Why a Holistic Approach May Drastically Change the Way Experiments Are Conducted
Contributed Commentary by Markus Gershater, Chief Scientific Officer and Co-Founder, Synthace
December 16, 2022 | The rush to digitalization has been underway for some time, with most industries—including manufacturing, healthcare, employee wellness and commerce—making the switch (if only partially) several years ago. But there is one industry that remains stuck in the past: the life sciences. While there have been numerous advancements in the way that (for example) drugs are developed, lab work continues to be dominated by out-of-date processes. Chief among them are specific point solutions that do a very good job of fixing problems that came to the fore many years ago. Improving them isn’t going to move the dial. While they may help, they are not capable of producing the level of innovation that’s needed to take us to the next phase.
That’s why, whenever I’m at conferences, attendees instinctively sigh and roll their eyes (among other reactions) whenever anyone utters the words “lab digitalization.” They’re likely surprised that this is still being discussed, and they may well be wondering: why weren’t labs digitalized years ago? It’s not as if the rest of the world has waited. For industries that were on the fence, COVID-19 proved to be the sledgehammer-sized nudge that change needed to happen sooner rather than later. In order for lab work to enter the digital age—and for us to change how we think about experiments—we should look back on where we are today and the limitations that continue hold us back.
Without A Unifying Link, Tools Remain Disparate And Incongruent
Make no mistake: the lab is filled with existing digital tools, but they are skewed toward individual and discrete tasks, including record-keeping and standalone operational execution. Whether relying on design tools before entering the lab, automation tools in the lab, or electronic lab notebooks to later record what took place, there are many ways that labs have gone digital. Lab hardware, each brand of which has been developed with unique interfaces and modes of operation, add to the complexity of digital offerings. But there’s no throughline or connective thread linking all of them—nothing that brings each of these disparate tools together in a way that makes sense.
This fractured landscape has interfered with the convergence and reliable lab-based implementation of three critical ideas: Design of Experiments (DOE), Lab Automation 2.0 and AI/ML (artificial intelligence and machine learning).
Let’s look at DOE first. While it cannot transform the industry on its own, it allows biologists to reach far deeper into the complexities of biological interactions than any other methodology out there. Multifactorial experiments have proven to be vital in the study of biology because they help us understand the interactions that make up the fundamental features of all living systems, rather than fixating on one factor at a time (the “tried and trusted” method of experimentation).
Second, let’s consider the benefits of lab automation. High throughput automation has been possible for some time, but its advantages are not limitless. It is now time for the next generation—Lab Automation 2.0—which will provide thorough traceability, reproducibility, usability, and an overall higher level of scientific value. What’s more, Lab Automation 2.0 can endure complexity without code, and give much higher levels of walk-away time.
Last but not least is AI and ML, which have received significant attention in recent years, but their full potential has yet to be realized. Progress is certainly being made, but the work of biology is difficult to represent in code and the data/metadata that lab work produces is hard to digitize. While it would be wrong to refer to these technologies as little more than a pipe dream, they may feel more like fantasy than reality until a breakthrough occurs.
And yet there have been advances made by innovators like AstraZeneca, which uses an experiment platform to combine both DOE and Lab Automation 2.0. This empowered the company to run experiments that were previously impossible to achieve. The end result could usher in a new era in the way experiments are carried out for pharmaceutical development all over the world.
Drastically Change The Way Experiments Are Conducted
When you consider what’s successful in the lab and what is merely following the status quo, you may wonder: where is the missing link? How is one group more successful than the other? The answer is quite simple and may have more to do with the way we think than it at first appears.
Whenever DOE, Lab Automation 2.0 and AI/ML are brought up, people tend to dive right into a discussion involving the “lab of the future.” But instead of focusing on whether or not a particular piece of software will help, R&D leadership should think more about the “experiment of the future.” Instead of asking about how much a particular piece of equipment will enhance lab work, it’s time to discuss what needs to change to improve the quality of our experiments. Moreover, how do we increase the value of our scientific output—what, specifically, is missing from that picture and how do we fill in the blanks?
By zeroing in on the experiment rather than the lab, it’s easier to stop thinking about everything else—processes, equipment, data, methodologies, etc.—as separate problems that need to be solved in isolation. It instead empowers us to take a more holistic approach that considers everything as a whole, and that may drastically change the way experiments are conducted.
Markus is a co-founder of Synthace and one of the UK’s leading visionaries for how we, as a society, can do better biology. Originally establishing Synthace as a synthetic biology company, he was struck with the conviction that so much potential progress is held back by tedious, one-dimensional, error-prone, manual work. Instead, what if we could lift the whole experimental cycle into the cloud and make software and machines carry more of the load? He’s been answering this question ever since. He can be reached at firstname.lastname@example.org.