Data Integrity in the Pharmaceutical Industry

Data integrity is an important current issue for regulators around the world. The data integrity-related cGMP violations have led to several regulatory actions, including warning letters and import alerts.

Data integrity is very important in pharmaceuticals to ensure that the final product meets all the requirements as per standard. Data integrity is defined as the maintenance and assurance of complete, consistent, and accurate data throughout the data life cycle

To make it more clear, lets first understand the common terms:

  • Data: The information derived or obtained from ‘Raw Data’.
  • Raw Data: Original records & documentation retained in the format in which they were originally generated (Paper/Electronic) or as a True Copy.
  • Meta Data: The contextual information required to understand the data.

For example: If Analyst “Mr. X” has reported a value of an analyte-A as 99.0 % and Analyte-B as 97.5 % from the HPLC chromatogram. Here, HPLC Chromatogram is Raw data, 99.0 % and 97.5%, is data and Analyst (Mr. X and Analyte A & B) is Metadata.

Regulatory Definitions of Data Integrity

MHRA: “The extent to which all data are complete, consistent, and accurate throughout the data lifecycle.”

USFDA: “Data integrity refers to the completeness, consistency, and accuracy of data.  Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded original, and accurate (ALCOA)”.

PICS: “Data Integrity is defined as the extent to which all data are complete, consistent, and accurate, throughout the data lifecycle”.

WHO: “Data integrity is the degree to which a collection of data is complete, consistent and accurate throughout the data lifecycle. The collected data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate”.

ALCOA Principle to maintain data integrity:

ALCOA Principle for Data Integrity

Attributable: All generated data must be traceable to the applicable instrument and the person who generated the data. The date and time of the collection or generation of data should also be recorded.

For example, A correction in the record should be initialed and dated to show when and who made the correction.

Legible: Data should be easy to understand, recorded permanently, and preserved in its original form. There should be no overwriting, All the corrections need to be clearly written with proper justification.

For example, when making corrections to a record, it should be struck using a single line, to ensure the data is legible.

Contemporaneous: Contemporaneous means data should be recorded at the time work is performed. Date and time entries should follow in chronological order. Data should never be backdated.

Original: Source data or Primary is a medium in which the data point is recorded for the first time. This could be an approved form or protocol or a dedicated notebook.

Accurate: To achieve accurate data, the data should be error-free, complete, truthful and it should reflect the observation made. If any correction is made to the data, it should record that who has made the corrected and when it is made.

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