theCUBE + NYSE Wired: MedTech Unplugged, the Future of AI in Healthcare & Life Sciences | Amber Salzman, Epicrispr

Clips
More from theCUBE + NYSE Wired: MedTech Unplugged, the Future of AI in Healthcare & Life Sciences

Amber Salzman

CEO

Epicrispr

play_circle_outline Epicrispr: Programming the Epigenome to Regulate Genes and Treat Disease

play_circle_outline FSHD: Impact and Therapeutic DUX4 Silencing Approaches

play_circle_outline Interim First-in-Human: Lean Muscle Increase, Functional Improvement, Biomarker Trends, and FDA Challenges with Natural History Comparators

play_circle_outline Upcoming milestones: World Muscle Society data and 12-month readouts for 12 patients

Info

Amber Salzman, Epicrispr

Amber Salzman

CEO Epicrispr

search

(INTRO)

Gemma Allen

>> theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired's MedTech Unplugged, a show where we connect Silicon Valley to the folks shaping what's next in technology, business, and health right here on Wall Street. Today on MedTech Unplugged, we're talking to a company developing what it calls epigenetic editing, essentially changing whether a gene is switched on or off, rather than changing the underlying genetic sequence. Amber Salzman, CEO of Epicrispr, welcome to NYSE Wired.

Gemma Allen

>> Thank you very much for having me, Gemma.

Gemma Allen

>> So I'm going to be honest, that is a lot to understand. So maybe let's just start there. Break it down for us, Amber. What exactly is Epicrispr? What's the mission?

Amber Salzman

>> Sure.So as we probably know, DNA, I kind of view as the hardware of our body. So that's the machinery. And then the epigenome is the software. So the software basically tells the hardware what to do. And the example I should give is, look, you have the same DNA in your muscle cell versus your hair cell. and yet it's expressing differently, even though you have the same DNA in every cell. But that's because of the instructions that are telling it what to do. So when it comes to diseases, often what's happening is your DNA is not expressing the right thing. It may express a toxic protein or it may not express a healthy protein. So if we can go in and basically give the right instructions and say, hey, turn it on, turn it off, turn it on halfway, turn it on all the way, depending on what you need for that particular disorder, we can really address a huge amount of very debilitating unmet need. So we're super excited to be able to use this technology, which is really proprietary to our company, and as we say, regulate any gene anywhere to address disease.

Gemma Allen

>> Wow. Well, it certainly fills us with hope when we think about the future of technology and where it can really serve the public good, right? You say this technology has lots of transferable opportunities and I want to get into that but first what problem are you seeking to solve first is there a very focused use case for this

Amber Salzman

>> so we had to pick which diseases we would pursuefirst in some ways to validate that our technology does what it's supposed to so our lead indication is for a disease called facioscapulohumeral muscular dystrophy, FSHD. And like the name says, it starts in your face so people don't, they have trouble smiling because they can't smile, can't close their eyes. So often people sleep with their eyes open. And then it travels down to the scapula. You can't brush your own teeth, wash your own hair. So it really takes away your independence. Travels down, about a quarter of the patients end up in wheelchairs. For the most part, the symptoms start late teens, early 20s. So it's heartbreaking because it's just when people think about what career they're about to undertake and then they start to lose their independence and ability to take care of themselves. And sometimes, as in my family's case, my cousin -in -law, who was a wonderful producer for NFL Films, could no longer work because he wasn't able to walk. So it's really it's heartbreaking. And the fact that there was no treatment for it was really tough. So when I learned about the technology that this company had, I'm like, OK, we can cure this disease because this is a disease that has a very well established cause. So we know that the problem is that there's a protein that gets expressed in the muscle. It's called DUX4 and that poisons the muscle. And little by little, you lose your muscle. So what we knew we could do is we could go into the root cause of this disease and stop the expression of DUX4 and stop the muscles from being poisoned so that the patient would have a chance to recover. Well, not everything, but if you still have some residual muscle, once you stop the poison, you're able to recover.

Gemma Allen

>> So, Amber, you've been in the industry a while, right? You started at GSK, you've worked across multiple companies, you're on multiple boards. there's a lot of excitement around biotech and health tech and the world of AI and how these two worlds will hopefully collide in a very fortunate way right for many individuals and we certainly hope that to be true but we also know that there is a lot of money being spent I think a few weeks ago we talked with some folks in the AI and bio space about 60 billion dollars in investment and still not a whole lot of FDA approvals right there is still a road and a bridge to be crossed here. Talk about that process. the technology is solid, you know you got the data. Why is it? What is the bottleneck?Like break it down for us.

Amber Salzman

>> So I think it depends how and where you apply it. So for example, where we're using AI is, as I described, this is a disease where patients lose their abilities, but there's a lot of variability. So when you want to test a new drug and see if it works, when you have that kind of variability, it can be challenging. So where we're using AI is we're working with a company called Springbok Analytics. And what they do is they have data from hundreds of patients where they've taken whole body imaging of those patients at baseline, six months, 12 months, 18 months, and they basically look at their lean muscle volume in about 140 muscles, and they also look at fat fraction. And what they've been able to do with machine learning based on that is say, for you as an individual, if this is your baseline MRI, and you're this gender, this age, this severity, this is what we predict you will look like on your MRI in six months, in 12 months. So it really gives a sense of, for each individual patient, what we call a digital twin would look like or digital placebo. And that really helps when it comes to showing that a drug works because you almost have a very well-matched placebo for each individual patient. So to me, in that regard, that's a brilliant way of using machine learning because it's taking data from hundreds of patients and from that being able to figure out for you how your MRI is going to progress. Because what happens if we didn't have that and we did a placebo controlled trial and we enrolled let's say 50 patients that we treated and 50 on placebo there's still so much variability that you could accidentally show the drug works when it didn't or accidentally show that it didn't work when it did but if you have for each patient their exact placebo twin then you can say here's what you would look like and here's what you look like treated. So at least from my perspective, it's a really good use of machine learning. I think you just have to be very focused in terms of what it can do and what it can't do. And as you mentioned, the FDA and other regulators, they're trying to leverage it in a constructive way. So we just have to be very thoughtful about how we use it. I don't know if that addresses your question.

Gemma Allen

>> Absolutely.Let's talk about data for a second, because when we think about data from the perspective of biotech and clinical data. it's a fascinating space, right? And, we know that there has been bottlenecks to data accessibility and data scaling in every industry. And I'm sure health is absolutely no different. But it's also the fundamental piece that's needed to prove its efficacy, right? How are things changing? Like you mentioned digital twins, which is a very interesting concept. You see that in tech all the time and manufacturing and automotive, are we going to see that more and more in biotech? And does that then make it synthetic data? Does that kind of, is that some sort of workaround for some of the issues that we've seen of old? Do you think that could speed things up?

Amber Salzman

>> So for rare disease, it actually could speed things up because of that variability and you can't be enrolling, thousands of patients. It could potentially really help with that issue of variability and speed it up. Now, I will say we're very fortunate in FSHD because, as I mentioned, there's been a lot of MRIs collected over hundreds of patients. And that, as with machine learning, you need a lot of data to learn from. But if we start to set our mind that way, I think we can do more and more of it and really speed things up that way or even do a trial that in the past we couldn't do. There's been some notable challenges that some companies have faced with the FDA when they tried to talk about using natural history as a comparator. And it kind of went to the challenges of variability. Was that natural history comparable to the patients that they enrolled on the study? So I think this is a place where regulators and sponsors and patients could really put some more effort into it to help speed up and get things to market that potentially otherwise would never make it.

Gemma Allen

>> what stage are you guys at from the perspective of FDA approval? And what does the kind of 10-year strategy and roadmap look like? Obviously, your technology is very useful and proprietary and highly transferable, I'm sure. What's the roadmap here? Is it to secure the drug, get a drug to market, which we know is a lengthy process, and also look at the commercial viability of the tech? or?

Amber Salzman

>> Yeah.So I feel like we're sitting on a technology that is too good to keep to ourselves. Look, as I grew up at GSK, I know that some of the bigger companies have a lot of understanding of disease. And if they knew what our technology could do, they could really leverage it based on their understanding of which protein needs to be suppressed or activated. And they also have sort of the testing environment to be able to see in a rapid way whether it works so at one level that's one way to use it but for us personally within the company we want to progress particular indications that are hugely meaningful to patients so the first indication as I mentioned is FSHD where we are running our first in human study we released early interim data at a recent FSHD meeting at the end of June in Chicago. And there was huge excitement because we were able to show that the patients that made it to their six -month visit, when they did the MRI at six months and compared it to their MRI at baseline, they actually gained lean muscle volume instead of losing lean muscle volume. And while it's early and small numbers, the really encouraging part was that in addition to seeing the lean muscle volume increase, we also saw that on functional measures, looking at strength of muscle and certain functional things like 10 -meter walk -run, the patients were also trending in the positive direction. And even more comforting was there's a novel circulating biomarker that we were looking at in collaboration with the University of Colorado. And when we looked at that circulating biomarker, it also was trending in the right direction. So we were pretty excited with this kind of early, I mean, to have such phenomenal data so early is super. So based on that, we're really driven to get it to patients as soon as possible. We're pulling together a request to have a meeting with the FDA because we want to make sure we're aligned with them. we've always had great exchanges with the FDA. And, this is a novel technology. So we want to make sure we're in sync. And we really want to have a shared understanding of what it would take to do a pivotal study so that based on what we would look at as an endpoint and what it would look like in terms of predicting clinical benefits so that we could pursue a pivotal study and file for approval in the not too distant future. So we're looking to engage with the regulators, especially when it's something so novel.

Gemma Allen

>> Talk about the role of big pharma, big health tech, right, in this. We know that, when I mentioned we had a lot of folks on, there is definitely a mixed view in terms of the role they play and at what stage. Right. Depending on your longer term strategy. Yeah. How do you see this and there's a lot happening. It's so great to see so many young companies and young guns and enthusiasm and excitement in something as critical as public health. Right. How do you see things changing? Like you said, you were at GSK. There's always been a bit of a big bad elephant in the room. We think about the world of large pharma broadly as it comes to buying up companies and convergence.

Amber Salzman

>> you bring up a really good point because big pharma, I mean, we're working on a disease which we could probably do relatively small compared to obesity or something like that. I mean, those are big, big studies. So those are really situated better for big pharma or midsize pharma. So the way I view it, we're all part of that same ecosystem. And the main thing is to be in conversations and engaging because there's different ways that we can leverage each other's strengths. So like I said, this technology that we have could totally be leveraged in some of these big pharmas to address some of the diseases where they really know the ins and outs and they have the testing infrastructure and really a deep understanding of how you would design a clinical trial. So that would be a good partnership. Look, there's always an opportunity, where they could do more and they could really leverage our technology in a greater way. They may even be interested in FSHD. FSHD, while it's rare, as I say, it's medium rare. I mean, there's at least 40 ,000 patients in the U.S. alone, and there's nothing for them. So for even a big pharma, that's a reasonable -sized market. So there is interest in it. And some of the big pharmas have active programs in this space. So there's different ways we can engage, with licensing, with partnering, with us doing things collaboratively, potentially. There's always M &A. I think we're in a great position to go it alone and really leverage the hell out of this platform that we have. But as I said, we just couldn't do all the things that this platform enables. So I want to make sure that others leverage it to go after all these diseases because patients need our help and that's what gets me excited in the morning just knowing what we can do to help patients so we can't do it ourselves and that's where we want to leverage

Gemma Allen

>> sort of the broader environment zoning out for a second and looking at this from the perspective of a woman who's been in this industry a long time and is quite seasoned when we think about tech investments, companies can be hemorrhaging cash and completely loss-bearing, but they have ARR, they have customers, they have early proof that the money will come. When it comes to biotech and this space, obviously there's a different challenge there, especially for all of these startups with so much enthusiasm in the industry. How do you think about that? How do you think about survival of the fittest in an industry like this?

Amber Salzman

>> Well, this kind of work does require much more significant funding than some of the tech companies, just the cost of running trials, et cetera, and materials. We've been incredibly cost efficient in terms of getting to where we are. So we wanted to be heads down, watch our cost, and make sure that we could get to the point where we got proof of concept that this novel technology works and it works in a very meaningful disease. As we started to have that incredible data emerge, we just announced last week that we closed a Series C, $90 million. But not only is it $90 million, we have an unbelievable who's who in the syndicate. I just feel so privileged to be surrounded by such great funds. the Janus, Fidelity, Cormorant, Duquesne, abrdn, and on and on. these are really good investors. It was oversubscribed, which means that we're in a good position as we keep going to have that kind of excitement. But as you said, it's expensive. It's hard to get the funding. So we constantly have to make sure that we're being laser focused and delivering what the market needs, which nicely does coincide with what patients need. I've always felt that if you do right by patients, the money does follow. So we kind of hand in hand, make sure that we're putting meaningful drugs on the market and getting the funding to keep taking it to the next level.

Gemma Allen

>> Well, that's what it's all about.Right. So, Amber, congrats on the raise. That's a nice little bit of runway too. Last question to you. What's ahead? Like what does the next six to 12 months look like for you and the team?

Amber Salzman

>> Well, we will be releasing additional data, more patients farther out at the World Muscle Society meeting in Hiroshima. That's the beginning of October. And then there's other medical meetings next year. And this time next year, we will have all 12 patients, 12 month data. So that should be a super exciting time for the company in terms of showing for all 12 patients. And we had six at the starting dose, six at a higher dose, and really have a meaningful readout at that time while we progress other indications as well. So we're pretty excited.

Gemma Allen

>> But I love it.I'm going to finish with your own words. If you focus on what's right for the patient, the markets will follow. I certainly hope that's true. Amber, wish you and the team all the best. Thanks for joining us on theCUBE and NYSE Wired.

Gemma Allen

>> Thank you so much for having me, Gemma.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is MedTech Unplugged, one of our programs with NYSE Wired, connecting Silicon Valley to the great minds on Wall Street. Thanks so much for watching.

Amber Salzman, Epicrispr

search

(INTRO)

Gemma Allen

>> theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired's MedTech Unplugged, a show where we connect Silicon Valley to the folks shaping what's next in technology, business, and health right here on Wall Street. Today on MedTech Unplugged, we're talking to a company developing what it calls epigenetic editing, essentially changing whether a gene is switched on or off, rather than changing the underlying genetic sequence. Amber Salzman, CEO of Epicrispr, welcome to NYSE Wired.

Gemma Allen

>> Thank you very much for having me, Gemma.

Gemma Allen

>> So I'm going to be honest, that is a lot to understand. So maybe let's just start there. Break it down for us, Amber. What exactly is Epicrispr? What's the mission?

Amber Salzman

>> Sure.So as we probably know, DNA, I kind of view as the hardware of our body. So that's the machinery. And then the epigenome is the software. So the software basically tells the hardware what to do. And the example I should give is, look, you have the same DNA in your muscle cell versus your hair cell. and yet it's expressing differently, even though you have the same DNA in every cell. But that's because of the instructions that are telling it what to do. So when it comes to diseases, often what's happening is your DNA is not expressing the right thing. It may express a toxic protein or it may not express a healthy protein. So if we can go in and basically give the right instructions and say, hey, turn it on, turn it off, turn it on halfway, turn it on all the way, depending on what you need for that particular disorder, we can really address a huge amount of very debilitating unmet need. So we're super excited to be able to use this technology, which is really proprietary to our company, and as we say, regulate any gene anywhere to address disease.

Gemma Allen

>> Wow. Well, it certainly fills us with hope when we think about the future of technology and where it can really serve the public good, right? You say this technology has lots of transferable opportunities and I want to get into that but first what problem are you seeking to solve first is there a very focused use case for this

Amber Salzman

>> so we had to pick which diseases we would pursuefirst in some ways to validate that our technology does what it's supposed to so our lead indication is for a disease called facioscapulohumeral muscular dystrophy, FSHD. And like the name says, it starts in your face so people don't, they have trouble smiling because they can't smile, can't close their eyes. So often people sleep with their eyes open. And then it travels down to the scapula. You can't brush your own teeth, wash your own hair. So it really takes away your independence. Travels down, about a quarter of the patients end up in wheelchairs. For the most part, the symptoms start late teens, early 20s. So it's heartbreaking because it's just when people think about what career they're about to undertake and then they start to lose their independence and ability to take care of themselves. And sometimes, as in my family's case, my cousin -in -law, who was a wonderful producer for NFL Films, could no longer work because he wasn't able to walk. So it's really it's heartbreaking. And the fact that there was no treatment for it was really tough. So when I learned about the technology that this company had, I'm like, OK, we can cure this disease because this is a disease that has a very well established cause. So we know that the problem is that there's a protein that gets expressed in the muscle. It's called DUX4 and that poisons the muscle. And little by little, you lose your muscle. So what we knew we could do is we could go into the root cause of this disease and stop the expression of DUX4 and stop the muscles from being poisoned so that the patient would have a chance to recover. Well, not everything, but if you still have some residual muscle, once you stop the poison, you're able to recover.

Gemma Allen

>> So, Amber, you've been in the industry a while, right? You started at GSK, you've worked across multiple companies, you're on multiple boards. there's a lot of excitement around biotech and health tech and the world of AI and how these two worlds will hopefully collide in a very fortunate way right for many individuals and we certainly hope that to be true but we also know that there is a lot of money being spent I think a few weeks ago we talked with some folks in the AI and bio space about 60 billion dollars in investment and still not a whole lot of FDA approvals right there is still a road and a bridge to be crossed here. Talk about that process. the technology is solid, you know you got the data. Why is it? What is the bottleneck?Like break it down for us.

Amber Salzman

>> So I think it depends how and where you apply it. So for example, where we're using AI is, as I described, this is a disease where patients lose their abilities, but there's a lot of variability. So when you want to test a new drug and see if it works, when you have that kind of variability, it can be challenging. So where we're using AI is we're working with a company called Springbok Analytics. And what they do is they have data from hundreds of patients where they've taken whole body imaging of those patients at baseline, six months, 12 months, 18 months, and they basically look at their lean muscle volume in about 140 muscles, and they also look at fat fraction. And what they've been able to do with machine learning based on that is say, for you as an individual, if this is your baseline MRI, and you're this gender, this age, this severity, this is what we predict you will look like on your MRI in six months, in 12 months. So it really gives a sense of, for each individual patient, what we call a digital twin would look like or digital placebo. And that really helps when it comes to showing that a drug works because you almost have a very well-matched placebo for each individual patient. So to me, in that regard, that's a brilliant way of using machine learning because it's taking data from hundreds of patients and from that being able to figure out for you how your MRI is going to progress. Because what happens if we didn't have that and we did a placebo controlled trial and we enrolled let's say 50 patients that we treated and 50 on placebo there's still so much variability that you could accidentally show the drug works when it didn't or accidentally show that it didn't work when it did but if you have for each patient their exact placebo twin then you can say here's what you would look like and here's what you look like treated. So at least from my perspective, it's a really good use of machine learning. I think you just have to be very focused in terms of what it can do and what it can't do. And as you mentioned, the FDA and other regulators, they're trying to leverage it in a constructive way. So we just have to be very thoughtful about how we use it. I don't know if that addresses your question.

Gemma Allen

>> Absolutely.Let's talk about data for a second, because when we think about data from the perspective of biotech and clinical data. it's a fascinating space, right? And, we know that there has been bottlenecks to data accessibility and data scaling in every industry. And I'm sure health is absolutely no different. But it's also the fundamental piece that's needed to prove its efficacy, right? How are things changing? Like you mentioned digital twins, which is a very interesting concept. You see that in tech all the time and manufacturing and automotive, are we going to see that more and more in biotech? And does that then make it synthetic data? Does that kind of, is that some sort of workaround for some of the issues that we've seen of old? Do you think that could speed things up?

Amber Salzman

>> So for rare disease, it actually could speed things up because of that variability and you can't be enrolling, thousands of patients. It could potentially really help with that issue of variability and speed it up. Now, I will say we're very fortunate in FSHD because, as I mentioned, there's been a lot of MRIs collected over hundreds of patients. And that, as with machine learning, you need a lot of data to learn from. But if we start to set our mind that way, I think we can do more and more of it and really speed things up that way or even do a trial that in the past we couldn't do. There's been some notable challenges that some companies have faced with the FDA when they tried to talk about using natural history as a comparator. And it kind of went to the challenges of variability. Was that natural history comparable to the patients that they enrolled on the study? So I think this is a place where regulators and sponsors and patients could really put some more effort into it to help speed up and get things to market that potentially otherwise would never make it.

Gemma Allen

>> what stage are you guys at from the perspective of FDA approval? And what does the kind of 10-year strategy and roadmap look like? Obviously, your technology is very useful and proprietary and highly transferable, I'm sure. What's the roadmap here? Is it to secure the drug, get a drug to market, which we know is a lengthy process, and also look at the commercial viability of the tech? or?

Amber Salzman

>> Yeah.So I feel like we're sitting on a technology that is too good to keep to ourselves. Look, as I grew up at GSK, I know that some of the bigger companies have a lot of understanding of disease. And if they knew what our technology could do, they could really leverage it based on their understanding of which protein needs to be suppressed or activated. And they also have sort of the testing environment to be able to see in a rapid way whether it works so at one level that's one way to use it but for us personally within the company we want to progress particular indications that are hugely meaningful to patients so the first indication as I mentioned is FSHD where we are running our first in human study we released early interim data at a recent FSHD meeting at the end of June in Chicago. And there was huge excitement because we were able to show that the patients that made it to their six -month visit, when they did the MRI at six months and compared it to their MRI at baseline, they actually gained lean muscle volume instead of losing lean muscle volume. And while it's early and small numbers, the really encouraging part was that in addition to seeing the lean muscle volume increase, we also saw that on functional measures, looking at strength of muscle and certain functional things like 10 -meter walk -run, the patients were also trending in the positive direction. And even more comforting was there's a novel circulating biomarker that we were looking at in collaboration with the University of Colorado. And when we looked at that circulating biomarker, it also was trending in the right direction. So we were pretty excited with this kind of early, I mean, to have such phenomenal data so early is super. So based on that, we're really driven to get it to patients as soon as possible. We're pulling together a request to have a meeting with the FDA because we want to make sure we're aligned with them. we've always had great exchanges with the FDA. And, this is a novel technology. So we want to make sure we're in sync. And we really want to have a shared understanding of what it would take to do a pivotal study so that based on what we would look at as an endpoint and what it would look like in terms of predicting clinical benefits so that we could pursue a pivotal study and file for approval in the not too distant future. So we're looking to engage with the regulators, especially when it's something so novel.

Gemma Allen

>> Talk about the role of big pharma, big health tech, right, in this. We know that, when I mentioned we had a lot of folks on, there is definitely a mixed view in terms of the role they play and at what stage. Right. Depending on your longer term strategy. Yeah. How do you see this and there's a lot happening. It's so great to see so many young companies and young guns and enthusiasm and excitement in something as critical as public health. Right. How do you see things changing? Like you said, you were at GSK. There's always been a bit of a big bad elephant in the room. We think about the world of large pharma broadly as it comes to buying up companies and convergence.

Amber Salzman

>> you bring up a really good point because big pharma, I mean, we're working on a disease which we could probably do relatively small compared to obesity or something like that. I mean, those are big, big studies. So those are really situated better for big pharma or midsize pharma. So the way I view it, we're all part of that same ecosystem. And the main thing is to be in conversations and engaging because there's different ways that we can leverage each other's strengths. So like I said, this technology that we have could totally be leveraged in some of these big pharmas to address some of the diseases where they really know the ins and outs and they have the testing infrastructure and really a deep understanding of how you would design a clinical trial. So that would be a good partnership. Look, there's always an opportunity, where they could do more and they could really leverage our technology in a greater way. They may even be interested in FSHD. FSHD, while it's rare, as I say, it's medium rare. I mean, there's at least 40 ,000 patients in the U.S. alone, and there's nothing for them. So for even a big pharma, that's a reasonable -sized market. So there is interest in it. And some of the big pharmas have active programs in this space. So there's different ways we can engage, with licensing, with partnering, with us doing things collaboratively, potentially. There's always M &A. I think we're in a great position to go it alone and really leverage the hell out of this platform that we have. But as I said, we just couldn't do all the things that this platform enables. So I want to make sure that others leverage it to go after all these diseases because patients need our help and that's what gets me excited in the morning just knowing what we can do to help patients so we can't do it ourselves and that's where we want to leverage

Gemma Allen

>> sort of the broader environment zoning out for a second and looking at this from the perspective of a woman who's been in this industry a long time and is quite seasoned when we think about tech investments, companies can be hemorrhaging cash and completely loss-bearing, but they have ARR, they have customers, they have early proof that the money will come. When it comes to biotech and this space, obviously there's a different challenge there, especially for all of these startups with so much enthusiasm in the industry. How do you think about that? How do you think about survival of the fittest in an industry like this?

Amber Salzman

>> Well, this kind of work does require much more significant funding than some of the tech companies, just the cost of running trials, et cetera, and materials. We've been incredibly cost efficient in terms of getting to where we are. So we wanted to be heads down, watch our cost, and make sure that we could get to the point where we got proof of concept that this novel technology works and it works in a very meaningful disease. As we started to have that incredible data emerge, we just announced last week that we closed a Series C, $90 million. But not only is it $90 million, we have an unbelievable who's who in the syndicate. I just feel so privileged to be surrounded by such great funds. the Janus, Fidelity, Cormorant, Duquesne, abrdn, and on and on. these are really good investors. It was oversubscribed, which means that we're in a good position as we keep going to have that kind of excitement. But as you said, it's expensive. It's hard to get the funding. So we constantly have to make sure that we're being laser focused and delivering what the market needs, which nicely does coincide with what patients need. I've always felt that if you do right by patients, the money does follow. So we kind of hand in hand, make sure that we're putting meaningful drugs on the market and getting the funding to keep taking it to the next level.

Gemma Allen

>> Well, that's what it's all about.Right. So, Amber, congrats on the raise. That's a nice little bit of runway too. Last question to you. What's ahead? Like what does the next six to 12 months look like for you and the team?

Amber Salzman

>> Well, we will be releasing additional data, more patients farther out at the World Muscle Society meeting in Hiroshima. That's the beginning of October. And then there's other medical meetings next year. And this time next year, we will have all 12 patients, 12 month data. So that should be a super exciting time for the company in terms of showing for all 12 patients. And we had six at the starting dose, six at a higher dose, and really have a meaningful readout at that time while we progress other indications as well. So we're pretty excited.

Gemma Allen

>> But I love it.I'm going to finish with your own words. If you focus on what's right for the patient, the markets will follow. I certainly hope that's true. Amber, wish you and the team all the best. Thanks for joining us on theCUBE and NYSE Wired.

Gemma Allen

>> Thank you so much for having me, Gemma.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is MedTech Unplugged, one of our programs with NYSE Wired, connecting Silicon Valley to the great minds on Wall Street. Thanks so much for watching.