Good afternoon. My name is Swayampakula Ramakanth, or known as RK. I'm a senior analyst in the H.C. Wainwright Healthcare team. This afternoon, we are gonna have a conversation with Jeff Hawkins, CEO of Quantum-Si. Quantum-Si is a commercial company developing next-generation protein sequencing instruments, the first of which is called Platinum, which is on the market. They're also developing a second-generation system, Proteus, which is actually a platform, and that provides differentiated capabilities. To discuss the development of Proteus and also.
Thanks for having us.
Just to get started, Jeff, could you give the audience a brief outline of not only Quantum-Si, but also the commercial strategy behind it?
Sure.
The corporate strategy behind it?
Sure. Yeah, you know, at a high level, you know, Quantum-Si is in the field of proteomics, in that field, you have essentially two different types of technologies. You have technologies like mass spec or affinity-based methods designed to screen for thousands of proteins from a sample, they just give you protein presence or absence. Where we fit in is we're in sort of the what's a new bucket, some call next-generation protein sequencing, some call high-resolution proteomics. Essentially, what we're doing is sequencing proteins at the amino acid level so that you can detect small changes such as single amino acid variants, post-translational modifications, sort of things that are very difficult to do with existing technology. As you mentioned, we launched a first-generation technology called Platinum in the beginning of 2023.
We're the only commercial stage technology for amino acid level resolution sequencing in the market. We've been using that largely to engage key opinion leaders, generate data, learn a lot about just operational and commercial and sort of support of that technology, and then took those learnings from that commercial phase, you know, both here in the U.S., but also through some direct and partners internationally. Took all those learnings and said, "Okay, what's the future need to really look like? What's the capabilities we need?" That led to the Proteus creation.
Perfect. You know, for 2026, you know, you deliberately have deprioritized, you know, Platinum, probe, and guiding just for about $1 million in 2026. Because you want to concentrate, on Proteus, not only done the development, but also making sure that you have it, you know, commercially ready for the launch. When Proteus comes to the market and, you know, let's say it's sitting in 2027, what sort of a, you know, revenue run would you like to see from, Proteus so that, internally, the team feels, that you made a valid decision?
Yeah. Maybe I position what we did with Platinum just slightly differently. We certainly have reduced as the sole priority of our commercial team. We do have our commercial team splitting their effort around you know, supporting that existing base, and helping to drive consumable utilization and publications, also really prospecting and building the market to be ready for Proteus. The other reality of, you know, sort of being a public company is we, you know, we shared the information about this new technology in the launch, some customers naturally hold back. That's why we introduced programs like our placement program, people can access the current tech and, you know, learn about it, get used to running it, without having to necessarily purchase that machine up front.
'Cause we knew with Proteus coming, that would likely, you know, impact people sort of timing. You know, do they wanna buy the current one and then turn around and buy a new one? That's sort of a natural sort of evolution that can happen. Some of this is also just people waiting. In terms of success, I'd say, you know, we learned a lot from the Platinum launch in terms of, you know, we went pretty broad as sort of a enabling technology with Platinum. Sort of, if you wanna try protein sequencing, here's a technology you could do it with, and sort of, you can try any application you want. That leads to, you know, some successes and some people having challenges. It leads to a certain sort of support infrastructure you have to build.
I think when we look at the launch of Proteus, you know, you really hear us talking about post-translational modifications. You hear us talking about single amino acid variants. You hear us talking about areas where there aren't good references, so you need to work in an unbiased or reference-free way. You're gonna see us do that 'cause we think that sort of depth of focus and concentration will lead to, you know, a more successful launch, sort of a smoother trajectory. We do expect it to build up sort of in a controlled way over, you know, the first few quarters and then really build that evidence base, have the users that can sort of reference other accounts, and then you can really see the inflection point and acceleration.
You know, not dissimilar to other companies who have come to the space with new technologies over the years.
With Proteus, you know, you actually have a master plan in the sense, you know, you not only want to start with Proteus, you want to grow it from, you know, 80 million wells to the Proteus Plus, which will be about 320 million wells, Proteus 2.0, you know, which could be up to 10 billion, right?
This is actually a multi-generation hardware cycle that, you know, you're starting off from here. For investors and also for people who would be using these machines, you know, what's the cadence that you are targeting, you know, as you grow this from Proteus all the way to, you know, Proteus 2.0, and, you know, would people just wait, saying like, "Let's wait for Proteus 2.0?
Sure. Yeah, I mean.
Should they start from here?
Yeah, I mean, there's always that question of you've given a glimpse into the long term. Will people wait? I think that maybe a good way to divide it is, the move to Proteus was to get onto a core architecture, in terms of the optics being in the instrument and the automation that's there, and then the consumable being the passive nanowell array. Getting to that architecture was required so that we could have sort of now a two-step sort of evolution. A lot of the improvements you talked about, you know, more throughput, a lot of these will actually come through a mix of chemistry improvements, some will come through software. Things like faster sequencing. Our standard sequencing time is 10 hours.
I can double the throughput of the machine if I lower that to five over time or further. We've shown data in the past around sequencing time, sort of sub two hours are feasible with improvements to the chemistry. We can improve the output by improving the number of single molecule reactions, something called sort of super-Poissonian loading, a sort of a techie term. Means getting a higher number of those wells in the array, delivering a quality sequencing result.
We can do most of what you're describing there for that, you know, Platinum or sorry, Proteus 2.0 through a mix of chemistry, some software, perhaps another embodiment of the consumable. It doesn't require any new piece of hardware. Anybody buying Proteus is gonna get that whole roadmap, you know, accessible through these other changes. To get to the sort of the billion scale, that's where the technology we shared last fall, controlled cleavage comes in.
What that means is we're able to control the sequencing reaction in such a way that you can then scan a much larger consumable. When, you know, at some point that platform comes out, that is a new platform, but we may also target perhaps a very different throughput in that machine. We're not sure yet. I think that's certainly a longer term roadmap item. I think anybody, any customer buying in now on Proteus is gonna have, you know, a pretty attractive multi-year sort of evolution coming through chemistry, software and, you know, maybe other consumable or reagent sort of approaches we might use that will be compatible in that machine.
The next couple of minutes, let's talk through Proteus, you know.
Okay.
Capabilities and also the strength that, you know, Proteus brings. you know, in your latest call, you know, you were talking about how Proteus has already delivered roughly twice, 2 x the, the read length that Platinum, you know, currently delivers, seeing a higher detection of amino acids and also the frequency.
So far to date, you know, what's the longest contiguous read length, you know, that has been achieved with Proteus?
Also, you know, how does that translate into the PTMs detection, you know?
Sure.
Flagging as the hallmark of this mission?
Sure. Maybe we'll start technology and then work down to and why does it matter for a customer. Technologically, when you think about protein sequencing, we sort of use the phrase coverage, and coverage sort of embodies a lot of things. It embodies, do you have the ability to detect an amino acid when it's there? There's a component of how frequently when it's there do you see it? Because there's a lot of complexity here to sort of the context of the sequence. There's how far into the peptide do you read? You mentioned the read length. We've really been focused on all three of those attributes over the last, you know, sort of two years as part of this program.
You mentioned we're now sequencing in our development lab at 17 amino acid detection. Again, there's 20 total. Part of that is we've also improved some of the existing recognizers that are in commercial kits, so the detection frequency is going up. The read length is a mix of, you know, just improvements to the chemistry, but also some fundamental advantages in terms of detection capability, signal-to-noise, how short of a pulse you can see with the Proteus technology that leads to that longer read.
You know, I think read length, we like to think about it in terms of average read length because there are scenarios where you have very short peptides and you read all the way through it before sequencing's over, and you have other examples where a protein may be digested into some really long pieces, so the sequencing could be longer. We try to have sort of quality control standards that we use, so every time we're improving the kit, we're measuring that and can see how the average coverage is changing or how the average read length is changing. To your point, apples to apples on a controlled set of samples, the read length on Platinum today tends to average around 8-10, and we're seeing, you know, closer to sort of an average of about 15 right now.
Why is that number important? Let's get to why it matters. You digest this protein into a bunch of small pieces called peptides. The enzyme we pick makes on average a 20 amino acid long peptide. Perfect would be average 20.
If we're already out at about, say that, about 15, about double the Platinum, we're getting a lot more information. Why do we care about coverage? Why do we care about read length? At the end of the day, if people wanna see a PTM, it doesn't mean that one's in the first position. Could be in the eighth, could be in the 12th, could be in the 20th. People want to see a variant, same sort of logic applies. People want to sequence something that doesn't have a good reference. They don't expect to get 100% coverage, but they're gonna wanna maximize the coverage they can see.
Anything we do on either of these three sort of pillars improves the number of scenarios, the number of cases that customers can use it for, the breadth of the coverage they get or the percentage of the PTMs they might be able to see. People doing biomarker discovery, doing translational work, trying to profile PTM patterns, these things will all lead to more complete sort of context of their analysis, you know, since we've been talking about PTMs, and One of the key differentiator, for Proteus against mass spec has been the detection of PTM.
That's what, you know, expecting that to be done. What specific PTMs, you know, can Proteus detect at large?
You know, versus, you know, what a mass spec can or cannot detect at this point?
You know, how, what are the plans to expand, you know, the PTM universe?
Sure. Maybe let's start mass spec. You know, phosphorylation, phosphoproteomics is probably the most well-studied area. There are a lot of, you know, sort of third-party software tools people can use to take the data off their mass spec and analyze, and ultimately resolve down to sort of site-specific, meaning I know there's phosphorylation at this amino acid. Once you leave phosphorylation, the availability of tools gets to be much more sparse. Then you start to be more where, what's the lab and do they have those in-house tools or not. If we look at Platinum, we haven't said exactly what we'll launch with on Proteus, but I wanna maybe make sort of a contrast. With Platinum, we have had people publish or present data on phosphorylation.
We've had people present on methylation, deamination, so sort of a people playing with a range, citrullination. On Platinum, you know, some of that is automated through the machine. In other cases, people have taken the data and come up with custom analysis tools. Our goal on Proteus is this is all baked into workflows with automated analysis tools. What we're excited about with Proteus is we've historically kept sort of three different ways of detecting PTMs moving forward until we were confident in which track we wanted to take for the long term. Two of the approaches require a lot of protein engineering, which are feasible, but they tend to be longer sort of R&D timelines with more R&D expense.
What we've just talked about on our recent call was we're really feeling good about the ability to detect these PTMs through the rich kinetic data that's there from the baseline sequencing reaction. More than just the amount of time the recognizer's there, the time in between binding events, the time in between cutting events, and just leveraging all of that with sort of modern AI tools, you can detect these small changes that come from PTMs. The value of that is if you're a customer and you buy Proteus, the next PTM, let's say we launch Proteus and it has, I don't know, 2 or 3 PTMs on, the fourth is a software update. It's not a new chemistry.
I think the key thing is doing it through kinetic signatures makes the sequencing universal, which means the extension of it comes through the data collection, the training of the database, and ultimately a software. We push a new software module into the cloud, the customer now gets the fourth or the fifth or the sixth. It's a much more extensible sort of path to add the capability and being universal also prevents us from some of the challenges you see with affinity methods where they need special probes for certain proteins. We feel good about that. I think we'll have a little more quantitative data to share soon in that regard, but we're sort of going all in at this point on the kinetic signature approach to doing the detection.
Okay. The next couple questions are on just on, like, the market itself.
Sure
You know, adoption and like that. I think the last year you presented a customer survey data. Where you said 65% of the study proposals are requiring to detect at least 2 PTMs. About 50% of them are requiring to do 3 PTMs or more. With that in mind, you know, when Proteus comes out at the end of this year you know, where do you think it's going to land, you know, in terms of which pool of these potential customers would you satisfy immediately? You know, the increase in terms of the detection, you know, is that I think you kind of answered it, is that going to be mostly on kind of kits and things like that or is it real proteomics expansion?
Yeah. We do have the data we've shared. We've also, as I mentioned, have had our sales force out since essentially late January after we brought them all together and trained them on Proteus, doing the awareness and sort of gathering of what customers wanna do with Proteus. I can tell you we've got now, you know, hundreds of data points from different users around the world on what they wanna do in terms of PTMs. I would tell you that PTMs is, sort of we're learning, it's really interesting, it splits sort of along two lines.
Are you working more in often a researcher, but they could be inside pharma, but if you're working in research, you're doing biomarker discovery, you're doing, you know, sort of translational research, there tends to be, you know, a set of PTMs that you would like to profile on every sample. Today, what happens in mass spec is most people do the phosphorylation, sort of the most commonly looked at, and then they may or may not continue that down to the other ones. If you look, people really wanna do phosphorylation with methylation and acetylation. They wanna do those three together, and you'll once in a while see a paper where a top-tier institute spent, you know, a month just analyzing one sample to profile all these different PTMs.
You know, if you're in that research space, there's that sort of combination of three that seem to really matter. The other ones are there in what they're looking for, it gets a little more sparse. If you go over into the other side, more in a manufacturing environment, you know, phosphorylation's still very common, but other ones are important as well. You tend to see more of a singular focus. I'm looking for a byproduct in manufacturing. I'm looking for something I would expect or not expect at a certain rate. You see a little more focused PTM work rather than, you know, more broad profiling work. That's one of the things we see in the data. In general, no one wants to buy, you know, an instrument to do one thing.
You know, you don't wanna buy a $400,000 or a $1 million platform to do one thing. I think that goes back to the second part of your question. Again, just to reiterate, with us using kinetic signatures, now the improvements to the chemistry or the improvements to the algorithms are simple upgrades to consumables and software, not a new piece of hardware. You know, customers investing with us, they'll have whatever capabilities we have on day one, but there'll be a roadmap to continue to expand that and they're not gonna need to buy a new piece of hardware, you know, or go find some software from some third party to do the analysis like they might in mass spec.
They'll be able to do that sort of through the tools we're providing or, you know, an upgraded reagent kit, you know, that might come out in the future.
Another interesting thing that in Investor and Analyst Day was, you know, put out the price point-
way ahead of time.
We did.
423,000 -
Yep.
As the list price. You know, you stated that there's not been much of a pushback yet from anybody. Also at the same time you have been doing sort of road shows, you know, not only in the United States, but in Europe other different cities in Europe. One, what do you think with not having any pushback, you know, there is a flexibility on the price going forward? You know, couple, you know, maybe not immediately, but, you know, down the line. Number two, when you do all these roadshows, you know, what sort of leads are you generating.
That, you know, when you launch at the end of this year, there could be a bonus effect, or what should investors expect?
Maybe start with we released the price, $425,000. We did that for two reasons. One was we had some customers who are on Platinum who have been using the technology for a couple of years. They're gonna wanna move. They wanted to know to just get working in their budget cycle. I also like that data point out there because if I have sales reps out prospecting, and I'm telling you about this great new thing I developed, but I don't tell you what it costs, it's probably pretty easy to get you excited 'cause you don't yet know what I'm gonna ask you to pay. It's more valid to have a price point. That said, we're not getting pushback, which means we probably didn't set it too high. It doesn't necessarily mean we didn't maybe set it a little low.
We'll find out. My experience in the industry says if you're, if you're low, you know, you can always over time step up the price to get to where you think it needs to be. The bigger problem you have is you go up too high, you don't have enough capability for that price point, and then the customer's like, "No, thank you." I think the weeding out of how right it is won't really come until people start to move into that next step, and that sort of speaks to what's gonna happen. I think, again, I've been in the industry for over 25 years. You get sort of above $250,000, people are going to want to see some data off the platform, probably send you a couple samples to analyze.
They're gonna have to run through a budget cycle. That could be a grant, that could be an internal capital expenditure. It could be a tender in an international location. Those are gonna range from 6-12 months. I don't think you're gonna get necessarily a bolus effect. What we're trying to do is use this awareness build now to turn into who's got samples that they wanna have evaluated so that we can open that access up over the next couple months. People will have data to start feeding into their budget cycles so that we have, you know, some number, not every budget request gets approved, but we wanna have sort of a consistent flow of those, so when we look out the 2027, we say, Okay, we wanna try to sell a certain number of instruments this quarter.
Do we have enough budgets that are gonna have a decision made in this quarter? We're thinking about it very methodically like that. I still think you'll see it, you know, transpire in a more logical and controlled way than a bolus, but the price point definitely gives you more valid information than being price-agnostic when everyone's excited. "Hey, that's cool. That's great." It's, you know, it's not gonna be free.
The other exciting thing or the other thing that you're doing in terms of getting ready for the launch is a summer program where you're bringing in a handful of sites. You know, you can kind of get a feel for how it is, you know. A part of it is also trying to get some publications from some of the KOLs.
Sure.
Who will be using it. What's the grand plan with that? You know, in terms of like, because there's a very short time between, you know, summer and launch day. Obviously, some of these publications will not make it in time.
Sure.
How do you use that knowledge internally? How much of that knowledge can be used externally in terms of detailing the product itself?
I think it will come in two steps. I think before we, you know, even before we place the machines in the end user environment, the ability for customers to send us samples and get sequencing processed by us and delivered back to them is the first step. Placing instruments in the end customer is because at the end of the day, customers wanna know somebody else ran it without you involved, and it worked. Our approach there will be to mix who we work with based on some being more academic in nature, such that they will publish and share their data. What I would say, it won't be exclusively that.
We may put some into more commercial environments where they might not, you know, publish their data, but they might be willing to speak about it. In terms of how the data gets out, I think we have multiple ways to do that. We could certainly, you know, sponsor a workshop at an industry conference or do that through an analyst or investor day, so that it can be presented even before it's able to be published. They could do posters or presentations at meetings. We've seen a lot of that with our Platinum machine. We think there's multiple ways to get data out, you know, sort of in advance of the launch, and then still have the flow through to the full peer review, on the backside of that.
Somewhat similar to what we've done with Platinum, and that's why we, you know, still have sort of a steady flow of those coming out on the Platinum device. I think that's how we, how we think about it. Then, you know, do we keep expanding that, you know, program a little bit all the way through the end of the year? You know, we haven't really decided, you know, how big to make that program yet, but we wanna do it. There's always a chance you learn something, that would, you know, maybe cause you to do something different before you launched. You know, hopefully by the time you get to that stage, you're not, you know, really learning something that's major.
You're learning maybe something about, like, a software feature or a workflow or a screen or something sort more minor that could be tweaked before you get to the market.
I know we crossed our timeline.
Yes.
Just in closing, with your financial strength at this point and, you know, with the launch coming up and obviously, your expenses, may be up, until the launch, how do you? You've been extremely smart about how to use cash. How are you thinking through this whole process with the current cash in the bank?
Sure. Today, we have a little over $190 million, which gives us cash into Q2 of 2028. Over the last couple years, we've taken advantage of that and, you know, sort of always erred on the side of being well-capitalized. Equally as much, we've been very stringent on cost controls. It's not, you know, it's not inexpensive to develop a new tech like this you know, we've held back in other areas to ensure we invest correctly in R&D. I think moving forward, we view two things. One is we'll continue to be, you know, sort of proactive and optimistic about capitalizing the company. I just don't know a different way to do it. Yeah, at the end of the day, you wanna be well-capitalized to execute on your plans and shoulder any sort of, you know, twist and turns in the road that you might encounter.
Also, we've talked about on the R&D front, you know, we have leveraged for Proteus several external partners in hardware, in software, in consumables, so that when we launch the platform, we have an ability to start to take out some of the R&D costs that are today being paid to partners, but when the development's over, that can come out. That gives us, you know, an envelope, you know, for some reduction, some redeployment into commercial. You know, we feel good about where we are. We'll continue to be optimistic, opportunistic if it, if it, if it arises. You know, with cash into Q2 of 2028, that's essentially 1.5 years from launch. There'll obviously be a lot of value creation in that timeframe.
We'll sort of assess what's the best, you know, sort of approach at that point.
Thank you. Thank you very much, Jeff.
Thanks for having us.