The Laboratory Data Integrity Guide illustrates safe ways to attain data integrity, with examples of analytical workflows and discussion of where data integrity might be at risk. Secure your measuring processes: learn more about data handling, SOP guidance and achieving data integrity.
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, which is preventable with the right solutions. The highest risks, when not working in a compliant manner, lie in import bans, product recalls 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 results is not enough: each dataset must be complete and contain all relevant metadata.
Laboratory 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 for regulated 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 presented as a feasible workaround may result in data loss or worse.
Data management with common software
Laboratory software can play a key role in a lean automated data integrity solution. Readily available software packages such as METTLER TOLEDO’s LabX or STARe need only be validated once, and every new analytical instrument attached will simply require an amendment via change control as the software, data management, audit trail, user management, result flow, etc. already are validated.