Good morning, everybody. Thank you so much for joining us here today at JP Morgan Healthcare Conference. My name is Bhavna, and I cover healthcare at JPMorgan. Today we are here with the team from Quantum-Si. I'm very excited to introduce you to Jeff Hawkins, who is the CEO, and Patrick Schneider, who is the President and COO. Over to you, Jeff.
Thank you for that introduction. I'd like to thank the organizers for the opportunity to share the Quantum-Si story with you today. Before I get into my presentation, just quickly, our safe harbor statement regarding forward-looking statements. If you have additional details, you can refer to our regulatory filings. Quantum-Si is the protein sequencing company. We have an experienced team from leading life science organizations such as Illumina, MilliporeSigma or Ion Torrent. We ended 3Q with $372 million on the balance sheet, so we are in a secure financial position. Our novel Time Domain Sequencing technology allows us to sequence peptides at the amino acid level in a massively parallel fashion using a semiconductor chip. It's protected by a broad patent estate and was recently peer-reviewed in Science in October 2022.
In December, we announced the launch of our Platinum sequencer. Earlier this week, we're excited to announce that we have begun shipping against those orders. You might ask, why sequence a protein? The field of genomics has shown us that there are about 20,000 genes. Those 20,000 genes encode about 20,000 parent proteins. It's the modifications of those proteins, amino acid substitutions, post-translational modifications, that lead to over 1 million proteoforms. Those modifications are the real-time indicators of health and disease. Today, roughly almost all drugs target a protein. However, 85% of the human proteome is currently undrugged. It's a significant opportunity to advance drug development. Specific to the technology, the ability to see single molecule in an unbiased way at the amino acid level is key to detecting those protein modifications.
Existing technologies, such as affinity-based methods, often miss these changes. The market opportunity is significant. It's estimated to be globally over $50 billion. Quantum-Si initial focus will be in the research market, and within that market, we're focused on the three areas shown on the right side of this slide. Protein identification, protein expression and quantification, and protein characterization, the e-exploration of those proteoforms. That's a more than $8 billion opportunity for the company. This is the Quantum-Si solution. In the middle, you see the instrument we call Platinum. That's the device that we launched in December and announced we've begun shipping. It's used in combination with the reagents you see on the right-hand side to do library prep, as well as sequence the sample. On the left-hand side, you see an instrument we call Carbon.
This is a sample prep device that we expect to launch in 2023 that will further automate the workflow for laboratories. Digging a little bit deeper, the key invention here is Time Domain Sequencing. We are able to interrogate a massively parallel number of reactions at a single time on a semiconductor chip. The first chip that we have launched has 2 million individually addressable wells that allow for single molecule sequencing reactions. Unlike DNA, where there's only four letters in the alphabet, protein sequencing has 20 letters. The option to use 20 different unique fluorophores is not possible. We use a single fluorophore, we take advantage of the intensity of the label, the time, and we take into account the kinetics of the binding reaction of our recognizers as they bind on and off the amino acids. This creates a sort of unique kinetic signature.
That's key because anything that disrupts the kinetic signature, we can detect. Here's an example of the power of kinetic signatures. Here we look at arginine dimethylation. Arginine methylation is been reported in the literature as an important biomarker in the fields of cardiovascular disease and oncology. Here we show dimethylation, in this case, there are two versions. There's asymmetric and symmetric. The important thing to understand is while these are two different modifications to arginine, they have identical mass. If you're using mass spec, you wouldn't be able to detect these changes. You can see in the chart on the right that the kinetic signatures are changing at the amino acid level as our technology detects the asymmetric versus the symmetric dimethylation. This capability will be key to the study of complex disease pathways and discovering novel biomarkers.
Even beyond the technology, the team here at Quantum-Si put considerable thought into the architecture of the product and the workflow to ensure that this wasn't going to be constrained simply to core labs. Here you see a typical laboratory workflow in proteomics. A protein is digested into peptides and conjugated to a linker with our library prep kit. That sample's loaded into a chip and placed on our machine. At the conclusion of the run, the data can be exported into our cloud analytics tool, and there's a simplified and automated data analysis tool. This is all done with a benchtop machine that has a very low capital cost. Here's the data analysis. In this case, we used a single protein, CDNF.
On the left-hand side, we sent that protein to a mass spec core lab and asked for the protein to be identified. You get returned to you a multi-tab, multi-line, multi-column Excel spreadsheet. The spreadsheet was pored over for hours by a PhD in our R&D department to determine what had been detected. The same protein on the right-hand side, sequenced by Quantum-Si's next-generation protein sequencing technology, and you see the report that we generate. In this case, a series of peptides have been identified, in each case on the right-hand side, the peptide has been linked to a protein and given a probability score. For all four of those peptides, CDNF is the highest probability protein.
The simplicity of this analysis, we think is key because not every academic research center who wants to do protein sequencing has the infrastructure in bioinformatics to be doing the types of analysis we see here with mass spec. We're fortunate at QSI to have an experienced team across industry leaders, across all of the areas in our, in our company, from R&D to sales and marketing and service. I mentioned earlier, we have a robust patent estate. We have over 1,000 issued and pending applications covering the whole range of what we do, from the biology to the semiconductor chips, to the instruments and other components. In Science in October, we published on this method. This was the first publication of a next-generation protein sequencing technology.
If you're interested in that publication, you can visit our website at the resource page and download a copy of that paper for free. We believe we're in a uniquely differentiated position. Proteomics technologies sort of fall into two general categories. Affinity-based technologies, like those from SomaLogic or Olink, which seek to look at a few thousand parent proteins at one time. Sequencing technologies that look to interrogate at the amino acid level. In that regard, Quantum-Si is the only commercially available technology that can sequence amino acids, detect those PTMs, and remember, we do that with a low-cost instrument and with automated analysis. The other component of this is we're the only technology up here, affinity or otherwise, that's truly an end-to-end offering for the customer.
There's no other machine needed to get a detection readout, no such as an NGS machine, used in some of these other technologies. This transition of industries from analog technologies to digital is something many of us have seen before in other places we've been in our career. If we go to the genomics industry as sort of an analog, microarrays were how it originally started. You could do tens of thousands to maybe hundreds of thousands of SNPs. EpiMatrix was a significant technology used. The original version of Illumina technology, which was an array, was a similar sort of approach. Digital technologies of next-generation DNA sequencing were invented, and those allowed for deep and broad interrogation of the genome.
It's because of those methods that so many discoveries have been made, and we now see that technology being used as a mainstay in drug development as well as diagnostic applications. Looking at proteomics, we're in a similar transition. We've seen affinity products like the ones listed here growing in use as people look to screen for a few thousand parent proteins. Quantum-Si is on the digital side, looking to bring this field over into the world of sequencing and unlock some of those discoveries that we believe we can unlock in protein sequencing, as others did in the world of genomics. 2023 is a significant year for us. As I mentioned earlier, we've begun shipping our first product, so there will be a large emphasis on the commercial build-out and the commercial priorities in the company. The first priority is always build a world-class team.
In December, we announced that we hired a Chief Commercial Officer, Dr. Grace Johnston, who will be charged with building that team across sales, marketing, and customer service. We wanna drive awareness through data and through peer-to-peer collaboration. You'll see us present at conferences and putting out data through posters, through peer review and webinars. For some customers, they wanna have a proof of concept before they adopt. We are offering a proof of concept testing service, where a customer sends us a small number of samples, and for a modest charge, we process those samples and show them the results. We think this is a creative way to help overcome some of the challenges new technologies have when they come to the market.
Finally, product launches like Carbon or new analysis tools and industry partnerships that will really round out the ecosystem around protein sequencing are going to be key initiatives for the company in 2023. Consistent with that, we were excited this week to announce two separate partnerships that address either end of that full workflow. If you remember back to the workflow slide, the first part is getting that protein ready. Aviva Systems Biology is a co-development partnership where they'll help develop immunoprecipitation kits that will allow people to do targeted proteomics work. What we've experienced in the field through our careers and in our recent launches, there are some customers who have their own methods and others who are looking for a standardized kit that will control the quality of the sample going in.
The Aviva Systems Biology partnership allows us to create those and have those available for customers, as they would like to adopt them. On the back end, after you've analyzed the data with our machine and you've generated your protein identification or the detection of the PTM, you might wanna use it if you're in drug discovery or biomarker discovery to see what other connections exist. Biovista, through their product called VIZIT, is an AI-powered custom visualization tool. You take the data from our machine, place it in their software, their software is able to help you see the linkages, whether that be the disease, the drug mechanisms of action, what's been published essentially in publications or is in databases around the world. We think these two together help put sort of a wrapper on either end of that total workflow.
As we look to 2023, we think we are well-positioned for a successful year. Listed here are the three priorities we have as a company that we'll provide updates on as the year progresses. First and foremost, commercialization of the Platinum device and that 2M chip. Later in the year, we'll launch the Carbon device, and again, automating the sample prep to just further ease that workflow for customers. We will continue to lead with innovation. The Science article is not the last thing we will lead on. We will bring new analysis tools to the market. We will expand the proteome coverage. These are things we'll do in 2023, and we expect to remain the leader in protein sequencing. Finally, we understand the value of preserving our financial position.
As I mentioned, we ended Q2 with $372 million on the balance sheet. We were successful last year in continuing to manage that OpEx and be able to reduce that OpEx guidance each at multiple points in the year. We're committed to extending the cash runway beyond our current guidance. We expect to provide more details on that on our upcoming Earnings Call. With that, I want to again thank the organizers for the opportunity to present. We welcome questions.
Thanks a lot, Jeff. Wanna take this opportunity to open up for any questions from the audience. I can get us started with one. Jeff, you joined Quantum-Si about three months ago. Can you tell us a little bit more about what brought you over and what you hope to achieve?
Sure. Yeah, you know, in the genomics field, we were always watching the proteomics field. You know, I was really looking for a chance to sort of experience what I had experienced in genomics. I wanted to do it with a really disruptive technology that could potentially unlock the market in a way that hadn't been done. I thought what Quantum-Si was doing, you know, could do that. Equally as important was sort of the quality of the people I met. I think disruptive technologies like this are very difficult from a technical perspective, and you have to have just an amazing team.
I got to meet some of these folks who have been involved in many companies we know, you know, Ion Torrent and, you know, Patrick from MilliporeSigma, and I really felt comfortable that they could execute on that roadmap. Finally, you know, the commercialization stage and making a market is something I've done in my career. I enjoy it. I'd rather be first and have to blaze the trail than follow. I just felt like it was the right sort of, you know, intersection of things that represented a great opportunity to really unlock the proteome.
Thanks a lot, Jeff. Moving on to talk a little bit more about the wave of proteomics companies that came into public markets in 2021. Can you tell us a little bit more about where Quantum-Si fits and how this technology solution is different from other proteomic solutions?
I mean, I think the key attributes we covered in the presentation, I mean, the ability to sequence peptides at the amino acid level is gonna be key to understanding those millions of proteoforms. I think that's what we think is gonna matter to really discovering those new biomarkers and advancing drug development. I think the other piece of it is, you know, it's a tough macroeconomic time, and the ability to do this at a low capital cost is really important. You know, not every lab is sitting on $250,000 or half a million dollars. I mean, it just depends on the industry you're talking about how big some of these sort of costs can get.
We think that gives us an ability to get the technology out there despite some of the budgetary challenges that might be in the market.
As the role of next-generation protein sequencing is still pretty new in the proteomics industry, can you tell us a little bit more about how you're trying to engage with the research community?
Yeah. Maybe I'll start, and maybe Patrick can add some sort of commentary 'cause we also collaborate at the R&D level. I think at the field level, you know, we're really trying to work with those thought leaders in the space, help them execute studies they're trying to execute, support them to get to meetings and be able to present their findings. We encourage people to publish whatever data they generate. You know, we too, you know, will continue to publish new things we discover that might still take a little more time to get to market, but we want the field to know these things are coming. Maybe Patrick wants to comment on sort of some of the collaborations we have.
Right. You know, for product adoption, it's important to collaborate and it's important to get the technology out there and show the application. We've been stimulating through catalyst conversations with all of our application notes, you know, some of the ideas that you can focus on as a researcher. Again, to really drive adoption, we need, you know, people to publish, and we need to support those folks as we go through this. It's primarily in the academia, although we're taking calls from, you know, pharma and biopharma. Certainly, in the academia world, we're finding people that wanna just, you know, pull the technology in and start tinkering and, you know, start moving forward with it. It's pretty exciting.
Building on that, can you tell us a little bit more about the range of applications that you see outside of research?
Today, our focus is in the research space. I mean, obviously, we believe over time this could follow a trajectory like genomics did, and it could be used in biomarker discovery and drug development. Historically, though, markets with new technologies, those commercial types of customers tend to move in a little more methodical pace than researchers who are excited about a new tool to be able to go maybe do a study they've been thinking about that they couldn't do for a long time. It's not that we're deprioritizing one over the other, it's just we're cognizant of the sort of the adoption cycles and the way to engage with those commercial partners. You know, we believe over time it will be a key tool used in commercial applications.
Yep. I definitely appreciate that color. Recently in December, the company launched the pro-Platinum protein sequencing platform. What has been the market feedback so far? Can you tell us a little bit more about your first customers?
Yes. The market reception to the launch has been positive. Again, academic customers have been the key folks. I think we've seen customers interact with us in sort of two ways. There are some customers, as Patrick was describing, who have some budget available and really just wanna get the technology and explore the edges of what it can do, and they're just moving forward directly to acquire the platform. There are other folks who have a study in mind who wanna make sure, because it's new, that it's gonna be able to sort of help them solve that problem. That's where that sort of proof of concept testing service comes from.
They send us two or three samples, they pay us a modest fee, we process those and show them the data, and if we, you know, are successful in that, we would expect them to acquire it and insource the technology. We do not expect to run a high volume service. That's not the business we're in. We're finding this sort of proof of concept to be a good way to help people with something that's so new get sort of the first-hand experience with it before having to make the full commitment to buy the machine.
As we think about how you're delivering on your goals and your progress, can you tell us a little bit more about how we can measure your progress? If you could also at the same way and touch upon your go-to-market strategy, that would be helpful.
Yeah. I think in terms of goals, you know, Right now we're focused on really learning the metrics and KPIs of selling a novel tech like this into the space. You know, we will, you know, on our upcoming call, certainly provide OpEx guidance. I think with respect to, you know, other commercial metrics, we haven't really decided the timeframe when we'll provide those. We expect to provide some during 2023, but just we're not sure the exact timing yet, because we wanna be very knowledgeable not only of how do people progress from interested and purchasing it to implementing and running. We're really learning a lot about that process right now.
That would inform, you know, our ability to sort of give useful metrics, you know, to those on Wall Street looking to sort of model the business or understand it. I think on the R&D front, I mentioned, maybe let Patrick comment, that, you know, we expect there to be sort of a sort of a flywheel of improvements and analysis tools and other attributes, maybe Patrick can talk a little bit about how he sees that unfolding.
Yeah. Yeah, I mean, we have a rich pipeline of other attributes. Not only on the kit side of the things, you know, versioning, getting more proteome coverage, for example, but also instruments and chips and, you know, we talk about our Carbon system, you know. I think you can expect to see a regular cadence of launches. Yep.
I think your other question was on go to market. Right now we're a lean and mean sort of commercial team. We were very focused in 2022 on really getting to that critical mass in R&D and really getting the technology, you know, ready to launch. I think publishing in Science and able to run a technology in your own lab with your best and brightest scientists and engineers is one level. The next level is can you make kits? Can you ship instruments? Do things work? Do they fail? That transfer to manufacturing is more complex, I think, than sometimes people give it credit for. Our focus was really in those areas, you know, product development, operations, transfer. Those were the big areas of investment. We have a...
As I said, we have sort of a lean and mean commercial team, that was, you know, prepping us for the market. Really, you know, bringing in, Grace as the Chief Commercial Officer is really the catalyst to now build that footprint out. We'll be measured with it, though. We're not just gonna run and put, you know, 20 reps in the field. You know, we're really much more focused on what's our pipeline looks like, what types of things are the customers doing, and then what are the right balance of skill sets across marketing, selling, application support. What's the right way to do that blend? Grace will sort of guide us through that throughout the year.
That's helpful to know. Taking a step back to go a little more deeper into the technology again, one of the key features of your technology is the ability to detect proteoforms and post-translational modifications. Can you tell us a little bit more about why post-translational modifications are important?
Yeah. Sure.
Patrick runs R&D. We'll let him talk to you about it.
I love post-translational modifications, actually.
How much time do you have?
You have 60 minutes.
Right.
All yours, Patrick.
Okay. Yeah, you know, proteins, obviously, they're the business end of biology, right? It's not just, is a protein there or not, or at what level is it there or not. Post-translational modifications actually modify whether or not a protein is turned on or turned off. Phosphorylation, for example. Phosphorylating, dephosphorylating at, you know, turns the protein on or off in terms of its function. If you're just scanning the expression levels of a protein, you're missing a whole bunch of information that is related to these proteoforms, right? Another example of a proteoform is if there's a mutation that is somatic that you can't really see in genomics, that you can identify that mutation.
We have an example of a white paper around beta amyloid and, you know, the plaque-forming beta amyloid versus the normal beta amyloid has different sequences that we can distinguish. You start to get a whole bunch of very rich information when you start to look at the proteoforms.
Can you tell us a little bit more about your IP portfolio and overall your approach to protecting your technology?
Yeah. Maybe you wanna describe sort of the breadth.
Yeah. As you saw on our on the slides, our technology includes an instrument, semiconductor chips, biology, you know, associated with our recognizers and our cutters. Each of those streams have a series of patent portfolio, you know, in our patent portfolio. It's, it's extensive. I mean, 1,000 patents or applications is pretty extensive, and it covers a wide range of different technologies associated with our total ecosystem.
Yeah. If your current platform, can you tell us what types of samples that it's currently compatible with? If you could briefly touch upon the prep workflow, while you load it into the instrument, that would be helpful.
Maybe I'll start. The workflow, you know, in a, in one of our customers' labs will look very similar to, you know, anybody else doing proteomics research, right? They're gonna isolate their protein of interest or proteins of interest. In our case, they'll use our library prep kit to digest those into peptides and add that linker before it gets sequenced. In terms of sample types, you know, many sample types can sit upstream of that workflow I just described. You know, those could be, you know, human samples. They could be samples, you know, from environmental. They could be other just pure proteins from different types of work.
It really is no different on that, those first sort of step, for someone doing proteomics research today than working with our technology. Do you wanna add?
Yeah. I can add also. It's application dependent, right, in terms of the input protein. Some customers want to, you know, they run a protein on a Western blot, and they wanna try to understand what that protein is, you know. They'll cut out of a band out of a gel and to identify that particular protein. Some customers are interested in, as I was talking about, these post-translational modifications, you know, treated versus untreated, right? In our chip, there are two chambers. One you could use treated versus untreated to look for those differences in either phosphorylation or expression levels. It really depends upon what the experiment is and what the customers want to try and do.
One of the things that we did with this partnership with Aviva Systems Biology is, you know, we're co-developing sample prep for those customers that wanna do immunoprecipitation. Sometimes they wanna do immunoprecipitation because they want to make sure that that protein is actually the one that's supposed to be binding or if there's any off-target effects. Or they're looking for the interactome, they're looking to pull down a protein and then doing some other, you know, experiments to identify particular proteins. It, you know, we know the workflows, and we're trying to basically fit within those workflows and make it convenient for the customer when they're trying to do their experiment.
That's very helpful to know. If you would like to zoom out a little bit and talk about Quantum-Si as a whole, we'd love to know more about the financial position and if you can tell us more about your operating cost trends or any cost optimization initiatives you have in place, and touch upon your cash runway, that'll be helpful for us too.
Yeah. As I mentioned, at the end of Q2, we had $372 million in cash on the balance sheet. You know, our OpEx target would suggest that we'll end the year sort of around $340 million. That's based on the guidance. We're comfortable with the guidance we've provided. You know, as I mentioned, our existing guidance is that we have cash through 2024 to get into 2025.
I'm working closely with the management team and the board on, you know, an operating plan and sort of a long-range plan that really takes into account the investments we need to make, the adoption scenarios we see for the technology and make sure that we, you know, maximize that runway wherever possible, but without, you know, us allowing ourselves to step back so far that we're not the innovator and the leader in the space. We've invested a lot to get here, and we intend to stay the leader in that regard. We also want to extend that runway as far as possible, because it's obviously not an ideal time in history to be raising capital.
We're quite pleased to be in the position we are, but we're looking to, you know, extend it wherever we can.
Yeah. Wanted to reach out to the audience again to see if you had any more questions.
Actually, for Patrick, you talked about in the future being able to identify more of the proteome. Oh, sorry. With Gen one, how much of the proteome can you identify? For Jeff, if I may,
Fill us in on the price per box.
Okay.
The price per consumables, What kind of gross margin trajectory we might look forward to. Thank you.
Yeah. I'll start. Our current system is what we call a five recognizer system. We have five recognizer proteins that can recognize 15 amino acids. If you calculate the frequency of, you know, amino acids is different in each, you know, in each case. If you calculate that, we have 70% coverage of the human proteome.
Chip 2, the 2... Sorry. The next chip, does it take the next chip to-
No, it doesn't take a next chip. It takes identifying and the next set of recognizers to get-
Okay.
More amino acids.
It's on the reagent side.
Yeah.
You can increase that proteome coverage through sort of new recognizers that can bind to amino acids that we maybe don't bind to today. It can be done. You don't have to sort of create a whole new chip just to expand that coverage.
Okay.
To answer your other questions, the Platinum sequencing machine, what you see on the screen here, it has a list price of $70,000, seven zero. The kits, It's essentially $1,000 a run for a library prep and sequencing, and you can load two unique samples onto that cartridge. Fairly similar to what a customer might see if they were running, you know, a mass spec device on a sort of a per sample basis.
Gross margin?
Yeah, we're not really providing guidance yet on gross margin. I mean, we're at the early stages of our business. You know, we think longer term, you know, we've had targets in the past where we've told people we think long term the business will operate at 70%+ gross margin overall. We have no data right now that would indicate that that's not, you know, a reasonable target at scale. Really the question we have to answer is, you know, how do we get there? What's the timeframe it takes to get there? We're comfortable that that as a long-term target is appropriate.
Do we have any other additional questions? If not, Jeff and Patrick, I'd like to hand it over to you for any closing remarks you'd like to make.
I think, you know, just we appreciate obviously the chance to be here. We appreciate the interest in the company, and we look forward to interacting with folks at our upcoming call and throughout the year at industry conferences. Thanks again for having us.