Verifying Data Integrity | Why it is Important
Data Integrity and Its Importance:
· Data integrity refers to the proper maintenance, accuracy and validity of data which is critical in the pharmaceutical industry to assure the quality and efficacy standards.
· Documented data is the only record of any activity. If the integrity of data is not maintained during the process, there will be no record to show the quality of products.
· Pharmaceutical Data Integrity is of utmost importance as any violation related to data integrity can lead to FDA warnings resulting in loss of trust.
· Even if the FDA has not found any instances of actual data deletion or manipulation, not have adequate data integrity controls and oversight in place is considered to be in violation of GMP rules, ‘Guilty until Proven Innocent’.
· The integrity of data maintenance is also crucial as submitting a false report is a Criminal Violation under the FD & C Act of FDA.
Consequences of Poor Data Integrity:
Poor data integrity can lead to:
· warning letters
· import alerts
· notices of Concern
· consent decrees by FDA
· ban on review of new products
· frequent inspections
In extreme negligence cases it can lead to complete ban/imprisonment.
Causes of Poor Data Integrity:
· Lack of System Control
· Lack of awareness and training in handling data
· Human error
· Poor data Storage
· Loss of data or misrepresentation of data
FDA, WHA, MHRA, PICS have provided detailed guidelines for the Pharmaceutical industry in these articles:
- FDA September 1991: Application Integrity Policy – Fraud, Untrue, Statements of Material Facts, Bribery, and Illegal Gratuities
- MHRA Guidance March 2015: GMP Data Integrity Definitions and Guidance For Industry
- WHO Guidance September 2015: Good Data and Record Management Practices
- FDA Guidance for Industry April 2016: Data Integrity and Compliance With CGMP
- PICS August 2016: Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments
- EMA Questions & Answers August 2016)
MHRA: GMP Data Integrity Definitions and Guidance for Industry
· Data integrity is fundamental in the pharmaceutical quality system to ensure the quality of products. The data should be complete, consistent and accurate. Data integrity requirements apply both to manual and electronic data.
· Data integrity involves generating and documenting data properly and timely in order to make all the processes and activities traceable.
· If any alteration is required, it should be duly signed along with the copy of original document and the reason for alteration.
ALCOA Principle:
ALCO relates to both paper and electronic data. US-FDA guidance defines ALCO as:
· Attributable
· Legible
· Contemporaneous
· Original
· Accurate
Attributable: This includes two main aspects of data integrity i.e. who performed the action and when?
Legible: Is data legible/ readable. This is an important factor in manual data record and as manual data cannot be edited with ease.
Contemporaneous: Record of time of activity, with date and sign.
Original: Recording of data on main record or must be a true copy.
Accurate: Should reflect reality and should be error-free.
ALCOA + Principle:
ALCO + is a framework that ensures the data integrity in addition to ALCO principle with addition of few other attributes i.e.
· Complete: All recorded data requires an audit trail to ensure that nothing is missing or lost.
· Consistent: Data must have a date and time stamp.
· Enduring: Long term storage of recorded data.
· Available: Data must be accessible.
Data Integrity Issues:
1- Data Recording Issues:
· Loss of data
· Backdating/backlogging
· Data Fabrication
· Data manipulation
· Use of old data
· Data Traceability issues
· A mismatch between recorded data
· No audit trail
· Omitting negative data
· Disabling data trails
2- Operational Issues:
· Lack of Process Knowledge
· Lack of regulatory knowledge
· Use of un-Validated Software
· SOP deviations
· Inadequate reporting
· Inadequate access authorization
· Re-running sampling
· Unofficial Analysis conduction
· Unofficial batch sheets and reports
Some data Integrity issues are more related to ethical and honesty issues rather than data, operational and technical issues.
