LABORATORY INFORMATICS GUIDE 2022


very much in a ‘brownfield’ setting, rather than setting up ‘greenfield’ labs, Milne said. And that brownfield environment will almost undoubtedly be very fragmented in terms of its legacy systems. So, connectivity needs to happen at the enterprise level, not just at the lab level, and encompass that existing ecosystem of digital technologies, Milne said. ‘The overarching aim is to generate


an environment that allows you to unravel that spaghetti soup of existing platforms, and make sure there’s that coherent organisation and the ability to use it all,’ Milne said. ‘Ultimately, this will allow organisations to purchase tools based on the capabilities and features of those tools, rather than on whether they will talk to the lab’s existing equipment.’ And this means wherever R&D teams, service providers or partners are located and whatever technology they use, they should be able to collaborate and share data across a cloud-based environment. ‘As well as solving the integration challenge for individual scientists, we want to make sure it can also be achieved at scale,’ he continued. ‘We are trying to square the circle a little bit, by creating a platform environment’ – and this will be a cloud-based environment, Milne noted – ‘that will give people the freedom to make those decisions at the level of their workgroup, lab or building, but expand the value of that integration at scale.’ This also means the concept of


integration doesn’t stop at the level of lab instrumentation and software. ‘Lab function will increasingly be married to asset performance management, inventory or resource management, and quality management,’ Milne said. ‘Again, businesses will have many options to choose from, and then the issue becomes, for example, how do I integrate my resource management tool into my existing laboratory information management system (LIMS)?’


Leveraging advanced tools The concept of holistic lab orchestration hinges on addressing three basic problems, said Trish Meek, director of marketing at Thermo Fisher Scientific. ‘Firstly, getting all of the data together so that you can use it for analysis, visualisation, and increasingly, for leveraging AI and advanced machine learning tools. Then


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there’s the human experience in the lab. How do you optimise the scientific experience for scientists day-to- day? Third is the need to improve and facilitate process optimisation.’ Instrument and software vendors


are already making moves to facilitate easier integration, Milne acknowledged. ‘From a lab connectivity perspective, when we look at instrumentation and software used for everyday lab work, such as sequencers, qPCR or flow cytometry, the vendors of these types of instrumentation are already starting to think about connectivity when they develop their new instruments.’ Thermo Fisher Scientific, for example, is building connectivity into all of its new instrumentation, he continued. ‘But then there will also need to be some sort of “retrofittability”, and that will be part of our initial offering. This will be achieved through the creation of a gateway that will make it possible to connect instruments and software in the lab, into a cohesive environment.’ While there are possibly multiple


aspects to the issue of achieving seamless connectivity in the lab, the ultimate aim is to make laboratory systems more effective at what they do, every day, Dave Dorsett, principal software architect at information technology consultancy Astrix Technology, suggested. ‘That’s a foundational concept: how to improve usage of systems – such as a LIMS or ELN platform – from the perspective of everyday use, and how to get these systems to work together to support the labs on a day-to-day basis.’ Consider the software and hardware


tools that a lab ecosystem relies on, and much of the interruption in integration will commonly be due to the diverse nature of instrument architecture, Dorsett noted. An organisation may have LIMS systems from multiple vendors in use across different departments for example, he said, mirroring Milne’s sentiments. ‘Some of these systems, whether LIMS platforms or other hardware or software, are more challenging to integrate than others. And this makes it costly for individual companies to set up and maintain them from an integration perspective.’ What this means at the most


basic level, is that many labs may still rely on manual data transcription or ‘scientist-facilitated integration’, Dorsett continued. ‘“Sneakernet” [physically transferring data from one


The overarching aim is to generate an environment that allows


“


you to unravel that spaghetti soup of existing platforms, and make sure that there’s coherent organisation “


PC to another using portable drives and devices] remains just part of everyday lab life. And no matter how careful you are with manual transcription and data input, or how effective your data review processes, the ultimate quality of that data is always going to be at risk.’ There are two challenges, in fact,


Dorsett suggested. Sometimes the issues are not so much with getting systems to talk to each other, as they are with aligning and harmonising the data that comes out: getting data out of point systems and enabling the flow to the next stage represents another stumbling block to seamless lab integration, Dorsett suggested. If it’s hard to get data from a LIMS, ELN or other key piece of software back out as accessible and meaningful, then it may not be possible to use that tool or platform to maximum effectiveness and efficiency. Dorsett continued: ‘One approach to


addressing such issues is to bring data from multiple systems into data lakes, where it can feasibly be compared, but again, you have to ensure your data are equivalent, particularly where your labs may be running multiple LIMS or ELNs, for example. You may have one LIMS for stability testing, and another for batch release, plus method data in an ELN.’ A typical problem organisations


face is how to compare all of that data once you have technically integrated your systems. ‘For any laboratory organisation, one of the biggest challenges to using the systems they want to integrate, is how to ensure both data quality and data comparability/ equivalence across systems, even once they are interconnected. Are your


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