AIforPharma.org

This report (written with input from 100+ independent experts across many countries and organisations) synthesises what frontier/general-purpose AI systems can do, what risks they pose, and how those risks can be managed. For pharma companies deploying GenAI/LLMs (quality, PV, medical writing, knowledge systems), it is a practical reference for “known failure modes” (evaluation gaps, reliability issues, misuse risks) and governance expectations.

International AI Safety Report 2026: what matters for pharma (quick take)

The International AI Safety Report 2026 (Feb 2026) is not a pharma-specific guidance, but it contains several points that are highly relevant for pharmaceutical companies using GenAI/LLMs or “AI agents” in R&D, labs, medical information, PV, quality systems, and regulated documentation.

Notably, the report highlights the following pharma-relevant themes:

• AI as a scientific accelerator (with dual-use implications). Advanced AI can support tasks such as molecule/protein design and other scientific work that can accelerate drug discovery, while also raising dual-use concerns (bio/chem misuse risk).
• Growing capability of AI agents in laboratory contexts. The report discusses increasingly capable agents (“AI co-scientists”) that can assist with experimental protocols, troubleshooting, and interacting with biological tools—useful for legitimate research but also relevant for risk governance.
• Use of “benign proxy tasks” in bio/chem risk evaluation. Because direct weapons testing is constrained, the report notes that safer proxy tasks—explicitly including activities like pharmaceutical synthesis—are used to estimate how much AI can increase capability.
• The “evaluation gap” as a central governance warning. Models can look strong in benchmarks but behave differently in real-world settings. For pharma, this directly supports the need for realistic testing, monitoring, and lifecycle control before relying on AI outputs in high-stakes processes.
• Reliability risks in high-stakes domains like medicine. The report highlights known failure modes (e.g., hallucinations/out-of-distribution failures) and notes that these issues matter in medical contexts, reinforcing the need for boundaries and human oversight.
• Safety practices that map well to pharma governance. The report emphasizes practices such as red-teaming and monitoring/control approaches, which align well with regulated “fit-for-intended-use” thinking and ongoing oversight.

Practical takeaway: this report strengthens the argument that GenAI and agentic systems should be treated as probabilistic tools with real failure modes—requiring realistic evaluation, red-teaming, monitoring, and clear boundaries, especially when outputs influence regulated decisions.

Publication page: https://internationa … i-safety-report-2026
Direct PDF: https://internationa … fety-report-2026.pdf

This document is a practical roadmap for how the EU medicines network intends to strengthen data interoperability, analytics, and AI-enabled regulatory capabilities, with annual updates planned. For pharmaceutical companies, it signals where regulators are investing: DARWIN EU (RWE), more systematic clinical study data analytics, AI governance and tools, and interoperability standards aligned with EHDS.

Notably, the workplan outlines the following focus areas and deliverables:

• A clear long-term vision: “Trusted medicines by unlocking the value of data”, with an explicit commitment to using data within an ethical framework and in compliance with EU data legislation.
• A structured program across six workstreams: Strategy & governance; Data analytics; Artificial intelligence; Data interoperability; Stakeholder engagement & change management; Guidance & international initiatives.
• Alignment with major EU initiatives impacting data exchange and evidence generation, including European Health Data Space (EHDS/TEHDAS2), revised EU pharmaceutical legislation, IDMP-related work (UNIWIDE IDMP), and broader interoperability frameworks.
• Scaling Real-World Evidence via DARWIN EU, including onboarding data partners, executing studies, and planning further expansion (DARWIN EU 2).
• Transitioning clinical study data activities from pilots toward systematic submission and analysis of patient-level clinical study data for centrally authorised products (CAPs), supported by tools/process updates and training.
• An AI workstream that includes: publishing Guiding Principles for Good AI Practice, an AI glossary, annual AI Observatory updates, AI literacy training (linked to AI Act obligations), and deployment of internal AI tools (e.g., Scientific Explorer, SPC Reader/SPC Search, AI assistants) supported by an AI Tools Framework.
• Interoperability deliverables such as data catalogues/metadata management, defined data roles (e.g., trustees/stewards), and a medicines-regulation data glossary—explicitly linked to EHDS readiness.

Workplan PDF (EMA): https://www.ema.euro … es-regulation_en.pdf
Work plan PDF (EMA): https://www.ema.euro … working-group_en.pdf

EMA has published the event page and supporting documents for the HMA–EMA AI group meeting with industry stakeholders (February 2026) and subsequently posted summary notes. This is a concrete regulatory mechanism for aligning expectations on acceptable AI use, governance, and evidence—also in areas that can impact GMP/CMC and lifecycle data.

Notably, the current guidance development activities mentioned include:

• Guidance on AI in clinical development (a concept paper is expected before a full draft guideline).
• Guidance on AI in pharmacovigilance, to be developed jointly with PRAC as a Q&A-style document.
• EU GMP Annex 22 on AI in manufacturing: following public consultation (≈1,300 comments received), it is now under revision, with the final document expected by the end of the year.
• Several industry interventions were noted, including an expectation for a more flexible approach to the use of AI in GMP. This includes the potential use of generative AI and large language models (LLMs) in critical GMP applications, provided this is supported by a robust, risk-based framework.

Event page (EMA): https://www.ema.euro … olders-february-2026
Summary notes PDF (EMA): https://www.ema.euro … february-2026_en.pdf

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

Welcome to AI for Pharma

Artificial Intelligence is rapidly transforming the pharmaceutical industry — from drug discovery and manufacturing to quality systems, pharmacovigilance, and regulatory decision-making. At the same time, it raises fundamental questions about compliance, data integrity, validation, and responsibility.

AI for Pharma was created as a space to explore these changes in a practical, regulation-aware, and industry-focused way. The goal of this blog is to:

• Share recent news and developments related to AI in the pharmaceutical industry
• Discuss regulatory expectations (GxP, data integrity, validation, EU AI Act, FDA perspectives, etc.)
• Translate complex AI concepts into clear, usable insights for quality, manufacturing, and regulatory professionals
• Highlight realistic use cases, risks, and limitations — not hype

This initiative is particularly focused on compliance-critical environments, where innovation must coexist with:

• Patient safety
• Product quality
• Regulatory accountability

AI has enormous potential, but in pharma it must be understood, governed, and implemented responsibly.

I hope this blog will become a useful and interesting resource for:

• Quality and compliance professionals
• Manufacturing and validation experts
• Regulatory affairs specialists
• Anyone interested in the intersection of AI and pharmaceutical regulations

This is just the beginning. Future posts will cover examples, regulatory interpretations, lessons learned, and emerging trends.

Welcome — and thank you for reading!