Exploring Key Challenges in Life Science R&D

Synthace recently released its Lab automation & experimentation in life science R&D 2023-2024 report, providing key insights into current challenges in scientific experimentation and digital transformation. Based on survey data collected from 250 scientists and decision-makers working in life science R&D, the report highlights issues ranging from integration to job replacement fears.   

 

To learn more about the report, its findings and their significance, Technology Networks spoke to Dr. Markus Gershater, co-founder and chief scientific officer at Synthace. In this interview, Gershater also explores some of the reasons behind the barriers identified in the report and shares his thoughts on what can be done to address them.

 

Anna MacDonald (AM): Can you give us an overview of the aims of the survey and the key themes from the findings?

Markus Gershater (MG): In running this survey we wanted to establish a baseline of understanding around the “state of the industry” when it comes to digitalization and automation in the lab. Our findings shed light on some fundamental challenges facing life science R&D experimentation today. Overall, we saw four key themes.

The first was data, and how it helps or hinders effective decision-making (e.g., 43% of decision-makers say the main difficulty with making clear go/no-go decisions was confidence in experiment data). The second was automation, and how it still represents a major hurdle for many teams (e.g., 50% of scientists report the main barrier to introducing new automation tools is implementation time and getting up to speed).

Third was experimentation and how it relies on and burdens the individual scientist (e.g., 82% of decision-makers also agree that more reproducible experiments would lead to savings in time, money and resources). The final theme was people, particularly how they are stretched too thin and under-supported (e.g., 52% of decision-makers believe that one of the main barriers to digital transformation in their labs is pushback from lab-based scientists).

AM: You mention that nearly half of decision-makers reported lacking confidence in their experiment data. Can you tell us about some of the reasons behind this lack of confidence and the wider impacts this has on R&D?

MG: It is common for biological experiments to give ambiguous or baffling results. This is for a number of reasons: experiments can be difficult to plan and execute, offering many opportunities for things to go wrong, but also experiments can often be under-powered when faced with the complexities and unpredictability of biological systems.

This can have far-reaching impacts: because biology is an inherently empirical science, experiments are at the heart of our biological understanding. Even when we take into account what we might learn with artificial intelligence/machine learning (AI/ML) tools, the emergent nature of biology still requires difficult work in the lab.

Given the complexity of the work that today’s biologists must now undertake, it’s perhaps no surprise that a reliance on individual scientists to hold everything together is a significant burden.

87% of scientists agree that one of the main challenges with complex experiments is reliance on the individual scientists holding things together. A majority of scientists (57%) also agreed that they have a hard time building on the experiments of other scientists. The main reason driving this was, again, data that was either incomplete or lacking full context.

AM: Integration was seen as a widespread barrier to digital transformation. Why do you think integration is such a problem and what can be done to address this?

MG: Biological research is extremely diverse and complex. This complexity is reflected in the hugely diverse ecosystem of hardware and software tools, which would ideally be integrated such that all data and metadata produced in scientific experiments would be automatically captured. However, the difficulty of this integration is very high, so this is one of the less surprising parts of the report for us.

Improving this situation will take a concerted effort on many fronts:

• All software and hardware companies need to be open and integrative. No one company can serve all needs in the biosciences, so solutions have to integrate. This openness has to be facilitated by interfaces that allow the transfer of data in and out of each solution. This open policy is one that is becoming more and more common but is not yet ubiquitous.
• Standards for data transfer and organization must continue to be worked on. There are initiatives such as Sila2 and Allotrope that have made some progress, and these should be continued, as well as other standards established where possible.
• As much of the work in the lab as possible must be digitized. We should ensure that the tools are in place that enable as much experimental detail as possible to be recorded, as automatically as possible.
  
  

AM: The survey also found that pushback from lab-based scientists is a significant issue. Can you tell us about the potential reasons for this pushback that the survey uncovered? How could these findings be applied to increase buy-in from lab-based scientists to digital transformation?

MG: Considering that 50% of scientists said the main barrier to new automation tools was time to get up to speed, and 87% of scientists agreed that the burden is on them to hold experiments together, it’s possible that the burden of change and transformation is, ultimately, falling to the individual scientist. If so, any “pushback” is perhaps understandable because it means more work on top of what is an already demanding and exhausting workload. The successful introduction of software and hardware that is proven to materially reduce headaches and workload for scientists will, in all likelihood, reduce the chances of this pushback.

AM: Almost half of the scientists completing the survey reported fear of job replacement as a barrier to digital transformation. Do you think these fears are justified? If not, can you highlight some of the benefits that digital transformation would offer to lab-based scientists?

MG: We don’t, no, because it’s not like there’ll be a lack of work to do even with the very best digital transformation. This is not a “zero-sum” game: as tools and new working practices enable us to make faster progress in the biosciences, evermore opportunities will open up. This will mean more jobs, not fewer. What’s more, the real challenge for biologists isn’t just the biology, but everything else that goes with it. The proof of this is in what we expect from scientists: experimental design, logistics, planning, management, dexterity, patience, steady hands, attention to detail, data handling, analysis and record keeping. All without error. Always consistent. All without guarantee of success.

The uncomfortable truth here is that this is too much. While there are a great many talented biologists at work producing incredible advances in our understanding, I can only wonder what we might achieve if it wasn’t so hard to do the work in the first place.

If we can lift the burden weighing on our scientists by even a fraction, the possibilities will be breathtaking.

AM: Were the trends identified in the survey consistent across company types, or were there any interesting differences between large and small companies?

MG: The difference in responses from companies of different sizes was quite interesting. Here are a few things we noticed from the survey results:

●  A majority of R&D decision-makers agreed that increasing reproducibility would be a good thing for time, money and resources. However, we saw a weaker agreement on this point from enterprise organizations, perhaps because they see a wider range of factors also moving the needle.

●  Larger companies, despite having shorter lead times for data production, often report more errors in their data, which could be attributed to the complexity and size of their datasets.

●  As companies grow, hardware and software integration issues seem to decrease.

●  The burden on individual scientists persists, regardless of organization size.

●  Resistance to digital transformation coming from the lab hovers around 50% regardless of company size.

AM: Did any of the survey findings surprise you?

MG: I think the most surprising of all is the lack of confidence in experiment data. There could be all kinds of reasons driving this, and I think we want to dive into this in more detail when we run the survey again.

This is the first time we’ve put together a report like this and it’s been enlightening in surprises like this but also in how unsurprising it is. The truth is that working in this industry is difficult. Building teams, tools and systems to improve how we work in the lab is never as straightforward as we sometimes hope it will be. Even with the best of talent and intentions, it’s complex, messy and fraught with risk.

AM: The survey was based on responses from individuals in the United States. Do you anticipate that the findings can be applied globally, or do you have reason to believe that there may be regional differences? Do you have plans to conduct a similar survey in the future with respondents in other geographical locations?

MG: We hope to run this survey in future years, hopefully expanding to UK/EU geographies. We don’t anticipate that there’ll be significant differences between US and UK/EU organizations, but that’s the point of running these surveys—we won’t know what we don’t know until we go and get the data.

About the interviewee:

Markus Gershater is a co-founder and chief scientific officer 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.

Markus holds a PhD in plant biochemistry from Durham University. He was previously a research associate in synthetic biology at University College London and a biotransformation scientist at Novacta Biosystems.

 

Markus Gershater was speaking to Anna MacDonald, Senior Science Editor for Technology Networks.