AIforPharma.org

Introduction

⇒ FDA and industry discussions at the 2026 PDA/FDA Joint Regulatory Conference point toward risk-proportionate AI assurance in CGMP, combining data readiness, validation, human oversight, source traceability and lifecycle control.

Artificial intelligence was no longer treated as a peripheral technology topic at the 2026 PDA/FDA Joint Regulatory Conference.

The conference, held in Washington, D.C. from 14 to 16 September 2026, included dedicated discussions on AI in regulatory oversight, inspection readiness, manufacturing data and validation of digital tools in a CGMP environment.

One message stands out:

AI does not remove existing CGMP responsibilities. The level of validation and control should reflect how the AI is actually used and the impact of its output.

The official PDA/FDA programme explicitly states that appropriate controls, human oversight and validation remain foundational requirements when digital tools are introduced into CGMP processes.

This is an important direction for pharmaceutical companies because it moves the discussion beyond the simplistic question:

“Does every AI tool require the same validation?”

towards:

“What evidence and controls are necessary for this particular AI use case?”

AI-Ready Manufacturing Starts with AI-Ready Data

One session, Advancing Digital Tools and Data Utilization in a CGMP Environment, brought together FDA and industry representatives.

Sanofi presented a practical framework for assessing whether manufacturing data are sufficiently mature to support advanced analytics and AI.

The concept evaluates not only which data exist, but also how those data are made available.

Relevant considerations include:

• coverage of critical quality attributes;
• process and yield data;
• batch and sensor information;
• equipment and material genealogy;
• derived process values;
• data freshness;
• accessibility;
• authenticity;
• standardisation;
• structure.

This is an important point for AI implementation.

An organisation may have very large quantities of manufacturing data without actually having data that are suitable for regulated AI use.

AI readiness therefore cannot be measured simply by:

How much data do we have?

A more relevant question is:

Are the data complete, contextualised, reliable, accessible and sufficiently controlled to support the intended AI decision?

CGMP Principles Still Apply

FDA participation in the session focused on the relationship between digital transformation and established pharmaceutical quality requirements.

The underlying message was not that AI requires abandonment of traditional CGMP principles.

Quite the opposite.

AI-enabled processes still need to operate within established expectations relating to:

• Quality Unit oversight;
• data integrity;
• documented procedures;
• validation;
• record governance;
• Quality Risk Management;
• appropriate human control.

This reinforces an important principle for pharmaceutical AI:

The technology may be new, but accountability remains within the Pharmaceutical Quality System.

An algorithm cannot assume responsibility that CGMP assigns to the Quality Unit, system owner, process owner or qualified personnel.

A Risk-Proportionate Validation Model Is Emerging

One of the most interesting discussions concerned the level of validation appropriate for different digital and AI applications.

Conference reporting described a distinction between tools that contribute directly to final regulated decisions and tools used primarily for investigation, exploration or information support.

This suggests a potentially useful continuum.

Higher-impact application

AI directly influences or supports a final GxP decision.

Examples might include:

• product-quality decisions;
• critical process decisions;
• batch disposition support;
• critical specification or release assessments.

Such uses may require extensive CGMP validation and strong evidence demonstrating fitness for intended use.

Lower-impact or exploratory application

AI helps users investigate information or identify possible signals while a qualified person independently evaluates the evidence.

Examples might include:

• investigation support;
• regulatory intelligence;
• trend identification;
• searching quality records;
• exploring historical inspection findings.

For these applications, a different level of evidence may be appropriate, provided that the use is clearly bounded and the output cannot bypass the responsible human decision-maker.

The important point is not that lower-risk AI requires “no validation”.

Rather:

The depth of assurance should be proportionate to intended use, GxP impact and the consequence of an incorrect output.

This Is Different from a One-Size-Fits-All AI Validation SOP

Pharmaceutical companies may be tempted to create one standard validation package for every AI application.

The conference discussion suggests that this could be unnecessarily rigid.

AI applications can perform fundamentally different functions.

Compare:

• AI that searches historical deviations;
• AI that drafts an investigation summary;
• AI that recommends a possible root cause;
• AI that predicts a critical process parameter;
• AI that influences a final batch disposition decision.

Calling all of these simply “AI systems” hides important differences in risk.

A more mature approach could therefore start with:

Intended Use → GxP Impact → Decision Impact → Failure Consequence → Required Assurance

Only after those questions are answered should the validation strategy be defined.

Inspection-Ready AI Regulatory Intelligence

A separate conference session provided an interesting practical example.

Eli Lilly described an AI-supported regulatory intelligence system connecting information that historically sits in different quality repositories, including:

• regulatory inspection findings;
• internal audit observations;
• deviation information;
• responses to observations;
• after-action reviews.

The architecture combines retrieval-augmented generation for unstructured documents with structured-data exploration.

The objective is not simply to ask a chatbot a compliance question.

It is to allow users to trace quality signals across:

time → site → health authority → observation → response → organisational learning

while retaining connection to the underlying evidence.

This represents a potentially important model for pharmaceutical Quality Systems.

AI may provide the greatest value not by replacing Quality experts, but by connecting information that humans currently review in isolated systems.

Source Grounding Becomes a Critical Control

For regulatory or compliance applications, an AI answer is only useful when the user can determine where it came from.

This makes source grounding particularly important.

A useful architecture may therefore require:

• controlled source repositories;
• retrieval of relevant source documents;
• citations linked to the generated answer;
• traceability to the original record;
• defined document versions;
• human verification of the cited evidence.

This can substantially change the validation question.

Instead of asking only:

“Is the generated answer correct?”

the organisation can also ask:

“Can the user reconstruct the evidence on which the answer was based?”

For GMP applications, that second question may be equally important.

Hallucination Risk Does Not Disappear with Better Models

The conference discussion also highlighted the continuing risk of hallucination and model variability.

General-purpose AI models can differ substantially in their behaviour, and performance can deteriorate depending on question framing, context length and the information supplied to the model.

For pharmaceutical compliance use, this means that selecting a more powerful model does not eliminate the need for controls.

A robust architecture should assume that:

the AI can sometimes be wrong.

The control strategy should therefore determine:

• how incorrect outputs are detected;
• when source verification is mandatory;
• when the AI must defer;
• what requires independent human review;
• what decisions AI is not authorised to make.

Human Oversight Must Be Real, Not Formal

The official conference programme explicitly identifies human oversight as a foundational requirement.

However, simply inserting a human approval step into a workflow may not provide meaningful control.

Effective human oversight requires that the reviewer:

• understands the intended use of the AI;
• understands its known limitations;
• has access to source information;
• can challenge the AI result;
• has sufficient expertise to identify an implausible output;
• has authority to reject the recommendation;
• remains accountable for the final decision.

This distinction may become increasingly important as AI becomes embedded inside routine quality-system software.

From AI Validation to AI Assurance

The conference discussions point toward a broader model than traditional one-time software validation.

A possible lifecycle could be:

Intended Use

↓

Risk Classification

↓

Data Readiness

↓

Validation / Fitness-for-Use Evidence

↓

Controlled Deployment

↓

Human Oversight

↓

Performance Monitoring

↓

Change Control

↓

Periodic Reassessment

The exact depth of each element should depend on risk.

This is increasingly consistent with the broader concept of maintaining an AI-enabled system in a demonstrable state of control rather than merely declaring the system “validated” at implementation.

Why This Is Important

The 2026 PDA/FDA conference is significant because AI is now being discussed within mainstream CGMP topics such as:

• inspection readiness;
• data governance;
• manufacturing data;
• validation;
• quality systems;
• regulatory oversight.

AI is therefore moving from an innovation discussion into normal pharmaceutical quality-system governance.

The emerging direction appears to be:

do not validate AI because it is AI; validate and control the regulated function according to intended use, risk and decision impact.

This may ultimately provide a more practical approach than attempting to apply one universal validation standard to every AI technology.

Regulatory Status Note

The observations described above originate from presentations and panel discussions at the 2026 PDA/FDA Joint Regulatory Conference.

The conference includes FDA participation, but individual presentations, panel discussions and interpretations should not be treated as new FDA guidance or formal regulatory policy.

In particular, the concept of proportional levels of AI validation discussed during the conference should be understood as emerging regulatory and industry thinking rather than a newly issued CGMP requirement.

Sources

FDA – 2026 PDA/FDA Joint Regulatory Conference:

https://www.fda.gov/ … -conference-09142026

PDA – PDA/FDA Joint Regulatory Conference 2026, programme and session details:

https://www.pda.org/ … tory-conference-2026

BioPharm International – FDA, Sanofi, Gilead Weigh AI Readiness and Validation Risk at PDA/FDA Conference:

https://www.biopharm … k-pda-fda-conference

Pharmaceutical Technology – Lessons For AI Governance in Quality Systems:

https://www.pharmtec … e-in-quality-systems

Introduction

⇒ Generative AI challenges traditional GMP validation because probabilistic models cannot always provide identical outputs for identical inputs. A new Bio-IT World commentary explores how intended use, traceability, human oversight, controlled model updates and continuous performance monitoring could make GenAI more suitable for regulated pharmaceutical environments.

A new commentary published by Bio-IT World on 11 September 2026 addresses one of the most difficult questions facing pharmaceutical AI:

How can Generative AI be used in GMP when its outputs are inherently probabilistic rather than fully deterministic?

The article, written by Valentina Armiento and Pierre Bonzani of Genedata, argues that GenAI cannot simply be validated in the same way as conventional GMP software.

Traditional computerized systems are generally expected to behave predictably under defined conditions.

Generative AI is different.

Its output may vary depending on:

• the prompt;
• the surrounding context;
• model configuration;
• model version;
• sampling parameters;
• changes to the underlying model.

This makes repeatability, traceability and lifecycle control considerably more difficult.

The authors therefore argue that GenAI requires a different validation mindset built around:

bounded intended use → risk-based controls → traceability → human oversight → controlled model lifecycle → performance monitoring

Why GenAI Is Different from Conventional GMP Software

The fundamental issue is non-determinism.

Traditional GMP software typically executes predefined logic.

When given the same input under the same configuration, the expected result should normally be reproducible.

Large Language Models operate differently.

They generate outputs probabilistically.

Even when the underlying model has not changed, variations in prompts, context or generation parameters may influence the result.

This creates a validation challenge.

The relevant question is no longer only:

Does the system produce the expected output?

but increasingly:

Does the system consistently remain within an acceptable performance range for its intended use?

That distinction may be fundamental for future GenAI validation in GMP.

The Current Annex 22 Position Remains Restrictive

The current draft EU GMP Annex 22 takes a conservative position.

It applies to static AI/ML models used in critical GMP applications and states that dynamic models that continuously learn during use should not be used in critical GMP applications.

The draft also states that probabilistic models that may produce different outputs from identical inputs are outside its scope and should not be used in critical GMP applications.

It explicitly includes Generative AI and Large Language Models in this restriction.

For non-critical GMP applications, however, GenAI may be considered where qualified personnel remain responsible for ensuring that the output is suitable for its intended use.

In practical terms, the current draft creates a clear distinction between:

critical GMP use

and

non-critical GMP support under human oversight.

Official draft Annex 22:

https://health.ec.eu … ion_guideline_en.pdf

But the Regulatory Discussion Is Still Evolving

The position above should not be interpreted as the final regulatory endpoint.

EMA held a multistakeholder workshop on 30 June and 1 July 2026 specifically to explore whether adaptive and probabilistic AI models could potentially be accommodated within a future risk-based Annex 22 framework.

The workshop examined topics such as:

• guardrails;
• human oversight;
• model evaluation;
• data governance;
• accountability;
• transparency;
• risk mitigation;
• validation approaches for adaptive and probabilistic models.

This is important.

The regulatory question is gradually shifting from:

“Should probabilistic AI simply be excluded?”

towards:

“Can probabilistic AI be controlled sufficiently for a defined GMP use?”

EMA Annex 22 workshop:

https://www.ema.euro … development-annex-22

Six Practical Controls Highlighted in the Bio-IT World Commentary

The article identifies several elements that should form part of a GMP-oriented AI control strategy.

1. Clearly Defined Intended Use

The role of the AI should be defined precisely.

The organisation should know:

• what task the AI performs;
• which decisions it supports;
• which data it receives;
• under which conditions it may be used;
• where its boundaries are.

This is critical because validation can only be meaningful when performance is assessed against a clearly defined use case.

2. Risk-Based Human Oversight

The level of AI autonomy should depend on the risk of the activity.

For high-impact decisions, the AI should remain advisory and appropriately qualified personnel should retain responsibility for the final decision.

The important question is not simply whether a human is formally present in the workflow.

It is whether the human review is capable of detecting and correcting an unacceptable AI output.

3. Data and Decision Traceability

Inputs, outputs, model versions and relevant user interactions should be traceable.

For GenAI this can potentially include:

• source data;
• prompt or query;
• model version;
• configuration;
• generated output;
• reviewer;
• final accepted result.

This may become especially important when AI contributes to GMP documentation, investigations or other quality-system activities.

4. Explainability and Confidence

The authors also highlight explainability mechanisms.

For some AI systems this may involve techniques such as feature attribution or confidence scores.

For GenAI, explainability may be more difficult.

In many cases, a more practical control may be strong linkage between the generated answer and controlled source information so that the reviewer can verify the basis of the output.

5. Integration with Existing GMP Controls

AI should not operate outside the Pharmaceutical Quality System.

It should be incorporated, as applicable, into:

• change control;
• validation;
• configuration management;
• supplier management;
• periodic review;
• deviation management;
• performance monitoring.

This is an important point.

GenAI does not require abandoning established GMP controls.

It requires extending them to address new failure mechanisms.

6. Cross-Functional Governance

AI validation cannot be owned by one function.

Effective governance may require cooperation between:

• Quality Assurance;
• IT;
• Data Science;
• Validation;
• system and process owners;
• subject-matter experts.

This is consistent with the draft Annex 22 approach, which explicitly calls for cooperation between process SMEs, QA, data scientists, IT and other relevant parties.

The Real Problem: Model Updates Can Break the Validated State

One of the most important points in the Bio-IT World article concerns model updates.

For traditional systems, software changes are generally identifiable and controlled.

With GenAI, behaviour may change because:

• the underlying model is replaced;
• the provider updates the model;
• system prompts change;
• retrieval data changes;
• configuration changes;
• context handling changes.

Even where the visible application remains unchanged, the output behaviour may be different.

This creates a difficult lifecycle problem:

How can innovation continue without repeatedly destroying the validated state?

The article proposes several mechanisms:

• version-locked models;
• formal change control;
• predetermined update strategies;
• risk-based revalidation;
• continuous performance monitoring.

From Exact Repeatability to Acceptable Performance

Perhaps the most important conceptual idea in the article is that validation may need to move away from expecting identical text outputs.

For GenAI, the relevant acceptance criterion may instead be whether different acceptable outputs remain within predefined quality boundaries.

For example, performance could potentially be evaluated using:

• correctness;
• completeness;
• critical-error rate;
• hallucination rate;
• source-grounding;
• human correction rate;
• failure-to-escalate rate;
• performance on predefined challenge cases.

The objective would therefore not necessarily be:

same input → identical wording

but rather:

defined intended use → controlled variability → acceptable and measurable performance.

This represents a major conceptual shift from classical software testing.

Continuous Monitoring Becomes Part of Validation

The article also argues that AI performance should continue to be monitored after deployment.

This is closely aligned with the current draft Annex 22, which requires regular monitoring of model performance and input-data drift for AI models within its scope.

For future GenAI applications, similar principles may become essential.

A lifecycle approach could therefore look like:

Initial Evaluation

↓

Acceptance Testing

↓

Controlled Deployment

↓

Performance Monitoring

↓

Drift / Failure Detection

↓

Change Assessment

↓

Revalidation Where Required

This begins to resemble a maintained AI state of control rather than a one-time validation event.

A Practical Place for GenAI Today

The commentary identifies documentation and knowledge workflows as particularly promising areas.

Examples could include:

• summarising controlled information;
• knowledge search;
• drafting documentation;
• supporting investigation review;
• querying validated datasets;
• assisting with scientific or technical interpretation.

These use cases are more suitable when the AI does not make the final critical GMP decision and qualified personnel remain responsible for reviewing the result.

This remains very different from allowing an LLM to autonomously:

• release a batch;
• make a critical product-quality decision;
• change manufacturing parameters;
• approve a deviation;
• close a CAPA.

Why This Article Matters

The significance of this commentary is not that it creates a new regulatory framework.

It does not.

Its value is that it clearly describes the fundamental problem that the industry now needs to solve:

GMP validation was historically designed around deterministic systems, while Generative AI is inherently probabilistic.

Trying to force GenAI into an unchanged deterministic validation model may therefore be unrealistic.

But abandoning validation principles is equally unacceptable.

The likely direction is a middle ground based on:

bounded use → measurable performance → controlled variability → traceability → human oversight → change control → continuous monitoring.

This may ultimately be one of the key concepts determining whether Generative AI can move from GxP-adjacent experimentation into genuinely regulated pharmaceutical workflows.

Regulatory Status Note

The Bio-IT World article is contributed commentary written by representatives of Genedata. It is not regulatory guidance and does not represent the official position of FDA, EMA or another competent authority.

The discussion of Annex 22 should also be understood in the context of an evolving regulatory process. The current draft excludes GenAI and probabilistic models from critical GMP applications, but EMA is actively considering stakeholder input on whether future risk-based controls and guardrails could support broader use.

Sources

Bio-IT World – What It Takes to Make Generative AI Fit for GMP, published 11 September 2026:

https://www.bio-itwo … ative-ai-fit-for-gmp

European Commission – Draft EU GMP Annex 22: Artificial Intelligence:

https://health.ec.eu … ion_guideline_en.pdf

EMA – Multistakeholder workshop on AI guidance development for Annex 22:

https://www.ema.euro … development-annex-22

Introduction

⇒ PMDA and PIC/S are making draft GMP Annex 22 on Artificial Intelligence a focus of regulator-only inspector training, signalling growing international inspection readiness for AI in pharmaceutical manufacturing.

Artificial intelligence in pharmaceutical manufacturing is increasingly becoming not only a guidance-development topic but also an inspection capability topic.

From 15 to 17 September 2026, Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) will hold its PMDA-ATC GMP Inspection Seminar 2026 in Tokyo.

The seminar is intended exclusively for regulators involved in pharmaceutical GMP inspections.

One of its primary objectives is explicit:

to build a shared understanding of the draft PIC/S GMP Annex 22 on Artificial Intelligence.

The event is aligned with the PIC/S training programme and is also described as a pre-learning opportunity for the 2026 PIC/S Seminar in Istanbul.

For pharmaceutical manufacturers, the event does not introduce a new GMP requirement.

However, it provides an important signal about where international inspector attention is moving.

Annex 22 Is Becoming an Inspector Training Topic

Until recently, much of the discussion around AI in GMP focused on:

• drafting regulatory guidance;
• public consultation;
• industry comments;
• regulatory workshops;
• interpretation of existing computerized-system requirements.

The PMDA seminar represents another stage:

training inspectors to understand the technology and discuss how it should be approached during GMP oversight.

According to PMDA, the seminar aims to create shared understanding among participating regulators through discussion of:

• the draft PIC/S GMP Annex 22;
• advanced technologies in pharmaceutical manufacturing;
• GMP inspection approaches;
• inspection reliance in Asia.

This is significant because implementation of any new GMP expectation ultimately depends not only on the wording of guidance but also on how inspectors interpret and apply it.

The Seminar Is for Experienced GMP Inspectors

The audience is deliberately restricted.

PMDA specifies that participants should be regulatory authority personnel with substantial practical inspection experience.

The criteria include inspectors who have:

• significant on-site GMP inspection experience;
• more than three years of work in GMP regulatory activities;
• intermediate knowledge of GMP regulations.

Approximately 30 regulators are expected to participate.

This is therefore not a general educational webinar about artificial intelligence.

It is a focused GMP inspector capacity-building activity.

Why This Matters for Pharmaceutical Companies

The event itself does not change the legal status of Annex 22.

The Annex remains a draft.

Nevertheless, companies should distinguish between two questions:

Is Annex 22 already an enforceable GMP requirement?

No.

and:

Are GMP inspectorates already developing knowledge and inspection capability around AI?

Clearly yes.

That distinction is important.

Waiting until the final Annex 22 becomes formally effective before establishing any AI governance could leave organisations behind the regulatory learning curve.

Companies already using AI in GMP-relevant processes should therefore be able to explain:

• where AI is being used;
• what its intended use is;
• what its GxP impact is;
• how the system was evaluated;
• what data it depends on;
• how its performance is controlled;
• what human oversight exists;
• how changes are managed;
• how ongoing performance is monitored.

These are not presented here as new requirements created by the PMDA seminar.

They are sensible readiness questions arising from the broader lifecycle-control approach already visible in draft Annex 22 and international AI regulatory discussions.

From Regulatory Text to Regulatory Capability

There are several stages in the development of a new regulatory expectation.

A simplified sequence could be:

Regulatory Discussion

↓

Draft Guidance

↓

Public Consultation

↓

Regulator and Industry Workshops

↓

Inspector Training and Harmonisation

↓

Final Guidance

↓

Inspection Experience

The PMDA-ATC seminar is important because it sits in the middle of this transition.

It shows that regulatory authorities are not only discussing what AI guidance should say.

They are also developing the capability necessary to understand AI during future GMP oversight.

PIC/S Alignment Makes the Event More Significant

PMDA describes the seminar as aligned with the PIC/S training framework.

PIC/S separately lists the PMDA event and confirms that its programme will focus on:

• the draft PIC/S GMP Annex 22 on Artificial Intelligence;
• advanced technologies in pharmaceutical manufacturing;
• GMP inspection reliance.

This matters because PIC/S provides an important international platform for harmonisation of pharmaceutical inspection practices.

The value of Annex 22 therefore potentially extends beyond the European Union.

A common inspector understanding of AI could influence GMP expectations across multiple PIC/S participating authorities.

For multinational pharmaceutical companies, this could gradually reduce the possibility of treating AI governance as an isolated EU compliance topic.

AI Governance May Become Part of Inspection Readiness

Historically, inspection-readiness programmes typically focus on topics such as:

• quality systems;
• data integrity;
• validation;
• deviations and CAPA;
• change control;
• computerized systems;
• process validation;
• supplier management.

As AI becomes embedded in these processes, a new layer may emerge.

Inspectors may increasingly need to understand whether AI is involved when reviewing:

• GMP documentation;
• deviation investigations;
• CAPA proposals;
• quality trending;
• process monitoring;
• predictive maintenance;
• laboratory activities;
• manufacturing control;
• decision-support systems.

This does not necessarily mean that inspectors will begin conducting separate “AI inspections”.

More likely, AI governance will become part of existing GMP system inspection whenever AI materially affects the regulated activity.

An AI Inventory Becomes Increasingly Important

One practical implication is particularly straightforward.

A company cannot adequately govern AI if it does not know where AI is being used.

A useful first step is therefore a controlled inventory covering:

• AI system or model;
• system owner;
• intended use;
• GxP relevance;
• supplier;
• model type;
• data sources;
• validation status;
• human oversight;
• change-management approach;
• performance monitoring.

This becomes even more important when AI functionality is embedded inside existing commercial software and may not initially be recognised as a separate AI application.

Do Not Confuse Training Activity with a New Requirement

It is equally important not to overinterpret the PMDA event.

The seminar:

• does not make draft Annex 22 effective;
• does not create a new Japanese GMP requirement;
• does not demonstrate that every participating regulator will interpret Annex 22 identically;
• does not establish an implementation date.

Its importance lies elsewhere.

It demonstrates that experienced inspectors are already being prepared to understand and discuss AI in pharmaceutical manufacturing.

That is a meaningful regulatory signal even before the final Annex is published.

What Pharma Should Watch Next

Several developments will now be particularly important:

• the finalisation of EU GMP Annex 22;
• any changes to the treatment of Generative AI and probabilistic models;
• the relationship between Annex 22 and revised Annex 11;
• PIC/S adoption and implementation;
• future inspector training materials;
• early inspection experience involving AI-enabled systems.

The most valuable evidence will eventually come not only from the final wording of Annex 22 but from how regulators apply it in practice.

Why This Development Is Important

The pharmaceutical AI discussion is entering a new phase.

The question is moving from:

“Should GMP regulators develop requirements for AI?”

towards:

“How should inspectors evaluate AI when they encounter it in pharmaceutical manufacturing?”

The PMDA/PIC/S training activity suggests that regulatory authorities are beginning to prepare for that second question.

For pharmaceutical companies, this is a good reason to begin treating AI governance as part of routine GMP inspection readiness rather than as a future digital-transformation issue.

Regulatory Status Note

The PMDA-ATC GMP Inspection Seminar is a regulator training and capacity-building activity.

Draft PIC/S/EU GMP Annex 22 remains under development and is not currently an effective GMP requirement.

The observations above concerning inspection readiness are therefore an interpretation of the regulatory direction and should not be read as new formal requirements arising from the seminar.

Sources

PMDA – PMDA-ATC GMP Inspection Seminar 2026:

https://www.pmda.go. … h/symposia/0351.html

PIC/S – Meetings and Training Events:

https://picscheme.org/en/events