AIforPharma.org

Introduction

⇒ FDA scientists propose “computable pharmacovigilance,” a framework for deciding which regulated pharmaceutical tasks can be automated by AI and which still require expert human judgment.

A new perspective paper published on 18 August 2026 in Frontiers in Drug Safety and Regulation provides an important new way of thinking about AI automation in regulated pharmaceutical processes.

The authors are scientists from the U.S. Food and Drug Administration, including the National Center for Toxicological Research and the Center for Drug Evaluation and Research.

The paper focuses specifically on pharmacovigilance, but the underlying concept may have much broader relevance for pharmaceutical Quality Systems and GMP applications.

The central question is not simply:

“Can AI perform this task?”

Instead, the authors propose asking:

“Can this task be translated into sufficiently explicit, auditable and verifiable computational steps?”

They call this concept “computable pharmacovigilance.”

Not Every Pharmaceutical Task Is Equally Suitable for AI Automation

The framework distinguishes between tasks that can be clearly formalised and tasks that depend heavily on professional judgment.

Examples of relatively computable activities include:

• checking whether required information is missing;
• detecting potentially duplicated safety reports;
• extracting predefined information from records;
• coding structured information;
• retrieving relevant information;
• performing defined rule-based checks.

Such activities have relatively clear:

inputs → processing rules → outputs → acceptance criteria

and can therefore be tested, benchmarked and audited.

Other activities are fundamentally more difficult.

The authors use case-level causality assessment as an important example.

Determining whether a medicine actually caused a particular adverse event can require integration of:

• temporality;
• alternative explanations;
• clinical context;
• pharmacological mechanisms;
• rechallenge information;
• professional medical judgment.

These activities cannot yet be reduced reliably to fully explicit computational rules.

The consequence is important:

AI suitability should be assessed task by task rather than system by system.

“Computability” Could Be a Useful Concept Beyond Pharmacovigilance

Although the paper concerns pharmacovigilance, the same reasoning could potentially be useful when evaluating AI use in GMP.

For example, some Quality System activities may be highly computable:

• checking whether required fields are completed;
• comparing a document against predefined requirements;
• identifying duplicate or recurring deviations;
• extracting equipment or batch information;
• checking dates, limits or predefined conditions;
• classifying documents against controlled taxonomies.

Other activities may remain much less computable:

• determining the true root cause of a complex deviation;
• assessing whether an unexplained event affects product quality;
• evaluating the adequacy of a CAPA strategy;
• making a final batch disposition decision;
• determining whether an unusual observation represents an unacceptable patient risk.

This suggests a potentially useful AI risk-assessment question for pharma:

How much of the intended task can actually be expressed as explicit, testable and auditable logic?

The less computable the task, the greater the likely need for expert oversight.

Large Language Models Are Not Automatically the Best Solution

Another important message from the paper concerns the current tendency to use large language models for almost every AI application.

The authors distinguish between:

• large models – general-purpose LLMs and foundation models;
• small models – task-specific machine-learning models;
• deterministic tools – rules, dictionaries, workflow logic and conventional software.

Large models offer flexibility and can work across multiple text-heavy tasks.

But they also introduce important disadvantages:

• hallucinations;
• variable outputs;
• prompt sensitivity;
• possible information loss;
• more difficult validation;
• more difficult auditability.

Smaller task-specific models can be easier to validate when the task has constrained inputs and clearly defined outputs.

The paper therefore argues that the future may not belong to one universal AI model.

Instead, the most practical architecture may be hybrid.

A Hybrid Architecture: LLM + Small Models + Deterministic Tools

One particularly interesting proposal is an architecture in which an LLM acts as an orchestrator but does not perform every regulated function itself.

The architecture could look like:

User / Reviewer

↓

LLM or AI Agent

↓

Controlled Tools / Retrieval / Rules / Small Models

↓

Structured Result

↓

Expert Review and Decision

The LLM can interpret the request and coordinate different tools.

But specific operations can be delegated to more deterministic components such as:

• validated rule engines;
• controlled terminology;
• structured retrieval;
• database queries;
• schema validation;
• task-specific ML models.

This architecture can reduce uncontrolled generative behaviour.

It can also improve auditability because intermediate actions and outputs can be recorded.

For regulated pharmaceutical applications, this may be a much more attractive architecture than asking one general-purpose LLM to perform the entire process.

An Interesting Finding: Bigger AI Was Not Always Better

The authors also report an illustrative comparison between a task-specific BioBERT model and a general-purpose Llama-4 model for extraction of safety-related information.

For the evaluated structured extraction tasks, the task-specific model performed better.

The authors do not present this as a universal comparison between the two technologies.

Instead, the example demonstrates a broader principle:

The most powerful general-purpose model is not necessarily the best model for a tightly defined regulated task.

For pharmaceutical companies, this is significant.

The correct question may therefore not be:

“Which is the best AI model?”

but:

“Which technical architecture provides the most reliable, verifiable and auditable performance for this particular task?”

“Trust but Verify”

The paper describes the boundary of AI automation using a “trust but verify” philosophy.

AI can increasingly perform routine, well-specified operations.

However, as the process moves from:

pattern recognition → interpretation → judgment → regulated decision

the requirement for structured verification and expert review becomes increasingly important.

The authors conclude that Generative AI does not currently mean the end of human pharmacovigilance.

Instead, the likely future is a:

Human–AI system

in which AI performs increasingly sophisticated operations while humans remain responsible for judgment-intensive decisions.

Why This Matters for GMP and Pharmaceutical Quality

This concept could be particularly useful when companies perform AI use-case assessments.

Instead of simply classifying an application as:

AI / non-AI

or:

GxP / non-GxP

an additional question could be introduced:

How computable is the task?

A possible assessment sequence could therefore become:

Intended Use → GxP Impact → Task Computability → AI Architecture → Verification Strategy → Human Oversight → Lifecycle Monitoring

This could help organisations determine whether a task should be performed using:

• deterministic software;
• a task-specific ML model;
• an LLM;
• a hybrid architecture;
• or primarily human expert judgment.

This may ultimately be more useful than assuming that Generative AI should replace existing software simply because it is more flexible.

Regulatory Status Note

The article was written by FDA scientists, but it is a scientific perspective paper and does not represent formal FDA guidance or FDA policy.

The authors explicitly state that the views expressed are their own and do not necessarily represent the official position of the U.S. Food and Drug Administration.

Nevertheless, the paper provides valuable insight into how regulatory scientists are thinking about AI automation, validation, auditability and human judgment in regulated pharmaceutical activities.

Source

Wu L., Xu J., Dang O., Ball R. – Does generative AI mean the “end of history” for pharmacovigilance automation? Towards a framework for the future of human-AI systems. Frontiers in Drug Safety and Regulation, 18 August 2026:

https://www.frontier … fr.2026.1846339/full

Introduction

⇒ PDA outlines how AI can strengthen pharmaceutical quality metrics and predictive oversight while remaining embedded within validated data, Quality Systems and human decision-making.

A new article published by the Parenteral Drug Association on 20 August 2026 provides a particularly practical view of how artificial intelligence could be integrated into pharmaceutical quality management.

The article, Using Artificial Intelligence and Metrics, was prepared as part of the PDA Quality Management Maturity Team’s series on metrics implementation. Its authors represent Bristol Myers Squibb, HarborView and Eli Lilly.

Rather than focusing on AI as a standalone technology, the article places AI inside an existing pharmaceutical Quality System.

This distinction is important.

AI as a Quality Intelligence Layer

The authors describe how increasingly contextualized and high-integrity data can allow AI to support more proactive quality and regulatory decision-making.

Potential applications include:

• real-time monitoring of quality metrics;
• earlier detection of deviations, trends and anomalies;
• predictive identification of potential quality problems;
• continuous process insights;
• dynamic prioritization of investigations based on emerging trends.

The concept therefore goes beyond using AI simply to summarize documents or automate administrative work.

AI could become an analytical layer over established quality data, helping Quality organisations identify signals earlier and potentially move from retrospective review toward predictive oversight.

But AI Cannot Compensate for Weak Data

One of the strongest messages in the PDA article is that AI-driven metrics depend on structured and reliable data.

The authors emphasize the importance of structured, validated data models so that metrics remain standardized, traceable and auditable.

This is particularly relevant in GMP environments.

An advanced algorithm applied to inconsistent, poorly contextualized or unreliable data does not create a mature quality system. It can instead accelerate incorrect conclusions.

For pharmaceutical companies, this suggests that AI implementation should often start not with selecting an AI model, but with evaluating:

• data quality;
• data structure;
• data ownership;
• metric definitions;
• traceability;
• data integration between systems;
• reliability of the underlying quality indicators.

AI Should Reinforce Existing Metrics, Not Replace Them

The PDA authors propose that AI-generated insights should be linked to existing quality metrics rather than creating an independent decision framework.

They highlight several important control principles:

• AI models should be validated as decision-support tools under defined conditions;
• outputs should be sufficiently explainable for quality and regulatory review;
• inputs, processing and outcomes should remain traceable;
• appropriate audit trails and system controls should be maintained;
• AI should be integrated into existing governance and management processes.

This creates an important boundary:

AI should complement the Pharmaceutical Quality System rather than become a parallel Quality System.

An Interesting Warning: Do Not Optimize the Wrong Metric

One particularly important part of the article concerns the risk of relying on a narrow set of metrics.

AI systems are very effective at optimizing measurable objectives.

But if an organisation measures the wrong thing – or relies too heavily on one indicator – optimisation can produce unintended behaviour.

For example, reducing investigation closure time could appear positive.

However, if the metric becomes dominant, it could unintentionally encourage faster but less thorough investigations.

Similarly, reducing deviations is not necessarily evidence of improved process performance if problems are being classified or reported differently.

The authors therefore recommend a multi-metric, context-aware approach, with metrics periodically reassessed and stress-tested against possible gaming or unintended effects.

This concept is particularly important for AI because algorithms may amplify the consequences of poorly designed performance indicators.

What This Means for Pharmaceutical Quality Systems

The article suggests a potentially important direction for AI implementation in pharma:

AI should not replace established Quality Management Review – it should make it more intelligent.

A future AI-supported Quality System could continuously analyze:

• deviations;
• CAPAs;
• complaints;
• OOS and OOT results;
• process capability;
• environmental monitoring;
• audit observations;
• supplier performance;
• training effectiveness;
• change controls;
• recurring quality signals.

Instead of reviewing each metric independently, AI could identify relationships between them and detect weak signals that are difficult to see during conventional periodic review.

But such a system would still require validated data, appropriate model controls, human oversight and clear accountability.

Why This Publication Is Important

Much of the current discussion around pharmaceutical AI focuses on validating algorithms.

The PDA article points toward another important dimension:

AI effectiveness may depend as much on the maturity of the surrounding Quality System and data architecture as on the performance of the AI model itself.

This is consistent with an emerging view that pharmaceutical AI should not be implemented as an isolated technology project.

Instead, it should become part of:

Data Governance → Quality Metrics → AI Analytics → Human Review → Management Decision → Continuous Improvement

The AI model is only one component of that chain.

Regulatory Perspective

The PDA publication is an industry article and does not establish a new regulatory requirement.

However, its recommendations align with several themes increasingly visible in regulatory discussions: defined context of use, trustworthy data, validation, traceability, explainability, human oversight and lifecycle governance.

For pharmaceutical companies considering AI in Quality Systems, the article provides a useful practical bridge between AI technology and established ICH Q10-style quality management principles.

Source

PDA – Using Artificial Intelligence and Metrics, published 20 August 2026:

https://pda.org/pda- … lligence-and-metrics

FDA Draft Guidance – Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products:

https://www.fda.gov/ … -drug-and-biological

Introduction

⇒ FDA experience with 15 approved drug applications shows how manufacturing process models are assessed according to intended use, risk and impact, with important lessons for AI in GMP.

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.

https://www.scienced … ii/S0378517326006976