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US FDA has released new draft guidance on data integrity in cGMPmanufacturing due to recent concerns over data manipulation and other data integrity questions in India, China and elsewhere. The guidance has been drafted in order to help the pharmaceutical industry ensure data is consistent and accurate.
At least 15 Indian companies were issued warning letters since 2013 over data credibility issues. Also in China FDA inspectors found that sample raw data file names were altered in an attempt to hide deleted data files. Hence companies in China were often banned from shipping drugs to the US because of such cGMP concerns.
The guidance includes 18 questions and answers on data integrity, alongside well-defined terms on data as they relate to current good manufacturing practice (cGMP) records, as well as recommendations on when workflows on computer systems need to be validated, and how to ensure electronic master production and control records (MPCR) are monitored and can only be used by authorized personnel.
According to the guidance,
• FDA defines data integrity as “the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate.”
• Electronic data generated to fulfil cGMP requirements, which is one of the focal points of many of FDA’s warning letters, should include relevant metadata.
• FDA also makes clear that there must be a good reason why certain data exclusions are made and raises concerns about shared logins to computer systems, which have also been documented in warning letters. To exclude data from the release criteria decision-making process, there must be a valid, documented, scientific justification for its exclusion.
• The audit trails should capture changes to critical data and be reviewed with each record and before final approval of the record. Audit trails subject to regular review should include, but are not limited to: “the change history of finished product test results, changes to sample run sequences, changes to sample identification, and changes to critical process parameters.”