In order to drive success, a business needs to focus on the things that will make the greatest difference to the customer. We have a technically rich platform that is scientifically differentiated and can offer unique insights into the protein interaction space, so what I want to do is build on connecting our technology with customers’ problems more effectively.

How can we produce, supply, and develop the technology, to give the most useful data to the end researcher?

If everything we do is centered around that question, we will be driving towards success.  

Our platform technology takes a new and formidable approach to a very classical problem – understanding how proteins interact with each other. The mission of the company is therefore very simple: to provide straightforward access to this protein interaction data to accelerate therapeutic, vaccine, and diagnostic advances.  

We’ve been able to do this by using a concept initially developed at the University of Cambridge – microfluidic diffusional sizing – and building an intuitive device and software to bring it into the hands of academic researchers, biotechnology and pharmaceutical companies. Crucially, the approach works in solution, avoiding the problems associated with immobilisation and surface-based technologies, and providing more impactful data on protein interactions in conditions more similar to those of the human body.

Our central product is the Fluidity One-M instrument, which uses microfluidic diffusional sizing to measure the size of binding molecules, allowing us to determine the characteristics of interactions between those molecules.  

In addition to instrumentation, we believe that being able to turn the data generated by the instrument into meaningful, interpretable, problem-solving information is fundamental to our customers’ success – and so we also have a product offering in data science tools.  

A good example of where these two elements combined are critical, is looking at something like PROTACs – where we have a ternary interaction. Rather than just providing a dataset on how the sets of molecules are interacting, we can provide a more complete picture, providing cooperativity and binding affinities around all the relevant interactions.  

Looking to the future, we have a rich pipeline of inventive ideas and techniques that we’ve licensed that allow us to continue the development of our instrumentation to the next level of sophistication. The products we have in the research stage, rather than look at specific species of interactions, look at subspecies and how they might interact and how those particular proteins might be characterized not by just a single particular form of the target but maybe in different forms of that target that can be measured in parallel.  

For our instrumentation, the fact that we work with microfluidics provides the ability to perform in-solution measurements. That means that the technology can examine the interactions of traditionally complex, difficult-to-measure molecules such as membrane-bound proteins or fibrils – like those associated with neurodegenerative disease.  

Being in solution, this also means the interactions are happening in an environment that is closer to the biological system in which they normally operate, giving results that are a closer representation of, for example, how a drug might act in the body.  

This combination of examining complex molecular targets, in complex background mediums, is really what sets Fluidic Analytics’ devices apart from the competition.  

The uniqueness of this technique, combined with sophisticated, clean data science tools, provides a simple way to access a whole new class of molecular information.

The field of proteomics has traditional been focused on identity proteomics – so whether a specific protein is present or not in a sample. The market has a lot of very good technologies out there for these kinds of experiments, at high sensitivity, or high multiplexing capacity, and so on.  

Beyond identity proteomics, though, is the understanding of how proteins are interacting with one another. For these purposes, the number of platforms out there significantly diminishes, and there are significant limitations to different techniques – for example the ability to work with complex molecular targets.  

The proteomics field is evolving, and the more scientifically interesting interactions come from the more complicated and sophisticated proteins, meaning there is a gap in the market for a simple technology that fulfils the needs of a changing scientific landscape. It’s this assessment of complex molecules, coupled with the fact that MDS can measure affinity, concentration, stoichiometry, all in one experiment, in backgrounds like serum and cell lysate, that gives it the edge over other techniques now and in the future.  

We are moving beyond identity proteomics, and beyond traditional, stable protein interactions into a much more advanced space, with MDS technology.

One of the pleasures of working for Fluidic Analytics is just the pure diversity of skills we have in the organization. It is an incredibly rich organization in terms of its capability and technical scope for resolving challenging and difficult problems.  

We have some super application scientists, some really highly specialist teams working in the Protein Interactions Lab, a set of design engineers and teams working on all areas of instrumentation development from electronics to mechanics to software. We have a very talented team that deeply understands microfluidics and the capabilities that it can offer.  

We also have a broad range of people that build, service and maintain the instruments as well as our commercial team that are out there talking to the customers in their language. So, one of the thrills about working for a company such as this is the depth of the skills our people have and the range of problems that they apply themselves to solving. That makes working in a challenging space such as this extremely satisfying, because of the people we have in the organization.

As I mentioned earlier, there are a lot of proteomic identity tools out there, so a key focus will always be to identify where opportunities exist and to find out from our customers the problems out there today that are not served by the existing technologies. To do this, we really need to engage with scientists directly, to understand how we can add value to their everyday work. Mapping solutions to the questions faced by our customers will help us prioritise what is going to make the most difference. 

I think it’s about building an organization that is flexible, that is lean, and that is responsive to the customer’s needs. Organizations that have an intrinsic ability to be perceptive, to respond, and to listen are often successful organizations, because we are paying attention to the stuff that is important. So, creating an organization that understands our customers that understands how our technology is impactful for our customer, allows us to focus on what matters most. 

That means that everyone has to feel comfortable to communicate, so I think the key lies in building an environment where knowledge is shared, and everyone has a seat at the table – particularly in smaller businesses where decisions affect all employees. 

My goal is to see this technology become a standard part of everyday lab tools and lab solutions. So that in every lab along with the standard lab equipment, we also see the Fluidity One-M – people talk about MDS as they talk about BLI and SPR. I think that would be a great result. 

But that’s a slightly longer-term goal – I think if we can start bit by bit, starting to get widespread adoption of the Fluidity One-M in the life science tools space in Europe as well as the US, we will have made a significant step in that direction to become a mainstream analytical tool. I would like to see us evolve partnership models with a strategic view in mind, and work with individuals and organisations that want to take our technology to different channels, but first and foremost I want to see a broad customer adoption of our technology, and that is obviously fundamentally purchasing of the platform and adoption of our data science tools.