October 31, 2025

Artificial intelligence (AI) is rapidly transforming drug development and regulatory decision-making, with the goal of improving efficiency, accuracy, and speed. Today, AI plays a crucial role in personalized medicine by identifying genetic markers, optimizing dosages, and minimizing adverse drug reactions.

Recognizing this growing role, the U.S. Food and Drug Administration (FDA) released its first draft guidance on AI in drug development in January 2024. The European Medicines Agency (EMA) also published a Reflection Paper on AI in the medicinal product lifecycle, showing a global regulatory trend toward structured AI oversight.

For stakeholders in the life sciences sector, understanding the FDA’s AI framework is essential for compliance, innovation, and market access.

FDA’s Objectives Behind the AI Guidance

In announcing the new guidance, FDA Commissioner Robert M. Califf, M.D. emphasized the importance of a risk-based framework that balances innovation with strong regulatory and scientific standards. “With the appropriate safeguards in place, artificial intelligence has transformative potential to advance clinical research and accelerate medical product development to improve patient care.” – FDA Commissioner

The FDA’s draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, provides broad principles for AI in regulatory submissions. Its goal is to help sponsors document AI credibility and ensure that AI-generated data is reliable enough to support FDA review and approval.

Key Highlights of the Draft FDA AI Guidance

1. Risk-Based Credibility Assessment

The FDA proposes a risk-based credibility assessment framework for evaluating AI models. Sponsors must:

  • Define the context of use (COU).
  • Provide source data used for AI training.
  • Document validation and reliability measures.
  • Show mechanisms to control bias and variability.

2. Transparency in Source Data

Companies must share the datasets used to train the AI model. For small and mid-sized companies, the challenge lies in securing high-quality training datasets, as public domain data is limited.

3. Early FDA Engagement

The FDA strongly encourages early interaction with sponsors to provide clarity on data standards, AI model validation, and regulatory expectations.

Context: Why FDA is Focused on AI in Drug Development

AI’s growing role in drug discovery, clinical trials, and manufacturing raises new regulatory questions. For example:

  • Moderna and Merck’s individualized neoantigen mRNA cancer vaccine (mRNA-4157) relies on AI algorithms to identify patient-specific tumor antigens. Since each vaccine may be unique, FDA requested the algorithm be “locked” before trials to prevent evolving bias.

This illustrates the FDA’s concern with consistency, quality control, and safety assessment when algorithms directly impact patient-specific therapies.

Examples from the Draft Guidance

  • Low-risk application: Using AI to measure the fill volume in vials (automated quality check).
  • High-risk application: Applying AI in clinical development or patient-specific therapies.

The framework makes clear that AI applications with higher patient risk will require stricter oversight, human review, and additional validation controls.

Challenges for the FDA Review Process

Unlike traditional biologics or recombinant proteins where the manufacturing process defines the drug product, AI introduces new complexity:

  • Proprietary AI algorithms may themselves be considered a critical component of the drug product.
  • FDA reviewers must assess not only the drug but also the algorithm generating sequences or decisions.
  • Reviewing AI requires new technical expertise, resources, and inspection methods.

Another challenge is whether FDA can leverage the massive datasets it holds from prior approvals without breaching data exclusivity rights. Budgetary constraints may also limit the speed at which FDA can build AI review capacity.

Industry Takeaways: How Sponsors Should Respond

Sponsors should carefully integrate the FDA’s AI guidance into their regulatory strategy. Key recommendations include:

  1. Define COU early – Clearly document how AI will be used within the drug development process.
  2. Plan credibility assessment – Establish a framework to demonstrate AI model credibility.
  3. Engage FDA proactively – Discuss the AI model with FDA during development, not after.
  4. Document transparency – Provide detailed records of training data, validation, and monitoring.
  5. Anticipate evolving guidance – Expect more detailed FDA guidance on complex AI applications in the future.

Conclusion

Artificial intelligence is revolutionizing drug discovery, clinical development, and biologics manufacturing, but it also introduces regulatory challenges. The FDA’s draft guidance reflects a commitment to ensuring AI-driven drug development is both innovative and safe.

For sponsors, the clear message is early engagement with FDA, strong documentation of AI model credibility, and compliance with a risk-based framework are essential to gaining approval.

At Maven Regulatory Solutions, we specialize in guiding pharmaceutical and biotech companies through FDA regulatory strategy, AI-based drug development compliance, CDSCO alignment, and EMA AI frameworks. Our expertise ensures that your innovations meet regulatory expectations while advancing safely to market.

???? Partner with Maven Regulatory Solutions to navigate AI-driven drug development with confidence.