The laboratory world is changing, with new technologies transforming our daily work, our communication, even our data management, coupled with the rapid change required and lessons learned during the COVID-19 pandemic. Developing the Lab of the Future will be the next step that many industrial and research laboratories will face — if they haven’t already.
Automating and digitalizing processes will make significant contributions to meet our future goals, not only in our laboratories. Driven by increasing complexity of many processes, sample volumes and the surge in legal regulations, efficient and safe work in the laboratory is becoming ever more important. It is, therefore, necessary to create an environment that is tailored to the users’ needs and at the same time to increase the quality of the results, combined with the possibility of collecting, organizing and automatically evaluating larger amounts of data. In the intelligent laboratory of the future, analyzers and measuring devices, sensors, processes and data are connected with each other. Automation and laboratory information management systems regulate and control this network.
Against the background of increasing digitization, various processes and structures must be rethought for the Lab of the Future. In this blog post I discuss some key elements that will shape future laboratories:
How could digitalization help you grow? Since the COVID-19 pandemic, many are starting to realize that digitalization defines the survival of their organization or business. Despite the general digitalization trend, the majority of the scientific community still holds onto their traditional ways, as for instance, the use of paper notebooks. The reason is often not money. It is because laboratories are complex structures with processes that are a subject of old guidelines or regulations, and therefore challenging to modernize. Or simply a “never change a running system” mindset. There are various obstacles to the switchover, which has made the uptake of digital platforms like laboratory information management systems (LIMS) and electronic laboratory notebooks (ELNs) slower than anticipated. In some respects, the community has been relatively slow in adopting new technologies that have long been commonplace in the business world. But we may see greater adoption, particularly in the application of virtual tools, in the years to come.
Why do we need a connected lab? The world is becoming a web of interconnected devices and people. Nowadays our homes are filled with smart technology, robotic devices, and voice-activated assistants. This process seems to be unstoppable and the Internet of Things (IoT) is growing in popularity and accessibility. The IoT is not a new concept, but now the size and cost of wireless devices has dropped dramatically, and Wi-Fi connectivity is built into many devices. The COVID-19 pandemic and local restrictions pushed us a step further toward digitalization and demonstrated the limitations or bottlenecks of our current laboratories. The situations, however, offered new opportunities and demonstrated how effective digitalization and connectivity can work for many industrial areas. Enhancing lab connectivity with virtually connected teams, group meetings, collaborations, devices and data management will improve the efficiency of R&D and free the scientists from doing unproductive work. We may even be looking at a future of robots or robotic co-workers automating experimental procedures and data analysis while scientists are freed up for big-picture thinking and problem-solving.
What will be the role of robotics and machine learning in the Laboratory of the future? Robots or robotic devices designed to work safely alongside humans offer new opportunities for automating laboratory processes and reducing human error in tasks. We see similar transformation in our daily lives, as for instance the robotic vacuum cleaners that help keep our households clean, while we are spending our time with something more meaningful. But it is the rapid advances in constantly improved programming, artificial intelligence, machine learning, and advanced robotics that are poised to make much more fundamental, even revolutionary changes to the scientific process.
Here is one simple example where automation is already improving the reliability of daily operations. It is often very difficult to reproduce and validate experiments accurately. Errors creep in as tired scientists have to enter high-throughput data manually, but automation could remove this hurdle. Better connectivity could also improve sample preparation to reduce waste and lead to fewer errors or out-of-specification results. Improved interoperability will also allow better compliance for regulation, better safety, and quality of the data. Conclusively, connectivity will create smoother workflows and improve the efficiency of the lab.
Will artificial intelligence (AI) revolutionize R&D? The chances are high it could. It is truly a big data revolution we are witnessing. But to make technology like AI work, we will need large amounts of structured data with robust sharing mechanisms and screening processes in place. In the last decades the generation of data in life sciences, as well as rapid advances in data processing technology, have led to growing interest in the promise of AI, especially in the pharmaceutical industry. Researchers hope AI could speed up drug discovery and help create better medicines. The move toward precision medicine will also accelerate the progress and adoption of AI as the industry becomes more focused on individual patient outcomes. And that is just one aspect of including AI in the development of a Lab of the Future. Artificial intelligence methods have already proven their ability to analyze and make sense of vast amounts of data. The question regarding mainstream use of AI and machine learning is therefore less “if” and “when” than exactly “how” the laboratory of the future will take advantage of the new opportunities.
How can we make our laboratories more sustainable? Pharmaceutical and research labs are resource-intensive and generate a significant amount of waste, including one-way consumables made of plastic, electricity, chemicals and biomedicals. While many people are recycling and reducing plastic waste at home or offices it can be problematic to translate this into resource-intensive R&D labs. On average, research labs use 10 times more energy, four times more water and generate 5.5 metric tons of plastic waste compared to an office. If labs could save just 2% of plastics from landfills it would be equivalent to saving 100 million metric tons of CO2. Incorporating sustainability into laboratory operations can help foster innovation. By increasing the efficiency of research operations, we will inevitably improve the impact on sustainability, and vice versa. Potential benefits to the lab include time and cost savings from reviewing processes to reduce waste may also lead to fewer operational steps and greater efficiency.
While the Lab of the Future will bring several benefits to researchers and companies, it is a massive undertaking and commitment. Despite the IT complexity of building an integrated ecosystem between analytical instruments, software, and data, the Lab of the Future is a business strategy, more than an IT strategy. Thermo Fisher Scientific commissioned Forrester Consulting to survey more than 200 executive strategy leaders to explore the short- and long-term impact 2020 had on lab transformation strategies. Learn more about what lab managers expect to change in the future and which steps are required to transform their laboratories. Download white paper.
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.