Artificial Intelligence is no longer just a fancy tool for predicting protein structures with AlphaFold. The FDA’s new draft guidelines outline a vision where AI permeates every stage of drug and biologics development. Whether it’s identifying drug targets, optimizing manufacturing, or—yes—even drafting Standard Operating Procedures (SOPs), AI is now under the regulatory spotlight.
And here’s what it means: Every use of AI must be documented, validated, and signed off by Quality personnel; Ready to be audited
🔑 Key Highlights from the FDA Guidelines
1️⃣ A Risk-Based Framework:
- The guidelines introduce a 7-step framework for assessing AI model credibility.
- It begins with defining the question, decision, or concern the AI model addresses, followed by determining the Context of Use (COU) and assessing AI model risk.
- The level of credibility assessments depends on the risk associated with the AI model and its intended use.
2️⃣ Early Engagement:
- The FDA strongly encourages early engagement with sponsors, biotech companies, and AI tool developers.
- Early dialogue helps address the appropriateness of AI use and identifies potential challenges tied to specific applications.
3️⃣ An Experience-Driven Framework:
- These guidelines build on the FDA’s experience from reviewing over 500 regulatory submissions with AI components since 2016.
- The recommendations have been shaped by 800+ public comments from 65 organizations, as well as insights from industry, academia, and public workshops.
📰 The FDA is seeking public comments on this guidance within 90 days. If you’re part of the industry, now’s your chance to weigh in and help shape the future of AI in drug development.
AI is already used in drug discovery and development. Question is, how to regulate it without stifling innovation?
When Can You Use AI? (Hint: Not Anytime, Anywhere)
Let’s say your Quality Control team decides to use AI for drafting SOPs. Seems efficient, right? But it’s not enough to simply use AI—you have to prove it works.
- Document it: What AI model was used? GPT-4 or an exclusive, custom-built tool?
- Report it: How was the model trained? Where’s the supporting data?
- Validate it: Did you stress-test the outputs? Was it independently reviewed?
And here’s a wrinkle: AI tools evolve fast. The $20 ChatGPT subscription might differ significantly from the $200 Enterprise version. Which one are you using? Is it consistent? Reliable? These aren’t just tech questions—they’re now compliance questions.
Validation: The Next Big Opportunity
Every AI model now needs to pass through a validation pipeline. Every AI decision must be backed by evidence. This is fertile ground for validation firms to step up and create standardized frameworks for assessing compliance, reliability, and risk.
If you’re in the business of compliance, consider this your open invitation from the FDA to innovate and build trust in AI tools.
AI Changes Faster Than Compliance Cycles
AI isn’t static. New tools, new versions, and updated models roll out every few months. Today’s validated model might drift tomorrow. The FDA guidelines acknowledge this reality and emphasize lifecycle management as a core requirement.
Every AI deployment needs ongoing oversight—not just a one-time certification.
Takeaways for Innovators and Auditors:
- Context Matters: Clearly define where and how AI is being used.
- Documentation is Essential: Every detail counts, from datasets to model updates.
- Validation is Key: Testing and cross-checking are non-negotiable.
- Ongoing Monitoring: AI is not a “set it and forget it” tool.
The FDA isn’t stifling AI innovation—they’re giving it a structure to scale responsibly. If you’re using AI in drug development, manufacturing, or quality control, these guidelines are now your roadmap.
The AI revolution isn’t on the horizon—it’s already here. And the FDA is watching.
👉 Read the full FDA draft guidance here: Link
👉 Let’s talk about what this means for your business. Get in touch!
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