RNA Delivery Business with Liberate's Shawn Davis, Ph.D. & Walter Strapps, Ph.D.

Earlier this year, Shawn Davis, Ph.D. left a pretty comfortable position at AstraZeneca (and Amgen before that, and Milliken, and others, before that) to lead Liberate Bio. He brought along another industry notable in Merck/Intellia alumnus Walter Strapps,  Ph.D., who serves as CSO at Liberate. Why'd they do it? Because RNA delivery is such a crucial step toward  revolutionary genetic medicine, but it's tough science. They think they can change that.  On this episode of the Business of Biotech, Drs. Davis and Strapps share how they're applying automation and machine learning technologies to minimize the challenges associated with targeted delivery of genetic medicine and  overcome the limitations of AAV.  During our conversation, they both leaned hard into how their previous experiences have shaped the work they're doing, and the wide-open business opportunities Liberate Bio is creating. 

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Transcript

Matt Pillar: 0:00

Hey business biotechers. This is Matt Piller and before we jump in today's episode, I have a big special announcement. On Monday, november 13th, at 11am Eastern, we're taking the business of Biotech live for a one hour highly interactive web conversation with a special guest you won't want to miss. I'm planning a good 30 minute grilling of BlueSphere Bio CEO and Biotech legal expert Keir LoIacono on legal and IP protection considerations for new and emerging biotechs. Keir is an Esquire and a veteran biotech attorney turned CEO with a ton of experience, and when I'm done with him, I'll turn him over to a select group of viewers you business biotechers who will have the benefit of 30 minutes of face to face Q&A and interaction with Keir and other members of the call. This hour will be incredibly consultative and offer great value at no cost but your time, and it's an opportunity for some face to face virtual networking with fellow business of biotech listeners who just so happen to be the coolest cats in the business. Go to the link in the show notes of today's episode to register and I'll see you there.

Matt Pillar: 1:16

I certainly don't need to belabor the fact that nucleic acid therapy development is flourishing right now. The market is pegged at around 5 billion today, with many projecting that figure to more than double well within the next 10 years. But chief among the factors that growth is predicated on is the safe and predictable delivery of those therapeutics to their targets. These are very large, very unstable molecules that require, to put it in rudimentary terms, some very specialized packaging to get them to their destination in one piece and to maintain their therapeutic effect.

Matt Pillar: 1:52

I'm Matt Pillar, and on today's episode of the business of biotech I'm talking with a couple of folks who left some of the flashier names in Biopharma to form a company that would tackle this challenge head on. Shawn Davis was executive director of drug delivery at AstraZeneca and director of tech strategy and innovation at Amgen before that. Dr Walter Strapps was director of RNAi Therapeutics at Merck, VP of Intellia Therapeutics and CSO at Gemini Therapeutics, among others. Today Davis is CEO and Strapps is CSO at Liberate Bio, a company formed up just last year on the back of research by the renowned University of Pennsylvania Professor Michael Mitchell. Shawn and Walter, welcome to the show.

Shawn Davis, Ph.D.: 2:43

Matt, thanks so much for having us. It's a fantastic opportunity to talk about what we think is the biggest challenge in the field right now, and so we're excited for the conversation and to convince you that you should be a drug delivery guy too.

Matt Pillar: 2:56

I appreciate that. Hey, we cover the whole gamut. So drug delivery is definitely on my table. We'll talk a little bit more about why Liberate Bio is a little bit of a departure but a stretch for the business of biotech. But, like you said, this being the challenge that it is, I'm all in on having this conversation about the business and the opportunity there and what you guys are going to bring to the space.

Walter Strapps, Ph.D.: 3:21

Thanks, matt. I'm happy to be here as well. This is something near and dear to my heart, working on oligos for 20 years, so getting oligos someplace other than the liver, that's the holy grail for the whole field.

Matt Pillar: 3:34

Yeah, yeah, and that's a good point, walter. You know, like I said, when I first looked at you guys, I thought, well, you know, I don't see like a very specific pipeline of candidates that are biologics. That would sort of be a box that I check before I invite an exec on my show. But I looked into the background of both of you and obviously your chops in this space and tackling these issues speaks for itself and, this being the challenge that it is, I thought, you know, this is a great opportunity to talk about what we're doing to overcome it and how the work you guys are going to do or doing will benefit the entire industry. But I want to start with sort of the Genesis story. You know, like I said, 2022, I think Liberate was officially founded 2022. So just last year. I want to know about about Dr Michael Mitchell's influence on the work and how this thing got started.

Shawn Davis, Ph.D.: 4:30

Yeah, so, like you said, we're just coming up on the first year anniversary for the organization as a whole and, frankly, it really started with a conversation between Mike Mitchell and Nessan Birmingham you know for many of your listeners who may be familiar with Ness's work at Antelia founder and first CEO of the organization, now with Coastal Aventures and so Mike and Nessan were discussing this incredible challenge. And you know Mike has published extensively in the space of lipid nanoparticles and other delivery vehicles. You know he's at the University of Pennsylvania for anyone that isn't aware and you know is, frankly, the director and group leader for their Penn Institute for RNA innovation. So not only published extensively but also really helped shape the field. And you know his recognition of the challenges of extra herpatic delivery.

Shawn Davis, Ph.D.: 5:18

As Walter said, delivering anywhere other than the liver really is foundational to unlocking the full potential genetic medicines.

Shawn Davis, Ph.D.: 5:26

You know it doesn't matter how much you optimize these nucleic acids, it doesn't matter how great your editor is, if it's not getting into the cell type that has the target, it's not doing its job.

Shawn Davis, Ph.D.: 5:37

So you know the fact that Mike not only had published extensively in the space and had a lot of fantastic ideas to move things forward, but had also created a unique way to barcode nucleic acids that serves as a key foundation for liberate bio as a whole, because our challenge in the field is really understanding how do you innovate in the space, how do you design and create new delivery vehicles that go outside of the liver but do it in a way that's actually efficient enough that you can go after multiple different nucleic acid cargos, go after multiple different cell types, because solving just one of these is fantastic, unlocks a number of different targets, but, frankly, isn't sufficient for our patients or for the industry, and so we had to start with a concept that actually would allow us to generate the biologic data as efficiently as possible, to make the company scalable and allow us to deliver on the mission of the organization.

Matt Pillar: 6:33

Yeah, yeah, and I'm going to ask you some questions to dig a little bit deeper into the business aspect, sort of the business proposition that liberate brings to the industry here in just a minute. But I'm curious you mentioned Nessin and the Intella connection. Walter, you served as VP at Intella and director of RNAi Therapeutics. Was that sort of the connection that brought you into the fold at liberate? Tell me how you two came together and mixed in.

Walter Strapps, Ph.D.: 7:09

Sure, so yeah, so I knew Nessin from Intella. Obviously I was the 10th employee at Intella. I led Discovery at that organization for about three and a half years up to the, until the group was about 200 people, and so I knew Ness from that. And when Ness and Mike had started talking about the idea behind liberate the barcoding, the high throughput screening, I had actually started as an advisor to liberate as sort of as that company, as the company was being ideated. So I'm a platform guy. I've worked on various oligonucleotide therapeutics and various platforms over 20 years.

Walter Strapps, Ph.D.: 7:48

I like problems like this, where there's an interesting technology and then the question is how do you turn it into something practical? How do you utilize it? We even think back to CRISPR-Cas9 and 2015,. When Intella was formed, it was the third of those CRISPR-Cas9 companies, after Editas and CRISPR therapeutics, and there was a lot of discussion right from the very beginning of okay, here's a very powerful technology, but what can you actually do with it? And so that's what the first several years of Intella were where figuring out practically what you can do with that technology.

Walter Strapps, Ph.D.: 8:24

The same thing is true here, the barcoding technology, which we can get into more details of exactly how that works, but it opens up opportunities. But the real question is how do you utilize it, how do you leverage it to actually find the delivery vehicles that are going to get you outside of the liver? And it's always a lot more work than people think it is to build a platform, and building a platform itself is not the end goal. Building the platform is, as I like to say, lots of conversations. It's step one. A good, robust platform is step one. Getting to the therapeutic is step 10. But if you don't have step one solid, you're going to be in big trouble by the time you hit step three or four.

Matt Pillar: 9:03

Yeah, yeah, you guys are anxious to talk about the technology. I can see it. I'm trying to get some background, you know, get a feel for how you two met each other, how you work together in the past, what brought you together. You're like no, let's talk about the technology.

Shawn Davis, Ph.D.: 9:20

I mean, this is what Walter and I, when we first met and started interacting. We talked about whether or not we would work together well and we said, if anything, the problem is we're too similar and we need more diversity of thought in this company as we go forward. But I have to admit, life is a heck of a lot easier when you agree on 90% of things and only have to really debate over the last 10%. So we've been making fast progress at the company and we're looking forward to build out that thought process. In terms of me being brought into the organization, you know, I was, frankly, having a fantastic time at AstraZeneca Great organization making great progress. You know, we worked on the pandemic prevention program with DARPA. You know, in this space, we're working on several interesting vaccination approaches and when NASA approached me, they sent over a quick paragraph for what they were trying to accomplish and I have to admit I read that paragraph and I thought to myself I could have written this paragraph. This is exactly where I think the world needs to go in terms of a scientific approach to this. This is exactly the right problem in terms of being big enough to dedicate. You know. You know, frankly, you know more than five years of my sort of short lifespan to try to address. So you know this is the kind of thing that can motivate me to leave. What a role that I really relished.

Shawn Davis, Ph.D.: 10:39

The challenge for me was at both Amgen and AstraZeneca and this isn't a knock on either organizations, because I'm huge fans of all of their work and the folks there.

Shawn Davis, Ph.D.: 10:49

But pharma companies have a particular set of incentives and frankly I bet they probably align with most of your audience's interests, which is identifying new targets and generating new molecules to address those targets, and that's the right thing for them to be incentivized Bye. Unfortunately, they are not incentivized for drug delivery, at least unfortunately for a drug delivery scientist who's been spending his whole career in this space. It's a great commercial differentiator in many cases for both biologics and small molecules, but until you get to a class of molecules like nucleic acids it's a nice to have, but it's not a requirement. Even when it's a requirement, the organization's incentives aren't really aligned to it because they're still thinking about targets and molecules. This is a big part of why it's so critical that we have a company like LiberateBio that is focused on the drug delivery aspects, whose incentives are aligned to achieving results in that space, to support partner organizations that are building those cargos that are going after those different targets, but also to eventually do some of that work ourselves.

Matt Pillar: 11:54

Yeah, that's a great segue into a couple more questions. I wanted to ask about that sort of market opportunity. Describe for me what you see pre-company like Liberate you can talk about whether there are other companies that are intending to do what Liberate's doing, but pre-concept of a company like Liberate. You have hundreds of nucleic acid therapeutic developers, probably globally and growing in the last couple of years I'm going to say hundreds. I don't know what are they doing. What are they doing in terms of solving this problem across the landscape right now? Are they each individually tackling delivery mechanisms on their own, playing with some different things like whether it's viral vectors and LNPs and just like? Is that sort of? Is it like everybody for himself right now, just kind of figuring out the delivery aspect?

Shawn Davis, Ph.D.: 12:58

Yeah, I think that's true. You work in the space longer. Yeah, go for it.

Walter Strapps, Ph.D.: 13:00

Yeah, so no, I think that's absolutely true, right, and it sort of depends on the size of the players there as well, right? So you've got companies that are small and they have a cargo and they try to do some work on delivery, but it's by definition, it's going to be very narrowly focused. You've got the other players in the space Pharma, who actually has the resources to pursue a more dedicated chemistry program, a more dedicated delivery program. So you've got them as well, and you've got a lot of interaction between those various players as well. But one of the keys to this is also the size of the cargo. So I referenced SIRNA earlier. Sirna is very, very small, especially in comparison to Messenger RNA or to CRISPR-Cas9, where you've got a Messenger RNA and a synthetic guide, for example, or even bigger things like the next generation of gene editors, right, they're even larger than that.

Walter Strapps, Ph.D.: 13:54

So the size of what it is that you want to deliver has a huge impact on even what delivery vehicles you can pursue. Lnps are common across all of that. So, essentially, regardless of the size of your cargo, lipid nanoparticles are probably going to be able to deliver it. Aav has a limitation. It has a size limitation. You can only put so much into an AAV, so you've got an upper limit there. Sirnas can be delivered by lipid nanoparticles, but actually the most success has actually been seen with conjugates from Elnilum, where they directly attach a ligand onto that molecule and then they don't need any other lipid nanoparticle or whatever to deliver it. So you have to think about it as being the delivery is going to have to reflect what it is specifically that it is that you want to deliver. We're focused on LNPs because all of those cargoes can fit in a lipid nanoparticle.

Shawn Davis, Ph.D.: 14:52

Yeah, man, I mean, I think if you look at some of the new entrants into the space, especially in the editing space, and you look at what indications they're going after and we just made those less the other day actually and basically it's not aligned specifically to where the greatest unmet need exists. It aligns to what they can accomplish. And so you see lots of liver targeting, you see tons of competition in the space and there are some great targets and a lot of value for patients in those. But it's not a coincidence that eight out of eight new organizations is going for a liver target when they're editing. It's because they're going where they can actually go. I'm an engineer by training. I'm a practical and pragmatic kind of guy. I get it Like if you have something that works, you should take full advantage of that. But we basically have to enable others to be able to go after all of these targets and create the same opportunity for ourselves.

Matt Pillar: 15:46

Yeah, yeah. So when you're forming things up right, I mean you could. I mean I don't want to say it's easy, but it's easier to say, well, hey, we're going to be a development company, we're going to focus on this challenge. You know, we're scientists, we're engineers I got a scientist and an engineer right here in front of me. Let's build a company, let's focus on the development of a solution to this challenge. It's quite another thing to say, okay, once we feel as though we've got some legs under our solution, let's figure out how we're going to play in this market. Let's figure out how we're going to serve as a solution provider to these development companies who could benefit from this technology. Where is liberate on sort of that continuum, the continuum of like? Yeah, we kind of have this business model. Maybe we're feeling out, Maybe it's mapped out Like and what's that going to look like?

Shawn Davis, Ph.D.: 16:41

Yeah, I guess there are a couple of considerations that come in there, Matt. I mean the first and foremost. We talked a little bit about the incentives for pharma organizations and how our incentives might be a little bit different. That also informs where we're willing to take risk and not take risk. And so for early programs, a large pharma company's a low probability of success on a new target, a new modality makes complete sense. They've got a broad pipeline, it amortizes out and works, but they take very little risk on the delivery vehicles side of things because they don't want to compound the risk. On new vehicles plus new targets, plus analysts, it's completely rational. But if you invert that for a delivery company, then it starts to inform what you're willing to do, if you want to build your own pipeline, and where you are and aren't willing to take risk. So I'd say that that's part of the thought process, which is there are a lot of very well-validated targets out there which have not been able to been addressed because of the lack of delivery vehicles, and those are great opportunities for us as a company. The second part of the consideration, I think really comes back to not just what's available but what are you going to be capable of going forward and how do you think differently about this? There was a really nice summary of the space from the Boston Consulting Group. I guess it was a month or so ago. They published and it was the coming waves of genetic medicines and they did a nice calculation of where are their validated targets, who's working on them, when do we think it's possible? And they inserted part of the calculation of how easy is it to actually accomplish the delivery. And as a delivery company, when we look at that, we'd see it completely differently because we recognize what's going to be possible when we're successful. And so when you redo that calculation, you come to quite different conclusions about where you should be partnering, when there's lots of competition and there are already players ready and willing looking for that access to those molecules in areas that people didn't think were possible, and so they haven't put the time and energy into. And I think that really is going to be a productive space for us in terms of building our own pipeline in the future, driven by the ability to actually differentiate ourselves in the market with these delivery vehicles.

Shawn Davis, Ph.D.: 18:53

But the final piece of the equation that has to come together is what we see is commoditization of many of these nucleic acid modalities. The COVID-19 pandemic was devastating but any devastating activity in history usually has some benefits that come out of it and I would say the dramatic expansion in the number of CDMOs producing a capable of making nucleic acids. And you've got new players coming in. You've got existing players dramatically making big investments to expand their capability. It's really shifted the market. I had conversations that when I was at AstraZeneca about would this ever be possible to use MRNA for anything other than niche applications? I mean we were paying thousands of dollars per gram for research grade material. We saw really no path to scale up with most of these players. And here we are with only two years of hindsight and it's a completely different industry and supply chain and that provides fantastic opportunities if you're controlling the differentiating factor of a delivery vehicle.

Matt Pillar: 20:02

Yeah, yeah, very good, very good, thorough response there, and now I'm going to give you an opportunity to get into some of the technology that you've been chomping at the bit to discuss. And there are words in your language that are super intriguing to me, given our coverage area, because I think there's applicability in some of these words to our entire audience, drug developers and otherwise. You use, and I'm going to touch on some specific words and ask you to drill into how you're leveraging these words or these technologies in your approach. Liberate is using automation, in vivo, high throughput screening and machine learning to discover, to accelerate the discovery of your extra hepatic delivery vehicles. So let's talk about each of those three things individually and where they overlap automation, in vivo, high throughput screening and machine learning.

Matt Pillar: 21:02

I did a podcast a month or so ago with a guy machine learning expert, ai machine learning guy, andrew Sats, and he I love this line. I've quoted it many times. He said machine learning and bio pharma is like sex in high school the people who say they're doing it really aren't and those who really are aren't talking about it. I love that. I love that. I get that right. So start start worried. Like automation in vivo, high throughput screening and machine learning.

Matt Pillar: 21:35

Let's talk about each of those individually.

Shawn Davis, Ph.D.: 21:37

Sure, maybe, maybe I'll give you like a big picture and then Walter can fill in the details to actually make it real for everyone to recognize that we are in fact doing something in the space. But it makes me think we need to. We need to incorporate at least one more buzzword technology to really hit like the trifecta or the bingo of investor interest. So what I, what I will say is like let's take a big step back and then the most basics for developing anything in the farmer space, that the design, make, test, analyze cycle, if you will. Dmta, I mean, it's been around for decades when it comes to small molecules. Running through that cycle allows you to come up with an idea, test to see if it works, understand the results and then make changes. It's the scientific method in a circle Right.

Shawn Davis, Ph.D.: 22:21

What we've really tried to do is look at what that cycle looks like for the development of new delivery vehicles and then identify opportunities to accelerate every aspect of that. You know the DMTA cycle with scientific method, it's proven, it's great. It can be very, very slow if you don't discover ways to improve the efficiency and the throughput of those discoveries and, frankly, find ways to reduce the cost so that you can scale it more efficiently. And so when we talk about automation high throughput, biologic, you know in vivo results talk about machine learning. Those are the concepts and the tools that we're using to accelerate each one of those cycles. Walter, you want to give some details on those pieces?

Walter Strapps, Ph.D.: 23:07

I will. I will attempt to keep it brief because I could go for a very, very long time on most of the aspects of these things.

Matt Pillar: 23:13

Some, some, some details. Good, walter, if you, if you get you, know if you go too far down and just give me the high sign if I'm going too far.

Walter Strapps, Ph.D.: 23:21

So, look, the overarching approach here is, as Sean mentioned, is we need to get as many things into testing as we possibly can. We want those things to be novel and we want to be able to iterate on success during during this cycle. So we start at the beginning of the machine learning piece of this right. There is a lot of work that has been done in the lipid nanoparticle space, looking at identifying lipids, testing those lipids sort of one one by one, in vivo, in mice mostly, but also in non human primates to some extent as well. So there is a certain amount of data out there that says these are the sorts of lipids that are going to work. These are the sort of lipids that probably aren't going to work. If you were a rational chemist, you would start with those and you would design things that are a small iteration on the things that have been successful before. That's that's perfectly natural. However, if you do that, all you're really doing is, if moving very slightly outside of where other, what other people have done already. So, with the machine learning, the approach that we're basically taking is OK. Here is a machine learning model that says these are the things that have worked, these are the things that haven't worked. Try something. That's way out here. It's making guesses about things that might actually work for us, which is great, but it's not useful if you can't test a lot of them because, especially at the beginning of this, most of the things that a machine learning model is going to predict are not going to work. There and for various reasons and you don't know what those reasons might be they might be at the level of you can't chemically synthesize the lipid that your machine learning model is predicting. You can't formulate it into a lipid nanoparticle, or even if you can, maybe, ultimately it simply doesn't deliver. It doesn't need what it needs to do in vivo. So you want to be able to test lots of things. The only way to test lots of things is to do it in vivo, because the cell based models that people have tried to use to predict whether or not lipid nanoparticles are going to work they're simply not good enough. Their failure rate is too high. Your rate of false negatives and false positives is simply too high, so you need to go in vivo.

Walter Strapps, Ph.D.: 25:28

So that's where the barcoding piece of this comes in, and what's key to this is we have a cargo, a messenger RNA. It contains a barcode directly in that cargo itself. You make these as you would make any messenger RNA. Each of those individual messenger RNAs with a barcode is made into a lipid nanoparticle with the lipids that are predicted by the machine learning algorithm. You make hundreds of these. Each of them is a distinct LNP. Each of them contains a unique cargo.

Walter Strapps, Ph.D.: 25:56

You take all of those LNPs, you pull them all together and you inject them into an animal. We've done work in mice. We've also done work in non human primates. Now I could probably spend another couple of hours describing why it should be NHPs, but we think NHPs are critical to this. But once you've injected that pool into the animal, you can sacrifice the animal and then you can take all of the organs that in that animal that you're interested in delivering to and look for the presence of that barcoded messenger RNA by using next generation sequencing technology. What that tells you is I found this barcode and it is now present in skeletal muscle. What that tells me is the lipid nanoparticle that enclosed that cargo delivered to skeletal muscle.

Walter Strapps, Ph.D.: 26:39

Then you say okay, and now I've done this with literally hundreds of lipid nanoparticles in a single animal. These are the ones that I am now interested in. You take those structures because you know what they are. You feed them back into your model and your model says oh okay, well, if you're interested in this, try these ones. Now you're moving into the white space that other people have not looked at. Chemically speaking, it all boils down to the liberate. Our platform, the raptor platform, is designed to test as many things as we possibly can, test them in vivo in the most relevant species, which is non human primates, and basically iterate on that cycle as quickly as possible. Sean referenced risk. Doing it this way puts all the most of the risk at the beginning of that cycle rather than at the end of that cycle. So you just build, you look for things that work and you build upon the success as you go.

Matt Pillar: 27:35

Therapies based on messenger RNA offer many manufacturing advantages over traditional biologics, including cost, speed and flexibility. On the business of biotech podcast, we delve into all things related to mRNA manufacturing, from making mRNA vaccines to their scale up, regulatory approval and more. The pod is brought to you in collaboration with CITIVA, a global provider of technologies and services that advance and accelerate the development, manufacture and delivery of therapeutics, including mRNA production and manufacturing. Check out their resources at CITIVAcom backslash emerging biotech. That's CYTIVAcom backslash emerging biotech and to see those things that work. I mean, the barcode concept is certainly integral, central to that. Shed some light for me on how you go about barcoding a lipid nanoparticle. What are we?

Walter Strapps, Ph.D.: 28:40

No, it's not the lipid nanoparticle itself that's barcoded, it's the cargo.

Matt Pillar: 28:43

It's the cargo.

Walter Strapps, Ph.D.: 28:44

Yes, we have a messenger RNA and then there's a tiny little additional piece of RNA. That's part of that sequence. That is the barcode. It's just a unique nucleotide sequence. That's key to this as well. If you had to barcode the lipid nanoparticle, there are ways to do it, but again the throughput's not going to be good enough. So no, you're basically you're tracking the cargo. And keep in mind, when we talk about delivery, we don't care about delivery. What we care about is delivering the cargo, and so we are directly tracking the cargo itself and we know that it is present in a particular organ, within a particular cell type. So it actually removes one degree of abstraction as well from that delivery piece.

Shawn Davis, Ph.D.: 29:23

I mean, matt, I think you hit on a critical point there, which is where are you putting this tag and what does it mean to you?

Shawn Davis, Ph.D.: 29:31

Hunter highlighted the differences that you see in delivery when you have different cargoes.

Shawn Davis, Ph.D.: 29:36

He was really highlighting what cargoes fit into what kinds of vehicles, but the truth is, the size of the particle itself and the cargo and its interaction with the biology also influences its distribution and bioaccumulation, and so we're being very cautious in ensuring that whatever we add to track things isn't giving us some sort of bias in a particular direction from an accumulation and expression standpoint. So that's really fundamental, and there are a number of other groups that are doing barcoding approaches, and some of them are on the nanoparticles, some of them are in the cargo, some are additional segments of DNA. Everyone has their approach. I would say that we feel very confident that our approach is not biasing the results that we're generating and ensuring that, when we do conduct non-human primate studies, that we are getting the highest value out of the sacrifice from these animals, that we're conducting the most efficient experiments possible, gathering literally thousands of data points per animal to inform these models. And I think your comment from your previous guest is really valuable, because as all of us know, you and entertaining right.

Shawn Davis, Ph.D.: 30:44

Yeah, of course I mean that's the best combination. For me. Funny and smart is a great combination. So what I love about a lot of the work that's going on is you still have to go back to the fundamentals.

Shawn Davis, Ph.D.: 30:56

We've all heard the phrase garbage and garbage out. Well, the truth is, until you can generate high throughput, high quality data in the right species, I would argue most of what you're putting into your models is garbage. It's published garbage, it's patented garbage, but it's garbage in quotes because it has bias in what's being published. It has bias in that it's almost all in rodents, and so I'm not sure about the translatability. It has bias in that it's almost all in the liver, because that's what most people have the access to. So how good of a model could we possibly build if we only looked at rodent data in the liver that's been published? Frankly, not very good. And so closing that learning loop for the algorithm, allowing it to make predictions, allowing Synthesizing the material, testing it and then going back and saying you know what? That was a terrible prediction. Here's how it actually played out. And closing that loop is critical to the long-term building of the model.

Matt Pillar: 31:54

Yeah.

Walter Strapps, Ph.D.: 31:55

I just like to give one more example, if I could.

Walter Strapps, Ph.D.: 31:57

So one of the things that we did as we were setting up our platform was pull lipids from the literature so that it had been published in papers or it had been published in patents that had demonstrated delivery of cargo to specific organs within a mouse, for example. But what we did was take our barcoding approach and our approach of looking at all of the organs within those animals. So even where these lipids and these lipid nanoparticles had been published and having known tropism as in, they went to a specific organ, because we actually looked at all of the organs, we actually found tropisms. We found the delivery of these published things going to organs that were not reported upon because no one had looked there. So it's sort of like, as we're starting to build out our novel set of lipids and testing them, because we're taking a very broad approach, not a focused spotlight, but a much more broad swath of things we're going to find things that others literally have already missed. So we have great confidence that we're going to find more things as we go forward here as well.

Matt Pillar: 33:01

Yeah, the testing that's taking place right now. Like, just to break it down into its simplest form you're analyzing, via this barcoding technology, where these cargos are ending up, the cargo itself. Is this something you guys are doing internally on your own, or are you partnering with companies, developers of those cargos, to do this testing? What sort of that? Can you share that? Who you might be partnering with, or whether you're doing it on your own?

Shawn Davis, Ph.D.: 33:32

Yeah, so far. Walter gave a great description of our building of a platform and inevitably when you build a platform, you want to start with a solid basis that you can trust and then expand out once you understand that. So we've been referencing lipid nanoparticles almost exclusively so far. We don't think the process is limited to lipid nanoparticles. One of our founders, teresa Reineke, is a real expert when it comes to polymeric nanoparticles, and so we will expand into that space.

Shawn Davis, Ph.D.: 34:03

The cargos themselves we've been working almost exclusively with mRNA today that we've had produced by CDMOs, as I described earlier. It's easily accessible. We can go out there. We're making it pretty traditional luciferase mRNAs, encoding for luciferase, so that we can understand not just the bioaccumulation but also the expression of these materials.

Shawn Davis, Ph.D.: 34:25

We're expanding into other modalities very rapidly, not only because it addresses some protocol challenges in terms of the timing of the experiments, but also demonstrates the breadth of capability for the platform. And so when you start with that core understanding of your design space and then slowly start edging it out and building things out further and further, you get to a point where you have a large number of different cargos that have been demonstrated in the platform. You have a large number of different compositions of those nanoparticles and a really robust designs based to work with. So I hope that addresses your question that today we're primarily focused on mRNA produced by CDMOs. Our expectation going forward, once we make some of these discoveries on the delivery vehicles, is that we will be partnering with organizations that have existing cargos that they want to deliver, as well as developing our own cargos going forward.

Matt Pillar: 35:17

Yeah. Yeah, I've got some questions for you around that too, the development of your own cargos. But before we move on to that, the technology itself, the platform technologies that you're developing, the infrastructure, if you will, that's supporting automation and machine learning and high throughput screening Is this stuff that is sort of inherent to the team that you put together and you've built internally, or are you working with outside computational folks and IT folks that's gone into the development and building of that tech infrastructure?

Shawn Davis, Ph.D.: 35:59

Yeah, it's certainly something we're continuing to build out right. We're a very young organization, we're operating very leanly, we're looking forward to expanding in all of these spaces. But core to the concept and I would say a differentiator for us relative to some other players in the space is that diversity of talent sets. So we started by bringing in one of the postdocs from Mike's lab in order to ensure that we could make an efficient translation of the barcoding concept from his lab into our more industrial setting. Then building out the biology team under Walter's expertise to cover what's necessary from a sequencing standpoint is in vivo studies.

Shawn Davis, Ph.D.: 36:39

And then one of our other co-founders, steven Scully, a former Intellia employee, also really strong on the chemistry side and frankly got a lot of this up and going One of the first people on the ground that actually did the hiring to find these folks brought in the formulation experience to complement that and so sort of the barcoding, then the formulation and creation of the lipid nanoparticles coming together.

Shawn Davis, Ph.D.: 37:02

And then about six months ago we brought in a former head of computational science from a couple of organizations Perminder, manco who is helping us build out our actual algorithms, and so she's been making incredible progress, driving the activity, along with some vendors, to build out a complementary set of these algorithms.

Shawn Davis, Ph.D.: 37:21

And so it's the fact that we've got a team with all of these unique core strengths coming together and frankly debating, slash fighting over what the right balance between some of these competing interests will be is making the company grow faster and helping us to understand what direction we should go. And when the computational folks generate a million different structures for you to go and synthesize, then it makes for a really fun conversation with the chemist about how in the world you're going to synthesize a million different structures. And when the chemist figure out how to make things really efficiently, then all the pressure shifts over to Walter to figure out how in the world he's going to test all of those materials. And so each one of those bottlenecks as we break through it just pushes the next team to go even further, and has been really exciting to watch over the last 12 months.

Walter Strapps, Ph.D.: 38:10

Sean mentioned, we're a small organization, right. So the approach that we've taken is where we need very tight control over what it is that we're doing. We have that in-house where we need stuff and we can outsource it. We've been outsourcing it. So, again, I think Sean and I are relative on the conservative end of things in terms of the building of an organization, probably in other ways as well, but just in terms of we want to make sure that we have what we need in-house, but we're utilizing there's a massive plethora of CROs in all of these spaces. We're utilizing that wherever we can. If and when we reach the point where a particular capability we feel it needs to be in-house, we'll build it in-house. But we're taking the approach of really identifying where the gaps will be, filling the gaps outside if we can filling them inside when we need.

Shawn Davis, Ph.D.: 39:01

Yeah, I'm loving when you get described as conservative when you're working in biotech in Boston and startup organizations where you're like staring into the abyss of finding funding when you move your family up from another state to take things on Everything is a relative concept, though, sean, I know Only in this industry? Are we the conservative ones?

Matt Pillar: 39:26

State or country. Walter, I'm picking up some Canadian.

Walter Strapps, Ph.D.: 39:29

Oh well, that's pretty good. Yeah, no, it's funny that usually comes up in the first five minutes or so of a conversation with me. Yeah, no, I'm actually from Nova Scotia, born and raised in Nova Scotia. I moved to go to graduate school. I moved in well, I won't say when a long time ago, it'll date me too much, but yes.

Matt Pillar: 39:50

Well, speaking of geography. So you guys are, you're both in Boston, you both live and work in Boston. Michael, obviously, is up in Philadelphia. Philadelphia is laying claim to cell and gene therapy 3.0, presenting themselves as sort of the hub for that. What's your take on like, to whatever degree it matters these days? What's your take on sort of the geographic sort of proximity between what's going on in Philly and all that IP and subject matter expertise there and what's been going on in Boston, where you guys are?

Shawn Davis, Ph.D.: 40:33

That's an interesting question, man. I would say so. I've had the benefit of living, frankly, all over the world, all over the US, both in my childhood and in my career, and you know, it's quite a different world post COVID-19 in terms of your ability to communicate and get things done across vast spaces. Right, I don't need to explain that to your audience, yeah, but I would say that, no matter where I go, when I return to the Boston, cambridge, us area and I step out of a hotel room into, you know, kendall Square, the energy I feel is like nothing I've experienced anywhere else. You can see I guess you can't physically see, but I feel the energy of the ideas. You see people looking to collaborate, you see the desire and, frankly, the capital to pursue new ideas.

Shawn Davis, Ph.D.: 41:30

That is so different from what I've experienced anywhere else that I've been. And so when I was ready to return to the startup space after a decade away with Big Pharma, it was the only place that made sense to me. And you know any startup look, I'm very confident and liberate any startup as a high risk proposition and relative to many other opportunities. But when I wanted to come back to the space, I knew the only place to do. It was in the Boston Cambridge area, because of the ideas, because of the capital, because of the people, and so I am incredibly respectful of what's going on in Pennsylvania and Mike's work and you, penn's work, and I love that we can have these centers throughout the world, but, at the end of the day, this is the place I think you need to be if you want to get a lot of these things done.

Matt Pillar: 42:16

Yeah.

Walter Strapps, Ph.D.: 42:17

Yeah, and I just like to emphasize the pool of talent piece of this right. So you know, obviously you're interviewing Sean and Sean and I, right, and we're C level executives at the company, but the fact of the matter is the company is really the people who are doing the work day to day. That's the pool of talent that you're drawing on within Boston itself. You cannot underestimate that. I mean, we've got people at our company who, actually, who have more years of experience than Sean or I working in Biopharma and they know how to do things as well. They, you know, do things internally and outsource things as well. So, you know, it's one of those things where there's a critical mass of multiple different things, but the I really like the team that you put together to launch a venture like we've launched with Liberate, is critical because every employee you know, when we're 14, 15 people, every employee is a massive part of what it is that we're doing. You can really only find that critical mass, I would argue, in Boston.

Shawn Davis, Ph.D.: 43:12

Yeah, it's the talent pool, but it's the culture too, right, like you can have a lot of really experienced folks that aren't willing to listen to new ideas and to try to think about things differently and discover a new approach to things. And you know, the first time something goes wrong, defaulting back to the original approach, as Walter said to me the other day. And I don't think that's. I mean, there aren't that many places where people are so open-minded and willing to try different things and just go out on a limb and see if it works and really follow the science. And you know, when we talked about automating things, you know there's automating physical activities, but I really think of some of the artificial intelligence work that's going on is automating the hypothesis generation. It's creating new ideas for you to go and follow up on and seeing.

Shawn Davis, Ph.D.: 43:57

You know, we have a fantastic chemist on staff and I remember this the first, as the first chemistry was coming out of the model. He's like boy, this is just garbage. Like I can never make this stuff. Like whoa, what are, what are these structures? And then a few weeks later he's like actually, there's some interesting stuff in here, this could be really good.

Shawn Davis, Ph.D.: 44:13

And then flash forward a couple of months later and he's like this is like having another fantastic chemist creating wild ideas and, yeah, some of them are crazy and probably not going to work, but some of these things are looking really good. We need to make these things and figure out how it works. Yeah, so you know, finding talent that has that sort of open-mindedness and that willingness to try new concepts is unique and you know, I love the fact that there's this core in the Cambridge Boston area, but I also love that I can take advantage of those people, no matter where they are in the world right now. If you find that talent in Singapore, let's sign them up, let's get her on board and have her contribute tomorrow.

Matt Pillar: 44:54

Sure, yeah, yeah, no, I like that. I like that you know the time and exposure, that sort of analogy you made about your chemists embracing some of these ideas, even though, even if those ideas are not being generated at the bench where they're, you know where they grew up and they're so comfortable. I've talked to many a biotech exec who went asked about sort of that marriage of computational and chemistry or biology. They say, well, you know what it's proximity and time. We put those teams in close proximity and let them work until they become friends, until they understand one another and can make good things happen.

Shawn Davis, Ph.D.: 45:31

Yeah, I think that's right.

Matt Pillar: 45:33

And Boston is a great place to do that, I would argue, sean, you were saying you know, I don't know. You're saying you know you can see it and you're like I don't know if you can see it. Well, I live in a very rural corner of Pennsylvania, opposite corner from Philadelphia, and when I come to Boston I could definitely see it, like you know, I think it is palpable, it's visual, it's you know, it's in the air. So, no, no, no, shame in your unabashed love for Boston. We're running short on time, guys, I. But I want to get to some questions about that. Possible, maybe someday.

Matt Pillar: 46:09

Pipeline, as I said, from the outside of this conversation, you know, I was like I don't know. These guys don't look like they kind of hint like their language you know the language on the site and the promotional line kind of hints at maybe you know a pipeline down the road, and I'm not going to pin you down on that, obviously. But what does and I'm quoting from your language a hybrid business model with an internal pipeline and partnered programs look like. I want to know what that looks like. And then I want to know you know whether or not there's, there's the potential for a pipeline and what when you have a cargo?

Matt Pillar: 46:47

These are the compound questions that I'm famous for asking. When you have a cargo and you have a delivery vehicle, and both are equally integral to the success, the potential success, of a therapeutic and you're in a partner program, I'm curious about what that looks like from a product ownership standpoint. What's the goods, what's the real goods, who owns it? What's that kind of look like? So a whole bunch of questions there in the short time that we have left. Pick an angle, pick one of those facets of the multi-point question I just asked and let me know what you think.

Shawn Davis, Ph.D.: 47:28

Yeah, let's see how complete of an answer I can give you. For me, it starts with if you're building a business, you need to build a business, in my opinion, that generates confidence, incredible results and, ultimately, revenue in a staged fashion. And so that means identifying early opportunities that will basically get you in the clinic, confirm that you've got something that's valuable and generate revenue to support the future growth of the organization. And so a partnership type of model, in which you have a number of big players in the space that have identified targets, that have cargoes that they want to deliver but don't have the right vehicle to make that happen, provide a fantastic opportunity for us to work together with them to create a therapeutic product that delivers patient value, delivers commercial value for them and for us.

Matt Pillar: 48:22

And in that let me just interrupt you real quick, sean In that scenario the clinical responsibility and the regulatory responsibility lies with the therapeutic, the cargo developer, correct?

Shawn Davis, Ph.D.: 48:36

No question, yeah, I mean, I think, you see. I mean you can use what happened in the COVID-19 vaccine space as an example of how that can play out, where you have suppliers providing the lip and nanoparticle structures and the chemistry being licensed into a parent organization that has created the vaccine itself, the two of them to come together. There's a split of royalties, there's payments exchanged in order to move that forward, but the end of the day, it's Pfizer's vaccine and that's always going to be the case. When you control the therapeutic modality, right, that's the primary mechanism of action. I don't think there can be any question about that. And so you know, I think that is the near term path, but it also, frankly and can be quite, I think, a valuable path for the company as a whole. But it is the near term path that we will build on.

Shawn Davis, Ph.D.: 49:27

I would say the mid range to longer range path is one in which we are creating our own internal pipeline based on the fact that many of these cargoes have become commoditized and that we could have produced without stepping on intellectual property or having to build out our own manufacturing capability in the nucleic acid space.

Shawn Davis, Ph.D.: 49:48

You can just look at, frankly, the number of failed trials in some of the genetic medicine space, where you see that you have a well validated target and you recognize what intervention you want to make, but without the approved delivery vehicle they either don't get the results they're looking for from a safety standpoint or they get the efficacy they want, but the talks and tolerability and immune response isn't acceptable.

Shawn Davis, Ph.D.: 50:14

I think those are fantastic opportunities for us to build on and go back and cure some of those issues. So that's how that could play out, and certainly in that case then we are owning the full therapeutic. You are seeing the lion's share of revenue concentrated in the company, and it is a way for us to progressively build value in the organization for ourselves over time and for patients. There's a lot of talk about speed in the market from a commercial standpoint, but I think we have to remind ourselves that our goal is to serve patients, and speed to market first and foremost means earlier access for patients to therapeutics that don't exist today, and so we talk a lot about speed and efficiency to make sure that we're providing what we need, but that's all underpinned by our desire to ensure that the thousands of diseases that are going untreated today in this space could be addressed, and we do that as quickly as possible. So that's our view on how to build a hybrid or bifurcated business model around this.

Matt Pillar: 51:13

Yep, Yep, I like it. Any hints, or will you not touch this question with a 10 foot pole down the line? You know saying, even if it's just an inspiration, like something that inspires you personally any hints or thoughts on what targets or indications an internal liberate pipeline might address? Or you could say no or yes, but I'm not willing to share, that's fine.

Shawn Davis, Ph.D.: 51:47

I'm just maybe what I'll say is you know, when you're building a platform that you hope can deliver to any cell type in the future, you still have to identify a few initial cell types. That are your priorities, to set your protocols, to set your assays, and we have done that. I would say that there's so much unmet need out there, it's actually a problem. It's not a value problem, right, Like you map what's valuable and there are so many things you can't put them all on a single slide. So then you start thinking about, well, what's actually scientifically tractable in the near future for us to focus on earlier, and you know clearly the there are some cell types that are more likely to have amenable solutions earlier than others that also have a lot of value. Maybe we won't get in the details on the podcast, but anyone that's interested in chatting about this, we can certainly have a conversation and give you our thinking about the directions it could go.

Matt Pillar: 52:44

Yeah, fair enough. Fair enough, you guys are a ton of fun to talk to. This has been a super insightful conversation, very important, very timely given the stage of development we're at in the RNA space and nucleic acid space in general. So I thank you. I thank you for coming on. I know there's a lot more. You know, walter said he could spend a couple more hours talking about some specific things. We'll get you back on sometime to do that and you know I'll follow the company with a lot of interest. You guys are terrific. So thanks for joining me. I appreciate it.

Shawn Davis, Ph.D.: 53:21

Thank you very much for the opportunity, matt. It's a really fun conversation and, most importantly, thank you for allowing us to share the mission of the company and get it out there for more people to be thinking about. The more people we have working on this, the faster patients are served and the better off for everybody.

Matt Pillar: 53:34

Yeah, no doubt. All right, Shawn, Walter, thanks, thank you. So that's Liberate Bio. CEO Shawn Davis and CSO Dr Walter Straps. I'm Matt Pillar and this is the Business of Biotech. We're produced by Life Science Connect with support from Cytiva, which demonstrates its support to you at Cytiva. com/emergingbiotech. Check that out. And if you're listening to this episode on its drop date, be sure to join us at 11am today for an interactive discussion on IP and legal considerations for new biotech entities, featuring BlueSphere Bio CEO Keir LoIacono. Go to the link in the show notes to register or type tinyurlcom/boblive23. i We'll see you there. Thanks for listening.