Embedded in nearly every quality or regulatory project for Clarkston is a focus on foundational data integrity principles to not only prevent deficiencies but to encourage a culture of data integrity. With that in mind, this blog series will explore some of the basic definitions, benefits, and approaches to maintaining data integrity, irrespective of the size or type of drug or medical device manufacturer.

Of the 46 warning letters sent by the Food and Drug Administration (FDA) to drug manufacturers for the 2016 fiscal year, a staggering 79% were related to data integrity. Considering that data integrity issues accounted for just 26% of warning letters in the 2013 fiscal year, it’s clear where the FDA is focusing their time and resources.

Scanning recent headlines from this month alone even further illuminates this focus by the FDA:

• FDA Warns Megafine Over Data Integrity Violations at Second Facility
• Data integrity and aseptic issues land Wockhardt with FDA warning
• FDA Warns API Manufacturer about Quality and Data Integrity Issues

As a means for mitigating further data integrity issues, the agency published draft guidance on the matter in 2016. The draft guidance, Data Integrity and Compliance with cGMP, was developed expressly “to clarify the role of data integrity in current good manufacturing practice (cGMP) for drugs.”

The FDA’s question and answer guide focuses on data integrity lapses and definitions of key terms.  The agency’s final report will reflect the current thinking and recommendations of the FDA for data integrity and cGMP compliance. Until the final report is published, drug manufacturers are encouraged to manage data integrity based on their own understanding, needs, and objectives.

So….what is data integrity?

Data integrity is the assurance that data records are accurate, complete, consistent, intact and maintained within their original context, including their relationship to other data records. This definition applies to data recorded in electronic and paper formats or a hybrid of both.

A data record is like any legal contract. A contract is valid only if all the pages of the document are complete and legible, contain the required, authentic signatures and properly state the terms and conditions.

In this sense, integrity denotes validity. Ensuring data integrity means protecting original data from accidental or intentional modification, falsification or even deletion, which is the key to reliable and trustworthy records that will withstand scrutiny during regulatory inspections.  According to the FDA, data integrity is the requirement for complete, consistent, and accurate data throughout cGMP.

The FDA uses the acronym ALCOA (Attributable, Legible, Contemporaneous, Original, and Accurate) as a guideline for compliance. However, it’s not just the record itself which must meet these standards but also the metadata must meet standards. Metadata is any data that describes the attributes of other data, providing context and meaning.  Drug manufacturers must also have procedures related to document and systems backup.

Benefits of Trust with the FDA

Lapses in data integrity are likely to obscure other problems.  The current good manufacturing practice rules are the minimum requirements drug companies must meet during the manufacturing process.  Data integrity underpins CGMP and therefore is a leading indicator to the FDA of other issues.   There are significant opportunities in ensuring data integrity, particularly with quality data, to help companies remain compliant.

Drug companies with highly controlled manufacturing processes have the potential to be inspected less often when they demonstrate controlled processes with a standardized set of quality metrics. High functioning, quality-centric organizations can use the FDA guidance on quality metrics as an impetus for addressing challenges that plague even the most highly-evolved organizations to promote responsible practices.

Check back in (or subscribe to our insights below) for part 2 of this series, where we’ll explore some of the most common risks and impacts of poor data integrity.