{"id":7002,"date":"2026-07-07T20:23:45","date_gmt":"2026-07-07T20:23:45","guid":{"rendered":"https:\/\/www.waters.com\/blog\/?p=7002"},"modified":"2026-07-07T20:25:01","modified_gmt":"2026-07-07T20:25:01","slug":"ai-in-gmp-navigating-evolving-expectations-for-regulated-labs","status":"publish","type":"post","link":"https:\/\/www.waters.com\/blog\/ai-in-gmp-navigating-evolving-expectations-for-regulated-labs\/","title":{"rendered":"AI in GMP: Navigating Evolving Expectations for Regulated Labs"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Why 2026 marks the shift from guidance to enforcement and what it means for your organization now.<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Written by Tracy Hibbs and Tony Sacchetti<\/em>,<em> Waters Corporation<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Regulators are already inspecting AI use, how prepared is your organization?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">For years, the pharmaceutical industry has anticipated regulatory clarity on the use of artificial intelligence (AI) and machine learning (ML) in regulated labs. This clarity has emerged, not only through guidance, but also through enforcement. For quality control (QC) labs, this moment signals both opportunity and accountability. AI can deliver speed, consistency, and efficiency, but only with strong AI governance and when deployed under conditions that preserve transparency, traceability, and scientific integrity.<\/p>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_a7b6d0-e1 .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_a7b6d0-e1 .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_a7b6d0-e1\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Foundations: The regulatory path that got us here<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In 2021, the Danish Medicines Agency (DKMA) became one of the first European regulators to address AI in pharmaceutical quality systems when they took a bold step toward defining the role of AI in regulated environments by publishing a draft guidance entitled \u201cSuggested Criteria for Using AI\/ML Algorithms in GxP.\u201d<sup>1<\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Their work informed reflection papers and draft guidance from the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA), all formally recognizing AI and ML as viable tools in regulated pharmaceutical operations, including QC. These documents do more than permit AI\u2014they define the conditions under which it can be trusted.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2021 DKMA<\/strong> <strong>Suggested Criteria for Using AI\/ML Algorithms in GxP<\/strong><em> <\/em>emphasizes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use of static, supervised models for critical functions<\/li>\n\n\n\n<li>Independent test data and bias mitigation<\/li>\n\n\n\n<li>Strong data integrity and validation practices<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2023 EMA<\/strong> <strong>Reflection Paper on the Use of Artificial Intelligence (AI) in the Medicinal Product Lifecycle<\/strong><sup>2<\/sup> emphasizes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Transparency and explainability<\/li>\n\n\n\n<li>Risk-based validation<\/li>\n\n\n\n<li>Human oversight<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2025 EMA with Pharmaceutical Inspection Convention and Pharmaceutical Inspection Co-operation Scheme (PIC\/S) <\/strong><strong>Draft Guidance Annex 22: Artificial Intelligence<\/strong><sup>3 <\/sup>explicitly excludes adaptive or generative AI from critical GMP applications and mandates:<strong><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Static, deterministic models only<\/li>\n\n\n\n<li>Locked training data and independent test sets<\/li>\n\n\n\n<li>Explainability tools (e.g., SHAP, LIME) and confidence scoring<\/li>\n\n\n\n<li>Defined operator roles in human-in-the-loop (HITL) systems<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2025 FDA<\/strong> <strong>Draft Guidance Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products<\/strong><sup>4<\/sup> introduces a risk-based credibility assessment framework, focused on:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Model transparency and traceability<\/li>\n\n\n\n<li>Data governance and independence of training\/test sets<\/li>\n\n\n\n<li>Lifecycle management of AI models<\/li>\n\n\n\n<li>Context-of-use validation<\/li>\n<\/ul>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_255a2f-b6 .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_255a2f-b6 .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_255a2f-b6\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Ten principles, two regulators, and one path<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The FDA and EMA jointly published Guiding Principles for Good AI Practice in Drug Development<strong>.<\/strong><sup>5<\/sup> The synchronized framework is significant, indicating a renewed alignment between two influential regulatory agencies and signaling that organizations operating across jurisdictions can plan against a stable target.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The joint announcement highlights 10 critical areas:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"371\" src=\"https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-1-1024x371.png\" alt=\"blog 1 fig 1\" class=\"wp-image-7003\" srcset=\"https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-1-1024x371.png 1024w, https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-1-300x109.png 300w, https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-1-768x278.png 768w, https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-1-1536x556.png 1536w, https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-1-2048x742.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_be8e8b-72 .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_be8e8b-72 .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_be8e8b-72\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Apply critical thinking<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Core skills developed across your organization, from risk management to data governance and vigilant monitoring, become more critical when leveraging AI. Your organization remains accountable for your quality.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI reliability: <\/strong>Data quality is foundational, requiring strict data segregation, traceability, and version control. Lifecycle management is a cornerstone, as are <a href=\"https:\/\/www.waters.com\/nextgen\/global\/products\/informatics-and-software\/informatics-and-software-education\/data-integrity.html\">data integrity<\/a> and bias mitigation.<\/li>\n\n\n\n<li><strong>AI successful implementation: <\/strong>Collaboration is an imperative requiring cross-functional teams including your analysts, QA, IT, AI ethics team, data integrity officer, and regulatory affairs team.<\/li>\n\n\n\n<li><strong>AI decision impact: <\/strong>Understanding the criticality of the decision is not optional. Risk assessment good practices and data flow and workflow maps identifying your intended use underpin your implementation.<\/li>\n\n\n\n<li><strong>AI oversight: <\/strong>Knowing the number of decisions being made and applying vigilant monitoring at each decision point are fundamental.<\/li>\n<\/ul>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_8d6bcb-ef .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_8d6bcb-ef .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_8d6bcb-ef\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Why this matters: The risk to product quality and patient safety<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The stakes in pharmaceutical QC are uniquely high. Every decision in QC labs has downstream implications for product quality, compliance, and, ultimately, patient safety. AI systems that are opaque, unvalidated, or poorly governed have the potential to introduce detrimental risks, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Undetected anomalies in chromatographic data could allow substandard or contaminated products to reach patients.<\/li>\n\n\n\n<li>Adaptive models that change behavior over time may produce inconsistent results, undermining batch release decisions.<\/li>\n\n\n\n<li>Lack of explainability can obscure the rationale behind critical decisions, making root cause analysis and regulatory inspections more difficult.<\/li>\n\n\n\n<li>Insufficient validation or biased training data can lead to systematic errors, disproportionately affecting certain product types or patient populations.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is why regulators are drawing a clear line. AI must not compromise the scientific rigor, traceability, or reproducibility that underpin GMP. Instead, it must enhance them.<\/p>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_aaabe1-59 .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_aaabe1-59 .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_aaabe1-59\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">What does this mean for QC labs?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">For QC labs, this is a pivotal moment. AI\/ML can now be deployed to:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"578\" height=\"303\" src=\"https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-2.png\" alt=\"blog 1 fig 2\" class=\"wp-image-7004\" srcset=\"https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-2.png 578w, https:\/\/www.waters.com\/blog\/wp-content\/uploads\/blog-1-fig-2-300x157.png 300w\" sizes=\"(max-width: 578px) 100vw, 578px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">But adoption must be deliberate. Labs and quality teams must ensure:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Models are locked and validated before use.<\/li>\n\n\n\n<li>Outputs are interpretable and reviewable.<\/li>\n\n\n\n<li>Human reviewers remain accountable and trained.<\/li>\n\n\n\n<li>Strong quality oversight persists.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is not about replacing analysts. It\u2019s about amplifying your deep expertise and focusing attention where it matters most.<\/p>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_faf676-2f .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_faf676-2f .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_faf676-2f\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Looking ahead: The message is straightforward<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Regulatory direction is clear: AI use in GMP areas is permitted, provided it operates within a framework of transparency, reproducibility, and oversight. As regulators continue to refine expectations, particularly around validation standards, model updates, and governance of agentic systems along with real-time monitoring, QC labs that invest now in compliance-enabled AI will be better positioned for the future.<\/p>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_f080c6-45 .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_f080c6-45 .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_f080c6-45\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Is your lab ready for the AI era? <\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.waters.com\/nextgen\/global\/products\/informatics-and-software\/waters-cloud-software-solutions.html\">Connect with our experts<\/a> to explore how Waters is enabling labs to take the next step toward intelligent, always audit-ready systems.<\/p>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_d46dd3-0b .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_d46dd3-0b .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_d46dd3-0b\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><a href=\"https:\/\/laegemiddelstyrelsen.dk\/en\/licensing\/supervision-and-inspection\/inspection-of-authorised-pharmaceutical-companies\/using-aiml-algorithms-in-gxp\/~\/media\/B02C888935984271BF61BD756ADDAB6B.ashx\">Danish Medicines Agency (DKMA), Suggested Criteria for Using AI\/ML Algorithms in GxP (2021).<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.ema.europa.eu\/en\/use-artificial-intelligence-ai-medicinal-product-lifecycle-scientific-guideline\">EMA Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle (Draft 2023, Final 2024).<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.gmp-compliance.org\/files\/guidemgr\/mp_vol4_chap4_annex22_consultation_guideline_en.pdf\">EMA GMP Annex 22 (Draft 2025).<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological\">FDA Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (Draft January 2025).<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.ema.europa.eu\/en\/news\/ema-fda-set-common-principles-ai-medicine-development-0\">FDA and EMA Guiding Principles of Good AI Practice in Drug Development (January 2026).<\/a><\/li>\n<\/ol>\n\n\n<style>.wp-block-kadence-spacer.kt-block-spacer-7002_2c0464-fc .kt-block-spacer{height:60px;}.wp-block-kadence-spacer.kt-block-spacer-7002_2c0464-fc .kt-divider{border-top-width:1px;height:1px;border-top-color:#eee;width:80%;border-top-style:solid;}<\/style>\n<div class=\"wp-block-kadence-spacer aligncenter kt-block-spacer-7002_2c0464-fc\"><div class=\"kt-block-spacer kt-block-spacer-halign-center\"><hr class=\"kt-divider\"\/><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Additional Resources<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/guidance-docs.ispe.org\/doi\/book\/10.1002\/9781946964854\">ISPE\u00ae GAMP\u00ae Guide: Artificial Intelligence (July 2025)<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/computer-software-assurance-production-and-quality-management-system-software\">FDA Computer Software Assurance (CSA) Guidance (February 2026)<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/q9r1-quality-risk-management\">ICH Q9(R1) Quality Risk Management<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/picscheme.org\/docview\/4234\">PIC\/S PI 041-1, Good Practices for Data Management and Integrity in Regulated GMP\/GDP Environments<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Why 2026 marks the shift from guidance to enforcement and what it means for your organization now. Written by Tracy Hibbs and Tony Sacchetti, Waters Corporation Regulators are already inspecting AI use, how prepared is your organization? For years, the pharmaceutical industry has anticipated regulatory clarity on the use of artificial intelligence (AI) and machine&#8230;<\/p>\n","protected":false},"author":127,"featured_media":7029,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_titles_title":"AI in GMP: Regulatory Expectations for Pharmaceutical Labs in 2026","_seopress_titles_desc":"Learn how evolving FDA and EMA guidance is shaping AI use in GMP-regulated labs and what QC organizations must do to ensure compliance.","_seopress_robots_index":"","_seopress_robots_follow":"","_seopress_robots_imageindex":"","_seopress_robots_snippet":"","_seopress_robots_primary_cat":"","_seopress_robots_breadcrumbs":"","_seopress_robots_freeze_modified_date":"","_seopress_robots_custom_modified_date":"","_seopress_robots_canonical":"","_seopress_social_fb_title":"","_seopress_social_fb_desc":"","_seopress_social_fb_img":"","_seopress_social_fb_img_attachment_id":0,"_seopress_social_fb_img_width":0,"_seopress_social_fb_img_height":0,"_seopress_social_twitter_title":"","_seopress_social_twitter_desc":"","_seopress_social_twitter_img":"","_seopress_social_twitter_img_attachment_id":0,"_seopress_social_twitter_img_width":0,"_seopress_social_twitter_img_height":0,"_seopress_redirections_value":"","_seopress_redirections_enabled":"","_seopress_redirections_enabled_regex":"","_seopress_redirections_logged_status":"","_seopress_redirections_param":"","_seopress_redirections_type":0,"_seopress_analysis_target_kw":"","_seopress_news_disabled":"","_seopress_video_disabled":"","_seopress_video":[],"_seopress_pro_schemas_manual":[],"_seopress_pro_rich_snippets_disable_all":"","_seopress_pro_rich_snippets_disable":[],"_seopress_pro_schemas":[],"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[25],"tags":[785,786,273,315,629,655,110],"class_list":["post-7002","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pharmaceutical","tag-ai","tag-artificial-intelligence","tag-data-integrity","tag-pharma-qc","tag-pharmaceutical","tag-regulated-labs","tag-regulatory-compliance"],"acf":[],"taxonomy_info":{"category":[{"value":25,"label":"Pharmaceutical"}],"post_tag":[{"value":785,"label":"AI"},{"value":786,"label":"artificial intelligence"},{"value":273,"label":"data integrity"},{"value":315,"label":"pharma QC"},{"value":629,"label":"pharmaceutical"},{"value":655,"label":"regulated labs"},{"value":110,"label":"regulatory compliance"}]},"featured_image_src_large":["https:\/\/www.waters.com\/blog\/wp-content\/uploads\/adobestock_833539647_full-res-presentation-ready-png.png",1000,601,false],"author_info":{"display_name":"Tracy Hibbs","author_link":"https:\/\/www.waters.com\/blog\/author\/thibbs\/"},"comment_info":"","category_info":[{"term_id":25,"name":"Pharmaceutical","slug":"pharmaceutical","term_group":0,"term_taxonomy_id":25,"taxonomy":"category","description":"We bring high-value technologies designed to solve critical, analytical problems. We enable profound discoveries, optimize lab operations and ensure regulatory compliance. We help customers turn global industry challenges into the medicines that offer hope.","parent":0,"count":100,"filter":"raw","term_order":"0","cat_ID":25,"category_count":100,"category_description":"We bring high-value technologies designed to solve critical, analytical problems. We enable profound discoveries, optimize lab operations and ensure regulatory compliance. We help customers turn global industry challenges into the medicines that offer hope.","cat_name":"Pharmaceutical","category_nicename":"pharmaceutical","category_parent":0}],"tag_info":[{"term_id":785,"name":"AI","slug":"ai","term_group":0,"term_taxonomy_id":785,"taxonomy":"post_tag","description":"","parent":0,"count":3,"filter":"raw","term_order":"0"},{"term_id":786,"name":"artificial intelligence","slug":"artificial-intelligence","term_group":0,"term_taxonomy_id":786,"taxonomy":"post_tag","description":"","parent":0,"count":3,"filter":"raw","term_order":"0"},{"term_id":273,"name":"data integrity","slug":"data-integrity","term_group":0,"term_taxonomy_id":273,"taxonomy":"post_tag","description":"","parent":0,"count":27,"filter":"raw","term_order":"0"},{"term_id":315,"name":"pharma QC","slug":"pharma-qc","term_group":0,"term_taxonomy_id":315,"taxonomy":"post_tag","description":"","parent":0,"count":12,"filter":"raw","term_order":"0"},{"term_id":629,"name":"pharmaceutical","slug":"pharmaceutical","term_group":0,"term_taxonomy_id":629,"taxonomy":"post_tag","description":"","parent":0,"count":11,"filter":"raw","term_order":"0"},{"term_id":655,"name":"regulated labs","slug":"regulated-labs","term_group":0,"term_taxonomy_id":655,"taxonomy":"post_tag","description":"","parent":0,"count":4,"filter":"raw","term_order":"0"},{"term_id":110,"name":"regulatory compliance","slug":"regulatory-compliance","term_group":0,"term_taxonomy_id":110,"taxonomy":"post_tag","description":"","parent":0,"count":15,"filter":"raw","term_order":"0"}],"_links":{"self":[{"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/posts\/7002","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/users\/127"}],"replies":[{"embeddable":true,"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/comments?post=7002"}],"version-history":[{"count":5,"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/posts\/7002\/revisions"}],"predecessor-version":[{"id":7027,"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/posts\/7002\/revisions\/7027"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/media\/7029"}],"wp:attachment":[{"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/media?parent=7002"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/categories?post=7002"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.waters.com\/blog\/wp-json\/wp\/v2\/tags?post=7002"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}