In order to make a dent in the world that we're going after, you have to measure millions and billions of molecules in a single run of an instrument, and that can't take more than a few days, and the reason you have to measure that much is that your cells, each cell has a lot of proteins in it.
Welcome to Tough Tech Today with Meyen and Miller. This is the premier show featuring trailblazers who are building technologies today to solve tomorrow's toughest challenges.
Welcome to Tough Tech Today with Meyen and Miller. Today, we have the honor of speaking with Sujal Patel, co-founder of Nautilus Biotechnology. Welcome, Sujal.
Thank you, Jonathan and Forrest. Thanks for having me.
To get our audience up to speed with what you're working on, could you tell us a bit more about Nautilus Biotechnology?
Sure, Nautilus Biotechnology is a incredibly interesting problem between the biological domain, physical sciences domain, and the tech domain. You know, if I'm to explain the company in story form, what I'd say is, is that if you look at biotechnology over the course of the last few decades, and specifically we look at genomics, we as humanity conquered genomics over the course of the last two decades today, if I take a drop of your blood, and I want to understand what your genome is, I can get you 99% of your genome for $1,000. In a couple of days, it's a complete commodity. But the issue is, is that your genome really doesn't change from the day that you're born, the day that you die, it actually is very static, it doesn't contain anything real time information about whether you're sick today or what's going on. And because of that, there is this big move to try to understand more of what's going on in your cells, and your cells are all made of proteins. And if you really want to understand what's going on inside of human body, you have to get to the protein level. And the issue that exists in the world today is that the very best techniques that we have to analyze proteins from that same drop of blood or from any biological sample are very different than genomics. In the genome, take a drop of blood, $1,000, in a few days, I get 99% of the answer. If I want the proteome, which is the makeup of the proteins, I could take that same drop of blood, the very best techniques on the earth, leveraging complex pieces of equipment, like mass spectrometers would take $50,000 in a month to analyze that sample. And at the conclusion of that, we would have identified 8% — single digit 8% — of the proteins that are the sample. And so Nautilus was founded to try to improve that dramatically.
So can you walk us through like exactly why we would want to know what's in the particular proteins? Because I imagine your DNA encodes for all of those proteins to allow them to exist? Are you trying to get information on environmental factors or different things from those tests?
It's much more than just environmental factors. But I think that Forrest you're on the right track there. So obviously, your genome encodes all of the possible proteins that could be created, but for a wide variety of reasons, what's occurring in your cells, and the proteins in your cells do not match in any particular way what your genomic — if we look at it in software terms, what your software code says. And that's the reason why if you took two twins, and you let them age to their 40, and one took care of themselves and one ate potato chips all day and never exercised, they'd look completely different. The environmental factors, the health factors, what's going on in that particular body, all those things are encoded in your proteins. This is the reason why if you looked at the FDA approved drugs, over 90% of the FDA approved drugs target proteins. Most of our diagnostics target proteins. And one of the side effects of this inability to measure proteins effectively is that drug development has gotten less efficient, and it's gotten worse. So over the last two decades, if you looked at the number of new FDA approved drugs every year, it's basically dead flat. And that's in the face of a quadrupling of the global R&D spend in pharma from about 50 billion to 200 billion so we've quadrupled spend, but FDA approvals is flat. Because of that, only two out of 12 marketed drugs that reach the market actually return the R&D dollars that went into the advocacy of the timelines have become worse and extended. And not understanding the proteins which make up all of your cells is a huge part of that problem that's that's been created here.
This is a... really complicated problem set. And so I'm curious with your background with as CEO of Isilon Systems which from my understanding was far and away from a biotech company. And so could you tell us a bit... how to connect those dots between your work with Isilon and then now find yourself at the helm of a really exciting biotech company.
Yeah, it's a great question, Jonathan. Because the path for me to get from my last company to here is a really interesting path. And it's one of those sort of small world types of stories. So my last company... well, let me kind of back up. Up until four years ago, my entire career has been in the tech world. And the last company that I spoke to was company called Isilon, which was a company founded in January of 2001, went public in 2006. And then we eventually sold it for 2.6 billion in 2010. That company was focused on building a new storage architecture for unstructured information, things like digital content, video and images machine generated data. Two decades ago, storage systems of the time, were really focused on string text based information and databases and credit card transactions. And so we built a scalable architecture. And we took that to the world focused on different verticals that were undergoing a digital transformation. So we started selling into companies doing photo sharing on the internet, into film broadcasts and television which was undergoing a transition from analog to digital. And then we moved into manufacturing and semiconductor and by '04 and '05, life sciences and biomedical research became a really big market for us. And this revolution that occurred in genetic sequencing was a big part of the success that we had in that vertical market. Back in 2004, which is 16 years ago, I met a guy named Parag Malik and Parag became a very large customer of mine at Isilon supporting his proteomics Research Lab at Cedars Sinai Medical Center, and Parag 16 years later, is my co-founder here at Nautilus. And that, our paths intersected many times over the course of those 16 years. First, Parag was in the role of a customer to me, and I got to know Prague really well and thought he was one of the smartest guys that I ever met. About nine years ago, Parag left his role at Cedars Sinai and went to Stanford to teach and start a research lab, which was really building technologies to pursue a personalized medicine, with a focus of sitting at the intersection of tech and biotech products, kind of a unique animal. And he has academic degrees in both biochemistry and computer science. And my wife and I were so impressed with the work that he was trying to do in his lab and then he has done that we decided to philanthropically support his lab. And we've done that for the last nine years, which built a really close relationship between Parag and I, and that led to Parag calling me up in 2016 and saying, Hey, I'm gonna start a company because I have an idea and let me tell you about it. And what started as a conversation, soliciting advice very quickly turned into two founders going off and trying to solve a really tough, really hard problem. And it's been an amazing ride for four years of kind of moving from the crackpot idea phase through feasible and now into real engineering.
That's so cool to hear how it's like an organic relationship that developed, that you found that you could... worked well together because of go founding a company is non-trivial, and absolutely a strain on a relationship. What were you doing when you got that call in 2016? And were you looking for your next big company to start?
Yeah, so it's interesting, right. So we sold Isilon in December of 2010. And we just closed our 100 million dollar quarter. And I had committed to the acquiring company that I would grow the business to a billion dollar run rate and then I would leave. We were in a very high growth rate — that only took 23 months, and then I left. And I did some consulting for them to help them through a few other things that were going on. But largely, I started to think about what I wanted to do next. And I hung out with venture capitalists, I made a lot of investments in companies that total I've made about 80 investments in the last 10 years in private companies. I had six boards at some point and I started to work with entrepreneurs and about six months in I realized that I couldn't be on boards and invest; I really was too young, too passionate, too excited to go and actually build something. And I knew I wanted to go out and do something again. But I also knew that I didn't want to do enterprise software, enterprise hardware, I didn't want to do something that was like what I did before. And I also had this burning desire to go work on really hard problems that have big societal impact. And so I was only really looking in three areas I was looking in healthcare technology, I was looking in clean energy, and I was looking at a couple of ideas in consumer, which would have very broad implications. And that's it. And none of those ideas ever graduated to the point where I said, that was the one for me until Parag came and it literally was that first one hour call with Parag, I went through it, I'm like, okay, if anyone were to bring me an idea like this, I'd call Parag and say, Parag, what do you think about this idea? And then I said to myself, Parag is going to go put his Stanford career which is really successful on hold, if this was an incredibly exciting in the opportunity of a lifetime. And I got there in one hour, obviously, there are a lot more conversations from there, but in one hour, I was like, Okay, this is it like this is that thing. And it's been it's been super exciting, getting into a whole new and different space.
So you called the early phase of the company, or the crackpot idea phase. Now was it really a crackpot idea? Did you shift a lot into what it is now? Or were you just getting more information to gain confidence that you had something really special?
So that's an interesting question, right? I mean, tongue in cheek, we call this the crackpot idea phase in 2016. But what I will tell you is that the idea was really unformed at that point. But we had an idea that the method that Parag was proposing to analyze huge numbers of proteins and get to a very specific identification with those proteins. And we an idea that that was possible, but there were a huge number of unanswered questions. And a big chunk of it was related to the core algorithm that Parag conceived of that is the underpinnings of what we do. And a big part of it is how do we scale this up and do it reliably at massive scale at low cost. And now, that part, is really the bulk of what we took the last four years working... what we've done over the last four years. The first six months were algorithmically moving it from crackpot idea to, okay, this really can happen. And in all honesty, I said to myself, well, let's go see what happens six months, I have six months to spare. If it doesn't work out, it doesn't work out, I'll go find the next thing. But two things happened during those six months. Number one, I realized that Parag and I work really closely together, and that there was a massive opportunity as we started to talk to customers for what we were trying to do. And the second thing is, is that in that six months, when we fully fleshed out the algorithmic part, and the computational parts of what Parag was proposing, we realized that the job was going to be a lot easier than we had originally had thought as well. And so once that was fully fleshed out, we realized, boy, this is actually not just feasible, but like I see a path to go and get this done. And that was when we really started to raise money and really start to hire and go after it.
In this particular sort of problem area that Parag identified as something that where he could really move the needle, my understanding of this area is that one of the big limiters is the data set, like just where do we get the data to be able to then do all what I'll call complexity analysis to be able to identify certain proteins or derivatives that could be great for drug target? How are you addressing that lack of data?
Yeah, if you take a step back for a second, I think there are three... you know, the underpinning your question is, well, how do you deal with the tough stuff, and you said, data. Data is one of the tough things that we have to deal with. There's a massive chunk of data at the end of the pipeline, there's a huge amount of information that needs to be gathered by instrumentation, which has never been built to gather this much information. And then on the biochemistry side, there's a huge amount of complexity in trying to analyze the molecules that we're going after. And it might be interesting just to kind of... I'll double click quickly on each of those areas, and then you guys can delve in and tell me which areas you think are most interesting. But in the biochemistry side of it, one of the things that made genomic sequencing possible was that nature already has a mechanism to go and copy and read DNA. It has to copy it for cell division so that each has a copy of the DNA and has to read it so that a process can be undergone to transform DNA into RNA, which then becomes protein. And in order to make a genomic sequencer, Lumina, which is the world leader by foreign genomic sequencing, built a system that took a DNA fragment, amplified it, meaning copied it many, many times and that enabled them to get signal amplification, which made the process of measuring it easier. For us, once something becomes a protein in nature, there are no mechanisms to read it back, or to copy it. And so without those mechanisms, without a way to optically look at it, because these objects are miniscule, they're well below the optical resolution of any microscope, because they're orders of magnitude smaller than the wavelength of light. Without a way of measuring these molecules, it's really, really... without a way to borrow from nature in order to measure these molecules, it's really difficult to develop a method. And that's one of the aha's that Parag had and the crux of this method is that instead of trying to make a specific identification of what a molecule is, if we borrow from computer science, a technique that probes the molecule, many, many, many times, each probe leaking slightly different information about the molecule that you could computationally combine that to get to a very specific identity of what that molecule is. And then you have to figure out how do you do that in parallel for a large number of molecules. That's the second part of the problem. In order to make a dent in the world that we're going after, you have to measure millions and billions of molecules in a single run of an instrument. And that can't take more than a few days. And the reason you have to measure that much is that your cells, each cell has a lot of proteins in it, roughly a million protein molecules in every cell. In a typical pharmaceutical drug development application, you might be dealing with 96 well plates, each one of them have 1000 cells in it. So if you can't turn through billions of molecules, you're not gonna want to make a dent, even if you could do better in terms of protein identification, and we've had to spend enormous amount of effort figuring out how can we, in our bio-chip have density so that when you're scanning it, that we're able to get a lot of information quickly, we've had to make a lot of innovations in microscopy to figure out how we can image these molecules at a single molecule level. At speed, we had to make a lot of innovations in the microfluidic system, and all the things that we need to probe these molecules over and over again, so as a huge body of work there. And then once that's done, it goes into a computer, a computer is dealing with 10, or 20 terabytes of raw information coming off the instrument every day, that has to get reduced in real time, these are images, so they have to first get deskewed, they have to deal with pincushion distortion, they have to deal with all the fuzziness of imaging at very low light levels, which is what we have to deal with. And from there, then we reduce the dataset, we send it to the cloud, and computation using this algorithm that Parag conceived of four years ago, requires hundreds of cores and many hours of compute power. And so that's just to get to quantification of what's in my sample, what's in that drop of blood. Then from there, the question is, well how do I use that information to build better drugs, to build diagnostics to personalized medicine? And that's a whole area of exploration that we will be a big part of, but our customers also be really integral piece of.
Now, when you say each cell has millions of different proteins, are you actually characterizing, you know, a million proteins in a single cell? Are you cataloging everything?
We believe, as a company that in order to be useful to pharma, you need to be able to analyze the vast majority of the proteins that are in a very large number of cells. So not just one cell, but hundreds or thousands of cells. And so we're building technology to be able to do that effectively. One of the things that's really interesting about this company is that let me kind of draw an analogy back to the genomic world in the genomics world. Before Illumina came along. There was a method of sequencing or a couple, but Sanger sequencing being one of them, which was slow and expensive. It was 100% right — or pretty much 100% right — and what Illumina figured out how to do is to make this a commodity: how can I make it fast, cheap, and that's 99% accurate. For us, there are some new companies in the proteomics world that are trying to do things like sequencing and trying to get to 100%, but those aren't techniques that are going to lead to a massively parallel approach that will democratize access to the proteome for drug development companies for pharma. We're building an approach from day one that's focused on we're not going to get 100% of the answer, but we'd like to get some very high percentage of the answer, but we'd like to do it very quickly at low cost. And so for us, being able to analyze not just the million proteins on average that are in one cell, but being able to do that for hundreds of thousands is the goal of this company.
This topic is... and I mean it in the nicest ways, it's the playground of PhDs who have studied this for their academic and professional lives, it seems. So what was it like for you who had a very different and equally specialized, but a different skill set, then starting to learn sort of the what Parag has been working on for so long?
Yeah, it's been very interesting, right? So one of the things that makes the combination of Parag and I unique is that, in order to bring this innovation to the world, we're going to have to build an entirely new instrument. That instrument has chemistry, it has biology, it has imaging, biophysics, hardware, software. On our staff, our mechanical engineers, software engineers, electrical engineers, biophysicists working side by side biochemists, organic chemists, bio engineering majors, all of these disciplines come together to build a complete solution. And you know, Parag's experience virtually spans the entire range, but I've spent the bulk of my career on the second part of that, which is from the hardware engineering, to the software and the data science side of things. And so the combination is quite powerful. And in addition to that, the distribution model for products in this world is very, very similar to the distribution model that we had at Isilon, my last company. Literally, you just change the titles of the different people in go-to-market organization and they match up almost identically in terms of how they function. But for me, I have a lot to catch up on to get going in this company. And it was really, it was a fascinating experience for me in the first six months to figure out how I was going to get up to speed in this world. So the first thing I did was that I went on YouTube. And I went and found every biology and chemistry class that I could and I set my speed to 1.5x. And I started churning through those as fast as I could. And every day I created... it was a list, I called it dumb questions of the day. And I would make a list. And at the end of the day, I'd call Parag, and sometimes it'd take 20 minutes, sometimes it would take two hours, but we would go through all of my dumb questions. And Parag is saint. He's an academic professor, he's very patient. And he went through all my questions. And I went through successive levels of classes that were more and more sophisticated to get to the point where I had a basic understanding of the subject matter that I was going into. And then I started reading research papers, once I got enough of that knowledge, and I read probably 500 plus research papers, maybe 1000 research papers in that first year. And I still go through roughly one a day or something like that on average. And then you start to really consume that information. And so today, I'm nowhere near an expert in anything. But in the one area where we are focused, I've been getting deeper and deeper and deeper.
That's amazing. So complete immersion is your strategy.
Yes, that's right.
That's got to be what you got to do. So I'm curious about the... you call it an instrument, and it has all these components and it seems very complex like is, is this actually like a physical device? Or is it more like a lab? What is the product?
Yeah so ultimately, what we are building is an instrument, much like a genomic sequencer — genomic sequencers, you can buy them from a number of companies; they show up as a box; the box sometimes would be the size of a small bench. So maybe it's a three foot wide by two foot by three foot high instrument, depending on how big it is and how fast it is. There are some companies out there that are building sequencers that are more like the size of a toaster today, but it's a physical piece of equipment. And so for us, that's about the first generation of sequencers; the next generation sequencing wave, we're about the size of kind of a few feet by a few feet by five feet and that's what our initial product will be. It'll be an instrument that's about that size. And our job will be to take that technology and make it reliable and reproducible enough that we can ship it to any lab in the world. And anyone can easily run a proteomic analysis. Now, between where we are today, and that world, there will certainly be a phase where we've cracked the code, and we can do it, we're just not ready to give you a box yet. And that'll be a really interesting phase of partnership between us a number of pharma organizations and diagnostic companies. And we're starting to talk to many of those partners today, as we are looking at starting some of those early experiments next year.
And then I assume you'll also have the the processing component, probably still in the cloud. So the instrument will just like, plug into the wall and upload the data?
Yes, that is, right. So there's kind of been... if you think about the data pipeline, the instrument is going to offload images to a local computer, that computer is going to have to receive massive amount of information and reduce it to something that's suitable for sending over... the wider your network. And then it's going to have to go to the cloud, the amount of compute power that we need is probably infeasible for most organizations to have on site. And so we'll send it up to the cloud for analysis. And then we'll have a platform there where customers can access their data, can look at their results, and then eventually, we'll provide more and more analysis capabilities in the cloud so that our customers are receiving insight, not just raw data.
How would you compare Nautilus Biotechnology... draw a distinction between that and say, Seer Bio, where my understanding is that they're somewhere in this space as well.
It'd be useful, I think, to discuss sort of the broader landscape of the companies that are out there. There are really two major existing categories of proteomic analysis. One is, there's a large amount of companies that produce assays, which are specific panels that will identify the relative quantities of some known proteins in a sample so if I take some cells and I run an assay that can identify 10 or 20 different proteins; what the assay will tell me is the relative abundance of protein A is three times as much as protein B, and I didn't see any protein C. It's a relative abundance, because these assays are very fuzzy and it only can support a very small number of protein molecules, because you have to have antibodies for all the proteins that you would want to analyze in an assay. And we as humanity, haven't built more than a few 1000 antibodies that will identify different proteins. And the human proteome has 10 times more, has 25,000 basic forms of proteins to go after. And then there's isoforms, and modifications and other things. So that's one category. So it's a very targeted analysis. And it's fuzzy because it's done in relative abundance. The next category is what's largely used for what's called discovery proteomics, which is I have a sample and I just want to know the most about what's in the sample. And customers who want to do the very best analysis use mass spectrometers today, and I think you guys are familiar, because I know that you did a recent video on mass spectrometry, but what a mass spectrometer is is a large, complicated instrument; it's actually built for the atomic program, and its job is to weigh fragments of molecules. And so what we do to use the mass spectrometer for protein analysis is we take protein molecules, we fragment those molecules into pieces, we send them through the mass spectrometer, and through a complex process, it's essentially telling you what the atomic masses of each of those fragments. And then on the other side, we use of that set of very complicated bioinformatics to reassemble that information into what the identities might have been for the various proteins that I put in. This is a pretty fuzzy process: it suffers from two dramatic limitations. One is that it has a very limited dynamic range. And so if you hand it blood, for example, without any treatment, most of your blood is made up of albumin, which is basically just a protein that's just there and is the bulk of your blood. If you sent that to a mass spectrometer, it would return everything's albumin because it's always going to return the most abundant stuff in a sample. And so that's problem number one. Problem number two is it's not a very specific instrument either, and we're talking about sensitivity and specificity here. And so the thing that customers have been doing is working on techniques in front to separate different types of proteins from one another, so that the mass spectrometer has a better shot of returning useful information. And typically what people use is they use a UPLC, which is essentially a purification method. So in one incarnation of this system, it may strip out from blood, the top 14 proteins that show up so that the mass spectrometer has a shot of identifying some interesting stuff beyond it. Those techniques are one complicated, expensive; two, they introduce bias into the results; and three, they're just not super effective. And so the 8% number that I gave you is roughly identifying 1500 to 2000 proteins; that's using a very complicated method of using columns that go and remove the abundant stuff from the blood and make a better analysis. This is really where Seer comes into play: Seer is a company that is focused on a better method of preparing the sample upfront than what a UPLC can do. And so that's a great method for improving the efficacy of what comes out of a mass spectrometer, but it doesn't fundamentally overcome the limitations of a mass spectrometer. And that's where this third group of companies comes into play. And I think that, in the third group, I would put those companies like us, which we don't know anyone else like us, and then those companies that are focused on trying to sequence proteins. Sequencing proteins is a hard and complex challenge: it requires very sophisticated biochemistry, the methods are not quite evolved enough where you know it's really feasible today. And the other thing is that you still have to fragment these proteins into peptides to be able to sequence them, because there's limitations in terms of the length that you can sequence. And so with that you lose sensitivity — a dramatic loss of sensitivity in the analysis. So when we look at the different competitors out there in the marketplace and different approaches, that's kind of the segmentation.
So my understanding then is that the real value is coming after hunting down these less abundant proteins. And so, connecting this back then to what the general public... what they need to know about the really importance of this new type of technology that you and your team are building, it's that if Nautilus Bio is able to help, say, a pharmaco and other parts of its customer set to be able to identify less abundant proteins, then that in turn, is valuable, because then pharmacos could develop better, more targeted personalized medicine.
That's correct. Yes, you're exactly right. So today, for a pharma organization, let's you know, in a simple model here: if a pharma company has a set of samples that have a disease cell, or disease cells, and they have a set of samples that have healthy cells, what they want to do is figure out what are the differences between these cells? And once I figure out what the difference is, and by the way, most of the time, over 90% of the time, the difference is there's a protein difference between these. The goal then is how do I build a compound that's going to be able to target that protein difference? And then I can deliver some kind of therapeutic? So if that's what I'm trying to do, I need to ignore the abundant stuff, because that's the same stuff, that's the stuff that is common between diseased cells and healthy cells. I need to focus on what are the rare things that are in these cells that are very different. We've been talking to one of the top 10 pharams for a long time, and the key scientist there, one of the things that he's told us is that in their mass spectrometry core, the most interesting targets they find, are at the very, very bottom of the detection threshold of mass spec. So his comments us was if you could just push that down 5%. All that next stuff is going to be super interesting, because it's the rare stuff that we're looking for. And so the ability to get deeper, has dramatic impacts on discovering what targets these pharma companies might want to use for their next therapeutic. And it also has broad implications to understanding how those therapeutics will work, right? Another example in pharma is that when you've developed a compound now you have to try to figure out well what's the therapeutic window? How much of this compound... how much of this drug could I give somebody in order to get the positive effect that I want but not have negative effects through the body? And today measuring negative effects through the body... it's really just a crapshoot, right? I tried an animal, I tried to human and we see what happens. Because we don't understand if I were to show this compound to all the other cells in your body, we have no way of profiling in advance what changes are occurring, where's the cross reactivity, the promise of being able to dig further into your cellular machinery in your proteins is that we'll be able to back up in the drug development process and understand well, how would this drug impact my cardiac system, my cells in my liver or my kidney? And if we could do that, it would make the drug development process much more efficient and much more effective.
Walk me through then the interface between your company and your customers? Is it that a customer comes saying, hi Nautilus, we'd like, help understanding an activation pathway for a particular disease. And then they give you that sort of requirements document and then Nautilus returns and says, okay, along the pathway are all these different types of proteins. So take a look at those?
You ask a complex question. I think that there are a broad range of business models that we will employ as a company. On one end, we will eventually have an instrument. And we will sell to customers the instrument, we'll sell consumables that power it and we'll sell them software as a platform in the cloud that they can use to analyze their results. And in that model, they're largely in charge of their innovation, and they're using our tools and our platforms to be able to get to that innovation. There are going to be some customers that are going to want a closer engagement with us, who are going to want help in trying to identify for a particular pathway what's going on, they are going to want to do things that are more customized on our platform than what our basic platform can do. And in those types of engagements, we are going to partner deeper with them lending data science capabilities and our knowledge base to help them in their mission. And some of those deals may look more like a traditional partnership that a biotech will often have with a pharma company where there will be milestone payments, and maybe some upside on the potential joint development. And then the other thing that we're thinking about is that in the first few years of this new technology being available, we're not going to be ready to ship a box to a customer and say, okay, it's all ready to go, it's packaged up neatly with a bow, just take it out of the box and run. And in that early phase, all of our engagements are going to be very close to a development and make engagements very close collaborations. And so we're up for helping our customers in whatever way yields joint discoveries faster, because that's in the benefit of both our customer and us, right? Our customer wants to get to innovation so they can build their next new drug, and we want to go and prove out this new platform and show that it's valuable to the world because it's going to be great for our business.
And just because it's like a hot topic right now is are you doing any partnerships, exploring potential treatments or studying COVID-19?
It's an interesting question. We're not far enough along today to make a dent in the COVID world. We've had many conversations with customers where they're like, boy, I would love to have this question answered, we're not there yet today, right? We're not analyzing customer samples, we can't give you a huge proteomic analysis but we'll see how fast this pandemic will go away, but if I were to bet on it, I think that our technology will get to the point where it's valuable in this pandemic at some point because this isn't gonna go away overnight.
And protecting against future pandemics as well.
For sure. That's a big part of what we want to make sure we do here.
I suppose when you're wearing your CEO hat, even though you may not have the offering on the table now to be able to help sort of fight this particular fight, but it must be awesome validation to have customers or prospective customers come knocking looking for... that as you build out these offerings that it'll be really potentially game changing for these folks. Rapid development of new medications and so much more between that.
Yeah, even more broadly than that, we're excited with our customers' reaction, not just in COVID, but just across a very broad range of applications. So I'll share with you a funny anecdote. We recently had a press release that we hired this guy, Nick Nelson, to be our Chief Business Officer. Well, Nick is a well known and super well regarded business executive in the biotech space. And he spent a decade of his career at Illumina. And now that Nick has been involved in some of our pharma conversations and had joint calls with Parag, my co-founder, one of the comments that he made to Parag, and I've talked to probably about this in the past was that, hey Parag, you realize, that these conversations aren't supposed to be 100% positive with customers. You're supposed to have a one in five hit rate. One time, they say, hey, can you do this, let's go do it at four to five. They're like, hey, this is great, come back to me when it works. And that's just not the case here. Every customer conversation is like, what can I do tomorrow? How can I get to the next step? How can you help me in this, or this or this? And it's really gratifying, right? Because what it tells us is that we're really scratching an itch that they have that's really important to their businesses, and we're working as fast as we can but we know that this technology will be really impactful when it gets out there.
So what sort of things are you doing, given the massive demand? And what are the things that you're doing to work as fast as you can, like what steps are you taking to kind of accelerate your development process?
Well, so that's an interesting question, right? We are building a company for the long run. So there is definitely a balance there of speed, and making sure that we're building a foundation that enables us to attract A players and build a great corporate culture, and build an organization that's going to be here in 20 years and be a huge player in this future proteomics industry. And so, there's a lot of tension to building a company when you have that kind of viewpoint, right? I mean, first and foremost, if you want to go and you want to move quickly, you should have a lot of capital. And, you know, we've been very fortunate that, you know, just in the last three and a half years, we've raised $109 million of capital, we have a blue-chip set of investors across the biotech and the tech world, including some public-private crossover money. And so we've got a great group of investors that's given us the capital to grow quickly, and to go after this problem aggressively. But still, today we're still only a 55 person company. I've told Parag and my recruiting team, I'd like to be at 100 like tomorrow, if I could. But it's just not feasible, right? We're trying to build an organization for the long term. And we had that healthy tension all the time of well, I know I need some bodies, but they're not the right people. And, we think we're making the right decisions for the long term but that tension is something I feel and I deal with every day.
With COVID-19, can I turn that question around then on to as a startup founder and managing not just like you and your co-founder's schedules, but now you've got 55 people and want to double that, how have you been navigating the challenges of whether it's shelter-in-place or — you are already sort of managing I understand two locations in San Carlos, California, Seattle, Washington — what's been working? And what may have failed — in that you sort of figured out like, ah, that didn't work — to make it better for running a company in this environment?
Yeah, I think that this has been an area where we have spent a lot of time and energy and we've been pretty intentional. And you have to kind of look at our two offices separately, because all of our wet science is done down in the Bay Area. And in Seattle, it's largely software engineering and administrative functions. I'm up here, my head of finance up here. And those are things that can be done from home pretty effectively. So kind of if you look at the Bay Area, once we got to the shelter-in-place order in California, we did what all companies had to do, which is we shut down and we assessed: what do we need to do as an organization to build a safe environment where lab workers can work once it's okay according to our county and our state regulations? And for us, that meant that we had to spend a lot of time energy and money on improving our office so that it could be safe in this COVID world. We added UV sterilizers in our HVAC systems, which was a huge amount of work and expensive. We added plexiglass and glass partitions between every office — in our open cubicle area, in our labs. We spaced things out, which made our space utilization much less effective. And we created policies to ensure mask wear. And we're created policies to ensure hygiene, we created a schedule for electrostatic sterilization for our facilities. Now, one thing after the other and that meant we were able to get back to somewhat effective work in three weeks, and then get back to pretty effective work within the first two months. Even today, we still deal with issues: childcare, people are on weird schedules because of their own childcare issues. We are very conservative with respect to employees if they're exposed, or even if they're two degrees of separation exposed to COVID. And so if your roommate's boyfriend had COVID, we don't want you back at work until you've had sufficient tests that say that you're negative. And so we've taken this conservative approach, and I think what it's done is it's helped us to maintain some goodwill with our employees. You know, up in Seattle, we were closed for a longer period of time, but eventually a number of us wanted to return back to the work office, and today we let people up in Seattle work from wherever they would like: from their house is fine, or from the office is fine. And so you know, at any given day, today, there's four or five, I think there's five of us here, but we have quite a bit of space relative to five people. And so we're able to physically distant, we have some UV sterilizers. And we kind of are smaller groups that were able to kind of keep our own pot. So that's how we've stayed safe. You know, what I would say, we could have done better and I hear this from other CEOs as well as is that this pandemic is... it's a really scary thing for people, it's unnerving, it's creating stress. And I think that, we tried to move very quickly. And we probably could be even more intentional with our communications, with our discussions one on one with employees about their concerns, and make sure that they are all kind of following our journey one step at a time. And I think we did a great job. And I think that that's always an area where we can improve, right?
That's... yeah, that's absolutely important, is the communication and being present, and so that you can really have your hands on the issues and trying to lead the ship, as well. What are your priorities then... looking about a year out? What do you prioritize over others for Nautilus Bio?
Yeah, so for us, there are, broadly, a few things that we need to do, and they're very interrelated. We need to get to the point over the course of the next 12 to 18 months, that we have the ability to routinely take customer samples in and return valuable information to them, that they have no way to analyze and get that information anywhere else in the world. And to start building the body of evidence that our technique is reliable and returning correct results reliably. That's job number one. In order to do that, I have to double the size of my SAP overnight. It's an interesting phenomena... a year ago, year and a half ago, you'd run an experiment, you'd get the results, you'd figure out what to do next. We're not in research mode largely anymore, we're in engineering mode, optimizing the protocol, figuring out how to build the next version of the thing that we have, figuring out how to make this thing go 50% faster to meet the spec that it needs. And in that world, there's 100 experiments in every single person's bucket that's piled up that needs to get done. And so we just need to grow our capacity, and we've got to be able to build our engineering and our research and development organizations to be able to get to the goal of going on the product side. On the commercial side of our business, we're really focused on refining our business models, widening our engagement with customers and signing the initial deals with customers that enable us to start to engage in some meaningful areas that will demonstrate the capabilities of our platform and those are that's kind of the stuff that is largely keeping me busy right now. When I look forward, one of the big goals for me over the course of the last year has been to transform the company from a leadership perspective from an early stage development organization to a company that's ready to go and introduce a product to the commercial markets. And so we entered 2020, with Parag and myself being the only two executives at the company. And over the course of the year so far, we hired Mary Godwin, who runs the operations organization for us. She's been at this for decades and has worked with me across a number of companies. We hired Nick Nelson, who I talked about earlier. And then I've already hired two other executives that are starting between now and the end of the year. And so we're going into 2021 with a team that's really set up to help us to move Nautilus into a commercialization phase.
I had a quick question, before we get the tying things up... so I wanted to ask you what's been the most fun part of your experience building this company?
What's been the most fun part of building this company? So, I, I am a computer scientist at heart. I am an engineer at heart, the most fun for me, has been actually getting involved and solving real problems and doing real engineering work. This company, my experience is actually quite different than my last company. And so when I found that Isilon in January of 2001, I was 26 years old, and no single thing about being a CEO. And I had to grow my staff from me to what ended up as 35 people at the end of year one because we had a massive chunk of software to tackle, to go and do what we're trying to do at Isilon. Because I didn't know how to do anything as CEO that consumed all of my time and that's what I did. At this company, Parag and I started with just the two founders for the first six or seven months, working through the tough challenge of hey, is that algorithm gonna work? And I took on a very different role, right? I mean, I've been a CEO from miniscule through public company, that stuff took 10% of my time, it's super easy for me to do. I spent 90% of my time on the biology and chemistry, education, and on coding, and actually working through the algorithmic pieces. So Parag was in the wet lab, six, seven months, when it was just the two of us, and I wasn't, I was in front of a computer coding. And it was a ton of fun. And believe it or not, even two to two and a half years in the company's life, I held on to doing some of the coding myself, which was a refreshing change that I did not get to do at the last place.
So the coding skills didn't just fade away as you're many years as CEO at the other company?
They didn't fade away. I mean, I've done plenty of side projects along the way, even as a CEO, but things those things come back to you very quickly. But now, I don't do any more coding, because there's a huge team of software engineers here now. But I still get involved in a lot of the engineering aspects of what we're building and how we're going to move that forward. And it's a really fun part of what I do, right? I mean, there's a lot of things that are part of a CEO's life that are mundane, or stressful or hard, or people-oriented, or whatever they might be. But for me, the engineering stuff is really energizing and fun.
To conclude the episode, we'll be glad to give you a space to share any thoughts or comments or shameless plugs that you'd like our audience to hear?
Shameless plugs? Well, look, I think I think that I've probably given you enough shameless plugs here. Hopefully, what I've been able to convey is that I think this is a hugely transformational company in the making. And I am incredibly excited and passionate about it. And I really appreciate you, Jonathan, Forrest, giving me the opportunity to go and talk to your viewers and tell you a little bit more about what we're doing. And I encourage you all to stay tuned, because I think that over the course of the next year or two, this company is going to be announcing some really interesting stuff that's going to have real impact on human health and human happiness. And that's, that's why I'm in this.
Oh, that's great to hear. So exciting and Nautilus Biotechnology for the audience is hiring. So if it looks like you've got the skill set and chops, and working with Sujal and his team, then it could be a great home for you.
I'm Sujal Patel, founder and CEO of Nautilus Biotechnology. Stay tough!
And that's a wrap of probing the proteome with Sujal Patel of Nautilus Bio. If you are a loved one suffer from allergies, our next episode may be right up your alley. We sit down with Conor Cullinane of Pirouette Medical. He and his team are developing an autoinjector drug delivery system. Their first offering is an epinephrine auto injector for emergency treatment of severe allergic reactions. Subscribe and join the mailing list so you stay in the loop. Meanwhile, stay tough.