Scientific and technological advancements continue to drive scientific organizations towards a complete digital transformation. These developments emphasize the importance of automation, both in the physical and digital world. The automation of processes, workflows, and data enables laboratories to reduce downtime, increase scalability, and optimize efficiency. Automation provides the time and space for biotech R&D laboratories to focus on innovations and improvements, which is crucial to a laboratory’s success. Scientific automation – whether software or instrument, a single-step or an entire workflow – is critical to accelerating laboratory productivity and unlocking innovation. Let’s take a closer look at how laboratory automation through laboratory integration and software advancements, is propelling scientific laboratories in their journey of digital transformation.
Understanding Biotech R&D Challenges
As we dive into the possibilities of automation, it helps to identify the most common issues that users face in the biotech R&D world and how automation addresses these:
- Maximizing throughput
- Enhancing reproducible results
- Reducing errors – this goes hand-in-hand with reproducible results. Manual processes are notoriously error-prone, especially when operating in a multi-user environment. Each user may be incredibly accurate and precise. However, when you introduce two or more individuals performing the same actions, there is bound to be variation.
- Flexibility to scale
- Decreasing FTE hands-on time – this is quickly being recognized as the most valuable aspect of the automation process. We’ve found that there can be benefits to automating only specific parts of an entire workflow.
Benefits of Automation
Now that we have a good understanding of the challenges biotech R&D labs are facing, we can start thinking about the benefits of automating those labs. The graphic below showcases the results of a recent Select Science survey of users like yourselves – users who are looking for a way to transform and automate their labs. You can see the alignment with the pain points and challenges listed earlier.
Not surprisingly, “Reducing employee time on manual tasks” took top honors. There is a common misconception that there must be a certain throughput need to justify automation. What is being realized is that even in a low-throughput environment (perhaps only a few plates per day), there is still value in allowing scientists to focus on other more meaningful tasks – like analyzing the data from the previous run.
The throughput component is still there – as scientists seek faster cycle times so that the data is available more quickly to be analyzed. But important to note, faster cycle times are more easily achieved when scientists aren’t burdened with loading plates into a series of devices.
When considering throughput, the question people ask themselves is “does doing it fast also cause errors and issues?” When you take a holistic transformation journey and automate in the right way, the answer is “no.”
There was a time not long ago when automation – especially automated pipetting and software control – was viewed with skepticism with respect to accuracy and precision. Scientists didn’t believe that a device could reproduce the techniques they had developed for interacting with their samples. It’s now generally recognized that the automated liquid handlers and the techniques that can be developed for manipulating all kinds of samples – from organic solvents to genomic DNA to highly sensitive primary cell lines – are highly reliable and accurate. When done properly, automation and transformation enable users to focus on the tasks at hand, while affording even better data quality.
A bottom-up approach
When it comes to digital transformation, one of the biggest challenges we find is that most companies will focus on analytics without that initial focus on the infrastructure needed to get the data to the analytics solution. Taking a bottom-up approach helps build a more sustainable and scalable solution.
A customer recently shared their idea of transforming their lab. It was very heavy in using AI and machine learning, but lacked focus on infrastructure. How do these systems connect? How do you get this data into one place? How do you orchestrate these labs or address the data silos? Using a bottom-up approach, we built out a landscape document of their entire ecosystem and it’s connected. This gave us visibility to the data all the labs are collecting, so we could understand what we had and what was missing. From there, we could build the orchestration layer that was critical to their transformation.
This example highlights the type of partnership necessary to transform a laboratory, connecting the physical world to the digital world. The right partner can help set objectives and success factors at a strategic level, before embarking on a technical discussion. And when the technical discussions begin, the right partner can help map the entire lab landscape, leveraging the investments already made in the existing ecosystem.