www.3-14.com People are still superior to machines when it comes to detecting data integrity issues, because the required level of analysis is too complex for a machines. That is why the review process remains part of the human domain.

1. Data Integrity – the review process
Different types of review process

2. People are still superior to machines when it comes to detecting data integrity issues, because the
required level of analysis is too complex for a machine. That is why the review process remains
part of the human domain.
• Review processes can be:
• Discrete or continuous
• One-off or repeated
• Scheduled or unscheduled
• They can be performed by
• Users
• Management
• Auditers
General background
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3. • Audit trail review
• Result review
• Review by exception
• Periodic review
• Data audit
Index
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4. • Audit trail report functions:
• Identify system security issues
• Errors in sequencing of activities
• Investigate errors and unexpected events
• Training issues
• Data integrity issues
• Can function both as a deterrent and a tool to aid possible investigation
• Audit trail review initially focuses on configuration:
• Configuration is correctly turned on
• Correctly configured as predetermined and documented
• Ability to change the configuration is subject to proper duty segregation
Audit trail review
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5. • Prior to the acceptance or rejection of product or data quality
• In-depth review of individual results
or set of results
• Comparison of all data against limits and specifications
• Evaluation of
• completeness and correctness of all metadata
• accuracy and integrity of manually entered values
• all decisions made and actions taken
• conformity to both sound scientific practice and documented procedures
• Investigation of
• Manual adjustments or alterations of (meta)data
• Changes made to the method versions in the creation of the result
Data review
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Audit
Trail
Review
Result
review
Data review
Result
review
Audit
Trail
Review

6. • Risk-based approach to data review
• Result review is only done on subsets of results, triggered by predetermined and documented
alerts
• Possible alerts:
• Within but close to the specification limit
• Manually integrated results
• Manually entered critical data that has been changed
• Reprocessed data
• Suspicious sample data / name
Review by exception
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7. • Performed at a defined interval, based on GxP criticality of the system
• Review of a longer period of time
• Includes:
• Audit trail review
• Review of SOPs
• System records
• SOP records
• Change control and system performance
• Back-up performance
• System access security
• Frequency may be increased where issues have been found in system operation or in previous
periodic reviews.
Periodic review
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8. • Data audit can be:
• Part of a scheduled review process
• Unscheduled as part of an investigation
• In preparation for a regulatory inspection or customer audit
• Possible reviews
• Inspecting a specific data-handling process
• Trace a single result back to the raw data (+ same exercise in opposite direction)
• Data audits based on FDA Compliance Program Guidance Manual
Data audit
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9. The Audit Trail Advantage by Carol Brandt
Pharmaceutical Engineering, March-April 2016, Charlie Wakeham and Thomas Haag
MHRA GMP Data Integrity Definitions and Guidance for Industry March 2015
EudraLex - Volume 4 Good manufacturing practice (GMP) Guidelines
WHO good practices for pharmaceutical quality control laboratories
Sources
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10. Our value proposition
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pi is the strategic partner of choice to some of the world’s leading life science companies.
We offer our clients unique expertise and strategic consultancy of the highest quality.
We dedicate ourselves to bringing excellence to the life sciences industry.
We’ve grown by learning how to be better.
Better resourced to focus our faculty of consultants’ singular experience and knowledge.
Better managed to share their depth of understanding.
Better prepared to challenge orthodox thinking.
And better able to redefine accepted best practice.
To accept no other standard than excellence.
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