>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

>> Announcer: Live from Austin Texas, it's theCUBE, covering South by Southwest 2017, brought to you by Intel. Now, here's John Furrier. >> Welcome back everyone, we are live at the Intel AI Lounge, end of the day, day one at South by Southwest, I'm John Furrier, this is theCUBE, our flagship programming brought to the events and extract a signal from the noise. What a day it is here, it's the packed venue, AI Lounge, with Intel, it's the hottest spot in South by Southwest, of course, where our theme is AI for social good, and our next guest is Bryce Olson with Intel, and your title officially is, global marketing director health and live services, but you are an amazing story, cancer survivor, but a fighter, you took it to technology to stop your cancer, and also, a composer with your friend, called FACTS, Fighting Advanced Cancer Through Song, the stories. Welcome to theCUBE! >> Thank you, it's great to be here, this is awesome, this is amazing environment that we're in today. But yeah, you're right, when you look at data, genomics data, which is looking at your DNA, and running that out and being able to understand what could potentially be fueling disease, that's the biggest of big data. And when I was working at Intel, I was in a non-healthcare oriented group, and then all of a sudden, I got hit with cancer, like very aggressive, advanced cancer. And I went through the whole standard of care, and I went through that one-size-fits-all spin that wheel of treatments and hopefully you get something kind of thing, nothing-- >> General purpose, chemotherapy, whatever, blah blah blah. >> Nothing worked. And I came to the point where I was start to come to terms with the fact that I may not see my daughter get through elementary school. So, cancer's starting to grow again, I go back to work, at this point, I only want to work in healthcare, because, why would I want to do anything else? I want to try to-- >> John: But you have terminal cancer at this point. >> I have terminal cancer at this point, but I'm not sick yet. You know, I went through all the chemo and all that crap, but I'm not sick yet. So, I asked to get into Intel's healthcare group, because I want to try to help healthcare providers make this digital transformation. They let me in, and what I found out kind of blew my mind. I learned about this new space of genomics and precision medicine. >> Well, it turns out, hold on for a second, you were telling me the story before, but you skipped a step, it turns out Intel has a lot of work going on, so you come into Intel, you're like, they open up the kimono-- >> Open up the kimono, and I learn about this new era called, just basically genomics, so what is genomics? Genomics, essentially, is a way to look at disease differently. Why can't we go in and find out what's fueling disease deep in the DNA? Because every disease is diagnosable by DNA, we just have never had the technology, and the science, combining together to get to that answer before. Now we do. So I found out that Intel is working with all these genomic sequencing companies to increase the throughput so you can actually take something that costs $2 billion dollars back in 2003, and took 10 years to do, get it down to $1,000 and do it in a day, right? So now, it democratizes sequencing, so we can look at what's fueling disease and get the data. Then I learned about Intel working with all these major bioinformatics open stores and commercial providers, the Broad Institute at MIT, Harvard, largest genomic sequencing place on the planet, about how they take that data and then analyze it, get to what is really fueling disease. And then I learn about the cool things we're doing with customers, which I could talk about, like actual hospitals. >> Well, let's hold on for a second on that, your shirt says Sequence Me, but this is really key for the audience out there listening and watching, is that, literally 10 years ago the costs were astronomical, no one could afford it. Big grants, philanthropy-funded R&D centers, now, literally, you had your genome sequenced for thousands of dollars. >> Well, so, and this is what happened, right? I learned about all this stuff that Intel's up to, and I get kind of upset. I get kind of pissed off, right? Because nobody's giving this to me. Nobody's sequencing my cancer, right? So I go back to the cancer center that I was working with, this is January 2015, turns out they were getting ready, they were perfecting their lab diagnostic test on this, it was like a perfect storm, they were ready, I wanted it, they gave it to me, turns out my cancer grows along this particular mutated pathway that we had no idea. >> So the data was, so in your DNA sequence step one, step two is you go in massive compute power, which is available, and you go look at it, and it turns out there's a nuance to your cancer that's identifiable! >> Yeah, a needle in that haystack, right? The signal in the noise, if you will, right? So there's a specific molecular abnormality, and in my case, there was a pathway that was out of control, and the reason why I say it was out of control is, the pathway was mutated, but then there's this tumor suppressor gene that's supposed to stop cancer, he's gone! So it's like a freeway of traffic-- >> So he's checked out, and all of a sudden, this is going wild, but this is cancer for everyone has their own version of this. >> Yes they do. >> So this is now a new opportunity. >> Yes! Now we understand what's fueling my unique cancer. We took data, we took technology and science, and we got to the point where we understand what's fueling my cancer. With that data, I find a clinical trial testing a new inhibitor of that pathway. >> So I just got to stop and just pause, because it's very emotional, and first of all, man, yours is an inspiration to me and everyone watching. I'm looking at some sign this year at the Intel AI booth, and it says, "Your amazing starts with Intel," this is truly an amazing story. >> Yeah, thank you. >> It's really beyond amazing, it's life saving! >> And that's what happened to me. >> This is now at the beginning, so take me through, in your mind, where is the progress bar on this, in the AI evolution, or when I say AI, I mean like machine learning, compute, end-to-end technology innovation. It's available, obviously, when is it going to be mainstream? >> Yeah, so, we're at a point right now where we can go in, if you have advanced cancer, we're at a point now where we can sequence that person's cancer and find out what's driving it, we can do that. But where it's going to get problematic is, look at my case. The mutated pathway hypersegmented by cancer, right, so prostate cancer, a common cancer, now became a rare cancer, because we hypersegmented it by DNA, and I went after a treatment that was targeted, so when my cancer starts to grow again, now I'm a rare cancer. So how are going to find people that are just like me out there in the world? >> So your point about rare being, there's no comparable data to look at benchmarking, so that's the challenge. >> Yeah, no given hospital will ever have enough data in this new molecular genomics-guided medicine world to solve my problem, because the doctors are going to want to look, and they're going to say, "Who out there looks just like Bryce "from a DNA perspective, uniquely? "What treatments were given to people like that, "and what were the outcomes?" The only way we're going to solve that is as all these centers and hospitals start amassing data, it has to work together, it has to collaborate in a way that preserves patient privacy, and also protects individual IP. >> Okay, so Bryce, let me ask you a question, if you could put a bumper sticker or a soundbite around what AI means to this evolution innovation around fighting cancer and using data and technology, what is the impact of AI to this? >> So, where I'm kind of going with this analogy is that without artificial intelligence to sift through my data, and all the other millions of potential cancer patients to start getting DNA data, humans can't do it, it's impossible, humans will not have the mental ability to sift through reams and reams of DNA data that exists for every patient out there to look at treatments and outcomes and synthesize it, we can't do it. The only way someone like me will survive into the long term will be through artificial intelligence. Without it, I will extend my life, but I won't turn cancer into a manageable disease without AI. >> So the AI will extend your life. >> Because AI is going to solve the problems that humans can't. When you have the biggest of big data-- >> Love that soundbite, love that, say that again! AI solves the problems that-- >> AI is going to solve the problems that humans can't, they simply, humans don't have the capability to look at the entire genome, and all this other genomic, molecular, proteomic, all this other data, we can't make sense of it! >> Alright, so let me throw something out at you, 'cause I agree 100%, but also, there's a humanization factor, 'cause now algorithms are also biased by humans, so what's your thoughts, given your experience, the role of the human race, actual human beings, that have a pulse, not robots or algorithms? >> Yeah, so let me give you a real practical example. So, the way that we fought my cancer was through a targeted therapy. Molecular abnormality, targeted drug. The other way that people are fighting cancer is through immunotherapy. Wake up the immune system to fight it. Guess what? Right now, there are 800 combination therapies going on with immunotherapy to try to stop people's cancer. How the heck are we going to know what is the right combination for each person out there? Unless we have like an algorithm marketplace where people are creating these, and taking in predictive biomarkers, prognostic biomarkers, looking at all the data, and then pushing a button to help an oncologist decide which of the 800 combos to use, we'll never get there. So-- >> That's awesome. So let me ask you a question, so for people watching that are younger, like my daughter, she's 16, my other daughter's a premed, she's a sophomore in college, they're like, school's like old, like, school's like linear, they get classes, but this younger generation are hungry for data, they're hungry, they want to, they're young, they're what people do, they disrupt, they're bomb throwers, they want to create value, and so their incentive to go after cancer, and the means are out there, cancer cells, we all have relatives who have died of cancer, it's a sucky situation. There's a motivated force out there of scientists, and young people. How do they get involved? How would you look at, based on your experience, and your experience, obviously, you got these songs here, but on a more practical level, what discovery, what navigation can someone take in their life to just get involved, not a catalog, not the courseware. >> I think, so there's a number of different things that can happen, if you look at the precision medicine landscape, and you start with a patient, patients don't understand this. "Genomic what? "Sequencing what?" They don't understand that there's a new way to fight cancer, so guess what's going to become a 20% per year growth rate job in the next 10 to 20 years? Genomics counselors. You don't have to be a doctor, but you have to be able to understand enough about biology-- >> And math. >> To be able to offload doctors, and have a discussion with patients to say, "Let me explain something to you. "There's a way to understand your disease, it's in DNA, "this is what it means," and then help them guide them into new clinical trials and other therapy that's got it by that, huge growth opportunity for kids. >> But also, it's compounded by the fact we just said earlier, where these become rare cases on paper, are also need to be aggregated into a database of some sort so you can understand the data, so there's also a data science angle here. >> Absolutely, and it's not just cancer, by the way, I mean, little kids in the NICU, pediatric ailments. Have you ever know anybody who's got a kid with a very rare neurodevelopmental disorder, and the parents are on a diagnostic odyssey for 10 years, they can't figure out what it is? So they go from specialist to specialist, specialist, $100,000 dollars later, guess what, the answer's in the DNA. >> DNA sequencing, number one. >> DNA sequencing, number one, and then, once you start sequencing that, you got to make sense of all this data, so there's going to be tons of jobs, not only in biology, but in analytics, to take all this data and start finding-- >> Alright, we got a few minutes left, I want to get a plugin for your little album here, it's called FACTS, Fighting Against Cancer Through Song. >> So here's the story on that. So, when you go through something that could be terminal, it's really nice when you can have something productive to channel that energy. So for me, to be able to channel feelings of sadness and frustration, I started writing songs. Music was therapeutic for me. I took that, started collaborating with a bunch of musicians throughout Portland, including cancer survivors, and we said, why don't we use music as a way to reach people about a new message of how to fight cancer? So we created that, I have an organization that is raising awareness for a new way to fight cancer, and raising funds, to bring sequencing to more people. >> So the URL is factsmovement.com, so factsmovements.com, check it out. Okay, now, I'm so impressed with you, one, you are on a terminal track, you go back to work. >> But I don't look like I'm terminal! >> You look great, you look great. Now, you're at Intel, Intel's got technology, you harness it, now, you're on a mission now, your passion, it's obvious, the songs, now, what's going on in Intel, 'cause now you're out doing the Intel thing, gives us the Intel update. >> I can talk to you about this precision medicine, it's personalizing diagnostic and treatment plan, which I've already done, I could talk to you about other things that we're doing to help hospitals transform. Predictive clinical analytics, let's look at something like rapid response teamed events. Have you ever been in the hospital and heard the alarms go off? That's usually somebody having a heart attack unexpected. Data is out there, if you look at all the data about people that have had rapid response teams events, we can create predictive signals to actually predict that an hour before it would happen! So predictive clinical analytics, and enabling hospitals to look at populations as a whole to treat them better in this new value-based care, is a technology-driven thing, so we're working on that as well. Yeah. >> Well Bryce, thanks for coming on to theCUBE, we appreciate it, really inspirational, great to meet you in person, and I'm looking forward to following up with you when you get back to Portland, we'll get our gang in Palo Alto to get you on the horn Skype in, and keep in touch, really inspirational, but more importantly, this is very relevant, and the technology's now surfacing to change, not only people's lives in the sense of saving them, but other great things. >> And I'm so proud to be able to work for a company that is using its brand and its technology to basically change people's lives, it's amazing. >> Bryce Olson, my hero here at South by Southwest, amazing story, really, really, you can choose to be a victim or you can choose to go after it, so excited to have met you, it's theCUBE, breaking it all down here at South by Southwest at Intel's AI Lounge, it's hopping, music tonight, music tomorrow night, CUBE tomorrow, panels, AI changing the future powered by Intel, #IntelAI, I'm John Furrier, you're watching theCUBE, thanks for watching, we'll see you tomorrow.