Great. Thank you. Good afternoon. Welcome day one of the TD Cowen Healthcare Conference. I'm Dan Brennan, I follow tools and diagnostics. Really pleased to be joined with me here on stage, management team of Nautilus. We have CEO Sujal Patel, who's co-founder and CEO, Anna Mowry, CFO, who's in the audience here. Sujal, welcome.
Thanks, Dan. I appreciate TD Cowen's invite.
Terrific. Maybe to start off, I'd like to just kind of zoom out a little bit, right? You've been at this a while, but it's always great, given we're in this age of proteomics, just to kind of see where Nautilus fits into the proteomic ecosystem, if you will. Just maybe speak a little bit about the profile of your platform and kind of how it's differentiated, you think, versus what's out there today.
Yeah, that's a great question. Let me kind of back up and just talk a little bit about the significance of the space, talk about why our company exists. Proteomics is the study of proteins, and it is an emerging field in the biotechnology space. If you think about the last 20 years, it's really the defining moment in the last 20 years has been the conquering of the genome, right? We can take a drop of blood or any sample and tell you what all of the DNA is. It's a commodity. It's $500. It takes a day. That DNA doesn't really change from the day you're born to the day you die. It doesn't have any of the real-time state of what's going on in your cells, what's going on in your body. It doesn't tell you anything about therapeutic response for a drug.
There is this real move to want to get to the protein level because proteins make up your cells. Your cells do all the work in your body. The proteomic space, the study of proteins, is far, far behind where we are in genomics. It is complicated. The answer that you get is fuzzy, it is not complete. Over the last 15-20 years, there has been a new method which has the potential to measure substantially all protein content inside of any sample from any organism, whether it is blood, tissue, cells. Nautilus is a company that has been focused on commercializing that technology over the course of the last eight years.
We went public about four years ago, and we've been building our Cowen relationship since then and still development stage today, but really building a benchtop instrument that will sell to biopharma, to biotechs, to DX, to academic nonprofit research, and focus on sample in and answer out the other side substantially the entire proteome.
What types of proteomic applications do you think you'll be able to unlock with your platform that other there's a lot of different ways to kind of look at proteins using instruments today. What will you be able to do that others can't?
Yeah, it's a great question. It's not a new applications. It's serving applications that exist today much better. To give you an example, inside of pharma, if I'm looking for a drug that potentially targets disease cells, the standard way to look for differences is to take samples that are healthy, samples that are diseased, m aybe you're looking just at the surface proteins, m aybe you're looking at all the proteins. You want to understand what the differences are. A common way to do that is using the mass spectrometer, which is an instrument that has been used for protein research for decades. You go and you send your cells through the mass spectrometer, and it will say, oh, here's the differences from healthy and sick. The problem is the mass spectrometer, state of the art, can only see 15%-20% of what's in the cell accurately.
You're missing 80% of the picture. What we've been told by a number of pharma is if we could see the other 80%, particularly the things that are rarer that differentiate healthy and sick cells, you'd be able to build better therapeutics. You'd be able to build therapeutics that have less side effects and less cross-reactivity. It is those types of applications, whether it be on the discovery side of things or it be in toxicity screening or mechanism of action studies in drugs, building better diagnostics, either for disease detection or as a companion. All of these applications are looking for a more complete proteomic picture.
Interesting. Kind of jumping the last week, and then I'd like to get more into the technology commiserate with kind of the announcement. Your timelines for commercial launch were pushed out to the end of 2026. You went through several of the reasons on the Q4 call last week. It'd be helpful to just unpack those factors again today. Maybe you can even talk about, maybe introduce a little bit more about the technology, what you've been trying to do, and then kind of what led to the timeline delay last week.
Yeah. Let's talk about what's ambitious about what we're doing, right? What we're doing is ambitious on, I would say, four different levels. Number one is that all the proteomic solutions that are out there, whether they're mass spectrometry-based or assay-based, only see a small subset of the proteins that are in a sample. We're building a solution that aims to be able to see 90%-95% of the proteins that are there. The second key thing is that you have a lack of sensitivity with current approaches. For example, in a mass spec, to be able to accurately quantify whether there's a fold change between two analy or two protein molecules that are in my sample, I need to be able to see 100 copies of a molecule before I can start to say accurately that there's a difference here.
Our platform's being built with single molecule sensitivity. It's super critical. Sometimes there are only 10, five copies of a molecule that make all the difference in disease state, and you have to be able to see that. The third is dynamic range, which is a really key one. We spent a lot of time with customers back in 2017 and 2018 understanding what it would take to be able to get a complete proteomic picture. One of the very key things that we heard was that existing solutions can't dig far enough into a sample. I'll give you a concrete example there. A common sample in pharma is 100 cells-1,000 cells. They have 96 well plates full of these 100 cells-1,000 cell samples. That sample is about 10 billion protein molecules.
If you're a mass spec and you can only interrogate one to 10 million, you're missing 99% of the picture in the sample. We set out to build a solution that matches the dynamic range requirements in pharma particularly. Our system can analyze 10 billion molecules per run per day. The last is really trying to tie together ease of use in a solution that ties together all those capabilities in a way that any lab in the world that wants to do proteomics can do proteomics, essentially delivering to the market a solution that is as easy as the genomic sequencers today. That's the ambitious thing we set out to go to. As you mentioned, we did on the last earnings call push out our timeline for launch again.
We can talk about the various pieces of it, but I think cutting to the chase, it is an ambitious project, one that we have a ton of confidence in as a management team, as a board, and the core investors that are in our company. We have been very capital efficient to make sure that we have the cash runway not only to get through milestones, but to get through the product development, get commercially launched, and start building our revenue ramp.
Is there a chance I know you've got, and we'll discuss, you've got the discovery platform where you can look at everything, and then you have more targeted platform. I mean, is there a chance you've been too ambitious that there is such a wide gap of where current technologies are to where you're striving for that if you can get halfway in the middle, that would be still very successful? Could you kind of titrate some of the performance features or just speak about, as you mentioned, the ambition that you have and the confidence that you'll ultimately be able to get to that level?
Yeah. You bring up a really interesting question. I think this came up in almost this exact form in each of the investor meetings, almost all of them, I think, that we've had so far today. Our platform we always envisioned had two modes. Mode number one is I take the sample and I tell you everything that's in it. I tell you what all the gene- encoded proteins are for all the protein molecules. That's the primary thing that we're trying to bring to market by late 2026. There's a secondary mode of our platform, which is a benefit from the architecture of it, that allows us to take content and antibodies in particular from other companies and use them in a way that it delivers a level of resolution for one biomarker that is well beyond what you can get in the world today.
We always thought we would deliver this broad-scale solution to the market first and then focus on this deep dive capability. It turns out that our platform matured at different rates. The deep dive capability is ready today. There were a lot of questions from investors of, well, how are you balancing your investment between broad-scale and the proteoform side? In particular, the last meeting we had was with a multi-billion-dollar AUM fund healthcare specialist, and they said, "I'm excited about proteoforms. Why aren't you doing more?" My answer was, well, we are doing more. We started with the Tau protein, which is a key neurological disease marker. In particular with AD, it's a critical marker to understand.
We are in the validation and verification phase of our Tau assay, which we've talked about now at the last two HUPO conferences, which are the Human Proteome Organization conferences. We just demonstrated data just before the earnings call at the US HUPO conference that showed data in mouse models, in human organoid models. We now have biological sample data, and we're working with pharma organizations to ink first a deal here in the first half of the year. Those deals today on the proteoform side are really focused on initially pilot studies, but then moving to studies where we look at cohorts of human brain and try to understand what the differences are in the Tau biomarker. Tau is just one biomarker. There are thousands and thousands of other critical biomarkers where the proteoform landscape could be mapped as well.
Internally, we're trying to balance the effort we put in proteoforms, which we think can be a really substantial business, but balance that with the investment that's required for our broad-scale product, which we absolutely believe is a category-killing product. We have a huge amount of confidence that we're going to get there. I'd say with that, we still have 80% of our spend allocated towards getting that product out in the marketplace, but still trying to make progress on the proteoform side.
Before we dig into the discovery platform, where you could see everything just on the proteoform side, how would you size that opportunity? What are the existing products that are sold in the marketplace today? How meaningful could that opportunity be, even though it's not the one for you? Just wondering, since that product is much closer to being a commercial reality.
Yeah. This is a question that we've asked ourselves. The answer is we don't know. We don't know because no one's ever measured it before. We've chosen to enter into the types of deals that allow us to explore the value. Instead of going and saying, "Hey, the Tau assay costs $2,000 a sample. Come get it," we don't want to give away a massive discovery for $5 million, $6 million, $7 million, $8 million of service revenue. Underscoring that is a belief that these proteoforms are biologically relevant and super important. Am I 100% sure that Tau has a biologically relevant protein modification? I don't know. But I do know for sure that there are thousands of biomarkers and a large number of them, t he proteoforms are going to be critical to understanding biology.
We're entering into these deals as joint partnerships and exploration where if discoveries are made, not at phase one, but as we progress, if discoveries are made that we can share on the upside of them. Yes, that pushes monetization way out, but we feel it's better to explore the space together with our partners as opposed to trying to monetize as a service for some early modest revenue.
How would you characterize, for instance, you're here a year from now, and on this proteoform product, what's the performance that you'd be able to achieve comparing it to a company that most companies can only see maybe a proteoform or two? I mean, they can't go that broad. I'm just trying to think through performance metrics that would detail the level of differentiation.
Yeah. I mean, I think we have to sort of define this for the audience, right? I can take a step back because this is a complicated piece of biology. When you say the word proteoform, most people, even proteomic scientists, don't necessarily know what you're talking about, right? You could back way up. When the human genome project was going on decades ago, we as a scientific community thought we were going to find hundreds of thousands of human proteins because a human is way more complex than a banana. You'd think, boy, we should have a lot more proteins. Turns out we didn't. We only have 20,000 proteins. We have less proteins, different protein types than many plants. Where's the complexity that makes a human?
It turns out that those proteins are modified and changed, and they show up in different forms in your body, leading to as many as some estimates are six million different forms of these proteins in your body. Those modifications have a huge impact on the protein's conformation, its distribution in a cell, what state it's in, is it being degraded. To understand cellular function, you have to understand what modifications are out there. All of the solutions that are out there have a significant disadvantage when you're trying to understand how a protein turns into a form. The mass spectrometer, by its nature, has to break a protein into lots of pieces to measure it. When you do that, you've completely lost the information about what modifications were there.
Assays out there, Quanterix is a good example, which is the gold standard for clinical assays in phosphorylation of Tau, which is one of the key things you want to measure, which is correlated to progression of AD. That's an assay. It's a great assay. It's sensitive, but it can measure one modification. It can't tell you on this one molecule, there's four phosphorylations, and they're in these different sites, and that means X from a biological perspective. When you ask for performance criteria, first and foremost, we're showing the world a level of resolution that exists in biology that they've never been able to measure before. That's an exciting new area, t hey're exploring at single molecule resolution, t hey're exploring as detailed as you can get with today's technology.
For everyone that we're talking to who's in neuroscience, there's a ton of excitement around what we're doing.
When you think about the challenges ahead as you move towards full commercialization of the broader discovery platform, what's in your control? Where are you leaning on third parties? I know you brought a lot of it in-house a few years ago, but just kind of walk through again the key steps and how much you have line of sight on kind of overcoming these challenges.
Yeah. What I would tell you, this is fully in our control at this point. One of the last big issues that we dealt with last year was that, as we've talked about in the past, our system relies on some 300-360 custom antibodies that are a different type of antibody that we need to make our platform work. We've been building those antibodies in a very large antibody development pipeline for many, many years. We've built thousands of candidates at this point, but the yield off those candidates was way too low. Thousands of candidates has only yielded tens of actual antibodies that perform well on our platform. Going and trying to get to 300-360 that way is not a cost-efficient way or a time-efficient way to do it. It's not really workable.
In the second half of last year, we spent a lot of time figuring out how do we adjust our assay to be able to use much more of the content that we have available. The types of problems that we have are actually similar to a lot of newer companies that are focused on things like peptide sequencing. We're focused on very short pieces of proteins that we need antibodies for, and those antibodies are more fickle, and we need to make the assay more resilient. That's the best way to go and tackle the challenge that's in front of us. As we talked about in the earnings call, by Q4, we had eight or nine different schemes that we're investigating to overcome this problem.
We chose one solution, and the solution has the least technical risk and performs the best out of the eight or nine schemes we're looking at, but at the expense of time. It will take us a little bit of time to redevelop that scheme, change the assay configuration, and then pick up where we left off. That is the work that is going on now. As we talked about in earnings, we felt it was prudent. We did that to also trim our operating expenses and headcount a little bit to make sure that we extend our runway through 2027 and give us the time that we need to complete these changes to get launched and get out into the marketplace.
Right. The $64,000 question is you've had a delay or two coming up to this point, and I'm sure investors would love to say, "Sujal, just kind of underwrite if it's not there by end of 2026, end of 2027." What can you say? Obviously, you can't do that. I guess, is there any way to kind of distill how much progress has been made? If timelines were to push again, is there a way to have more of a certainty for investors or, obviously, for yourself, more importantly, that it's just the incremental changes get smaller and smaller such that the goal line just keeps drawing nearer?
Yeah. I mean, that's the $64,000 question. What I would answer for you asked two different questions in there, right? One is about confidence. One is about checkpoints. I think one of the things that we've heard from investors is that they would like more checkpoints along the way, even if they're pretty technical. We're going to endeavor to provide a little bit more clarity on the path towards the first big milestone, which I think is a really big milestone for us, where the full assay is decoding proteins from a complex sample like cell lysate. We will endeavor to do that. As you kind of heard on the earnings call, it is going to take us a couple of quarters to get through the assay configuration change before we can start reporting on that progress.
I think that right after, we will start reporting on that progress. The other question you had is really about confidence, right? I mean, small-cap biotech is a tough place to be right now. Investors certainly have watched us flip our timelines. What I would tell you is that I have a lot of very smart people on my team, and every single one of them is confident in the approach we have in front of us. I'm personally confident. My board and investors are confident. We're an extremely tightly held company that has mostly just bought stock in the open market. If investors are hanging in there with us, we're all in the same boat together.
In terms of any of your scientific collaborators, who we spoke to, two or few of them, how far you've got technical paper describing the method, but in terms of scientists not inside the company that have seen what you're doing and have followed what you're doing, I don't know, could you speak to any of the relationships on that front, their support or their guidance towards kind of how to get to the goal line?
Yeah. I mean, I think, so I mean, we talk to scientists in the community all the time about our approach. We also have a scientific advisory board with folks like Lee Hartwell, Nobel Prize winner from the genomics era. Everyone believes that the process we're taking to get the product out is the right one. As well, I think one of the things for those investors that have deep science background, one of the things you will observe from our company, when you look at our preprints, you look at our conference presentations, we have a lot of diligence that goes into the quality of the data we produce, understanding what the conclusions are that we're drawing from it. The scientific community notices that.
They know that we are a rigorous science company and that we'd like it to go faster, but it is a steady path to getting this approach into the world.
When you think about ultimately, if the product is successful and it launches, how would you characterize the use cases today that are either being done by mass spec or other technologies? Out of the gate, what would be the first applications?
Yeah. I think that the first key applications are drug discovery and various pieces of that workflow. Biomarker discovery, mechanisms of action studies, toxicity profiling. Another is in the diagnostic space, particular biomarker discovery. We're seeing a ton of interest on that front, even from pharma on the companion diagnostic side, as well from the standalone DX companies. In academic and nonprofit research, there are a ton of use cases from basic science to translational. Those are the early feeders for some of those other applications. To us, that early piece is at least a couple of billion dollars of the market, a big chunk of it that's serviced by mass spectrometry-based proteomics. We think that's an ideal spot for us to initially target.
There are a handful of other companies who are kind of following single molecule protein sequencing, but I think they're different in their technology, kind of their approach. How would you characterize who else is trying to follow this path? Do you think anyone else is ahead of you or kind of has a similar product?
Yeah. I mean, let's correct that first of all. We are the only single protein molecule company out there who's attempting this. Everyone else is chopping their proteins into small pieces and attempting to do something. Mass spec weighs it and infers what it was. Some companies try to sequence it, but those are not full proteins. We're the only ones focused on measuring full intact proteins, which has a huge advantage for proteoform detection and a huge advantage for sensitivity to orders of magnitude. We think ultimately being whole protein and having that huge dynamic range where we can analyze 10 billion molecules in a run, we think those two advantages are durable and will produce a solution that far exceeds the specifications of anything that's kind of in the conception phase right now.
Is there anything on the informatics side that's really differentiated? I mean, I can imagine when you're trying to measure these very large, extremely complex molecules, like informatically, that is also quite challenging. Is that like when you think about the informatic complement to your product, how much of that is built in-house, externally, or just maybe that aspect of the technology?
Yeah. I mean, the informatics, the algorithmic side of what we're doing is the key insight that enabled us to get to a place where we can provide an accurate identification of what each molecule is. That is a big piece of what we do. Most of that is secret sauce inside of our organization. Our head of bioinformatics, who's been with us, he was the first employee other than the two founders who's been with us for the entire journey, is at our conferences. He's one of our prominent speakers. I mean, he's the guy who developed the entire algorithm and has been focused on making sure that we can get these accurate identifications and prove to the world that we are. Certainly, bioinformatics is a big piece of what we do.
The algorithms behind the identification and the decode methodology that we've got preprints out that describe it, that really is a lot of the secret sauce behind this company.
Is AI, I mean, it's all emerging, but is that helping now, or is it locked, or just how does it?
If I put AI in my company name, will the market cap go up?
No. How does that factor into the informatics? I mean, it's there. I mean, it's machine learning, right? So obviously, machine learning is part of the process.
Machine learning is not AI. Yes, we use machine learning in our system in an initially kind of a unique way, right? I mean, most people, when you say AI, they're like, "Oh, I have a bunch of data. I apply AI, and something magical comes out the other side." Our solution actually uses machine learning on the inside of the measurement in order to understand what's going on in terms of binding patterns. That is a really great piece of technology that helps us to not have to figure everything out experimentally as we're building the product, and it's really valuable. After we have a product in the marketplace, there'll be insights. Yes, AI will be great because when there's a large volume of data and humans can't make sense of it, that's where AI does the best work. We're a crawl, walk, run, right?
We got to get the product out the door first.
Right. Maybe just back to the commercial opportunity this year with early access on the proteoform side of the business. I do not think just kind of, I know we talked in the beginning, size that, and you said it is a challenge to size that. Is there any way in how will investors think about that? We are sitting here year-end. There is going to be some revenues generated from this. How will you help investors understand that opportunity as it progresses?
Yeah. I don't think revenue is a good way to do it because I don't think we're going to really generate any revenue. If not, and if we do, it's not going to be material. I think what you have to look at is, take a look at two-year lens, right? Because I don't want to try to guide what and when. In a two-year lens, it's not just going to be Tau. It's going to be more than one biomarker that we're focused on. We're going to have data with multiple partners that show what the biology is behind what we're seeing in terms of the form of the protein. I think from there, what investors need to look at is, okay, this is a new type of biology. This is no different than spatial, right?
When spatial really started to hit its stride, it was a new piece of biology that had never been seen before. Once we're able to see it and we have partners who understand what data is there, I think we'll be able to understand what's the impact on therapeutic development, on diagnostics, how do we think we can push these, what's the path to the clinic. From there, it becomes a more mechanistic type of analysis that'll help you understand what the value might be.
I forget, have you guys said how many partners you think you'll have in this pre-launch?
What we said is that we intend to sign one in the first half year. And we're talking to a few that we think are good prospects. I mean, one in the first half year, I don't know how many more, but in that two-year time frame, there'll certainly be more than one.
Got it. In terms of the checkpoints, which we alluded to earlier on the broader discovery platform, what will be, since it sounds like there is interest from investors to get a little more granular, maybe they're going to be very scientific, but what will be some of the checkpoints you think you'll provide to show the technological progress towards your goals?
Yeah. I think with the scientific community, we bring them along on smaller milestones along the way. I think what we've talked about with investors is highlighting the things we bring to the science community more and trying to draw a line to what that means towards the big milestones that are coming up. Most of the investors we're talking to are trying to figure out, how do I know this big milestone's coming before it actually gets here, right? It's a little bit of a game.
Okay. Okay. Obviously, cash flow, you guys kind of extended your cash runway with the push out or with the delay in the commercial timing. Just kind of walk through how you're managing that, keeping the right people, the right talent, the right investment at the same time, cutting back. Is it fairly efficient? Are you cutting into bone? Just the level of cuts that you're having to make to kind of extend the runway.
We're not cutting into bone. I mean, we are running a tight business prudently. We took a very hard look at what headcount could we live without, what pieces of the platform are far enough along that we can do it with less staff. For us, it was a very healthy exercise and a collaborative one. We feel great about the resources that we have to prosecute on the remaining development that's in front of us. I think for me as a CEO, looking at it from my shareholder hat, I mean, it's the right move. It also lowers our jump-off point for our expenses on R&D, which are tough to right-size after a launch. It helps me not just with this piece through 2027, but it helps me on the path to cash flow positive as well.
Eventually, when this product launches, what's the kind of consumable pull-through? Again, without numbers, but what's a relative way to think about that?
Yeah. I mean, we haven't announced final pricing for the product, but what we've said is that we expect an instrument deal to be roughly a million dollars. That's your cost to get into our platform. And what we've said is that our per-sample costs are a few thousand dollars per sample, and they'll decline over time. The instrument can run four, eight, or twelve samples in a day. You could look at that as it could be a few thousand dollars all the way up to it could be $20,000 in a single day. When we think about ramping a customer over the course of the first kind of six, eight quarters of ownership of an instrument, we'd like a customer to ramp to a million dollars of consumables a year.
That's a significant price point, one that Illumina hits with their sequencers, one that Olink nearly hit with their Explore platform prior to the acquisition by Thermo Fisher. It's definitely a price point that's big, but it's one that has precedent out there.
For investors who are looking at the stock today, I mean, it's come down with a lot of things in kind of mid-cap universe and with the delays, but they're looking ahead and they're seeing this novel disruptive technology, but trying to figure out how to value it. If they looked out three years beyond launch, is there any way to frame what's a reasonable range of revenues that could materialize then?
I don't have guidance for three years out, but I will tell you that we're big believers in this platform. I look at it as, boy, we're trading below 1x MOIC. That seems like a crazy place to me.
All right. Good. With that, I think we'll end. Sujal, thank you very much for being here. Thanks to everyone in the audience.
Thank you.