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Non-standard Amino Acids in the Development of New Medical Therapies

There are 20 standard amino acids in Earth biology.
Other non standard amino acids are rare or cannot be found in nature, plus can only be made in a lab.
GRO Biosciences is a company at the forefront of using non-standard amino acids to develop new medical therapies.
Non-standard amino acids can improve protein stability, prolong drug effectiveness, enhance cellular delivery, and modulate the immune system.

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare. 

Written English is built around an alphabet of just 26 letters, which act as the building blocks for every possible word or sentence. 

In the same way, all life on Earth is built around a standard set of just 20 amino acids, which are the building blocks of all proteins. 

But sometimes our 26 letters aren’t enough to express everything we want to write—which is why we’ve also invented all sorts of special characters, like the @ sign and the hashtag sign.  

Well, it turns out that we can do something similar with amino acids. 

So, how many amino acids are there in nature? Beyond the 20 standard amino acids in Earth biology, there are other non standard amino acids that either occur rarely in nature, or that can only be made in the lab. 

And biologists are figuring out how to assemble these non standard amino acids into designer proteins that can do specific jobs inside our bodies, such as helping drugs last longer in the bloodstream before they get broken down, or helping to train the immune system not to attack the body’s own tissues. 

One of the companies on the cutting edge of doing that is called GRO Biosciences.  

It’s a spinout from the laboratory of the renowned synthetic biology pioneer George Church at Harvard Medical School, where GRO’s CEO Dan Mandell was a post-doc. 

Mandell co-founded the company back in 2016. 

And today he’s here to talk with us about the work the company is doing to explore the applications of non standard amino acids in the development of new medical therapies. 

He’s really good at explaining how the science of non standard amino acids works an what do amino acids create, so I’ll let him do that, rather than trying to summarize it myself. 

But the basic idea is that new chemistries built around these so-called NSAAs could help overcome some of the limitations that keep today’s gene and protein therapies from being used more widely, while also expanding the kinds of jobs that protein-based therapies can do. 

I think it’s an exciting time in the biotech business, when it feels like some of the remarkable laboratory advances we’ve seen in synthetic biology over the past decade or two might finally make it to the clinic.  

And that excitement comes through in my interview with Mandell, which I’ll play for you now. 

Harry Glorikian: Dan, welcome to the show.  

Dan Mandell: Thanks, Harry. It’s great to be here with you. 

Harry Glorikian: Yeah, no, I was really excited about having this conversation. And then I was thinking about, okay, how am I how am I going to frame this? Right? So we go step by step and so that everybody listening can sort of wrap their heads around everything you guys are doing. And so you know your company GRO is all about making it possible to build protein based therapeutics where the proteins are made from what you’re calling non standard amino acids. And so before we go too deep right into the tech. I’m hoping you might be able to give sort of, you know, listeners a quick refresher maybe on what proteins are. I mean, you know, all of us, every living organism has standard proteins. And as far as we know, all are made from the same set of, you know, a 20 amino acid, you know, set. And I think there’s this view, there’s an inherent limitation, um, when you’ve only got 20 things to work with, although, you know, biology has done a pretty good job up till now, right? But maybe you could explain a little bit about, you know, proteins, what they do and then, for the non-expert to understand what you’re doing, what do you mean by standard and nonstandard amino acids, and what is the difference between standard and non standard amino acids? And so I’ll let you take it from there. 

Dan Mandell: Yeah, absolutely. That’s a great set up, Harry. And indeed, as you said, you know, the company’s mission is trying to redefine a particular class of medicines that we call protein based therapeutics, right? By granting access to this what we call a new alphabet or even a new universe of unnatural amino acids. Right. So what does what does all that mean? So as you as you pointed out, proteins are really the workhorses that carry out pretty much every function in your body. Every enzyme in your body that’s used to metabolize your energy is a protein. Every antibody in your body, which is fighting off pathogens, is a protein. Much of your skeletal structure is protein, your hair is protein. Right? So these are really the sentinels or workhorses that carry out every function in your body. And if you think about your DNA, your genome, that determines who you are as a person, really what that is, is a blueprint for which proteins should be made. And so all the variation that you see in us is driven at the genetic level, but at the end of the day, it doesn’t mean anything until it’s turned into a protein. And so that really is a difference between all of us. 

Dan Mandell: Now, life has, for the last 3.5 billion years, miraculously used the same 20 building blocks over and over again to create all the diversity that we see around us. Right. Every human being, every plant, every bacterium, viruses, everything is made of the same 20 amino acid building blocks, plus or minus a couple rare ones. And that’s staggering. Right. And to your point, you know, life seems to have gotten along pretty well with this this 20 amino acid alphabet. Now, what was also critical in what I just said, it’s been 3.5 billion years. Right. And so given these incredibly long timescales and given a particular mission, which is survive and reproduce, by any metric, life has been incredibly successful on Earth. But as engineers and and scientists and clinicians, we don’t have 3.5 billion years to wait for a particular treatment to arise for a disease that’s affecting people today. And so we think about what we call forward engineering, how do we take a problem and solve it using tools that are at our  rdisposal in a reasonable time scale to be able to help people that need it now? And that is what these new amino acids give us access to. We can take functions or chemistries that weren’t accessible to us ever really, and now bring them into our tool kit. And so I can talk to you a little bit about what are the new functions that we’re enabling. But just at a very high level, that’s what we mean. We want to take the process of therapeutic development, dramatically expand the capabilities that are accessible to proteins to solve some of the toughest challenges facing medicine, but do it in a way that’s expedient and that can bring something to patients that are in need in the short term. 

Harry Glorikian: But you’re not talking about standard amino acids that have been chemically modified or, you know, you’re not talking about manufacturing new kinds of amino acids that aren’t found in nature. You’re talking about non standard amino acids. I think you’re talking about amino acids that occur naturally, but rarely or never get incorporated into proteins. And I just want to make sure I’ve got that correct. 

Dan Mandell: That’s a great question. And it’s in fact both. So there are some non standard amino acids that can be found in nature, but none of our tools we have available for making proteins allow us to build proteins with them in any kind of efficient way. So part of what we’ve done is bring rare non standard amino acids into what we call our platform, the GRO platform, that now make them engineerable, we can put them into any protein wherever we want, right? So one of the protein properties that we’re trying to improve, for example, is stability. We want to make therapeutics that can be dosed in a convenient fashion and that have fewer side effects. When you think about the pharmacodynamic profile of a drug, it always has a peak and a trough, right? And that peak to trough ratio will heavily dictate how much of a side effect that confers on a patient. Right? So you have a high concentration of something, you’re more likely to have a side effect. And if you have a trough, you know, you might lose efficacy. So shortening that ratio, right, is something that is of high interest. So there are non standard amino acids that are found in nature that can confer proteins with a really incredible level of stability. 

Dan Mandell: So what happens if you make a protein more stable? Well, you can work to prolongate its half life and flatten that pharmacodynamic curve, as it were. Right. And so now when you give the drug, you can give less of it because it’s not going to have that big peak to trough, right? It’s not going to be cleared so quickly, which means oftentimes fewer side effects. And also it lasts longer. And so you can give it in a more convenient fashion. Right? So that’s one example of something that occurs in nature. There are other non standard amino acids that don’t occur in nature, and we synthesize them in a laboratory and they’re also accessible to our platform and they can do all kinds of crazy things, anything ranging from what I just described to improve stability, to creating proteins that can better access the internals of cells, really for delivery,  to directly modulating the immune system. And that latter application is something we’re actually focused on right now in with a great deal of effort. 

Harry Glorikian: So when you’re thinking about this, you know, could you use a non-standard amino acid to make proteins with 3D shapes that hold together better. I’m just trying to figure out all these different applications because, you know, protein therapeutics have been around for decades, and we keep figuring out how to make it better, how to improve on them, how to stick something on them to last longer or have, you know, the effect that we want. So, you know, how does what you’re doing, how do you, what’s your vision of saying that it’s going to really change the dynamics of from where we are today? 

Dan Mandell: Yeah, that’s a great question. And you actually used a key phrase there, which is 3D shape. And maybe for in the interest of your listeners who might not be so familiar with, you know, how proteins are made and how they end up actually doing a job when a protein is made inside your cells. It’s, as we talked about, comprised of these 20 different building blocks. And when they’re synthesized, it’s almost like they’re a string of beads on a necklace that come out one by one, first amino acids, second, third, fourth, as it’s been read off from what’s ultimately been the DNA. And so that string now of amino acids will have each of those amino acids has different chemical properties. And that means in the aqueous environment of a cell, which is like mostly water, that protein will actually then fold into a three dimensional shape. And that shape is heavily determined by how much each of those standard and non standard amino acids wants to be exposed to water and does not want to be exposed to water. And a very fundamental level, the protein folds in such a way as to hide the standard or non standard amino acids that don’t want to be exposed to water in the core and expose the ones that like being in water on the outside. 

Dan Mandell: And that’s how you get what’s called the fold or the shape of the protein, and that heavily dictates its function, right? So in order for a protein to do its job, it has to reach that folded state. And we have a number of diseases that arise from protein misfolding, right? In fact, a lot of the neurodegenerative diseases that we face, we believe are heavily driven by aggregated proteins, which means those proteins didn’t fold appropriately and they stuck together in ways that are pathogenic, right? So in order for the protein to fold right, there are actually special standard and non standard amino acids that we can use, to your question, which assists that process and help maintain that fold. So one of our chemistry families is called DuraLogic. And you can almost think of these as staples. They’re non standard amino acids that form a very strong bond that’s so strong that it can persist inside a cell and it holds the protein in that shape. And that’s really what helps prolongate the half life of that protein. 

Harry Glorikian: I’ve seen you quoted as saying that GRO Bio is interested in problems that, quote, really can’t be solved in the clinic without, you know, this new expanded universe of chemistries at the amino acid level. And you said, we want to play in areas where we think we can solve Holy Grail challenges, and there isn’t another way to go about this. So. What are some examples of these Holy Grail challenges? 

Dan Mandell: Yeah, great question. So this is a great segue into yet another family of what we call non standard amino acids that we’re working on. And you’re absolutely right, this is a big part of our mission. You know, we’re not trying to make incremental improvements. We’re trying to figure out what are some of the key unsolved challenges facing patients and clinicians, which really can’t be addressed using therapeutics constructed from the 20 standard amino acids so that we can move on to non standard amino acids. Right. So this brings us to what I’ll call ProGly, this other family of non standard amino acids that we’re now working with in our platform, Right? Um, ProGly is actually not quite a portmanteau, but it’s, it’s two acronyms put together. It’s short for programmable glycosylation. So glycosylation is really the method by which your body distinguishes self from non-self. And here we’re going to be delving a little bit into the immune system to go after really big problems there. And the reason why I’m bringing this up is there are little sugar molecules called glycans that decorate most of the cells and proteins in your body. And Harry, you and I share the same glycan composition, but we have a different glycan composition than a cow or corn or a blade of grass or in fact, bacteria, right? And so when your immune system sees a particular sugar composition on the surface of a cell or a protein, it goes, aha, this is this is me, stand down. But when it recognizes the sugar composition that over evolutionary timescales your immune system has learned tends to be associated with a bacterial engineering pathogen, it mounts an attack.  

Harry Glorikian: Right. 

Dan Mandell: And so this is a language, if you will, this sugar language that is incredibly important to how immunity stays in equilibrium. Right. You need to have your immune system to fight off pathogens, to fight off cancer cells. But you also need to have it balanced against all the things in your body or else you get what we call autoimmune disease. Right. And autoimmune disease is where your body has decided something inside of you is foreign and it mounts an attack. Now, everything I just mentioned about microbes and cancer cells, they actually use this glycan language, this sugar language, to try to trick your immune system. So there are bacterial pathogens that will pluck off your glycans and put them on to pretend that they’re part of you. There are cancer cells that will overexpress those self glycans to try to avoid surveillance of the immune system, right? Those are all ways in which this can go bad. Now what we want to do is co-opt these approaches to make better therapeutics to treat things like autoimmune disease, right?  

Dan Mandell: So the way this works is when you have an autoimmune disease, you are reacting to a particular, let’s say, protein sequence in your body that you’ve decided for one errant reason or another is now a foreign protein. We want to now re-educate your body to recognize that protein as a self-protein and stop reacting to it. That would cure the disease. Now, typically a patient facing an autoimmune disease, because we don’t have a way to do that right now, has to take what we call a broadly immunosuppressive therapy. We have to knock down the entire immune system to get you to stop responding to that one or a few proteins that are causing the disease. And if you think about what I just said about how important your immune system is for fighting off infections and cancer, now you’ve just completely changed the problem. So maybe your autoimmune disease is better, but your risk for infection goes up. Your risk for cancer goes up. Right? We don’t want to expose patients to these kinds of solutions. And so why can’t we just what I’ll call telorize, re-educate the immune system to that one protein or set of proteins that they’re reacting to? Well, part of the reason why we can’t is we’ve never had a way to engineer in that special language, this glycan language. Well, it turns out that all of those glycans that I’m talking about are attached to amino acids. So why can’t we just call one of these glycosylated amino acids as non standard amino acids and build proteins out of it in our platform? Well, the answer is we can now. And so we can take a, what we’ll call an autoantigen, the protein that you’re reacting to in case of an autoimmune disease, but now re-express it in our platform decorated with these particular glycans that your body recognizes as being the signature for self. And we take that protein and we administer it to the patient. But now when the immune system sees that protein, it goes, aha, that’s the self signature. Stand down. A nd next time it sees a copy of that protein, it remembers that and reduces the autoimmune response to that protein. And so by retraining the immune system using these special glycosylated amino acids, these are these ProGly amino acids I was alluding to, we can, in fact, in a durable and persistent way, re-educate the immune system to recognize that autoantigen as a self protein and reverse or eliminate that autoimmune response.  

Harry Glorikian: So you guys now are a spin off from Harvard with George Church. As well as a gentleman by the name of Christopher Gregg, I think who’s the one that worked on that glycan protein area, right? If I’m correct.  

Dan Mandell: Yeah, that’s all right. So. So the company spun out of George Church’s lab. I spun the company out about five and a half years ago, and Chris is our chief science officer,  who co-founded the company and also has a strong background in glycobiology. And so Chris’s background there has been instrumental in, you know, us coming up with the inspiration behind this approach and also executing on it. 

Harry Glorikian: Yeah. And I was actually I was reading up and I was like, I think it was like 2013, 2014, when this whole thing got started. And it always reminds me like, oh my God, everything in our world takes so much longer than people actually realize. Like until it becomes something that spins out,  becomes a company and eventually hopefully becomes useful in in some health care application or, you know, other application. But can you walk us through like some of the early ideas you were thinking about in protein design. I mean, you know, why did it seem like such a compelling area of research? And if I’m not mistaken, because we’re always trying to integrate AI and machine learning, you’re trained in AI and machine learning at Stanford before you got into this whole synthetic biology. So. You know, how did those two, you know, come together? And was there any, like, key technology or computational advantages, advances that made it practical to bring these two things together and then turn it into a commercial platform. That’s a lot to digest, but. 

Dan Mandell: Yeah. No, it makes perfect sense. Absolutely. Yeah, that’s. That’s correct. So. So my original training was in was in AI, machine learning. My degree at Stanford was heavily computational, and then I did my master’s degree in Edinburgh in AI, and I was in fact trying to use machine learning techniques to create a system where you could give me two protein sequences, just the amino properties, not the three dimensional structure, just the list of amino acids. And then I would try to tell you if those two proteins would interact or not, and it was an incredibly misguided project at the time and it had basically no chance of success. And I didn’t even know what an amino acid was. To me in this project an amino acid was a bunch of numbers, right? How hydrophobic is it? What’s its isoelectric point? Basically a bunch of chemical properties that we would encode into a vector and then use machine learning to try to predict interactions. Right. The irony is, Harry, like now 20 years later, we actually are doing projects like this with astonishing success. And there have been incredible advances both in the computational models that underlie this. So our understanding of how basic macromolecular structure forces work, as well as advances in computation, right? So just being able to apply massively parallel computing power to these problems, both of those have been important. But in the early 2000s,  this was, it was silly that I tried to do this. And in the end, like I was I was so fascinated by it, almost like, why doesn’t this work? What even is an amino acid? So that got me to do a PhD at UCSF, where I focused on a field called computational protein design. And this is where we did have enough data where we could take 3D structures of proteins and then redesign them to have new shapes and new functions. And this blew my mind. I couldn’t believe that we could do this. But when I say we, I’m really talking about the field of structural biology where, you know, over the previous several decades, many scientists had done the painstaking work to get proteins to form crystals, blast them with X rays, and then figure out what the actual three dimensional structure is at atomic level accuracy. And each of these projects could take months to years. But now we had tens of thousands of examples of these from which we could learn. Those of us who have no idea even how to do crystallography. And so our field was really starting to accelerate in the early to mid 2000s because we could begin to prove that we could predict the three dimensional structure of a protein from its sequence, and we could do what’s called design. We could change that structure to do something new. And I thought that was incredible. 

Dan Mandell: So I joined a lab, my PI was Tanja Kortemme at UCSF. And, you know, one of the great things was one of the first kind of hybrid labs where she had both the computational setup and a wet lab, which is basically, you know, laboratory benches where we can make and test these proteins. So I fell in love with this field. And, you know, it’s still very slow, right? And I over the course of my PhD over six years, I did design proteins and test them. And, you know, we had some success there. But this is, you know, six years, I still was feeling kind of like, wow, I still feel kind of limited in the Lego blocks I have to play with here, right? It’s, you know, and all of my friends who are in protein design know what I’m talking about. You know, it’s like you you’ve got a trapezoid and you wish it was a square or you wish it was two square stacked together. And nature just doesn’t have that. And so you just wish like, oh, I wish that this amino acid could be one carbon group longer, right? 

Dan Mandell: So, you know, after I finished my PhD and I started thinking about where to do my postdoctoral fellowship, I’d been talking with mostly big technology development labs because I had been in a very small lab where I learned a tremendous amount from my PI and I was ready to kind of be a bit more independent and build something, you know, almost like hybrid engineering and science. And so when I learned about what was going on in George Church’s lab, that they had created for the first time these organisms that could put in new amino acid building blocks, I was just you know, I was it was it was an epiphany to me that this was even possible. And so I came out and did my postdoc with George. And so sort of around your question of how we got started, the first problem that we worked on was not how do you make better therapeutics. It was actually how do you create an organism that’s contained? And the reason why, and this gets into a little bit of what is special about these organisms, why is it they can put in these new non standard amino acids? And without going into great detail, what we’ve done is modified the genetic code in these organisms.  

Dan Mandell: So the way the genetic code works, if you think about a string of DNA. You’re reading along, you know, it’s all A’s, C’s, G’s, and T’s. Every three letters of those is called a codon. And if you do the math quickly in your head, there are 64 ways that any three letters can be combined when there’s four choices per letter. Four times four times four. In life, all of those 64 possibilities are are taken up. And so if you put three A’s in a row, you get alanine every time. That’s one of the non standard amino acids, right. Each of those codons codes for one of the non standard amino acids. Right. And so there are codons that actually code for stop. How do you know when to stop making the protein? Well, there’s a codon for that. And in fact, there are three codons for that. And in fact, there are multiple codons for almost every amino acid. What I mean, Harry, is there are redundancies in the genetic code. There’s more than one way to say the same thing. So we can exploit those redundancies. And that’s exactly what we did. And so what was done to create this first organism that can put in these new non standard amino acids was we took one of those codons that means stop. And we switched every instance of it to another codon that also means stop. Okay. So from the perspective of the organism, nothing has changed, right? Making the same proteins, they’re all stopping in the same place. But one of those codons is now gone from the genetic code. It’s not present in the organism anywhere. 

Dan Mandell: So we can now repurpose that codon to incorporate a new amino acid. And that’s exactly what we did. And so now these organisms have codons which used to code for one of the standard amino acids, used to tell the cell, put in a standard amino acid or stop. But now they tell the cell put in a new amino acid. So that’s kind of how we’ve reprogrammed the genetic code. Now, what are some of the implications there? Well, one is we can put in new non standard amino acids. That’s great. But there’s another implication. And that has to do with viruses. And you might be familiar with how viruses reproduce in your cells. They give you their DNA or RNA and they kind of like coerce your cells to make their proteins for them. Okay, So what does that depend upon? Well, it depends upon the virus and you having the same genetic code. If everywhere that’s supposed to be alanine is instead stop in your cell, then the virus that puts its DNA or RNA into your cell is all going to be mistranslated. It’s almost like an Italian person and a Chinese person trying to talk together when they don’t know each other’s language. Right. And so in the end, some of these early organisms that we’ve made actually are, it’s very difficult for viruses to infect. And that is really powerful for biotechnology because in biotechnology or in pharma, when you’re using microbes to create protein therapies, you can get infections in your bioreactors and that shuts the whole thing down for months. And there have been instances where a therapy for an orphan disease population can’t be produced because there’s one factory and it’s been shut down due to an infection and those patients suffer. There’s no there’s no medicine for them for some period of time, which is an awful thing that we need to avoid. Right. And so this is an interesting advantage, right, of what we call virus resistant organisms. But there’s another thing that you might raise an eyebrow at, which is, well, wait, these organisms can’t catch a cold, right? They’re impervious to getting sick. So what happens if they escape? Right. Could they outcompete naturally occurring microbes in our environment? And so that’s a question that’s, it’s a fair question. Now, I will say that the reality is, any time that we take a microbe and we engineer it to produce something, that microbe is actually at a disadvantage relative to environmental microbes because our microbes are diverting all their energy to making this, in this case, medicine for us, right? So there’s never been an instance of a laboratory, you know, production strain, you know, running amok in the wild, but it doesn’t mean that it could never happen. And so the phrase that that George likes to use is building the seatbelts before the car. 

Dan Mandell: And so I mentioned this first organism where we took one of those stop codons and we switched it. We actually are now finishing an organism which is going to have seven codons changed and it’s effectively going to be impossible for any naturally occurring virus to infect it. Right. So how do we get those organisms to stay put? Well, this is where that notion of biocontainment that I alluded to before comes into play. So when I got to George’s lab, I had this background in computational protein design. My co-founder, Mark Lajoy, was the guy leading the project to create these new organisms. And we got together and said like, how can we solve this problem of biocontainment? And what we came up with was to use computational protein design to look at every enzyme in this organism which is essential for its survival and ask the question, Where can we stick an amino acid that can’t be found in nature right into the middle of this enzyme and then change the shape of it right around that, that that new amino acid so that no naturally, naturally occurring amino acid can fit there. Right? So that if we were to withdraw that synthetic amino acid from the organism’s food source, these essential enzymes would not fold, they would not function and the organism can’t survive. That’s what we mean by biocontainment. And so this was a multi year project that really combined all the cutting edge of protein design and what we call a genome recoding. But in the end we ended up developing an organism that we have to feed one of these synthetic amino acids for it to survive. And when we withdrew that amino acid, we could not detect any escape of this organism up to about a trillion cells, which is the most we could we could test in our laboratory. So this was this was a fun sort of demonstration. I think it’s an important demonstration ultimately of how robust biocontainment mechanisms can be built. But it also, for the first time gave us confidence, hey, we can take this expanded amino acid alphabet and rationally engineer proteins that critically depend upon them. Right. And that when we turned our attention to questions of the ones you alluded to at the top of the call, what are the really pressing problems facing the clinic that could only be addressed with this expanded amino acid alphabet? And now we have the tools, we have the protein design, we have the recoded organisms. And it’s really now up to our imaginations to figure out what new capabilities can we bring in that will address these these really pressing unmet needs.  

[musical interlude] 

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And one more thing. If you the interviews we do here on the show I know you’ll my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.  

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And now, back to the show. 

[musical interlude] 

Harry Glorikian: So we’ve talked about a bunch, you know, a lot of the science and the history of the company. So just turning back to the platforms. And I know you talked about your I think it’s called your DuraLogic platform, which is, you know, making proteins more durable. And then the,  you called it the ProGly for treating autoimmune disease. Like if you had to make a prediction, which is going to be more commercially important, right? I mean, at some point you got to. You know, being a venture guy, right? You got to pick a horse and you got to get one horse to cross the finish line. Right. Is it stabilizing proteins so they last longer in the body or engineering proteins that affect the immune system in new ways. 

Dan Mandell: It’s a great question, and we are a company who uses that that polarizing term “platform company.” Right. And you mentioned venture. Right. And so that’s always going to bifurcate the capital that you can access when you’re a platform company. But the other thing that the most important thing when you’re a platform company is to have a pipeline. If you say you’re a platform company and you have one product in development, nobody’s going to evaluate you as a platform company. Right? And so, you know, I think the answer to your question is a phrase that we often use at our company, which is “porque no los dos,” right? Why not both? And in fact, we have pipelines across all of our different chemistries in development. And we we try to capitalize ourselves to bring multiple products forward to the clinic. We also have the possibility to combine these chemistries. So if you’re going to have an immune-tolerizing therapeutic, why not make it more stable, too? Right. And so, you know, as we go forward, we’re increasing our capabilities to actually combine multiple chemistries that are novel into the same protein. So I think you’ll see more and more on that coming from our company.  

Dan Mandell: In terms of like, you know, the bleeding edge of of what we can access, I think you can look at ProGly as really blowing the doors open on some exciting new applications. I talked a little bit about autoimmune, right, and how we’re taking a protein that the patient’s reacting to, putting on these glycosylated amino acids and then re-educating the immune system. And that works to help them stop reacting to a protein that’s inside of them already. But we can actually apply that to proteins from other sources, too. You know, as you and I just discussed, there are many therapeutic proteins out there. Antibodies. Every antibody therapy is a protein. If you have a deficiency in an enzyme that causes you to be very sick, there are therapies called enzyme replacement therapy where we just give you that enzyme and it carries out that function for you. But there’s a problem here because oftentimes those enzymes come from non-human sources, right? And that means what? It means you’re going to react to it. You’re going to generate antibodies against that therapy that’s trying to help you. Right. So how do we eliminate those antibodies and keep those therapies efficacious? Well, guess what? We can use the same approach, the same way we can tolerize you to proteins in your body, we can tolerize you to a protein from somewhere else. Same process. We express it in our platform decorated with these glycosylated amino acids. But now when we administer that to you as a therapy, your body sees that sugar signature and says, “Ah, this is a self protein, stand down.” right? And now that enzyme replacement therapy can carry on for as long as you need. And there are many chronic diseases that are treated by enzyme replacement therapies. And so we actually have a product in our pipeline where we’ve taken a marketed enzyme replacement therapy, something that’s currently used by patients that’s incredibly efficacious for refractory patients for whom there’s no other treatment. But in almost every case, within weeks to months, those patients develop antibodies against that therapy and it stops working. So what we can do is take that, we have actually now done this, taken that same marketed therapeutic and used our protein design tools, some of the ones I just told you about, and then engineered glycans on the surface of that enzyme and shown that we can get that enzyme to be just as efficacious in carrying out the reaction, which is degrading a toxic product, as the wildtype enzyme, the enzyme that’s marketed. And so this is totally possible, right? And so you could do this for effectively any enzyme replacement therapy. And so that’s, when I talk about a pipeline, that’s one of the products in our pipeline in addition to autoimmune. 

Dan Mandell: Another area where you can apply this, which is I think, incredibly exciting, is gene therapy. And so any of your listeners who are familiar with that field might… just to give you a really quick primer, what gene therapy is, is if you have a defective copy of a gene that’s causing you to have an illness, and these are oftentimes incredibly serious diseases, what gene therapy does is it actually uses a virus that can’t reproduce in your body to deliver a healthy copy of that gene to your cells. And the virus has the machinery necessary to get that DNA into your chromosomes. And so while the gene therapies, you know, we’re still trying to make them have better targeting to the right tissue, we’re trying to increase the payloads they can deliver. We do have some incredibly successful products now that are either currently marketed or about to be marketed that are saving the lives of patients who had, you know, nothing before this. And if we can get these gene therapies to work, well, we can treat and cure, you know, a very large number of diseases that are deadly or debilitating. But there’s all these problems. And I think that, I think many people would agree with me that the key problem to gene therapy access being expanded to more patients is immunogenicity. This problem that when your body sees that virus, it’s a virus. So it raises an immune response and it tries to neutralize it with antibodies. Right. Right. And in fact, it’s much worse than that. Harry, you and I have a very decent chance that we’ve already been exposed to that virus in our everyday lives. Right. And that means we already have antibodies to it. 

Dan Mandell: So heaven forbid you or I need a gene therapy for a serious disease. We can’t get reimbursed by insurance because we already have antibodies for it. And these are million dollar therapies, Right? Right. So there are patients, there’s a large populations of people who for whom these therapies will be life saving are out of their reach. So how do we get rid of that neutralizing antibody problem? And again, we can use a very similar approach. We can take the proteins that decorate the surface of that virus, right? And now in our platform, decorate them with these tolerogenic, these re-educating glycan NSAAs, and administer that back to the patient to tell the immune system, oh, when you see this protein, don’t react. It’s a self protein. So it’s the same process. So, you know, we’re hopeful that we can use this approach to dramatically expand the access of gene therapy to patients who can’t get it and to solve an important problem of re-dosing. Now, when you’ve seen this virus, you won’t react to it. And for the large number of diseases that will require two, three, four doses, you can now finally do that. So that’s sort of what I was getting at with something like even just this one family of glycosylated amino acids, we can go after a large number of problems that are, number one, really important. And number two, we don’t see a compelling solution using the standard 20 amino acids today. 

Harry Glorikian: So this begs me to, you know, to go to the business model question. Right. Because when you’ve got a Rubik’s Cube and you tell me, okay, just tell me which colors you want and I can dial it up is, do you guys partner with other pharma companies to help them get new protein therapeutics into their pipeline. Do you develop your own novel protein therapeutics for specific indications? I mean, where are you guys in your thought process of where this is going to go? Or maybe it’s beyond thought process and you’ve already got some, you know, deals in the works. 

Dan Mandell: Yeah. So it’s the “porque no los dos” answer again. Right. So for the two programs that I mentioned before, so the autoimmune program and the enzyme replacement program, these are large commercial opportunities, but orphan populations. And so that puts them in a regime where we can capitalize ourselves to bring these to the clinic ourselves. And that’s in fact what we’re doing. For the gene therapy approach, we are not a gene therapy company. And in fact, a subtle distinction that’s important to note from what we’re doing compared to what the ultimate therapy producer will do: we’re not making the whole virus with the payload inside. What we’re doing is making an empty viral capsid that has this tolerogenic signature on it as sort of like a way to educate the immune system to accept the therapy. So a few weeks or whatever before you got the therapy, you would get our tolerogenic version of it and then your immune system would know when you get the therapy not to react to it. So this is a perfect application to partner because there are many different variants of those capsids that will target to different tissues for different diseases being made by different companies, right? And so we can use the same approach to tolerize a capsid that’s being used for hemophilia or spinal muscular atrophy, depending on whatever you’re working on. And so that’s a program, n umber one, we’re not a gene therapy company. We’re not trying to be a gene therapy company. But number two, it’s modular in the sense that we could be partnering it with many different players who all have the same problem. They all have this problem that their vectors are immunogenic and that has issues for safety and efficacy. And, you know, we think we can address both. 

Harry Glorikian: So you founded the company back in 2017. It’s been six years. Any, you know, lessons, you know, how’s your understanding of the best way to introduce non standard amino acids, has it changed? What are your learnings about what works and what doesn’t? I mean, these are always long gestation periods and it’s, I’m sure you encounter this, we always think we know what’s going to happen and then we do it and we’re like, Ah, didn’t that didn’t exactly go the way I thought it was going to go and you have to modify your way through. 

Dan Mandell: Yeah, absolutely. Maybe we’re getting into the kind of, you know, advice for founders topic area, right? So lessons learned. I think a couple of things there, you know, so I started the company with $2 million and three people, right? Um, you know, I would never advise somebody these days to try to start a platform biotechnology company that way. It was 2016 when I raised the round. It wasn’t, it was not an unusual amount of money to raise at the time. Um, but, you know, we started really small and the technology is mind blowingly cool, but we needed time, right, to industrialize this organism. I haven’t talked about that, but you know, anything you take out of the academy is not ready for production. Right? And so we needed time to make the organism fitter, more productive, you know, higher yielding in terms of these products we’re making. Right? We needed to show that when you make NSAA-containing proteins, NSAA is the acronym for non-standard amino acid, that they don’t induce an immune response or toxicity in animals, right? So in the early days of the company, we were really proving out these fundamental things, which is totally fine. But you know, one of the things I think that we, any young company can always use more of is, you know, sort of corporate strategy and business development, right? We couldn’t afford to hire, you know, a full time chief business officer until our series A financing, which was announced about a year and a half ago. And we’ve brought in a fantastic CBO. And I can’t tell you how much great stuff he’s done to make my life better and the company better. But, you know, resources weren’t there for that, you know, but this is one of those things that the earlier you have that in your company, the more likely you are to be driving towards a viable product. Right?  

Dan Mandell: And I think this brings us to an important second point, which is, you know, I think over time we’ve got better at what what we call customer discovery. You know, this is where, you know, I think as a scientific co-founder, one of the traps you often fall into is you create something that’s really cool and sexy and you presume that people want to pay for it, right? You know, what’s the difference between a really good science paper and a product? Right. And, you know, all of us, I think, run into that, that and the issue with making that distinction early on in this transition. And what customer discovery does is it’s a process by which you talk to your potential customers, be they pharma companies, clinicians, KOLs, patients if you can get to them and in an unbiased fashion, ask them what their needs are. And the problem always with taking your idea around and shopping it is people are too nice and they’re all going to tell you, hey, this is a great idea, I love this and I’ll definitely buy it when it’s ready. And then you you two years later, you come back and they’re like, well, what we have already is close enough and I don’t want to pay this premium to get what you’re building. Right. And that’s deadly, right to your company. We’re laughing because it happens all the time, but that’s deadly. And it’s probably like the number one biggest pitfall that scientific co-founders can fall into. So, you know, I guess we were a little fortunate in that as part of a ironically, a grant that that we won through the NSF I had to do this boot camp that involved doing tons of these customer discovery interviews. And over time we got better at this, which has helped us learn the difference between a nice to have and a need to have, which is the difference between living or dying in this business. 

Harry Glorikian: Yeah, there’s so many times I’ve gone to people and I’ve been like, you know, take, take a look at this. And they’re like, Oh my God, this is so cool. Oh, you know, blah, blah, blah, blah, blah. And then you say, Would you buy it? And then, yeah, no, you know, what I got is actually good enough. And you’re just like, wait a minute. We just spent half an hour talking about how great this thing was and you’re not willing to make that switch. And so, you know, I find that interesting. But I do have a question for you in these last few minutes. When I look at what you’re doing, one of the first things that that that jumps into my mind is, you know. The whole field of synthetic biology. Couldn’t you use your technology to grow a new kind of biomaterial or design, you know, a synthetic product that we’re looking to make. I mean, that was one of the first things that jumped to the forefront of my mind. And I know we didn’t cover that at all in our discussion, but just throwing that out there, so, like, have you guys thought about it? 

Dan Mandell: Yeah, great question. And absolutely. And you know, in the early days of the company, we explored applications beyond therapeutics as well. And that could be anything from, you know, commodity chemicals to pet care to green carbon capture technology to personal care, hair products and things like that. Right. We thought about all of them and we even explored some of them. And to your point, there are synthetic biology companies working on all of them, be they fragrances, biofuels, commodity chemicals, you know, cheaper ways to make cannabinoids, you name it. The field is definitely, you know, poised to deliver some very impressive results in all these areas. I think maybe, you know, a decade later then it was originally sold as doing. But it’s happening now.  

Dan Mandell: Now, why aren’t we in those spaces? Well, they’re really important problems and they’re really valuable spaces to play in. At the same time, you’re oftentimes in a race to the bottom against a more traditional, cheaper alternative, like petrochemical engineering. Right. And again, this comes back to like, what do we want to do? What is our mission? Right? And if we’re going to say, let’s find what we, as you called it, a Holy Grail problem, it isn’t so much about, you know, can we, you know, squeeze every penny out of this as can it even be done? And if we can do it, there’s a great commercial opportunity there. And there’s a huge unmet need being fulfilled, in this case for patients. And that makes it much more tolerable to be using all these cutting edge technologies, because the truth is this is all very new and it’s not yet cheap. It’s not going to be, you know, dramatically more expensive than what’s already available. And quite frankly, if you do this right, you’re going to have incredibly outsized benefits to our health care system. Right? Anytime you can take a patient who’s being treated in a hospital and make them, you know, much less sick or cure them, that obviously has huge benefits, long term. And that’s the deal you make by having all of this expensive upfront R&D. But ultimately by playing in this space, you know, we can really open up our imaginations and go after these incredibly important problems with any of the sort of creative ways we can imagine without constantly being having to think like, well, you could have done this, you know, ten times cheaper if you used oil, right? Or something like that. And that’s a bugaboo is kind of always there if you’re going after a commodity product. So super important problems across the board. But at this stage, given how novel everything is, it lets us be much more creative and innovative, I think.  

Harry Glorikian: You know, in these last few minutes, sort of like, trying to project out into the future here. Right. You’re talking about re-engineering protein production, you know, how to manipulate the immune system. And given how much data is being produced from multi-omics data and machine learning models to analyze the data, and it seems like every week I’m seeing a new modeling about, you know, structure.  What are the kind of advances in medicine do you think that’s going to be possible in the next, I don’t know, three, five, ten years. 

Dan Mandell: Yeah, I think I think pointing to the advances in what you call structural modeling, right. Is, is that’s something that’s been a very tangible improvement in the last few years. If some of your listeners are familiar with AlphaFold, which is, you know, everyone’s talking about large language models and deep learning, this is the application of that to the protein folding problem, right? And what’s been shown is, I think that there’s been a there’s been an incredible progress and a little bit of hype as a result of this work, because what’s happened is we can now take proteins for which we only know the amino acid sequence. We never knew how they would fold. Nobody did that “blasting them with X-rays and figuring out the structure” thing yet. But we can now predict their 3D structure in many cases where we couldn’t predict it before. Now, what does it actually mean to, quote unquote, predict the structure? Right. Because at the end of the day, there’s a question of the level of accuracy. And it’s an incredible success that we can now predict many, many proteins we couldn’t predict before. You know, much of the human proteome. As you know, you talk about ‘omics, can now be predicted. But to what level of accuracy? And different level of levels of accuracy permit different applications. With that technique I talked about where we actually solved the structure of a protein with with X rays, that is atomic level accuracy typically. And so we can use those proteins for drug design. We can actually say like these particular atomic level interactions are going to happen or not, some of the time at least. But with something that’s maybe half that accurate, you know, even though we have a general sense of the shape of the protein, we probably can’t get that right most of the time. So I think what’s going to happen is with these deep learning approaches, we’re going to be able to bring our drug targets into the realm of possibility that were previously excluded. Right. And the way we’re going to solve this is basically through large numbers. So we’re going to say, you know what the term they use in deep learning is hallucination, right? So you take a bunch of examples of what similar proteins look like and then you hallucinate large families of these proteins. And what you’re going to see is a integration of applying massively parallel computations across these hallucinated protein structures to massively automated robotics systems that can test those different proteins.  

Dan Mandell: And in fact, that’s, that’s very similar to what we’ve built at GRO Bio. We built this setup that we call our high throughput bio foundry, and it hooks up with our computational protein design tools in such a way that lets us test many of these designs in a high throughput fashion right. And as our company grows, that throughput is going to grow. And I think you’re going to see this in a lot of, you’re already seeing it in a lot of the synthetic biology companies, Massive computation coupled to massive robotic systems. And that’s going to let us get at solutions to problems we couldn’t get at before due to what we call the sampling problem. Right? There’s just the space you had to explore was too big. And we’re now able to access that space. And so that’s I realize that’s a bit high level, but if you think about that conceptually, you know, there’s so many problems that can’t be solved due to quote unquote sampling. And whether it’s understanding the molecular driving forces of a disease or it’s taking that pathway and finding ways to interact with it, drug it, modify it, there’s going to be incredible pieces of that space that are now accessible to us. 

Harry Glorikian: I totally agree. I mean, I’ve had a number of guests where we’ve been talking about, you know, the automated lab and then feeding information back in. And then, you know, it’s constantly improving, you know, their systems. So it’s been great to have you on the show. I wish you guys incredible success. You know, it’s fascinating how far we’ve come in in all these areas. Hopefully we’ll see a product from you guys or something, you know, in the next I don’t know. I’m going to guess 3 to 5 years is is a good guess. But you probably know better than I do. 

Dan Mandell: Yeah. I’m right with you, Harry. I hope so too, and expect so. And it’s also been wonderful speaking with you. These are great questions and very much appreciate the time to chat. 

Harry Glorikian: Thank you. 

Harry Glorikian: That’s it for this week’s episode.  

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