Hi, Michael. Welcome to the show today. Thank you very much. Thank you for inviting me. Our pleasure. To start off with going a little bit into your background, and then, I was hoping you could tell us what exactly are exozymes. Yep, absolutely. Let's do it. Yeah, so I'm the CEO of eXoZymes. We are a company that specializes and obsesses about making a new generation of biosolutions, meaning using the power of nature and biology to basically build things for us. And we'll dive into what exozymes does a little bit later. If we start with myself as an introduction, I was born and raised back in Denmark, hence the accent. I've lived here in Southern California for more than 10 years now. In my early days that is kind of like 20, 25 years ago, I was one of the guys that basically sat down and decided to build a bioinformatics company that should be the next generation. So 25 years ago, that's obviously a long time ago in regards to all the things that have happened. What we did realize with that first company was that computational power and molecular biology kind of going hand in hand would be very impactful for the world. The amount of knowledge and insight we could help uncover via building good tools for scientists and clinicians and other kinds of researchers in life science. So first company was focused on bioinformatics. Back in those days, there was nothing that was called, for example, next generation sequencing. There was this very, very new space that was called high throughput sequencing, where people were playing around with new technologies to basically get information out of information out of information out of nucleotides. And the sequence analysis of that was where we started. Because of that, we started working together with some guys over in the UK. We were sitting in Denmark. And they were called Sulexa. They had found a new way of doing sequencing by synthetists. And a couple of years later, they were bought by the microarray company from San Diego called Illumina. That was how Illumina became a sequencing company back in 2006, if I remember correctly. And that was how Illumina became a sequencing. And that was where we started realizing, okay, there's other people that have our eyes on this new way of doing sequencing, what was then later on termed next generation sequencing, or around that time called next generation sequencing. And this ability to read DNA of everything, of you and me and everybody, but also everything, and fundamentally try to uncover what we back then a little naively thought would be kind of like the source code of life, as people called it. Like, this is super intriguing. If we can just read all the DNA, we will just basically know how to code life. It reminds me a little bit of when I was a young teenager, and I got to mess around with some HTML the first time. I was like, I can just copy this. I was basically a script kitty sitting here taking kind of bits and pieces. And because I could get them to do something, I could build things. That was very intriguing. And next generation sequencing was a little bit the same in the beginning, where there was a few things that were going to do. We could do things we could do with it. But it was a long journey to kind of figure out how to get the error rate down and how to actually use these short reads that could technically seem when you are what was called aligning back up to a genome. It could belong multiple places. And then you could have read errors. Was it a mutation or was it a read error? All of those computational challenges that the world had to go through before next generation sequencing started becoming valuable. And discovering from an entrepreneur, perspective where the applications of these new technologies were fitting with problems and could become solutions to those opportunities or problems, depending on what it was. Intriguing background. It was very informative for me in my young days to kind of get to be a part of building a company. How do you build companies? How do you build teams? How do you make sure everybody is aligned and pulling in the same direction while at the same time developing like super, super complex technology? And when you get it to work, how do you actually kind of make that value proposition known to the world and to the potential customers? How do you find your customers? And all of that. So CLC Bio became one of the leaders in next generation sequencing analysis and was sold to QIAGEN and is one of the cornerstone softwares of QIAGEN Bioinformatics today and still a very used software. platform platform that you can argue that you can argue that you can argue that you can argue like being able to have been a part of some software that is now 20 years old and is still one of the best commercial offerings in the world is in itself a little bit of a success. That gave me an opportunity having built that first thing to become a part of a number of other startup and startup projects. And that's basically what I've done for a living since I. I have this entrepreneurial urge in me. My personality. I would like to see the world be a better place, solve the problems that are facing us. And I very much believe that technology can do that. And I also very much believe that there's a truth to that biology is technology and technology is biology. It's just not technology built by man. It's technology built by mother nature. And let's just say she has been much more inventive and hardworking. She has been much more inventive and put 3 billion years, 5 billion years into it. She has a little bit more time. Yeah. Exactly. So very, very, and with a tenacity that makes us humans look slow and lazy. But the reality is maybe just to play on this picture, also been a little lazy on the documentation on the code. What does this part do in the genome? Like there's not a lot of manuals and documentation, but there is a lot of code that is commented in and out. So this whole thing of like, you have a lot of code. What is it coding for? Is it even expressing? So therefore, is it just legacy code? Or is it actually something that's actively used? Is it maybe less one-to-one as we thought in the beginning when you were doing sequence analysis? Or like, let's find a gene, let's find a disease, and then turn it off and on. That would have been nice if the code stack of living matter would have been set up that easily. That's obviously not how it works. It's much more about all of these different types of influences and stages that things are in. What does that allow for? But overall, that's a fascination of mine between molecular biology and computational power and what can be built. And I'm now on my fourth or fifth company, depending on how you think about it. So being exoscience. So I very much believe in that we humankind can solve a lot of the problems we are faced with by utilizing the things that have already been developed in nature. Just we haven't been in control or even at a level where we could understand or interface with the solutions. And that is what DNA sequencing, opening up the whole universe, is giving us. It's the computational powers of machine learning, pattern recognition, and AI that really opens up those things. And it all seems to me to be like a very straightforward line of opportunities that keeps kind of building on top of each other, the bioinformatics company. I remember the first time when I realized by our, back then it was our chief science officer, Bjarne, that pulled me aside. And I was like, hey, Michael, let me explain to you that these algorithms over here, we have coded. Decision making. Decision making. When this, then that. Human controlled. These algorithms over here. Look at these patterns in these datasets. That's the rule. We're not coding anything here. We're just setting pattern recognition engines up. And every time you see a pattern, then that becomes a tool. And I was just mind blown. Like, what are you talking about? That's the core of AI, right? It's pattern recognition. Put on math so you can re-execute it with a click of a button. And that's where some people still misunderstand AI as a thinking individual. It's not. It is a pattern recognition engine that based on everything seen before can make really, really informed and smart guesses on what is the next thing in the sequence, being for example, a word in a text string, or if we're working with proteins as we do at eXoZymes. Basically, what is a change in the DNA that leads to a functional change? What is the protein in the protein? And what is the enzymes? And being able to master those tools and math and pattern recognition opportunities is really what is giving us all the breakthroughs that we've seen today. Okay. Yeah. Well, thanks. Thanks for sharing. So how old is eXoZymes since it uses machine learning to help with designing the enzymes? LLMs have become, you know, more popular in the last couple of years. You know, machine learning has been around a little bit longer, but really not too long because, you know, the bottleneck, I think, really was the compute power available to power the, you know, the learning algorithm. So how did eXoZymes come about? And, you know, what prompted you to get started with it? Yep. So there's a number of elements we have to sew together to answer that question. So eXoZymes have two major technology breakthroughs that we're building the company on. The first one that was where the company started at UCLA is basically cell-free biomanufacturing. So what happened back in, well, the company was founded in 2019. So some years before that, maybe let's call it a handful of years before that. Tyler and Paul and Jim, that are the three technical co-founders of our company. Jim was the PI of the lab in a biochemistry-focused lab. And Paul and Tyler, the two PhD students, that was basically celebrating that they had genetically engineered a cell to make a little bit of some biodiesel. And that was enough to do a really nice publication. So academically, it was a success. But they were also honest enough with themselves to sit down afterwards and say like, but hey, guys, this is never going to work in reality. This cell is never going to allow us to do this at scale. Then they basically, they looked at each other and they tell the story as Jim kind of like left the lab saying like, okay, guys, then just build me a cell-free biomanufacturing system where the pathway, the enzymes that are breaking down from feedstock, the breakdown enzymes, break it down into building blocks and the buildup enzymes build it up to kind of the end molecule. That happens inside of the cell typically. If the cell is such a problem, just build me the pathway. Without the cell, without the cell, built me a cell-free biomanufacturing system. And I was meant halfway as a joke, halfway as a like, why is the cell such in the way for us to do these things? And that was where the journey of cell-free biomanufacturing started. And maybe just to unpack it a little bit more, the reason why synthetic biology have been so challenged from turning really good ideas and really great initial work into commercial solutions, into something that makes a commercial and a real life impact is basically that fundamentally cells are set up to only make the chemicals they need and only in the amounts they need them. And everything else they try to regulate down. So you're basically on a production platform, if you will, in cell-based biomanufacturing where you're kind of fighting the platform a little bit. You're trying to make it make chemicals it doesn't need and for sure not at the volumes it needs it. So you have all of these regulatory things. We already talked about a little bit from the genome that, again, it's not well-documented code. So it's not like these are the five things we need to turn off and then this will fly. It is multi-year often development programs, trying all kinds of combinations. And sometimes it works and then the next day it doesn't work and nothing has changed. Like what's going on? So it's a very, very challenging task. It's why it's so amazing that there are even some people that have succeeded in building cell lines that produces chemicals of commercial value and being able to put it up to scale. Argument number one was basically they don't need it. Number two is because they don't need it. Kind of, if you take it all the way up, it's also toxic for them and might even kill the cell. So you end up having basically your cell-based chemical factory breaking down every time you use it. That's not a super good starting point from a how do we make commercial business out of that and repeat it? And then the third argument that has unfortunately challenged a lot of the student bio manufacturing companies over the years is like, even if you build a cell line that is now making enough of this material without dying or at least making it before it dies, then the isolation cost of taking a specific small molecule out of this universe that the cell is that has a million other things going on. So you have like all these other things that are polluting your pure end product. So you have a hard time getting to purities that are commercially relevant without having spent more time on isolating than the value of that compound. That means you're never going to make commercial success. You're never going to have a business on it. So these things were the first area that Tyler and Paul and Jim sat down and said like, okay, we fundamentally want to be able to run enzymes outside of the cell and the whole pathway, not just one enzyme that time, but the whole pathway, but the whole pathway. How do we do that? And spent what is now combined a close to a decade on what figuring out what is it that a cell does for enzymatic pathway? Okay. It has these cofactors and cofactor regeneration capabilities. How do we substitute that or build a system that does the same, but outside of the cell? How do we have energy balances balanced the right way over in our solution when we don't have the cell that naturally does that? How do we do that? How do we do a lot of development? How do we do a lot of helping functions for the enzymatic pathway? And that is obviously a lot of first directed evolution. Then after what? It became the more engineering style. We have a sense of if we make this change, then that might benefit. Let's try that. Let's go out and express it in the lab. So this is by, sorry to interrupt you, but by directed evolution, what you mean by that is just making a lot of random mutations within the, gene that's responsible for the protein you're looking to study the function of and then looking at the activity and performance of that protein and then figuring out which mutations are the ones that are beneficial to what you're trying to get the protein to do or enzyme in this case is that correct okay you're exactly right that um so so this is what francis arnold got the nobel prize for directed evolution can we sit down and just with random mutations basically let evolution play out and then pick the winners and say like wow there's now this one enzyme over in this cell line is doing so much better let's go back and look at what was that random mutation that led to that development that's how evolution works right one mutation at a time out in nature if it becomes significantly better then that will be the one that wins over time so that's that's that's that's the the the r&d program of uh of of mother nature that's how that works and just massively parallel trying a lot of things we don't have the same scale available so so despite directed evolution is still a strong methodology and can be used sometimes it became the rational design methodology where we kind of like could sit down and with the team that has a lot of experience in engineering enzymes saying okay instead of it let let it be random why don't we control where the mutations are we kind of know where the winners are going to show up what part of the genes that expresses the enzyme what part of the gene is most likely to to influence this and let's sit down and and try that out so that's that's generation two directed evolution and i'm going to talk about generation three that is what we use today and where ai plays a role but let's just map this out because it is this pretty fascinating how you can use basically the drive of nature to to optimize and then start learning from that looking at those patterns so the first generation of software used in this industry was you can argue less that we consider ai today and more kind of like rational design we now go with these things for example and we be concrete if there's an enzyme we want to lift outside of the cell we need to make it strong enough that it survives outside of the cell so sturdiness and robustness there is a lot of knowledge about what it takes to genetically optimize and in time to become robust enough so so you kind of get a knowledge packets around that and every time you then look at a new enzyme you build it based on all of that historical knowledge and those data sets before and then when i joined the company i joined the company a couple of years ago as ceo and came in with also the perspective of now we have all of this knowledge lying around in our data all these patterns you can argue knowledge is pattern and patterns is knowledge lying around why don't we train on that why don't we and we don't need to do it ourselves necessarily because alpha fold that we talked about from that that is is basically a way of looking at the the sequence of all the way down from DNA but also up to the protein level of the expression what is that linear sequence can you calculate based on that what the 3d structure of the protein and in our case enzymes what they look like now of course starting to have a lot of data starting to have a lot of data with a lot of data with a lot of knowledge patterns inside and these algorithms becoming both available but also cost effective so that it didn't cost the farm every time you needed to run the calculations were then an opportunity to sit down and say okay this is really interesting can we based on our own knowledge and our own experts guess what the mutations are and compete with an AI version that guesses on what it is and we started having a lot of overlap and it's not that surprising because we obviously somewhat training on the old knowledge but nevertheless this ability to do something with a click of a button versus spending a couple of weeks worth of meetings sitting and debating forth and back and what do you think and so on so that that really was the first implementation of of of AI force and then the the team and everybody sat down and said like oh if we only had more data if we only had more patterns and knowledge we could train this because like this ability to to to to execute everything in in milliseconds instead of seconds or seconds instead of having to do it in days and weeks and months is obviously a huge opportunity if you can speed things up so a part of our team sat down and said like well we have some ideas to how we could how we could basically express the enzymes much faster which was the bottleneck so let me just unpack that when we had the AI come and say yep I have found the thousand mutations that can influence this enzyme everybody was looking at each other and going like yeah but we have like capacity to go out and implement implement maybe 10 different genes and grow up the cell lines and harvest those cell lines from the enzymes performance test them and then get data and it's going to take us month and month to do like a hundred and it wants to do a thousand there was a disconnect so so our team sat down said like what can we do to much faster test if these changes actually ends up with an enzyme that performs better and our cell-free capabilities really kicked in as an opportunity because it started becoming all so it's obvious to us that we could basically express the DNA over to enzyme without cells so we could take the DNA we could basically set up enough helper systems to express the DNA over to the protein that falls up to the enzyme and then in the same workflow we can actually do the performance testing of the enzymes and when you say helper systems so that's taking into account everything that's needed for the transcription of the DNA but also the transcription of any cofactors that the enzyme is that the enzyme is that the enzyme is that the enzyme needs to function properly right? Yep. So this is a really good example of how biology is becoming technology and technology is becoming biology and of course we borrow a lot from biology where it's like where we can get it fast and cheap but also sometimes we have to have to add up things that makes the system run to a degree where it expresses enough enzymes so we can performance test enzymes but we we found ourselves taking AI and we found ourselves taking AI and this data generation via cell-free and matching them brought us to a position where all of a sudden we were kind of like okay let's let's express a thousand mutations on this one enzyme to see if it's better and basically just brute force evolution let's sit down and calculate all mutations that we think would happen over the full stretch of all of evolution if there was such a thing. Just a quick question why would you need a thousand of the mutations when you can take you know if you're looking for a specific function the AI should predict that the mutations that should give you the best you know results so why not take 50 or 100 of the best ones and express those and see how well they perform compared to what the AI was hypothesizing right instead of you know why did you need to take all of a thousand or were you also trying to get more data to go back and train the AI to perform better next next time around. You're almost answering the question here so there's two reasons the first reason we were thinking as you were thinking in your question here like why don't why don't we just take the top ones they're supposed to be the winners right turns out that biology is more complex it is often number I'm making up numbers here but number three number 17 and number 52 in combination that gives the best version so so just picking the winners is can can can can can get a little harder so so what I'm saying here is if again we only want to develop an enzyme tool that it's good enough it's no longer the bottleneck in the production pathway so if it's one mutation and we get it there then that's great and why is it not number one two and three like top three or top five that we just implement it's because they often interfere with each other so it becomes a little bit more of a puzzle piece and that leads to your your your second part of that question is we actually learn a lot of the what's the data from running this data both learn so we can take it fundamentally back to the core algorithm and said like when you with so high confidence scored this one to happen this and it didn't happen at all then you need to learn from it so that's kind of like one one of the advantages of generating very large data sets is that you're actually training your base you at least have the opportunity to do so the other thing is that it's it's a combination of things and some of the mutations kind of like the changes they do they interfere with each other so if you take one two and three then you get one two and three then you get not an improvement but maybe a non-working enzyme out of it so it's still a little complex and back to that mother nature didn't write this to be human understandable and easy it brute forced it by evolution but by running these systems we can still in a matter of days and a few weeks do things that would take i don't know a another five billion years of evolution to happen naturally out in nature so we have invented this language we have invented this language packets to kind of packets to kind of fit ourselves and explains our name basically an enzyme is inside and cyme is a chemical reaction exozyme is outside and a chemical reaction so an exozyme is a enzyme that is robust enough with this evolution that that we are we are building to live outside of the cells so it doesn't die unfold crash at the same time it's also an enzyme that can work together with the next steps in the enzymatic pathway so that you can have a enzymatic pathway so that you can have a feedstock start then break down and then build up and then with your end molecule and because we only have the feedstock and the exozyme we put in we're now in programmatic control we are like step by step in in control of what is happening in our biomanufacturing and that means we have complete purity in the end it also means that all the feedstock are used to go to the end product where over in the cell good luck getting just 10 percent of your feedstock to end up in your end product and it makes it so what i'm saying is like we have taken bio out of biomanufacturing and turn it into a scalable chemical process though with the power of mother nature via these enzymes and exozymes so so it's finding the best of both worlds of chemistry and biology and what it really allows for is that we can start not just being much better scaling control because we can we can control these enzyme steps based on these enzyme steps based on these tools we've talked about but we can we can also start building things that are typically not present so let me let me be concrete about it now so we have for example a project where we are building a small molecule called sandaline sandaline is the active component in sandalwood oil sandalwood oil is one of the most expensive natural products you can buy why is it so expensive basically because it takes between 10 and 30 years to grow the tree that it comes from it can only grow one of two places in the world india and australia when you harvest one time you take the hardwood out you put that in chemicals and steam for a week and voila you have your sandalwood oil and a little bit of that is sandaline so what we have done is we said like okay that that is clearly very hard to get at so supply side limited this small molecule has pharmaceutical use cases it has new pharmaceutical use cases and in the fragrance industry that that frankly consumes most of the available thing in the available thing in the world right now of sandaline is is a very positive fragrance that is in a lot of the very very high end so it's one of the reasons why really expensive perfumes are really expensive and very nice so we just sat down and said like hey let's look at that tree what are the genes that codes for those enzymes inside of that tree that sometimes takes a feedstock breaks it down to these building blocks and builds it back up to sandaline we studied that pathway inside of the tree then we we are saying okay those enzymes one by one how do we turn them into exozymes so they can live outside of the cell we're using our ai platform and our the forced evolution data generation and ai training circles so that the enzymes aka exozymes becomes good enough isn't a bottleneck and then we just run that pathway and only that pathway we don't have to build the whole tree and we don't have to wait 30 years for all the other things that we want to do we basically just go from feedstock breakdown build up and and end product and that's basically the description of how our business work we take very valuable natural products that are often very valuable because they're very difficult to get hold of or impossible to get hold of and then we build biosolutions for those and what we can then do on top of that is not only building the natural product version we can build new to nature and more potent or better whatever the molecule is versions of it so for example that's that's another product line of ours it's called nct that's a small molecule that's a molecule that's a molecule that's a molecule that boosts human metabolism that boosts human metabolism that means burns more fat and creates more energy that's a positive value proposition for a nutraceutical calling it nutraceutical because you you've already eaten it you've already had it because it's in black pepper the challenge is just that you need to eat a a house full worth of black pepper to get enough of one dose for nct for it really to do a difference for you so there's no commercial practical way of getting your hands on nct today as a nutraceutical same story sit down look at where does it exist in nature in black pepper what are the genes that code for the enzymes how do you lift them over make them exozymes and basically build a biosolution that makes nct and nct exactly the same way nature does it that's a natural product that we can get grass approval generally recognized as safe that's an fda regulatory framework and that means it can be sold as a supplement that is one business vertical of this but then we can also take nct and say okay now we can take nct and say okay now we can take nct and say okay now we get to really play around with some of the other ai tools available this small molecule is doing this nice thing over to this specific drug receptor but it's random it's just completely the universe is random enough that this nct ends up clicking into this protein drug receptor and therefore triggers some downstream effects but what if we optimize that small molecule to be even more potent have much more longer treatment time those kind of things you can do you can do typically open drug discovery and drug development with medicinal chemistry we can do that with enzymes now so what we are also doing building our company is building a new generation of medicinal chemistry tools that biotechics and pharma companies typically haven't had access to because they use normal chemistry to kind of try to build the small molecules and some people are using enzymes to kind of enhance those small molecules that means make additions and so on but as we are building the whole pathway for this small molecule we are building the whole pathway for this small molecule we are much more programmatic control engineering level control of how we evolve enzymes to do very special chemical steps and only those so we can build we can build a lot of what is basically called small molecule drug candidates that have been theorized but not been able to be built before well I know I know that a lot of natural products are modified modified by the pharmaceutical industry and then used to treat various diseases I know various diseases I know you know in cancer that's done and then those small molecules once they I guess become a viable candidates you know then they established a manufacturing process to at scale do you think your technology could replace some of these manufacturing processes the chemical manufacturing processes that you know exist right now is that a possibility definitely you can argue that we are just focusing on nutraceuticals that also can in optimized versions be pharmaceuticals that also can in optimized versions be pharmaceuticals that's kind of the space we play in but fundamentally this is a new way of doing chemistry so you you can apply it not just over in the pharmaceutical space you can build a lot of different I would I would argue most nothing all but many or most chemicals that we today use gas and oil as the starting point and break down chemistry to get to to where we are the different products we make from it that you could use natural feedstock starting points and you can have the enzymes break down to the right building box and build it back up and we have had projects in the company and have projects in the company that that is kind of outside of our focus space that that we have been talking about so for example the DOE have given us a grant and the DOD actually as well have given us grants to look at isobutinol that is if you take two isobutinol to put them together you have sustainable aviation fuel so they have given us basically a research task of seeing if we can build sustainable aviation fuel that they ended up running into that all the companies that had given money that was cell based their cell lines kept dying on them or crashing on them or not producing or in the very end even if it worked the isolation cost was higher than the value of the sustainable aviation fuel so there was no business case and they came to us and basically said because you're cell free and because this is actually really important to us we would like you to work on this and we have those programs running yes you're right it can be used for all kind of chemistry but as a startup we have to focus so that's why we're in the nutraceuticals and nutraceutical inspired to be upgraded to potent drugs exactly as you were describing you mentioned that you know you have to make an enzyme more robust to turn it into an exozyme so is any pathway and any enzyme able to be pathway and any enzyme able to be turned into an exozyme and you know how much modification is required or is not every enzyme that exists a valid candidate because it's just not as able to exist in an exogenous environment you know well enough to be part of the biosynthetic pathways that you guys are building at scale so overall i would say that the enzyme and protein universe is so great that anyone that's anyone that's anyone that told you everything can be done with one thing you shouldn't believe them so so no not everything not every enzyme and and there's a couple of things that are not that we have met that many where we we don't have anything we can hold on to but there's definitely some of them that are much much harder to to kind of engineer on where there's there's some that that are super easy to work with so there's kind of like a scale in that what also happens is let's say we have like just making up the number here seven enzymatic steps from feedstock to end product and it's one of the enzymes that is the bottleneck all the other ones they could run three times as fast when we're done building them as as exozymes but there's this one specific one and we have optimized and we've done all but like from a performance point of view we can't really get it to jump instead of just keep hammering on it as we can do and then brute force our way through it and in the end make it good enough what is actually often easier for us is to take a step back and say okay what is the chemical step this thing is doing specifically okay this specific step where else in nature is the same step done by another enzyme okay time to to kind of change around and put a new football player in with fresh energy here because then we we come in with a candidate that technically let's see it was from a tree right these seven steps were one to one the same enzymes from the tree but we are taking one out and replaced it with one that does the exact same thing but under other circumstances and that often allows us to then make those very large jumps in performance enhancement or whatever the bottleneck is it can also be that the performance is actually good enough but they're just not good at surviving outside of the cell and stabilizing them as hard or it can be that we need them to be heat stable up to 75 degrees that's like some sometimes you end up in these trade -off situations where if you optimize for the one then you start hurting the other ones and sometimes starting with a new new player is then easier than being stuck in the same challenges it's also because we are obviously the technology we have today is version one it's the worst version it will ever be in all the future but it's the best existence on planet earth and it didn't exist going back in history so when we start updating on the fundamentals of the system it will also come back I'm sure in three five ten years from now we'll look back and say like do you remember when we had a problem with these these these kind of things and we don't have that anymore it's also going to be true at its core we can build biosolutions that makes small molecules that are otherwise not available at all only in tiny amounts at extremely high prices and that makes us a viable business here now and then gives us time to kind of keep optimizing on on the core technology and the core capabilities so that hopefully with time there's nothing nothing we can't do but we are dreaming when we dream really far and really out in the future we're talking about a world with a sustainable abundance of everything there's still a little bit of work to be done before we're there and if it's if it gets done in my lifetime then I'm also more than happy and if it's afterwards but we have paved the way then that would also be a success thanks for sharing do you guys have some sort of plan you know what what are you sort of working towards in the next three three to five years we are working on making sure these first product lines so we talked about NCT that is is good for metabolism and metabolic health is is a very big initiative of ours that that we talk about because we we have it out publicly that we have sandaline that we have talked about then we are very fascinated by cannabinoid like that is not anything you get high on no no psychoactive cannabinoids is a part of our program and we are actually getting federal grant funding for cannabinoid research because we're so in control of our process we can guarantee there will be no THC and there will be no psychoactive challenges that is is the reason why they are scheduled as a schedule one drug and therefore not something you're allowed to otherwise work on typically as as a research organization this is from a federal perspective not from a state perspective we really we really admire that there is such molecules that there is such molecules that binds to so many things different drug receptors in the body in so many different cell types specifically we also recognize that a lot of people have been using cannabinoids but from plants where all approximately 200 cannabinoids are just mixed together in different ratios from plant to plant from from from growing season to growing season so sometimes it helps some people something other times it doesn't help and nobody can kind of detangle that network network of what what what small molecule does what for a certain disease and that's basically what what we're working on being able to build the rare cannabinoids in pure form and build new to nature versions of cannabinoids that are specifically tailored to specific diseases so we're very intrigued about that natural product direction and and we are taking it very serious that the federal government is paying us to help detangle that whole big opportunity for for humankind and then we have a number of human kind and then we have a number of other natural product ideas that we are basically we have in what we call our incubator so we have a idea states incubator where we do proof of concepts and other things either financed by ourselves internally or via partners or via grant agencies that comes and says could you build this small molecule it would be really important for x reason and then we basically without spending a lot of money on it we can map out if it's possible possible and we can build a prototype of it and then if that turns into something that is commercially viable and worth financing and we find the right partners for it we go from the incubator up to the accelerator program where nct and sandaline and the cannabinoid like the molecules lives right now but that's basically product packages that in a number of the cases will be spun out as individual companies as we're doing with with nct and in in other cases it's going to be joint collaboration joint ventures and similar style businesses where we hand over the buyer solution to a partner that then brings it to market it's very very interesting and i'd love to i'd love to continue this conversation and ask you you know about a dozen more questions but unfortunately we're we're out of time for for this time so thank you again for coming on the show and uh telling us about exozymes i'm i'm super fascinated i think it's very cool what you guys are doing and i think there's a ton of ton of potential you know for it to lead in just a lot of different directions yeah thank you so much for the opportunity and maybe sometime out in the future i can jump on again and give you an update on how things are going and i just want to i want to end on the note that the future is bright it's going to be okay we're going to build solutions for the problems this this whole narrative of that the future is dark and everything is going down the drain and so on don't subscribe to it subscribe to that we are a generation that can just build our own solutions and when we get to harvest the raw power of mother nature then there's going to be enough for everybody i agree with that and i definitely subscribe to it definitely thank you so much for the conversation i enjoyed it thank you