As technology continues to evolve rapidly, the concept of a digital lab has emerged as a way for life sciences R&D to drive efficiency. Digital transformation opportunities are across all stages from the digitization of information and AI drug discovery to robotics and automation, the internet of things, and much more. This blog shares the current trends in digital lab transition and provides insights for biotech labs to make use of these new technologies.
Laboratory robotics solutions are growing rapidly, with an almost infinite number of ways that they can be used to support lab teams. Robots are increasingly deployed to handle tasks that can be tedious, time-consuming, and susceptible to human error. Astech Projects, a UK-based company that has been providing automation solutions for over 20 years, breaks laboratory robotics into six categories:
These solutions free up lab teams to focus on more complex work while improving the speed and accuracy of routine procedures. However, while the advantages of robotic systems in laboratories are clear, they can be expensive to implement and maintain. Existing lab processes need to be adapted to fit automated workflows, which can take time and require training. Despite these challenges, the long-term benefits seem certain to outweigh the initial costs, but committing to the investment is a significant decision.
With the increasing need for biotech companies to comply with track and trace rules, robotics and automation can streamline the process by tracking samples and products, logging every movement or test in real-time, and reducing human error. Advanced robotics systems integrate with laboratory information management systems (LIMS) such as LabLynx, ensuring compliance with regulatory standards
For instance, devices such as those created by Flow Robotics can scan and track hundreds of samples at once ensuring no mix-ups. Automated serialization and digital record-keeping systems such as Tulip’s ensure that products can be traced from creation to market.
AI is increasingly present across all parts of life, but a particularly valuable use is in the potential for fast, targeted drug discovery. The BioIndustry Association (BIA), which represents the UK life science and biotech industry, breaks down data-driven drug discovery into two life-cycle stages in their TechBio report:
Dr. Emma Lawrence, Senior Policy and Public Affairs Manager, BIA, shares that applying machine learning and AI solutions to data mining, “enables predictive medicine where you can know that someone is more likely to get a disease and target medicine, so you can get the right drugs to the right patients, at the right time.”
It’s not just within the lab that robots are proving effective. In clinical settings, robots can accurately dispense prescriptions, executing doctor’s orders and minimizing the risk of medication errors. Robotic prescription systems such as Omnicell’s Prescribing Cabinet can track stock, monitor expiration dates, and deliver prescriptions, freeing up medical staff to focus on patient care.
These systems use barcode scanning and electronic health record (EHR) integration to ensure accuracy, but this must be partnered with careful set-up of the system and full staff training
While digital lab benefits are clear, the journey to full digital transformation in research can be long, costly, and bring new challenges of adapting workflows, providing staff training, and staying abreast of an ever-changing regulatory and technological environment.
There is a wide range of uptake for AI-driven drug discovery, for example. Of BIA members, Lawrence says, “It’s a real mix; the most mature and largest companies tend to be furthest along the process in terms of understanding and using technology to identify drug targets, but there will still be lots of targets for clinical development identified in the traditional way.”
The BIA supports biotech companies by representing their needs to policymakers. The potential for drug discovery and accurate targeting is huge, but Lawrence highlights that a challenge currently facing all labs in getting the full benefit of AI and machine learning is the lack of access to genomics or clinical health research data.
In some cases knowing what data exists depends on a researcher’s personal knowledge and network, and support from information professionals who understand how to access data could be valuable. “There has traditionally been a culture of protectiveness in the way health data is reported and there could be amazing datasets in academia that people don’t know about,” says Lawrence. “There's lots of hoops to jump through to get access to the data. It can be quite costly and difficult, particularly for smaller companies, to navigate all of the governance.”
There have been improvements in access to data, for instance, the Health Data Research UK Innovation Gateway offers a central access point for researchers to find and request health data, tools, and resources. There are other large cohort datasets globally, such as the NIH’s All of Us Research Hub in the US and the forthcoming European Health Data Space.
Navigating the availability of data and ensuring lab staff are trained in how to optimise digital workflows and tools is part of the balance in making the move to a digital lab and gaining the benefits. Staying well connected with your information professionals will be an essential part of ensuring ongoing success in the journey to digital transformation.
For further insights into the benefits and drivers of digital lab transition, read the report The Digital Transformation of R&D: Navigating the Digital Lab and Solutions for Efficiency.
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