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Introduction
This is an interview with Matthew Osman and Fabio Boniolo, the co-founders of Polyphron.
The thesis behind Polyphron is equal parts nauseating and exciting in how ambitious it is: growing ex-vivo tissue to use in organ repair.
And, truthfully, it felt so ambitious as to not be possible at all. When I had my first (of several) pre-podcast chats with Matt and Fabio to understand what they were doing, I expressed every ounce of skepticism I had about how this couldn’t possibly be viable. Everybody knows that complex tissue engineering is something akin to how fusion is viewed in physics; theoretically possible, but practically intractable in the near-term. What we can reliably grow outside of a human body are simple structures—bones, skin, cartilage—but anything beyond that is surely decades away.
But after the hours of conversation I’ve had with the team, I’ve began to rethink my position. As Eryney Marrogi lines out in his Core Memory article over Polyphron, there is an engineering system that has reliably produced viable human tissue for eons: embryogenesis.
What if you could recapitulate this process? What if you could naturally get cells to arrange themselves into higher-order structures, by following the exact chemical guidelines that are laid out during embryo development? And, most excitedly, what if you didn’t need to understand any of these overwhelmingly complex development rules, but could outsource it all to a machine-learning system that understood what set of chemical perturbations are necessary at which timepoints?
This does not exist today, but Polyphron has given early proof points that is possible. In their most recent finding, which we talk about on the podcast, their models have discovered a distinct set of chemical perturbations that force developing neurons to arrange themselves with a specific polarity: just shy of 90°, arranged like columns. This is obviously still a simple structure—still a difficult one to create, given that even an expert could not arrive to that level of polarity—but it represents proof that you can use computational methods to discover the chemical instructions that guide tissue self-assembly.
We discuss this recent polarity result, what the machine-learning problems at Polyphron looks like, and the genuinely insane economics of the whole endeavour. The last of which is especially exciting; it is rare you hear biotech founders talk about ‘expanding the Total Addressable Market’, and actually believe them. But here, it is a genuine possibility if the Polyphron approach ends up working.
Enjoy!
Links
Timestamps
(00:02:16) Introduction
(00:02:37) Why replace tissue rather than the whole organ?
(00:10:34) Why not do simple stem/progenitor cell injections?
(00:13:51) Can organs repair themselves naturally?
(00:18:21) What does “structure” actually mean in tissue engineering?
(00:21:04) Why are skin and bone the only FDA-approved tissues today?
(00:23:45) What exactly are tissue scaffolds?
(00:27:52) Why are organoids a “dead end” for this field?
(00:35:08) The argument for recapitulating developmental biology
(00:40:28) Walk us through the Polyphron experimental loop
(00:47:56) Can you simulate morphogenesis with only small molecules?
(00:49:49) How large is the set of possible tissue scaffolds?
(00:52:32) How reliable are developmental atlases?
(00:56:45) What is the machine learning model actually optimizing for?
(01:04:04) Polyphron’s first big tissue engineering result: polarity
(01:15:33) What comes after polarity?
(01:17:09) Why is vascularization the hardest problem of tissue engineering?
(01:20:33) Why can’t you just wash angiogenesis factors over the tissue?
(01:22:25) How does the graft integrate with the host’s blood supply?
(01:25:45) How do you validate tissue function before implantation?
(01:29:01) How do you design a clinical trial for a biological pacemaker?
(01:37:01) The argument for being a pan-tissue company
(01:41:57) What are the biggest scientific and economic risks?
(01:45:23) Who are Polyphron’s competitors?
(01:47:07) Expanding the TAM beyond transplant lists
(01:52:28) Autologous vs. Allogeneic approaches
(01:55:07) Is a 3-year timeline to the clinic realistic?
(01:56:28) Cross-species translation
(01:58:05) What would you do with $100M equity free?
Transcript
[00:02:16] Introduction
Abhi: Today I’ll be talking to Matthew Osmon and Fabio Boniolo, who are co-founders of Polyphron, a startup working to grow ex vivo tissue to use in organ repair. We’ll be talking about the history of the tissue engineering field, the science of existing approaches, and their argument for why computation is necessary for the field to move forwards. Thank you for coming onto the show.
Matthew: Thank you for having us. Happy to be here.
Fabio: Happy to be here.
[00:02:37] Why replace tissue rather than the whole organ?
Abhi: The first question I have is somewhat of the obvious one: why do tissue replacement as opposed to either cell replacement or full-out organ replacement?
Matthew: Right. So, first, let’s give an overview of the kinds of conditions and diseases where tissue replacement is potentially warranted as a strategy. There are a lot of them. So it’s any indication where there has been tissue loss, 3D architecture loss, and where function is downstream of that 3D architecture. You’re looking at indications where really it’s not plausible that you could drug your way out of the fibrotic tissue. So you just can’t plausibly drug scar tissue in the heart back into being a beating heart.
Now on the other end, you have kind of whole organ transplantation, which is the existence proof that tissue replacement at all—writ large—works as a strategy. There are obviously some serious limitations there, particularly around supply, eligibility, of course, the need for lifelong immune suppression, and ICU time. Organ transplantation is enormously invasive; thoracic or abdominal surgery.
And so our thesis is that actually for certain indications—a lot of the chronic age-related diseases—what you should really try and do is build functional units of tissue and intervene a lot earlier. So instead of waiting for total organ collapse, which is basically the organ transplantation model, what you do is you identify early focal forms of damage, which are highly predictive of eventual failure, and intervene at that level. With modular tissue replacement, that can essentially prevent the collapse of the organ occurring in the first place.
So places where this makes sense: heart—so scarring after a myocardial infarction, left ventricle tissue there. Advanced CKD, liver, fibrosis in the lung, COPD, pancreatitis, a bunch of CNS disorders around trauma, and in the retina. Am I missing any? There are like a hundred million people in the US with these chronic diseases that involve organ damage. And so drugs, we don’t think can modulate; devices, we think can route around the problem, but never restore full function.
Abhi: It makes instinctive sense why you would want to replace individual aspects of an organ. If most of it works fine, there’s just a tiny few bits of it that don’t... The reason I thought people usually opt for full organ replacement is more that they don’t understand what bits are damaged and what bits are not damaged, and so they just want to replace everything outright. But you told me that that is in fact actually not true. There are usually pretty clear markers of damage that you could just excise and replace, and I’d love to just hear you repeat that.
Matthew: Yeah. So, that’s absolutely the case. So you are really looking for places where there is focal damage before it gets fully diffused. Now, obviously when an organ is close to full collapse, damage is probably so widespread that it would be difficult to select individual portions to resect and replace with replacement tissue. But there are many, many steps before that.
So you will see clearly in imaging, damage to heart tissue in ischemic cardiomyopathy, and also all the other indications that I mentioned, with a couple of exceptions. So you should be able to locate focal lesions, focal damage that you could potentially treat with this replacement strategy—maybe across more than one site, right? We’re not saying it’s necessarily one site per organ, although within the heart I think it probably would be one site, but in the kidney it’s probably multiple sites. But you would intervene early enough that it becomes a tractable problem.
Abhi: And for the example of left ventricular damage... those are pacemaker cells, correct?
Matthew: Uh, so left ventricular tissue is actually not pacemaker cells. Left ventricular tissue is the tissue that is very often damaged in a myocardial infarction, in a heart attack. So what you get is reduced ejection fraction, so your heart stops pumping enough blood out of the ventricle. And that causes obviously a huge amount of health problems down the line, leading eventually to death.
Abhi: And the damage you would see is so visually apparent that a doctor could just see like black speckles or something and just extract that out?
Matthew: It could be visually apparent to a surgeon. It’s certainly apparent to all of the diagnostic tools currently used in modern surgery. One of the things that we wanted to do when trying to build a platform—because we are trying to pioneer tissue blocks, functional replacement tissue as a new modality—is require as few people to change what they do as possible. And so we’re always looking for ways that we can piggyback off existing reimbursement pathways, surgical workflows, et cetera.
So yeah, all the indications that we’re initially looking at have whole organ transplantation as a covered treatment. Because what you really want to do is intervene kind of much, much earlier. Just to make it super concrete: there were 48,000 organ transplants last year, give or take a few hundred. Of those, maybe 10,000 might have been heart transplants. You have 6.7 million people in the US with heart failure. So that’s anything from New York Heart Association category one all the way up to category four.
What we are proposing initially doing just in the heart case—and we have multiple tissue types we’re going after—is to intervene just before they would need a heart transplant. So it would be a deferral strategy for that specific product. We have other heart products we’re working on, including a pacemaker actually, which we can chat about. But for that product, what we’re trying to do is act as a bridge to prevent them from needing a heart transplant. So it’s either a deferral strategy for someone who needs more time, or it is an alternative strategy for someone who is not able to get a heart transplant—so either for comorbidities, for age, for adherence, or psychosocial reasons, which is one of the eligibility criteria that would prevent you from getting a heart transplant.
And then what you would do is you would kind of move earlier and earlier in the progression of the disease and much more minimally invasively. So all of these tissue products are delivered through surgical workflows that are considered to be minimally invasive. So in our case, it’s not a full thoracic—you’re not cutting open the sternum. So it’s much, much easier to slot into that existing surgical workflow. And it’s already reimbursed and there’s an anchor price. So you know how much a heart transplant is going to cost, which is 1.6 million in the US plus everything else. So you have, for the insurer, a really strong argument about “this is how much you should pay to defer that happening.” And it means that you get a product in the sort of low to mid six figures, which is important because you’re having to figure out how to manufacture this stuff at scale.
[00:10:34] Why not do simple stem/progenitor cell injections?
Abhi: I think the economics here are really crazy in the sense that you get access to this entire patient population that currently no one is really able to touch. I think that is something I will want to talk about a little bit later.
I think the craziest part about Polyphron is this extreme importance of tissue structure. Which I did not naively appreciate before talking to you. Before speaking to especially Fabio, I kind of had the belief of, well, why can’t we just squeeze in some progenitor or stem cells into the site of the tissue damage? Like excise the tissue, squeeze in the stem cells. The body will figure out how to work with it. Why doesn’t that work? Obviously it doesn’t seem to work, but *why* doesn’t it work?
Fabio: No, absolutely. It would be great if it worked. Unfortunately, it does not. And it has been tried, especially in the heart. So people have tried to inject cardiomyocytes into the infarcted area to see whether there was any regain on function. And this did not happen. And it really goes back to the fact that in nature, in vivo, cells really exist in a specific environment within which they can perform whatever function they’re supposed to perform—meaning they can proliferate, they can commit or differentiate into a specific lineage, they can grow, they can assemble, so on and so forth.
And this relation between the microenvironment and the architecture within which these cells grow, and the cells themselves, is something that is established throughout development. And of course, this is something that is lacking at the injury site after any type of traumatic event. Therefore, cells that are just injected there are not able to assemble properly, are not able to signal the microenvironment their presence properly, and they’re not able to learn what the microenvironment is telling them properly. And therefore they’re just basically unable to understand what to do. They’ll just be either washed away or they will start moving around and then they either die or they are killed by the organ.
And this is quite important because, again, one of the things that we really have at the core of the company is this idea that structure is the fundamental underpinning for any tissue engineering approach that has the potential to cure people.
Matthew: I would just kind of piggyback off that—Fabio mentioned the work that was done in cardiomyocytes. So structure is important for function; it is also incredibly important for safety. So particularly where you derive the function of the tissue from its structure—so anything that has signaling or conductivity—if you don’t have structured tissue, what you get is incredibly aberrant effects that are really, really damaging. So in the iPSC cardiomyocyte work that was done, you get arrhythmias because heart tissue is part of an electrical system. And likewise, if you try and inject excitatory neurons that aren’t in a proper structure, you get epilepsies. So it’s very, very important from a safety profile to have as close as possible to native in vivo morphology.
[00:13:51] Can organs repair themselves naturally?
Abhi: Is there any organ that, if damaged, will be able to repair itself to some reasonable degree? Like most of organ development happens while you’re an embryo... is there any ability for repair to happen after you’re born?
Matthew: I mean, the liver is probably the case of fairly persistent regenerative capacity. It is an extreme outlier. You just do not see that in the heart or the lung, for example, to nearly the same extent. So the liver is a very specific case. For what it’s worth, the liver is a really interesting example of an existence proof of not needing to replace the entire organ in order to get clinical benefit, because you have had successful liver segment transplants for a while. So that actually gave us sort of comfort that there’s not something that we would be missing by not having to fully recapitulate the entire organ before doing the transplant. There are also examples in the intestine, I think, and a couple other organ systems as well where segmental transplantation gives you a huge clinical uplift.
Fabio: So we... I think we will be talking about quite a few different axes of complexity today, but I think one of the most interesting ones is the regenerative potential of these different tissues. You can put them on a continuum going from, as what Matt was saying, from the liver to the heart. Now the interesting thing is that people noticed quite early that a few organs could actually be intervened on by having some type of structured framework scaffold with isolated cells coupled in. So much so that the first real applications of tissue engineering, which were probably in the 1980s, were all about getting specific plastics or biomaterials, filling them with cells and putting them in animals to see whether there was any regenerative potential.
And you know, these applications were called *Chimeric Morphogenesis*, just to remember this idea of trying to recapitulate what happens in development that leads to tissue formation. So all of these kind of small elements point to the fact that we can indeed regenerate organs. And I think the exciting thing and cool thing about tissue engineering is that we are not necessarily asking the organs themselves to regrow tissue, but we’re rather using engineered solutions to support regain of functionality.
Abhi: The primary thing I was trying to question was: on one hand, the heart is not able to repair an aorta by itself, so you need to go in and replace it. On the other hand, given the fact that sometimes you do see this—like the liver is able to partially repair itself—is there any pathway to being able to convince an organ, primarily through genetic or chemical means, to repair itself? Or is that just out of the picture? Is there no developmental pipeline that does that, or is it kind of unknown?
Matthew: I mean, it hasn’t worked. I can tell you that. I think that if it were to work, it is most likely to work in the liver, but unlikely to work in other organ systems where the niche of the damage is so fundamentally changed from the developmental program that it’s hard to know how you would kind of act on it in the way that you’ve just described reliably. So it’s somewhat of an unknown right now, but I can tell you it hasn’t worked.
Fabio: I would speculate there is some threshold below which the regenerative potential is not enough to actually bring back functionality. In many of the chronic inflammation indications that Matt was mentioning, and especially in their acute phases, there is necrosis happening. So the actual focal location in the organ dies, it stops working. Therefore, there is not much the body can do.
[00:18:21] What does “structure” actually mean in tissue engineering?
Abhi: That makes sense. And when we like vaguely gesture to “structure” and “tissue”, what does that actually mean? One axis is clearly that there exist multiple cells and multiple cell types in this environment. What other types of structure exist?
Fabio: This is quite important and actually it is so important that at Polyphron what we are doing is trying to establish metrics that can tell us how close our grafts are to in vivo structure. And the way we look at structure is really at the three-dimensional architecture and composition of the tissue. Where by this we mean we look at how different cell types are patterned within the tissue, how they locate themselves with respect to each other, and whether they have specific orientations or polarities—so where there are specific distributions of proteins that tell us what is up and what is down. And we can see that these elements—so polarity, multicellularity and cell composition, and of course also the layering and the geometry of the tissues—are something that happens across different organs and different tissues. Of course with different features, but macroscopically, we can identify these. And one of our hypotheses is that really we should try to recapitulate all of these single steps towards our goal of recapitulating tissue structure.
Abhi: Do you think there are dimensions of structure that are not well either currently unknown today, or not legible entirely and you need a model to encompass it all for you?
Fabio: As many things in biology, it really depends at the resolution level at which you look at tissue. We have had decent ways to measure tissue morphology or tissue structure for quite some time, either imaging-based or fluorescence-based. And what we’re learning with more and more powerful technologies such as electron microscopy, for example, is that we can really go down and look at the nanoscale organization of these tissues. Now one question is how relevant it is to understand all of these different scales to actually be able to recapitulate structure in the lab or manufacture it. But it is for sure true that we can see this continuum of complexity scales. And as for many complex systems, the macro features and behaviors we see really arise from this continuous scale of complexity.
[00:21:04] Why are skin and bone the only FDA-approved tissues today?
Abhi: There is an existing proof point today—beyond just an organ’s ability to regenerate or organ transplantation—that you can do this sort of fractional replacement, and it has only popped up in, as far as I can tell, three areas: skin, cartilage, and bone grafts. These all exist. There seem to be FDA-approved products in the market that do this. Why hasn’t the tissue engineering field moved beyond these three?
Matthew: So, I can give the very naive response, which is that those are easier to engineer for a couple of important reasons. One, skin is very thin, which means you don’t have to solve the vascularization problem of perfusing vasculature that you have to solve for thick tissue. I mean, something like cartilage as well often doesn’t have blood vessels, so there you aren’t having to solve the vascularization problem either. They’re metabolically less demanding tissues to produce as well. And you can do a lot of this work in thin 2D sheets, which is what some of the original skin work was done with.
Fabio: Following up on what Matt is saying, he has identified two more axes of complexity, which are metabolic demand—we don’t need vascularization, which is a bottleneck for any tissue engineering approach, and I’m sure we’ll discuss that. So we don’t need vascularization as much for these products. And also they have a relatively simple structure again. So there’s a relatively small number of cell types. They’re organized in a very, in a relatively simple configuration—so there are ways... it’s simple layers.
And for example, for bones... I actually had my first experience in tissue engineering in a bone graft production company. And the incredible thing about bones is that there is this mineral inorganic component that we can find elsewhere in nature. Bovine bone is exactly the same basically as human bone in terms of mechanical features. Corals can be used as bone graft, and they’re just the perfect scaffold to put in the body. There are maxillofacial applications, there are spinal cord applications for the bone component of course. And it is quite simple to insert them and have the body repopulate them and basically make them their own. So you know, in a way they were the low hanging fruits of tissue engineering. And now the challenge is kind of on us to go to the more complex structures.
[00:23:45] What exactly are tissue scaffolds?
Abhi: We’ve mentioned scaffolds a few times in this conversation. I probably should have asked this question earlier. What *is* a scaffold in the world of tissue engineering?
Fabio: So the scaffold is basically one of the key elements of tissue engineering. If we look at the field in general, you basically need three elements. And then every single approach combines these in different ways. These elements are: some type of isolated cells [whether iPSC derived or primary cells] , you need some type of bioactive factors that help make whatever you want to make, and then you need this kind of scaffold, which is basically the framework or the structure that cells need to develop, mature, and make the tissue you want.
Now, in tissue engineering, this scaffold is looked at as a structure that can be either a plastic or a biopolymer that can be transplanted, or it is a nature-derived material such as collagens. And you can imagine them as sponges or basically 3D porous matrices that you can use to seed cells in. You can use them to create gradients. And you can also use them to tune the mechanical and physical properties of the sponges so that cells receive very well-defined stimuli. And the hypothesis in the tissue engineering field has been: if we give the initial structure to the cells and then we let them do their thing, basically, they will remodel the scaffolds on their own. And then what we will get out at the end is the desired graft that might be then transplanted. And what people quickly realize is that unfortunately, this is too much of an artificial setup for the cells. So they will not be able to actually go and remodel and restructure these scaffolds. They will kind of go midway, and the product will not be as effective as a real graft might be.
Abhi: What do you mean when you say the scaffold is “too artificial”? Like what does artificial concretely mean?
Fabio: So in this case, what I mean is that what happens naturally in vivo—once again looking at development—is that the scaffold [which in this case for natural tissues means the extracellular matrix and the environment within which cells grow and proliferate] evolves and changes together with the cells that are developing and committing to specific cell lineages.
Abhi: So it’s not just secreted during morphogenesis and then populated by the cells and it stays static?
Fabio: No, it actually changes throughout development. So the physical properties, the stiffness of the scaffold changes because it has to support different cell types and different functions. And this change will be impacting developing cells, but will also be impacted by developing cells. And what we are seeing and what we hypothesize is that this complexity is really a process that basically reaches equilibrium through these different steps in vivo. While what I was describing earlier of this artificial setup where we give the scaffold from the outside and hope the cells will grow inside, is a very kind of non-natural setup where we’re trying to define complexity from the top down and not having it grow and stabilize on its own.
And one of the things we’re trying to do at Polyphron, or that we’re actually building with our technology, is really a way to allow cells to create their own three dimensional microenvironment, their own scaffold, and therefore also the structures that are relevant for function.
[00:27:52] Why are organoids a “dead end” for this field?
Abhi: And I think gesturing back to the current FDA approved products, most of the way that those worked is like bioprinting—layering on one layer of cells at a time works well for those particular cell types. Obviously it doesn’t scale to more complicated ones. One of the other approaches people seem to be working on is organoids, and Matthew has not positive opinions about organoids with tissue engineering, and I’d love to get your take on them.
Matthew: Uh, so I think that organoids are a dead end for therapeutics. I think that as a strategy, it is just not gonna lead to meaningful therapies that could bring the need for organ transplantation to an end. I think that there are definitely some useful drug screening use cases with organoids, but for a bunch of reasons they lack the complexity and in vivo structure that you would need to get any of these functional restoration effects that we think that we need.
Abhi: But you do see the organoids are willing to like mangle themselves into some sort of structure. And so you do get something that’s clearly better than single cell replacement. Why is that not enough? Like where does that start falling apart?
Fabio: Yeah, so you are right in saying that what we’re seeing with organoids is some type of self-assembling behavior. Um, and this is mainly dictated by trying to intervene on typically pluripotent stem cells in a way that simulates how different cell lineages come to be during development. The, there are multiple issues though, with this approach. Um, the first one is that, once again, development really is successful because cells develop not in a vacuum, but in a very specific microenvironment. And this is not recapitulated in typical organoid cultures. Cells will grow in collagen or in some type of extracellular matrix, but they will not be receiving the chemical and especially the mechanical stimuli that cells need to create structure in vivo.
Secondly, what you typically see in organoids is that due to this self-assembling behavior, you will see sporadic structures arising. And here with structure, I mean micro features that resemble natural structure. What we’re missing though is the micro features that kind of put all of these smaller components together. So I can give an example. If you take for example, kidney organoids, you will see within an organoid some cells that make glomeruli-like structures, some other features that will make renal tubule-like structures. But we will be missing the union between structure one and structure two. And this of course, is quite important for what we want to put in in vivo because we need that graft to be able to accomplish its function.
And this is not always possible, actually, it is not possible with organoids. Um, and one more complexity is that organoid cultures are still an intrinsically stochastic process. And of course this puts quite some limitation in terms of approval and in terms of manufacturing. So one of the things that we’re thinking quite often at Polyphron is how to make the whole process of production of replacement tissues as robust as possible so that it can be, you know, a proper technology that can scale.
Matthew: Sorry... I was just going to—maybe we’ll come to some of the commercial manufacturing challenges later, but one of the things I also wanted to point out that I think is unusual about the way that we are designing the various loops that will allow us to build functional tissue units is that we are taking cost into account as part of the cost function of the overall loop. So we’ll talk about like how we try and recapitulate morphogenesis, I’m sure. But one of the things we’re really trying to do as well is to select the cheapest way down the mountain. So we take into account the price of reagents, we’re optimizing the pathways to recapitulate morphogenesis because the last thing we want is to pull off this kind of technical miracle and have a commercially non-viable product at the end of it. So we’re like trying to build in manufacturing COGS viability even in our initial ML approach.
Abhi: Have potentially organoids... like do they not work at all? Has it ever been successfully—or like ever a transplantation has ever happened and it just didn’t take? Like the native functionality was not restored, or has it still never been tried?
Fabio: There are examples in rats, specifically in the intestine where there seems to be integration. Restoration of function though is, has not been proven yet. Okay. So these, these organoids are recognized as self, they’re integrated, but you know, there is no real restoration of the functions that they’re supposed to carry.
Abhi: Yeah. So it sounds like there’s multiple hard problems. You first need to grow the tissue in the first place. And then the second hard problem is you need the body to be willing to accept that tissue and to integrate into the rest of the body. Is that connected to the structure problem or is that an independent thing?
Fabio: No, it is fundamentally connected. I would actually say it is... you know, structure is the underpinning element to being recognized and integrated properly. And this is because if you think of organs, they work as this extremely well integrated kind of setup. And the question is what is the smaller functional unit we can use that can be recognized, connect and basically restore function. And we believe that, you know, in order for this to happen, the graft should be as similar as possible to what was lost. In terms of structure, in terms of cellular composition. And this should ease the way that the body integrates and makes the graft its own.
[00:35:08] The argument for recapitulating developmental biology
Abhi: Okay. So it seems like bioprinting is too simple for more complicated things. Decellularized scaffolds are also potentially too simple to do the most complicated things. Like scaling up organ transplantation via xenotransplantation is both super technically risky and also... you don’t wanna do organ transplants for everyone. What is the way out of this conundrum that you’ve set up where every approach is either too simplistic or too complicated?
Matthew: Our approach...
Abhi: What is it?!
Matthew: It’s to make much smaller functional units of tissue that are recognized as self and integrate and restore function. So to give you a rough sense of the order of magnitude, these are tissue chunks in the sort of centimeter-cubed volume or less, right? We’re not trying to build entire hearts in terms of biomass. But yeah, so that’s the way out of the conundrum. You have to be able to do it in a repeatable way with exceptionally low variability. You need to be able to control COGS. And ideally you should try and get this as much clinical effect as you possibly can with the smallest unit of tissue, because the less biomass you have to produce, the cheaper it’s going to be. Whereas we think the pricing will probably stay where it is, because there are these anchor prices for transplantation, for assisted devices.
Abhi: But it seems like the core tenant is almost like: if everything else is too simple to recapitulate developmental biology, and now your answer is basically “let’s just recapitulate, let’s just do developmental biology straight up.”
Matthew: So if I had to kind of sum up like one of the precepts of the company, it’s that there is an engineering system that has already produced functional human tissue—and that’s human development. And why don’t we try and recapitulate that as much as possible? And that over the past few years, the data sets that allow us to have at least a fuzzy starting prior of what development does have come online and become available. So these are developmental atlases that are often multi-omics based or including increasingly spatial transcriptomics. And so the prior kind of set of tissue engineering approaches involve this highly mechanistic understanding where you’re trying to smooth out the complexity of what’s happening in development to fit it in the brains of the scientists that you have working on the problem and to kind of fit it experimentally.
Um, our view was that now the data sets are rich enough and wide enough that you can start throwing them at some of the exciting new architectures that we are seeing and have models learn latent rules of morphogenesis in a way that doesn’t need to be legible to a human. This obviously needs to be very, very tightly paired with wet lab validation, which is something that we are super explicit about. We have a kind of a closed loop between the developmental references—which are kind of our fuzzy priors—and what’s being tried and validated in the wet lab in this loop.
But our view is that now there’s a plausible path to us being able to start from what happens in development, potentially find alternative pathways to achieve the same goal—which is like super, super exciting—and eventually end up with a functional unit which is similar to what nature produces. And one advantage, and I’m sure we’ll talk about kind of like model architecture and some choices that we’ve made there, is that we have a strong hypothesis that there will be like an ultimate latent logic of development that models will learn when they see more and more tissues. And there are kind of already hints of that in some of our experimental data, that you might be able to kind of transfer things across tissues. We don’t yet know, I should be clear, whether, you know, you only need to do three tissues and then you can do everything. Or whether it’s you can do 10 and therefore you can do 20. But we do fully expect there to be transfer learning across tissues as we go.
Abhi: I think the pan-tissue aspect of Polyphron is something I really wanna talk about because I think it’s one of those crazier...
Matthew: It’s kind of insane. But actually I think it makes sense because the human body doesn’t produce a liver separate from a kidney, right? The human body is an engineering system which is holistic and comprehensive. Um, and therefore you would expect there to be redundancies and kind of the same techniques across different tissue types. And it’s very, very important for us that the space of interventions that you can use to manipulate morphogenesis is bounded. It is finite. And in natural development it is by definition.
[00:40:28] Walk us through the Polyphron experimental loop
Abhi: To look at Polyphron’s experimental loop with more of a concrete lens... Like what do you start... like you have a box of like collagen or something, you start as the existing scaffold. You seed that with induced pluripotent stem cells, iPSCs. What’s the next step? Like, let’s say you’re trying to produce some functional heart tissue. What would you do next?
Fabio: Yeah, so the first step really is to look at developmental atlases, where we are looking at single cell atlases, spatial and transcriptomic atlases of the developing human heart. And this allows us to first understand which developmental time points have been sampled. And, you know, this then dictates what type of dynamics and what type of lineages we can try to recapitulate in vitro. We then use different types of computational approaches to mine these high dimensional data sets and extract temporal trajectories and dynamics that we care about—these being specific lineages, when they arise and when they commit, or specific microenvironmental interventions or perturbations.
And then we move to our in vitro setup where we have, as you’re saying, these kind of three dimensional boxes within which we can use different types of extracellular matrices depending on what microenvironment—or what developmental microenvironment rather—we might want to try to simulate. And we then seed these scaffolds with different types of either progenitors, pluripotent, or committed cells, depending again on which type of cell type we want to recapitulate, which type of structure we might want to achieve. And what happens then once we have our cells in this kind of 3D box, is that we can start perturbing them using the same kind of perturbations that we have learned are effective during development based on our atlases.
Abhi: But the developmental atlases are, as far as I know, telling you like the ligands that it sees on like day 15 of heart development. How do you relate that back to like a causal relationship that like these ligands caused—like was essential to day 15 of heart development?
Fabio: So, couple of things. What basically state-of-the-art computational biology approaches allow you to do right now is to go from a discreet sampling of a developmental trajectory to a continuous trajectory. So you can really start to see kind of continuous dynamics, whether there are peaks, valleys, whether there is a steady state at one point in development. And this then tells us basically which molecules to apply when. But one important thing to your point is that we really do not care about understanding the relationship. Because what we want to do is to just define the broadest set of interventions that might matter for the structure we want to recapitulate. See how those perform on our cultures and then optimize based on that. So that is what Matt was saying: our atlases just become our basically first and initial prior and then we quickly move into the lab, we start generating relevant data, and then we optimize on this data only so that we can see how different interventions when combined in a certain way, give us certain structures that we can then optimize on, select for, et cetera.
Abhi: So you have like a box of scaffold with cells on top of it. You have this developmental atlas that tells you like at each of these time points what ligands was noticed in that developmental environment. And you sample those and apply them to your Polyphron sample and just see how well it recapitulates like the native tissue.
Fabio: That is correct.
Like, what you can do potentially is actually do this at a whole transcriptome scale. You don’t necessarily need to focus on ligands. We focus on ligands because again, we have this quite strong hypothesis that the microenvironment is what matters. Not only cell intrinsic transcription factor related dynamics. But yeah, the other advantage really with ligands is that you can get small molecules for them to simulate their activity oftentimes. So it’s okay to actually go out buy them and then apply them to our experimental setup.
Abhi: My impression is that there are just like thousands upon thousands of small molecules going on inside an embryo while it’s developing. Do you guys have the ability to also put in thousands upon thousands of small molecules in your tissue sample? Or is it like you picked up like a dozen?
Fabio: Yeah. Okay. So, let me first say what happens in vivo and then there’s kind of a jump to what we do in the lab. But spoiler, the jump is mainly due to our current constraints in terms of teams and instrumentation. Um, but what happens in development is that there is actually—and also Matt was referring to this—there is quite a finite space of molecules and pathways that is activated at specific time points to get the, basically to get cells through morphogenesis. And the other interesting thing is that there is a lot of redundancy. So there are parallel pathways where one might be active, the other one might not be active. And all of this basically becomes a quite constrained space to start from.
What we then do is to pick from this list of molecules and proteins that are activated, the ones that we can easily source. The ones that we can cheaply source. And the ones that actually have quite a clear MOA [mechanism of action] , so that we can at least predict what we are really perturbing in vitro. Let us say that this brings us to a hundred molecules, we can create specific sets of combinations from these hundred molecules and then use them to perturb our cultures. Um, what we want to do moving forward is to scale up in terms of automation. And, you know, the more we have robots that allows us to make more and more complex interventions, the more we can explore different regions of this space. For now, we’re limited in that as we are doing this kind of semi manually. But the idea is to potentially browse the space using automation and robots.
[00:47:56] Can you simulate morphogenesis with *only* small molecules?
Abhi: My impression is that while morphogenesis is going on, there’s a lot more going on than just small molecules alone. There’s electrical fields, there’s mechanical forces. Like right now, are you just thinking “well, small molecules get us 80% of the way there, we’ll deal with the other 20% later”? Or what are your thoughts on the subject?
Fabio: Yeah, so it is exactly as you’re saying. Um, and we were discussing this earlier also when we were talking about organoids... what really pushes tissues across the line in terms of functionality and maturity is something that goes beyond chemical perturbations—with this being either mechanical stimuli or electrical stimuli. And this actually has been proven where, for example, to get mature cardiomyocyte fibers, you need basically periodic electrical or mechanical stimuli that can basically bring your tissues to function.
We are fully aware of that, and this is something we are taking into account for our next generation of experimental setup where we will try to integrate chemical perturbations and mechanical and electrical ones. What we can do for now is to push as much as we can with small molecules and also be smart about the way we design our extracellular matrix. So one of the cool things with these kind of gels or plastics is that you can play with their chemical structures or you can kind of embed them with different molecules so that their chemical or physical features change. And by putting together different types of molecules with different type of ECMs, we’re actually able to find proxies for most of the knobs that one might want to tune during... while growing a tissue graft.
[00:49:49] How large is the set of possible tissue scaffolds?
Abhi: I can vaguely understand like, oh, there are this universe of small molecules that happen in developmental biology. Let’s just recreate those for ours. For ECMs, how much do you need to stick to the world of natural things versus explore that into novel chemical territory?
Fabio: Yeah. There’s a trade off and a fine line to thread there in the sense that natural ECMs are better used to culture cells in, so it is easier to culture and grow cells in these natural ECMs. They recognize, you know, again, the familiar kind of microenvironment and they will be happy basically, and grow. The kind of other side of the metal is that you even do not have the ability to really fine tune the features you might want to get as you might have for example, with biopolymers or any type of, again, plastic that can be used. Therefore, for us it has been easier for now to use natural derived extracellular matrices. It might be that at one point in the future we start playing with biomaterials, which I find as a very, very interesting venue or direction of research and application. But for us, natural extracellular matrices have worked quite well. We can now control and engineer them quite well.
Abhi: How large is like the universe of natural ECMs? Are there like a flat dozen or like... hundreds?
Fabio: Yeah, so there are kind of macro categories or buckets, but then there is a plethora of modifications you can apply. So you can take different types of collagens, you can take different types of any type of extracellular matrix that you know is present in other tissues. And then you can start tuning it in terms of how cross-linked it is. And this will determine the physical qualities. You can start again, embedding different types of molecules. You can have different types of porosity of the matrices. And all of this moves on different continuums, right? So you can potentially tune it for as long as you want, and to the resolution you want.
[00:52:32] How reliable are developmental atlases?
Abhi: With regards to like... you’re treating the developmental cell atlases as almost like a ground truth of the real system. I’m curious as to how trustable are those? How heterogeneous are they amongst different embryos? Like do you have one golden standard data set that you can derive everything from, or do you need to average across like hundreds of these?
Fabio: Yeah. So, just a few words on how these data sets come to be. So for the most studied tissues, such as the brain or the cortex, really, there is quite a lot of public available data out there in terms of single cell developmental data sets. So what is possible to do is to compile all of these data sets together and create what is called a gigantic or very large atlas where different samples come from different studies, and of course you will have multiple individuals from every study, right? So you have a multi-donor, multi study starting atlas that gives you some confidence that you can actually—that you’re actually capturing enough heterogeneity and cell types for whatever your goal is. And of course this is also function of the actual number of cells in the dataset, due to single cell technology themselves. But also, you know, due to basically random sampling, you will have an overrepresentation of some cell types versus others. So the hope is that by accumulating again, different data sets and different donors, you’ll be able to have a good representation of the cell population of the tissue of interest.
Unfortunately, this is not the case for every tissue. And incredibly, there are some tissues that are quite important in terms of human disease, such as the kidney, for which there is not much data out there. Other tissues such as the heart... we’re somewhere in between the cortex and the kidney in terms of representation. So a lot right now comes in identifying which data sets we can use and how to integrate them properly in our kind of technology.
Matthew: I’ll just add that as a company, we’ve signed a partnership agreement with, I think it’s one of only two places that you can get developmental tissue as a commercial entity. It’s extremely hard to get it. And we essentially have tissue that arrived maybe a week ago. It was an extraordinary freight process. Um, so there’s the possibility that for some of these tissue types we’d want to tackle like the kidney—because CKD by itself would be a mega blockbuster product as a tissue construct—we may want to create our own analysis.
Abhi: Is the usual process for creating these developmental atlases... like you get an embryo at like day 30 of development and then you sequence stuff from it? Or do you sequence continuously while the embryo is developing?
Fabio: No, so these assays are destructive. You get one sample per developing tissue. I mean, you can get couple of samples if the tissue is big enough, but once it is sequenced, it is over. Um, so when you are really looking at different time points, every time point is typically actually from a different donor. Also because, you know, of course developmental tissues come under very, very tight regulation and control. So it’s not very easy to source them. And then there are also like quite brutal tissues to handle. So there are specific sequencing protocols one needs to apply. And there are consortia out there that have optimized the whole process. Uh, and yeah, the idea is you get them, you isolate the cells, you in some way, you sequence them, and then you have these kind of gigantic tables that you have somehow to make sense out of.
[00:56:45] What is the machine learning model actually optimizing for?
Abhi: And so like I... we haven’t actually... like we’ve just discussed a lot on the data collection problem here, where you have like... you pick from many ligands through the days of trying to recapitulate the developmental process. Is the machine learning task you’re trying to solve selecting like the minimum number of ligands you need to reconstruct the native tissue? Is that largely the primary problem?
Fabio: Actually it is quite the opposite of the limit in the sense that what we might want to do if we had infinite manpower and infinite automation would be to try every possible combination of ligands. What we must use the developmental reference for is to go from all the potential ligands in the human genome to the set of ligands that matters for that cell type in that tissue. Right? So there’s already like a funnel there happening. And then from there we don’t really care of understanding what each ligand does, but it is rather: can we try them in our cultures and see how they perform? And this, in my opinion, is quite important because what we want to do is to extend our protocols to different cell lines, for example, right? And different iPSCs. And that has been proven in different settings—we respond differently to the same ligands. So the idea is: can you build enough redundancy in your set of interventions to take into account, for example, donor variability or interpersonal differences?
Abhi: So is it fair... actually, if a model is trying to help you decide which ligands I should be... what ligands should I introduce at this time point for this specific tissue to lead to this final outcome... What is this model actually trained on in terms of the labels? Like what is the final readout of this whole tissue creation process?
Fabio: Yeah. So there are two things that have to be clear here. On the one end we have the reference developmental atlas that is only used to create this list of perturbations and maybe the order. And after that we move it to the lab, right? We move to generating our own data from these 3D structures and boxes that we were discussing earlier. And this is where the actual optimization and model training happens. So what we do is we have ways to non-destructively monitor what happens in our cultures. Right now we are looking at microscopy mainly, but the idea is to extend this to other modalities to get more and more complex readouts.
We semi-continuously basically take pictures and videos of our cultures, and then we use these images and this data to build basically a digital twin of our experiments. We look at embeddings we see how different cultures and cells are growing. We see how these differences are linked to different ligands. And then we define one direction that matters for us. So one basically cost function that we want to minimize to get to the tissue of interest.
How do we do that? I think this is one of the most innovative approaches in our platform. In order to identify what we want to maximize or minimize, we have to first identify what feature of natural tissue we want to recapitulate. Once we do that, let us take for example, the heart. As we have been discussing the heart for quite a few times today. We take the heart and we want to try to recapitulate fiber orientation and alignment. We quantify what these measures look like in a mature, healthy human heart. That becomes our quantity we want to get as close as possible to. And then we optimize with an active learning setup what happens in our culture so that whatever chip or whatever combination of interventions brings us closer and closer to this quantity of interest.
Abhi: Is the cost function or the loss or whatever the model is optimizing for... is it usually right now, as of today, like a single metric of interest? And in the future you’ll extend to multiple things, but for now it’s just a single metric?
Fabio: Uh, so it is a single... yeah. A single structural feature.
Abhi: Kind of relatedly, we... this is something I guess like the conversation didn’t naturally lead to, but I think it was fascinating enough that I wanna divert back around to it: in the limit case, you can imagine that whatever Polyphron comes up with will create the natural end result that developmental biology does, but go about it in a way that’s potentially more compressed and cheaper than it is in the real world...
Matthew: We hope.
Abhi: Are there existing proof points that this is indeed possible? Like you can have a model that instead of these tens of thousands of ligands, it compresses down to like 50 that do most of the work?
Matthew: I mean, iPSC differentiation protocols, this probably is like an existence proof. Transdifferentiation... these are like non-developmental pathways that get you to a cardiomyocyte that on qPCR and sequencing looks like a cardiomyocyte. So that’s a pretty strong existence proof in our view.
Abhi: And with regards to the experimental loop, how long does each experimental loop take? And is it the sort of thing where you need to like... like each one costs a million dollars, you need to really think about it each time before you go in? Or you can kind of just throw it and see what comes out?
Fabio: Yeah, so, um, what we’ve been trying to do so far—I think I should preface this—is to again, recapitulate one specific feature of structure, which is polarity. Okay. So what we set out to do to de-risk our platform was to say, okay, can we control polarity across different cell types? Once we have identified polarity, we can then say... we can first decide which tissues to try. And then we can basically define the set of ligands and the set of differentiation protocols that allows us to get to this, to basically to try to control this cell type for the feature of interest.
This is where the time comes in. Depending on which tissue we’re looking at and what polarity looks like for that tissue, the time for one experimental loop, one experimental round might change. Um, what we’re actually seeing is that it is pretty fast. Okay. So, like we are running two programs right now. Uh, we’ll be publishing about them, but one is in the cortex, one is in the Heart with cardiomyocytes. And what we’re seeing is that for both of them, we can run hundreds of experiments really in the span of one week. And within this week we will basically try the first pass of our developmental inspired interventions.
[01:04:04] Polyphron’s first big tissue engineering result: polarity
Abhi: Is that... I think this actually lends naturally well to the next question of what is the first interesting slash promising result that Polyphron is willing to share? And it sounds like it is along the lines of this polarity thing.
Matthew: Yeah. So I mean, to put some more numbers on it. So we started with the cortex as our first program. We have a cortical program, a cardiac program, and then potentially have other couple programs, which we’re not kind of being public with right now. But we started with the cortex as the sort of proof of concept in part ‘cause of data availability. It is the most mapped tissue type, like really, really beautiful deep developmental atlases. So if you wanted to kind of prove that that could be your sole prior, it’s a good place to start.
And what we did was we identified a key feature of native morphology in the cortex, both developing an adult, which is neurite orientation. So there are these things called neurites, exciting neurons, that in the cortex have a polarity, like an angle relative to the apical surface of the developing cortex or the adult cortex, which is about 90 degrees. So it looks like it’s kind of beautiful row of neurons. Now it’s a specific subtype of neuron. It’s not all neurons in that kind of section of tissue. So if you were ever going to try and recapitulate cortical tissue, for example—which we actually don’t think is a good initial therapeutic, which we can discuss later, but it’s a very, very good proof of concept for the platform—if you ever wanted to produce cortical tissue, you need to be able to have those neurites have be 90 degrees to the—give or take five degrees—to the apical surface. And just those neuronal subtypes.
And so what we did is we basically created a developmental atlas out of all the available public data. And it was a 10 week period. The total experiment took 10 weeks. It was three loops. We went from a starting orientation, which is measured in an angle. So the, you know, in vivo is 90 degrees. An organoid—going back to our favorite approach—gets you about 45 degrees on average. So the neurites are random, but it kind of averages out to 45 degrees. And we took it from 45 degrees to 82.2 degrees, which is damn near close to in vivo morphology in a three iteration loop that took 10 weeks. Um, we’re extending that right now to the heart. That experiment is ongoing. It looks like we will have sped up experimentally, which is good. Like one of the things that we’re trying to do here is to make it easier and cheaper to onboard each incremental tissue.
Abhi: Is polarity a pretty important phenomenon in a lot of tissues beyond like... it seems like the brain and the heart? Is it important in a lot of other tissues besides that?
Matthew: I mean, it’s important in all tissues, but it’s definitely like the polarity of specific cell types is super important to any tissue that has like an outside and an inside, for example, any tissue that is conducting a signal, be it electrical or otherwise. It’s kind of most of them. And it’s also one of the first macro features of tissue-ness that emerges during a kind of a classic developmental pathway. It’s like one of the first things that’s laid down in development is figuring out... like, development, you’re just this one long tube and you need to figure out which way is up and which way is down. Like, that’s one of the first things that is done. So it made sense to start there for a bunch of reasons.
Abhi: And how long did it take? So like you had scaffold, you seeded with these neuronal subtypes. Eventually polarity emerged after experimental iteration. What was that experimental iteration process like in terms of time? In terms of I guess cycles? Or is there like a single foundation model at Polyphron that decides all ligands, or is it all like you have a new model for each new experimental loop?
Fabio: Yeah. So... so. Keep in mind this was our first program, so there was no model before this one. But the whole idea is, we train our first—let’s call it V zero model—after the end of the first iteration of the neuronal program. And this was a model based on imaging data that was supposed in a self supervised way to learn features across all of our experiments. We then use this model to basically dictate what the next set of interventions might be to optimize for polarity. This then led us to round number two. And then the same happened between round two and round number three, which was our final round. And that’s where we got to. So we started from 45 median round one to 82 max in iteration three. This all happened in 10 weeks. Of course, we are basically still working a relatively low data regime, so we’re not using most cutting edge type of architectures approaches. But one of the very cool things about the way the active learning field is moving is that these are relatively non data hungry approaches. So they’re really effective even if they do not see very, very vast amount of data.
Matthew: I just wanna add something about why starting with polarity is both... so I think we’ve covered why it’s kind of useful from a technical perspective. I think we always have our eye on the clinic. And so something else that we considered as well is: are there tissue types and cell types within that tissue where solving polarity gives you a huge clinical unlock that was otherwise not available? And so that’s why we have a cardiac program. Because in myocardium, a couple things are interesting about myocardium. One is that you have to have like the right alignment. And if you have incorrect alignment, you have arrhythmias. Also the contractile tissue has this kind of helical arrangement, which is kinda interesting. So most of the approaches in cell replacement for heart failure and also some of the engineered muscle patches have not successfully solved this alignment problem. And we believe that... we actually will have a number of advantages relative to those approaches beyond solving alignment. But we believe that if we can solve the alignment problem we’ll have a much, much better safety profile. So even though it’s like the first element of tissueness, just solving that gets you something that is potentially clinically transformative.
Abhi: Sorry, I may be bit confused. Alignment is equivalent to polarity here?
Matthew: Uh, in this case, alignment is a sub feature of polarity. Polarity is like a broad category of directionality.
Abhi: Is polarity the sort of thing where getting like near native polarity after three experimental cycles just feels like crazy given how large the initial search base is? Is it that you suspect polarity is like a pretty low dimensional thing? Because the way that I’m imagining is like first experimental loop, the model gets like... okay, it seems like the model’s getting three data points in total. That’s a lot of extrapolation.
Matthew: Well, I should point out that this is being done in a high throughput chip.
Abhi: Oh, so this is not like you apply a bunch of perturbations, you get a single readout at the end?
Matthew: Sorry. No, no, no. This is like a relatively high throughput. Right now it’s a microfluidic system. We’re gonna build our own slightly more than microfluidic, like meso-fluidic system. So the model at each round is seeing like per plate, there are what? 40. And we do it in duplicate or triplicate. So you’re not just seeing one per round. It is a significant compression. I think we worked out the total combination set of all the ligands and conditions, et cetera, like cell density, ECM, was like 1.7 million or something. And in total, the model probably saw like 90 different experimental conditions. Less than 99%.
Abhi: So when you refer to three experimental loops, what does the three refer to?
Fabio: Yeah, absolutely. So imagine that taking the mental framework of what we are doing. We start from a developmental atlas. We have this list of molecules that we might care about. And then we define random sets of these interventions at the very beginning. So we start from a hundred different molecules. We want to perturb ourselves with three molecules at a time. And then we define all the potential triplets from this list of a hundred ligands, right? We have our microfluidic high throughput set up that allows to try as many of these triplets in parallel as possible. Taking however many plates it takes. And this gives us many, many data points from which we can learn which interventions are more efficacious and which interventions are less or even deadly for the cells. And that’s what we then use to optimize. And that’s why the active learning setup is very useful, because not only it will tell us which triplets that it has seen are interesting, but also it will predict which unseen triplets might be very cool to try. There of course is this play between exploitation and exploration. But you know, all in all, what we see is that we can get enough triplets to go to round two. We already see an improvement in round two, and then we can have further improvement going to the one additional round, which is round three.
Abhi: Or for the case of polarity, is that like a single step perturbation in that like you’re not doing like one set of perturbations and then tomorrow you’re doing another set of perturbations?
Fabio: So right now it is, we’re looking at one time point. And then this time point, it’s one set of perturbations that lasts a few days. In the future, you know, the more complex the structure we will have to recapitulate is, the more complex this kind of protocol will be. So we’ll have different interventions at different time points. And we are already playing with this a little, but the idea is for now was, okay, let us see if the active learning setup and the closed loop system can actually work.
[01:15:33] What comes after polarity?
Abhi: And so you mentioned that one of the reasons you opted for polarity is that like alone polarity is like cool. It’s almost like sufficiently MVP to some capacity. What is the second lowest hanging fruit that you would wanna optimize for after polarity?
Fabio: So there are three things that we mentioned that I discussed at the very beginning that we believe are structure. One is polarity, two is multicellularity, and three is basically reaching the size and the shape you want to achieve for the graft to be clinically meaningful. And that involves vascularization. But our next step will for sure be multicellularity. Again, these things will not happen sequentially, right? We will optimize polarity first and multicellularity... they will rather happen altogether, so they will be optimized as one system, but I think that conceptually it makes sense to think at them as three kind of things we need to care about. So yeah, we’ll basically start adding multiple cell types and seeing: can we preserve the polarity structure we defined in our first programs while having multiple cell types that interact with each other?
Abhi: Do you imagine like... at what point will you need to move into the realm of like multiple time points? Is it kind of like unclear?
Fabio: Right now it’s happening. So as soon as you go above polarity and really... for some tissues, we’re already past that. Just for polarity, you need to have multiple, multiple time points.
Matthew: Lots of robot arms.
[01:17:09] Why is vascularization the hardest problem of tissue engineering?
Abhi: No, automation seems like pretty essential for this. It’s a very interesting direction and cool initial result. And you mentioned vascularization as the final thing that needs to be done for any tissue engineering company to eventually take off. And I think while I was researching for this, like, it just seems like everyone is like talking about like vascularization is like a fundamentally unsolved problem in the tissue engineering field. Why is it so hard?
Fabio: Yeah. So let me go once again back to our reference, which is development, human development. Vascularization is an incredible system and the way it arises throughout development is incredible because as you can imagine, every developing organ needs nutrients. So as soon as the first stage of development is over when basically diffusion is enough, organs and tissues need vascularization. So it is incredibly complex. It has very specific phases that it uses to first define kind of the general vascular framework of the body, and then to generate all the capillaries and this incredible dense network. And once again, we have not been able so far to recapitulate this complexity or to trace this complexity properly in the lab. So all the approaches that have been tried so far have been quite simplistic and reductionist. So we were not able to really achieve the vascular complexity needed to feed growing tissue grafts and make them, you know, and bring them to the necessary size and shape.
Interesting... what has been happening in the space is that people have started understanding how to use different approaches to have vessels grow and how to engineer them. Where the key insight has been: we have to have a clear starting point and we have to have a final point towards which the vessels can grow, right? We have basically have some attractor that the cells can use to point at. Our insight is, once again, you cannot use or recreate this complexity artificially or top down. You have to grow it. So our approach is as we are growing different tissue structures by tracing development similarly, we are also trying to grow vessels in these three dimensional boxes full of extracellular matrix of some type. And again, the way this will happen is that we will have computational models that try to drive vascularization and optimize for different features. And this is important because one of the very cool things about vascularization is that it varies across different organs. So the brain with a blood brain barrier needs a very specific type of vascular network. The heart, a different one. Kidney, pancreas, liver, different ones again. So there is a lot of optimization to be done there as well.
Matthew: And we have a vascularization program underway. Like, we know it’s a showstopper. We’re working on it. It’s not an afterthought.
[01:20:33] Why can’t you just wash angiogenesis factors over the tissue?
Abhi: I spiritually get why you guys want to just like mirror developmental biology. ‘cause that’s kinda like the thesis of Polyphron as a company. Why, why, like, naively, why can’t you just like seed the whole thing with endothelial cells at the very beginning and wash over angiogenesis factors to create the vessels? Like why doesn’t the naive solution work?
Fabio: Yeah. So the problem is that cells need to interact in a very specific way in order to create the structure first and then to gain functionality. If the first steps are skipped or not properly recapitulated, you will not be able to obtain a functional network by the end. That’s why the majority of vascularization approaches first implies some type of kind of blob of endothelial cells that need to exist. And then from this blob, you know, the vessels will start to arise and kind of diffuse throughout the growing graft.
Abhi: Is there like any world in which you grow the vessel separately and then you can join them back in?
Fabio: It is extremely difficult. Imagine that for many tissues there is basically one capillary per cell.
Abhi: I was not aware of the complexity. It’s not like flooding a rough neighborhood of cells...
Fabio: It’ll change from tissue to tissue, but the density you need to achieve is astounding. So the chances of being able to do this ex-graft and then plug this in, in a way I think are relatively low. Um, and again, it’s really difficult to reach equilibrium of a complex system by combining things. It’s just easier to have the features arise together with complexity.
[01:22:25] How does the graft integrate with the host’s blood supply?
Abhi: Makes sense. Um, and let’s say like you do solve this like grand challenge of the field. Um, you’re able to get vascularization working. You give it to a surgeon, they’re about to implant it into a patient. Once it’s in the patient, it’s not like integrated with the rest of the vascular system of that patient. How does that integration occur?
Fabio: Yeah, so there is a process that needs to happen and this regularly happens in the surgical rooms during organ transplant or any type of surgery, which is anastomosis—where the vessels in the entering body have to be connected to the existing vessels. One way is to do so is surgically. The other way is to try to exploit the natural way the body reacts to foreign bodies, which is basically by perfusing them with blood or with fluids having their own immune cells and cells kind of try to colonize the graft and try to find anchor points that can be used to make basically, um, integrate this, this addition.
Abhi: By anchor points... is that like a physical vein that they’ll attach?
Fabio: So it’ll be first cells and then it’ll be, you know, it’ll be either like some type of fibrotic tissue. I should preface, I’m kind of speculating here. It hasn’t been, I think, fully proven across grafts. But what I can tell you, for example, in bone replacement, this is exactly what happens. So the bone replacements are designed to have basically to be exposing anchor points that the body sees, recognizes. It latches onto, and this basically helps the body integrate the new graft. Right? So we can imagine something similar happening with our grafts, whether we have to insert these kind of anchor points—this might be like peptides, so nothing too problematic for the body—or we should find ways to stimulate angiogenesis, which of course is kind of tricky due to oncogenic potential. But you know, one way to approach the problem will be how can we exploit the way the body reacts and basically use it as a way in.
Abhi: I didn’t know about that bone thing. That’s interesting. Like, I know like skin doesn’t really need vascularization all that much. ‘cause it’s thin enough that like, diffusion just works fine. I didn’t know bone grafts even had like needed blood flow into it.
Fabio: They do. So imagine that a bone graft is basically again, this rigid sponge. And it’s immediately perfused. And then you have these tiny pores that kind of are exposed and cells will just pass by, latch onto them. There’s a small period of inflammation. And this then will have other host bone cells that colonize the graft and then start basically remodeling it and making it new bone from the host.
Abhi: This is just my own curiosity, but like, in bone grafts, do they also replace the stem cells within it, or is that ignored?
Fabio: No, no. You can just put the graft. This mineral kind of thing.
[01:25:45] How do you validate tissue function before implantation?
Abhi: Interesting. Um, okay. I wanna zoom out a bit, like more the future. And so like right now you don’t necessarily have functional tissue in like an absolute sense, but someday you will. When you get to that point, are there functional assays for this sort of thing that you can use to prove out that like, oh, this chunk of cardiovascular tissue is actually gonna be useful? Once I implant into someone, like, how do you prove that out in advance? Beyond like measuring polarity, the proteins are there, is there anything else?
Matthew: Yeah, I mean, so in vitro you have like cell identity sequencing, qPCR, all of that stuff. But you also have electrophysiology. You have functional readouts where you can measure the function of the tissue. So like contractility in heart tissue, pacing if it’s pacemaker type tissue. So that’s all the stuff that you have in vitro. And then obviously you have animal models. So in the heart you can’t really do functional readouts in rodents because the physiology is so different. Like the heartbeats are like an order of magnitude apart in terms of beats per minute. So it’s a very poor translational model for cardiac interventions, for most cardiac interventions for that reason. So you would probably do it in a pig. So there is a panel of fairly robust functional assays. You can do the in vitro level and then you would do a large animal study in a pig first before you put it in a human.
Abhi: I remember when I interviewed Hunter, the Until Labs guy, my last episode... he said that, “oh, well we’re good with functional assays because the organ transplant field has already like, figured out most of them.” Is that also true for the tissue side where like... I know that there are discrete tissues that are transplanted from like one person to another person. Are those metrics like pretty well flushed out?
Matthew: So, I mean, my understanding is that—and I’m not a complete expert in organ transplantation to the degree that Hunter must be by now—but my understanding is that there are pretty minimal assays that are done on those organs before they’re transplanted. So you’ll probably have like an ischemia time window check. You probably have like a biopsy, maybe you have some imaging. But these are basically being done in a helicopter, so your QC/QA is: “it’s human tissue and human tissue is good, so let’s put it in and this person’s gonna die otherwise.” So that obviously changes your risk profile.
Abhi: He did mention like the big advantage of cryopreservation was like, you get to do more testing right now.
Matthew: And actually it’s an equivalent advantage of being able to do these ex vivo grafts, which is that you get to do QC/QA like really, really deeply.
Abhi: Like as much as you want.
Matthew: And I think that’s gonna be a core advantage going forward.
[01:29:01] How do you design a clinical trial for a biological pacemaker?
Abhi: Makes sense. The other big question I have is like, this is a brand new therapeutic modality, in basically every fashion. And so how do clinical trials work for this sort of thing where you were going up to a patient who—like for the pacemaker case—like they already have a pacemaker? How do you convince them? Like, “oh, can we put this engineer tissue into you to see if you like, can go without the pacemaker?” How do you recruit these patients in the first place?
Matthew: Yeah. So... so we have—before I get to that step, I just wanna kind of touch on how we think about indication selection. ‘Cause I think it’s quite important. So because we are you correctly pointed out, trying to pioneer a completely new therapeutic modality, we have a barbell strategy for every new tissue we approach, which is that we are looking for two potential products. One we call a Regulatory Pathfinder. One is like a Commercial Workhorse.
So in the cardiac case, our regulatory pathfinder is a biological pacemaker. And what you’re looking for for a regulatory pathfinder product is the ability to have a unbelievably unambiguous clinical readout, incredibly fast, with the circumstances that would allow you to have a very, very small clinical trial. And then the commercial engine, which in our case is a left ventricular muscle patch for heart failure with reduced ejection fraction in stage three and stage four... that’s basically 95% of the revenue, but the readouts take a lot longer because the clinical readout for the reimburser for your commercial engine is reduced hospitalizations, which you just have to measure over the course of a year or two years even. We need to find proof of efficacy as quickly as possible in the most unambiguous way we possibly can to give a kind of a halo effect to both products in the barbell strategy.
So in the biological pacemaker, what you are looking for—and back to your kind of the initial challenge that you gave—is you’re actually not looking for patients where they have something that’s working perfectly well. You are deliberately trying to find cases where this can be a salvage therapy. And because you have the commercial engine, you don’t really mind about the size of the patient population that you’re gonna go after there. Because these things work together in tandem. So you are really trying to optimize for: can I have a first, like a phase one trial that is like almost N of 1, potentially even compassionate use so that you can get as much regulatory speed up as possible and have an unbelievably clear binary signal.
So for the biological pacemaker, what we’re looking for is “hardware exhausted” patients. Uh, they’re often pediatric or neonatal, sometimes they’re adult. So these are patients for whom they have a device that is going to fail. It’s either because of infection or its device rejection in some form. They’ve had multiple surgeries. There’s nowhere to put a lead left, like occluded veins. There are a bunch of reasons that this could happen, but basically your quality of life is gonna be anything from very, very bad to palliative care. And you need to find one of those people.
From a patient recruitment perspective, a lot of these cases are clustered in the same three or four surgical centers in the US. So you need to basically build inroads with the cardiologists who are doing these lead extraction, lead placement operations. But it’s essentially the same surgical workflow that they’re already doing. So again, no one needs to be like retrained on anything. Um, but the way that the trial would be designed would be as like a weaning trial. So you would have someone who is on device who has a pacemaker right now, but they are hardware exhausted—that device is gonna fail.
Abhi: And that’s usually predictable.
Matthew: It’s incredibly predictable, right? They will be classified as a hardware exhausted patient. That is known. There’s a code, like you can find them. And you essentially run a parallel trial where you have the engraftment of our biological pacemaker and they have the device still there as a safety backup, which again makes it should make it a lot easier to get buy-in from all of the patient groups and FDA and cardiologists because you have a kind of a baked in safety valve, which is that you already have this device and what you do is you toggle the device on and off and you see how much time can you have off device. And that readout you should find out whether you’ve built a functional tissue that does the thing that we said it was gonna do within days, hours, certainly, certainly weeks. And so it is the fastest possible way that you can get a human clinical readout. And so patient recruitment, you are really looking for like one, maybe two people for that trial.
Abhi: Why are these types of patients congregated at three medical centers? Is just like the rarity of this condition?
Matthew: Yes, because so much of it is pediatric. So it tends to be clustered in like where there are pediatric cardiologists, which is obviously a specialism within a specialism.
Abhi: This is maybe... these are difficult questions to answer because it’s something that’s gonna take a while for you guys to get to, but finding these sort of patients, or like convincing clinicians that they should be using this seems challenging. How much do you convince the cardiologist themselves that this is a worthwhile approach versus like, you just file a clinical trial and if like the parents of the child are interested, then they’ll sign up for it?
Matthew: So, I mean we think there’s a pretty good shot that... obviously we wanna engage early and often with the cardiology community, and we want ‘em to be very, very supportive of this as I think they should be. When it comes to the first use, it could be done through a compassionate use pathway, which has a much, much lower regulatory bar. There are large number of functional assays that we would do before even attempting to put this in a human, including successful large animal trials, obviously. And then the good thing about the way that the pacemaker trial would likely be designed is that there are no other options for that patient. Um, and there’s already a device in place that you could switch on to take over from the graft if something were to go wrong. So you are looking in these regulatory pathfinder indications—which we have for every tissue—we are looking for something with these features where you can try and have safety built in almost by definition. So looking to take someone off a device is a good way for looking at indications.
Abhi: Is there some analog to that in anywhere else? Like “you are on a device right now, we’re gonna introduce an intervention to try and get you off the device”?
Matthew: Is there an analog? I mean, I would guess that in kidney there must be... you know, to get you off dialysis. Time off dialysis is an endpoint. And that would probably be our endpoint for what it’s worth for nephron units and stuff.
[01:37:01] The argument for being a pan-tissue company
Abhi: One of the other... this is something we touched on earlier, but right now you have cortical programs going on right now. You have heart programs going on right now. And like the hope for Polyphron is that you become this pan-tissue company that you are involved in every single organ, every single system in the body at once. And when I first talked to you guys, it seemed like clearly insane. After you explained to me the rationale, it makes a bit more sense. I’d love for you to just like, repeat that basically.
Matthew: I mean there’s a... there’s I suppose a technical dimension to this and there’s like a commercial dimension as well. Um, so from a technical perspective—and Fabio can chime in if I absolutely mangle this—but as we said before, we have a relatively strong hypothesis that there are gonna be some latent rules of morphogenesis and development that extend across tissue. And we think that there’s a critical mass of tissue systems and tissue products that we can onboard, after which we could probably do most other products. So we actually think that there’s a race to reach that critical mass and that the company that does it first likely has a very strong competitive position relative to the market. Helped by the fact that most of the data is actually being driven by the lab. So after you have this initial bootstrap from reference data, it’s really, really hard to come in as a fast follower and attempt to try and produce this stuff. You would have to build the loop and run through the loop the same way that we’ve done. And we think that that will take a lot of guts and capital and maybe wouldn’t even work if someone were to try it afterwards. So the race is on in our view to try and get to that critical mass of tissues.
Not only that, but from like an organizational perspective, you need to master the manufacture, production, deployment of new tissue products at commercially reasonable cost of goods, which is something we’ve built into the loop from day one. And we’ll get better and better at that over time. And then we’ve also built these kind of nested flywheels where we should not only get better as a company and then at the company level, at the model level, each new tissue type we onboard, but each graft, each tissue product that is sent out into the world. So we’ll be very, very careful about collecting super deep telemetry data about what these grafts are doing, engraftment rates, clinical readouts, et cetera, all of which will be incorporated back into our loop.
And then the last thing I’ll say is that from like a company compounding perspective, we believe that these tissues will not suffer the patent cliff problem. So the value is not a composition of matter, which you would normally protect with a patent—which is the kind of the grand bargain of pharma is that you get 10 to 12 years of like monopolistic profits, but you have to disclose exactly how you’re doing it. Process power beats patents basically every time, and ours is a very, very process power driven company. So we anticipate for each product to have quasi-perpetual revenue streams at the limit. Like a bunch of stuff can happen, people can produce better products than us, but I don’t think we will have this kind of drop off. And so we should get quicker and quicker and faster and faster with every new tissue type and every new tissue product we produce. And the company should get more and more defensible as we go.
Abhi: Is that last point along the lines of like, oh, the defensibility of what we produce at Polyphron will be a combination of one, it’s just very hard to produce, and two, how we made it is a trade secret?
Matthew: Basically. So it’s model weights and process. It’ll... it’s closer to a semiconductor fab, honestly.
Abhi: That’s interesting. Um, I do have a friend who actually considers that like this will happen to the biology field in general because... it’s just like, like if China can just like pick up the molecule, then like why would you ever give away the... yeah.
Matthew: I would anticipate actually across the field, like classic pharma and biotech approaches working on regular modulations will start to move more and more into trade secret. Particularly with all of the target crowding that we’re seeing.
[01:41:57] What are the biggest scientific and economic risks?
Abhi: What is the most risky thing that could possibly happen at Polyphron that is either scientific or economic or both?
Fabio: Scientific... I think we made this explicit, but we have quite a few technical problems to solve. Adding complexity in terms of structure. We will have to see how well our platform gets to that level, or which knobs we will have to tune. Of course vascularization, we have this very strong hypothesis, but I can’t lie, it is something that we’ll have to face and that we are already starting to think about. The more on theoretical side, I find this idea of learning these latent rules of development as quite an interesting challenge. It’s not really a roadblock, but I think it’s something very fascinating that we will try to prove.
Matthew: Economic... Um, so I think I’m gonna kind of give like a non-answer. Here’s my non-answer. So when investors invest in biotech or tech bio companies, I think there is a delusion... which I don’t know who it serves... but there is a belief that you are only taking technical risk and you’re not taking market risk. And that’s what kind of deep tech investment is. That’s what biotech investment is. That’s absolutely not true. You’re taking both. And if you are not appreciating the commercial risk you’re taking, then... well, it’s not ideal.
So the worst position to be in as a company that has pulled off technical miracles—and we’ve pulled off maybe like one or two of the technical miracles we need to, but there are plenty more we need to pull off to make this successful—the worst thing that could happen is that we end up in a position where we don’t know how to manufacture this profitably. We don’t know how you would sequence the regulatory strategy, the market rollout strategy. Like Bluebird Bio is the now, I think, canonical example of what happens where you can do unbelievably good science and build like an unbelievably impactful product that can meaningfully benefit lots and lots and lots of patients, and you get sold in a fire sale for like 5% of the money you raised. So as much as possible, not just in how we build the organization, how we think about things strategically, but actually in what we are trying to optimize in the lab, we are baking in, as I said before, like COGS, vendor redundancies, like where we are sourcing this stuff from, to ensure as much as possible that we’re building a technically viable and commercially viable organization at the same time. So I suppose the risk is I’m wrong about any of that. But we’re trying as much as possible to kind of think around the corner even well in advance of when it would actually be going up for reimbursement by a payer.
[01:45:23] Who are Polyphron’s competitors?
Abhi: Relatedly, who do you need to worry about in terms of competitors in the tissue engineering space? I can’t really think of anyone. You the only...
Matthew: So... I mean, there are people working on tissue. I think we are the only ones with this pan-tissue approach. I think it’s because it probably does seem insane to people initially and you have to think about it a little bit to get more comfortable with it. But there are tissue specific companies that are working on things. It’s difficult to find out exactly what their approach is from the outside sometimes. But Bob Langer has a company called Satellite Bio that, as far as I know is doing liver. Aspect Biosystems, I think is taking a bioprinting approach. They’re doing some endocrine stuff, liver as well. And then they did a Novo Nordisk deal, which I think is obviously gonna be obesity related. And then you have, I suppose at the other end you’ve got the Xeno companies as well. Again, like I don’t believe that if you could produce human tissue or you could have pig tissue, you’d pick human tissue every time. And I actually believe that the pace of the field in sort of human tissue engineering is gonna exceed xenotransplantation pretty quickly in terms of technical maturity. And so I think that you’ll always want human tissue. If you had like a head to head, you’re always gonna want human tissue for a bunch of reasons. Not least of which is the genetic engineering, which adds some safety concerns and may not even be transferrable patient to patient.
[01:47:07] Expanding the TAM beyond transplant lists
Abhi: You mentioned earlier on about how by going after this non-super advanced stage of patients where they need organ transplants, you get access to this patient population that there is not really any therapeutic intervention available to them other than perhaps like medication. I’d love to get your take on that because I think it does dramatically change how I think about the economics of Polyphron.
Matthew: Yeah. So I think that’s something that you probably should believe in order to be bullish on like the extremely successful case of Polyphron. So let’s take an example. Let’s take the heart, which I know we’ve been talking about a lot, but we know a fair amount about it. So there are 6.7 million Americans with heart failure. It is the second biggest cause of death in the US I think. And of those 6.7, maybe 10,000 roughly will get a heart transplant. There are a large number of people who just cannot get the heart transplant even if they kind of were on the list. So either for frailty, age... like you’re just not gonna give it to someone who’s over a certain age. Comorbidities, lifestyle. You know, there are potentially other kind of exclusionary factors that would prevent you—like say you can’t take immunosuppression, for example. You can’t deal with being in the ICU because the heart transplant involves this like full sternum being cut open.
So of those 6.7 million, only 10,000 will make it to a heart transplant. But you have 670,000 roughly—so about 10% of those—are in stage three or stage four of the categorization that is used to categorize heart failure. And of those 670,000, we think that automatically with our first product, you can start to kind of address most of that population. So these are people who would not be able to get a heart transplant, who would be kind of allowing to defer that potentially forever. So you can meaningfully expand the population by, I mean, 10 to 20x roughly.
And it remains to be seen how much you can push it, but to kind of give you a sense of like TAM expansion: Okay, so let’s imagine that you start with a patient population who have heart failure with reduced ejection fraction below 35%. So there will be like a kind of a cutoff limit. And these are people in stage three or stage four. Your initial patient population is gonna be about 40,000 people. And the pricing for that product, you probably would anchor against a left ventricular assisted device. So call it 150 to 200,000 dollars, let’s say 200,000 dollars. Again, you’re probably anchoring against deferring a heart transplant and not having a left ventricular assisted device. So you have... let’s take the midpoint between 20,000, 40,000, which is number of these patients. You have a TAM just there of like 6 billion dollars in the US alone. That’s probably 10 globally. But you can meaningfully expand that just by going up. You can potentially even get to 50 plus billion dollars of US TAM by being able to address like a fraction of the people who have like progressive chronic heart failure. So right there, just in like expanding the patient population window slightly to say, you know, a hundred, two hundred thousand people, you begin to be in a revenue range of like Eli Lilly. And we believe that for most of these tissues, right of the list that I gave of like 10 different tissues we could go after, we believe that there are these mega blockbuster products waiting if you can just slightly—not even fully—just slightly increase the patient population window. And at the limit we think that we can increase it significantly.
Abhi: Is this particularly true in heart or do you imagine like similar dynamics would occur in almost every organ? Maybe except the brain?
Matthew: Almost every organ except the brain. Maybe eventually the brain, but that requires a bunch of technical work. There’s no brain transplant, right? So you take a lot of reimbursement risk and then there’s a huge amount of translational risk that you probably don’t wanna layer on top of the other risk that you’re taking here. But yeah, conceivably for pretty much every other organ that we currently have transplantation for.
[01:52:28] Autologous vs. Allogeneic approaches
Abhi: In the limit case of like Polyphron is now outwardly open to patients in need, would you go autologous or allogeneic for whatever your tissue construct is and why?
Matthew: So I think it depends. I think it depends on the indication. I think that there are manufacturing considerations obviously around autologous. So the optimal COGS scenario is probably immune-cloaked allogeneic, so that you can amortize the cost across multiple units. Because our system is designed from the get go to be resistant to starting donor line heterogeneity, you could actually just as easily imagine doing HLA matched allogeneic. So there are 40,000 roughly different subtypes, but that’s not normally distributed. There’s a kind of a fat head of subtypes that people belong to. Um, you could have a kind of an inventory of like a hundred different cardiac products and cover 70, 80% of the US population. It’s lower in more homogenous, genetically homogenous countries. So like Japan, I think you can do like 20 and you can cover 80% of the population. So I think that really, if you’re gonna be able to produce this at scale, you are better off going with a kind of immune matched or immune cloaked allogeneic product.
Our system is designed to be able to produce both hypothetically. Now this is like a bit more of like a sci-fi case, but if Hunter is successful, you could imagine biobanking frozen tissue well ahead of time, right? We know that there’s cluster dysfunction. That’s how age-related chronic diseases work. We don’t necessarily know the order, but we know that your kidneys are gonna fail, your lungs gonna fail, your heart’s gonna fail. And you could imagine a future where you would create a bank of constructs that you think you might need, and those are kept on ice for you. But I think for near term commercial solutions, you would want to do immune compatible, allogeneic.
[01:55:07] Is a 3-year timeline to the clinic realistic?
Abhi: The future of Polyphron in terms of like... when will the first clinical trial start? To me, when I first talked to you, I thought like, okay, it’s gonna like six to 10 years away. You had a rather aggressive timeline: three years. What’s the rationale on that?
Matthew: So now just to be clear about what the claim is. I am not claiming that within three years we could produce every single tissue. I’m also not claiming that any tissue that any of all the possible tissues could get into to a human within three years. I am claiming that if you are very specific about indication and tissue product selection and you parallel track some stuff—you are properly financed to allow you to parallel track some stuff—then yes, you could put the Regulatory Pathfinder product in a human within three years. So I think you would do, you know, large animal takes you some amount of that, and then you would jump straight to a large animal, and then you would go into human from there. And I think you could do it in less than three years if you had a compassionate use.
[01:56:28] Cross-species translation
Abhi: This isn’t something we actually talked about, like how how big of the translational risk is there that you might optimize something really good for large animal, but might not be very good for human?
Fabio: So our whole computational setup is designed to optimize for human structure. We are using human cell lines. So there’s always some type of translational gap. There is the limits of predictive validity of animal models. But you know, in terms of what we’re trying to recapitulate, we’re looking at like human tissue.
Abhi: But like in that case, that means it’s very difficult to do this like large animal study because you don’t know how to create tissue to their species. Is that not fair?
Matthew: I mean, you’ve got a decent similarity. Enough of a similarity between like in the cardiac case between pigs and humans. So the heart is similar enough in terms of size. Alignment is not perfect for what it’s worth, which is another issue with like xenotransplantation is that there is like a slightly non-natural morphology that you’re having to deal with. But that I believe would be considered kind of the best in class model. Porcine... maybe, but probably pig.
[01:58:05] What would you do with $100M equity free?
Abhi: The last question I have is: if someone gave you a hundred million dollars equity free tomorrow, where would that be best allocated to make the company move faster?
Matthew: Doing that thing I said in three years? No, in all seriousness, I think that a hundred million dollars would be best spent on... because we believe that this company is truly a platform and that there are some very particular moats that will emerge at a certain scale... sizing up the automation fairly aggressively early on. Uh, it can be sequenced. It doesn’t all need to be done at once, but a lot of the investment would go there. A lot would go on compute, honestly. And then the remainder would be the steps that you cannot skip and you should not skip, around like CMC and QC/QA and like all of the regulatory stuff to be able to put these products in humans. But I mean, with a hundred million dollars equity free, we would imagine that we could try and have two products in human, one within IND underway. And of those two products, they’d be in two different tissue systems. So we wanna prove that this... it’s not something weird about the heart that allows this to be the case. That this transfers across germ layers. Because it transfers across tissue systems, it transfers across germ layers. So that would be my use of funds.
Abhi: I’m a little bit curious... I felt for like your take of like on one side, I can imagine this a hundred million dollars is like really well spent in gathering like a lot more embryo data. But is that actually like not super useful? Like you have plenty like of set line data that you need?
Fabio: Yeah. So. We do not have it right now for sure. So there is gaps both in terms of stages for one specific tissue as much as we do not have pretty much anything for other tissues. But I do agree. My sense would be that after a certain size of the data set, the amount of information you can extract kind of plateaus. Just you need the feedback loop instead. So what we really have to do is to scale up as much as possible the scale of our experiments. So having automation that allows us to create very, very complex intervention kind of groups. And secondly, it is how many readouts we can get out of our experiments. What we’re doing right now, it’s mainly imaging based and it has of course its limitations. There are solutions out there that allow you to do spectroscopy or collect ‘ome or any other type of sensor based data. And you know, by building this kind of 360 view of our experiments, we can really start exploiting our computational approach and then really try to optimize for the structures we want to get.
Abhi: Okay. Cool. I think those are all the questions I had. Thank you so much for coming on, Matthew and Fabio.
Matthew: Thanks for having us.
Fabio: It was a pleasure. Very fun. Thank you.
