Ensuring data integrity by protecting original data from accidental or intentional modification, falsification, or even deletion
is the key for reliable and trustworthy records that will withstand scrutiny during regulatory inspections. Many assessments
and action plans stop at the system security level discussed in the "Access Security" article in this series. However, merely
controlling and securing access to a data system does not address the real challenge for today's laboratories ensuring data
Data integrity means that data records are complete, intact, and maintained within their original context, including their
relationship to other data records. To use an analogy from the paper world, a contract is valid only if all the pages of the
document are complete, legible, and if it contains the required authentic signatures and properly states the terms and conditions.
In this sense, integrity denotes validity.
In the case of a chromatography data system (CDS), data integrity gives a high degree of certainty that a given record (such
as a calculated chromatographic result) has not been modified, manipulated, or otherwise corrupted after its initial generation.
In the context of CDSs, data integrity requires automatic change management of metadata (for example, storage of method setpoints),
control" for reanalyzed data. Data integrity also means that data cannot be entered out of context. Operational checks enforced
by a computerized system should check user permissions and enforce a certain sequence of permitted steps according to a defined
Another technical challenge for data management systems is to ensure referential integrity, that is, the integrity of the
record's relationships (dependencies). A database record is traceable, reliable, and trustworthy only if the complete set
of related (dependent) records is available.
Think of the following scenario: Sample XYZ needs to be analyzed using Method A, revision 4 (the current revision). Due to
a shortage of solvent during the analysis, chromatogram 1 of sample XYZ is invalid, and the sample must be re-injected. The
system stores revision 2 of the binary chromatogram without deleting or overwriting the original. (Check whether your data
system can really do this!) Chromatogram 2 is now processed, generating the result XYZ.2-A4-1. One of the points on the calibration
curve is subsequently marked invalid because the reviewing analyst found a previously undetected sample preparation error.
Chromatogram 2 must be reprocessed a second time, generating result revision XYZ.2-A4-2. The results are reviewed, approved,
and archived. Over the course of the following months, method A is updated to revision 5 due to a specification change. In
the course of an FDA audit, the results for sample XYZ are revisited.
A system with appropriate measures for maintaining referential integrity will retrieve the requested revisions of those results,
including the correct references to the revisions of the raw data (XYZ.2) and processing methods (A4). Many current systems
will allow retrieving the final result and the original raw data. But will they show the correct version (A4 instead of A5)
of the processing method used at the time? Will they really show the history of revisions? If your system does not do this,
you should develop appropriate procedures to capture at least a paper trail of the iterative changes.
Audit Trails and Change
After security, traceability is one of the first prerequisites for the trustworthiness of a record. The computerized audit
trail of a laboratory's data system holds the evidence of who did what to a record and when. According to McDowall, "audit
trail is a software utility that monitors changes to selected data sets within the main application."1
Section 11.10 (e) of 21 CFR Part 11 requires an audit trail for "actions that create, modify, or delete electronic records"
and that it be "secure, computer-generated, time-stamped."2 It is neither new nor surprising that previous entries in the audit trail must not be obscured, a practice well known to
the keepers of paper records in a cGMP environment.
During FDA inspections, auditors typically refer to laboratory logs for the sequence of analysis and manufacturing steps.
Similarly, audit trails help to manage, control, and also inspect the history of changes made to raw data and intermediate
results that are used to calculate final results. However, the audit trail is only a subset of the change management of electronic
records. Change management for electronic records requires both an audit trail (frequently called logbooks) and revision control
of records. Logbooks merely describe what happened and when, but keeping the record under revision control establishes the
exact details (for example, the chromatographic result before and after the change). When implemented properly, change management
therefore can be used to answer the following questions:
- Did any instrument or processing errors occur during the analysis of a certain sample that could have caused the result to
be invalid? A recent FDA warning letter specifically addressed the lack of required instrument maintenance records: "Failure
to maintain records of the inspections of automatic, mechanical or electronic equipment, including computers or related systems.
[211.68(a)]. For example, the firm failed to maintain any background data to verify that testing of laboratory HPLCs (b) had
been performed or produced acceptable results. Also, written and approved protocols for testing of these HPLCs were not maintained."3
- What particular changes were made to the integration parameters of a certain injection within the sequence?
- Why was the analysis result reintegrated and subsequently reprocessed?
- When was the analysis result reviewed and by whom?
- Why was a particular analysis result rejected and excluded from the result calculation? A recent FDA warning letter specifically
addressed missing records associated with out-of-specification investigations: "Out-of-specification (OOS) results were invalidated,
without a thorough investigation, supporting data, documentation, or justification. For example: Confirmed OOS results (...)
were invalidated by Quality Assurance, that concluded that the chromatographs were incorrectly integrated. The Chromatographs
were reprocessed with adjusted baseline parameters, yielding acceptable results, and the lots were released for distribution.
However, the laboratory investigation concluded that the results could not be invalidated and that no problems were observed
during the chromatographic run."4
- Were there any "on-the-fly" changes to the processing parameters (for example, integration) that were subsequently discarded?
A recent FDA warning letter specifically addressed the lack of traceability and doubt about the integrity of required laboratory
records: " Laboratory records do not include complete data derived from all tests including a record of all calculations performed
in connection with the test [21 CFR 211.194(a)(5)]."5
Obviously, the capability of attaching audit comments to an electronic record helps the originator as well as the reviewer
in documenting an action and justifying why it was done. Part 11 does not explicitly require entering a reason for a change,
but some predicate rules do (for example, Good Laboratory Practice regulations). Some modern data systems therefore offer
a function for fixed or user-definable audit comments. The data system can record, for example, that a
certain method parameter was changed from value X to value Y, and in the comment section the analyst may state that this was
because of a revised SOP.
Finally, in addition to operational controls that enforce the sequence of permitted steps systemically, audit trails also
play a role in preventing "pencil whipping," that is, "the entry of data before an action occurs or at the end of the day,
as an afterthought."6