Synthace explains how to solve bioscience’s software problem

Guy Levy-Yurista, CEO of experimental platform developer Synthace, explains what is missing in increasingly software-dependent life science laboratories: a holistic way of thinking to improve experimental methods.

There is a major problem holding back progress in biotechnology and in the life sciences in general. It’s not obvious, but based on the conversations I have—with friends, colleagues, clients, and at every conference I attend—it’s there. These conversations vary in subject and tone, but all show the outlines of something else, yet hidden from view and somehow enticingly within reach.

It’s a software-as-a-service (SaaS) issue. Life Sciences and Life Sciences more broadly need SaaS to increase their potential. Specialized SaaS solutions are the norm in every other industry, and abstracting and delegating difficult tasks—tasks that computers can do better than humans—allows us to do a lot more.

And while the current SaaS landscape is filled with exemplary companies doing incredible things, it is deeply flawed. It’s a flaw that, if fixed, could lead to a revolution in biotech computing before the end of this decade and a new generation of SaaS companies that are enabling incredible new things for those companies that see a good opportunity , if you see one.

Today’s biotech SaaS landscape is powerful but highly fragmented

Until now, software in the lab environment has been geared towards individual and discrete tasks, particularly for things like keeping records or performing surgeries on their own.

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There are the design tools we use before we enter the lab, the automation tools we use once we’re in the lab, and even more—the eponymous electronic lab notebooks (ELNs)—that we use after the fact to manually record what happened. In addition, we have a whole world of laboratory hardware, each with its own interfaces and functionality.

All of these different systems and tools cover different parts of the experimental process: the loop we go through when designing experiments, running them, analyzing experimental data, and starting over again. But there’s a problem: there’s no consistent line, no common thread that ties all these disparate tools into a unified whole.

We do have an ecosystem, but it’s a fragmented one; If we step back and look at the big picture, we see islands of data, islands of skills, islands of understanding, islands of data and metadata—all of which are difficult to connect, integrate, and interact with.

This is the crux of the problem, and it is a two-pronged problem. The first reason is the difficulty of making these tools. But the second is the limitation imposed by how we think about the problem in the first place.

Our current model limits the progress we can make

How we think about this fragmented ecosystem problem—the “mental model” we adopt—determines the answer we find. But not all answers are created equal. And getting it wrong has a massive impact on our long-term prospects. Let’s take ELNs as an example.

For now, ELNs are seen as something worth preserving, maintaining, and improving. That makes sense: Better ELNs mean more chances of understanding and recording what’s going on, more than the current alternatives (the “we didn’t record it” or “it’s in this Word document” sort of thing).

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But does improving an ELN, a “point solution” that requires manual intervention and is potentially subject to human error, directly correlate to better science? With greater scientific value? The answer is, “Maybe. Unfortunately, it’s not as direct a correlation as many of us would hope.

We can draw the same example and find the same big question with any other discrete element of laboratory software, whether it is laboratory information management systems or automation tools. This forces us to ask an even bigger question: if there is no direct correlation between improving a single thing, what are we improving to improve our science, and how do we do it?

We need a new model to move forward

Instead of the eponymous “future laboratory” we should frame our thinking around the “future experiment”. This subtle but profound shift challenges us to examine the many assumptions about how we should work in the first place and think about the broader system.

When we think of experimental records, we need an ELN. When we think in terms of sample management, we need a laboratory information management system. But if we think about it the experiment itselfLet’s stop looking at processes, equipment, data and methods as separate problems to be solved in isolation.

One such team committed to this mindset is AstraZeneca’s Discovery Biology department. A team in the department found a way to run an assay using 50% less reagent with the same assay quality. Another examined the entire design range of an assay to definitively confirm that buffer choice had no effect. A negative result, but a clear one that allowed this project to move forward quickly.

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They did all of this with an emerging methodology that combines experiment design with automated delivery of reagents in 1536-well plates, what we refer to as high-dimensional experiments (HDE).

When we think about improving the experiment as a whole, rather than fixating on individual point solutions, we get a clearer idea of ​​how and why we should connect and integrate the many different elements that contribute to progress. We also understand what tools, platforms or features may be missing from the broader landscape.

If we can continue to build on that understanding and act on it, the next few years will allow us to explore even more unimagined possibilities.

Guy Levy-Yurista has been Synthace’s CEO since May 2021. He has more than 20 years of experience in strategy, marketing and product leadership roles in startups and Fortune 500 companies.

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