, "The guiding principles of good AI practice in drug development are a first step of a renewed EU-US cooperation in the field of novel medical technologies. The principles are a good showcase of how we can work together on the two sides of the Atlantic to preserve our leading role in the global innovation race, while ensuring the highest level of patient safety" (1). This foundational work is expected to evolve alongside the technology, maintaining a focus on demonstrated quality, efficacy, and safety (2).

References

1. European Medicines Agency. EMA and FDA Set Common Principles for AI in Medicine Development. Press Release. Jan 14, 2026.
2. FDA. Guiding Principles of Good AI Practice in Drug Development. Accessed Jan 14, 2026.
3. FDA. Artificial Intelligence for Drug Development. Guiding Principles of Good aI Practice in Drug Development. Accessed Jan 14, 2026.
4. American Association of Pharmaceutical Scientists. AAPS Community Digest for Wednesday January 14, 2026. Email Newsletter.