Achieving Data Integrity, Quality & Compliance in Manufacturing

by , , | Nov 1, 2023 | Life Sciences & Medical | 0 comments

Achieve Data Integrity, Quality & Compliance Across Your OrganizationIn highly regulated industries such as pharmaceuticals and biopharmaceuticals, the data accompanying production is critical to the successful release of products. In a recent webinar, Achieve Data Integrity, Quality & Compliance Across Your Organization, Emerson’s Michalle Adkins and Hilary Mills-Baker share how data integrity means complete, consistent, accurate data throughout its lifecycle. This webinar is now available on demand, and I’ll highlight a few key points from their presentation.

Michalle opens by noting that about half of the U.S. Food & Drug Administration (FDA) warning letters included aspects related to data integrity. Potential financial severe consequences of enforcement actions could result from data integrity-related issues found in an inspection, including fines, shutdowns, recalls, import/export bans, delayed drug approvals, remediation costs, damaged reputation, and more frequent inspections.

To improve overall data integrity, the FDA has encouraged manufacturers in the Life Sciences to use digital capabilities. Here is their guidance from the fall of 2022.

In recent years, advances in manufacturing technologies, including the adoption of automation, robotics, simulation, and other digital capabilities, have allowed manufacturers to reduce sources of error, optimize resources, and reduce patient risk. FDA recognizes the potential for these technologies to provide significant benefits for enhancing the quality, availability, and safety of medical devices, and has undertaken several efforts to help foster the adoption and use of such technologies.

Much of the data, but not all, is subject to GxP regulations. Data integrity GxP data addresses these questions:

  • Does data demonstrate regulatory compliance? (dispensing, batch/cleaning record, alarm/event)
  • Does the data directly support GxP Business Processes? (release, recall, batch reporting)
  • Is the data directly fulfilling a CGMP regulation, a ‘predicate rule’? (e.g. 21 CFR Part 211.188) A predicate rule is a requirement set forth in the Federal Food, Drug and Cosmetic Act, the Public Health Service Act, or any FDA regulation other than Part 11.
  • Could data be part of an investigation for a deviation or non-conformance? (alarm, batch history)

There are two ways to think about the characteristics required for data integrity: ALCOA and ALCOA+ cover these characteristics. ALCOA—Attributable, Legible, Contemporaneous, Original (or true copy), and Accurate. ALCOA+ adds Complete, Consistent, Enduring, and Available/Retrievable.

Data integrity is ensured via an appropriate approach to data. This includes government and management processes, procedural and technical controls, and consideration of human factors. Data integrity is not just about the enabling technology. The overall framework must include two other components to ensure data integrity. The overall governance and management processes must also consider the culture and people aspect. And, of course, standardized and proceduralized work processes need to be documented and followed to ensure data integrity.

With this groundwork laid, Hilary discussed how the DeltaV system can help manufacturers in the Life Sciences solve any data integrity issues. Some of the solutions to data integrity issues are to automate manual data collection, use DeltaV to collect data with inherent data integrity, use DeltaV electronic records and electronic signature capabilities for 21 CFR Part 11 or Annex 11 compliance, and use DeltaV to protect the underlying software running the validated process.

Watch the webinar recording and visit the Life Sciences & Medical section on to learn more about how to meet the challenge of ALCOA+ level data integrity, quality, and compliance to drive improved manufacturing performance.

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The opinions expressed here are the personal opinions of the authors. Content published here is not read or approved by Emerson before it is posted and does not necessarily represent the views and opinions of Emerson.

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