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LABORATORY INFORMATICS


Research ‘misconduct’ will be big... very big


Mark Newton, a consultant from Heartland QA, gives his take on the scale of research misconduct taking place in laboratories


Research misconduct – and other integrity issues – are poised to become a major issue for research publications, eventually forcing major changes in the way laboratory data is summarised, reviewed, and retained. Greater transparency and data lifecycle management will not be a best practice, but a mandate for researchers who want to continue to publish original research. Allow me a personal moment. By


background, I am a scientist whose career has been largely spent in quality control laboratories and lab informatics teams, so I have experienced the lab from both scientific and data management views. For the past several years, I have been deeply involved in data integrity (misconduct) identification and remediation for pharmaceutical labs and manufacturing, so I have a view of this topic from a GMP (good manufacturing practice) perspective. As I read articles about misconduct in research, I see parallels to the numerous data integrity citations given to pharmaceutical and clinical research firms by regulators around the world. These parallels form the basis of my opinion here.


The issue is far larger than believed There are several reasons to believe this issue will be big: (1) the issue of misconduct was more prevalent in GMP labs and manufacturing than anyone would have believed; (2) there is already indirect evidence that misconduct is widespread in research labs; (3) systematic controls to prevent misconduct are less rigorous in research than in GMP-regulated QC labs; (4) research labs are less likely to be inspected than their GMP counterparts; and (5) research labs have similar motives to GMP-regulated QC labs and manufacturers.


Data integrity among GMP manufacturers (reason one) In the past three years, about 130 US


22 Scientific Computing World August/September 2018


FDA Warning Letters have been given to pharmaceutical manufacturers or clinical research organisations for data integrity- related infractions. These are serious infractions that cost companies millions (or even billions) of dollars, and typically two years or more to remediate. So, what caused the spike in these infractions? Regulators learned to do data forensic auditing. Once they understood how to look for data discrepancies, and the ways that data could be manipulated to create a desired outcome, they were able to increasingly find bad data practices that were missed in the past. They stopped looking at procedures in


a conference room and started looking at data in the company’s systems. And they found data manipulation: re-running samples to get a desired (passing) test result, keeping ‘official’ and ‘unofficial’ batch records for manufactured pharmaceutical products, deleting unfavourable data or storing it in other places to hide it from inspectors – these are but a few examples. The shift in focus to inspecting original data directly in the electronic system exposed an industry-wide issue that will require several more years of efforts to improve.


Indirect evidence in research (reason two) Gupta [1]


lists two relevant statistics in


discussing the matter: nearly 40 per cent of researchers were aware of misconduct and did not report it, and 17 per cent of surveyed authors of clinical drug trials reported that they personally knew of fabrication in research occurring over the previous 10 years.


Systematic controls (reason three) By the term ‘systematic controls’, we are describing processes or procedures that are routinely used to assure that reported data values are complete and accurate. GMP regulations require companies to use equipment that is calibrated periodically, use test reference standards, formally train personnel, retain all testing data (even if not used to report a result), have all testing data reviewed by another scientist prior


to using the data to make any product decisions. Computer systems must not allow people to share accounts, and must limit key activities (enhanced access, such as administrator) to a limited set of people who have no conflict of interest in the work they perform. Manufacturers typically use standard


reports to look for product issues or to potential data integrity issues. They are required to record and track any unplanned events, and determine the root cause of the event, so its recurrence is unlikely. In contrast, research labs will calibrate instruments, provide basic science training for personnel and will use reference standards and controls for tests. But they have no requirements for individual user accounts. Sometimes accounts are shared to save license fees, and many labs allow everyone in the lab to be a system administrator and make system changes as needed. Unplanned events have no requirement for investigation. While original data exists in computer


systems, that is usually not the data reviewed by another scientist; rather, the summarised test data is reviewed before determining the test result as acceptable for use. Data is retained for future use, but it is the summary data, rather than the complete set of original data values collected at the time of testing. Articles submitted for publication are peer reviewed, but the host lab provides the summary data to a peer reviewer. And there is no requirement to save all data – even the results that were not summarised for publications.


”In three years, about 130 US FDA Warning Letters have been given to pharmaceutical manufacturers or clinical research organisations for data integrity-related infractions”


@scwmagazine | www.scientific-computing.com


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