Seer, Inc. (SEER) Earnings Call Transcript & Summary

Hello, everyone. Good morning. I'm Tejas Savant and I cover the Life Science Tools and Diagnostics sector here at Morgan Stanley. Before we begin, important disclosures. Please see the Morgan Stanley research disclosure website at morganstanley.com/researchdisclosures. And if you have any questions, do reach out to your sales rep. So it's my pleasure this morning to host here. And on behalf of the company, we have Omid Farokhzad CEO; and David Horn, CFO. So thanks, gents, for spending the next 30 minutes with me. Omid, I want to start at a high level, right? So despite some of the near-term headwinds, which we get to later in the chat, I want to talk to you about sort of what's gone right over the last 12 months for SEER? At an industry level, the excitement around multiomic in general and proteomic specifically is tappable. What are you most proud of in terms of what you've accomplished since IPO?

Okay. Number one, we built a great team, and I'm super proud of that group of people like minded that came together to execute. Number two, we aim to put a product together that could give us access to the Proteo in an untargeted unbiased hypothesis freeway. In ways that would open up a new gateway to the Proteo. It's important for context that before that instrument was put in hands at a customer, if you try to do proteomics in an unbiased way, deeply, meaning you begin to see low concentration proteins in the biological samples, that you are limited. You have to make -- you have to compromise between how deep you wanted to go because the workflows were tough. And then what high studies you could do in the studies were all under powered by market discovery with a challenge. For context, the deepest plasma proteomic study published before our instrument went out the door or the study with 48 samples, it's the biggest. The deepest study, meaning the number of proteins that they could identify was 4,500 proteins that were studied out on the road, okay. So then we shipped the first instrument, and by the way, as of the end of last quarter, an average customer has had our instrument in their hand, just about 1 year, 3.9 quarter to be exact. So most customers have had on the average about a year of the instrument use. And so what are we seeing? Number one, -- we've now had multiple customers complete studies of 1,000-plus, one customer Proteomic starting studies of 15,000, gets a protein coverage in plasma has now reached 8,000. At the ASMS conference in June, when federal officials launched their new mass spec, Orbitrap Astral, they actually exemplified the performance of that instrument with the Proteograph and they showed in their own data that they get to 6,000 protein with 45 minutes per sample mass spec time. I mean these are just levels of performance that were fundamentally not possible, highly disruptive, I think what's possible. So I'm very proud of the way the instrument is performing and as a customer and how easy it is to use and the data that's being generated. And -- but we're earning this game and the path from where we are to really putting proteome in an unbiased and have the customer the way access is available in genomics, that path isn't going to be a straight shot, what we're plowing through it, and I'm very optimistic about our opportunities ahead.

Got it. So on that last point to me on the last earnings call, you talked about sort of the 3 factors that represent mainly key barriers to adoption for the Proteograph. Maybe just starting with access to mass spec, right? That's probably the most important one. You've identified that as one of the key barriers. Can you remind us what the installed base for mass spec worldwide looks like? And what fraction of this installed base are using mass spec for biological use cases?

The installed base, I don't have updated information as of now, but the number a couple of years ago were 50,000 growing at an 8% CAGR -- 50,000 globally growing at an 8% CAGR, and about 1/3 of them are used for proteomic applications. Within that segment that are used for proteomic applications, about 1/3 of that are kind of involved in the -- if you would, prior to see the tinkering that goes on to kind of try to get workflows that goes deep into the proteome for various different applications, whether it's plasma or tissue or cells, et cetera. That's the mass spec talking about this. Then if I look at -- what does that mean for us? And what have we learned in this process. We had always expected that our customer base in on our TAM would be a combination of genomic and proteomic. In fact, early on, right after the IPO, if you remember, we did partnership with ThermoFisher with Bruker with SCIEX because our value proposition to those providers was that we expect to put the Proteograph in the hands of genomic customers that are not mass spec users and in fact, them accessing our instrument will open up entire market for you that you don't even tap into today in addition to the traditional proteomic market that you're in. So we had identified those 2 customer types from the very, very get-go. But what did we learn as we went to the market? Here's what we learned. Number one, the proteomic customer -- this is a mass spec user that does proteomic work. Because they could never scale, as I mentioned, the first -- the biggest study was 48 samples, the biggest was 4,500 proteins. And by the way, in that study of 4,500 proteins, that was a 16 sample study. Because they could never scale, they don't have freezers full of samples. Also, their business model, in large part, is that they would invest a lot of money on CapEx, and then they didn't really have a consumable business. So they weren't used to this OpEx continuous paying for samples. And the way they worked was they had FTE costs and those people kind of give fractionation depletion in the seems of time. But that's the way those labs are operated without samples really. So now here we come -- and by the way, and the uniqueness of each of those labs and the publications that will come from it was the very unique nature of their workflow, and they all different from each other. And so we did this method of depletion, and that method of fractionation as somebody says differently and they will go deeper and deeper or doing it a different way. Now here comes SEER with an instrument that takes the black art out, standardizes the process, automates it and gets through depth throughput and precision and reproducibility that is totally disruptive and takes away all of those uniqueness of these different workflows and just fundamentally stabilizes it. Okay. Now -- but those folks don't really have samples because now you're letting a mass spec user that the biggest study would do would [indiscernible] samples. So okay, now you're ready to go with thousands of samples. They just don't have freezers full of samples. Now that same lab that did the deepest paper, they just presented data at the ASMS. The deepest paper that was published in 2017 was 16 samples, 4,500 proteins. Now they did a 300 sample study going to a depth of 6,000 proteins. For them going from 16 to 300 is a big step-up. But we're actually interested in customers that can do thousands or tens of thousands of samples -- We want to see papers get published equivalent to the U.K. BB Nature paper worth 450,000 exomes were sequence that 450,000 deep unbiased proteomic has got done. With the latest product launch the Proteograph FXT, you really do now are able to impede and match the throughput by which we access proteome and genome with CapEx that is modest that most organizations that are scaled in that way can actually tolerate and do. Okay. So then what about the genomic customer? The genomic customer actually doesn't really care about what the protocol is, whether it's a mass spec or not, mass spec is complicated for them. They're users of biological data. They just want their data, and they don't want the complexity of -- I have this amount of [indiscernible] chromatography time with this. They just want what is the biological insight that comes. Given those 2 different customer segments and what we learned in the market, I think commercially, we've now adjusted. I'm very proud of the team in the way they saw the opportunity and the shortcomings of the way we were viewing it, but also the differences in their viewpoints with their constraints and addressing them. First, we were always adamant about wanting to have a distributed commercial model where we would place instruments and consumables, and we wanted the instruments everywhere. We did not want to be in a service business. We're still don't buy [indiscernible]. That said, in order to catalyze the community, we need peer review publications. So you've got to fill the top of the funnel with as much data as possible. So on the bottom, papers come out because that is so important. In order to fill the top of the funnel with data because the data that's coming out that I have visibility to now as a customer, just fantastic. But in order to fill the top of the funnel more, we installed the stack, which is a SEER Technology Access Center. The stack essentially does 2 things. One, it gives end-to-end solution to a genomic customer that typically does work on a service basis. And they send us a sample, we send them data back, simple as that. But also for the same genomic customer, we provide what is called an XTM service, our latest product release was Proteograph XT. The XTM is that we sell a kit to a customer and that kit, the price point of that kit is designed that the customer can have a Proteograph. They can run their own Proteograph. But as part of that price, they send the peptides back us here, and we run the mass spec for them. Again, this is all to lower the barrier for the genomic person to get hooked on the data. Because once you understand the value proposition of the content that comes from the Proteograph in an unbiased way, I think necessity is the mother of all invention. Once the genomic customer knows what that means for them, they will figure out a way to bring the mass spec in or the mass spec guys will figure out a way to provide that for the genomic -- the right level, and we want to facilitate that. For the mass spec guys, that doesn't really have a lot of the samples, and we want them to engage, by the way, that applies to the both customer type. We also introduced the SIP, and I will have David comment on it. And the SIP, essentially the strategic placement of the instrument where we place the instrument but is tied to consumable purchase upfront. We screen folks that are able to run samples. The motivation is that you've got to publish your studies. And then those are customers are screened in order to be able to buy the instrument later, but it's really again to lower the barrier, all of that sets. Every piece of evidence, by that, I mean scientific data that I see coming from any customer that use the Proteograph, totally makes me feel great about what is possible once access to unbiased proteomic becomes readily prevalent. And my prediction for you and I made the same production a couple of months ago, is that within 18 months -- so by that, I mean, by the way, a couple of months have gone by since the last time I made a prediction. So I'll stick to it within 15 months, by the end of 2024, I predict the first population scale, deep unbias proteomic study will either be done or at least well underway. And I think the content value of that would be unprecedented.

Got it. I mean we love forward-looking statements, me.

You can keep my [indiscernible] to file that.

I guess the question is, how confident are you that it will be on the Proteograph? And as a follow-up to that, I mean, you had user presentations, you're actually at MS a little while ago. And we got a lot of follow-up out of that around like could someone like [indiscernible] perhaps like run a bunch of U.K. Biobank, scale, samples or perhaps samples from the UK Biobank on the Proteograph?

Yes. Tejas. We're seeing an enormous amount of innovation on the mass spec side. If you look at the Orbitrap Astral or even the Bruker HT, I mean, these instruments are doing so much more than they could before. But if you just look at the Thermo Fisher data that was presented at the ASMS, the Astral, when you do a direct injection or something like plasma, so what would, for example, comes from the U.K. BB sample. They get to a depth of 675 proteins in their own study. Now 675 proteins in plasma in a single injection on mass spec is phenomenal. It's just so different than 3, 4 years ago if you would look at on bioproteomic but then the same instrument, you put a proteographXT in front of it, and suddenly, the plasma produces 6000 proteins. And we now have customers that have used the combination of those 2 and are getting to 8,000 proteins because of the sample size increases, content also increases. So the thing is I don't see a path today to a commercially viable, scalable solution. None that I have any visibility to today nor if I look over my shoulder and I look backward. That can do a study like unbiased proteomic that does not involve the Proteograph.

Got it. That's great. So I mean, I want to spend a little bit of time on XT. I mean, obviously, you've mentioned it a couple of times already. Can you just give us an overview of how the assay improves upon the performance of your original Proteograph speed? And just feedback you've heard from early adopters to date?

Yes. When we launched the first product, the Proteograph assay, it involved 5 injections for samples. And it's because every sample was splitted into 5 wells. Those wells had our proprietary gene nanoparticles in them. And then those 5 wells each went into a mass spec at a separate injection at 30 minutes each, total time was 2.5 hours of injection time. That was a very significant step-up to what was possible before in terms of depth and throughput. What DXP does is, it further increases the throughput by more than 2x to 1.5 to be exact. You go from 5 wells per injection down to 2. And so it's 2 injections per sample type. Now the -- if you look at the Orbitrap Astral data that I shared with you that 6,000 proteins, that is actually done on 22 minutes per injection. So the total sample injection time is 45 minutes, right? So 1 Astral and 1 Proteograph together now can generate 10,000 samples deep unbased proteomic per year. That is not a big commitment in terms of infrastructure to the large scale. What that means is if you now want to run 50,000 samples, that's 5x of that. And so we're definitely getting to a point where you should be able to do large-scale population scale studies deep. With that number of proteins, the kinds of precisions that just are phenomenal, it gives you so much power in terms of discovery for biomarker changes in proteomic signatures. And so that is how DXP operated. It reduced the throughput preserving the content and preserving the precision of the assay.

Got it. Makes sense. Do you think the price tag of the Astral is a bit of a gating factor to seeing that elasticity of demand play out on the consumables side for you guys?

I think the Astral is a very expensive instrument. But I think the performance of the Astral is also highly differentiated. Obviously, we have Astral in the lab. We have 3 of them in our lab. And my expectation is that the value of the content will drive demand. I mean, I know genomic prices are down, and we've now sequenced the million genome 10 million exomes. But you guys have all been around the block for a long time. Just look back a few years, it cost 10 times the amount to do a genome sequencing and exome sequencing and people get paid because the value of that content at the time was appropriate. I think that we don't need to chase the price down, we need to demonstrate the value of the content. I think the value of the content will speak for itself. And that said, until the value becomes clear, it is a bit of a price war, but I think once you're differentiated, then I think it stands on its own to feed. Now for a customer that doesn't want to spend the money on an Astral or Orbitrap Astral, the Bruker HT that's also an exceptional performance at a much lower price point.

Got it. I want to talk about the strategy for getting more publications out there because that's clearly one of the gating factors to broader adoption. Collaborations are something that a lot of companies use to spark these publications from key opinion leaders. Can you just tell how you utilized that strategy? And what is the cadence of publications that you're expecting between now and perhaps the next 6 to 9 months or so?

Yes. Tejas, it's always -- I mean having spent most of my life in the academic world where your life is writing [indiscernible] writing papers. It's always difficult to [indiscernible] process from a publication perspective, I'm not doing it now because I'm not, as you're a public company, but our customers are doing it that are trying to publish this. There is an inverse correlation usually other than the COVID time with regards to speed to publish and the quality of journals. If you want to go to the top journals, premium process takes longer, you can do multiple cycles. If you go to a lower quality, you can get things published very quickly. And some of our customers that are now in the manuscript submission process or review process, some of whom figures you know, have targeted very, very high-level journals. And by the way, appropriately so, because the types of content that they're trying to publish was never possible, and so they're aiming high. You can imagine those top 1, 2 journals in the scientific community. We have a customer that's in the revision phase there. We have another customer, another one of the sister journal of the same journal that just got reviews back from a reviewer that is very positive, and so I expect that paper be published. But the cycle is long. And from my perspective, I sit here pulling my hair, waiting for these things to come because every one of them that I'm aware of the data is just fantastic content. It's just the process is long. Now keep in mind though, as I said, that the average customer has had a Proteograph products within their hand in exactly 3.9 quarter, 1 year. It takes time to do a study to then go through a [indiscernible] published. We're early. We have not been around as an organization commercially since 2000 or 2004. We shipped our first instrument 2 years and 8 months ago, and an average instrument has our customer in a year. But what's coming and the visibility I have is, the content is great. My expectation is that we'll begin to see at least 2 or 3 publications from customers. In prereview publications this year, and then the visibility I have to the pipeline is about 11 or 12 papers that are in various stages. So I think those will then trickle into 2024 in terms of when they'll come out.

Got it. Is there a possibility that we might see some of those in preprint form before they make it out?

Yes. There's a -- actually there's 2 of them in bioarchive. One, looked at, for example, other than biofluids or liquid samples, looked at skeletal muscle, tissue and looked at tissue with a Protograph, and that's interesting because remember what the Proteograph does is, it compresses the dynamic [indiscernible] in tissues like muscle where most of your proteins are 1 protein or 2 proteins, which is like myocyte than -- that to be able to decrease the compression of the dynamic into the lower bundle protein, probably adds a lot of value for that. And so that paper is looking at exemplification of tissue, scale of a muscle. That's a customer from Auburn University. That's now on bioachieving, that paper is in the review. And then cost and service paper, which is looking at PQTL is a bioarchive. That paper, by the way, their review came back that was -- that I alluded to, that looks really good. So I expect that paper to come out in a period for, hopefully, by the end of the year.

Got it. Got it. Switching to macro. I mean, obviously, we talked about sort of the cautious spending from customers. And you mentioned SIP as one of the initiatives that you've launched in response to that to avoid the upfront CapEx involved. Tell us more about how you go about sort of selecting appropriate sites to be part of SIP. And how do you see the mix between SIP placements and straight-up placements evolving over the course of the next 12 months?

Yes, Tejas. Thanks for that. In terms of the SIP, you're exactly right. What we've tried to do is, look at kind of these key opinion leaders and folks that we think, have projects in near term because again, getting back to the -- we're trying to put as much data on the top of the funnel for folks. And just given that some of the both the academic folks and increasingly some of the commercial folks have constraints on their capital budgets at least this year as they think about budgeting for next year or they're in the grant cycle still in that process. What we've said is purchase consumables upfront. So if you purchase a certain amount of consumables upfront, we will then loan the instrument for the period of time up to 12 months. And then they have projects that we expect the consumable pull-through to work through with that instrument. And then certainly, as Omid said, they have -- we expect they have the intent to purchase that instrument and -- so really, it's a budget cycle type loan program where you're able to place the instrument, therefore, they can get started on those projects and start to produce that data and then work through the budget process on their end to ultimately purchase the instrument.

Got it. I mean are you starting to get sort of customer inbound interested in looking at SIP and accessing the platform that way?

Yes. We've been pretty successful with that in terms of there's been good uptake around that. And I would say, certainly, we've seen some traction in that -- we really kind of launched it in the first quarter. So we've seen quite a number of placements under the SIP program.

Got it. Is the consumables package that customers have to commit to before they participate in, is that standard? Or does it sort of depend by customer, their circumstances and how large of a commitment they're willing to make?

Yes, that generally -- we kind of work with them to determine what size of projects they're looking at, what types of commitment. There is a threshold, by under which we won't go below, right. So there's a minimum that we would consider it. And so certainly, that's a consideration as we evaluate the customers.

Got it. One of the questions we've done, David, following the launch of SIP is, just, passing the SIP placements out from straight up placements, right? And to your point, I mean, over the long term, it doesn't matter because of the installed base and that drives consumables growth. But at least for the next year or two, would you consider sort of separating those out when you update the installed base at the end of the year?

Yes. I think we'll certainly give you some qualitative feedback around what that is. And I can tell you now Tejas. It's around -- it's been around half, in terms of this year, in terms of what we're looking at.

Switching to China. I mean, obviously, a topic of great interest in this conference and varying levels of bearishness around it around some people calling it sort of a structural reset, others saying it's more of a temporary thing. Talk to us about what you're seeing in China in July and August, Omid?

It's been soft Tejas. It's been soft, little activity, certainly a lot more than we had expected. And I don't have visibility to get -- actually changing the balance of the year for us either. David, do you want to add to that? .

No. Again, I think we certainly expected more. And again, we just haven't seen the demand come through China.

Just in terms of the conversations you have there, like, in the sense that, this reset is here to stay and then 2024 might see a continuation of the trend? Or is it more a situation of you don't know where the bottom will be over the next 2 quarters or it will be 3 quarters?

Yes. Look, I don't think -- that's the part of the frustrating part, though, with our partner in light over there. We do have discussions with them. But they don't have a ton of visibility. They're optimistic but -- so that hopefully next year is more encouraging, but that's part of the frustration on our part too. It is just a lack of visibility. When did it turn? And again, we're small, right? But just from what we've seen, again, a lot of conversations going on, but unclear as to when the funds will free up for that.

And just more broadly at a global level, Omid, biopharma versus academic, how are the trends looking there? I mean on the one hand, you have the funding headwinds, on the other hand, this year seems to be okay, but there are some concerns around potentially a low to mid-single-digit decline in academic budgets next year. So just walk us through what you see in both of those end markets.

Yes. The pipeline in terms of demand is almost split in the middle. If I look at sale, it's probably 2/3, 1/3, commercial and academic. And then if I look at pull-through very strongly towards commercial versus academic. And keep one thing in mind, which is academic entities usually run on grants. When you submit grants, usually you cite references and papers to support your methodology and approaches. There's just a paucity of data today to really support even an academic grant process. But we have [indiscernible] , we have placed instruments in several high-level academic labs. And one of the strategic changes we made is that there are academic labs that are also hardly -- largely supported by philanthropic approach sources. We're targeting some of those. By the way, they just happen to also be kind of like genomically oriented academic labs. . And so we are now beginning to place and there is one very significant one that is happening in this quarter placement into labs that are genomically focused academic labs that are heavily stacked with samples, very used to running very large scale studies and they're funding it in your traditional kind of government grant funding like.

To finish on an optimistic note, right? So you've talked in the past, Omid, about increasing the content on the platform, reducing sample input volume and throughput, you've got the third part now via XT. So when can we expect to hear improvements on the other 2 dimensions? .

Yes. Tejas, the sample volume part of it is actually something that we're working on. In fact, I was in the therapeutic space for most of my life and in the therapeutic space, it's almost like a label extension, if you would, in terms of once you get a drug approved, you don't get it for other indications. Even for XT, our Apps Lab has exemplified the use of that with lower volume. So we're going to do lower volume, not as a new product per se. I think it's too big of a -- for me, a product needs to be really radically different. So sample volume, to me, is an exemplification that we're doing on Aps Lab, You should expect that in DXP already happened. We'll write white paper on it and will support customers to lower volume on DXP. I think you can also expect from us that will do about a cadence of a product a year. For me, product is could be -- by the way, an instrument could be -- it could be a software. But if it's a software, it needs to be radically different. Like, for example, on the 18th of this month, will do a software update. It's a pretty important update, but I don't consider new products. So if it's a software, it will be dramatic. If it's a hardware, it will be something very different. Just to give you a context, we're not doing hardware upgrades for the DXP, but I don't consider that a new product. So if it -- so for me to call something a new product the magnitude of the impact it needs to have -- has to be as different, for example, as DXP one. And you can expect about one of those per year. And I think it's also fair to say that the next product will be kind of a content focused product.

Got it. Yes. Fair enough. We're out of time. So that's a great place to leave it out. Thank you so much.

Thank you so much Tejas.

Appreciate it.