On March 09, 2018, United Kingdom's Medicines & Healthcare products Regulatory Agency (MHRA) has released its guidance on 'GXP' Data Integrity20. Data integrity is fundamental in pharmaceutical quality system which ensures that medicines produced are of the required quality. Safeguarding data to ensure patient safety and the quality of medical products is at the forefront in this new guidance from the UK-MHRA. Data integrity issues have often been cited frequently in US FDA Form 483s and in warning letters for pharmaceutical and active ingredient manufacturers, as well as in statements of noncompliance with GMP from the MHRA.
Background
Over the years the way regulatory data is generated, has continued to evolve in line with the ongoing development of supporting technologies such as the increasing use of electronic data capture, automation of systems and use of remote technologies; and the increased complexity of supply chains and ways of working, for example, via third party service providers. Systems to support these ways of working can range from manual processes with paper records to the use of fully computerised systems. The main purpose of the regulatory requirements remains the same, i.e. having confidence in the quality and the integrity of the data generated (to ensure patient safety and quality of products) and being able to reconstruct activities.
The agency defines GXP as Good 'X' Practice where 'X' is used as a collective term for:
- GDP – Good Distribution Practice,
- GCP – Good Clinical Practice,
- GLP – Good Laboratory Practice
- GMP – Good Manufacturing Practice
- GPvP – Good Pharmacovigilance Practice
Guidance on Data
Data is facts, figures and statistics collected together for reference or analysis. It is also all original records and true copies of original records, including source data and metadata and all subsequent transformations and reports of these data, that are generated or recorded at the time of the GXP activity and allow full and complete reconstruction and evaluation of the GXP activity.
The Data should be:
A – Attributable to the person generating the data
L – Legible and permanent
C – Contemporaneous
O – Original record (or certified true copy)
A – Accurate
Data governance measures should also ensure that data is complete, consistent, enduring and available throughout the lifecycle, where;
Complete – the data must be whole; a complete set
Consistent - the data must be self-consistent
Enduring – durable; lasting throughout the data lifecycle
Available – readily available for review or inspection purposes
Establishing data criticality and inherent integrity risk
The MHRA guidance says that Data has varying importance to quality, safety and efficacy decisions. Data criticality may be determined by considering how the data is used to influence the decisions made. Therefore, the risks to data are determined by the potential to be deleted, amended or excluded without authorization and the opportunity for detection of those activities and events. The risks to data may be increased by complex, inconsistent processes with open-ended and subjective outcomes, compared to simple tasks that are undertaken consistently, are well defined and have a clear objective.
Data may be generated by:
- Recording on paper, a paper-based record of a manual observation or of an activity or
- Electronically, using equipment that range from simple machines through to complex highly configu- rable computerised systems or
- By using a hybrid system where both paper-based and electronic records constitute the original record or
- By other means such as photography, imagery, chromatography plates, etc.
Paper
Data generated manually on paper may require independent verification if deemed necessary from the data integrity risk assessment or by another requirement. Consideration should be given to risk-reducing supervisory measures.
Electronic
The inherent risks to data integrity relating to equipment and computerised systems may differ depending upon the degree to which the system generating or using the data can be configured, and the potential for manipulation of data during transfer between computerised systems during the data lifecycle. The use of available technology, suitably configured to reduce data integrity risk, should be considered.
Simple electronic systems with no configurable software and no electronic data retention (e.g. pH meters, balances and thermometers) may only require calibration, whereas complex systems require 'validation for intended purpose'. Validation effort increases with complexity and risk (determined by software functionality, configuration, the opportunity for user intervention and data lifecycle considerations). It is important not to overlook systems of apparent lower complexity.
Within these systems, it may be possible to manipulate data or repeat testing to achieve the desired outcome with limited opportunity for detection (e.g. stand-alone systems with a user-configurable output such as ECG machines, FTIR, UV spectrophotometers).
Hybrid
Where hybrid systems are used, it should be clearly documented what constitutes the whole data set and all records that are defined by the data set should be reviewed and retained. Hybrid systems should be designed to ensure they meet the desired objective.
Other
Where the data generated is captured by a photograph or imagery (or other media), the requirements for storage of that format throughout its lifecycle should follow the same paramaters as for the other formats, considering any additional controls required for that format. Where the original format cannot be retained due to degradation issues, alternative mechanisms for recording (e.g. photography or digitization) and subsequent storage may be considered, and the selection rationale documented (e.g. thin layer chromatography).
Data Integrity
Data integrity is the degree to which data is complete, consistent, accurate, trustworthy, reliable and that these characteristics of the data are maintained throughout the data life cycle. The data should be collected and maintained in a secure manner, so that it is attributable, legible, contemporaneously recorded, original (or a true copy) and accurate. Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices.
Data Integrity Paper Based vs. Electronic Data
The guidance notes that both paper-based and electronic data can be used but data generated manually on paper "may require independent verification if deemed necessary from the data integrity risk assessment or by another requirement," whereas the "inherent risks to data integrity relating to equipment and computerized systems may differ depending upon the degree to which the system generating or using the data can be configured, and the potential for manipulation of data during transfer between computerized systems during the data lifecycle."
The guidance also defines certain terms and its interpretations at length, including what 'raw' or source data means, what metadata is, what audit trails are, comparisons between 'original record' and 'true copy' and definitions of data governance, data lifecycle and data transfers or migrations.
In terms of validating computerized systems, the guidance notes that they "should be validated for their intended purpose which requires an understanding of the computerized system's function within a process. For this reason, the acceptance of vendor-supplied validation data in isolation of system configuration and users intended use is not acceptable. In isolation from the intended process or end-user IT infrastructure, vendor testing is likely to be limited to functional verification only and may not fulfil the requirements for performance qualification."
Data retention
The guidance notes that Data retention may be for archiving (protected data for long-term storage) or backup (data for the purposes of disaster recovery).
Data and document retention arrangements should ensure the protection of records from deliberate or inadvertent alteration or loss. Secure controls must be in place to ensure the data integrity of the record throughout the retention period and should be validated where appropriate. Data (or a true copy) generated in paper format may be retained by using a validated scanning process, provided there is a documented process in place to ensure that the outcome is a true copy.
Procedures for destruction of data should consider data criticality and, where applicable, legislative retention requirements.
The guidance also lays procedures for Archive, Back-up, File structure, Validation, Selection of IT Suppliers and Service Providers, Business Continuity etc.
Conclusion:
'GXP' refers to the various good practices regulated by the UK MHRA, including the Good Laboratory Practice Monitoring Authority (GLPMA), Good Clinical Practice, Good Distribution Practice, Good Laboratory Practice, Good Manufacturing Practice and Good Pharmacovigilance Practice. The MHRA's GXP data integrity guidance has a high degree of alignment with documents published by other global regulators. It is designed to facilitate compliance through education, whilst clarifying the MHRA's position on data integrity and the minimum expectation to achieve compliance.
Footnote
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
