Data integrity challenges in the quality control (QC) labs

Data integrity is fundamental to comply with current good manufacturing practices and ensure patient safety. Unsurprisingly, quality control (QC) lab data is heavily scrutinized during audits as it directly supports a product's quality, safety, and efficacy.

Most QC labs have worked hard to establish the fundamentals of data integrity. Many have digitized to eliminate paper, some systems have compliance features built-in, and a series of mitigations and workarounds cover the rest.

At the same time, there has been an evolution of systems and business practices, rising expectations for efficient investigations, and the desire to gain insights through better data access. The next steps in data integrity are key to the QC lab of the future and the digital maturity of the manufacturing capability.

This paper identifies five key data integrity challenges in the QC labs and presents a problem statement, good practices, and desired future state for each:

1. Legacy systems

2. Integrated lab systems

3. Contract labs

4. Cloud systems

5. Next-generation sequencing in the GxP environment.

The paper should be considered when designing, implementing, and reviewing data integrity actions for new implementations and upgrades (particularly those involving legacy instruments, multiple organizations, or cloud solutions).

It establishes a baseline definition of key data integrity challenges and uses common problem definitions so that standard solutions can be mandated by biomanufacturers and implemented by vendors. This will allow investment to be focused and lead to greater industry robustness.

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