Motivated by an increase in the number of nonconformities in the Pharmaceutical Industry, regulatory agencies, FDA, MHRA, OMS and PIC/S have issued guidance in recent years on how to keep intact critical data related to the production of their products.

Most of these Data Integrity Guides are in the Draft version, to receive contributions and then move on to the final version. They don’t have new guidelines to follow, but they make it clear that regulatory bodies are paying more attention to the issue of data integrity and the industry should pay special attention to the issue and avoid non-conformities that lead to a series of sanctions.

Data that must have preserved integrity

All final data regarding the production process, Metadata, which carries information about the main data and Raw Data should be part of the integrity assurance process.

The first action that must be considered in the search for Data Integrity is to record the current situation of the company with an analysis of compliance with the regulatory standards in the management of critical data.

Subsequently, a joint work of the validation and quality assurance team in the preparation of a risk analysis is necessary to define which data are related to Good Practices and to the release of batches of medicines.

The specification of the data that must enter in this process is intended to prevent all company data, even those that aren’t subject to the regulatory standards, from being included in the Integrity Assurance process, because it would increase costs and bureaucracy of the production process and the release of the data in the Quality Control analysis results.

According to the GAMP 5 Guide and ANVISA’s Validation Guide, Raw Data is all the data on which quality decisions are based.

Some examples of Raw Data – Photographs accompanying product complaints, environmental monitoring and calibration records and equipment qualification, equipment cleaning and maintenance records, batch production records, manual transcripts of displays and equipment, HPLC chromatograms, inventory records, training records.

Master plans and procedures should consider this information so that the Quality System has clearly defined which data will be subject to the Data Integrity Best Practices.

Data Integrity Characteristics – FDA

The ALCOA concept of data integrity is based on the accurate, complete and consistent recording and management of data or information, either on paper or electronically. The term refers to the characteristics of Data Integrity – FDA that a data must contain, to be considered integral by the regulating agencies:

Attributable – The generated or collected data must be attributed to whoever performed the action and when the action was performed. This assignment can be done manually, through a signature and electronically dated by Audit Trail.

LegibleThe collected or generated data must be registered in a legible and permanent way.

ContemporaneousThe data must be registered at the moment that the action happens.

Original The data must be directly noted in the official Log Book of the company, avoiding posterior a1nnotations and the subsequent transcription of these data.

Accurate The generated data must be free of errors, complete and unfeigned, reflecting exactly the determined actions in the productive process.

ALCOA + PIC/S Concept

In addition to the ALCOA concept defined by the FDA, the PIC / S agency based in Switzerland uses the ALCOA + concept, which highlights other characteristics to a data considered intact, they are:

How the process can contribute to critical data integrity

Some actions may be taken to include in the production process the execution of procedures that contribute to the requirements of Data Integrity of regulatory agencies. They are measures to ensure that processes are defined so that critical data is generated and protected against changes.

How regulatory bodies and industries treat the issue

REFERENCES:

AUTHOR;  FIVE VALIDATION
EMA: Questions and answers: Good manufacturing practice – Data Integrity Section
FDA: FDA Data Integrity Guidance
MHRA: MHRA Data Integrity Definitions and Guidance
PIC/S: PIC/S Guidance
WHO: WHO Guidance