Name: Kärt Tomberg, ExpressionEdits
Uploaded: 2026-07-20T17:00:00.000Z
Duration: 14 min 47 s

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Kärt Tomberg

CEO & Co-Founder

ExpressionEdits

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Kärt Tomberg, ExpressionEdits

Kärt Tomberg

CEO & Co-Founder ExpressionEdits

(INTRO)

Gemma Allen

>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired and today for a very special program we are covering AI in biotech. Joining me now from Cambridge in the United Kingdom, is Kärt Tomberg, CEO and co -founder of ExpressionEdits. Welcome.

Kärt Tomberg

>> Yeah, super excited to be here.

Gemma Allen

>> So your story is very interesting because you, Kärt, were working on the COVID conundrum, if we could call it that, back in 2020 and really came up with this, or I guess I could maybe say accidentally or incidentally discovered this opportunity for this company. maybe start there. Talk to me a little bit about you and the origin story for this.

Kärt Tomberg

>> Yeah, for sure. So my background is in genetics. I spent 19 years across different countries and institutions looking at genetics. And then just like you say, during COVID, we were all told in the Institute, either you go home or you start working on SARS-CoV-2. And we didn't want to be at home. So we started looking at how can we help with this pandemic. And as a geneticist, my focus was looking at the vaccines that we were creating and looking at the sequences of how we make this S protein that became the basis of the vaccines. And I looked at it and I was quite interestingly surprised by like, oh, but this does not look like a human gene. And I started to pull that thread and be like, why is that? Why is this? Is it just this vaccine? Is it the whole industry? And what I realized is that 50 years ago, our industry was started with this company called Genentech, which was also traded on this floor under ticker DNA for a while, right? And they really were looking at how do we speak to cells? How do we make a cell make a protein? What's the DNA elements required? That was the start of our industry. However, 50 years later, these instructions looked exactly the same.

Gemma Allen

>> That's crazy to me. Truly, as somebody on the outside, we hear so much about the progression in science and in technology, and you just assume that every layer of that discovery is moving up. Right. So why was that? Why? Why did we not find a new way to talk to cells or a more effective way, I guess, perhaps?

Kärt Tomberg

>> I think that it's been the question I've been asking a lot and trying to really look back at why industry makes certain decisions. I think there's definitely the technology revolution that's enabled us really to be able to look at it. This is also a really complex problem to solve. And now with the AI era, we can actually do it. So part of it was it wasn't so easy to revisit that question. But partly, I think it's about risk. You don't want to stack risk when you're dealing with drug development. And so if the technology works, you kind of don't want to touch something that works. And you want to work on the bits that are the uncertainties. And so I think that there is an element of that in the story as well.

Gemma Allen

>> Wow.Talk about what is so powerful and impactful about finding new ways to talk to cells. In our day to day lives, what could that change for us?

Kärt Tomberg

>> Yeah. So I think that the one most important thing for the non -bio folks is to understand that unlike DNA and RNA, we cannot make proteins. So what we have to do instead is we have to convince a living cell to make the protein for us by giving it DNA instructions. That's what Genentech hacked, right?

Gemma Allen

>> Right.

Kärt Tomberg

>> However, at the time when Genentech was doing their work, they only could draw inspiration from bacteria and viruses. So unsurprisingly, our instructions look like viruses and not like human genes. And 50 years later, we have a much, much better understanding of how genes look in nature. And so the cells in our body, viruses are their mortal enemy. Right. They've been seeing viruses for a long, long time. And so they know these instructions look like viruses. and they're doing their best to turn it off, to silence it, to deprioritize it, and to empower their own programs for making proteins. And so the idea is that if you make the instructions look more like native genes, the cells will recognize them as self and start making a lot more of it. The opportunity here is really across the entire industry because we need proteins everywhere. I think that the biggest opportunity really is in therapeutics where the cells are actually in our human body. So the in vivo therapies, the gene therapies, the vaccines, the DNA medicines, CAR-T and cell therapies. There, the factories are really our human cells. And it's really, really tricky to get these instructions into the cells. And so every single instruction needs to really be best optimized to get the output for the patients. So really unlocking the potential of making more proteins unlocks new drugs in that modality. But it goes way beyond that and way beyond also applications in medicine to all the wider biotech as well.

Gemma Allen

>> That is truly fascinating. So 2020, you look at this does look like a huge one. So you're like, okay, there's an opportunity here. There is a commercial opportunity too. Talk me through what happened then. You decided, okay, I'm going to split this off. I'm going to set up my own endeavor. I'm going to fundraise. 2020 was an interesting time in the world of medicine and science and the world, right? Talk me through how you thought about the commercial viability for this, especially when, like you said, sometimes people have the attitude, if it's not broke, don't fix it, right? And I'm sure that applies to many industries.

Kärt Tomberg

>> Yeah, so 2020 was when I discovered the problem. It actually took a long, long time to figure out if we can actually do the engineering bit. It is not trivial here. And I'm really downplaying our team's efforts for years in creating billions of data points to actually solve this problem. But let's assume that that was doable and we could do it. Then, yeah, you're right. The next obvious question was, where is the pain? What is the real beachhead here that we should go after? And we were really, really lucky that already at a very early stage when we were publishing our preprint on this data and we were still in academia, we started to get inbounds from industry. So we really started to get really strong signals of everyone saying, oh, but we cannot make this. And oh, we need to shelve these assets because we cannot make enough to purify. Or in gene therapy, we have this situation where this is the therapy of the future, right? But we have patients having major immune responses and patient deaths because we're giving them these high doses of virus, but they can't lower the dose because then they can't cure the disease. And so all of these stories were coming to us and that really enabled us to think about where is the value proposition? How do you spin this out as a company? While also, obviously, we spent a lot of time thinking, how do we actually technically deliver on it, which is non-trivial and where AI comes into play heavily.

Gemma Allen

>> So that marriage between academia and industry that I think we've always seen in the world of science and even in technology, but in technology, things have moved at a very different speed right and the expectations in that industry tend to be the speed of sound quite frankly science and especially medicine has been quite intentionally slow for good reason right but AI is also shifting commercial expectations and I'm sure investment expectations what has that been like for you especially as you raise money as you go to build out your partner strategy. How is that shifting broadly in terms of what you're expected to deliver tomorrow?

Kärt Tomberg

>> Yeah, I think that it's really, really interesting. We are talking to two historic capital pools, which is like software and drugs. And all of these AI bio companies, most of them actually sit in between the two. And so there is no very good precedent to build upon. And so you have to kind of bring actually those investors around the table together and be like, guys, we think that it's in the middle that is the biggest value opportunity. And how do you then kind of have that dialogue with these two very, very distinct pools of investors to understand that in the short term, we can do partnering, we can leverage the data, we can bring in non-dilutive capital from large pharma. But at the same time, we can start to build our own drugs so that in the long term, we also get the value from the drug side. And so I think that it's a balance that is constantly changing. And a lot of the people downstairs today are talking about, how do you bring in these people with these different ways of making money and investment around the table and build a new way to make sure that it happens?

Gemma Allen

>> So what stage is ExpressionEdits at now? you've raised 13 million. Where are you at from the perspective of your own commercial strategy? Have you started partnering with any large pharma players? Are you in ongoing conversations? Fill us in.

Kärt Tomberg

>> Yeah, so we already have multiple large pharma partnerships, mainly in the gene therapy space that we were just talking about where the medicine really goes into human bodies. We have a publicly announced partnership with Boehringer Ingelheim and other partnerships that are not disclosed. And so those are the ways that we can already learn from partners, get non-dilutive capital and really validate our technology. At the same time, we are working on our internal assets as well. And so we have a lead asset actually in a different modality, which is recombinant proteins. Now, this is basically like insulin, right? These are the proteins that Genentech started working on where you make these in cell cultures, kind of like brewing beer. You extract then the protein you need and that protein becomes the drug for the patient. We've been doing that for 50 years. However, when we started to look at that opportunity space, we realized there's still so many proteins we cannot make either at all or not in sufficient amounts. And so we were really thinking, OK, that's a really interesting proof of concept and value opportunity for our technology. Because if we can now make things that people haven't been able to make, we can bring a new drug to the field. And so our internal assets are really in that field for now.

Gemma Allen

>> Wow, you're kind of hedging your bets in both spaces, right? In that substrate for pharma, I love it. So talk about the broad industry challenges, data access. You're based in the UK and it's no longer part of the EU, but it's a very different beast from the perspective of commercial viability, right? When we think about what's happening here in the US, what are your thoughts on some of the regulatory and policy changes that would be needed to truly optimize the use of AI from the perspective of patient data and scaling discoverability in ways we haven't been able to do before.

Kärt Tomberg

>> Yeah, I'm sure that there are other people who are at the forefront of actually solving that. We are not accessing patient data yet. So we haven't had to solve this geopolitical landscape on it. I think that everyone is watching as well as us very, very closely because it's changing so, so rapidly. Also, mainly for the better, FDA is rolling out a lot of interesting ways how to interact with them much more efficiently, much more data driven. So we're super excited to follow it, but we're not at that space ourselves. So we're not really leading the thought process there.

Gemma Allen

>> Okay. What's ahead? You've achieved a lot in six years, I guess, or so. What does the next year even look like for you and your team, Kärt? What's on the roadmap here?

Kärt Tomberg

>> Yeah, so we're definitely, like you say, hedging bets and pushing on both frontiers where we think the value sits. And on the platform side, we really want to build our models bigger. We need to do, there is so much more to learn in all of that process from DNA to protein because no one else is really looking at that space. So we're really trying to double down on those upstream problems and building the models and being the best in the world to know how that journey is, how that speaking to the cells needs to look like for effective protein production. So we're really doubling down on that space. And on the pipeline and the future value of the drugs, we're pushing the assets forward. So we are right now in in vivo models and we're in mice. We're getting closer to getting our candidate status and then moving towards humans.

Gemma Allen

>> Wow. And lastly, you're here at the NYSE. You've flown in for this, I assume. I would imagine that in this space, conversation, knowledge sharing, dialogue is so incredibly important. Like it is in any innovative space. But in a space like this, we're talking about public health. how do you think about this point we're at in the industry where folks are trying to commercialize faster there is like in every world a battle for funding and a battle for partnerships do you think the industry is evolving withthe right level of collegiality

Kärt Tomberg

>> and what do days like today mean for you yeah I feel like if you go down there in the room you will not see it as a battlefield I feel like everyone who is actually building in that intermediate space, is so grateful to have peers, to have a chat about how are they thinking about BD? How are they thinking about bringing the investors together? The investors are in the room. Really, they want to understand how can we unlock the value? How do we build it? So I think that there is not a lot of like, oh, who is doing what? And it's all about us as a collective. How are we pushing this industry? And as well as all of these really fast-moving pieces, We are going to hear a lot about them on the stage and we're going to learn from each other. And so I actually think this is the reason to fly in, is because in that room, the community is really openly sharing where they're at. And it's super fast moving. And so it's the best place to be today.

Gemma Allen

>> I love it. The best place to be today for sure. Well, Kärt Tomberg, thank you so much for joining us on theCUBE.

Kärt Tomberg

>> Thank you so much. It was a lot of fun.

Gemma Allen

>> I'm Gemma Allen here at theCUBE studio at the NYSE. Today, we are talking all things AI and biotech, a hugely important space right now. Now, some great guests coming up. Don't go anywhere.

Kärt Tomberg, ExpressionEdits

(INTRO)

Gemma Allen

Kärt Tomberg

>> Yeah, super excited to be here.

Gemma Allen

Kärt Tomberg

Gemma Allen

Kärt Tomberg

Gemma Allen

>> Wow.Talk about what is so powerful and impactful about finding new ways to talk to cells. In our day to day lives, what could that change for us?

Kärt Tomberg

Gemma Allen

>> Right.

Kärt Tomberg

Gemma Allen

Kärt Tomberg

Gemma Allen

Kärt Tomberg

Gemma Allen

Kärt Tomberg

Gemma Allen

Kärt Tomberg

Gemma Allen

>> Okay. What's ahead? You've achieved a lot in six years, I guess, or so. What does the next year even look like for you and your team, Kärt? What's on the roadmap here?

Kärt Tomberg

Gemma Allen

Kärt Tomberg

Gemma Allen

>> I love it. The best place to be today for sure. Well, Kärt Tomberg, thank you so much for joining us on theCUBE.

Kärt Tomberg

>> Thank you so much. It was a lot of fun.

Gemma Allen

>> I'm Gemma Allen here at theCUBE studio at the NYSE. Today, we are talking all things AI and biotech, a hugely important space right now. Now, some great guests coming up. Don't go anywhere.