Exhibit 99.3
SomaLogic Investor
Presentation Transcript | Transcribed By: FINSIGHT 530 7th Avenue New York, NY 10018 |
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Keith Meister
Slide 1
Good morning and welcome. We are thrilled to be here today to announce the business combination transaction between CM Life Sciences II and SomaLogic. I am Keith Meister, the Chairman of CM Life Sciences II. I am joined on the call today by Eli Casdin, our Chief Executive Officer and by Dr Roy Smythe, the Chief Executive Officer of SomaLogic. Let me begin by giving some background for those of you who are not familiar with what the mandate for CM Life Sciences II was.
Slide 5
We raised the $276M SPAC with the view of helping lend our capital, our industry expertise, and our board of directors and their expertise, to a company to help accelerate its growth and position it to succeed as a public company. Our view was by lending Casdin Capital skills and expertise as one of the leading life sciences investors, and my expertise around capital formation and helping businesses use the public market to accelerate growth; one plus one would be equal to more than two. That Eli and myself could come together and be good partners to help a business grow.
We specifically targeted the life sciences space with the view that the Casdin team had unique expertise, that we would be a partner of choice given the ecosystem of companies with whom we have relationships, and that the space was going through profound and rapid change. There were so many wonderful businesses that were growing at a rapid rate, leveraging technology for growth, and that if we could combine our capital, we could help accelerate these businesses in their path to growth. In addition to our capital lending expertise, our board members who are leaders in industry, would help drive value and help position a company to succeed as a public company.
We are thrilled that we found SomaLogic as a partner and when we think of what our mission was, which is to help a company with $1 billion or $2 billion value with a world class entrepreneurial management team who doesn’t want to sell out but wants to partner with the public markets to grow from a $1 or $2 billion business, to $5 and then $10 and then $20 billion public company, we could not have found a better partner than SomaLogic and Roy. I’m confident when you hear from Roy today, that you’re going to be equally as excited. My partner Eli has unique knowledge of SomaLogic. He led their most recent round of financing and has served on their board of directors. This knowledge of the company and the proteomics space, and the relationship he’s been able to develop with Roy and his team, served as a great competitive advantage as we came together as a partner of choice to help SomaLogic become a public company.
What we thought we would do today is, I turn it over to Eli, who would spend a few minutes sharing his perspectives on the company and the industry and why this is a great company for CM Life Sciences II to partner with and then we’ll turn the show over to Roy, so you can hear about this wonderful business, Eli.
Eli Casdin
Thanks Keith, for that introduction and good morning everyone and glad to be here. We are really excited about this merger with SomaLogic and CM Life Sciences II. We know that with the group of board members that are joining and the huge influx of capital, it will allow a company that has been 20 years in the making and is now really just transitioning into a commercial stage enterprise, really accelerate its growth and the impact, not just on the field of proteomics but the clinical market for improved disease identification and management.
We have known SomaLogic for a long time but became investors last year when we led their Series A. It is always a little bit interesting to call it a Series A when you are investing in a company that’s 20 years old and had about $500M already invested in it. And what we saw, as we had seen early on in the field of genomics, the maturation of a field of proteomics that has an order of magnitude bigger potential to impact human life and we’ve been looking for a dominant platform company that could not only bridge the enablers but also take the information and data base of proteomics and leverage it into clinical applications. SomaLogic was an obvious choice and Roy was an obvious person to back, having been a very successful physician scientist and a commercial and strategic leader at Phillips as CMO and strategy leader there. In that Series A, the vision was to enable Roy to invest in the commercial enterprise and grow this business in the diagnostic market.
The purpose of this SPAC transaction is to just throw gasoline on that already lit fire. At the end of this transaction, Roy will have over $650M in capital on his balance sheet, world class board including Kevin Conroy, CEO of Exact Sciences, Troy Cox former CEO of Foundation Medicine and behind the commercial enterprise at Genentech, Steve Quake, a renowned technologist in the field of tools and diagnostics and this collective group will take this company forward in a way that could not have been done without this capital infusion. So we are very excited to have; Roy will lead you through the presentation and along the way, I may have some comments.
Roy Smythe
Slide 6
Great, well thank you Keith and Eli for the introduction and for your confidence in the company and your desire to join us. We are very excited about the partnership as well.
If you look at slide 6, there are 5 hexagons that I think help to demonstrate how you might want to think about SomaLogic at the highest level. We are an evolutionarily advanced platform, and that platform has technology like other proteomics companies do, but ours is unique and proprietary and has technical specifications and abilities that others do not. But we also have the world’s largest and only clinical proteomics database with more than 500,000 samples, about half of which have clinical data and then we’ve developed a bioinformatics capability to look at that data and create derivative products over the last decade.
We are market validated. We are not conceptual about what we are planning to do in the market; we are in the market, we have more than 300 customers and collaborators. We generated almost $60M in revenue this past year. And we’ve actually been moved up the food chain by some of those customers. One example is that Novartis has moved us from being a discovery tool, to a clinical drug development tool that requires regulatory audits on data and so forth. So not only are we in the market and researching biopharma, but we have actually moved up the food chain in that market as well.
We are an enablement company, so our technology like other proteomics technologies identifies and measures proteins. But we can measure 4.5x more proteins than any other commercial enterprise in the world. We currently measure 7000. We are on the way to 10,000 and there is no limit to the number of proteins we can measure on this platform over time.
But the unique thing is that were not just an enablement company, and this is where most of the market is currently, we are also a company that’s begun to create actual clinical applications by using the data that we have been looking at for the last decade in the database.
We have created and launched a pipeline of first in class high-plex protein powder recognition diagnostic tests with very special characteristics that I will describe later, and these are in market, they’re in people’s hands in a demonstration market, and we are working with the organized medicine and health systems to help them better understand these so that we can broaden access and distribution over the next few years.
And lastly, it may be a bit seductive to think about a 20-year-old company as having a technology that’s stale when you compare it to other new entrants in proteomics that we’re all aware of. But by then, a scientific historical accident perhaps, and our founder would say that it is no accident, we use nucleic acid as our protein capture identification to measure and analyze. And so, the technology trajectory for this platform is significant in regard to moving off of our current measurement readout system onto alternative systems like alternative arrays and next generation sequencing and also even using our re-agents and the newer protein sequencing and fingerprinting technologies as well; so the trajectory of this technology is still significant. Most of the benefits of using nucleic acids are still in front of us despite 20 years of effort.
Slide 7
On slide 7, I think it is important to note that people have known for several decades that proteins would be the most informative source of clinical data, power precision health and individual health. However, the problem has been measurement. Proteins are dynamic; they change over time. Proteome turns over about 40,000 times during your life. DNA obviously has a lot of information embedded in it, but it is not a dynamic construct. Messenger RNA is more dynamic but it only correlates about 50 to 60% of the time with the actual structural and functional molecules of life the DNA codes for and those are proteins. The problem again has been not being able to measure enough of the numerator of the denominator of 20,000 canonical protein structures to attain a signal and then interpret that signal until SomaLogic.
Slide 8
We know the market on slide 8 is very large for proteomics. We think about the evolution of genomics over the last 25 years where you went form measuring one gene to many genes to all genes and along the way, data was created and looked at and people realize that that data being generated was the raw material for clinical application products. And as that happened, the genomics pie grew from a very small pie from when I was a post-doc to the large pie it is today with both enablers and applications companies involved and some that straddle. The same thing is going to happen to proteomics. It is a very large market now with a research component, basic research and discovery component, with a biopharma component, with a diagnostics component and we actually believe over time, there will be a direct-to-consumer component. This market is going to grow just like genomics did, and the end market could be even larger.
Slide 9
So, on slide 9, you can see that there are a number of things that are needed in order to capture market share in that growing market over time, and these are on the left-hand side of the slide. These include technical specifications, that your enabling technology might demonstrate such as you need to obviously be able to measure lots of proteins. As I mentioned, we measure 4.5x everyone else in the market currently. You need to measure across concentration; you need to be able to see those proteins that are common and those that are less common, in lower concentrations, and obviously it has to be scalable, reproducible, your coefficient and variance has to be good, cost has to be appropriate. But in addition to these technical specifications and these commercial capabilities for enablement, if you want to create clinical applications, you have got to be able to do things like create a database and then know how to look at that data to create clinical applications over time.
In the middle of the slide, you can see where most of the industry is today. While some of these are partially met, many of these needs are not being met by the vast majority of those in the commercial space today. And in the right-hand side of the slide, you can see that we actually at SomaLogic have met both these technical and commercial scaling needs and also have met the needs that are required to develop clinical applications and increase your market share over time.
Slide 10
So, on slide 10, what this has given us is a significant first mover advantage in the market, a market that is going to grow. As mentioned earlier, we’ve been around for 20 years and I think it’s a testament to the founders and the scientists that have been at SomaLogic for the last 20 years. I don’t know if there are other companies like this that have been around this long without actually having gone to Wall Street to support further growth. But we are certainly there now, we spent $550M of total money invested between strategic investors over time in our Series A that closed last year. As was mentioned, we have real products, real customers, and real revenue. $55M last year in the biopharma and research market; more than 300 customers over time have been on the platform and collaborators. But on the left-hand side, we’ve got this proprietary database that no one else has. Almost a half a million samples, half of which have clinical data. We have 250 publications over time, some of these are leading publications or landmark publications in proteomics; these aren’t just references to using our technology in the methods section. And then, in addition to all of this technical work in this biopharma market and the creation of the database, we have begun at the bottom, to work on a pipeline of more than 100 first in class protein pattern recognition tests with a significant, specific and unique capabilities. And 20 of these have actually been validated or are in our demonstration market now and we will be taking this forward in our health system work over the next year; it’s not conceptual.
Slide 11
Our platform on slide 11 consists of these 3 components. We have a proprietary technology as I mentioned earlier. We use aptamers, small pieces of nucleic acid that by dint of their 3D conformational shape and solution, bind to protein shapes and allow us to identify them and quantify them. We measure 7000 going to 10,000 in 2022. Our technical specifications are as good or better than anyone else in the market. And there are a very large number of potential shapes based on the number of aptamers that can be created that allow us to measure the entire proteome over time.
We have the largest proteomics database in the world; this is a unique asset. As I mentioned we are at almost a half a million samples. We also have the ability over the next few years to grow this database substantially and I will talk about that as we move forward with biobank contracts and as I mentioned, clinical partnerships that are being developed this year with 4 large healthcare delivery systems and more to add as the year goes on. This is not only where we create our clinical product, but it is also an unmatched resource for our regulatory filings moving forward.
Perhaps most important of all, we have been looking at protein data for a decade. We have a unique bioinformatics stack; we have a tools group, we have a production group, we have an A.I. group, we have bespoke tools, and this allows us to take protein data in ways that others cannot currently and create clinical applications of both financial and human value.
Slide 12
On slide 12, we will just talk about these commercial characteristics that are required to have a successful and scalable proteomics business. On the left-hand side of the bar graph you can see what is required: breadth, depth, speed, cost, scalability, reproducibility, low sample size, and detection capabilities. And the traditional ways of measuring proteins using antibodies or mass spec or nanobeam and mass spec, it currently falls down in some of these areas. We anticipate this will get better over time and of course we’re going to be improving our technology over time as well. We do believe protein sequencing is interesting. The ability to put you know, a small 12 by 12 inch box on a desktop and perhaps measure hundreds of proteins in an hour. In the future, we do think there will be some unique characteristics here that we might want to be involved in. When we talk about organic inorganic plans later, I will show you what our plans are there, but we do believe that protein sequencing and fingerprinting is several years away from significant market penetration.
Slide 13
So on slide 13, this is really the way the business works and I believe this could be considered a real virtuous cycle. So, the platform in the middle of the slide is obviously how everything starts. We created first the Biopharma and Research businesses which is where $55M in revenue was generated last year and where most of our growth over the next 2 years will come. Biopharma and Research clients have validated our base line technology. That technology will create increasing numbers of applications later on. This facilitates not only FDA recognition and approval but also clinical uptake. We have actually in our first set of these protein pattern recognition diagnostic tests are being used as RUO tools and biopharma clinical trials. As they are used by the customers on this side of the circle, this will also facilitate clinical uptake overtime. As I mentioned we are moving into this emerging clinical diagnostics business. We collect data from biopharma customers. We will be collecting data from our clinical customers. We also still have access to a number of biobanks we haven’t even assayed the samples yet to put them into the database and then we plan to access more about banks over the next several years. These 3 sources of data feed the database, the database feeds the development of clinical products and the cycle therefore is quite virtuous moving forward.
Slide 14
On slide 14, probably the most important consideration from this sort of introductory part of the talk and that is. As I mentioned earlier, the evolution of genomics you know moving from one gene to many genes to all genes, the creation of large amounts of data that were then taken and developed into clinical applications and products, the same thing is happening to proteomics. However, we are the only company that has moved down the evolutionary scale to this fairly aggressive development of clinical applications and new diagnostics.
Everyone in the space is an enabler. We continue to invest in our enablement technology so that we won’t fall behind anyone there over time. You know if you think about what’s going to happen in proteomics over the next 10 to 15 years, we believe that it’s quite likely that they’ll be somewhat of the convergence over the next 10 to 15 years in measurement and quantification enablement capabilities. That’s what’s happened in genomics overtime, but the winners are going to be those that have moved down the evolutionary scale, that have created derivative products, that have developed partnerships for those derivative products. You know things like proteomic genomic combined tests and products and other partnerships, and this is where we plan to win over time. We are doubling down on this comprehensive strategy and that is why we are excited to have the involvement of the SPAC to help us do that.
Eli Casdin
This is a very important slide, and what really defines the opportunity set here, which is that for the last decade in life sciences, genomics has driven innovation, and both from the enabling side like the Illumina’s of the world and in the application side, the diagnostic of application companies, those like Adaptive that are taking this information and applying it to both diagnostics and drug development. This TAM you see in genomics is as large and potentially larger in proteomics because the proteins, while DNA is the source code of life, the proteins explain that source code and are the business end of biology. Understanding them and reading them as proteomics enablers do, is going to create a tremendous amount of value, and therefore the applications of them are where you are going to draw a lot of revenue, and SomaLogic is the only company in that space. So, when we talked in the beginning about this being a platform or a platform company, this is exactly what we mean and what we see in the market.
Roy Smythe
Yeah I would agree with Eli and just to sort of compound his observation, we know that, between 5% and 10% of human disease is caused by dominant mutations in the genome. A lot of clinical products, a lot of clinical value, a lot of human value is being created by clinical genomics tools and applications. However, about 90% of human diseases are driven by genomic predisposition and your exposure or your exposures alone. It is this 90% of human disease that proteomics potentially opens a window into overtime and we are well into that project already, and I’ll give you some examples of that momentarily.
Slide 15
This concept on the next slide that we are an enabler and an applications company. We have moved down the evolutionary path much further than others, is a very important consideration we believe and we’ll show you some examples of that.
Slide 16
So on slide 16, this is our biopharma and research market and this is where basically all the revenue is generated now as we are developing the diagnostics clinical market to follow. Across the bottom here you can see the use cases for biopharma and research proteomics tools. I want to talk just a little bit about the use cases and how we compare to others in each of these areas. So in the 2 icons immediately below the basic research and discovery bubble, you can see drug and vaccine target identification development and the discernment of new biologic insights and certainly every proteomics enabler should be doing these 2 things, as we are as well. However, it is really just a numbers game, it’s not that complex. If you are measuring 7,000 proteins versus 1,500 or 3,000, and the number of targets that can be identified in sort of traditional clinical trials and basic pharma discovery work, and the number of biologic insights that can be developed both by pharma and by basic researchers it is just a larger number driven by the fact that you can measure more protein. So, while everybody’s work needs two icons, there are certainly differentiating competencies that we have by how many proteins we can measure.
The 2 icons on either side of that genomic and proteomic discovery and the identification of PQTL targets. We do have, I would say, an even more significant advantage. We know that when you are looking for targets, if you were taking full genome sequence samples, and you are looking for drug targets based on genomic abnormalities. We know from experience that about half of those targets are not the protein related to the gene that is abnormal it is a protein somewhere else in the network that is call the Trans Effect and again this is a simple numbers exercise. If you can only measure 1,500 of the canonical 20,000 proteins versus measuring 7,000 or 10,000 you are not going to find as many targets when you look at full genome sequence samples and lay proteomics on top, and we have done work with some of our customers including Amgen to demonstrate this. There is an important paper coming up in the next few weeks that will illustrate the significant advantages of high-plex proteomics in this combined genomic proteomic discovery model, one that will be increasingly important and obviously the determination of protein, gene correlations PQTL’s is also just a numbers game. If you are measuring more proteins, you are finding more PQTL’s and more targets as a result. On the right-hand side are two icons that we because really separate us from the competition.
As I mentioned earlier our protein pattern recognition diagnostic tests are now being sold back into the pharma business as RUO clinical trials tools. An example is we have a set of Nash test panel that can not only tell you if you have Nash with about 90% sensitivity without a liver biopsy, it’s just from a blood draw. We can also tell you the 4 histologic subtypes as accurately as an inter-pathologist read. You can imagine the benefit of that in clinical trial’s use, in order to get patients into trials, and to monitor drug effects as you go along the way of clinical trial work. But also, we are releasing a handful of cardiovascular risk tests that tell you what your risk of death are from CHF, what your risk of an MR stroke is in the near future or what the likelihood of a positive workup for cardiovascular disease is and those with ambulatory chest pain.
A host of tests that can be used in an RUO fashion, that nobody else in the world currently has access to because we have developed them as these high-plex protein pattern recognition tests off of our platform. Then lastly, we have been asked by many of these customers “can you take this unique capability to create these protein pattern expression diagnostic tests and create complementary companion diagnostics for us?” So these are 2 use cases that others currently don’t have the ability to deliver, and then I can add another icon here, bioinformatics and analytics itself. Something we have sort of been getting away from for the last several years, we will be productizing over this next year, that will also be fed back in and will be a unique offering compared to others. While there may not be tons of revenue associated with bioinformatics and analytics, large genomics companies will tell you that it certainly makes customers that are buying in all these other areas much more sticky over time.
Slide 17
Slide 17 just to mention, some of our customer and collaboration relationships. As I mentioned earlier, we had more than 300 of these over the last 20 years. We have a 10- year deal with Novartis, we basically, as I mentioned earlier, we have been moved up the food chain and we are now not only a discovery tool there, but we are actually a clinical drug development tool, and have been through the HIPAA data certification and other audits to make sure that we are capable of delivering under those requirements.We have a very large deal with Amgen that was a 2-year deal that stretches into 2021. We have signed enterprise deals in the last few months with Bristol Myers Squibb and Alkahest and many others under discussion. So this is a real business with real customers. We believe the trajectory of the business here is based on hiring a much larger sales force and bringing strategic sales expertise into the company, that there is a lot of fruit that’s low hanging and lying on the ground for us to rapidly grow this, in addition to the offerings that we put into this market that others do not have. Bill and Melinda Gates Foundation just as a validation point, funded our work with COVID-19 over the last year. The number of investigators did not have funding to run their samples. We ran several 1,000 by using Gates as a funding source for those investigators and we are involved in a number of other projects with Bill and Melinda Gates. They feel like our technology might be very useful in the developing world, in the future where you can imagine crowding into sub–Saharan African village, drawing blood from individuals, running these new protein powder recognition tests, going back into the village and delivering care based on needs rather than putting pathologists, CT scanners and MRI scanners on the back of that truck.
Slide 18
On slide 18, I would like to talk about our emerging clinical diagnostic business, and the use cases for this, which are considerable, and as I mentioned earlier, thinking about how we moved down the evolutionary path further than others with the database, with the ability to create derivative products, is very unique. What these tests do, just to let you know, in the first generation is that we run thousands of proteins every time we evaluate an individual for a condition, or disease, or future trajectory of the disease. We’ve created machine learning models that are between 16 and 360 proteins that correlate with that disease, or condition, or trajectory, and within those models we can bend the risk of individuals using a data source that no one has ever used before. And what we are seeing is not only can we detect early disease, but we can predict risk in ways that was not possible previously.
We know the 10% to 15% of individuals that have acute cardiovascular events for example, have no known traditional risk factors. We are seeing this in our demonstration market. We also, and frankly I know as a former clinician getting phone calls at 2 o’clock in the morning from the emergency room, that even when you think you are managing patients at the top of your license, acute events still happen. This is sort of one of the holy grails of medicine, to be able to predict acute events and intervene before they happen. These tests have the capability of doing that. They are sensitive to change, for example, polygenic risk tests that are only really applicable to a small number of diseases so that 5% to 10% that are driven by dominant mutations or strong polygenic risk, often times are not sensitive to change. But the proteome changes from day to day, from moment to moment, from year to year, and because of that, we can see changes in diseases based on just the natural history getting better or getting worse, and then what the intervention’s impacts are on that disease as well.
The icon with a cross in it, we have developed a handful of tests in the past or a handful of models that could be tested in the future to determine the first few weeks of drug administration, to see if the drug is going to be effective or have side effects. One of our discussions currently with a very large payer is around developing a panel of tests that can predict if high-cost biologics are effective and we will likely be placing our SomaScan laboratory capabilities on site for them. Lastly, the ability to use individual biologic data to risk stratify is something that we have been waiting for, for a long time. Currently we use EHR data and claims data but what these tests will be able to do, for example, is to tell you in your population of diabetics, if you are health system A, of these 1 million patients with diabetes these are the 20,000 most likely to have adverse events in the near future. You can imagine the impact on the ability to determine what resources need to be deployed or which clinicians need to be hired or brought into the system and then of course if you are a single payer: Brazil, or Germany, or Japan, this is an imperative to understand this and know how to spend money wisely.
Eli Casdin
I think this is a huge untapped opportunity set for Proteomics broadly but specifically for SomaLogic. No other company in the space is this far along or is really anywhere in the clinical market. SomaLogic has close to a half million samples, almost half of them have rich annotated clinical data associated with them and it has allowed them to accelerate the insights of the proteome and its application in clinical markets and there is an enormous amount of value to be created in this company that is not currently reflected in the business model, in a substantial way.
Roy Smythe
And Eli, I think if you look at the 2 icons that have the dotted lines through them, these are 2 examples of that. One is that we can develop, you know we have got 100 tests in our pipeline, but there are hundreds that could be developed on this platform because again, proteomics gives you a window into 90% of human disease that we have not been able to look in to or peer through previously. So developing tests for others and letting them use them, you know either in a license way or royalty arrangement. We have been asked to consider this already by many, and then on the right-hand side of the slide, I would say people have always been curious. We have always had some of these friendly calls from genomics providers, but within the last several months since we closed the private placement round, this is likely due to both Casdin and the investors they brought to the table. We have had 3 or 4 very serious conversations about combining proteomics capabilities with genomics capabilities to create an even additional layer of clinical diagnostics applications, to create value.
Slide 19
On these next two slides, just a couple of examples of the SomaSignal tests. This one is what we call our secondary cardiovascular risk example, on slide 16 this is a really interesting test that can predict the risk of an MR stroke in the near future if you had one previously. As it turns out, it also predicts that for primary risk for individuals over the age of 65 and diabetics. Their biology, not surprisingly, sort of recapitulates a secondary risk of biology. Then perhaps even more interesting, when we ran through all of our existing models to see if we could make some predictions around COVID with the samples that we ran over this last year, often in combination with Gates funding. What we have found is this test also predicts your risk of dying or intubation at the time of hospital admission with COVID, again, not so surprising if you understand the mechanism of death and as individuals it is usually cardiovascular. But this test has been validated in more than 15,000 participants around the world. For all of our tests, we develop them with sensitivity and specificity that is as good or better than existing diagnostics in the same area. The AUCs of this are 0.7 to 0.8 or higher. We actually launched recently with NEC Computing in Japan, a company called FonesLife and this will be the front door for selling all of our tests overtime in Japan. One of the things we have noticed is that as well as in other sample sets from around the world is that these tests or racially, gender and geographically robust. So far, all the testing we have used in the Japanese population, one that you can consider to be both geographically and dietarily isolated have work just great. And of course, these are sensitive to change over time as I mentioned so that you can understand if your interventions are improving a patient’s condition or not.
Slide 20
NASH is another interesting example of the test that we validated so far. Again, this is the liquid liver biopsy, this can tell you if you have got NASH with 90% sensitivity, without a liver biopsy, with just a blood draw, it can also predict the histologic subtypes. As you know, one of the problems getting patients in the invasive diagnostics trials is their unwillingness to have things like needle biopsies done, this allows them to facilitate that. This test has been validated by the largest liver consortium in the world, liver disease consortium of the world, the litmus consortium in Europe, and they basically evaluated over the last few months, the 20 known noninvasive tests using biomarkers, and ours was by far and away the number one test in that study.
Slide 21
Slide 21 is our pipeline of SomaSignal tests and this is not a conceptual pipeline, it is important to note that this is a real development pipeline. The first ten tests or eleven tests are over on the left-hand side here, launched at the end of 2019 in our demonstration market. We also published a paper in Nature Medicine, 1 of those 250 papers I mentioned earlier, we did talks about how our test are developed, how the models are created. It was one of the most highly cited papers in Nature Medicine for about 6 weeks.
The tests in the middle, about 25% of these, we completed validation on in 2020. Namely the cardiovascular risk test that you see, CHF, rule out symptomatic coroner disease, and then additional applications under the secondary cardiovascular risk testing I mentioned earlier. We are very excited about a set of cancer susceptibility tests, so we have brought a set of samples to determine the feasibility of creating a set of tests, that look at the top 4 adult cancers, and the ability to predict your predisposition for developing that cancer in the near future or over the next 3 years or so, as compared to early diagnosis. What with a number of early diagnostic companies in the market using things like cell free DNA, there is currently no test that biologically correlates your actual risk for developing cancer in the future and you can imagine that this could change the way that we screen individuals for cancer which is currently done by demographics. And also, you could easily link these tests on the front end of things like cell free DNA early detection and we’ve been in discussions with one of those companies whose name you would recognize already to do that.
All the tests on this list, even the ones on the right-hand side are either in bioinformatics, have been created and validated or they are in a bioinformatics development process, where we have the data to create them. As I mentioned earlier, the conceptual pipeline for high-plex proteomic pattern recognition diagnostic test is in the hundreds, if not more.
Slide 22
So on slide 22, I want to switch from the markets to talk about our financials. At the bottom of this graph, you can see the dark blue bars, these are our base case assumptions, these were the financial assumptions and modeling we put in place in the private placement round. About a 75% gross margin over time, break even in the 2025 time period, and then about a $250M cumulative cash burn over the next several years as well. There are things that are not modeled in these base assumptions, including significant cost reduction on our assays, we will take 25% of the cost out this coming year. We are currently in discussions with 2 large public technology companies, one a genomics company, and one a biotech company with other technology capabilities to move on to an alternative potential read platform, which could dramatically drop our cost for the assay, and obviously have an impact on our breakeven as well. So that is not modeled into the base case, and of course, a lot of other things aren’t modeled into the base case either based on assumptions around bringing additional capital into the company as we make this public transition with our SPAC partner. And these include things like creating small plex products in addition to our large discoveries’ platform.
Pushing kits out in the market, we had kits in the market but we pulled this all back in 2017. Also, taking these kits and boxing up the assays so that we have a hardware in reagents play over the next couple of years. Both of these technology partners I mentioned to you a moment ago, are sort of vying with us to create that box so that when some of these other new entrants are in the market in 2 or 3 years, that’s the primary motive, making money.
We will have not only a service business that is generating $100M+ plus of revenue, but we will also have a hardware and reagents business, deployed business as well and a number of other things that we can do with this money that we believe will drive the CAGR from conservative 30%+ percent between now and 2026 to a conservative 40%+ CAGR as we use these funds to create these commercial opportunities. Again, the significant advantage that we have, is that we do not have to invest large amounts of money over the next few years just to make our technology work, as some of our competitors will be doing, as they bring money onto their balance sheet. We’ll be obviously pushing the technology trajectory downfield, but we will use the bulk of this funding for commercial opportunities.
Keith Meister
Roy – let me just add a few points here. The first point I would note is the cumulative cash burn in the plan of $250M. That is as compared to the in excess of $650M the company’s going to have on its balance sheet pro forma, for the transaction. So clearly the view here is to overcapitalize the balance sheet, not for the sake of overcapitalizing the balance sheet, but because Roy and team have some pretty exciting organic and inorganic ideas for growth and that does not just include expanding the salesforce, that does not just include accelerating health system partnerships, it does not just include clinical application genomic partnerships for accelerated growth, it also includes inorganic actions. So we are going to position this company to leverage the technology advantage it has and be a first mover. That is one point I would make.
The other point I would make is the momentum in the business. Q4 2020 the company did $55M of revenue. In Q4, it had achieved $25M, which I think was the best quarter in the company’s history. The plan for 2021 is, you know, slightly in excess of $65M of revenue. In Q1, that – of 2020 – that number was $6M. It looks like Q1 in 2021 is about
$16M in terms of where it is shaping up and that’s an estimate, obviously, at this point. But the momentum in the business is real. The $65M going to high $80M’s in 2022, is all revenue that the company has very good line of sight on that comes from multi-year partnerships, for the most part, tied to the biopharma business.
The clinical applications business and the exciting tests that Roy went through on the previous slides, and the partnering with health systems, and where we are talk about the 4 partnerships the company has that, with this money, can be expanded to eight. That is what helps move the blue line up to the pink or fuchsia line on the presentation. So the whole purpose of this big capital transaction is to allow Roy and team to move fast, to solidify their technology advantage, and as Roy pointed out, as others are investing in technology, Soma can be investing in commercialization and accelerating growth and advantage.
Roy Smythe
Slide 23, 24
Thanks, Keith. So I’d like to talk a little bit now on slide 24 moving forward a little bit more about the specifics of how we will accelerate growth using this transitional funding.
Slide 25
On Slide 25, we can advance our tech trajectory using this funding. So as I mentioned earlier, we are primarily a service business now. We can push kits back out into the market. We plan to have quite a number of those over the next couple of years; we are asked continually to deploy these. In addition to that, we can obviously develop a product version—the technology—a box version, if you will. It gives us tremendous market access. There is nothing about this technology that doesn’t lend itself to that. We just never funded that project. We can speed up the content capabilities, so we can speed up the 7,000 to 10,000 proteins measured to 10,000 to 15,000 proteins measured. These are basically just money and people and time coefficients to get this done.
We can grow the database at a much faster rate. Currently, we have a large number of backlogged research samples – the clinical samples. We will have the protein data once we assay them. We will have the clinical data as well. We haven’t run these because of capacity issues, and we have to put customers at the front of the line. We will be able to dramatically increase our capacity over the next couple of years because of this funding and pour these existing discovery samples into the database.
We can develop more health system relationships. You know, we plan to have 4 announced by mid-year this year, 4 brand name health systems that will be doing benefits proof work with us. You know, utility proof, health care economics, care improvement. We have about 8 projects online with these 4 health systems. But these will also be sources of data. Since each of these relationships requires an investment from us, we can double that over the next couple years with this capital as well. The other course we can take is to buy into more bio banks. We already have a number of bio bank samples that we haven’t actually accessed yet because of about 5 years of contracting effort on the part of our clinical team. But we will be able to buy into a couple of large bio banks around the world that we have not accessed yet. And of course, all these things will accelerate the database to turn that flywheel that I showed you earlier, faster. We do believe that there some inorganic opportunities as well that I will mention on the next slide.
Slide 26
So the right-hand side of the slide are the inorganic opportunities that we are currently thinking about. The left-hand side are the partnerships, many of which I have mentioned going through the presentation. These are more sort of organic ways of growing, above obviously internal R&D efforts but also investing in partnerships to move other technology trajectories forward, to box up the assay, to create combined proteo- genomic products, and so forth. And the important thing about this set of hexagons is that these aren’t conceptual, we are either in feasibility for these now so we have either already started working on feasibility for these projects, for the ones that we could afford, based on the capital we had access to at this time. We are in contracting discussions or we are in the late stages of discussions that will lead to contracting - basically to get all these things done with partners and not only to increase our tech trajectory, but also increase the number of commercial opportunities that we have at our disposal.
On the right-hand side, we really do want to double down on this first mover comprehensive commercial position that we have so, in addition to the organic opportunities, we do think there are some others we could consider. We are interested in protein sequencing and fingerprinting. We do believe, however, it’s a number of years out before it’s commercially applicable or useful but we also know that we can use our reagents and some of these platforms and so we’re actually engaged in the feasibility experiment right now with an early-stage protein sequencing company. And that feasibility work turns out to be promising - this is an opportunity for us potentially to move that on our platform.
We also, despite having a lot of bespoke bioinformatics capabilities and tools, do not have a customer facing data or bioinformatics interface. We think this is also another opportunity for us to potentially acquire these types of capabilities, and put them on to the platform as well.
<In Reference to Slide 36>
Just to comment on the health system relationships, I’m going to move to slide 36 in the appendix to show you that as I mentioned, 4 very large health systems, have either agreed or are in the process of agreeing with us to partner on benefit proof studies over the next year to 18 months. We believe we should be able to double these with funding, over the next year, and of course these health systems you work with on innovative products, you know, that you are trying to get accepted into healthcare markets are likely to be your first customers and so by doubling the number systems we work with, we should be able to convert more of these in the 2023 or so time frame, paying customers for this transformational set of diagnostic tools.
Slide 27
Slide 27 just talk about our leadership group for a moment. This is a very experienced group of leaders. This is not, you know, a startup company with people that are necessarily learning on the job. There is a huge range of experiences from clinical care, to biotechnology development, to data, to genomics, to basic research capabilities. I would put our 3 core R&D executives, Nebojsa Janjic, Steve Williams and Alan Williams right up there with any group that I have ever seen in my life, these are the 3 that I inherited. We added Jason Cleveland this year as our CTO. Jason is a physicist and polymath and when we think about tech trajectory and integrating with other technologies over time, he will be very beneficial in that regard too and I think of all the joy of meeting both Jason and the others in the R&D group.
On the commercial side, we had about 3 salespeople in SomaLogic when I arrived 2 years ago, but now we begun to build out true commercial capabilities and expertise so we can pluck that low hanging fruit, pick up the fruit that has been on the ground waiting for us to find it. Tracy Hervey, comes out of the clinical trial services arena. She has managed billion-dollar campaigns, she has been responsible just in the last few months for 2 or 3 new enterprise deals. She is building out a global team of salespeople and she is just fantastic. Angel Bakker-Lee runs our emerging healthcare business. This is our diagnostics business, where we will be selling our SomaSignals test. Angela has had a ton of experience selling innovative first-time products in the healthcare. She was one of the first people to sell Watson into healthcare facilities around the world. She was Chief Strategy officer of a voice recognition AI company, Quib, she is very comfortable talking and selling new technologies and new approaches.
Before we talk about open hires, I want to mention and introduce Melody Harris, who is our president and COO. Melody came to us from Qualcomm Life and HealthyCircles, where she was the Chief Legal Officer, but was also involved in many operational roles and Melody is both the President, with a number of the units in the company reporting up to her, as well as the Chief Operating Officer and she is a fantastic operator for the company that is all I can say. She keeps the trains running on time and keeps the trains on the tracks as well.
We are adding a CFO this year - we lost our CFO early in the private placement round, but luckily, we have Manda Morris, who runs our strategic finance and commercial finance activities and if you are getting involved with us and would like to learn more about finances, Manda is an incredible resource. But we also believe will be hiring a Chief Strategy officer someone that many people that are in a life science and diagnostics tools markets whose name they will recognize is being a leader in this space and someone that will help us a great deal with our partnership, M&A, and other decisions moving forward.
Slide 28
On the next slide, on slide 28 our existing SomaLogic board also has an array of experiences and capabilities. Chuck Lillis, our board chair, was the number 2 executive at GE. He also ran Media One Group which was a several hundred-billion-dollar public company and Margulies, Chief Information officer at Harvard. Of course, Eli Casdin has joined our board so lots of great experience here but what we are really excited about in the process of merging with CM is the access to board members with a lot more real time and recent experience in life sciences tools and diagnostics. These were mentioned earlier Kevin Conroy, Troy Cox, Jason Kelly, Steven Quake and we believe that at least 3 of these individuals will be joining our board as well you know certainly as the CEO looking forward to that.
Slide 29
Roy Smythe
So I’m going to turn it over now to Keith to discuss the dynamics of this deal.
Keith Meister
Thanks Roy. Let me quickly walk through the terms of the transaction. CM Life Sciences 2 begins with $276M of cash on our balance sheet. We originally anticipated entering into a $250M PIPE. However, we received a very warm response from investors, and we were substantially oversubscribed and decided to upsize the PIPE to $375M. As detailed in our press release file this morning, investors in the PIPE included some large strategic partners, some of the leading growth investors in the world, and a significant participation from existing SomaLogic investors. The $276M on our balance sheet plus the $375M PIPE, when combined with the $120M of net cash expected to be on Soma’s balance at closing will leave the pro forma enterprise with approximately $686M of net cash at closing. As a frame, we mentioned earlier in the presentation that Soma expects to have a cumulative burn through profitability of approximately $250M. The incremental cash, the difference between $686M and the $250M expected burn, will give Roy and team significant excess firepower, to invest for growth both organically and inorganically. Let’s jump to slide 30 for a minute.
Slide 30
Slide 30 lays out a graphical depiction of what I just spoke to. A pro forma enterprise value of approximately $1.23B and a company with net cash of $686M. The transaction, which we announced today, we expect to close in the third quarter of 2021. We expect to file a registration statement with the SEC within a month and then we expect the longest lead time item between filing and closing just to be working through the SEC registration process.
In terms of just a frame on valuation, as I think about the $1.2B valuation, I think it represents a real value on the expected Revenue. So if you think about it as a multiple of 2022 revenue of $85M, it appears to be a reasonable valuation and a win-win for the company and investors and positions the company, in my opinion, attractively verses its other proteomic peers, most of whom don’t have revenue today and anticipate having a lot less revenue in 2022 than SomaLogic.
Slide 31
Where I get most excited is the asymmetry of the transaction. We are pricing this as a real company with a real revenue base. None of that revenue base today comes from the exciting platform opportunity of not just being enabling technology but a clinical applications company. And as what you just heard Roy and Eli walk through of, the company executes on that vision, the opportunity to invest in this game changing proteomics platform at a $1.2B valuation, we think is asymmetric as you comp it verse other genomic and life science platform businesses, the EV of the opportunity to the TAM just seems quite compelling. So as you look at this you know and go back to the original vision of CM Life Sciences, empowering Roy and team, with expertise from our board, with lots of capital, and the ability to execute, to take a billion dollar business and grow in the public markets to a $5, $10, $20B company. We think we have laid out the roadmap for that. Roy.
Slide 32
Roy Smythe
Thanks, Keith. I would just like to sum up the presentation by saying that this is a unique market, it is a market that is going to grow considerably over the next several years. It is going to recapitulate, I think, but in a bigger way to what’s happened to genomics and for all the reasons we discussed earlier. Taking all that into consideration, we are truly uniquely positioned with a proprietary platform, a first mover advantage, more than $500M of total investment in our technology so that we don’t have to use these funds to make our technology work, that some of our competitors will over the next several years. We are well into applications, based on our superior measurement and identification capabilities. We are market validated. We can measure more proteins, and enablement sense than anyone else in the world and I talked about the true advantages and use cases, especially in the biopharma and research market when you can measure proteins at this level. We have the world’s largest proteomics database that is already throwing off significant applications, we have the most sophisticated bioinformatics capabilities in the world. To do that, we have developed more than 20 of these first in class protein pattern recognition tests, with more than 100 in development. And then, our core measurement reagent is nucleic acids so the technology trajectory for this platform is still out in front of us, despite being here for 20 years. It’s truly, I think, perhaps of all the statements here the most remarkable because of all the things that we will be able to do in a commercial sense based on that reality. I think we have a team and we will obviously be improving that team as part and parcel of the SPAC merger, that can execute on all of these things that put us in this unique position. So, thank you very much for taking a look at the presentation, and we will look forward to meeting with some of you as we move forward overtime.
Keith Meister
Thank you Roy. So, I will conclude by just expressing how excited both Eli and I are to enter into this tremendous transaction with SomaLogic. Roy, we thank you and the Soma team for trusting us as partners to help you on this next leg of your journey and to all the investors and prospective investors we look forward to sharing more with you in the years to come. Thank you for your time today.
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