The US Food and Drug Administration (FDA) issued the Warning on April 1 but published the letter yesterday, detailing significant violations of current good manufacturing practice (CGMP) at Sri Krishna’s Unit II finished formulation plant in Nacharam, Hyderabad.
The violations centred on problems with data observed during an inspection in December 2014, including laboratory records failing to contain all raw data generated during each test, and the firm’s quality unit using incomplete records to make batch release decisions in support of regulatory submissions to the Agency. For example:
“A QC analyst injected eleven identically or similarly named samples for impurity and assay analysis approximately one to fifteen seconds apart from one another… A second analyst injected eight similarly named impurity and assay samples approximately twelve to sixteen seconds apart,” the Agency wrote. “Neither analyst reported all results obtained during testing.”
“The laboratory incident reports concluded the first analyst deleted 28 original files due to pressure fluctuations and ghost peaks, while the second analyst deleted original trial injections of working standard and sample testing data due to a problem associated with peak shape.
“However, your laboratory incident reports provide no evidence to support these conclusions. Both analysts also changed the clock prior to reanalyzing the samples.”
Sri Krishna did not respond when contacted by in-Pharmatechnologist.com.
Integrity Issues
The Warning is the latest to be issued to an Indian drug or API maker based on data integrity problems. According to Regulatory Affairs Professionals Society (RAPS), 15 were slammed with Warning Letters between May 2013 and January 2015 due to problems with their data.
But the problem is not restricted to Indian drugmakers, as facilities in Italy and the Czech Republic (or ‘Czechia’) have also been hit with warnings over data.
The FDA is looking to address the problem, and last week issued draft guidance consisting of 18 questions and answers aimed at assisting drugmakers to ensure the quality of their data, including how and when to limit access to computer systems controlling data input.