Welcome to the 9th edition of Mareana Deep Dive. This edition includes our updates from June and July 2026.
[Whitepaper] The AI Never Told Us: Lessons from the First FDA Warning Letter on AI Misuse
AI is rapidly entering pharmaceutical manufacturing, but regulatory expectations are evolving just as quickly. The first FDA warning letter citing AI misuse highlighted a critical reality: organizations remain accountable for every decision, record, and process influenced by AI. For Quality, Manufacturing, and Compliance teams, the challenge is no longer whether to use AI, but how to implement it in a way that supports validation, human oversight, traceability, and regulatory readiness. This whitepaper examines the regulatory lessons emerging from AI adoption and outlines the architectural foundations required to use AI responsibly in regulated environments.
You can access the whitepaper here: The AI Never Told Us: Lessons from the First FDA Warning Letter on AI Misuse
[Blog] Inside FDA’s First Warning Letter on AI Misuse
The April 2026 FDA warning letter didn’t invent an AI rule. It applied 21 CFR 211.22(c), a forty-year-old rule, to AI output. A contract manufacturer let AI agents generate specs and production records and released them without Quality Unit review. So the real subject is the review duty the Quality Unit can’t outsource.The piece then turns to who actually carries the risk: virtual and asset-light sponsors. They hold the license and 100 percent of the liability while their CDMOs run the floor and send records back weeks late, in mixed formats.
The core idea readers take away is the line between AI-assisted (a reviewer checks the output against the source before signing, which is acceptable) and AI-generated (the same content reaches the cGMP record with no real review, which is what FDA cited). It closes by naming the work this creates: proving that distinction on every record, every lot.
Read Inside FDA’s First Warning Letter on AI Misuse.
[Blog] When the AI Lives at Your CDMO: Making 21 CFR 211.22(c) Executable Across the Contract Chain
The regulatory ground: FDA’s Parts 210 and 211 already reach AI output, and the EU’s draft Annex 22 permits only static, deterministic models for critical decisions, leaving most generative AI needing a human in between.
The build is a four-function, sponsor-side architecture:
• Ingestion turns paper, PDFs, and system exports into structured data, scored field by field.
• Contextualization links records into a knowledge graph with traceability across the chain.
• Deterministic rules return a reproducible pass or fail with severity grading.
• Exception review is where the Quality Unit re-enters, judging only flagged items and signing the audit trail.
Read our blog When the AI Lives at Your CDMO to dig deeper.
[Blog] How a Knowledge Graph Connects Data into a Single Queryable Network
Pharmaceutical manufacturing generates vast amounts of data across batch records, Certificates of Analysis (CoAs), equipment logs, laboratory systems, quality systems, and paper documentation. These records often remain disconnected, making it difficult for quality and manufacturing teams to quickly trace relationships, investigate deviations, prepare for audits, or assess the impact of material and equipment issues.
A manufacturing knowledge graph connects these records into a single, queryable network that preserves the relationships between batches, materials, equipment, test results, and quality events. By bringing together structured and unstructured data, manufacturers can gain complete manufacturing context, accelerate investigations, improve recall readiness, simplify annual product reviews, strengthen cross-site visibility, and support regulatory compliance with end-to-end traceability.
Read How a Knowledge Graph Connects Data into a Single Queryable Network to learn more.
[Blog] 7 Factors to consider while choosing AI for Batch Reviews
AI-assisted batch review can cut release cycles from weeks to days, but only if the right data architecture is in place underneath.
This article breaks down seven evaluation factors that pharmaceutical QA and operations leaders should assess before selecting AI batch review software, from batch genealogy and bidirectional lot traceability to 21 CFR Part 11 compliance for AI-generated decisions, ALCOA++ audit trail requirements, connected electronic batch records, recall readiness, and the manufacturing intelligence layer that ties it all together.
Learn the 7 Factors to consider while choosing AI for Batch Reviews .
[Video] How AI Helps You Achieve Exception-Based Review
Exception-based review lets QA verify only the batch record fields that need a human, instead of reading every page. See this video to understand how AI makes exception-based review possible in pharma manufacturing, and how it cuts batch record review time.
[Video] Batch Genealogy: End-to-End Traceability in Pharma Manufacturing
See how batch genealogy turns a full manufacturing batch into one connected view, with every value traced back to where it came from.
[Video] Accelerating Root Cause Analysis with an AI Chatbot
This video shows how an AI chatbot speeds up root cause analysis by letting QA query that genealogy in plain language.
[Video] AI for CPV in Pharma: Automated SPC Charts, Drift Detection, and Inspection-Ready Audit Trails
Explore how an intelligent Continued Process Verification (CPV) workflow automatically maintains SPC charts, detects meaningful process drift in real time, and keeps every chart inspection-ready.
[Inside Mareana] Events and Celebrations
At Mareana, we believe great teams are built both at work and beyond.
A few moments from our International Yoga Day celebration at Mareana.
Taking time to connect beyond work. A few moments from our team dinner together.
Honouring five years of commitment and contribution !
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