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19.Jul.2018

Data Integrity: To see or not to see? – that is the question

Thinking point 1: “Quality” is a living sense of awareness that should pervade the pharmaceutical industry, data integrity is but the product of this awareness.

Thinking point 2: Data integrity in the pharmaceutical industry: is Quality’s insistence on data integrity a hindrance to business; or sour tasting medicine, with long term health benefits for your organisation?

  • By Anju Nadarajah, Director, inglasia pharma solutions

Quality professionals often have the same problem as accountants – I should know, having had experience with both industries – we are often viewed as the stumbling blocks to lucrative commercial ventures. On the one hand the accountant is infamous for turning down yet another proposal due to “lack of funding”, the Responsible Person of a wholesale distributor may be insisting on holding out for confirmation of licences or insisting on performing a GDP audit – which may be considered (in some circumstances) a deal breaker. In the case of the pharmaceutical GDP/GMP Quality professional the key is confirming the integrity of the data with which we are presented. This is integral to patient safety.

Data integrity – what do we mean by this and how does it affect the safety of patients?

Various definitions are available throughout the different industries, with slight variations but a common running theme that covers in one way or another – data integrity strives for data that is complete, reliable, accurate and timely:

The MHRA’s recent guidance published in March 2018: GXP Data Integrity: Guidance and Definitions provides us with a useful starting point. This succinct twenty-one page document covers in six sections the principles of data integrity; application of these principles being mindful of the inherent risk associated with the particular stage of data lifecycle and nature of the data itself.

MHRA GxP data integrity guidance

Another definition that stood out was the following, particularly the words highlighted in red:

Data integrity is the overall completeness, accuracy and consistency of data. This can be indicated by the absence of alteration between two instances or between two updates of a data record, meaning data is intact and unchanged. Data integrity is usually imposed during the database design phase through the use of standard procedures and rules. Data integrity can be maintained through the use of various error-checking methods and validation procedures. –Techopedia: data integrity and databases

ALCOA – the data integrity acronym: as per MHRA guidance referred to above

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

Just another set of restrictive guidance? Or something that facilitates business long term?

“A company with a highly developed culture of quality spends, on average, $350 million less annually fixing mistakes than a company with a poorly developed one” – Harvard Business review, Creating a Culture of Quality, April 2014 issue

A few practical industry examples may help to answer these questions. Please note that all names and places and products in these examples are purely fictional but are situations that could plausibly occur in the “real world”.

Example: Pharman Ltd – a fictional manufacturing plant in China has just been informed that its customer MA holder Ltd, wishes to move manufacturing operations to another plant in Taiwan. Method transfer documents are sent through to the Taiwan plant. Batches of Fixme 100ml are manufactured at the new site however, at the QC Stability test stage there is a failure. Batches continue to be manufactured but cannot be released causing a backlog.

After two months of testing and re-testing and negotiating with the Chinese plant who confirmed that the manufacturing process had been written out accurately, there was a breakthrough. The older samples of Fixme that had been stored for 10 days or more passed the stability test. The Chinese site had always stored the samples for a week before testing. The one-week wait was a site-wide procedure but had not been written down for this particular product as part of its QC test method.

We can see in this example as to how there is a breach of data integrity: a critical component of the testing process is missing – data is not complete and does not accurately reflect the process.

Now, the financials associated with this mistake?

Let’s say for argument’s sake one vial of Fixme has a sale price of £8,000GBP. One batch produces 20,000 vials. It takes one week to produce one batch.

The cost to the company in lack of sales over the two-month period can be calculated as follows:

£8,000 x 20,000 vials x 8 weeks = £1.28 billion GBP in sales

Overheads remain the same, as the manufacturing plant will operate as usual, however there is a delay in cash inflow resulting in cashflow difficulties.

Pharman Ltd: What would be the ramifications if the QC failure result had been overruled by management for commercial reasons?

It so happened that the batches of Fixme 100ml were actually stable and suitable for human consumption, however a routine complaint case may arise. Upon investigation it is discovered that the initial QC test failure had not been looked into and the batch had been released. This would be flagged up as a breach of compliance resulting in a possible recall and regulatory sanctions.

Back to thinking points 1 and 2

Quality professionals do indeed have an uphill task on our hands – how to breathe life into Quality? How do we quantify our efforts to reflect monetary value added to the pharmaceutical industry? It has been suggested that “A Strong Quality Culture is best indicated by what it is done when Nobody is Looking”. Taking due care and diligence, the application of common sense and sense of responsibility are the essential ingredients that help sustain the integrity of data that is produced, reproduced, maintained and archived.

 IPS Data Integrity course: 15th October & 19th November 2018 – book now