In this exclusive interview, Markus Gershater, the Chief Scientific Officer and Co-founder of Synthace, shares insights on his career path, the pressing challenges in life sciences, and how Synthace aims to revolutionise R&D through automation and advanced methodologies. He discusses the potential of AI, Machine Learning and automation to transform the industry and highlights significant scientific advancements enabled by Synthace’s platform.
Can you tell us about your career journey and what led you to become the Chief Scientific Officer at Synthace? What initially sparked your interest in the life sciences sector?
To me, biology is the most complex, fascinating and powerful phenomenon known to humankind. It can solve the biggest problems facing us as a species, but for it to do so, we must learn how to work with biological systems as effectively as possible. This has been my drive throughout my career and was the main driving force when we started Synthace.
What are some of the most pressing challenges currently facing the life sciences industry?
The biggest challenge is the same one that the industry has always faced, and that is the complexity of biology. Except these days, we must reach further into that complexity as we look to solve increasingly challenging problems. This has resulted in an unrelenting increase in R&D costs and a corresponding decrease in return for all of that investment over the decades. We must find more effective ways of working so that the life sciences industry can continue to function effectively.
When we talk about biological complexity, we’re not just talking about the sheer number or diversity of the components that make up a cell or an organism. We’re also talking about how these components interact in extremely diverse and often unpredictable ways. But the problem is that most bioscience is being done with experiments that are too simplistic, experiments are blind to these fundamental interactions. Maybe with this context, it’s not so surprising that biological R&D is tough going!
How is Synthace set to transform the landscape of life science R&D, particularly in terms of automating high-throughput experiments and structuring complex datasets? What excites you most about these advancements?
The mission of Synthace is to enable more powerful experiments that can shine a light on the complexities of biology. This means using software and automation to lift the burden of running complex experiments. The Synthace platform does a huge number of the tedious and involved planning and calculations that are needed for an experiment, then it can automatically convert the detailed plans that it generates into the scripts that are needed by automation. Once the automated experiment is run, it can automatically gather and structure the data that is produced.
The effect of all this is that scientists no longer have to worry about a lot of the more demanding and tedious parts of running an experiment, so they can run and analyse way more powerful experiments than they could have done before.
Can you elaborate on how Synthace integrates methodologies like Design of Experiments and what advantages this brings to scientific research?
Design of Experiments is a bit of an odd term, in that it seems to refer to a very generic concept: after all, every scientist designs their experiments! But what it actually refers to is multidimensional experimentation. What this means in practice is that a great many factors that might affect a biological system can be tested at once, instead of doing one at a time. But even more critically, it also enables us to identify the way these factors interact with each other. Put simply, it allows us to understand the interactions that are at the heart of biology. One of the key capabilities of Synthace is that it enables scientists to run this kind of multidimensional experiment a lot easier.
Synthace aims to enable scientists to tackle humanity’s most pressing challenges. Could you share some examples of how the platform has contributed to significant scientific advancements?
We work with scientists across many different fields- drug discovery, agricultural sciences and food production to name a few and we’ve seen dramatic results across all of these. It never gets old to see the science that hugely innovative scientists have been able to do with our software! Examples include being able to produce dramatically more cells for cultured meat production, establishing complex cellular assays for drug discovery and doing so way quicker than would otherwise have been possible.
We’ve been lucky to have scientists from AZ, GSK, Uncommonbio and Virica BioTech publish and present at conferences about the dramatic impact of the automated, powerful experiments that Synthace enabled for them.
How do you see the broader life sciences industry evolving over the next decade, particularly with the integration of new technologies like Artificial Intelligence, Machine Learning and automation? What trends are you most excited about?
AI is already having a great impact on the life sciences. In drug discovery we’re seeing a huge number of new therapeutic targets being identified, each one of which could become a new therapeutic programme, and ultimately yield a new treatment. But we’re also seeing the impact that this glut of new targets is having on other parts of the drug discovery process. In short- the lab is the new bottleneck.
So, what I’m really excited about is the prospect of powerful software and automation to alleviate this bottleneck: ever more, more powerful experiments that not only provide rapid answers to today’s problems but also generate the high-quality, high-dimensional datasets that will form the foundation for even more powerful ML-generated insights for the future.
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