GOOD PRACTICES FOR DATA MANAGEMENT AND INTEGRITY IN REGULATED GMP/GDP ENVIRONMENTS

GOOD PRACTICES FOR DATA MANAGEMENT AND INTEGRITY IN REGULATED GMP/GDP ENVIRONMENTS

This document was written with the aim of:

Providing guidance for inspectorates in the interpretation of GMP/GDP requirements in relation to data integrity and the conduct of inspections.

Providing consolidated, illustrative guidance on risk-based control strategies which enable the existing requirements for data integrity and reliability as described in PIC/S Guides for GMP2 and GDP3 to be implemented in the context of modern industry practices and globalised supply chains.

Facilitating the effective implementation of data integrity elements into the routine planning and conduct of GMP/GDP inspections; to provide a tool to harmonise GMP/GDP inspections and to ensure the quality of inspections with regards to data integrity expectations.

This guidance, together with inspectorate resources such as aide memoire (for future development) should enable the inspector to make an optimal use of the inspection time and an optimal evaluation of data integrity elements during an inspection.

Guidance herein should assist the inspectorate in planning a risk-based inspection relating to data integrity.

This guide is not intended to impose additional regulatory burden upon regulated entities, rather it is intended to provide guidance on the interpretation of existing PIC/S GMP/GDP requirements relating to current industry practice.

 The principles of data integrity apply equally to both manual and computerized systems and should not place any restraint upon the development or adoption of new concepts or technologies. In accordance with ICH Q10 principles, this guide should facilitate the adoption of innovative technologies through continual improvement. 3.6 This version of the guidance is intended to provide a basic overview of key principles regarding data management and integrity. The PIC/S Data Integrity Working Group will periodically update, amend and review this guidance in light of inspectorate feedback, experience in using the guide and any other developments

  

DATA GOVERNANCE SYSTEM

 What is data governance?

Data governance is the sum total of arrangements which provide assurance of data integrity. These arrangements ensure that data, irrespective of the process, format or technology in which it is generated, recorded, processed, retained, retrieved and used will ensure a complete, consistent and accurate record throughout the data lifecycle.

The data lifecycle refers to how data is generated, processed, reported, checked, used for decision-making, stored and finally discarded at the end of the retention period. Data relating to a product or process may cross various boundaries within the lifecycle. This may include data transfer between manual and IT systems, or between different organizational boundaries; both internal (e.g. between production, QC and QA) and external (e.g. between service providers or contract givers and acceptors).

Data governance systems

Data governance systems should be integral to the pharmaceutical quality system described in PIC/S GMP/GDP. It should address data ownership throughout the lifecycle, and consider the design, operation and monitoring of processes / systems in order to comply with the principles of data integrity, including control over intentional and unintentional changes to, and deletion of information.

The data governance system should ensure controls over data lifecycle which are commensurate with the principles of quality risk management. These controls may be:

· Organizational

o procedures, e.g. instructions for completion of records and retention of completed paper records;

o training of staff and documented authorization for data generation and approval;

o data governance system design, considering how data is generated recorded, processed retained and used, and risks or vulnerabilities are controlled effectively;

o routine data verification;

o periodic surveillance, e.g. self-inspection processes seek to verify the effectiveness of the data governance policy.

· Technical

o computerized system control,

o automation

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