Modelling Biology: A Q&A with Cellarity’s CSO Laurens Kruidenier

Company to Watch – Cellarity

In the final article in the Company to Watch series on Cellarity, Big4Bio editor Marie Daghlian spoke with Chief Scientific Officer Laurens Kruidenier about what impressed him about the company, what he sees as the strength of the Cellarity platform and how it works, and why we should keep our eyes on the company. 

by Marie Daghlian

Marie: You just joined Cellarity. What’s your background, why did you decide to join Cellarity, and what impresses you about the company?

Laurens: First and foremost, I joined Cellarity for its disruptive platform, a platform that I believe has the potential to deliver groundbreaking new medicines for many patients, something that has always motivated me throughout my career.

Speaking of my career, I started out as a research biologist with a PhD in mucosal immunology. I realized early on that I wanted to spend my time and energy on problems that really move the dial, finding solutions that break new ground. That has remained important to me throughout the last 25 years, in academia and in larger and smaller biotech companies. I’ve always had a pioneering mindset and worked with that mindset across the full gamut of drug discovery and development. When I learned about Cellarity, I felt this to be the ultimate pioneering approach because the vision of the company is to fundamentally change, not one aspect of the drug discovery and development process, but basically the whole paradigm.

Cellarity CSO, Laurens Kruidenier

Changing the paradigm starts with a complete focus on the cell, something else that impressed me about Cellarity.  I think across the industry, we’re facing a major challenge with growing unmet clinical need in many complex diseases. At the same time, we don’t often know a lot about the underlying molecular pathology of these complex diseases. That’s a big challenge because what we’re trying to do in the industry is to address these diseases by making more and more selective drugs—small and large molecules designed to act on just one particular protein in your body—and we’ve seen successes there. It can work. But I think it’s now time for a paradigm shift that uses a target agnostic approach that will allow us to look for more and see more of what’s happening under the hood of that disease, and if we are able to address that, do more for more patients. For me, that was really something that hooked me when I looked at Cellarity.

It’s important to understand that now the time is ripe for this approach; I don’t think we could have done this maybe five or 10 years ago. We’ve seen major advances in high resolution data, single cell technologies, machine learning, and AI. At Cellarity, we’re using those technologies to embrace the complexity of disease and really see it not just as a biological problem, but also as a computational one. We can understand computationally the changes that occur at a cellular level and that make the cell transition from a healthy to a disease state. Then, we can model the actions of various pharmaceutical interventions on cells and design novel therapies that yield a desired set of changes, going beyond a single target and instead, broadly addressing cellular behavior that is driving disease. That turns discovering drugs into engineering drugs, which makes sense because most things we currently don’t discover, rather we engineer them and why would that be any different with drugs? I think that’s another thing that really impressed me about the approach. Further, because this approach is target agnostic, you would expect it to work in basically any disease.

Marie: There are other companies focused on single cell biology. What is unique about Cellarity versus the others? Or do you think they are all racing for the same solutions?

Laurens: There are a couple of aspects to this question. Some companies are service providers, and we are not. We are not using our platform to drive classic target or biomarker discovery. We’re not using our platform to identify basic, let’s say, combinations of existing drugs that might help a certain indication. We’re not using our technology for precision medicine basically to match existing drugs to the right patient subgroups. We’re not using our technology to improve the current drug discovery process, make it more efficient, for example, by improving target identification or drug screening. All of that is not what we do.

What we do entirely reconfigures the way that we do drug discovery. We solve for a functional readout combined with a cell behavior to change, and not a proxy like traditional drug discovery does by finding a particular target. What this approach allows us to do uniquely, versus the other companies, is to understand biology at a more complex level and uncover previously undiscovered mechanisms and complex behavior that you would not have discovered with a traditional target-centric approach. Basically, it’s about modeling biology. It’s about testing interventions and regarding medicines, then, essentially as reprogramming devices. To my knowledge, Cellarity is the only company that uses this kind of technology in combination with machine learning approaches to address a particular problem like this.

Marie: How do the various teams work together to ensure the data generated is of high quality?

Laurens: It’s a really good question because for this approach to work, we really need to have the various disciplines work very closely together. We have biologists, medicinal chemists, data scientists, and computational scientists. This platform can only work if we foster a deep multidisciplinary approach and that’s exactly what we do. It’s one of the reasons I joined this company because in large pharma companies, that level of collaboration is hard to achieve. This is ingrained in every program we do. Our program teams are multidisciplinary, and they work together to ensure the key questions are asked, not just from a biological perspective or from a computational perspective: are we collecting the right kind of data from the right kinds of samples? Is this data helpful? It sounds like an obvious thing, but sometimes people just collect data for data’s sake, but you want to collect data helpful in addressing the particular question you ask as a team. I see this as a dialogue between the disciplines, and we really foster a culture where we also learn from each other because we’ve all been trained in different areas and we speak different languages. But in time, you start to speak the same language. You start to understand each other better, and therefore you can formulate new questions and dive much deeper into scientific questions than if a single discipline were to just ask such questions in isolation.

Marie: Can you give an example of how the technology worked in a certain situation?

Cellarity Maps identify cell behaviors that can be targeted to resolve a disease

Laurens: There is a great example of this in our metabolic disease program. In this program, we started with fat cells, and we asked ourselves the question: “What if we could reprogram white fat cells, the fat cells that store fat, into beige fat cells, the fat cells that burn energy? Wouldn’t that be great because we might then have a significant impact on several metabolic diseases.” So, we built a Cellarity Map based on mouse fat tissue. We have since also looked at other fat cells and other species, but we started with the mouse. We created a Cellarity Map to model the transition between these white fat cells to the beige fat cells, and then deployed our platform to design compounds that drive this transition. And what we quickly discovered through our machine learning algorithms was multiple interventions that were predicted to drive this biology. At that point it’s only a prediction for intervention. We have the map, and we say, “Hey, here’s a bunch of molecules that can drive, really promote that transition from white fat cells to beige fat cells. That’s the effect we wanted to see.” What was really encouraging, of course, is then when we took these predicted interventions and we tested them, in vitro and in vivo, we actually found really impressive improvements in insulin sensitivity, glucose tolerance, and lipid profiles. This is, in my mind, quite remarkable because it showed us that our in silico predictions translated into experimental results.

Marie: What’s the future look like? What are near-future plans?

Laurens: I think the future looks very rosy. Of course, the reason I joined is to progress our pipeline, and a future priority will be to push our lead programs into the clinic. That’s number one. Number two, we want to do more with the platform in order to expand our pipeline. We want to prove that this platform works in many different kinds of cell types and disease indications. So, the plan will be to start new programs, investigate new diseases, and importantly, as a platform company, we’re not forgetting the platform itself, you know, the goose that that lays the golden egg. I see a lot of potential in how we can enhance this platform by adding new data types and new analysis capabilities. So, our goal really is to build Cellarity into a next-gen biotech that leads the way into a new age of discovery and development. It’s the reason I joined. And it comes back to the theme in your first question, the pioneering mentality here is definitely a key feature to which I’m attracted.

Marie: These are good reasons why we should keep our eyes on the company. I have a feeling you’re probably going to spend a bit of time talking to potential partners to broaden the platform’s reach.

Laurens: Of course. It will be a key part of our strategy.

Marie: Thank you for your time.

Laurens: Thank you.

This interview has been edited for clarity and readability.


This is part of the Big4Bio Company to Watch program for March 2022: Cellarity
For more information on the series, click here