窪蹋勛圖厙

Author: Saskia Hoving

As technology continues to evolve rapidly, the concept of a 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.

Trend 1: Clinical lab automation

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. , a UK-based company that has been providing automation solutions for over 20 years, breaks laboratory robotics into six categories:

• Liquid handling robots, usually fitted with a pipette to allow, for example, transferring specific volumes of liquid.
• Plate handling robots which move microplates around laboratories.
• Microplate readers that automate the analysis of the contents of microplates.
• Automated chemical synthesis robots which can follow all steps in a predefined process including measuring, heating, mixing and purifying.
• Biobanking robots supporting large collections of biological materials and ensuring sample integrity.
• Robotic arms which can be described as the ���workhorse��� of laboratories, supporting lab staff in any number of areas.

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.

Trend 2: Improved track and trace functionality

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 , ensuring compliance with regulatory standards

For instance, devices such as those created by can scan and track hundreds of samples at once ensuring no mix-ups. Automated serialization and digital record-keeping systems such as ensure that products can be traced from creation to market.

Trend 3: AI-driven drug discovery and predictive medicine

AI is increasingly present across all parts of life, but a particularly valuable use is in the potential for fast, targeted drug discovery. The (BIA), which represents the UK life science and biotech industry, breaks down data-driven drug discovery into two life-cycle stages in their :

• Preclinical testing: Identifying drug targets, optimizing drug candidates, and matching existing drugs with new drug targets.
• Clinical development ��� phases 1-3 trials: Identifying the appropriate patient segments for clinical trials to increase the likelihood of success and reduce potential side effects.

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.���

Trend 4: Robotic prescription systems

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 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

Challenges and transition strategies for digital laboratories

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.���

Streamlining health data access and digital lab transformation

There have been improvements in access to data, for instance, the 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 in the US and the forthcoming .

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 .

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