Hello, everyone. I'm Sujal Patel, Founder and CEO of Nautilus Biotechnology, and I'm joined with by Anna Mowry, our Chief Financial Officer. We're here to talk about Nautilus unlocking the power of the proteome to revolutionize biomedicine. As we begin, this is our standard safe harbor statement. This presentation and my oral remarks have forward-looking statements. All those statements are subject to risks and uncertainties that are in our SEC filings on Form 10-K and 10-Q. One of the major developments over the last two decades in biotechnology is that as a community, scientific community and humanity, we've conquered genomics. We can take a drop of your blood, we can run it on a sequencer, tell you what 99.9% of your genes are. It's reliable, it's accurate, it's really low cost.
Your genes really don't change from the day you're born to the day you die. They don't tell you anything about the real-time state of disease. They don't tell you about what's going on inside of your body, and so they have very little power to affect drug development, to affect precision medicine. Outside of oncology, which itself is a disease of the genome to some degree, the genomics revolution hasn't really had a huge impact on drug development and biomedicine in general. The scientific community has recognized over two decades that the issue is that we need to be able to analyze proteins in samples, not just your genes. Proteins are the little biomolecules that do all of the work in the human body. 95% of our FDA-approved drugs target proteins. Most molecular diagnostics target proteins.
Today, the state-of-the-art technologies that analyze proteins within a human body from a blood sample, from cells, from tissues, those technologies return extremely poor, extremely low-quality results. This is a huge challenge in the scientific community that has led to over 90% of new drug development programs failing. Billions and billions of dollars are spent on every single drug program that begins. We recycle the same mechanisms, the same targets, and most of those programs fail. One example of that is Alzheimer's disease. Alzheimer's disease is one of the first applications of the Nautilus platform, which I'll describe shortly, that has emerged. It's a disease that affects 7 million Americans. It's a disease that has a worldwide economic impact that's over $1 trillion. We've known about this disease for 120 years.
For 40 years, we've understood that the disease's pathology is driven by the aggregation of proteins within the brain, which causes neurodegeneration. In 40 years, we still have zero effective therapies. We still don't understand, outside of understanding that there's a single protein in the human body, the tau protein, which has a accumulation of errors and modifications that cause this disease, we don't understand what's the molecular root of the disease. We don't understand how to stop it. This is the sort of application that we expect to be able to use the Nautilus platform to begin to make a significant dent. This slide here outlines a little bit around the market opportunity, and it takes a very specific lens here.
Just looking at how proteins play a role in different disease areas and just looking at the NIH research dollars, just America, just NIH, that goes into these areas, you see there's massive spend. Autoimmune diseases, $1 billion. Cardiac, closing on $3 billion. Almost $7 billion for aging. Almost $4 billion just in neurodegeneration and Alzheimer's and other related dementias. These billions of dollars go into these programs, and in none of these areas have we had significant game-changing new drug introductions over the last decade. We haven't seen a significant improvement in the understanding of these disease areas. Our belief is that in order to make a dent here, you really have to have a huge upgrade in the types of protein analysis tools and platforms that exist out there, and that's what Nautilus is focused on.
The last slide that I wanna highlight as we set up our conversation here is that one of the things that I'm sure as investors in healthcare companies you've heard a lot about is AI. AI is a technology that has been transformative with technologies like ChatGPT, creating new applications in consumer and large enterprise types of environments. The existing AI tools like ChatGPT trained on the entire internet. They consumed every piece of text, image, and video on the internet to create a massive training set. In biology, we wanna apply those same technologies. There's no training set today. No one can accurately tell you what proteins are in our cell, and without that, we can't create a cellular model. We can't understand how cells work with each other. We can't understand what goes wrong in disease.
Proteomics data is the key piece of data that needs to exist in a high-quality, accurate, reproducible form to be able to leverage AI to the fullest for this next generation of drug development, and we think that Nautilus is a key enabling technology of that. A couple of times in this presentation, I have pointed out that existing technologies are really inadequate. If you look at the types of technologies that exist today, there are a few types of technologies, and you see some graphics that show the outputs of them. The gold standard to analyze proteins today is using a workflow around the mass spectrometer. The mass spectrometer is a $1 million-$2 million instrument sold by large cap companies like Thermo Fisher, Danaher, and Bruker. These technologies have a wide range of applications, food safety, metabolites, airport security, metallurgical analysis.
There's been a workflow created over the last couple of decades around these mass spectrometers to analyze proteins. There are also other types of things. There are assays from companies like SomaLogic, which is now owned by Illumina, and companies like Olink, which is owned by Thermo Fisher, which is one of those large caps I just discussed. These approaches all take a slightly different approach to trying to analyze proteins in samples. Ultimately, what you have is you have a bunch of methods which haven't improved a whole lot in 20 years, and that produce very, very incomplete results. The results don't show you any meaningful subset of the 20,000 basic canonical proteins that exist in a human. They give you very limited information on the millions of forms of those proteins that exist in a human body.
They have results that don't agree with each other. They don't, they're not reproducible across samples. The results are very biased. They show you things that are the most abundant. They show you the largest changes. Biology is often driven by small changes, and scientists need a completely new way to analyze proteins to be able to effectively impact drug development programs, diagnostics, and precision medicine. This next slide really just highlights for you what I said with actual data. This is a chart that's based on a paper that was published in 2025. There's a second paper in 2025 which corroborates this data. Nautilus has nothing to do with these papers. These papers completely highlight the problem that I just described.
What this is a Venn diagram that shows the overlap between Seer at the bottom, which is essentially, you could call it a gold standard mass spectrometry-based analysis approach, SomaLogic, and Olink, the other two companies I mentioned earlier. Here, we're taking the most basic analysis. How many proteins did you identify, and what proteins, and how did it agree with the other solutions in the space? This is the most basic question. Customers are really looking for more complicated questions. How are these proteins changing over time? How are they modified? But just the most basic question, did you see the same thing that the other products saw? These products don't agree at all. They agree on less than 2,000 proteins between the three of them. The coverage is incomplete. They don't agree. There's very poor data quality.
What we find in talking to customers who use all these proteomics technologies, they combine the data from all of these and try to put together insights, but it's extremely complicated, even though billions and billions of dollars are spent every single year on these solutions. If you think about the requirements from a customer standpoint, one of the things that we as a company have done is we've spent two separate formal customer studies and spent time with hundreds of biopharma organizations, companies building new drugs, companies in the diagnostic and precision medicine space, and we've really determined what the customer believes the core requirements for this next generation set of technologies in proteomics is.
They've told us time and time again that we need to be able to be sensitive enough to see very minute changes in cells because those minute changes often drive disease. We've heard time and time again that the data needs to be reproducible and robust, that they need a very easy-to-use solution, which has been elusive with current products in the market today, and they need to be able to analyze these samples with enough throughput that they can get through a large number of samples in a reasonable amount of time so that they can accelerate their drug development programs, which speaks immediately to efficacy of those programs and speed and cost of those programs. This was the set of criteria that we set out to build nine years ago. Nautilus was founded just over nine years ago.
We've been a public company for nearly five years, and what we're building is a solution that is a completely new disruptive solution that implements the core requirements that we've heard time and time again from customers using a brand-new method. That method is different than everything else that's out there because it leverages this very unique combination of disciplines that Nautilus uniquely has strength in. It combines the disciplines of life sciences and biochemistry with physical sciences and engineering and computer and data sciences. Both myself and Parag Mallick, my co-founder, who's Stanford faculty and is our chief scientist, both of us have very complementary skill sets across all of these areas. I have skill sets across physical sciences and engineering and computer science, where my degrees are.
Parag has a degree in computer science, degree in biochemistry, and his Stanford lab is a lab for the last 13 years that has sat at the intersection of data and computer science and biochemistry focused on precision medicine. All of our team that we have built over the last nine years is a team that is unique, has unique strengths to deliver on a very interdisciplinary approach to proteomics that hasn't been attempted before. The approach itself, I won't get into too much detail because this is not a great setting to get into a lot of detail on the approach, but the approach is a completely new method that hasn't been attempted before, and it's an approach that no other companies are pursuing. Underneath our approach is this technology called iterative mapping, which I'll touch on briefly in a few slides.
The key thing that I wanna highlight is that in order to implement iterative mapping, we had to build a platform technology, instruments, consumables, and software that did a number of things that have never been done before. We had to build a semiconductor fabricated chip and flow cell system that is capable of immobilizing protein molecules. Think about it on a giant chessboard, but not 100,000 or 1,000,000, which is the typical for assays today, but immobilized 10 billion protein molecules so that we had them on substrate ready to analyze. We have had to spend over nine years now building a brand-new class of antibodies that we use to give us information about every one of those molecules that's on this chip.
We've had to build a set of sophisticated software technology and algorithms that are able to analyze the data coming off of our instrument. The output of that is actually very simple, very robust data. Over the course of the last year, we've just now gotten to the point where not only have we started showing significant reproducible data to the scientific community, but we have an early alpha customer in the Buck Institute for Research on Aging who has had our instrument in alpha form for just under a year now. The Buck's data has been shown to the scientific community, it's generated a huge amount of buzz, and we'll touch on that as we get a little bit further along here. I'm gonna take a minute to describe all of the things on this slide here.
At the bottom of the slide is this method that I talked about, iterative mapping, which is this disruptive technology that measures proteins in a sample. It doesn't matter what sample type, whether it's blood, cells, tissue, or whatever, it measures them with a depth and resolution and reproducibility that isn't possible with today's solutions. On top of that, we have the Nautilus platform. At the core of that platform is an instrument that we call Voyager, and it is a benchtop instrument about the size of a large genomic sequencer, and that instrument is what does the analysis. It takes any sample from any organism in and returns data to the customer in the cloud. That instrument we expect to cost roughly $1 million, which puts the instrument in line with the cost of a high-end mass spectrometer that's used in protein discovery environments today.
That instrument is expected to move into beta access in the second half of the year and then as well into general availability as we start to get into next year. The other pieces of our platform are one, and most importantly for a recurring revenue model, the reagents and consumable. Every time someone wants to run the instrument, and the instrument is capable of running four, eight, or 12 samples in a day. Every time you run the instrument, you have to buy a consumable kit, which includes reagents, flow cell and a chip, and those consumables are a significant revenue stream for the company. We haven't announced final pricing for our kits, but we've said that roughly we expect it to be a few thousand dollar per sample.
You can imagine that if you're running 4-12 samples per day, the instrument is capable of significant pull-through numbers that could exceed $10,000 a day in consumables. Our thinking is that over the course of a year, as a customer gets used to the instrument and operationalizes in their environment, that we expect to see them start to grow their consumables pull-through to $500,000 a year. Our goal as a company will be to continue to work with our customers and grow that over time.
In addition to that, the platform contains a set of software technologies that analyze the data on the instrument, send that data to the cloud, and then there's a whole cloud back-end, which uses a significant amount of computing power to turn all of the data that's coming off of the instrument into protein identifications, protein abundance information, and deliver that to the customer in a really easy-to-use way. On the top of this slide, what you see is a set of applications that the platform is capable of running. You can call these applications, you can call them assays. The iterative mapping technology that is implemented by the Voyager platform is actually quite general.
When we started as a company, we talked about one application which was the biggest and broadest of those applications, and that is the largest box on the left here that we call broad scale proteomics. Broad scale proteomics is essentially an application where you give us any sample from any organism, and we tell you what all of the proteins are in the sample. It gives you quite a lot of information that's very precise and reproducible around what's in the sample, and this is an application that competes directly with the products that I've described earlier, the mass spec-based proteomics workflow, as well as some of the other products that we talked about. The other use cases in the center here are an area of application space that we call targeted proteoforms.
Inside of the human body, a protein does not just exist in one form. It actually exists in hundreds or even thousands of different forms. This is an area of biology that is incredibly understudied because there are no ways to fully measure a proteoform with any product on the market today, and there's really no method to do it in a scalable way. These forms of proteins are critical to study because every form has a different function in a cell, different conformation, different degradation characteristics, different messaging, different presentation. If you don't understand what form of a protein you're looking at, you don't understand its function. For example, you see tau listed as one of the targeted proteoform applications that we support today.
This is an assay that we just introduced to early access to customers just a couple of months ago. This is the first application of our platform that is ready for customers' use. Tau is the key biomarker that's implicated in the pathology of Alzheimer's disease, and tau exists in hundreds of different forms in the human body. The aggregation of tau in the accumulation of modifications on tau that turn it into different forms is what the scientific community believes drives the pathology of Alzheimer's disease and disease progression. We introduced an assay that is capable of measuring 768 different forms of tau in the human body, something that is a data set that has never been seen by the scientific community.
With our work that we did with the Buck Institute, with Genentech, Mount Sinai, and the New York Stem Cell Foundation last year, we've been sharing over the course of the last year this data with the scientific community. There's an incredible amount of excitement around it. We're in early access today, and just last week or maybe even this week, we just announced that Baylor is gonna be the first early access customer, leveraging this tau assay to be able to analyze samples, and then really move towards a larger study in the future. This targeted proteoform space is a really interesting space, and we intend to build other proteoforms as well.
We've talked about on our last earnings call that the next target that we're going to focus on is an oncology target, and I'll walk through the timing on these things. That's expected this year in early access form as well. We also recently announced a partnership with the Michael J. Fox Foundation, where Michael J. Fox has provided a $1.6 million grant, of which $1.2 million of it comes to Nautilus, that's focused on building a brand-new assay for alpha-synuclein. Alpha-synuclein is the key protein that's the key player in the development of Parkinson's disease, which is an area that Michael J. Fox is almost exclusively focused in. That's another one of the targeted proteoform applications that we expect. Then over time, we expect to continue building these assays.
These applications have different immediate market focuses and different, uptakes in the market. These targeted proteoform applications are incredibly valuable biology, but it's a new area of biology where no one has ever done any work. We largely are today focused on academic and nonprofit research customers. We expect that those early insights will lead to more biopharma interest in these proteoform assays. On the broad scale side, both biopharma and academic and nonprofit research use all of the existing solutions today, and there is an incredible desire for technologies that are transformative to their workflows. We expect that with broad scale, when it's introduced in early access and general availability, that the business will likely split much more so 50/50 initially between biopharma and academic and nonprofit research.
We've talked about that, and I'll show it in a slide form and a timeline in a second, but we've talked about it on our earnings calls. We think that the combination of these targeted proteoforms and the broad scale technology, when they're both generally available with our platform, we think that that's the key to unlocking the significant revenue ramp that we would expect for such a transformative technology, and that is something that we expect by the middle of next year. One of the things that I've highlighted in this presentation so far is that the type of solution that we're building is very new, and it includes a tremendous amount of innovation across a wide range of disciplines.
We have had to build, over the course of nine years, a team of people capable of delivering something this ambitious and this complicated. Myself, I've been an entrepreneur in the tech world prior to Nautilus. The last company I was founder and CEO of was founded 26 years ago and went public in 2006, and then ultimately sold for $2.6 billion in 2010. That business today is ultimately owned by Dell and is producing billions of dollars of top-line revenue every year. Parag Mallick, I talked about his background. He is a key opinion leader in the proteomics world. Mallick Lab at Stanford is doing groundbreaking work in precision medicine using multi-omics approaches, largely proteomics. Subra Sankar, for example, is our head of R&D, and he runs an organization that's largely the whole company today.
Subra is a veteran of the genomics world. He was the head of engineering that delivered the first genomic sequencer at Solexa, which is the company Illumina purchased to eventually become the leader in genomic sequencing. Subra built eight platforms for Solexa and Illumina up through the MiSeq and the HiSeq platforms. He's a guy who is steeped in experience in building complex technology and bringing them to the market. There's others on this team as well, who round out our team from the commercial side and from the business side. Matt Murphy is our General Counsel. He was the general counsel of 10x and PacBio, you know, decent-sized publicly traded companies today. Ken Suzuki, our Chief Marketing Officer, was the head of mass spectrometry business unit at Agilent.
Amber Faust joined the team this year. Amber has been at every one of those proteomics companies I described earlier for the most part, and she has run sales for proteomics types of environments for many years. She was at SomaLogic twice. She was at Olink. She spent time at Seer and Waters earlier in her career as well. Just to touch on market opportunity. From broad strokes, the proteomics market, which has been well-studied, is expected to grow to $57 billion over the course of the next few years, taking us to 2030. That's about a 13% CAGR. That market breaks up about 50% biopharma, 30% academic nonprofit research, and applied applications. That in and of itself is a huge opportunity.
It's an opportunity where billions of dollars in mass spectrometers, just the instruments, are sold every single year. It's an opportunity where there's tons of new ideas and new companies and lots of growth that's expected. More importantly, our belief is that this technologies like Nautilus will unlock new clinical applications, new drugs, new opportunities for precision medicine, and that there's a significant long-term opportunity to grow this market even more because of those opportunities that get unlocked. On this slide, what you see is how we expect to go to market. If you look at where we are today, we've got in early access that tau proteoform application, which I talked about earlier. The lighter blue is early access and the darker blue is general availability. What you see is that tau proteoform assay is in early access now.
That early access is as a service, so customers send us the samples, we analyze them, we send them back the results. You start to see in the second half of 2026 that the Voyager platform will enter early access beta and get instruments, and then move to general availability by the end of the year for launch with first instrument placements at the beginning of next year. Once we get to that point, customers will be able to access the tau proteoform assay by buying a consumable kit from us, running it on their own instrument, and we'll see. You'll start to see other applications come.
You see the oncology proteoform, the first one that we expect to announce in early access in the second half of this year, and we expect just around the end of the year for it to flip to general availability as well. You see in the future there's more proteoforms coming, and most importantly, from an application perspective, our biggest application, broad scale, we expect to enter early access in the second half of this year and expect to be able to get to general availability in the first half of next year. Together, we believe that once all of those things by the middle of next year reach general availability, that we'll have a product that will be able to ramp on the top line in a more significant fashion. Today, if you looked at our engagements, these are really early engagements.
We've said that we really don't expect material revenue from these engagements today. The engagements are really about proving out the market opportunity, proving out our science and our technology, and building the body of evidence that we need to start getting customers interested in instrument purchases starting at the beginning of next year. Anna.
Great. With that, I will touch briefly on our financials. At the end of last quarter, we reported cash on our balance sheet of $156 million. What's really exciting about that is that this is 45% of the funds we raised as part of going public almost five years ago. Sujal and I and the entire team have been very focused on us making the best use of our resources throughout the life of the company. You can see from our headcount and operating expense results that in 2025, we took steps, additional steps to bring down our spending. This was really intentional to make sure that we have cash runway through the product and commercial milestones that Sujal talked about.
You can see from our Q4 cash burn, we have a significant runway if we need it. With that being said, what we've communicated is that we expect our cash to last through 2027. This is a full year beyond our launch announcement timeline, which we said we expect in late 2026. This really accounts for some growth in spending as we finish our development and as we start to make our first investments in market development and commercial expansion. With that, I'll pass it back to Sujal to wrap up.
Great. Thank you. I just wanted to highlight a few things as we wrap up our presentation here. Hopefully, what came across is that Nautilus is building an incredibly disruptive product that has a significant potential in the marketplace, and we've built a team over the course of the last nine years that knows how to deliver on this type of complex product that's as different and disruptive as we've described. One of the things I wanna highlight is that this company has an unusual amount of alignment with shareholders. Parag and myself, the two founders of the company, own one-third of the company. Our board table owns about half. We're extremely tightly held with beneficial holders, over 70%.
Of that eight people I showed you on that team slide, not one of those people has sold a single share in the public market since we've been public for five years. Six out of eight of them have bought shares in the open market. We're aligned with shareholders and focused on long-term success. We think this is a really exciting coming 1.5 year , where there's a lot of catalysts and opportunities to generate value. We're excited about talking to any of you that wanna learn more about it. Feel free to reach out to us for a one-on-one meeting. Thank you.
All right. Thank you, sir. Now we'll move into a quick Q&A session.
Great. Thank you. I know we have, Sujal, we have one question, and it's: for the non-scientific investor at a granular level, how does having an expanded view of cell proteins and proteoforms ultimately lead to drug development? After collecting the data, what does the drug developer do with the information?
It's a really good question, and I'll try to answer it in the most simple form possible. Proteomics, study of proteins, is used in a wide range of applications within drug development. First and foremost, it's at the very beginning of what they call target discovery. If you're looking for a new drug that helps with heart disease, you can take cells that are afflicted with heart disease or from tissues that are afflicted with heart disease, you can take cells that are healthy, and you could run them through a proteomic analysis and analyze what are the differences. Those differences could tell you what might be a target that's druggable, where I can affect that quantity of those proteins up or down.
It might inform you of a mechanism where there was an effect that occurred that created this change in the sample. You could use protein analysis to help understand how those changes occurred. Once you have a drug candidate, or in case of pharma, there's generally hundreds of candidates that you wanna see how they affect disease cells and tissues and organs, you can go and run these studies that they call mechanism of action studies, where you expose cells to these potential compounds that you think are good drugs. You can then look at the changes within the proteins of those samples. The next step from there, once you've got candidates that you like, you have to understand the toxicity of the potential compounds that you wanna go and push for clinical applications.
You can expose those compounds to cells from all over the body. You can look for where cross-reactivity exists. The customer's desire is to do as much of that upfront work as possible so that when they move to clinical trials, which are the most expensive part here, they've got a much higher chance of efficacy and getting a drug out on the other side. That's the desire that we see from customers. There's plenty of other use cases as well, but in the most simple form, those are the steps where you start to see proteomics used in significant ways in drug development.
Great. Also, if anyone has further questions, we'd be happy to address those. We look forward to some of the one-on-one meetings we have scheduled, so we'll see you in those. Hand it back to the investor summit group team. All right. Thank you so much for your time, and thank you so much, guys, for your pres-