I do, okay.
Question. I'll answer that.
Oh, you're right. It does have that. Okay. Great. Good afternoon, everyone. I'm Matt Sykes, Life Science Tools and Diagnostics Analyst at Goldman Sachs. I have the pleasure of welcoming Anna Mowry from Nautilus Biotechnology. Thank you, Anna, for being here. I appreciate it. Maybe we're just starting off with the big picture. Can you give us kind of a brief overview of the proteomics market as you see it today and how Nautilus fits into that broader proteomics ecosystem? Particularly, what differentiates Nautilus platform versus what's out in the market today?
Matt, thanks so much for the introduction and for the invite to participate in the conference. We're happy to be here. In terms of proteomics, we think that this is a really exciting business opportunity as well as a key area of research for academic and pharmaceutical organizations. We estimate that this market will grow to $55 billion by 2027, and billions of those dollars are spent in discovery proteomics, which is an area that's really relevant for us. Now, proteins are very different from DNA. DNA is the same in every cell in your body from the day you're born to the day you die. On the protein side, proteins are the key drivers of biology. They are often the difference between whether you're healthy or sick, and 90% of drugs target proteins. Despite all of this spend, our ability to measure proteins is very limited.
The key workhorse is the mass spectrometer. It is the gold standard today. With that being said, it's very difficult to use. It requires specialized labs and skill sets. It takes weeks of analysis on complex samples. Still, the end result is you can still only see 8-30% of proteins in a sample, which doesn't give researchers a clear picture of what's happening in the sample. Newer techniques have come about to try to address these challenges, particularly through affinity or sequencing-like approaches. The challenges with those techniques are that they typically work on peptides, not proteins or specialized sample types, and don't have the scale and sensitivity that is necessary to answer the questions that our customers are asking.
Nautilus was founded to meet these challenges head-on with a bold new approach to democratizing access to proteomics by bringing to market a new instrument with the aim of comprehensively measuring the proteome from any sample, from any organism. What I mean by comprehensive is that through our in-house developed what we call multi-affinity reagents, combined with our computational approach, we believe we'll have the potential to see 95% of proteins in a sample. Our platform is also designed to measure single-molecule intact proteins over a chip or three chips that has 10 billion analytes. That scale and sensitivity we think will be transformative for those developing new drugs and have the potential to kick off a new wave of precision and personalized medicine.
Got it. Super helpful. Maybe just drilling down a little bit, what types of applications can you unlock with your instrument versus other proteomics tools in the market?
Yeah. Because our technology is built to see comprehensive coverage of the proteome, it really makes sense to go after use cases where customers need to see everything. In particular, this is helpful in the drug development workflows. In the early stages of drug development, customers or pharma companies do not necessarily know which proteins they are looking for. They are looking to see the rare differences between healthy and sick cells. From there, that helps them identify which targets to go after. Our technology is also really useful in the later stages of drug development because once you apply the drug, you want to measure the response. You want to understand the mechanism of action. You can also dig into later stages like toxicity and even diagnostics over the long term.
Through our hundreds of customer conversations, our customers tell us that what they're discovering today is at the very edge of what can be seen by the mass spectrometer. We believe that once we unlock greater visibility into the proteome, it can kick off a new set of discoveries.
Got it. I think I can easily say in my time in covering this sector, you guys are attempting to do one of the more difficult scientific challenges that I have seen. It is incredibly impressive what you have built and what you are moving towards. What you are doing is highly innovative and novel, and it comes with its challenges. Can you maybe talk a little bit through about what led you to kind of delay your commercial launch to the end of 2026? Any color you can provide on the progress you have made to address some of these challenges.
Absolutely. It's no secret it's taken us a bit longer than we anticipated. It can be true when you're trying to solve as difficult of a challenge as we have with a completely new approach. Let me give you a little bit more color on what led to the delay. In our platform, there are a few key pillars of our technology. Number one is our instrument and software. We've got instruments running in our facilities in San Carlos, California, every single day. We also have our flow cell. The third aspect is those multi-affinity reagents I talked about.
Over the past couple of years, we've developed thousands of probe candidates and have demonstrated to ourselves that the antibodies we've developed do, in fact, bind to a diversity of epitopes within proteins and have the ability to differentiate amongst proteins, which is necessary for our platform. What we've also realized is that we have defined the specifications of what type of antibody we need or the characteristics of antibodies that will work on our platform. Some portion of our probe candidates do convert into platform-ready labeled probes. What we found is that our fallout rate is just too high. There are a number of ways we can address that. We could scale up our development pipelines and just keep working that process. It's just a very inefficient way of doing that.
Over the past couple of quarters, we've been looking at ways that we can make our platform better align or the specifications of our platform better align to the characteristics of our existing probe candidates and also make our development pipelines more efficient. In Q1, the path we chose is ultimately one that we feel will result in a more robust assay and allow us to tap into more of our probes that we've developed, but it does take longer. That is what led to us pushing our launch timeline to late 2026. Now, we're still in the middle of that process, although we do have pilot experiments with our new approach that we're working through. It just will take us a little bit more time before we can give any meaningful updates.
Got it. Maybe just walk us through the differences. You've talked a little bit about it at the outset, but just the differences in capabilities across a targeted analysis and broad scale or more basic research. How would you characterize use cases from a customer's perspective across those two offerings?
Right. Everything we've been talking about so far is really focused on our broad scale application. This is where we want to comprehensively see each of the 20,000 gene-encoded proteins that exist in a sample. As you point out, we do actually have two modes of operation in our platform. Both use the same common core platform, but the targeted application is really where we look deeply at a particular protein of interest that's already been well characterized with or at least has existing antibodies that have been developed by others. Because our platform is an open platform, we can leverage those antibodies that are raised towards site-specific modifications on proteins of interest and use them on our platform to see what's never been possible. We started this just as an example with our collaboration with Genentech, where we were looking at the tau molecule.
Tau is a highly modified antibody that is known to play a role in Alzheimer's disease. Now, there are other techniques out there that do measure how many of a particular post-translational phosphorylation site is present, but there's no technique on the planet that has the ability to say how many tau do we have that's modified two times or three times. In that context, this is where our platform comes into play. In Q1, the update Parag gave is he said we completed our internal verification and validation of our tau assay, and through that process, we used 12 distinct antibodies to see which allows us to differentiate up to 4,000 different unique forms of tau.
Got it. Staying on tau and neurology, you've talked about it as being sort of an initial focus for the targeted proteome offering. Can you kind of talk through why kind of existing targeted proteomic methods like mass spec have challenges in the neurology space and how Nautilus differentiates itself, particularly in these low-abundant proteins?
Yeah. One of the key features of the mass spec is that it works on peptides. The downstream implications of analyzing peptides have some of the ramifications that you're pointing out. When you work on peptides, it really eats into your dynamic range. If you're measuring one whole protein, but then you, as we are, that takes up one analyte, you chop that into, let's say, 100 peptides, now you've got 100 more molecules to analyze. That impacts your dynamic range by, let's say, two orders of magnitude. You also have to reassemble that data to try to figure out what existed on the whole protein level. You need to see hundreds of copies in order for you to confidently say what proteins are present. That can lead to you making calls on the most abundant proteins.
The other aspect is that you lose the ability to say, as I mentioned before, which whole protein molecules had multiple phosphorylations on one molecule versus a set of independent ones. Our technology was really designed to look at single intact proteins, which allows us to address many of those challenges. We combine that with our 10 billion analytes per run. That means that we have, theoretically, we are now impedance matched to the pharmaceutical workflows where they're looking at 100- 1,000 cells. We spread all those proteins across the surface of our chip. We can potentially see even the rarest proteins in that sample.
Got it. What are your expectations for the early access partnerships in terms of either revenue impact or just relationships in 2025? Can you remind us sort of on the economics around the EAP program that you've got, and will you be actively placing instruments during that time?
Yeah. So I think what we've said is that we expect to put our targeted assay into the hands of researchers in 2025, and we're working to sign a partnership for that in the first half of 2025. The goals of this are really around helping potential customers both get that external validation that we need, as well as to demonstrate the power of what it means to be able to dig into a protein of interest at this level. What we're not doing is modeling revenue associated with this in 2025 because this is really more of that demonstrating the power of this technology and helping us to evaluate the use case. Just in terms of the go-forward business model, that's still something we're evaluating. I think, as you mentioned, we could do this through a services offering.
We could do it through joint development because there are customers that have significant investments in particular proteins, and they may need some help in evaluating those proteins. We could also do platform in the traditional way. Those are all options that we're evaluating, and the collaborations and partnerships we're working to establish in 2025 will really inform that business model as opposed to being the source of revenue.
Got it. Understood. And then outside of neurology, are there other applications that you're intending to pursue when thinking about potential partnerships in the targeted offering?
Yeah. A lot of what we talk about is tau-related at this point, but the platform itself can actually work with any number of proteins of interest, especially ones where there are a critical mass of well-established antibodies that have been shown to work for site-specific modifications and so on. We are at the stage where the tau assay is pretty far along, and we can start to think about what's next. In Q1, we established a series of customer conversations with more than 30 customers across academic, nonprofit, and pharma. These conversations are really intended to help us understand which type of customer we want to go after, understand their priorities, and think through how we go to market.
I think in terms of the possible proteins of interest, it ranges from furthering our investments in the neuro side, but we could also start to look at proteins of interest in immunology or cardiology or oncology and areas where we think that or where it's estimated that a particular form of that protein is known to have a biological or thought to have a biological impact. That would be a really great place for us to go next.
Understood. Turning to the broad scale opportunity, how should investors track progress of your assay reconfiguration in order to gain more confidence in that late 2026 commercial launch?
Let me make two points here. I know I talked a lot about the opportunity with tau, and I think there's a big business opportunity or there's a business opportunity we're evaluating on that side. One of the really great benefits of moving forward with our targeted assay in tau is that it does leverage the same common platform. Our instrument and software is being used regularly in that assay, and starting with partnerships and collaborations starts to build our customer-facing muscle and gives us, as we start to process samples, that's just all aspects that ultimately lead to commercial readiness when our broad scale application comes online. In terms of the broad scale application, what I've said is that we need roughly 300 reagents to get comprehensive coverage, and we don't need all 300 to be able to begin our commercial launch.
What we tell investors to look for is that those initial data where we start to see any meaningful number of proteins in complex samples could be 1,000 proteins. That is the sign that the core pieces of our technology have started to come together. From there, I think it is a very different timeline to get the remaining reagents that we need to get to comprehensive coverage.
Got it. And then just given the novel nature of the broad scale platform, how would you characterize use cases versus what's out there today? I mean, part of me thinks that you're going to be potentially finding proteins that have not really been known before. And so there's a lot of it sort of like, we'll kind of tell you when we get there. But could you kind of talk about maybe what some of the attractive use cases would be for someone for the broad scale, maybe in the academic end market?
There is a huge amount of research being done today with data that generates a subset of visibility into the proteome. We believe that in the existing market, once customers have access to a complete picture of proteins in a sample and have this single molecule sensitivity and dynamic range, it unlocks a whole new set of discoveries that customers will learn to incorporate us in more and more places. The second thing I would say is that proteomics is really concentrated in folks that have access or specialized labs and skill sets with a mass spectrometer. We believe our platform will be more like a genomic sequencer where it is push button simple, easy to use, sample in, data out in the cloud.
What that does is it potentially brings in a whole new set of scientists who want to do proteomics but can't because they don't have those specialized labs and skill sets. We think that would be a market expanding opportunity that brings in new folks into proteomics that aren't able to access it today.
Yeah. I want to kind of drill down on that point because one of the challenges with mass spec, which you've said already, but we would definitely agree with, is that the workflow and the prep is actually pretty challenging. When you're trying to do something at scale or at speed, it just makes it difficult. Maybe dig a little bit into your kind of sample tech workflow for your instrument and how that is sort of sample in, answer out, and how simple that is, and maybe compare it to what a scientist would do with a mass spec.
Yeah. On the mass spec side, it's a fairly complex process to get a sample through the mass spectrometer. The first step is you take your sample, you extract the proteins, then you digest them into peptides. You also fractionate your sample. Instead of having one sample, you have many samples. Those proteins get ionized. On the other end, you have to go through this very complex process of recombining back your data points to determine what's in the sample.
On our side, we consider our sample prep process to be more similar to on the genomic side where we extract, in our case, proteins, not DNA, but we extract the proteins, we attach them to our proprietary label, and then our instrument will take those proteins and spread them across the surface of our chip so that each well will have a single protein molecule. From there, our instrument will do its multi-cycling and the data goes into the cloud. Very different.
Got it. Maybe just walk through how you're thinking about pricing the instrument and sort of what the potential pull-through on consumables would be in your sort of early estimation.
Yeah. Our first opportunity is really going after the mass spec budgets. Those instruments today are priced depending on the vendor and application. They're priced below $1 million, up to closer to $2 million, especially with some of the more recent instruments being released. Given our value proposition relative to the mass spec, we haven't released official pricing yet, but we estimate we will price our initial instrument package for roughly $1 million. This includes both the instrument itself as well as that initial install and training, as well as some support, maintenance, and software service contracts. In terms of pull-through, we anticipate pricing our samples at a few thousand dollars per sample. Our instrument is designed to run 12 samples per run.
At modest utilization estimates, we think that our pull-through per instrument will approach $1 million there as well.
Understood. As you think about sort of the mass spec market, even during times of more challenging macro spend environment or CapEx constraints, there always seems to be a market for cutting-edge mass spec, and really specifically in proteomics. We have actually seen research dollars over time gravitate more towards proteomics because a lot of it has to do with what you have already talked about, which is proteins as the mechanism of action of disease. It is more of the commercial end, the biopharma, and some researchers are more interested in. As you come out with this novel new instrument that can give you far more range and depth than what a mass spec can be, do you think that that instrument will allow you to penetrate into a market that still might have some CapEx constraints?
Given the novelty of the instrument and your pricing is not off from where those high-end proteomics mass specs are, do you feel like that will allow you to kind of penetrate that market? Therefore, the mass spec environment, whenever we enter it into when you launch the instrument, might not be as sort of a macro headwind for Nautilus.
Yeah. I think there's a number of we like to think that's true. Yes. We think that for a product that's as differentiated as ours, that there will be an ability for us to still sell into these customers despite the market conditions. I think that what we hear from our customers is that the data they're getting today is incomplete. We know customers have like one of every tool, right, because they're trying to assemble a more complete picture of the data that's in their sample. We think that we can provide the most complete picture. Of course, we think that there's always going to be a market for that. The other thing I would say is that by the time we launch, we hope market conditions will be different. Our pricing is also market-driven.
To the extent that, and not cost-driven. We have a very different cost structure because we're using fairly readily available components and reagents. To the extent that we have to adjust our pricing to meet market demand, we will have the ability to do so.
Got it. That is kind of like my next question is that despite the novelty of the instrument, there still is obviously well-known constraints within the U.S. academic and government market, which is a large customer base for mass spec and for proteomics. Would you expect to offer maybe discounting for those types of customers depending on what the environment looks like at that time, just if funding remains somewhat constrained? Maybe sort of as a part of that question, to the extent you are willing to talk about it, sort of what is sort of the gross margin and the cost structure of this instrument, meaning how much room do you have to be able to do that? Because it would be an important lever for you to pull initially, at least to get things off the ground.
Yeah. I would say that we're already talking with potential partners and collaborators who have well-characterized samples where we can really jointly benefit from because we get the ability to compare our results to their well-characterized samples, and they get the benefit of furthering their studies. We're already starting to think through those partnerships as a way to generate data and publications, which helps then create the flywheel where we can get into some customer accounts where they do need that data in order to move forward. Academic collaborators is the place where we anticipate starting some of those more formal engagements. In terms of discounting, I think that to the extent that customers help us with the data and publications, that's always something we would be willing to discuss.
In terms of cost structure, what we've said in the past, and it's still true today, is that over the long term, we see margins being around 70%, and that's a blended number. I think that depending on the product type, the instrument, maybe it'll be a little bit less than that. Our software and reagents would be higher than that. I think overall, there's plenty of room there for us to work through market conditions as needed. Of course, we're always focused on finding ways to bring costs down.
Yeah. I think you mentioned earlier, and I think the services offering is something that a lot of your peers in other areas have done, and it's actually been a big success. One, it allows customers to kind of try it before they buy it. Two, it allows them to see the results. You're obviously very familiar with how the machines work, so there's probably some speed to that. Is that something that you would consider having a services offering, sort of like an internal lab, to be able to outsource some of that stuff for customers?
Yeah. I think services is something we envision will always be part of our product. Initially, our services is really more intended to help validate our platform, and it generates that data and publications. It also will be our primary mode of operation for our early access program, which is where customers send us samples. We analyze them in our own facilities. From there, that really gives them the data that they need to go write the grant or to get the approvals they need to be able to buy the instrument. We really think of that services offering as a way to promote lead opportunity generation to support the commercial launch. Now, beyond that, there are some customers that do not have the sample volumes that require them to purchase an instrument.
Initially, we would tap into our services program to support customers who have a need to process a small number of samples, or it's their pilot set of samples, which then they can use to go write the grant and support the larger study. As we get into the later stages of commercial development, of course, we can use the services mode to be a proof of concept to support instrument sales, but also we'll be coming out with new reagent kits and improvements and enhancements. There will always be a need to have some level of services to help get customers access to our data prior to buying an instrument.
Got it. And just thinking about data interpretation, I mean, you're generating a vast amount of data with the platform ultimately. How do you expect to integrate this data that comes from your instrument into sort of bioinformatics platform or an output that's understandable by your customers?
Right. In our platform, we think we'll be generating some of the most complete data. It's 95% proteome coverage. It's 10 billion analytes per run. There's a lot of data there. We do anticipate there will be a need for some bioinformatics tools to be able to make sense of this, to pull out the insights from it. Some of those we'll be developing in-house and will have some data tools that customers will be able to access, particularly through our cloud portal. We do know that with the rise of AI, there's a need to feed the machine. It will make sure that, and we're already starting to think through, of the data we generate, how does it enable those types of more advanced platform analyses.
Got it. You have a cash runway target out to 2027. How should investors be thinking about key priorities of investment through the upcoming years just to ensure, one, sufficient resources ahead of the commercial launch, but also being prudent with spend, which has sort of been your MO for the last couple of years?
Yeah. We've been focused on efficient use of our cash and cash preservation from the beginning. When we went public in 2021, we raised $345 million. Our last reported cash balance was $193 million. We still have over half of the cash we raised on our balance sheet. This has really been through our continuous efforts to invest in a very focused and disciplined way while still making sure that the company has what it needs. We've done that also by finding more efficient ways to do things, being innovative, bringing costs down, and so on. I'm really proud of that. I think it's really what's put us in a position of strength. In terms of priorities, we're continuing to be very focused on our development and being as efficient as we can.
We have preserved that cash so that when it is time to go commercial, we have the cash we need to do that. In our runway that we said through 2027, that does include those initial commercial investments that we would anticipate happening in the months leading up to our commercial launch.
When do you expect to start kind of layering in the commercial resources over time? I know you made a big hire, someone who's head of marketing. There's obviously been some movement. How do you think about sort of cash disbursements and spend? At what point do you start layering in the commercial resources?
Yeah. We said that our commercial launch will be late 2026. This is really when we start shipping instruments and reagents. In the lead-up to that, we'll be doing early access, both internal and external type of programs. I think that set of activities will really come when we see those initial meaningful number of proteins coming out of our broad scale. At that point, we know that all the various components of our technology have come together, and we're on the path. We have greater certainty in our lines. We really want to make sure we hit that milestone before we start investing in commercial because commercial, as you know, is a very cash-intensive exercise.
Got it. How should we be thinking about catalysts, data releases? I mean, you guys have been a pretty regular fixture who owns some of the other industry conferences. How should we think about either partnership, data catalyst, data generation over the next year leading up to the launch?
Yeah. In that timeline that I was talking about, we've got in 2026, that's when our early access begins, or that's when we, in the lead-up to commercialization, we have mapped out our early access program. Prior to that is when we would need to see those first set of protein decoding, let's say 1,000-2,000 proteins. Once we have that, we would work through publications, both publications as well as presentation at scientific conferences. There are the various mass spec conferences. There is an Alzheimer's conference. At each of those conferences is when we give updates both on the tau or on the targeted or broad scale side.
Got it. And then maybe, I mean, it's interesting, one of your co-founders, Parag, is a KOL in and of himself in terms of mass spectrometry and proteomics. But what are you hearing from KOLs that you're working with in terms of feedback for the instrument, the process, sort of like areas of feedback to improve, but also areas that they're super excited about?
Yeah. What I would say is that we have continued excitement and interest from our KOLs, both on the targeted and broad scale side. What we hear from them is that the data they're getting from existing techniques is just not sufficient. I think there's still a gap in what they need versus what they can see. They are really excited about both sides of the technology. They are just, whichever one becomes available, they'll want to get in front of it.
Got it. Maybe just to wrap up, sort of in speaking to sort of the investors, how would you characterize Nautilus in terms of an investment, and what would you have them kind of look towards to evaluate, one, your performance until launch, but then also what's something for them to look forward to in terms of the market opportunity?
Yeah. What I would say is just to build on some of the points I made earlier is that this type of data, both on the targeted and broad scale side, does not exist today. On the targeted side, we can see a level of granularity on particular proteins of interest that is just not possible with any other technique on the planet. We have already shown that with tau in our ability to measure thousands of different forms of tau in a sample. On the broad scale side, there is no one that can see comprehensively every protein in a sample.
We think that the ability to see this data will be game-changing and unlock a new wave of discovery, as well as finally kick off, deliver on the promise of precision and personalized medicine that cannot be seen when you are dealing with data that does not give you a complete picture.
Awesome. We'll leave it there. Thank you very much.
Thank you.
Appreciate it.