Amber Salzman, Epicrispr | theCUBE + NYSE Wired: MedTech Unplugged

Hello out those studio connecting Silicon Valley and Wall Street. >> I'm John Furrier co-host of the Cube here with Dave Vellante my co-host. >> Welcome back to the Cube studio here at the New York Stock Exchange. I'm Gemma on with NYSE Wired's MedTech Unplugged, a show 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 where the regime is switched on or off rather than changing the underlying genetic sequence. Amber Salzman, CEO of EpiCrispr, welcome to NYSE Wired's. >> Thank you very much for having me, Gemma. >> So I'm going to be honest, like 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? >> 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 used to give is look, you have the same DNA in your muscle cell versus your hair, and yet it's expressing differently even though you have the same DNA in every cell, but that's because of the instructions and 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 full 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 reg- as we say, regulate any gene anywhere to address disease. >> 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? >> [snorts] >> 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? Like, is there a very focused use case for this right now? >> So, we had to pick which diseases we would pursue first, and some ways to validate that our technology does what it 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 cuz 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's really takes away your independence, travels down about a quarter of the patients end up in wheelchairs. Um for the most part, the symptoms start late teens, early 20s, so it's heartbreaking cuz it's just when people think 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, um my cousin-in-law, who was a wonderful producer for NFL films, could no longer work because he wasn't able to. >> Wow. >> So, it's really it 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, okay, we can we can cure this disease because this is a disease that it's 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 in to 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 do recovering. >> So, Aubrey, you've been in the industry a while, right? You start GSK, you've worked in cross-functional 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 bio space, about $60 billion 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. Like you you know your technology is solid, you know you you got all the data. Why is it like what what is the bottleneck? Like break it down for us. >> So, I think it depends how and where you apply it. So, for example, where we're using AI is that 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 Springboard 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, 6 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 severe, this is what we predict you will look like on your MRI in 6 months, in 12 months. So, it really gives a sense of for each individual patient what their what we call 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 that's a brilliant way of using machine learning because it's taking, you know, data from hundreds of patients, and from that being able to figure out for you what your how your MRI's 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, I don't know, 50 patients and 50 that we treated and 50 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 do like like treated. So, at least from my perspective, it's like 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, like 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. >> Absolutely. Let's talk about data for a second because when we think about data from the perspective of biotech and clinical data, I mean, it's a fascinating space, right? And you know, 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 if efficacy, right? How are things changing? Like, you mentioned digital twins, which is very interesting concept. You see that in tech all the time in manufacturing and automotive. You know, are we going to see that in in more and more in biotech? And does that then make it synthetic data? Does that kind of Is that some sort of workaround from some of the issues that we've seen of old? Do you think that could speed things up? >> So, for rare disease, it actually could speed things up because of that variability and you can't be enrolling, you know, thousands [snorts] 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. Um, and that as you know, 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 into to help speed up and get things to market that potentially otherwise would never make it. >> 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 what's the road map here? Is it to secure the drug, get a drug to market, which we know is a lengthy process, and also look at the commerciality of the tech, or >> So, we have Yeah. So, I feel like we're we're sitting on a technology that is too good to keep to ourselves. Um look, I 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 personal, you know, within the company, we want to progress particular indications that are hugely meaningful to patients. So, the first indication, as I mentioned, is FSHD. Um we're we are uh 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 6-month visit, when they did the MRI at 6 months and compared it to their MRI at baseline, they actually gained lean muscle volume instead of losing lean muscle volume. And while the 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-m 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 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 we I mean, to have such phenomenal data so early is is super. So, based on that, you know, we're really driven to get it to patients as soon as possible. We're pulling together a request um to have a meeting with the FDA because we want to make sure we're aligned with them. I mean, we've always had great exchanges with the FDA and you know, 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 we're looking we we you have to engage with the regulators especially when it's something so novel. >> Talk about the role of big pharma, big health tech, right? What in this? We know that, you know, 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 kind of like and it there's a lot happening. I it's so great to see so many like young companies and young guns and enthusiasm and excitement in something as critical as public health, right? >> Yes. >> you see things changing? Like you said you were at GSK, there's always been like a bit of a big bad elephant in the room. We think about the world of large pharma broadly as and as it comes to buying up companies and convergence. >> There's Yeah, now you bring up a really good point because big pharma, I mean, we're working on a disease which we could probably do relative relatively small compared to like obesity or something like that. I mean, those are big big studies. So, those are really situated better for big pharma or mid-size 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. >> Mhm. >> 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. Look, where they could do more, and they could really leverage our technology in a greater way. They may want they may even be interested in FSHD. FSHD, while it's rare, as I say, it's medium rare. I mean, there is at least 40,000 patients in the US alone, and there's nothing for them. So, for even a big pharma, that's that's a reasonable size market. So, there is interest in it. Um and some of the big pharmas have actually programs in the space. Um so, there's different ways we can engage with licensing, with partnering, with us doing things collaboratively, potential I mean, there's always M&A. I think we're in 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 could never, no matter how I mean, 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 that's why I 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 sort of the broader environment. >> zoning out for a second and looking at this from the perspective of women who's been in this industry a long time and is quite seasoned. When we think about tech investments, right? Companies can be hemorrhaging cash and completely lost bearing, but they have ARR, they have customers, they have early proof that the money will come, right? When it comes to biotech and the space, obviously there is a different challenge there, especially for all of these startups with so much enthusiasm in the industry. How do you think about that? Like how do you think about survival of the fittest in an industry like this? >> Well, this kind of work does require much in some ways significant much more significant funding than some of the tech companies. Just the cost of running trials, etc. 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 like who's who in the syndicate. I mean, I just feel so privileged to be surrounded by such great funds. I mean, you know, the Genesis, Fidelity, Cormorant, Toucan, Aberdeen, and on and on. I mean, 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. Um 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 patient needs. I mean, I've always thought that if you do right by patients, the money does follow. So we kind of hand-in-hand make sure that we're doing putting meaningful drugs on the market and getting the funding to keep taking it to the next level. >> 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 6 to 12 months look like for you and the team? >> Well, we will be releasing additional data, you know, more patients farther out at the World Muscle Society meeting in Hiroshima. That's the beginning of October. Um 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 start, and those 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. >> I love it. I'm going to finish on 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 in the Cube and NYSE Wired. >> Thank you so much for having me, Jenna. >> I'm Jenna Allen here at the Cube 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.