Ensuring Data Integrity in Regulatory Submissions

🔍 Data Integrity (DI) Remediation & Validation in Biomanufacturing: Compliance is Non-Negotiable! In cGMP biomanufacturing, data integrity (DI) is the backbone of compliance. Without robust DI controls, the risk of regulatory scrutiny, product recalls, and patient safety issues escalates. Yet, many facilities still struggle with DI gaps, leading to FDA 483s, Warning Letters, and even Consent Decrees. So, how should organizations approach DI remediation and validation effectively? ⚠️ Common DI Pitfalls in Biomanufacturing ❌ Incomplete or altered records – Missing or manipulated batch records, audit trails, and electronic data raise red flags. ❌ Lack of ALCOA+ principles – Data must be Attributable, Legible, Contemporaneous, Original, and Accurate, plus Complete, Consistent, Enduring, and Available. ❌ Inadequate system controls – Poorly configured manufacturing execution systems (MES), laboratory information management systems (LIMS), and electronic batch records (EBRs) can compromise DI. ❌ Unvalidated data systems – Failure to validate computerized systems leads to unreliable data and regulatory noncompliance. 🔄 DI Remediation: A Risk-Based Approach A reactive approach to DI remediation is not enough. A well-structured DI remediation plan should include: ✅ Gap Assessment & Risk Prioritization – Identify DI gaps across paper-based and electronic systems. Prioritize remediation based on product impact and regulatory risk. ✅ Governance & Training – Establish DI policies, SOPs, and cross-functional training programs to embed a culture of DI compliance. ✅ Data Lifecycle Management – Implement controls for data generation, processing, storage, and retrieval to ensure compliance throughout the product lifecycle. ✅ Audit Trail Reviews & Exception Handling – Routine monitoring of electronic data trails to detect and correct DI issues before inspections. ✅ Periodic DI Assessments – Continuous review of DI controls through internal audits and self-inspections to maintain readiness. 📊 DI Validation: Ensuring Trustworthy Data Validation of GxP computerized systems ensures that data is reliable, accurate, and compliant. Key steps include: 🔹 System Risk Assessment – Categorize systems based on DI risk to determine validation effort. 🔹 21 CFR Part 11 Compliance – Ensure electronic signatures, access controls, and audit trails meet regulatory expectations. 🔹 IQ, OQ, PQ Execution – Verify system installation, operation, and performance meets DI requirements. 🔹 Periodic Review & Revalidation – Validate updates, patches, and system changes to maintain DI compliance over time. 🏆 DI Excellence = Compliance + Business Success A proactive DI strategy strengthens compliance, minimizes regulatory risk, and improves manufacturing efficiency. Organizations that invest in DI remediation and validation today will be the ones achieving inspection readiness and long-term success in biologics and cell & gene therapy manufacturing. #DataIntegrity #GMPCompliance

FDA Puts Data Integrity on Notice — AI Developers, Are You Ready? https://lnkd.in/dhHda8-2 The FDA just issued a formal warning on data integrity lapses in medical device submissions — a wake-up call not just for traditional diagnostics, but for every AI developer in healthcare. This isn’t just about rogue labs. It’s a broader signal: Your internal processes and any third-party lab must be GMLP compliant to defend your submission data. That’s where Gesund.ai comes in. AI developers now need more than great models — they need infrastructure that can defend their evidence from scrutiny: 🔐 Audit trails for every annotation and inference 🛡️ Role-based access controls 🧳 Data sequestration ♻️ Immutable versioning & lineage tracking ✅ FDA-aligned GMLP compliance by design Gone are the days of ad-hoc reader studies and opaque validation. Regulators expect traceability, reliability, and full lifecycle control. Regulatory-grade AI needs regulatory-grade infrastructure. That’s what we’re building at Gesund.ai. #GMLP #FDA #DataIntegrity #AIValidation #MedicalAI #RegulatoryTech #Gesund #AIinHealthcare

𝗧𝗵𝗲 𝗙𝗗𝗔 𝗶𝘀 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝘀𝗰𝗿𝘂𝘁𝗶𝗻𝘆 𝗮𝗿𝗼𝘂𝗻𝗱 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝘁𝗲𝘀𝘁 𝗳𝗼𝗿𝗺𝗮𝗹 𝘄𝗮𝗿𝗻𝗶𝗻𝗴 𝗹𝗲𝘁𝘁𝗲𝗿 — 𝗗𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝘀𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗱𝗮𝘁𝗮 𝗼𝗿 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘃𝗲𝗿𝗶𝗳𝘆 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗿𝗼𝘂𝗻𝗱 𝘆𝗼𝘂𝗿 𝗔𝗜/𝗠𝗟 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗱𝗲𝘃𝗶𝗰𝗲? At Innolitics, our team works closely with FDA reviewers, and guidance like the "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions" which provides recommendations for ensuring data integrity including: • ✍️ 𝗖𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Using digital signatures or message authentication codes (MACs) to verify data authenticity and integrity. • 📑 𝗖𝗵𝗲𝗰𝗸𝘀𝘂𝗺𝘀 𝗮𝗻𝗱 𝗵𝗮𝘀𝗵 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Employing algorithms to detect unintended data changes. • ✅ 𝗗𝗮𝘁𝗮 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Checking data for completeness, accuracy, and consistency with expected values. 𝖳𝗈 𝖺𝖽𝖽𝗋𝖾𝗌𝗌 𝗍𝗁𝗂𝗌 𝗍𝗒𝗉𝖾 𝗈𝖿 𝗈𝖻𝗃𝖾𝖼𝗍𝗂𝗈𝗇, 𝖼𝗈𝗇𝗌𝗂𝖽𝖾𝗋: • 𝗗𝗲𝘀𝗰𝗿𝗶𝗯𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀: Specify the methods used to protect data integrity during transmission and storage. • 𝗝𝘂𝘀𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗰𝗵𝗼𝗶𝗰𝗲𝘀: Explain why your chosen methods provide adequate protection for the data and the intended use of the device. • 𝗣𝗿𝗼𝘃𝗶𝗱𝗶𝗻𝗴 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Demonstrate that you've tested your integrity controls and that they're effective in detecting and preventing data corruption.Audit trails for every annotation and immutable Version Control AI developers now need more than great models — they need infrastructure that can defend their evidence from scrutiny: • 🔐 𝖠𝗎𝖽𝗂𝗍 𝗍𝗋𝖺𝗂𝗅𝗌 𝖿𝗈𝗋 𝖾𝗏𝖾𝗋𝗒 𝖺𝗇𝗇𝗈𝗍𝖺𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝗂𝗆𝗆𝗎𝗍𝖺𝖻𝗅𝖾 𝖵𝖾𝗋𝗌𝗂𝗈𝗇 𝖢𝗈𝗇𝗍𝗋𝗈𝗅 • 👜Proof of Data Sequestration • ✅FDA-aligned GMLP compliance by design Ad-hoc reader studies and opaque validation are no longer acceptable. Regulators are now expecting traceability, reliability, and full lifecycle control. In other words, regulatory-grade AI needs a regulatory-grade development team! How will you ensure that your internal processes and any third-party lab are GMLP compliant to defend your submission data? Visit our article on documenting AI/ML algorithms, or reach out to us here! #GMLP #FDA #DataIntegrity #AIValidation #MedicalAI #RegulatoryTech #AIinHealthcare