Nautilus Biotechnology, Inc. (NAUT) Q2 2021 Earnings Report, Transcript and Summary

Good morning and thank you for standing by. Welcome to the Nautilus Biotechnology's Second Quarter 2021 Earnings Conference Call. At this time all participants are in a listen-only mode. After the speaker's presentation, there'll be a question-and-answer session. [Operator Instructions] I would now like to hand the conference over to your speaker today, Carrie Mendivil [ph] with Investor Relations. Please go ahead.

Thank you. Earlier today, Nautilus released financial results for the quarter ended June 30, 2021. If you have received this news release, or if you'd like to be added to the company's distribution list, please send an email to investorrelations@nautilus.bio. Joining me today from Nautilus are Sujal Patel, Co-Founder and CEO; Parag Mallick, Co-Founder and Chief Scientist; and Anna Mowry, Chief Financial Officer. Before we begin, I'd like to remind you that management will make statements during this call that are forward-looking statements within the meaning of federal securities laws. These statements involve material risks and uncertainties that could cause actual results or events to materially differ from those anticipated. Additional information regarding these risks and uncertainties appears in the section entitled forward-looking statements in the press release Nautilus issue today. Except as required by law Nautilus disclaims any intention or obligation to update or revise any financial or product pipeline projections or other forward-looking statements whether because of new information, future events or otherwise. This conference call contains time-sensitive information and it's accurate only as of the live broadcast August 10, 2021. With that I'd like to turn the call over to Sujal.

Thanks, Carrie, and good morning to all of you. I'm excited to welcome you to Nautilus' first earnings call as a public company. This morning, we'll review our results for the second quarter of fiscal year 2021 and provide some color and context around the significant opportunity we see in front of us. Before I get started, I'd like to take a moment to express my sincere thanks to our remarkable team for their hard work and focused execution. Our continued progress as a company is a reflection of the resiliency and quality of the individuals that make up team Nautilus in the Bay Area and Seattle. I'm very proud to work with each of them. I'd also like to thank our longstanding investors for their continued support and provide a special welcome to our new investors. We look forward to continuing to develop meaningful long-term relationships with you as we grow the company and pursue our ambitious goals. Speaking of those goals and to put it as clearly as I can, we believe that Nautilus has nothing less than the potential to revolutionize biomedical research by unlocking the potential of the proteome. The world needs a dramatic acceleration in drug development. An acceleration that we believe will be key to ushering in a new era of precision and personalized medicine. We believe a bold scientific leap is required to overcome the current limitations and to radically reinvent proteomics, something we view as one of the last and most significant untapped opportunities in biological science today. Our vision at Nautilus is to bring to market a complete end-to-end massive scale protein analysis platform, comprised of instrumentation, reagents, and software, which takes sample in and returns unique biological data and insight out. To accomplish this, we've designed a single molecule analysis platform of extreme sensitivity, scale and ease of use. Leveraging a unique architecture combined with advanced machine learning and algorithms, we believe our platform has the potential to identify substantially all proteins in sample from almost any organism. What I'm describing is not an incremental or evolutionary improvement over existing methods, it represents the bold scientific leap I referred to earlier, a complete re-imagining that we believe has the potential to unlock the promise of the global proteomics market. Estimates indicate that currently the proteomics market is approximately $25 billion and growing at a 12% CAGR. While this market opportunity is an immediate and exciting area of focus for us, we're looking over the horizon at the potential long-term transformation of healthcare that could be fueled by proteomics. Much as democratizing access to the genome was a catalyst for the development of a broad, vibrant and healthy genomics ecosystem. We believe that Nautilus' innovation has the potential to spark a renaissance in proteomics that could unlock high value applications in precision and personalized medicine in drug discovery and in diagnostics. With that, I'll now turn the call over to Parag.

Thanks, Sujal. As many of you know, Nautilus grew out of my personal frustrations in performing large scale multiomics and system medicine studies. In particular, my frustration was born from the extraordinary effort required to simply comprehensively and reproducibly analyze the protein sample. For reference using mass spectrometry, the average lab can only identify about 8% of the proteins present in a blood sample, and only about 20% to 30% of the proteins in a cell or tissue sample. After years of work that only incrementally improved the mass spectrometry based proteomics workflow, it became clear that we had to do something radically different. Starting with a blank sheet of paper, we wrote down what scientists and researchers like me want. This effort resulted in a very clear set of design criteria. First, we wanted to be able to measure the whole proteome not just 8% of it. In addition, the instrument had to be easy to use and ultra-sensitive, and the process needed to be reproducible and robust, complete, fast, and fully integrated. As Sujal mentioned earlier, we set out to design a platform that would enable biologists to go from sample to insight easily, to put a sample in and get an answer out. With these objectives as the design criteria, we set out to create something from the ground up to achieve these criteria and have now created a platform that has key technical innovations across sample preparation, instrumentation and machine learning. Our first platform innovation was the recognition that we would need a single molecule protein array. This is extremely challenging to do and the inverse of how people typically think about protein analysis workflows. In typical workflows, antibodies or aptamers are mobilized in bulk on a surface and used to capture proteins out of the sample. Contrary to traditional thinking, we instead and mobilized each individual protein molecule from the sample onto a hyperdense array. This allows us to go beyond bulk measurements of proteins to unlock the sensitivity and scale we want to deliver. That leads to our second major breakthrough, which is the instrument itself. Here we designed a stable ultrafast, ultrasensitive, multi-cycle imaging process, which we can use to repetitively probe each individual protein molecule on our array with different binding reagents. Each cycle allows us to get more and more information about each individual molecule. The third significant platform innovation is the integration of a sophisticated machine learning framework within the measurement itself. We have recognized that by bringing machine learning up into the measurement process, it fundamentally changes the landscape of what's possible and potentially unlocks our ability to measure substantially the entire proteome. While the underlying technologies are quite complex, the product we ultimately expect to deliver will be an easy to use fully integrated sample-in, answer-out platform. As we continue to run hundreds of samples through our prototype instruments each month and analyze the resulting data, we have become even more confident that our platform will ultimately enable our customers to pursue the research they want to pursue without experiencing the same frustration that led me to conceive of Nautilus in the first place. Going beyond broad-scale profiling, our platform also has the potential to unlock a tremendously exciting opportunity to measure proteoforms. For those who aren't familiar, each protein exists in potentially hundreds or thousands of different forms with different patterns of modifications. For example, one protein molecule may have phosphorylations at positions 15, 25 and 217, and another may be truncated and have phosphorylations at positions 25, 300, and 325. It's believed that are – there are potentially millions of these different proteoforms and that the pattern of their modifications defines which drugs may work and how. Consequently proteoforms are important for both understanding disease mechanism and as novel biomarkers. Peptide centric analysis methods are unable to discern individual proteoforms, but in our platform, because we study intact proteins we are able to provide a detailed view of the proteoform landscape, its molecular heterogeneity and that of individual proteins and pathways. Last, it's important to understand that our technology is highly open and customizable with compatibility to a wide variety of binary reagents. With a simple labeling kit our platform is designed to be able to use virtually any reagent in the library of biologicals that have been created by biopharma, academia or commercial antibody manufacturers today. This flexibility is an incredibly powerful aspect of our technology, which makes it customizable for the biological research community to ask and answer the questions that they want depending on which binary reagents are introduced. We continue to meet key internal milestones including advances in the manufacturing design of our instrument, hardening of our protein analysis workflow and of our key consumables as they transition to our manufacturing partners. These activities all support our goal of commercialization in late 2023. I'm very excited for what lies ahead. I'm also excited for our team to begin broadly sharing their work through a series of planned publications, the first of which will describe our use of modular fluorescent nanoparticle DNA probes for detection of peptides and proteins. That paper was recently submitted and is available on bio-archive now. We anticipate that an additional foundational manuscript on Nautilus technology will be submitted later this quarter with other manuscripts being submitted later this year. With that, I'm going to turn the conversation back to Sujal. Sujal?

Thank you, Parag. The substantial technological progress we've made has allowed us to transition fully into the partnership phase of our commercial strategy. Late last year, we signed a research collaboration agreement with Genentech to use the Nautilus platform to analyze and map the proteoforms landscape of a particular protein target in which they have interest. The key goal of our collaboration with Genentech is to submit a paper for publication by late 2021; and we remain on track to do so. Through our work with Genentech and in speaking with other potential collaborators, we continued to refine our understanding of the ways in which customers will ultimately leverage the Nautilus platform to advance their research initiatives. That understanding would deepen even further through a series of voice of the customer interviews that we conducted with 17 key opinion leaders and researchers in the first half of the year. Those lengthy detailed conversations confirmed enthusiasm for our product concept and their specific feedback has been incorporated into our product development process. As we continue to accelerate our product development efforts, we expect to move through a number of milestones related to our broad proteomic profile and capability. From our initial goal of quantifying 2,500 proteins per run by early 2022 to comprehensively quantifying the proteome by mid-2023, I hope you share my enthusiasm for the fact that every step of this journey will represent a breakthrough, a fundamental advancement of what's possible in terms of unlocking the value of proteomic data. I looked forward to briefing you on our progress against these and other important milestones as we make our way down this path. These product milestones map to a three-phase commercial strategy. In the first phase, we'll engage in collaborations and partnerships like that with Genentech to create foundational publications that demonstrate utility and validate the value of our technology. Our early access period should start in mid- to late 2022, and allow us to deepen our engagement with a larger number of biopharma and academic customers. Our goal in this phase is to seed the market for instrument presales, by expanding the applications we focus on and by continuing to show value and utility. And then the commercial launch of our anticipated for the end of 2023 should capitalize our business growth. That growth will be driven in large part by the leadership team that we've assembled at Nautilus. It's comprised of individuals with diverse backgrounds, each of whom is uniquely suited to help us accomplish our mission. Adding to that team, I am pleased to share that we recently appointed Karl Voss, a 13-year Pacific Biosciences veteran as our Vice President of Life Sciences Research & Development. Karl will build on his experience leaving Pac-Bios consumables R&D and material and surface science efforts as he grows and lead the team that will help deliver Nautilus as first generation product. We're excited to have Karl on board and I look forward to regularly updating you on the progress of his team's work. I'm also pleased to share an exciting addition to our Scientific Advisory Board. Dr. Emma Lundberg, a pioneer in the field of Spatial Proteomics. Dr. Lundberg is currently a professor in cell biology proteomics at KTH Royal Institute of Technology in Sweden and the Director of the Cell Atlas of the Human Protein Atlas, an international proteomics and cell mapping project. It is an honor to have Emma joined us and we look forward to her contributions and guidance. With that let me introduce Anna Mowry, our CFO.

Thanks, Sujal. Total operating expenses for the second quarter of 2021 were $10.7 million compared to $3.4 million in the second quarter of last year. Research and development expenses for the second quarter of 2021 were $6.4 million compared to $2.8 million in the second quarter of last year, that increase was primarily driven by growth in personnel costs for the purpose of accelerating the development of our platform. General and administrative expenses for the second quarter of 2021 were $4.3 million compared to $649,000 in the second quarter of last year. That increase was primarily driven by growth in personnel costs and professional services as we prepared to operate as a public company. Overall net loss for the second quarter of 2021 was $10.7 million compared to $3.4 million in the second quarter of last year. We ended the quarter with approximately $388 million in cash, cash equivalent and investments. During the second quarter, we completed our business combination with ARYA III generating gross proceeds of approximately $345 million. This capital will enable us to add more staff across our scientific, engineering and commercial teams and already we are on-track to more than double our total headcount this year. The growth in headcount combined with our increasing capacity for experimentation is driving a material increase in the pace of investment in the second half versus what we've reported for the first half. This anticipated step up and spend it in-line with the full year operating expenses we've communicated previously. We are targeting long-term gross margins at around 70%. We anticipate the initial deals including the instrument will have approximately $1 million average selling price. We plan to grow our top-line quickly by putting instruments into the marketplace and then pairing those sales with their recurring revenues from related consumables and software sale. We anticipate that this strategy will drive a recurring revenue stream, that's highly profitable and predictable. We plan to continue investing in research and development activities and have established a long-term expense target of 15% to 20% of revenue. In the near-term, we are focused on product development and preparing for commercialization, but over the longer term we will use our research and development spend to extend our anticipated lead in the proteomic space by building on early successes as well as expanding into new categories and use cases. For our selling, general and administrative spend we have given a long-term expense target of 20% to 25% of revenue reflecting our expectation of significant leverage from our high average selling price, but also from the land and expand model that we expect will drive a high lifetime value for each customer accounts. Our highly profitable sales combined with an efficient sales motion are expected to lead to a long-term operating margin target of 25% to 30%. However, we believe there are many adjacent opportunities and we expect to reinvest additional profits back into research and development and strategic opportunities to grow our top-line even further. With that, I will turn it back over to Sujal.

Thanks, Anna. This has been a very exciting year, both for Nautilus as a company and for the broader field of proteomics. I'm grateful for the efforts of our exceptional team for the collaborative spirit of our partners and for the support of our investors, both longstanding and new. I look to the future more confident than ever that Nautilus is well positioned to achieve the ambitious goals we've set out for ourselves and I know I speak for the entire team when I say that we look forward to sharing this journey with you in the years ahead. With that, we'll now open the call up for questions.

Thank you. [Operator Instructions] And our first question comes from Brandon Couillard with Jefferies. Your line is open.

Hi, thanks. Good morning. Sujal, I'll be curious to get your views as to – maybe can you just tell us about any tweaks that you're making to the platform as you run more samples month to month and what are you learning in terms of tweaks that you can make or improvements to the platform out of the Genentech relationship, anything you can share on that?

Sure, Brandon. Thanks for the question. So if you recall the stage that we're at today is that we really transitioned our activities from research into formal development and manufacturing. And so, there are a number of tracks that are going on right now within the [indiscernible] organization. For example, one track is focused on moving our instrument to the final formal design, finalizing our flow cell and the other activities related to the final instrumentation. Other tracks are focused on the various subsystems that are within our solution. For example, we're optimizing the conditions and the chemistry related to creation of a hyperdense uniform single molecule array. We're developing and qualifying the reagents. And then as well as we've – kind of as we've talked about in the past, we are continuing to work closely with the teams at Genentech, our first collaborator, to move and analyze new samples this year that build upon the work that we did in Q4 of last year. Parag, maybe I’ll kick to you and see if there is any other comments that you'd want to add to Brandon's question.

Yes. And I'd say one other area that we're continuing to advance is on the data analysis side, but just further refining and formalizing our data analysis workflows, machine learning components and those have been key advances.

Okay. I'm curious are you involved in any talks you're considering any other – taking on any other biopharma partners right now, or are you really kind of waiting for the outcome of the initial Genentech study before adding a second partner?

Yes, Brandon. This is Sujal. So we continue to have many conversations with potential collaborators and partners, and we've been very excited that we've gotten enthusiastic reception to our technology and platform, but as well the proteomics space in general from all of the potential collaboration partners that we've been talking to. As we've previously said when we conducted our PIPE road show and we're in the early stages of our business combination with Arya III, we do anticipate that we will find another collaboration agreement with a pharma company in a similar proteo form analysis, use case to what we're doing with Genentech prior to the end of the year.

Got you. All right, thank you.

Thank you. Our next question comes from Matt Sykes with Goldman Sachs. Your line is open.

Hi, good morning, everybody. Thanks for taking my questions. I appreciate it. Just in terms of the early access program that you mentioned in your prepared remarks, do you have sort of anticipated size of that program and/or the mix of biopharma versus academic that you would like to see to make it a robust program?

Good morning, Matt. So I think that when you kind of look at the different types of partnerships and collaborations that we will have as a company between 2021, 2022 and 2023 prior to the instrument launch, there is going to be a good mix there as you're leading to. The types of collaborations today are really focused on platform validation, on proteoform analysis on showing the foundational pieces of our technology and explaining how the system works. As we start to transition to the early access program in the middle of next year, we'll add more customers in different use cases. And as we get through that – that first year of that early access program, you'll see us migrate more and more towards more transactional types of arrangements. You could look at them almost as paid proof-of-concepts that have the aim of us analyzing samples, showing the value of the system, and then using that data to enable us to drive pre-order business. When you think about the mix, which is kind of the key piece of your question, I don't know if I have an exact mix for you, but the early access programs certainly is going to include biopharma organizations as well as some KOLs to continue to build out the body of evidence around our proteomic profiling capabilities as well as prove out the reproducibility of the platform. And I mean, if you asked me, I'd say that – if you asked me to take a guess today, I'd say probably three quarters are going to be in the biopharma space and the rest would be on the academic or non-profit research KOL side.

Great, that's very helpful. And then just in terms of not just for early access, but just as you think about your business plan and the types of customers you're looking for, I know you've spoken in the past about the mass spec market and where you feel like you can add value to users of mass spec for proteomics. Is that primarily who you're targeting? Or is it a wider net in terms of those that are doing proteomics research in terms of the instruments they're currently using?

Yes. I think that the existing solutions that our potential biopharma customers are using is actually pretty diverse and I – over the course of the last three months, we have spent a significant amount of time with another round of voice of the customer engagements, this time more on the biologist side and the scientist side. And one of the things that came out was that the existing products that these potential customers are using is actually – it's more diverse than just mass spec, they're using mass specs, they're using microarrays, some of them are using transcriptomics. And so I do feel that the capabilities that we're designing our system to have will enable us to go after a market share from different pieces and different types of platforms within the proteomics world. Parag, maybe do you want to add to that a little bit?

Yes, I'd just like to recall that our broader mission is not limited to mass spectrometry users, but instead internally in the company, we say anybody who wants a proteome, gets a proteome. And it's really meant to address the wider biomedical research community in the same way that we've seen the genomic technologies democratized access to the genome. So that nearly any biologist who is interested in measuring the genome or transcriptome has that capability, that's our long-term objective for our platform as well, but nearly any scientist has the ability to access the proteome.

Got it. Thanks. And just my last question, you mentioned the expected paper to kind of be released towards the end of this year in regards to the partnership with Genentech. Is there anything else that we should be expecting with your partnership with Genentech outside of that paper? Is that sort of the first the first type of data point that we should expect to get?

Yes. Look, why don't I dive in and then we'll let Parag add some comments. So, I think, one of the things that – that we said in our process at the beginning of the year as we combined with ARYA III. We said that our goal was that we would submit for publication, a manuscript related to a foundational piece of technology in our platform, and then as well submit for publication the early data that we have coming out of our collaboration with Genentech. And we continued to be on track for both of those papers to be submitted. I just kind of tie the – tieback to a comment that was in Parag's prepared remarks. We do have a paper that was – that's related to fluorescent labeling nanoparticles that we did submit and is available on bio archives today. But that paper is an additional paper that's not one of the two that we previously referenced in our comments. Parag, anything to add there?

Yes, I'd say that in general, I think your question has a couple of parts. One is what can one look at to see the progress that we're making. And the foundational manuscript, that Sujal just referred, is on target for Q3 submission and was planned for before the end of the year same thing with a submission on our collaboration with Genentech. We also anticipate potentially presenting our work at relevant conferences as well.

Great. Thanks taking my questions. Appreciate it.

Thanks, Matt.

Thank you. [Operator Instructions] Our next question comes from Tejas Savant with Morgan Stanley. Your line is open.

Hey guys. Good morning. Sujal, just one quick question for you on the early access program that you hope to launch next year. How should we think about sort of the stage of the program that you'll be processing samples as a service in house versus customer placements so that they can start playing with the instrument and generating data at their own – in their own labs?

Yes. Good morning, Tejas. So when we start with our early access program next year, we will be operating completely in a model where the customer will be sending us a sample. We'll be analyzing it on our prototype equipment and the third-generation of that equipment will be available around the middle of next year for internal use. And we'll be giving them the results and working with them closely on the analysis of the data. Well that is the predominant model that we will use prior to instrument launch in 2023. As we approached the instrument launch we'll probably – we'll probably have a couple of sites that we will choose to be testing the instrument onsite prior to its official release, but I would expect that to be a smaller number of sites. The instrument engineering team is a team that's very seasoned, has built many, many life sciences instruments in the Dx and tool space in the past. And so we're confident in their capabilities and we don't expect that we'll need a large number of physical placements prior to the instrument launch.

Got it. Very helpful. And then one for Parag, I mean, a question that that we occasionally get is just around the fact that given that your price point is going to be pretty analogous to that if a mass spec at least initially, what are you doing in terms of making sure that the Nautilus instrument is going to be future-proofed versus the mass specs not of today, but those of 2023 or 2024

Now, that's a great question. As Sujal mentioned, one of the key activities over the past several quarters has been extensive voice of customer conversations. And part of that is asking exactly that question you just did. What is it that customers want today? What do we anticipate them wanting in the future, and how does our platform achieve not just today's anticipated milestones and requirements, but the future ones? And so that's a huge element of us defining what are the key characteristics and performance criteria of our MVP? And so that's something we're watching very closely and we believe that our platform will continue to be a tremendous value and a high-performing instrument well into the future?

Got it. And then one – one final one for me on the data side of things and just, your approach to the data that's coming off the platform post-commercialization. So Parag, I mean, given that you're leveraging these multi-affinity binding reagents to generate compatibility signatures for every protein essentially, and you've spoken in the past about sort of getting access to that data to make your algorithms smarter. How long do you think you need to be having access to that data that's coming off customer instrument? And do you anticipate sort of getting to a point perhaps in 2023, perhaps in 2025 will essentially the learning process for the algorithm is done, then you have these compatibility signatures in terms of looking at all these millions of proteoforms that you're hoping to analyze?

So I think – I think one of the great opportunities of having machine learning workflow inside of the instrument is that it can be continuously learning, and it can learn about each individual reagent. It can learn about how those reagents work together. It can learn about how they work in different locations in different environments. Additionally we're likely to change the reagent mix over time or particular customers may want to include targeted reagents alongside their multi affinity probes. So I anticipate that the – that opportunity just gets better and better and better and more refined over time. I also think that there are advantages in allowing on the customer side, on the analysis component of helping, helping our customers interpret and analyze their data. And so that'll be an aspect of the platform that we're very excited about, and then I think we'll provide value for a long time.

Got it. And one final one, actually that I just sort of thought up and you've referred to it in your prepared remarks, but I think it's an important point. You talked about sort of peptides centric approaches and those having sort of limitations when it comes to proteoforms. I mean, can you just elaborate on that a little bit more?

Yes. So I'd – and I would say if one looks at proteomics overall, it's a giant field with a wide range of applications. And they're within that range. There are two big categories that are addressed by a variety of methods – targeted methods and discovery approaches, and within the targeted approaches, there are the mass spec targeted approaches and the affinity based targeted approaches, which are a nice complement to our own targeted platform for proteoforms analysis. What is particularly unique about our proteoform analysis is that it does require intact proteins and so any – it also requires the ability to multiply and iteratively probe each individual molecule. And that's really how you are able to get to not just knowing that some portion of a protein is modified or in bulk, but really to get to that molecular heterogeneity any approach that digests the protein and the peptides loses that contextual information. So you can no longer say that a given molecule had modifications that positions ABC. You can only know the percentage of molecules and solution that have a modification at position A, the percentages that have modifications of position B. And so that information we believe is tremendously valuable and are the problem partners that we're working with and other KOLs we've talked to, agree that it is an incredibly valuable piece of information that is lost by peptide centered methods.

Very helpful. Thanks so much guys.

And, Sujal may also have some thoughts on that.

Yes. I was just going to say, what Parar’s describing here that he is really an important capability. I just wanted to make sure to add on it a little bit here. So in the 17 most recent voice of the customer interviews, it was very clear that there was a lot of focus on different isoforms and different modifications. And as you know, these modifications affect the behavior of these proteins, and if you want to understand what's going on inside of your cellular machinery, you have to understand these modifications. And so the importance of understanding, not just the identity, but the modification is there with our customers. And one of the very unique things that Parag said that is unique about our platform is that we have the ability to take a single protein molecule and ask it many, many, many questions. Some of those questions are related to positively and accurately identifying what the molecule is, but then you can follow-up with questions about different modifications at different sites. And we anticipate that those questions will give us the richest source of data to deliver to our customers. But we also envision that this is a source of continued improvement of our platform. As we add new reagents that ask more questions, and our customers ask us to build custom reagents for them to ask particular questions that give them unique biological insight. And I think that that is a very unique and exciting aspect of our platform that we expect to take a good advantage of over the course of the next three to five years.

Got it. Very helpful. Thank you.

Thank you. And that's all the questions we have for today. This concludes today's conference call. Thank you for participating. You may now disconnect.