Mark Fish, VP and GM of Digital Lab Solutions, Thermo Fisher discusses the importance of making automation more accessible for labs across industries with the next generation of innovation.
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What specific routine tasks are most targeted for automation in current lab environments?
It’s an unfortunate truth, confirmed through a lot of research, that scientists spend a lot of time on activities that are not helping drive their scientific or business outcomes. This includes repetitive manual laboratory processes like pipetting, weighing or preparing samples, transcription and checking of records from paper to computer systems, searching for and wrangling data for reports and presentations. Automation, both physical and digital, is perfect for accelerating or eliminating routine or repeatable tasks, freeing up scientists to focus on higher level activities, like doing experiments.
Beyond taking on mundane tasks, automation accelerates the production of high-quality data sets, providing quality data at volume that enables things like simulations, digital twins and in silico science, ultimately accelerating scientific discoveries. Labs across industries have expressed interest in democratising data due to its ability to push the bounds of scientific discoveries. Leveraging technologies like a laboratory information management system (LIMS) or an electronic laboratory notebook (ELN) can help improve data analytics with automated workflows to support research across industries. Even more important today is modelling the physical world digitally, enabling science to be performed by in silico, and the transition of work from “wet labs” to “dry labs.”
Expectations for human experience are also changing, and many organisations are working to ensure that it is simple for technicians of all skill levels to engage in the scientific process. It is no longer reasonable to expect that scientists have highly technical knowledge, and attend long training courses, to operate certain technologies. With automation, many complex technologies in the lab can be programmed and orchestrated, making complex equipment far more accessible.
Automated technologies also enable easier compliance with current and emerging regulations and standards by taking humans out of the loop and streamlining traceability and interconnectivity between labs. One nice example is the emerging food safety regulations stemming from new research on PFAS contaminants.
What are the biggest barriers to adoption for automation in pharmaceutical R&D labs today?
As automation is becoming more mainstream, laboratory teams are looking to integrate automation and collaborative approaches, both digital and physical, into routine laboratory workflow. This can help ensure compliance with standards and regulations and engage in more thorough analysis. As teams are looking to further integrate robotics and automation into the lab, they must still balance it alongside a human scientist, because scientific discovery cannot be fully automated.
Amid heightened automation, laboratory technicians and scientists may be hesitant to leverage the technology due to perceived complexity and lack of understanding of these new systems. While the benefits may speak for themselves, hesitancy to engage with new technologies and to learn how to operate them can hold laboratories back from realising the full impact of technology. This is why leading with human experience and being thoughtful about organisational change management and education is so important as organisations look to adopt new technologies. Somewhat ironically, taking a people first approach is often the most important decision in a successful technological transformation.
Lastly, rising concerns over data security and regulatory compliance can hold laboratories back from embracing automation. This is particularly acute in downstream areas closer to manufacturing processes, like quality control labs. As laboratories integrate automation, we often find that the ownership of the computing and operational technology can be felt between laboratory operations, quality assurance, and site, corporate information technology and security teams. Things like network design and information technology (IT) and operational technology (OT) security are very hot topics, especially given impact of recent high-profile and widespread security outages. Fortunately, with the right combination of governance approach and technology solutions, these security and regulatory concerns can be easily addressed, while also giving organisations the benefits that come with automation.
How are data integrity and reproducibility being managed in these automated systems?
As organisations deploy automation and digital solutions to orchestrate lab workflows, it is really important that the data flows and data products are considered. This requires that the right foundations are in place to manage the data from samples, experimental planning, resource management, work assignments, service and calibration records, reagents and consumables, instrument results, and things like telemetry from equipment, power consumption and even the environment in the lab. This wealth of information today in most labs is managed in silos, sometimes even on paper. It’s critical to realise that all these dimensions might impact the quality and reproducibility of an experiment, assay or sample result.
It’s important to consider the data foundation and how we might become more data driven in science. Transforming to a truly data led organisation means understanding how to manage the integrity and reproducibility of their data by leveraging technologies like a LIMS or an ELN. Considering the constellation of data around the experiment and connecting the ecosystem of data sources in a lab to the enterprise can help drive decisions. New, innovative technologies are enabling the generation of reproducible and accurate data while simultaneously minimising human error, orchestrating laboratory and scientific processes, and facilitating automating the process of information capture, storage and sharing.
The impact of pairing digital solutions with lab automation to ensure precision in science is constantly evolving, but recent studies have shown the reduced risk of human error. Research from BioData found that, when comparing duplicate versus single screen results of a preclinical immunogenicity assessment, the results were statistically equivalent, validating that the streamlined workflow did not compromise the quality of the data. The reduction in human error in the lab due to the increase in automation and digital technologies will further scientific research and discoveries, helping accelerate results and improve accuracy compared to manual workflows.
What training or upskilling will be needed for scientists to effectively leverage these new tools?
One of the main benefits of deploying digital technologies and automation in the lab is the reduced need for training and upskilling, meaning scientists will not need to have highly technical knowledge of how to operate these technologies. Scientists of all skill levels will be able to engage in the scientific process with these new tools, making the lab more accessible to everyone – from junior scientists to experienced technicians.
This upskilling of the scientific workforce does require thought and planning though, as it may require new ways of working. It is important to consider the mindset of the user and culture of the organisation. People respond to change far better when they understand the reason and the why for the change; teams can then be helped to understand how the work can be better and what they will need to do differently. People, processes and technology all need to be aligned to realise the ultimate benefits of digital and robotic automation.
How do you foresee automation impacting the roles of laboratory technicians and junior researchers?
Acceleration is a theme across industries and across different parts of the value chain. Most organizations are trying to work through the puzzle of how we might deliver products much faster that are more complex and with high quality within the constraints of our current headcount and resources. Automation can help science deliver results faster. Laboratory technicians and junior researchers will be able to jump in at any stage of the workflow without needing rigorous training, helping democratize scientific discovery in the lab. They will also be able to have a more hands-on role in the laboratory since automation will tackle administrative tasks and analysis, enabling technicians and researchers to engage in more complex aspects of experimentation. Digital technologies and automation will be integral players in the future of the lab as we know it today, and it will help scientists uncover new discoveries at a faster rate.