U.S. and European Regulators Set Principles for Good AI Practice in Drug Development
Artificial intelligence (AI) is rapidly transforming drug development—from target identification and trial design to manufacturing optimization and post-market surveillance. Recognizing both the promise and the risk, U.S. and European regulators are now aligning on foundational principles to guide the responsible use of AI across the product lifecycle.
Rather than issuing rigid rules, regulators are establishing Good AI Practice (GAIP) principles designed to promote innovation while safeguarding data integrity, patient safety, and regulatory confidence.
A Converging Regulatory Approach
Regulatory authorities on both sides of the Atlantic—including the U.S. Food and Drug Administration and the European Medicines Agency have emphasized a risk-based, lifecycle-driven approach to AI in drug development.
The message is consistent:
AI is acceptable and encouraged, when it is transparent, controlled, and fit for purpose.
Core Principles of Good AI Practice
While guidance continues to evolve, several shared principles have emerged:
1. Transparency and Explainability
Sponsors must be able to explain how an AI model works, what data it uses, and how outputs influence development or regulatory decisions. “Black box” systems without interpretability raise concerns, particularly when AI informs safety, efficacy, or quality determinations.
2. Data Quality and Governance
AI models are only as reliable as the data behind them. Regulators expect:
Well-characterized, traceable datasets
Controls for bias and data drift
Documentation of data provenance and curation processes
Poor data governance is viewed as a direct regulatory risk.
3. Human Oversight and Accountability
AI is positioned as a decision-support tool—not a decision-maker. Human oversight must be clearly defined, with accountability resting on qualified experts who can challenge, override, or contextualize AI-driven outputs.
4. Model Validation and Lifecycle Management
AI systems require ongoing monitoring. Regulators expect:
Defined validation strategies aligned to use-case risk
Change management for model updates
Continuous performance assessment over time
Static validation at a single time point is no longer sufficient.
5. Risk-Based Application
Not all AI uses carry the same regulatory weight. A model used for internal research prioritization will be treated differently than one supporting clinical trial design, manufacturing release decisions, or safety signal detection.
Implications for Drug Developers
For sponsors, these principles translate into practical expectations:
Early integration of regulatory thinking when deploying AI tools
Cross-functional collaboration among clinical, CMC, data science, quality, and regulatory teams
Documentation that aligns AI development with existing GxP frameworks where applicable
Importantly, regulators are not asking companies to slow innovation—but to operationalize trust in AI-enabled systems.
Looking Ahead
As AI adoption accelerates, we can expect:
Additional clarifying guidance tied to specific use cases
Greater emphasis on inspection readiness for AI-enabled processes
Continued global convergence rather than fragmented regional rules
Organizations that proactively align with Good AI Practice principles will be better positioned to leverage AI as a competitive advantage—without introducing unnecessary regulatory risk.
Final Thoughts
Good AI Practice is not a new compliance burden; it is an extension of existing regulatory fundamentals applied to a powerful new toolset. Transparency, quality, oversight, and accountability remain the cornerstones of regulatory trust—whether decisions are made by humans, algorithms, or a combination of both.
For drug developers, the path forward is clear: innovate boldly, but govern wisely.
