Dispatches from CPhI

Around 80% of data integrity issues QC related, says CPhI expert

By Dan Stanton

- Last updated on GMT

Around 80% of data integrity issues QC related, says CPhI expert

Related tags Data integrity Food and drug administration

Increased efforts by the US FDA have made data integrity the hot topic on GMP compliance, according to a regulatory expert.

“Data integrity is nothing new​,” Octavi Colomina, President of the Asociación Fórum Auditorias, told delegates of the CPhI Worldwide Pre-Connect Congress in Madrid this week. “It is a requirement as old as GMP itself.”

But the issue has become “the hot topic”​ for industry over the past few years, being brought to the forefront through a rise in US Food and Drug Administration (FDA) warning letters containing serious GMP violations referring to deficiencies in data integrity.

“From mid-2013 to mid-2014 seven Indian manufacturers received warning letters referencing data integrity [examples can be found here​ and here​], but it’s not only in India [see here​ and here​].”

Colomina attributed this statistic to stronger regulatory focus on data integrity. “Now the FDA is training people and employing experts in data integrity,”​ he said, adding when this was extended to facility audits such issues came to light.

Definition and Causes

Data integrity refers to the accuracy and consistency of data generated during GxP, and Colomina said inspectors require such data to be attributable to the person who generates it, legible and permanent, contemporary, original and accurate.

The FDA itself, he said, cites “invalid or not reliable data is a sign of poor control on the operations and equipment and therefore the company is unable to ensure the expected quality of process.”

While sources of data integrity violations include batch records, equipment cleaning records and training records, Colomina said 80% of issues are quality control (QC) related, with HPLC the number one cause.

Within chromatography, many companies have been pulled up on the deletion and overwriting of data, the altering of integration parameters, and continually testing until they arrive at the results they want.

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