The industry signal: FDA is formalizing how AI should be trusted
On January 6, 2025, FDA published draft guidance titled “Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products.” It provides recommendations on using AI to produce data supporting regulatory decisions and introduces a risk-based credibility assessment framework tied to a model’s context of use. (U.S. Food and Drug Administration)
In December 2025, Reuters reported FDA qualified AIM‑NASH, described as the first AI-based tool qualified to help speed development in a liver disease setting—reinforcing that FDA is willing to recognize AI tools when performance and governance are credible. (Reuters)
Separately, U.S. government programs are explicitly pairing advanced manufacturing and AI concepts in initiatives aimed at evolving manufacturing capacity and capability. (ASPR)
What this means for API manufacturing and QC teams
Many organizations already use AI/ML in “quiet” ways:
- anomaly detection in process data
- multivariate analysis of spectra
- prediction of impurity formation
- batch-release decision support dashboards
The risk is that teams adopt AI because it is powerful, but fail to document credibility in a way QA and regulators can trust.
FDA’s framing pushes sponsors toward a more disciplined model:
- Define context of use (COU)
- Assess risk
- Establish credibility evidence proportional to risk
A practical credibility framework you can implement without a full data science department
Step 1: Write the context of use in one sentence
Examples:
- “Model flags atypical impurity profiles for QA review prior to disposition.”
- “The model recommends reaction endpoint timing to reduce over-processing risk.”
Be explicit whether the model is:
- advisory (human decides), or
- automated (system decides).
Step 2: Classify decision risk
High-risk COU examples:
- automated batch disposition
- real-time release decisions
Lower-risk COU examples: - prioritizing lab investigations
- route scouting hypotheses
Your credibility burden should scale accordingly. (U.S. Food and Drug Administration)
Step 3: Prove data fitness and governance
Document:
- data provenance and integrity
- missing data handling
- training/validation splits
- representativeness across lots, scales, and instruments
Step 4: Validate performance for the COU
Avoid generic “accuracy” claims. Use COU-relevant metrics:
- false negative rate (often the critical one in quality settings)
- stability across time (drift)
- robustness to known variability drivers
Step 5: Build lifecycle management (model monitoring + change control)
AI models are not “set and forget.”
- monitor drift
- define retraining triggers
- version control the model and its data pipeline
- maintain audit trails
Vendor and partner questions (useful for sponsors and CDMO clients)
- What is the exact COU and who is accountable for decisions?
- What data was used and how was it governed?
- How is performance measured in the real workflow?
- What is the change control plan for model updates?
- How are audit trails, access control, and cybersecurity handled?
Where Agere Sciences fits
Agere’s audience includes teams dealing with real-world chemistry, analytical, and manufacturing constraints (APIs, research compounds, CDMO services). (Ageresciences)
The site also appears to be building topical authority in this area with a published AI checklist post. (Ageresciences)