Artificial intelligence in pharmaceutical manufacturing is often discussed as something regulators will need to address in the future.
A new FDA-authored paper provides an important reminder that model-based manufacturing is already part of regulatory reality.
Published online on 28 July 2026 in the International Journal of Pharmaceutics, the paper FDA regulatory experience with drug manufacturing process models in approved applications reviews FDA experience with manufacturing models submitted as part of approved pharmaceutical applications between 2012 and 2025.
The findings provide unusually concrete insight into how FDA evaluates models that influence pharmaceutical manufacturing and control.
At Least 15 Approved Applications Used Manufacturing Process Models
According to the FDA authors, process models were successfully used in at least:
15 FDA-approved applications from 8 companies between 2012 and 2025.
The applications covered both drug substance and drug product manufacturing and included NDAs and BLAs, including biosimilars, original applications and supplements.
Eleven of the model-supported applications involved continuous manufacturing, where understanding residence-time distribution is important for tracking material through the process.
Models were used for activities including:
- establishment of design spaces;
- real-time process control;
- process monitoring;
- in-process control;
- diversion of non-conforming material;
- release-related decisions.
FDA encountered different types of models, including mechanistic models, empirical models such as NIR/Raman chemometric models, and hybrid approaches.
The Most Important Message: Model Risk Depends on What the Model Does
Perhaps the most useful GMP lesson from the paper is that FDA’s assessment is linked to the risk or impact of the model’s intended use.
The authors describe examples across different impact levels.
High impact:
- release decisions;
- parametric control.
Medium impact:
- in-process controls;
- diversion of non-conforming material.
Lower impact:
- definition of design space;
- investigation and monitoring.
The amount of information included in regulatory submissions was generally commensurate with the determined risk or impact of the model.
This principle has major implications for future AI applications in GMP.
The fundamental regulatory question may not be:
“Is the technology AI?”
but rather:
“What GMP decision does the model influence, and what happens if the model is wrong?”
That distinction is critical.
An AI model identifying unusual process patterns for investigation is not equivalent in risk to an AI model automatically determining whether material is acceptable for release.
The technology may be similar, but the required level of assurance should not necessarily be the same.
FDA Can Examine the Model Very Deeply
The paper also reveals an interesting aspect of regulatory review.
FDA modelling experts are frequently consulted when reviewing applicants’ modelling and control strategies.
In some cases, FDA experts have even developed their own internal models calibrated using experimental data submitted by the applicant.
This demonstrates that model-based regulatory submissions should not be viewed as a black box accompanied only by a headline performance figure.
A regulator may need to understand:
- model structure;
- assumptions;
- input variables;
- data used to establish the model;
- operating range;
- predictive performance;
- relationship to the control strategy;
- consequences of model failure.
For future AI/ML applications, explainability may therefore need to mean more than providing an attractive dashboard showing accuracy.
The organisation must be able to demonstrate why the model is appropriate for the specific manufacturing decision it supports.
Does Modelling Slow Regulatory Approval?
Interestingly, FDA’s experience does not suggest that the use of manufacturing models necessarily creates regulatory delay.
After excluding three submissions with issues unrelated to modelling, the model-supported submissions reviewed in the study were approved an average of 20.3 days before their regulatory goal dates, including several applications under accelerated review.
This observation should be interpreted carefully.
It does not prove that using models accelerated approval.
However, it provides useful evidence against the assumption that sophisticated manufacturing models automatically create regulatory obstacles.
When modelling is scientifically justified and properly integrated into a manufacturing and control strategy, regulators clearly have experience assessing and approving such approaches.
Is This an AI Paper?
Not exactly — and this distinction is important.
The FDA paper covers pharmaceutical manufacturing process models, including mechanistic, empirical and hybrid models. These should not all be described as artificial intelligence.
However, the paper is highly relevant to AI and machine learning because it provides practical regulatory precedent for the broader question of how models that influence GMP manufacturing decisions can be assessed.
AI does not eliminate the established regulatory principles used for modelling.
Instead, it makes several of them even more important:
context of use, model risk, data quality, predictive performance, control strategy and lifecycle management.
Lessons for AI/ML Used in GMP Manufacturing
1. What exactly is the intended use?
Does the model:
- monitor?
- detect anomalies?
- recommend action?
- control a process?
- divert material?
- contribute to release?
The answer determines the potential GMP impact.
2. What happens if the model is wrong?
A model can have impressive average accuracy and still create unacceptable risk if rare errors affect critical quality decisions.
Performance criteria therefore should reflect the consequences of different types of error rather than relying only on one overall accuracy figure.
3. What is the validated operating region?
A model should not automatically be assumed to remain reliable for data or manufacturing conditions substantially different from those used during development and validation.
The boundaries of reliable operation need to be understood.
4. Can the model and its decisions be reconstructed?
For regulated applications, companies should be capable of identifying:
- the model version;
- relevant input data;
- output or prediction;
- configuration;
- decision subsequently taken;
- applicable human review.
Traceability becomes increasingly important as models influence higher-risk decisions.
5. How will changes be controlled?
Changes to:
- algorithms;
- training data;
- process data;
- sensors;
- model parameters;
- software infrastructure;
- manufacturing processes
may affect model performance.
AI/ML therefore needs to be integrated into the Pharmaceutical Quality System rather than managed solely as a data-science project.
6. How will continued performance be demonstrated?
Initial validation shows that a model was suitable when tested.
Lifecycle monitoring needs to demonstrate that it remains suitable.
This becomes particularly important for data-driven models that may be sensitive to changes in raw materials, equipment, sensors, operating practices or process distributions.
A Useful Regulatory Principle for AI in GMP
The FDA experience suggests a practical principle for future AI implementation:
The level of model assurance should be proportional to the influence the model has on product quality and GMP decisions.
This is more useful than treating every AI application as equally risky.
For example:
AI for trend detection → AI for decision support → AI for process control → AI for release
represents increasing influence on the final GMP decision and therefore potentially increasing requirements for validation, transparency, monitoring and governance.
That is consistent with long-established Quality Risk Management principles and may provide one of the most practical foundations for integrating AI into pharmaceutical manufacturing.
The latest FDA experience demonstrates something important:
Regulators are not starting from zero when evaluating advanced models in pharmaceutical manufacturing.
A substantial regulatory foundation already exists.
The challenge for AI will be to extend those principles to models that may be more complex, data-dependent and probabilistic — without losing the fundamental GMP requirement that the process remains understood and controlled.
Source
Fisher AC, Chatterjee S, Madurawe R, Tian G, Tran R, Lee SL. FDA regulatory experience with drug manufacturing process models in approved applications. International Journal of Pharmaceutics. Available online 28 July 2026. Article 127249.