FDA and EMA set good AI practice principles for drug development

Summary

The FDA and EMA have jointly released 10 guiding principles for good AI practice in drug and biological product development. The principles aim to steer safe and responsible AI use in evidence generation and monitoring across the medicines lifecycle, from early research and clinical trials to manufacturing and safety monitoring.

The FDA summary emphasizes human-centric design, risk-based approaches, clear context of use, data governance, and lifecycle management among the core themes. That framing signals the guardrails regulators expect as AI adoption expands in regulated drug development.

Signals for Investors

• Shared FDA/EMA principles create a common baseline for AI-enabled drug development; expect more emphasis on documented context of use, data governance, and lifecycle management in vendor diligence.
• Risk-based performance assessment and monitoring requirements could increase demand for validation, audit, and model-governance tooling across pharma pipelines.
• Because the principles apply across the full lifecycle, platforms that span discovery through manufacturing and post-market monitoring should be better positioned in regulated workflows.

What to Watch Next

Watch for follow-on FDA and EMA guidance that translates these principles into submission expectations and for EU guidance that builds on the 2024 EMA AI reflection paper. Track how pharma and biotech teams operationalize the principles in AI governance and evidence-generation workflows.