Each day the quality control laboratory generates an enormous amount of electronic and paper raw data. It is a huge daily challenge to fully ensure the integrity of this data, but what is data integrity and just why it is so important in the pharmaceutical industry?

When visiting a pharmacy we want to be sure that medicinal products purchased for us or for our loved ones will be safe, effective and do help to overcome the disease. We don’t have possibility to check quality of these products by ourselves. Luckily pharmaceutical law strictly regulates this area in order to protect patients. All pharmaceutical manufacturers are bound by law to guarantee that the medicines on the market are of high quality and above all that they are adequately effective and safe for the patient’s use.

In Poland, the main pieces of legislation regulating drug manufacturing processes are the Pharmaceutical Law of 6 September 2001 and Minister of Health regulation of 9 November 2015 concerning requirements of Good Manufacturing Practice (GMP).  This legislation is consistent with EU-wide law and the guidance provided by regulatory bodies like the European Medicines Agency (EMEA) and the US Food and Drug Administration (FDA).

GMP (or cGMP where the c stands for “current”) is a quality system for the pharmaceutical industry which refers to each step of a medicine manufacturing process: starting from plant and laboratory design and raw material storage, on through the production process and analysis to packaging, and ending on release of the final product to the market. Every pharmaceutical company is periodically inspected by regulatory authorities to confirm the level of compliance with quality legislation. One of the inspection subjects is raw data recording and integrity ensuring through the entire data lifecycle.

Data integrity refers to completeness, consistency and accurateness of raw data in the data lifecycle (Figure 1). Basically it means that raw data has to be complete, consistent and accurate and that any unauthorized changes or deletions cannot be introduced (1). All information about raw data is captured within the metadata which is an integral part of raw data, providing details such as date, time or author of data. Release of medicinal products to the market is always based on raw data, therefore ensuring data integrity is crucial for manufacturers. Lack of data integrity may lead to false assessment of quality, effectiveness or safety of product, which can trigger an unnecessary risk for patients.

Figure 1: Data Lifecycle

Ensuring data integrity is difficult and often demands a lot of resources from the manufacturer. Data integrity refers to electronic and paper raw data and but it should be ensured not only on a technical but also on a human level. According to The Medicines & Healthcare products Regulatory Agency (MHRA) guidelines, the integral data must meet ALCOA+ requirements presented below (Figure 2) (2):

Figure 2: ALCOA+ requirements for data integrity

Raw data in the quality control laboratory can be generated by simple devices, sophisticated computerized systems or by laboratory staff as paper records.  Ensuring integrity of paper data starts from the proper design of the document. An appropriately designed document is identifiable, unique, has sufficient space for records and its storage and distribution are strictly controlled to prevent destruction, falsification or unauthorized changes.

In order to ensure data integrity of electronic raw data, computerized systems should have the following attributes: automatic registration of action time, possibility for keeping records in place of action, secured and limited access to changes, audit trail, automatic local data backup or connection with internal server. Additionally, electronic raw data should be periodically verified by the dedicated laboratory personnel to confirm integrity of generated data.

Lack of data integrity may arise from variety of reasons – it can be an intentional act of product falsification, but in most cases it results from inappropriate data oversight or an inappropriate level of control measures in comparison to data criticality. Examples of lack of data integrity can be as follows: reporting incorrect results, modification of time, date or time zone in computerized systems generated raw data, incorrect manual peak integration, etc. In case of lack of data integrity detection, the investigation procedure must be started immediately.

To avoid lack of data integrity in the quality control laboratory it is necessary to implement consistent procedures designed to assess criticality of the raw data and also to introduce proper measures to ensure data integrity. Measures should be based on:

• Quality system – lack of data integrity generally results from bad practices and incorrect work organization, giving opportunity for data manipulation. A quality management system should be continuously improved in terms of procedural, technical and behavioral scope. 
• Appropriate control tools – e.g. computerized systems validation, periodic audit trail review, data safety audits.
• Training system – raising awareness amongst employees.

Reliability and integrity of pharmaceutical data has a fundamental meaning for compliance with the regulations and for patient safety. It should be always taken into consideration during implementation of quality management system in the quality control laboratory.

• Author:
  
  Aleksandra Choryłek
  Quality Assurance Specialist
  
  To contact the author please email at selvita@selvita.com

• References:
  
  1.Włodzimierz Gogołek, Wiesław Cetera: Leksykon tematyczny. Zarządzanie, IT. Wydawnictwo Wydziału Dziennikarstwa i Nauk Politycznych UW, 2014, s. 103. ISBN 978-83-63183-585.
  
  2.https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/687246/MHRA_GxP_data_integrity_guide_March_edited_Final.pdf