AI Empowering Regulatory CMC Strategy

AI is rapidly transforming regulatory Chemistry, Manufacturing, and Controls (CMC) strategy, especially for emerging biotechs navigating limited resources and growing complexity. With the FDA’s 2024 draft guidance on Quality Management Maturity (QMM) pushing for data-driven, forward-leaning operations, AI has become a critical tool for smarter decision-making, faster submissions, and deeper regulatory alignment. Here’s what that means now, and where it’s headed.

AI in the CMC War Room: Turning FDA’s New Draft Guidance Into an Advantage for Emerging Biotechs

AI is rapidly reshaping CMC drug development, giving emerging biotechs the ability to make faster, sharper, data-driven decisions.

In short, AI is becoming the engine behind smarter CMC strategy, cleaner submissions, and a more mature, inspection-ready organization.

Artificial intelligence is no longer just a buzzword floating around discovery and R&D decks. With FDA’s January 2025 draft guidance on Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, AI has officially entered the CMC war room.

For CMC leaders in emerging biotech, this is a pivotal moment: FDA is now telling you how they expect AI-generated data and models to be justified, documented, and defended when you use them to support decisions about product quality, manufacturing, and release—not just clinical efficacy and safety.

This isn’t abstract. The guidance gives explicit examples that sit squarely in our world, including using AI to facilitate the selection of manufacturing conditions and to support automated visual analysis of critical quality attributes (CQAs) such as fill volume in parenteral vials.

The message is clear:
If you want to use AI to justify CMC decisions to FDA, you now need a structured, risk-based credibility story—not just a clever model and a slide deck.

What This Guidance Actually Covers – And What It Doesn’t

First, scope. FDA is very explicit.

This draft guidance applies when AI is used to produce information or data that directly supports regulatory decision-making for drugs and biologics, specifically decisions about safety, effectiveness, or quality.

That includes AI used in:

• CMC development and commercial manufacturing

• Product and process understanding

• Quality decisions that feed into submissions or GMP activities

The guidance does not cover:

• AI used purely for internal operational efficiencies—for example, drafting submissions, managing workflow, or resource allocation—as long as it doesn’t affect patient safety, drug quality, or reliability of nonclinical/clinical results.

For CMC, that means:

• AI that decides, predicts, classifies, or quantifies something you rely on for product quality, release, or spec justification = in scope.

• AI that just helps your team write Module 3 faster = out of scope (under this guidance, but still something you should govern).

So if you’re using AI to set process parameters, monitor fill volume, classify defects, or support real-time release testing—this document is aimed squarely at you.

The Centerpiece: A 7-Step “Credibility Framework” for AI Models in CMC

The heart of the guidance is a 7-step risk-based credibility assessment framework—FDA’s playbook for how you should justify, challenge, and document AI models used to support regulatory decisions.

Think of it as a structured way to answer:

“Why should FDA trust this AI output enough to rely on it for a quality decision?”

The steps are:

1. Define the Question of Interest
   What are you actually asking the model? For CMC:

   • “Does each vial meet fill volume specifications?”

   • “Is this lot within the acceptable process performance envelope?”

2. Define the Context of Use (COU)
   What is the model’s role and scope?

   • Is it the sole determinant of release (high influence)?

   • Or one data stream among several, with human/other checks (lower influence)?

3. Assess Model Risk (Influence × Decision Consequence)
   FDA defines model risk as a combination of:

   • Model influence: How much does the AI output drive the decision?

   • Decision consequence: What happens if that decision is wrong?

   An AI system that solely determines vial accept/reject for a critical injectable has higher risk than one that flags potential issues which are then confirmed by independent QC testing.

4. Develop a Credibility Assessment Plan
   For higher-risk CMC models, FDA expects a structured plan covering:

   • Model description and architecture

   • Data used for training/tuning (relevance, representativeness, reliability)

   • Model training methodology and controls against overfitting

   • Model evaluation on independent test data, with appropriate metrics and uncertainty characterization

5. Execute the Plan
   Run the analyses, collect evidence, and refine as needed. FDA explicitly encourages early dialogue on your plan instead of waiting until NDA/BLA time.

6. Document the Results (Credibility Assessment Report)
   Summarize what you did, what you found, and any deviations from the plan. This report can be:

   • Included in a submission or meeting package, or

   • Held and made available on request (e.g., during inspection).

7. Decide Whether the Model Is Adequate for the COU
   You and FDA may conclude the model is:

   • Adequate as-is

   • Adequate if supported by additional non-AI evidence

   • Or not adequate for the proposed COU—requiring a change in influence, controls, or approach.

For CMC teams, this framework is essentially your blueprint for “regulatory-grade AI”.

CMC-Specific Examples: Where the Guidance Hits Home

The guidance gives a manufacturing example that could easily mirror what many sponsors are actively piloting:

Scenario: A parenteral injectable (Drug B) in a multidose vial. Fill volume is a critical quality attribute. The manufacturer proposes an AI-based visual analysis system to perform 100% automated assessment of fill level.

Key regulatory signals embedded in that example:

• AI may be used to enhance performance of visual checks and identify deviations.

• However, independent verification of fill volume is still performed as part of release testing on a representative sample, so the AI output is not the sole determinant. Model influence is therefore mitigated, and model risk is classified as medium rather than high.

Translated into practical CMC terms:

• FDA is open to AI-enabled 100% inspection—but expects you to be very clear whether AI is advisory or decisive for release.

• The more the AI model replaces traditional quality verification, the more stringent your evidence and documentation need to be.

Apply the same logic to:

• AI-driven PAT or process monitoring models (e.g., predicting CPPs or CQAs from multivariate sensors)

• Predictive maintenance models that indirectly affect product quality and supply continuity

• AI-based classification of visual defects for particulates, container closure, or surface flaws

If those outputs factor into acceptance/rejection, trending, or batch disposition, this guidance is your playbook.

The Part Everyone Will Miss: Life-Cycle Maintenance of AI Models in Manufacturing

One of the most important sections for CMC—especially for commercial manufacturing—is life-cycle maintenance of AI model credibility.

FDA knows that AI models:

• Are data-driven

• May be self-evolving

• Are vulnerable to data drift when new data differ from the training data

So the guidance is very clear: if your AI model is used over the drug product life cycle (manufacturing is explicitly highlighted), you must have a risk-based plan to monitor performance, detect drift, and manage changes under your pharmaceutical quality system.

What FDA expects you to do on the CMC side:

• Define model performance metrics and monitoring frequency.

• Treat model updates and retraining as controlled changes within your Q10-aligned PQS and change management system.

• Re-run portions of your credibility plan when changes materially affect model performance or context of use.

• Use ICH Q12 tools (e.g., established conditions, post-approval change management plans / comparability protocols) to pre-define which AI model changes can be handled without prior approval.

In other words, AI models used in CMC are now regulated objects over time, not one-and-done validations.

Practical Moves for Emerging Biotech CMC Teams

If you’re an emerging biotech with limited headcount and a complex CMC agenda, how do you turn this guidance from burden into advantage?

1. Be Ruthless About Context of Use

Before you ever talk to FDA:

• Map every AI use case touching CMC.

• For each, write one sentence for:

  • Question of interest

  • Context of use (role + scope)

  • Decision consequence if the model is wrong

This lets you quickly rank model risk and decide which AI tools need heavy, formal credibility packages and which can be governed more lightly.

2. Start Small, but Make It “Regulatory-Grade”

Pick one or two high-leverage CMC AI applications—e.g.:

• AI-supported 100% visual inspection (with traditional sampling as a backstop)

• AI-driven pattern recognition for process deviations that feeds your continued process verification

For those:

• Build a clean data story: relevance, representativeness, reliability, and data management practices.

• Treat training and test data separation seriously; no sloppy leakage.

• Pre-specify performance metrics, acceptance criteria, and uncertainty handling.

You don’t need a data science empire, you need one well-documented, credibly justified use case that will stand up to regulatory scrutiny and can be replicated.

3. Embed AI into Your PQS and Change Management

Make sure your PQS and SOPs explicitly cover:

• AI model development, documentation, and code/version control

• Data governance for training, tuning, and test sets

• Ongoing monitoring, deviation handling, and trigger points for model retraining or rollback

This isn’t just a regulatory expectation, it’s how you avoid getting blindsided when a model that used to work “just fine” suddenly starts misbehaving after a shift in materials, equipment, or upstream processes.

4. Use FDA’s Early Engagement Pathways Strategically

FDA doesn’t want you guessing.

The guidance explicitly encourages early engagement to align on model risk, credibility expectations, and documentation strategy.

For CMC/AI in manufacturing, your go-to channels include:

• Emerging Technology Program (ETP – CDER)

• CBER Advanced Technologies Team (CATT)

Both are specifically named for AI in pharmaceutical manufacturing and encourage early dialogue before implementation or submission.

For cross-cutting or modeling-heavy programs, the MIDD Paired Meeting Program or RWE/AI interfaces may also be relevant.

Use these meetings to:

• Pressure-test your Context of Use and risk assessment.

• Confirm what level of credibility evidence FDA expects.

• Align on how and where you’ll present the AI story in your CTA/IND/NDA/BLA or supplement.

What This Means for CMC in 2025 and Beyond

This draft guidance is not just an “AI policy note.” It’s a marker of where CMC regulation is heading:

• AI is welcome in CMC—if you can prove it’s credible, controlled, and well-governed.

• Model risk and Context of Use now determine how hard FDA will push back.

• Life-cycle management of AI models is now part of your PQS responsibility, especially in commercial manufacturing.

For emerging biotechs, the upside is real:

• Done right, AI lets a small CMC team perform with big-pharma sophistication—better process understanding, tighter control, stronger quality narratives, and more resilient supply.

• A well-designed, well-documented AI use case can become a regulatory and commercial differentiator, not just a tech experiment.

The opportunity in 2025 is to move from “we’re playing with AI in CMC” to
“we run regulatory-grade AI that FDA can trust—and it’s built into how we develop, control, and release product.”

That’s where this guidance is pointing. And that’s where the competitive edge will be.