Data integrity issues

[Pavan (Pavan Reddy)]

Plz give me some better examples for data integrity issues happens in QC and what the CAPA for that?

[sunilrbudhkar]

Ensuring data integrity is difficult and often demands a lot of resources from the manufacturer. Data integrity refers to electronic and paper raw data and but it should be ensured not only on a technical but also on a human level. According to The Medicines & Healthcare products Regulatory Agency (MHRA) guidelines, and other Regulatory authoroties like, USFDA, EMA the integral data must meet ALCOA+ requirements presented below,

Attributable -Record who performed an action and when.

Legible - Readable throughout the entire life cycle of the record.

Contemporaneous - Documented at the time of the activity.

Original - Retained in the format in which they were originally generated.|

Accurate - No errors or editing without documented amendments.|

Complete- The data should be complete.

Consistent- The data should be self-consistent.

Enduring- Durable; lasting throughout the data lifecycle.

Available - Readily available for review or inspection purposes.

Raw data in the quality control laboratory can be generated by simple devices, sophisticated computerized systems or by laboratory staff as paper records. Ensuring integrity of paper data starts from the proper design of the document. An appropriately designed document is identifiable, unique, has sufficient space for records and its storage and distribution are strictly controlled to prevent destruction, falsification or unauthorized changes.

Some examples of data integrity issues in QC laboratory are,

• Missing or incomplete records.
• Deficient system access controls (e.g., shared logins)
• Mishandled chromatography samples and data, including reprocessing, reintegration, and manual integration without proper controls.
• Deleted or destroyed original QC records.
• Audit trail deficiencies. (Chromatographic data)

In order to ensure data integrity of electronic raw data, computerized systems should have the following attributes:
automatic registration of action time,
possibility for keeping records in place of action,
secured and limited access to changes,
audit trail,
automatic local data backup or connection with internal server.
Additionally, electronic raw data should be periodically verified by the dedicated laboratory personnel to confirm integrity of generated data.

Lack of data integrity may arise from variety of reasons – it can be an intentional act of product falsification, but in most cases it results from inappropriate data oversight or an inappropriate level of control measures in comparison to data criticality. Examples of lack of data integrity can be as follows: reporting incorrect results, modification of time, date or time zone in computerized systems generated raw data, incorrect manual peak integration, etc. In case of lack of data integrity detection, the investigation procedure must be started immediately.

To avoid lack of data integrity in the quality control laboratory it is necessary to implement consistent procedures designed to assess criticality of the raw data and also to introduce proper measures to ensure data integrity. Measures should be based on: (CAPA)

• Quality system – lack of data integrity generally results from bad practices and incorrect work organization, giving opportunity for data manipulation. A quality management system should be continuously improved in terms of procedural, technical and behavioral scope.

• Appropriate control tools – e.g. computerized systems validation, periodic audit trail review, data safety audits.

• Training system – raising awareness amongst employees.

Reliability and integrity of pharmaceutical data has a fundamental meaning for compliance with the regulations and for patient safety. It should be always taken into consideration during implementation of quality management system in the quality control laboratory.