Serial entrepreneur Michael Heltzen, CEO of eXoZymes, reveals how his NASDAQ-listed company is "liberating enzymes from cells" to create a new generation of chemical manufacturing. Instead of using living cells as factories, eXoZymes isolates enzymatic pathways to work as pure chemistry — achieving engineering-level control previously deemed impossible in conventional synthetic biology.
Michael discusses eXoZymes’ AI-powered enzyme evolution, six-week development timelines, bold IPO strategy during biotech's funding winter, and applications in pharmaceuticals like NCT for liver disease. This is synthetic biology's next chapter: sustainable, scalable enzyme-based manufacturing that could replace both petrochemicals and natural harvesting.
Podcast episode available here
🎧 Simplecast
🎧 Apple Podcast
📺 Spotify (Video Podcast)
Transcript of interview below
Welcome back to Inside Biotech, an exciting podcast from Biotech Connection Los Angeles. I'm your host, Gabriela Rupert. For those listeners who might not know, BCLA is a nonprofit organization dedicated to inspiring, educating, and connecting emerging scientists and entrepreneurs to grow and diversify biotech in LA.
We invite you to get a sneak peek into this growing sector along with us. Every month, I'll talk with scientists, entrepreneurs, investors, and more about the cutting edge science going on inside their companies.
Here, we bridge science with storytelling to bring you the most novel, up to date, and thrilling news on the biotech world in SoCal. Today, I'm chatting with Michael Heltzen, CEO of eXoZymes, a company reimagining how we make chemicals by liberating enzymes from cells and harnessing AI to create sustainable, scalable alternatives to traditional manufacturing.
From his roots as a serial entrepreneur in biotech, bioinformatics to leading an IPO in one of biotech's toughest years, Michael shares how exozymes technology could transform pharmaceuticals, nutraceuticals, and more.
More after this. Hello, I'm Dylan Kassane, host of Beyond Biotech, the weekly podcast from La Biotech. Each Friday, we explore the latest in biotechnology, sharing insights on breakthroughs, research, and the innovations shaping the future of medicine.
From advancements in gene therapies to AI-driven drug development and cutting edge cell therapy, from top pharma and venture capital perspectives to fresh thought leadership from rising stars, we deliver deep conversations weekly with experts, entrepreneurs, and change makers.
Discover how biotech is changing the world, one innovation at a time. Subscribe to Beyond Biotech on Spotify, Apple Podcasts, or your favorite platform. Thank you for tuning in to another episode of Inside Biotech.
Today, we're joined by Michael Heltzen, CEO of eXoZymes, a company that's pushing boundaries of enzyme science, AI, and sustainable energy to replace traditional chemical production methods with a sustainable and non-polluting alternative.
So thank you for being here, Michael. We're so happy to have you on the show. Thank you for having me. And so, can you give us just a brief background of how you ended up as the CEO of Exozymes? Yeah, absolutely.
I was born and raised back in Denmark and Scandinavia. And very early on in my life, my professional life, I started realizing I have these very strong entrepreneurial traits in my personality. Very curious.
I'm very kind of like, if it doesn't work, let's go fix it kind of mentality. And where other people see limitation, I see opportunity. So in my early 20s, I was one of a few guys that started up a startup called CLC Bio. There was a bioinformatics company.
There's a lot of bioinformatics companies that have been started over the years because you just need a couple of laptops and a couple of crazy guys, then you have the ingredients, right? So we were lucky and hardworking enough that we ended up basically teaming up with another startup.
over in the UK called SELEXA at the time, you know, the Masaluna today. And the other sequencing, high throughput sequencing, as it was called in the beginning, but next race to sequencing is what we know it as today.
And we basically built a bioinformatics platform that became a tool for genomics and the genomics workbench is still used by a lot of people around the world under the Kaigen workbench brand today. So that was the start of my career.
I hadn't really, I'd just started at university under a finance direction, so completely off the thing, but it was passion driven because early on in my life, I realized that it's the combination of molecular biology.
That wasn't how I was formulating it back then, but like the power of life and the computational world that really fascinated me. And I loved both. And they seemed so far apart until I fell into the conversation about bioinformatics.
That is using computational power to study and understand the tech stack of biology. Right. That is a reference I am going to try to build here so you and I, we can have a conversation about what epigenetics and genetics is to, for example, small molecules and how that all stacks up.
So as a personality entrepreneurial ended up in, in the commercializing technology and science breakthroughs. So that when, when I met scientists that was kind of like all fired up because they had made a big breakthrough, but maybe either didn't have the tools or the setting to communicate that in wide matters and wide swath building.
And also, as we all know, just because you're having an academic breakthrough doesn't mean that the next day it's kind of implemented. Exactly. That can be a 30 year journey. And it can be a never ending journey that never succeeds if you do it wrong.
And a lot of people unfortunately do that wrong. So becoming entrepreneurial, understanding how to translate these opportunities as a person, that's my professional life. As a person, I'm driven by the impact that can make.
And sometimes I work a little longer, a little harder than the average person because I'm very driven on these topics. And the reason why I'm the CEO here today is because I've been CEO a handful of times before because I've literally zero entrepreneur style.
Every time we build one company and sells it, we get right in and build the next thing because there's so many things that is either in need of building or the opportunities is too great to not jump in.
And after I sold the last company on behalf of us, the shareholders and the founding group and the team of that company, last year I was basically recruited over to this. And it's based on that I, by being a non-trained scientist, which is kind of how I see myself, I'm so curious that I've studied up on a lot of things and I like nerding deep into technology, but I have not had a formal training at least yet.
I'll do that when I get really old and I can't do this entrepreneurial things. I'll go to university at that point. For retirement and you'll go back to school. Exactly. Exactly. So, yeah, that's a little bit about who I am.
And I have ended up building companies in this intersection of computational power and new ways of signal detection. So biosensors and new ways of recording data for the computational side. AI and machine learning have been an integrated part of all of those companies over all those 20 years that I've been kind of active.
And then on the other side, how do we actually take biology apart so we can see it as technology so we can start building with it? And that's the interface that I like. You sit in this very unique and skilled position of, and also this tough job of having to be able to simply relate or convey these scientific ideas and breakthroughs, which is not always a logical scientific approach in the way you communicate things.
You have to, you know, utilize emotion or other things to connect with the people that you're trying to sell this idea or conclusion to. Think about the audience. Yeah, exactly. How do I get them to listen? But at the same time, while you're doing that, you still have to almost like respect the conclusions and make sure you don't over speak or under speak.
Yeah, it's all it's an exercise that I go through with my with my PI all the time and my advisor when I'm putting together presentations of, well, if I say this, is that 100% accurate? And he goes, well, it's 90%, but you're still getting the idea across.
And for this presentation, it's okay. But, you know, for a different one, you might have to be a little bit more. It's all just thinking about your audience. And so it makes sense if you, you know, if a company is looking for someone that is able to do that while leading a biotech company, that it's perfect, a perfect fit.
It's true with science, right? You can be 100% accurate and 100% not understood. Exactly. Especially with all the jargon that exists. And as this technology is getting growing faster and faster and faster and new terms are developing faster than the late, like late citizens or non-scientists can learn them.
It's I feel like the job is getting not harder and harder. I think it's a little bit more fun. And so I guess with off that, can you we can start a little bit about now your role at Exozymes and and you can maybe show us a little bit of that skill.
So Exozymes is I think on your website, it says you are liberating enzymes from the confines of working in cells. And so can you break down that sentence a little bit in terms of Exozymes mission? Yep. And let me do that by stepping a couple of steps back and talk about why it matters.
Okay, yeah. So the reason why it matters what we do is basically that we're trying to address a very, very large challenge on the humankind scale. So we humankind have access to chemicals, chemicals being everything that is our modern life.
We have access to things, small molecules, chemicals in drugs. We have it in the things we wear, the things that makes the semiconductor and the computer industry happen. It's chemistry. It's everything modern life.
And humankind kind of had like two, three ways of getting chemicals. And if you start thinking about how fragile that is, that we only have a couple of ways to sustain everything we're doing, we start getting to why it matters.
But like the first part before oil, that is number two way of getting chemicals. Before that, we basically lift off nature. That meant from a chemical perspective, we would find a chemical produced in a plant and the bark and the root and the leaves.
And we would harvest that and extract the chemical out. So that's natural products. These things we basically harvest from nature. And then of course, in the 60s, 70s, the whole boom of we can take oil and gas and we can chemically break that down to different versions of it that gives us very different everything from everything from plastic to colors to food ingredients to all kinds of things.
All the things that humankind have done with oil. Both of these two sources of chemicals are challenged. We are over-consuming what we can harvest from nature. We're depleting nature. We, humankind, are depleting nature.
That's a problem for the future. Oil and gas does not just benefit us. It also leaves a bill for future generations, also our own generation, that needs to be cleaned up to be cleaned up and we haven't really addressed that.
So that's also not sustainable. Then there was this grand vision of synthetic biology coming around that a lot of us got super excited about. Like, wow, we're going to make the cells be small chemical factories.
It's going to be amazing. Genetically, we'll just cook them to make the chemical. See, I can do it over here in the press. It is. Hence, it's real. Only problem is that it's not really real because it's not here.
Why is it not here after 10, 15, 20 years or billions and billions of billions? of dollars gone into it. Well, it's because we forgot to ask the cell if it wanted to be a chemical factory. And a cell is literally optimized over evolution, over 3 billion years to make only exactly the chemicals it needs and only in the amount that it needs them.
Nothing else. So when we come with our clunky DNA code, puts it into it. And we have technology to virtually gene transfer this or with CRISPR insert DNA. So we can make cells make things for us at petri dish level.
So we can kind of get our publication. We can get our proof of concept, but it doesn't scale up. And if you don't make it at scale, you don't make it available to people from an impact point of view, kind of doesn't exist.
So there's this research bit that works with these cell factories. I think about me doing in vitro transfections, you know, giving cells a microgram of DNA, which is a very small amount, and then getting a readout.
But not enough. I'm not manufacturing levels of this new, this exogenous protein that I can, you know, aliquot into a bunch. It's just not at a manufacturing scale. And you're taking advantage of that the cell doesn't react right away.
But if you look at it as a production system, the cell will very quickly look at this protein or small molecule or whatever you ask me to produce and go like, I don't need this. Let's turn that off. So it has a lot of, let's turn it off methodologies and regulation to use of word.
It's not as simple as giving a command to a computer and it takes a command and spits it out. The cell will think a little bit, be a little bit simplistic. We'll think a little bit, like you were just saying.
This is maybe a little bit too much energy. Yeah, I'm not, I'm not, I'm not getting anything out of it. Like, why am I doing this? Yeah, it's kind of like a, like a petulant teenager. It's like, I'm not going to do that.
So, so, okay. So if the cell was a chemical factory that doesn't want to produce anything for us. Okay, let's move to argument number two, why cells are a challenging platform to manufacture for us. Cells are often hurt by what we ask it to produce.
It's toxic, but it can even be to a degree where we're actually killing the cell because of the toxicity gets so high. So not only do I have a cell factory that doesn't want to work through, it will fight you as if its life depends on it because it literally does.
So it's, it's not just going to be a little bit like, hey, you need to convince me a little bit. It will literally fight you. So that's, that's the argument number two, why cells as, as a manufacturing platform, the biomanufacturing platform is, is, is talented.
And I'm not discounting all the great work that has been done and all the positive progress because there are areas where there's positive progress, but we also have to be realistic about it has not been as impactful as we hoped.
And, and compared to all the work that has gone into it, the, the, the, the harvested fruits are very, very little, relatively seen. So argument number three for why cells, when you really start thinking about why cells are a challenging manufacturing platform is, if you study the cell scene from a, a hardware perspective, you have this amazing, almost small city worth of production methodologies, so many things going on inside of a cell.
And just one of the factories has one production line that maybe is the one you have inserted and it's, it's producing for you, but they also have a million other things going on. That's why I can regulate this, why I can have it.
Not will of its own, but you know what I mean, kind of. Definitely. And from that perspective, we have all of this going on. So if we feed it, a, a feedstock, let's say a kilo of sugar, just to be concrete and European about it, a kilo, kilo of sugar, then very little of that kilo will go into your production chain.
It will go into the million other things that is happening as well. So like co-ops it for its other processes that need to go on. Of course. Right. Of course. It's much more interested in living its life and doing what it's supposed to do than what you're forcing it to do.
Makes sense. In that. So it's kind of, of course it doesn't, of course it takes some of that sugar. It taxes you really. It's really hard if it can. So that is an argument for, for efficiency. And it also mirrors up against another problem that is like, when you are to harvest your end product out of this cell factory, then it's not a nice production line where it comes out in a box in, in the end of the building.
You have now taken a product that is embedded in the roof, in the floor, in the walls. It's, it's literally stuck inside of this. And you need to break down the whole thing and isolate out. And the isolation cost is often higher than the value of the compound.
And your business idea just dies right there. Right. So this was the realization that our technical co-founders at UCLA, by the way, after having had done an amazing scientific achievement, but they were honest enough with themselves to say like, guys, this is never going to scale in reality.
So it's not going to really matter. And we tell the story that Jim Bowie basically said, like, okay, guys, if that's your attitude, then liberate the enzymatic pathway from the cell and just give me the pathway and everything that it needs to run.
Then we'll just do it like that. Ha ha ha. Just get rid of all the extra bits. You're like, oh, sure. But that became in the research direction. What does it actually take to run enzymatic pathways outside of the cell on their own? And that's, that's why we kind of named them exoscipes.
So instead of enzymes inside, we call them exo outside doing their chemical catalysis or chemical acceleration steps. So you can argue we're taking biology out of biomanufacturing. We're just making it, we're just making it chemistry.
Of course, it's super, super difficult to do so. But it is just chemical reactions where we are saying, okay, if you have this feedstock starting point, and you want the following steps to happen, what are those enzymes that breaks it down to the right sizes? And what are the built up enzymes that builds it up exactly to the molecule you want? You just want that.
And then any support system that is needed to keep that manufacturing going. So let's just build that. So that's what we built into the exoscipes and biosolutions. And we are the only company in the world that has it at a scale where we can do like multiple enzymatic steps and make sure that the whole balance, we were talking about cofactor and energy balances that we have had to figure out in thermodynamic balance.
And a lot of things like what is it actually the cell does to the enzymatic pathway? And how do we make enzymes that are strong enough to survive outside of this very protected environment of the cell? That's basically what we have built.
And it allows us to build a new generation of biosolutions. That means we can build chemicals. And where we specifically apply that is in high valuable nutraceuticals and pharmaceuticals. So new versions of pharmaceuticals that cannot be made either by ketochemical or harvested in nature.
And that's what we focus on. But the platform can in principle do most kinds of chemists. So it's a new way of making chemistry. Yeah, you're like taking a page out of the cells books and kind of using their toolkit or, you know, their hammer and their nail, which are their enzymes and putting in this chemistry setting.
I'm thinking of almost when I was in my organic chemistry lab, struggling to synthesize something out of something else and having all of these steps. But instead of having like, oh, my gosh, it's been so long since I've been in chemistry, but the round bottom flask and you're heating it and turning it.
You're also kind of giving it these little, those, these exoscipes or these special enzymes. So you can drive a certain product. The work you were doing by heating it or putting solvent on and all of that.
We just have enzymes that can do that trick. Wow. So we just make small workhorses for each step. Small, small guys that knows how to do that one thing really precisely. And then we just dose the exact right amount of those exoscipes.
We can literally drop feedstock in, in the one end. Could be sugar, but can be different things. And then the breakdown enzymes and the buildup enzymes. The only thing that happens as a chemical reaction is those things.
And then the support systems that needs to be there for, for example, cofactor regeneration. So you remove the non-essential, like if there's any non-essential intermediate steps or let's, I know sometimes there's two steps that happen catalytically.
Can you put them together? Is that something that, that this technology can be used for? Or is it, you know, skipping maybe a step that's a little bit draining or uses up a lot of the, the supporting factors, but isn't necessary for the final products? You can kind of take that out.
Is it as easy as just removing a puzzle piece or is there some adding steps that you need to do to link the, the intermediates of those other two enzymes? There's nothing easy about state of the art technology, but yes, it's that easy.
It is, it is literally, we're taking the enzymes and stripping them down to me doing that one thing they're supposed to do or by design, making them do multiple things because we benefit from it. But we're getting into what makes us different than a lot of the other soon bio companies.
It's like we are an engineering level control of things. You never, as we talked about with the cell, you'll never be in, for sure, not control, not even in the known of what's going on. We know exactly what's going on because there's nothing alive in our, and it's, it's a one batch.
There's nothing alive in there. If it doesn't work, we can measure on it and we can go like, okay, in the pipeline or the production line, it's this guy that isn't doing his job because we can literally just look at the intermediates.
It's like this, the next step is not being performed or being performed fast enough. Hence it's, it's this guy. And if it's not fast enough, we dose some more in or we take that exosime out again and run our artificial evolution on it that basically pressures the enzyme to become even better at that pressure with a reference to evolutionary pressure.
Can you speak a little more about the artificial evolution of pressuring an enzyme to evolve? So it's, it's, it's, it's a, it's, it's a reference to the, the, the, the great fundamental work done by a lot of people.
And obviously there can only be one or a few people getting the Nobel prize, but basically a directed evolution at, at our great colleague over at Caltech that is basically in between where you sit and I sit right now in this podcast.
So, um, directed evolution, having the, the, the foresight to sit down and say biology is so complex. So many things going on that let's just try to take out a system of it, the chemical system, the enzymatic system and focus on that.
And, and how does that work with realization of like it's, it's, it's mutation that happens per generation out in nature that have allowed evolution to, to kind of work its way in different directions.
Why don't we sit down and just speed that up a little bit? And then we randomly see which mutations lets an enzyme become better at something. And then just by letting it randomly mutate, when every time there's a high performer, you take that out and say like, what, what's mutation is that? That, that that's a driving positive mutation.
So that, that's directed evolution. Then, then, then you have the rational design, second generation coming and say like, hold on, just randomness is, it's a little slow for us because we know a lot about our enzyme.
Why don't we sit down and rationally design and say like, it's probably one of these mutations. We start guessing, we start using knowledge. We start using the rational design to guess how to drive. the evolution forward.
That's generation two. That's, that's, that's what we use as well as everybody else, but we have built a third generation. And that is basically saying, okay, if you sit down and you want to, to, to use the computational power, there's now so much knowledge via AlphaFold and other programs.
That is this software program made by DeepMind, the Google company in the UK that got known for first, kind of solving, kind of solving, kind of solving, kind of solving, go and chess from, from, from a computational neural network perspective.
And then later on were the, the pioneers of taking last language models on protein amino acid data and foreseeing the structure of, of, so, so, very big technology breakthrough. We obviously put our eyes on that.
It was like, that's kind of interesting because now we can start calculating what, what would a change in a nucleotide and a, amino acid lead to just, just, just estimating it. We, we, we, we, that's why I'm calling it guessing.
It's not like, yeah, I'm absolutely sure. But, but we, we, we, but it's less, less blind than, than the randomized mutations. You're going a little bit like, I think this could happen, but less, you know, based on, it's just less combinations to try out, you know.
And, and where, where the second generation was very much like, we worked with this enzyme for years now, we have kind of messed around with that. We knew this enzyme. We got to a, a, a, a tool package where it was more like, based on, all knowledge, all humans has on all proteins, aka also enzymes.
We, we have a model for when we change that, what might happen. Why don't we set those and kind of go like, okay, now, instead of us guessing based on human cognition of like seeing patterns, why don't we train models to, to guess what it is? And then at the same time, and that's actually what has given us our big AI breakthrough is, not, not the, the, the AI models only.
It is that we also sat down, how do we figure out a way of generating a lot of data so we can train the models specific. Specifically for this purpose, not just, okay, here's this information and now use it.
You're spot on. So under a certain condition. So now back to that guy that was not doing his job in the workflow before on the production line, like the slow guy. Yeah, the slow enzyme. We, we take him out and he go like, dude, you, you need to speed up.
And, and maybe you're right. It's about taking something away that he's doing that actually doesn't benefit the process. It can be, it's something where it's like, let's, let's just set it up so it can move faster.
It can consume more energy. It can, or maybe it's not stable enough. So maybe it stopped working because it fell apart. Like when, when he falls apart, it's not really his fault. It's just because he wasn't kind of set up to succeed.
Not that fault and enzymes has anything to do with each other. But again, just from a storytelling perspective, getting him trained in what other mutations we can make so that we make the best version of this enzyme.
So that when, when that enzyme goes back in that workflow, it's not the bottleneck anymore. And it can be everything from, from instability to kind of like this enzyme was just where we found it. So when we started, it was kind of something that was trained out in nature as, as a starting point under very different circumstances.
We, we, we don't want like super high heat because that's just a way of heat in our production system. So can we train the enzyme that did something out in nature to do that at, at, at colder or hotter degrees depending on, and, and, and it's, it goes both ways because it's, it's often kind of like, can we make it happen without making it too hot, but also we want it to be stable.
Up to 75 degrees. So we can just express an enzyme via a cell if we want to, then we can just hit the cell with heat. Everything dies because it can't take 75, but the enzyme can't because it's stirred enough if we pushed it with artificial evolution.
It's, it's engineering tricks we're using. And this is really where I'm probably different and blessed by not being scientifically educated because I would know all the things where I'm wrong and it's not possible and all of these things.
Right. You're more blind, unbiased. I'm just like, just, just, what is the problem in front of us? How do we solve it? And let's not try to understand everything, but let's try to be in control over what is in front of us.
So, so I think the world much more, as you probably heard before, expressions like biology is technology and technology is biology. It's just not human made technology. Yes. It is evolutionary made technology.
And by trying to understand and control the whole tech stack of biology, we fail again and again and again and again by taking out a small area or valuable area such as being able to make chemicals and mastering that area by, by engineering level control and, and being in, in cause effects training loops where, where the, the, the, the change leads to either better or worse.
When we build AI algorithms, it's basically reinforcement training. I'm talking about where, let's see, let's give an example of that. One of my favorite examples is, is the self-driving cars. The self-driving cars are actually not training really well when you're driving it perfectly.
It is when you are kind enough for humanity to drive into a tree, then the algorithms can say like, that thing is not good. So we shouldn't do that again. So everything where you punish the algorithm.
We, we talk about rewarding and punishing algorithms. It sounds a little harsher than it is, but it's basically just like, you need to go back and say like, don't do that again. Yeah. That's, that's actually what it learns from.
So when we use AI to foresee, for example, a couple of hundred or a thousand mutations that potentially can have an impact on the, the exosign, the enzyme for a specific role in a specific circumstance.
We try all of those off when we get hits where it's like, this is working better. And especially the ones that can be combined with each other. We love those because it helps us on the specific project.
But it's actually equally much when we're kind of like, we're super certain about this mutation is going to do something good and do something bad instead of, or does nothing. Because then we go back to, to the algorithm and go like, now you need to learn from this because like, you were wrong here.
You steered us into the tree. No, thank you. Don't do that again. So, so, so, so every time we use our platform, we get a better enzyme, but we also get a better platform. That's, that's what artificial evolution is.
Wow. That's amazing. So that's probably how you're able to develop your platform. Now you can go from enzyme discovery or hits about certain modifications to production of your target compound in under two months, or I think it's six weeks.
Do you think that was from this kind of feed forward teaching where everything's just happening faster and faster or what are the other contributors to that, that efficiency process? Overnight success takes approximately 10 years to prepare for.
The reality is that it's not one thing, but, but it's, it's very much our capabilities to pick the right enzymes and get them into the right place and make them exosyme so that they are sturdy enough so that they can, they can work better.
with the support modules that we have available. We can't do exactly everything what the cell can do. At least yet, we haven't figured out all the tricks of, of all of biology, but we have figured out a lot and we can basically therefore have a choice between the enzymes we can support.
So, so it's, it's the combination of those things. And then yes, it is a competitive advantage that where other people with rational design will spend months and years and millions or tens of millions of dollars that we can do it.
in, in, in days. And if, if we're counting in, in whole million dollars, it's zero million dollars that we're spending on it. So it's kind of like, it's a new generation of how we do inside engineering.
Wow. And then so can you give an example or like a tangible way of visualizing the level of production and scalability that you can achieve now with this generation three platform compared to that initial example we were giving about, you know, like a cell or cell in a petri dish that we typically do at a research scale.
Yep. Absolutely. So, and, and I'm going to pick an example that at least the use case you know much more about than I, than I do just to get myself into, to trouble here. Um, so we. Specifically started building a, uh, exosign biosolutions from, for NCT. That is a small molecule found in the husk of the hemp seed.
You have this tiny, tiny amount. Trace amount. Out in nature. There has been enough that people with painstaking the effort have been able to isolate enough to run some, some mouse studies. That basically shows that the NCT small molecule allows the, uh, the mitochondria of our liver cells to become more active and, and more in volume.
And that's obviously a, a very interesting opportunity because, as you know, and that's why I'm saying I'm getting myself into trouble here. Humankind have a challenge with, that we have lived, too well the last generation.
30% of everybody has non-alcoholic fatty liver disease. Today, it's one of the largest unmet medical needs and we don't have anything for it. Having healthier livers is like super important simply because it's, again, in layman terms, it's, it's the recycling center of the body.
It takes, it takes everything in and it takes like the bad things that shouldn't be there and make sure it leaves the body and all the good things it sorts it up and make sure it goes out to the right places.
Having a breakdown in your liver makes you grow old faster and get more diseases faster. Those are not nice value propositions. And I said 30% of everybody else has it already today. And it's basically a disease that we don't do anything about.
We don't even try to diagnose it because we can't help people. We're just like, yeah, just, just, just grow sicker. And then we will deal with it when your liver is, is really messed up and really started closing down.
Then we have some, some wise medication. We'll throw at it or start saving up to a new liver, but like, that's not going to help you because there's not enough livers to all the people that need livers.
So it's a very large challenge to have unhealthy livers. And do you care to share a little bit about the reference I'm making about your work? Yes, definitely. Yeah. But you're right. It's just, you know, too much fat in the liver affects its ability to function.
And so with the increasing cholesterol and processed fats that we're consuming just with our diet and also increased sedentary lifestyles, there's just an explosion of obesity and obesity related diseases.
Exactly for the reasons that you're saying for the cleaning and also sending out jobs that the liver does. I love that you pointed that out because it's my favorite organ. And then in the, in, in 10, 15 years, that number is projected to skyrocket even more.
If we can't do anything about it. So in half a generation, it's going to be more than half of everybody. We're just, we're just walking into this huge problem. And other than we are somebody that really cares about, it doesn't seem to be something that's taken super serious, but it's fair enough because there's no solution.
So why should you kind of concern yourself about it? We'll try to change that. But what is really interesting in this conversation is that the kind of work, if I understood what I read, basically using epigenetic changes.
So changing not the DNA itself, not the genes itself, but the expression of it. So that little programmer note that says like this gene is not to be expressed or this gene is to be expressed at this level.
So basically changing that, it's, it's a slightly different part of the tech stack that you're coding on than, than where we are coding. What we're doing is we're taking the, the mitochondria, the powerhouse of the cell and, and basically making work.
Faster so that it, it can burn some more fat and, and probably also some fat it couldn't burn before. By, by activating the drug receptors that our small molecule activates. Right. So it's working over time to just burn the fat more, more efficiently and faster.
Just boosting. Yeah. What, what, what is there? Well, give the layman version of what you guys are doing. You definitely, you definitely expressed that right. Well, we're kind of doing something a little bit similar in our lab.
We're interested in identifying enzymes that are contributing to lipid metabolism, either, you know, knocking genes out. And we find that the mice are protected against certain diets or like high fat diets.
Or if we overexpress a certain gene, either by knocking out a gene that causes another one to skyrocket or just giving it exogenously. If that also confers some kind of protective benefit. And then we'd like to find the mechanism of why, you know, what, what are the actual metabolic pathways that are going.
But so, so I'm, also interested in like the, the epigenetic bit of what genes can we kind of fine tune to protect or predispose. And then what does that tell us about the generation of these diseases and how they contribute to these common metabolic diseases.
And then what you said about epigenetics, the analogy that I tend to use is that, so you're saying it's not a question of genes or the DNA itself, which is whether or not a gene exists in that person's genome or that mouse's genome.
And so I think of that as, do they have the light switch to even turn a gene on or off? Do they exist? Do they have, like, if you think of a gene as a light switch and the epigenetic sense, it complicates the light switch analogy to be more like a switch dimmer.
Exactly. And so, and the cell has blueprints that says, you know, at this time, or when you see this metabolite in the system, turn this gene on or off. But it's not just turn this gene on or off. It says, turn this gene up 25%, but don't let it go above 40, you know, those kinds of instructions.
And so if you can manipulate the, or jailbreak the dimming instructions, then you can essentially create an environment where genes can be on in ways that they wouldn't be naturally and have certain partners that they wouldn't be.
I think it's clear that we, humankind, have messed up our own factory settings. So again, I'm trying to make, without turning us into kind of a Terminator conversation, that's not where I'm going. I will fight against that.
But us as technology, we're basically a Windows program where somebody had been messing with all the settings and now the computer just went slow. And getting back to the factory settings, that sometimes takes some intervention.
Because we have not set ourselves into that not optimal state by everything from smoking to eating and drinking and stressing and not sleeping and all of these things that one or the other way messes with our, both our epigenetic settings, but could also be all the way down to mutating.
Yeah, tanning too much or the UV exposure, you can damage the DNA itself. Yeah, it's a fascinating world we're living in. And I think finding this reality balance of what can actually be done now from an impact perspective and what can be done later and which order does it come is basically the core of my job as a CEO, because we could be working on a million things.
But the reality is that we would get nowhere. But the reality is that we would get nowhere. It would be like trying to boil the ocean. So my job is basically to sit down and say, okay, how do we go from where we are now to a value inflection point where we have done something that really makes a difference? Or is at least a step in a defined chain of events that would make a difference? And by taking those steps, we can.
So that's just to kind of say that as the CEO, I don't get to mess around with the technology as much as I probably would if I had more time. But on the other hand, I get to communicate what we do. I get to translate what we do.
And I get to make sure we work on the right things. That's the strategic part of it. And because we are deliberate about that, both using very, very new technology and very high potency science, really big impact opportunity.
But at the same time being orchestrated about how we do it, so we don't just apply it to everything, but we go and find a problem or an opportunity and then we build that solution and we bring that to market.
And the value generation of that can then be invested into the next thing. Allowed us to basically, for example, raise money in a year where everybody told us it's not possible to raise money for a thing like you guys are doing.
And when they heard we wanted to IPO on Nasdaq in a year where everybody said it's impossible. We allowed for that by showing that we can orchestrate very, very complex. That's not just in science and technology, but in commercially relevant ways.
Right. I'm glad you brought up the IPO. I did want to ask what led to the decision to go public on Nasdaq, as that's not common in biotech companies. And so what informed that decision? Did that come from your previous experience building and strengthening companies? Or what really led to that? I remember seeing it come up on my LinkedIn in November, and I was just, then I really, really wanted to get you on.
So it does have a background story. I will share it in a minute here. I'll just start by mapping out a little bit of like what is usual. So everybody's kind of on the same page. So typically a great scientist, it meant something maybe at the university that is spun out in a company, some seed investors or pre-seed investors, because we kind of ran out of before ways of talking about it.
So pre-seed is really, really, really early on. Seed is early. People started saying, you know what? It's clearly not fully big yet, but like, I think those guys and gals right there, like super skilled and talented and driven.
So I'm going to bet my dollar on investment alone that they're going to build something valuable of it. So getting that kind of investing is obviously, if you ask your investment advisor, very, very different than investing into a company that has revenue or even to a bond that is kind of like an agreed upon contract, you know exactly what the return on investment.
So the risk reward profile on these early investments are obviously through the sky because you can look at all kinds of statistics. Basically, it's kind of like nine out of 10 companies, startup companies won't survive.
So it's risky. But at the same time, sometimes people are still driven to a degree of like those guys and gals over there are the ones that are going to win or they're working on something that is so important that even if I take this risk, I actually want to see that.
Just an extreme example of that is, of course, if you have a disease yourself and people are working on that disease, it's a kind of like, what is this numbers on a piece of paper worth to you when you have a disease? So there's always been, especially here in the US, this entrepreneurial drive, innovation drive to go and try to make a better future.
And it's that drive that have started a lot of things that you can argue is the venture. So venturing out into an uncertain journey, but like, had this technology breakthrough, the science breakthrough, can we make that become something if we build it? How do we kind of get investors into that whole journey? That's the startup journey.
And it was basically then set up as venture capital VC, where it was like, okay, there's a specific type of investments. The investment thesis behind VC is like, if some smart guys and gals takes a lot of choices on which of these startup companies might have 100x return on investment, then because you can only lose one x, you can only lose your money one time down to zero.
But you can gain them x many, many, many times. So the thesis is like, if you once in a while pick a 50x or 2x, as long as you're doing that with less picks than what it is, you should in principle mathematically kind of have a risk adjusted model where you can say with a portfolio that is set up as long as the return profile is high enough, then you can pay for a lot of like, that didn't work out, but we took a chance and it could have had big potential.
That's the core of venture capital. Then you started having all of these people saying like, yeah, okay, we do that, we do support the startups. We're just going to write into the papers some terms that if it doesn't go well, then this happens.
And even if it goes well, it has to go this much well before anyone else sees it. Like basically, we don't want to risk the co-founding scientists or the entrepreneurial guys in the beginning to basically kind of get presented of the money invested.
So there's all of a sudden, these VCs term sheets turned into like small Bibles worth of terms. And the shorter ones can be even more toxic because they're basically making it very, very risky on top of the normal risk, just from the financing perspective.
So that's the VC model. I'm doing it a little bit of a disfavor here, but I'm going to use it as the bad guy. So that's why I'm doing that. But I'm obviously a guy that I've invested both as the investor under those terms and I've received money under that.
So it does work to some degree. But the challenge with it is that now after the interest rate has gone up and there's all the insurgents in the world, the cost of capital is much higher. That means there's not as many people that are willing to invest into the future, which is a human mechanism of when there's uncertainty, you focus more on short term.
So all of a sudden, we don't have a world function VC market anymore. Dollars are not moving. The terms that have been asked are so extreme that you from at least the co-founding side can go like, well, even if we're successful, we're not going to get a part of the cake.
So why should we do the 100 hours a week and brain damage every day, late hours that it takes? And it ceased up. And then there was already many, many years ago, even you can argue, even 100 years ago, people that went a different model and said like, hey, you know what? Why don't we just make it a lot of people taking this investment risk, spread it out a little bit like an insurance.
And that's the public market. So 100 years ago, people said like, why don't we build railroad here from East Coast to the West Coast or wherever they were going? And people were like, can you do that? Is that a business? How are you going to make money out of that? What if you build your tracks to a place where nobody want to go? Then that's a difficult one, right? But that was a venture.
That was public venture money where it's like invested into the future and everybody was like, yeah, there might be something about it. It might become a big thing as it did. Everyone can benefit a little bit.
So they're willing to put some in. Exactly. Exactly. And that has been before VC also how a lot of biotech and other things were started up. So you can argue we are just walking back to the previous model.
And MDB Capital, that is the investment bank that co-founded our company back in 2019, they have been using that model for 20 plus years and just saying like, we're not VC. We help build the companies.
We help not take a lot of like nasty terms. So if something goes wrong, we're in there as a co-founding group. We help build the company. We are in the same. There's no need for a lot of lawyers to read all the contracts because like if you start getting lawyers involved in a relationship, it's probably not a good relationship in the first place.
So with that methodology, MDB Capital have basically started this public venture methodology. And the investment perspective is we go out, we find really big technology breakthroughs. And if they're IP protected, that's our downside protection.
It's not some crazy terms. It's like, you know what? There's a really skilled group of people here. It's a really big breakthrough. If it goes really bad, it might be someone else that ends up building in this area.
But then we have some patents and we basically, we're going to get some licensing fees from that. So that's a little bit of downside protection. But it has the 100x or more potential on the upside. And that's how the company was co-founded by in 2019. And there's a large network of people behind MDB Capital.
People like myself that have maybe had an exit or make money other places that goes like, you know what? That public venture model, we're actually pretty intrigued by that because it allows for some things to happen that otherwise would not happen.
Something as crazy as taking. And liberating inside pathways that still a lot of scientists will say like, that's freaking impossible what you're doing there. Over in my book here, it says it's not possible what you're doing.
So you must be wrong. We're still there. So this is the overall landscape. And this is why it was possible for us to IPO in a year where everybody said it was not possible because there was still a group of people in the hundreds that said, you know what? This is such a big science breakthrough.
And if it's ready to start the commercialization journey, now, and the team is standing ready, we're not going to basically sit and wait for a better IPO year. We're just going to go right away. But you're right.
We were one of the very few IPOs on Nasdaq that was biotech related last year. And a lot of people told me to stay away from it in all kinds of ways because they thought it would be like a number of other companies that has introduced and just basically the bottom fell out of the market.
And we're lucky that we have enough investors that have enough patience to say, long term, this is going to change how humankind makes chemicals. Medium term, there's a number of products like NCT that will be brought to the market that will be valuable in themselves.
And that means shorter term, they will help finance us to get that. Wow. What did it feel like when you rang that bell? When you guys got to ring it? That was a good feeling. It's something I would like to try again.
It was very much like, okay, let's celebrate for a minute here. This is the start line. This is not the end goal where a lot of other people that IPOs, they IPO as an exit, as a way of getting out of a company where for us, it's a way of fueling the future and really more into it.
So it's probably also a little bit more of a, when people are celebrating at IPOs, it's because they ran the marathon, they got all the gold line, and they don't have to do it. And they're done now. I see.
Yes. Where my personality, I'm never done with these things. So I would be trying the day where I would have to step away. It was more of extending the lifeline for you. Yes. And scaling up the communication of it as well, that has been a big advantage, by the way.
We're getting a lot of attention and interest because we're taken serious. Because it's very clear that this is not just something we're kind of messing around with. We mean it seriously, because being a, NASDAQ lasted a public company makes us live up to SEC rules and consequences if we don't follow those rules and a lot of, a lot of other regulation that, that is just a lot of work.
So, so we are, we are, we are proud of it, but we also take it very serious. Yeah. I hope that other biotech companies can, maybe if anyone's listening, can learn from this success story and, and the factors that you explained that contributed to the decision and the, and the positives of being a public versus privately traded.
And so yeah, I'm, congratulations on, on your IPO. And we talked about how the short and long-term trade-offs are, were really important for getting that IPO. And you mentioned the, the NCT is a potential, you know, short-term result of, of your technology, you know, being able to isolate that chemical from the corn husks and, you know, potentially help mitochondria fire up more to prevent obesity or other types of metabolically related diseases.
So that's in, you know, a pharma medical kind of sector. What other kind of sectors are, do you think most primed to be disrupted by, by exozymes? Maybe in a five-year, 10-year scale? So, so NCT as an example is, is a good example of something people have had just a little bit of access to.
So they have had the chance to, to find the nutraceutical and the pharmaceutical applications. That's, that's literally, it's, it's, it's our strategic focus area for, for that one reason that when you built, for example, NCT, the, the, the natural product version, you can go nutraceutical routes.
And that means you can go faster to market if, if they're, they're grass recognized. So generally recognized as safe by, by the FDA or can, or can become so. So that's like one way, but then they are not always as potent as you would like them to be.
They're like a treatment or something like that. So, so we, from, from the, from the pharmaceutical path, what we do is that we take that small molecule and said like, is there any way where we can make a new version of that small molecule that is more potent, have a, a, a slower absorption time so that the treatment is longer? Or it's like, is there some of the side effects that can be engineered around? So, so we focus on the nutraceutical and then what we call the analogs.
And it's very, very unique that we can design and engineer small analogs, small molecules. Small molecules is typically something you have in, in a big, just like, you broke down things in all kinds of ways and you have all these fragments of small molecules and you throw it at the target.
You see what sticks. We do it the other way around. We go and say like, okay, what is the specific drug receptor? And there's a lot of AI today that is working on this specific area of like, what are the drug receptors and what would the optimal small molecule look like? So what, what, what is the key to the lock kind of methodology? But what a lot of people forget about when they come out celebrating, yeah, I buy information.
They come out of the perfect small molecule. It's like, can you make it? Can you have access to it? Otherwise you're just back in court of like, yeah, I know about it, but I can't do anything. We can't do anything with it.
Yeah. And what is very unique for our platform is that we can sit down and we can make the enzymes do different things step by step and we can feed them with different building blocks. So if we want building blocks that, that have never been in the small molecule, been attached in a certain way, we just make sure those building blocks are available.
So we take artificial evolution pressure on an enzyme to find a way of combining those small chemical interactions that otherwise is considered not possible. So what we open up for is a lot of medicinal chemistry space where a medicinal chemist will say, you cannot do this thing because of, and then something about how shake flask and heat and solvents and so on, why that won't allow for it.
But that's not how we're doing it. We're doing it in a dramatic way and that's why it's possible. So the first application area overall is a lot of the natural products that are known by humankind already to be valuable, but maybe not as potent or maybe not accessible in bigger quantities than enough to treat a couple of mice.
It solves the problem other than we don't have access to it. Yeah. If you can't go past the mice. Yes. So, so we have, we have a number of products, natural products in, in basically our focus areas that we would like to take to market.
NCT is going to be one of the first. We got a project with National Science Foundation to take sandaline, basically build, build a biosolution for sandaline. Sandaline is a small molecule found in a very, very fancy tree sort that, that just allows for a little bit of sandaline wood oil where the sandaline molecule is inside of.
So, so again, one of these frustrating stories of like, can only go a few places on the world and we're already over harvesting it. Basically there's not enough. So despite the molecule has some very interesting nutraceuticals and pharmaceutical potentials, nobody has touched it because it's like, you can't get your hands on it.
It's too expensive. And the little bit that is there is actually used in the fragrance industry. So it's some of the fragrances in the really, really high end perfumes. It's really valuable, small molecules, but they actually have anti-cancer properties.
Interesting. People just can't use them. So, so we're going to, we're going to make a sandaline and we're going to make analogs of sandaline that are optimized as a nutraceutical and pharmaceutical. Maybe the nutraceutical.
We'll have to figure out if we can make sandaline as a nutraceutical because again, it has to be the natural product version for it to be a nutraceutical. That's amazing. That's amazing. That's really cool.
I didn't even think about the idea of not even just scaling up molecules that are small, but then you're thinking like, how can I, how can I make it better now? That is exactly why we need to be in engineering level control because otherwise this conversation doesn't exist.
Yeah. If it's a cell where all kinds of things is going on, you will never be down at like, we want you now to not do this, but do this. And it ends up in a slightly different analog that computationally, at least very quickly, or maybe even before we have run it has shown to be better.
And then of course you can test that in different assays and depending on the drug receptor, but basically everything from binding and the whole pharmaceutical value creation steps. But it is, it is because of the engineering level control is because we have taken biology out of the biosolutions.
It is chemistry. It is chemistry. You're removing all of the extra messiness that could be clouding the information that you could be getting and needing to make it better. And then we can operate at very, very fine levels where that can never happen inside of a cell.
Definitely. So I guess with this level of engineering, is there a problem child or a problem enzyme that comes to mind that has been particularly difficult to modify or scale? Or do you find that they're pretty ubiquitous in their ability to modify? Yes, there is a distribution.
Okay. Interesting. Inzymes, they're kind of like the holy grails of P450s that are membrane bound. They need to be in a membrane to work. And therefore they're super, super difficult for us to kind of like, are we going to try to engineer that reality of a membrane? Are we just going to go like, if that, sorry, my friend's here and go over.
And say what other enzymes can do the same thing? So the reason why we don't run into a lot of enzymes that misbehave is because we screen our enzymes up front. We only get enzymes that have good development potential.
So we kind of sort away from the problems in the beginning. And you actually said it during the part here that we sometimes, instead of having a reaction that takes three enzymes in nature, we find an enzyme that can basically jump all steps in one chemical reaction.
And then we just train that enzyme to go and do that. So we can plug and play enzymes where we go like, okay, this is the natural product pathway. But these three steps we can avoid if we go in another organism over here and we borrow that enzyme, we train him to be good at that specific one step and we plug that in.
So this is engineering level control. You start being able to use the hardware together. And that also means when we build these pathways, when we start a new project, we often start by saying, if we're to reuse most of this production line, but just changing a couple of enzymes so it becomes a different product, what would those products be? And it is, chemistry is still mind-blowing to me of like, it's this thing and then it's this thing.
And you talk about something you think you know, and then you say like, and then you change this thing, then it becomes something that's completely different. And you just go, how can that be the same? Yeah, that is, that called me back when you figured that one out because I'm not going to crack that one.
Yeah, this conversation is reminding me so much of my, I was a biochemistry major in undergrad. And for this exact reason, I loved biology in high school, but I loved the chemical applications in biology and just how they work together.
It's just so fascinating how something so quickly, you can have an enzyme that's doing one job and then you add just one group and it's completely different now almost. It's just, it's incredible. It has me in awe when I think about it.
The coolest thing is though, that they're consistent. So there's cause effects. So it's not kind of like, it's a blue moon Tuesday, now it's doing it. And now it's not a blue Tuesday and now it's not doing it and we don't know why or something else is competing.
So this ability to break the systems out to smaller enough components that they're stable and there's cause effect, then we can start applying a lot of the machine learning and methodologies we know from engineering, which changes the game from a commercial point of view.
Wow. Well, that's about all the time we have now. I could keep talking about this for another three hours, but I do want to give you a chance to, if there's anything that you want our listeners to remember from this episode, if you could, just what would that sentiment be? So if it was just like one thing, there is a new generation of Synbio where we are not dealing with cells.
We're dealing with chemistry empowered via enzyme and exercise. And my shout out to the world is like, come build together with us because this is not a technology we want to keep to ourselves. This is not something we want to sit on for the 20 years of the patents and then someone else can come and look at it.
We're very open for partnership collaborations and we want to build biosolutions simply because we, humankind, needs a new way of making the chemicals that makes us have the good life because we need to know that the future is a bright place, that it's a good place, that it's going to be better than it is now.
And this is one of those opportunities. And this is why we want to roll it out as soon as we can. And it's going to take time, but as soon as we can, we want to make sure that the world is powered by this raw power of nature that is the enzyme exoscience.
And at the same time, have the scalability that petrochemical have given us. Because as much as I just talked, oil and gas, let's also not forget that that is what has given us the good life that has been there for us in our lives.
And we owe to find the next year. So we don't just continue until we kind of run out of it or had polluted so much that there's no way back. And that's a shout out to the world. Come build this together with us so the future will be bright.
I love that tone of positivity that you're leaving us with. Well, Michael, it has been an absolute pleasure having you and talking to you about this. For more, for our listeners who want more information about the company, exoscience, or even follow your journey, where should they go? They should definitely go to our webpage.
We have put out a lot of videos and a lot of materials so we can be studied from abroad. Or even if you are our neighbor, feel free. We try to share the mission we are on, also to call out for partnerships, as I just said, and to get insight to everything from the science to the team that is here to the motivational factors that drives it all.
We'll link the website in the show notes below for anybody that wants to learn more then. Thank you very much. Thank you so much again for joining us. And again, congratulations on everything Exozymes has achieved so far.
And I look forward to seeing what comes next. Thank you so much. It was a pleasure. That's a wrap on today's episode. A huge thank you to Michael Heldson for sharing his incredible journey from serial entrepreneur to NASDAQ-listed CEO.
And for opening our eyes to how his company, Exozymes, is revolutionizing the production of chemical compounds. If today's conversation got you excited about enzyme engineering and sustainable manufacturing, be sure to check out Exozymes' website.
We'll have that linked in our show notes along with all other resources mentioned today. Thank you so much for tuning in to this episode. If you're interested in exploring other BCLA events, check out the events page on our website, bc-la.org, linked down in the show notes.
For more information. As always, if you like the show, please subscribe. We'd love to keep sharing the inspiring stories shaping Southern California's biotech landscape with you. This podcast is a BCLA production.
Thank you so much to the rest of the BCLA podcast team. Serena Gao, Daniel Arce, Kat Merklin, Brian Jimenez, Stephanie Wu, Amarilla Signia, and Danielle Liu. Thank you to Daniel Graves for the fantastic theme music.
And of course, thank you to BCLA's core sponsors. California Nanosystems Institute, LA Biospace, the USC Michelson Center Bridge Institute, California Life Sciences, and Keck Graduate Institute. My name's Gabriella Robert, and I'll see you here next time on Inside Biotech.