Data Integrity and data quality: ALCOA+, SOP guidance, data management in the lab
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How to Achieve Data Integrity in the Laboratory

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A knowledge collection on the ALCOA+ principle, data management and practical tips to ensure data integrity in the laboratory

The Laboratory Data Integrity Guide illustrates in various examples of analytical workflows safe ways to achieve data integrity and discusses where data integrity might be at risk. Secure your measuring processes: learn more about data handling, SOP guidance and achieving data integrity.

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Laboratory Data Integrity Guide — Data Handling, SOP Guidance, Practical Examples

 

 

Lack of data integrity is the main reason for most FDA warning letters

In 2017, data integrity issues were cited in 65% of all FDA warning letters. The main reason was incomplete data, an aspect that can be prevented by using the right solutions. The highest risks, when not working in a compliant manner, lie in import bans, product re-calls or even the closing of production plants.

For laboratories that must comply with GLP, GMP and GAMP regulations it is important to have records or documented evidence of all relevant analyses that can be checked by a second person and are also readily available for audits. Storing the result is not enough, each result set has to be complete and contain all relevant metadata.

Data integrity in the context of 21 CFR part 11 and EU Annex 11 Compliance

The US FDA and European Commission have defined the criteria for ensuring trustworthy and reliable electronic records and electronic signatures in computerized systems of regulated pharmaceutical industries. Several pharmaceutical companies have recently asked for 21 CFR Part 11 certification for instruments not connected to a PC, just using the instruments' firmware. But according to experts, standalone instruments cannot be 21 CFR Part 11 compliant and concepts, which are presented as a feasible workaround, may result in data loss or worse.

Data management with common software

Common software can play a key role in a lean automated data integrity solution. Common software, such as METTLER TOLEDO’s LabX or STARe, has to be validated only once and every new analytical instrument attached will just require an amendment via change control as the software, data management, audit trail, user management, result flow etc. already are validated.

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