AIforPharma.org

Key AI GMP-relevant documents where regulators explicitly address AI / ML or the core compliance expectations that govern AI used in manufacturing (computerized systems, validation/assurance, data integrity, lifecycle control) are listed below with relevant links (Last link check: 2026-02-08, note: links to draft documents may stop working after the consultation phase ends).

FDA (US) — AI in pharma manufacturing & quality

• Artificial Intelligence in Drug Manufacturing (Discussion Paper, 2023) — FDA CDER discussion paper focused on AI use in drug manufacturing (not guidance, but important signal of expectations). https://www.fda.gov/ … edia/165743/download - It also includes many other useful links.
• Considerations for the Use of AI to Support Regulatory Decision-Making for Drug and Biological Products (Guidance, Jan 2025) — framework for establishing credibility of AI models used to support decisions about safety/effectiveness/quality (highly relevant if AI supports GMP/quality conclusions in submissions). https://www.fda.gov/ … -drug-and-biological
• Guiding Principles of Good AI Practice in Drug Development (Jan 2026) — FDA + EMA aligned principles; explicitly spans lifecycle including manufacturing. https://www.fda.gov/ … edia/189581/download
• FDA “Artificial Intelligence for Drug Development” hub page (collects the above + related FDA AI resources). https://www.fda.gov/ … nce-drug-development

GMP/quality foundations that apply to AI systems

• Data Integrity and Compliance With Drug CGMP: Q&A (Dec 2018) — the core FDA data integrity expectations that AI systems must meet (ALCOA+, audit trail, controls, governance). https://www.fda.gov/ … uestions-and-answers
• PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance (Guidance; PDF) — not “AI”, but foundational for model-based control/monitoring and advanced analytics in manufacturing. https://www.fda.gov/media/71012/download
• Process Validation: General Principles and Practices (Guidance; PDF) — validation lifecycle principles that also govern AI-enabled control/monitoring when it impacts product quality. https://www.fda.gov/ … es-and-Practices.pdf
• Emerging Technology Program (ETP) (CDER) — FDA program supporting innovative manufacturing technologies (relevant pathway when AI is part of novel manufacturing control/automation). https://www.fda.gov/ … chnology-program-etp
• Advanced Manufacturing Technologies (AMT) Designation Program (Guidance, Dec 2025) — program guidance for advanced manufacturing approaches (useful if AI is embedded in AMT strategy). https://www.fda.gov/ … -designation-program

EMA / EU medicines regulators — AI + EU GMP updates

• EMA Reflection paper on the use of AI in the medicinal product lifecycle (final, 9 Sept 2024; PDF) — covers principles across lifecycle and regulatory expectations when AI outputs are used in regulated submissions (incl. manufacturing-related evidence). https://www.ema.euro … uct-lifecycle_en.pdf
• HMA–EMA Multi-annual AI workplan 2023–2028 (PDF) — network strategy for AI in medicines regulation (important “direction of travel”). https://www.ema.euro … teering-group_en.pdf
• EMA/FDA: Guiding principles of good AI practice in drug development (Jan 2026; PDF) — joint high-level principles (explicitly spanning manufacturing). https://aiforpharma. … in-drug-development/

EU GMP (EudraLex Volume 4) — computerised systems + new AI annex (draft)

• EU GMP Annex 11: Computerised Systems (current; PDF) — the core GMP anchor for any AI used as a computerised system in manufacturing/QMS. https://health.ec.eu … x11_01-2011_en_0.pdf
• European Commission consultation on revising Chapter 4 + Annex 11 and introducing New Annex 22 (Artificial Intelligence) — this is the major EU GMP move specifically targeting AI/ML in manufacturing. https://health.ec.eu … s-chapter-4-annex_en
• Draft “Annex 22: Artificial Intelligence” (consultation PDF) — outlines GMP expectations for AI (intended use, acceptance criteria, test data independence, explainability, operation, etc.). https://health.ec.eu … ion_guideline_en.pdf
• Draft update material for Annex 11/Chapter 4 (consultation PDF) — background and change rationale. https://health.ec.eu … ion_guideline_en.pdf

PIC/S (global GMP inspection cooperation)

• PIC/S PI 041-1: Good Practices for Data Management and Integrity in regulated GMP/GDP environments (final; PDF) — widely relied upon by inspectorates; very relevant for AI data pipelines and governance. https://picscheme.org/docview/4234
• PIC/S PI 011-3: Good Practices for Computerised Systems in Regulated “GxP” Environments (PDF) — inspector-oriented expectations for computerised systems (validation, supplier management, control). https://picscheme.org/docview/3444

UK MHRA

• MHRA GxP Data Integrity Guidance and Definitions (Rev. 1, March 2018; PDF) — strong practical expectations for data integrity controls that apply directly to AI toolchains. https://assets.publi … rch_edited_Final.pdf
Health Canada
• Annex 11 (GUI-0050): Computerized Systems — Health Canada adoption of Annex 11 principles for GMP computerized systems (useful for “regulatory convergence” arguments). https://www.canada.c … ystems-gui-0050.html

Artificial Intelligence (AI) has the potential to transform the way medicines are developed and evaluated, ultimately improving healthcare outcomes. In this context, the EMA and FDA issued joint guidance in January of this year outlining 10 international guiding principles. These principles identify areas where international regulators, standards organizations, and other collaborative bodies can work together to advance good practices in drug development.
Areas of collaboration include research, the development of educational tools and resources, international harmonization, and the establishment of consensus standards. These efforts may help inform regulatory policies and guidelines across different jurisdictions, in alignment with applicable legal and regulatory frameworks.
Further details can be found in the relevant documents (links below). However, the 10 guiding principles are particularly worth highlighting:
1. Human-centric by design
The development and use of AI technologies align with ethical and human-centric values.
2. Risk-based approach
The development and use of AI technologies follow a risk-based approach with proportionate validation, risk mitigation, and oversight based on the context of use and determined model risk.
3. Adherence to standards
AI technologies adhere to relevant legal, ethical, technical, scientific, cybersecurity, and regulatory standards, including Good Practices (GxP).
4. Clear context of use
AI technologies have a well-defined context of use (role and scope for why it is being used). 1 For the purpose of this document, the term “drug” is used to refer to drugs and biological products as defined in the United States of America, and medicinal products as defined in the European Union.
5. Multidisciplinary expertise
Multidisciplinary expertise covering both the AI technology and its context of use are integrated throughout the technology’s life cycle.
6. Data governance and documentation
Data source provenance, processing steps, and analytical decisions are documented in a detailed, traceable, and verifiable manner, in line with GxP requirements. Appropriate governance, including privacy and protection for sensitive data, is maintained throughout the technology’s life cycle.
7. Model design and development practices
The development of AI technologies follows best practices in model and system design and software engineering and leverages data that is fit-for-use, considering interpretability, explainability, and predictive performance. Good model and system development promotes transparency, reliability, generalizability, and robustness for AI technologies contributing to patient safety.
8. Risk-based performance assessment
Risk-based performance assessments evaluate the complete system including human-AI interactions, using fit-for-use data and metrics appropriate for the intended context of use, supported by validation of predictive performance through appropriately designed testing and evaluation methods.
9. Life cycle management
Risk-based quality management systems are implemented throughout the AI technologies’ life cycles, including to support capturing, assessing, and addressing issues. The AI technologies undergo scheduled monitoring and periodic re-evaluation to ensure adequate performance (e.g., to address data drift).
10. Clear, essential information
Plain language is used to present clear, accessible, and contextually relevant information to the intended audience, including users and patients, regarding the AI technology’s context of use, performance, limitations, underlying data, updates, and interpretability or explainability

Documents published by EMA and FDA :
https://www.ema.euro … g-development_en.pdf
https://www.fda.gov/ … edia/189581/download