LABORATORY INFORMATICS
to their interpretation of the regulations. Laboratories should never have to go out and buy a whole new system because new regulations have been introduced.’
Common sense regulation Much of what regulators are asking laboratories to provide with respect to their data is more or less common sense, comments Daniela Jansen, director product marketing at Dassault Systèmes’ brand Biovia. ‘Any laboratory or company should want to have good quality data, because decisions will be based on that data, whether we are talking about developing a new product, or releasing a product batch.’ The quality, completeness,
reproducibility and integrity of data are at the top of the list of regulatory compliance must-haves, although the topic of data integrity hit celebrity status when FDA released its latest guidelines in April 2016, in light of potential data integrity breaches identified during routine inspections. ‘But it’s not just relevant to regulated industries,’ Jansen notes. ‘Setting in place steps to ensure data integrity is something that all companies should strive for, whether they are in a regulated sector or not.’
One of the major differences between
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”We are now seeing this whole concept driving down into other industries and also into research & development workflows”
regulated and non-regulated industries is the level of documentation, she states. ‘From FDA’s perspective, if you haven’t documented it, you haven’t done it. This is adding a lot of additional work for organisations.’ Data review and approval is another ‘burden’ that is tied to data quality, and keenly reviewed by regulators, Jansen continues. ‘The timely and adequate appraisal of both the data and the process by which that data was derived is central to the issue of data quality.’ In today’s digitally driven labs there should perhaps be no excuse to claim ignorance, she believes. ‘Informatics systems such as ELNs can dramatically help to reduce the likelihood of intentional or accidental errors in data input and alteration and ensure data integrity and quality.’ Direct acquisition of measurement and analytical results from instrumentation
removes the need for manual data entry, while automatically flagging up changes to data, highlighting anomalous results and forcing step-by-step review and approval, ensure that reviews are becoming easier and procedures are followed’. This is data handling for good
practice, adds Stephen Hayward, product marketing manager at Dassault Systèmes’ brand Biovia. ‘Laboratories are increasingly looking for digital solutions that reduce the potential for manual error, acquire data in real time, and provide chain of custody. Ultimately they want to make sure that their data is complete, can be reproduced and, importantly, re- examined, in context, at a later date, both for regulatory review and also to aid future decision making.’ For an R&D-driven organisation, the
drive to fulfil data quality requirements shouldn’t just start at the point where the regulator may step in and want to look at data, Jansen believes. Rather, data quality, integrity and accessibility should become an integral consideration at any stage where those decisions may impact on development, manufacture or release. Ideally labs should have an infrastructure that can record data in combination with all contextual information, in formats that can be viewed and interrogated
June/July 2018 Scientific Computing World 17
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