{"id":38686,"date":"2025-08-05T09:00:54","date_gmt":"2025-08-05T07:00:54","guid":{"rendered":"https:\/\/www.qualifyze.com\/?post_type=resources&#038;p=38686"},"modified":"2026-04-07T16:46:51","modified_gmt":"2026-04-07T14:46:51","slug":"gmp-annex-22-ai-compliance-pharma","status":"publish","type":"resources","link":"https:\/\/www.qualifyze.com\/what-gmp-annex-22-means-for-pharma\/","title":{"rendered":"What GMP Annex 22 Means for Pharma\u00a0"},"content":{"rendered":"<p><b>What GMP Annex 22 Means for Pharma\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Artificial Intelligence (AI) is moving from pilot projects to real-world pharmaceutical manufacturing applications\u2014but not without regulatory oversight. With the <\/span><b>publication of the draft of Annex 22<\/b><span style=\"font-weight: 400;\"> to the European Guidelines for Good Manufacturing Practice (GMP), regulators are officially entering the conversation. This new annex, now open for public comment, represents the <\/span><b>first structured framework<\/b><span style=\"font-weight: 400;\"> for the application of AI\/ML in GMP environments.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Annex 22 is not just regulatory housekeeping\u2014it&#8217;s a signal that AI is here to stay. And the message is clear: AI can be used in regulated environments, but it must be<\/span><b> transparent, traceable, and well-controlled.<\/b><\/p>\n<p><b>What Annex 22 Covers\u2014and What It Doesn\u2019t<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Annex 22 provides <\/span><b>additional guidance to Annex 11<\/b><span style=\"font-weight: 400;\"> for computerized systems, focusing specifically on <\/span><b>AI and machine learning (ML) models<\/b><span style=\"font-weight: 400;\"> that are used in <\/span><b>critical GMP applications<\/b><span style=\"font-weight: 400;\">\u2014those impacting <\/span><b>patient safety, product quality, or data integrity<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The scope is <\/span><b>narrow and deliberate<\/b><span style=\"font-weight: 400;\">. The annex:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Applies to<\/b><span style=\"font-weight: 400;\">:<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">AI\/ML models with deterministic output (i.e., identical input yields identical output).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Static models\u2014those that do not continue learning during operation.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Excludes<\/b><span style=\"font-weight: 400;\">:<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Generative AI (e.g., ChatGPT, image generators).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Large Language Models (LLMs).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Probabilistic or dynamic models that adapt continuously.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Any model used in <\/span><b>non-critical GMP applications<\/b><span style=\"font-weight: 400;\">.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">If you\u2019re exploring AI for tasks like real-time process monitoring, predictive maintenance, or supplier risk classification, the annex is relevant\u2014but only if these systems directly impact critical GMP outcomes.<\/span><\/p>\n<p><b>Themes<\/b><\/p>\n<ol>\n<li><b style=\"font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\">Intended Use and Risk Assessment Are Foundational<\/b><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">AI isn\u2019t plug-and-play. Annex 22 stresses the need for a <\/span><b>detailed, documented description of a model\u2019s intended use<\/b><span style=\"font-weight: 400;\">, input data types, expected output, and risk to the process. Clear documentation ensures traceability and helps QA validate whether AI outputs can be trusted.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Define subgroups of data (e.g. sites, materials, defect types).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identify potential bias or limitations in the data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Use subject matter experts to approve intended use before acceptance testing.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b style=\"font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\">2. Test Data Must Be Independent and Representative<\/b><\/p>\n<p><span style=\"font-weight: 400;\">A robust AI model is only as good as the data it\u2019s tested on. The annex mandates:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Independent test datasets<\/b><span style=\"font-weight: 400;\"> not used in training or validation.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Inclusion of <\/span><b>all expected input variations<\/b><span style=\"font-weight: 400;\"> (e.g. across suppliers, shifts, equipment).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Verified labeling of test data by experts or validated systems.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">This avoids \u201cmodel overfitting\u201d and ensures generalizability, which is crucial in dynamic supply chains with global variability.<\/span><\/p>\n<p><a href=\"https:\/\/hubs.ly\/Q03yM0q10\"><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-38731\" src=\"https:\/\/www.qualifyze.com\/wp-content\/uploads\/2025\/07\/Banner_-Zuzana-Horakova.png\" alt=\"\" width=\"1200\" height=\"324\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><b>3. Explainability and Confidence Are Non-Negotiable<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The annex calls for <\/span><b>explainability<\/b><span style=\"font-weight: 400;\"> in models used in GMP-critical tasks. QA and supply chain leaders must be able to audit AI decisions and understand when human review is needed. AI outputs should never be blindly accepted in critical decisions. Annex 22 reinforces that responsibility always lies with trained personnel.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Prominent interpretability tools like <\/span><b>SHAP (<\/b><span style=\"font-weight: 400;\">SHapley Addictive exPlanations) or <\/span><b>LIME<\/b><span style=\"font-weight: 400;\"> (Local Interpretable Model-agnostic Explanations) should be used to explain which features contributed to outcomes.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Confidence scores<\/b><span style=\"font-weight: 400;\"> must be logged, and thresholds set to prevent overconfident predictions.<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Human-in-the-loop (HITL)<\/b><span style=\"font-weight: 400;\"> setups are encouraged. Operator roles must be well-defined and consistently trained.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b style=\"font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\">4. Post-Deployment Monitoring and Change Control<\/b><\/p>\n<p><span style=\"font-weight: 400;\">A well-performing model at deployment may deteriorate over time. Supply chains evolve, and so must the oversight of AI tools.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Once in operation, models must be treated like any other GMP-critical system:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Change control applies to the model, software, and input data sources.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Performance metrics must be continuously monitored.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Input data drift must be tracked to ensure ongoing validity.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>What This Means for the Industry<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Annex 22 is a <\/span><b>playbook for adopting AI responsibly<\/b><span style=\"font-weight: 400;\">. It bridges the gap between innovation and compliance, ensuring that patient safety and product quality remain at the core.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0For manufacturers, this is an opportunity to embed AI into GxP processes <\/span><b>without ambiguity<\/b><span style=\"font-weight: 400;\"> about what is or isn\u2019t allowed.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u2705<\/span> <b>Regulators recognize the need\u00a0 for AI-driven innovation, but they demand rigor.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">\u274c<\/span> <b>\u201cBlack box\u201d models, uncontrolled drift, and unsupported automation will not pass inspection.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Importantly, Annex 22 is currently in draft form and open for stakeholder comments until October 7, 2025. Feedback from industry experts may result in changes to the final version. This is your chance to shape the conversation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If your organization is considering AI in critical GMP environments, it will soon have a regulatory guidance to do so.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Do you want to learn possible applications of AI in pharma compliance? Listen to the talk of Zuzana Horakova, performed at Sync Space Barcelona 2025 on 17.6.2025\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The session covers:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Real-world use cases<\/b><span style=\"font-weight: 400;\"> of AI in pharma quality and operations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Predictive compliance<\/b><span style=\"font-weight: 400;\"> tools: where they help, where they can hurt<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Responsible AI adoption<\/b><span style=\"font-weight: 400;\">: why pharma can\u2019t afford to get it wrong<\/span><\/p>\n<p style=\"text-align: center;\"><a class=\"btn-green hidden\" href=\"https:\/\/hubs.ly\/Q03yM1lr0\">Watch <span style=\"font-weight: 400;\">the Sync Space Session here<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What GMP Annex 22 Means for Pharma\u00a0 Artificial Intelligence (AI) is moving from pilot projects to real-world pharmaceutical manufacturing applications\u2014but not without regulatory oversight. With the publication of the draft of Annex 22 to the European Guidelines for Good Manufacturing Practice (GMP), regulators are officially entering the conversation. This new annex, now open for public [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":38689,"template":"","topic-tags":[79],"resource-type":[51],"class_list":["post-38686","resources","type-resources","status-publish","has-post-thumbnail","hentry","topic-tags-audits-compliance","resource-type-blog"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What GMP Annex 22 Means for Pharma\u00a0 - Qualifyze<\/title>\n<meta name=\"description\" content=\"GMP Annex 22 explained: discover what the new AI\/ML regulatory framework means for pharma manufacturing, compliance, and quality assurance in GMP environments.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.qualifyze.com\/es\/what-gmp-annex-22-means-for-pharma\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What GMP Annex 22 Means for Pharma\u00a0 - 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