Harry's guest for this unusually frank and urgent episode is Jacob Glanville, the founding partner, CEO, and president of Distributed Bio. The company is using its skills in computational antibody analysis and optimization to help the drug industry develop new vaccines and antibody-based treatments for a range of diseases, potentially including the coronavirus that causes COVID-19.
If you've seen the recent Netflix docu-series "Pandemic," about efforts to check previous viral outbreaks, you've seen former Pfizer scientist Jacob Glanville in action. The inventor, entrepreneur, and Ph.D. immunologist capitalized on the advent of cloud computing to provide vaccine and drug developers with high-throughput genomic sequencing of antibodies in humans and other species. He calls it "using the ability to look deep into these maelstroms of antibodies to try to understand why vaccines fail to hit conserved epitopes [where antibodies attach to antigens] on influenza or HIV, or how to better produce an antibody medicine." Revenue from the service allowed the startup to grow without outside capital. Today the company is developing a universal flu vaccine for pigs and humans.
Glanville says we'll know by April whether existing anti-malarial, anti-HIV or anti-Ebola antivirals work against the COVID-19 coronavirus. A vaccine will take far longer to develop, he says. Meanwhile, Distributed Bio is using its search platform to find new antibodies—derived from antibodies that neutralize the SARS virus—that could recognize the new coronavirus and provide instant (but relatively short-lived) protection. Glanville compares the search to "taking five billion spaghetti noodles and throwing them against the wall and seeing what sticks."
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Harry Glorikian: Hello, I'm Harry Glorikian. And this is Moneyball Medicine. The show where we meet executives, entrepreneurs, physicians, and scientists using the power of data to reinvent healthcare from machine learning to genomics, to personalized medicine. We look at the biggest trends in patient care and healthcare management.
And we talk to people behind the trends to find out where data is making the biggest deal.
Well, my next guest and I talk about how data can make all the difference across all areas of human health, diagnosis, monitoring, and therapeutic development. We discussed the company he co-founded and its mission and tools. And we have a very blunt fact driven conversation about Corona virus. If you want to hear an unvarnished conversation, this is it.
His company is made up of computational immune engineers with a mission to create breakthrough technologies in areas that are typically challenging to treat. Imagine something like a universal flu vaccine or a way to retool something. We already have to treat an existing pandemic. Dr. Jacob Glanville is an entrepreneur inventor and computational immuno engineer.
He developed the core business model. The research teams and the technologies that enabled his company distributed bio to become profitable without any external investment with cool product names, like superhuman tungsten app, Genesis, tumbler, and scent evacs, distributed bio hopes to accelerate the race, to solve very big problems.
Now that brings me to Netflix. Netflix you ask, where does that fit into this? Well, as luck would have it, Netflix recently premiered its new docu-series pandemic, how to prevent an outbreak. And the timing could not have been better. Especially if you look at it through their lens incorporated into this docu-series are the people on the frontline working to keep outbreaks in check and even eradicate them altogether, such as the work coming from the company, Jacob co-founded.
A quick warning and a couple of spots. This interview contains strong language. Jake, welcome to the show.
Jacob Glanville: Hey Harry. Thanks for having me on. It's nice to have a little bit of break from all the coronavirus. Uh, Freak out.
Harry Glorikian: Yeah. I mean, w w we're we're in a little bit of a hot mess here. I mean, there's a, there's, there's a lot going on in you are, you are everywhere, man.
I mean, Netflix, um, you know, I don't seem to be able to find a new shore YouTube channel that doesn't have you on it. I mean, you're, you're famous.
Jacob Glanville: Well, we had, you know, I told my team when the Netflix documentary was coming out, I was just like, guys, there's 10,000 things on Netflix. Like, no one's going to watch this, but then it happened.
That it was released right around the time of this outbreak took place in China. And so suddenly we were on the front page of Netflix for three straight weeks. And so we became like inadvertently the, uh, one of the faces people turn to, to ask about the nature of the,
Harry Glorikian: Oh my, that, that I'm sure that the producers are going, Oh my , it couldn't have had, I mean, talk about ratings, right, right off the chart.
Jacob Glanville: Yeah, they were, uh, they are talking about a second, a second series that, or a second season might be happening.
Harry Glorikian: Oh, I could give them some feedback if they, if they wanted it. Like, there was a few things in there. I was like, not so sure about that.
Jacob Glanville: Okay. I'll put you in touch. I'm sure that they're cool people they'd like to hear it.
Harry Glorikian: Um, so listen, I, you know, one of the things, you know, everybody's bombarding you about, you know, COVID right now, but, but. Let let's step back here for a second. I mean, you, you didn't just, you know, fall from the sky and, and they're calling you, right? This is, you've been working on Distributed Bio now for, for quite some time, you know, tell me about the company.
Tell me about, you know, How this thing started in, you know, I think I said to you, even before we started recording, I mean, I think if you had tried to do this, you know, 10, 15 years ago, I'm not sure it would have worked exactly well because of where data was and where computing was
Jacob Glanville:. Yeah, no, I completely agree.I think Distributed Bio is a computational immuno engineering company. I couldn't have started five years before I did, because it didn't have the cloud compute capabilities that helped us launch. And I think it would have gotten too competitive five years after I did, because then more people would.
You'd be able to start thinking about these things well and taking advantage of them. So at heart, what my company is doing is taking advantage of high throughput, data acquisition, um, against immune system. So using genomic sequencers and the arms race to make the better, faster genomic sequencer. And instead of pointing it at the 26,000 genes in the genome, if we're pointing it at like a hundred million different antibodies that a person can produce.
Or looking at mice or looking at antibody libraries or Hyperdome or vaccinated individuals, and using that, that ability to look deep into these, these mouse rums of, of antibodies, to try to understand why vaccines fail to hit conserved epitopes on influenza or HIV, or how to better produce an antibody medicine.
So these are technologies that I, um, was, was beginning to build in 2008 to 2012. When I was at Pfizer. Then they let me publish on it and it traced back to work. I was interested in doing back at Berkeley before I joined Pfizer, where I was taking the kind of nascent big data, which at the time was like 7 million sequences.
And in our, which seems trivial now. But back then, it was amazing. And going down to the stacks because all the data and all the, all the damn papers weren't even up online yet. So I'd go down to the stacks and I'd collect statistics on population genetics and different of HLA and different populations.
And I'd bring them up and enter them into Excel sheets. Um, and now we're where we are now. I can generate 20 million sequences, no problem in an afternoon. And so I was able to just surf on those new emerging technologies and realize that they could really crack into the immune system and help us understand these like deep, annoying mysteries of why are vaccines missing and why are we not producing antibodies against the hardest and most valuable drug targets?
And so. That's what distributed bio is and because of the nice timing around when I launched it, and because of the way I built the vertical stacks, I first built a cloud-based computational platform for analyzing antibodies and a bunch of pharmaceutical companies paid me to do it. So I never needed funding.
That was profitable. Uh, client money, which is the best money right at the start. Right. And then I folded that money back in to the initial reluctance on my partners, antibody discovery, physical wetlab technologies, which was pretty damn scary. In the beginning they were like, Hey, what are you doing?
You're spending all of our money. We could have just sat on it. Yeah. But, but, uh, the inflection point of value, there was huge because suddenly we had a technology that could command like 10 times the amount that the software could and we could get milestones and royalties. But from my perspective, the killer thing was I really just wanted to build data, money, laboratories, and technologies that would let me build a new generation of trucks.
And that's what I wanted to go first is I feel like we could, we could realize the real value of the biotechnology, like a data, heavy revolution and building kinds of medicines that previously would have been impossible.
Harry Glorikian: But it's interesting, right? I mean, I'm, I'm, I'm, I'm in, you know, I'm always talking to different companies and what you just described is.
The new model, right? It's, it's a unique technology. It's a service in the beginning. It's being paid, it's putting the money back in, it's building it out. And the next thing, you know, you're able to actually generate product in the end. So it's not just a plain service anymore. I mean, it's a completely different model than I think what we've seen in the past.
I think it's just because of the data and because of you can repurpose that data or tweak that data in different ways. You're able to sort of take the company through a metamorphosis in a sense
Jacob Glanville:, if you can build something that's useful along the way, you should always do that because it gives you resources.
But second, like your clients are giving you money, but they're also forcing you to work on things that are important, right? Forcing you to make things that work and. I remember looking at, uh, you know, some of the traditional funding model is you have some guys that come out of there, you know, arrogant and ignorant.
They're really good at one thing and probably bad at most other things, but they come out and they everyone's been telling them they're smart, their whole lives. And so they go, I just emerged from Harvard and Stanford. Somebody gave me $30 million. Then they do that dumb thing where they have too much money.
And so they end up hiring all their friends and getting a nice facility with a pool, like nice pictures for their mom. And then, and then you have a bunch of people sitting on sitting on their hands because I heard a bunch of people they don't need yet. So they're on this crazy burn rate. And then they're trying to.
When they're trying to grow, what they're really trying to do is like not actually grow the fundamentals they're trying to and impress the next round of investors. So there's a little bit of an alignment incentive happening. So that, that whole system it's comfortable, but you know, you have the pool, but it's a dangerous value proposition.
And a lot of these companies fail. So yeah.
Harry Glorikian: But, but you're coming at it. Look, I, you know, I admire your, you know, let me solve the big problem. Right. I mean, I, you know, this all started with, correct me if I'm wrong is sort of a universal flu vaccine, right. And that's a big idea that it's not, it's not every day that somebody gets to start, that moves forward on, it ends up getting money, continuing, you know what I mean?
It's, it's a big problem. It's not going to be solved overnight.
Jacob Glanville: Yeah. I think I, part of my incentive to push this thing was I had this idea for how to create a whole new class of vaccines that would work against broad spectrum, like rapidly mutating pathogens, so broad spectrum vaccines. So the first one we're doing is flu that one ultimately got me in front of Bill Gates and we want a Gates grant.
Um, and we were also applying the same technology to see if we can make a, an HIV vaccine that would actually work. And there's a number of other pathogens. Right. And I thought of the idea and the approach, um, Pretty early on. And I, I just felt like if I went and talked to people, I didn't have a PhD. I was not a professor.
Um, I was too low in the totem pole where either people would just ignore the idea entirely or they would take it from me. And so I actually built a company because I'm like, I don't need that many resources. I plotted a path towards proving this. And it was a little slower than if I'd gotten all the money up front.
But on the other hand, I was able to maintain control and I was able to execute because I think it really, I think people just wouldn't have even heard me back then.
Harry Glorikian: I love, I love this story. Right. I love this story because I've been getting my whole life of, you know, where's your PhD. Right. But, um, you know, let you know, but stepping back here for a moment, cause I haven't watched the whole series.
Right. I tried to watch part of it, but so I was wondering like what happened after the last pig? You know, you were working on that and, and it was the, towards the end of episode two. So I was really like wondering, did you get the, the Gates grant and, and, um, You know what happened next
Jacob Glanville: Spoiler. Yeah, we got it.
Yeah. So, I mean, that was four years, right? So we, you know, I came up with the idea in 2012, we built up the company started and then it was really like 2014. We started building an animal animal facility. Uh, 2015, mostly in 2015 down in Guatemala. My brother is a construction worker and my father had extra property.
I have a friend, professor, Erin Caguas at the university of San Carlos and Guatemala that we were collaborating with. And we basically built this thing up because we realized we could, um, I'm actually, let me step back a little bit. What initially we were doing is we were going around and trying to talk to VCs, the normal process, and they weren't that hot into vaccines at the time.
And I was a No- namer and I was talking about a very different type of technology. So at one point Giles and Chris and I, my, my co-founders, we went to this, like meet the VCs evening. John was like, come on, come on, you got to come out and meet the VCs. He's British. And, uh, it brings me out there and I was, I don't know, I was kind of just over it because I feel like the, the meetings weren't productive and there was like a T there's, like, I don't know, like 150.
Psycho fence, like all hanging out, you know, being very nice to the people on stage from like six different VC firms. And the questions were like, they were not, I'd say they're not hardball questions because people wanted to get funded. So
Harry Glorikian: Of course
Jake Glanville: People would get up and be like, so would you say you're very happy or very, very happy that you could, you know, flow innovation into the community?
And they're like, Oh, that's a great question. The answer is very, very happy. And so I'm sitting there just being like, this is ridiculous. And then John was like, elbows means like, say some genius shit so they remember us. if we could get their attention right?. So I'm like, all right. So I raised my hand and I'm like, yeah. So what are you guys doing about the funding gap? And they're like, what? And I'm like, yeah, the funding gap, the area between like innovation of an idea. And it being proved out enough that you guys want to invest in it.
They don't do that. A lot, a lot of them don't do that. There's a select few that do.
Jacob Glanville: Yeah. Well, they kicked me in the balls pretty hard. So like first one of them said, yeah, I know we've got a good portfolio. And I'm like, well, look, I've actually looked at all your portfolios. You guys are heavily leveraged towards like things that are de-risked and you're externalizing early risks to academia.
But the problem is the academics have the same problem in their grants. They can't make them too innovative or they won't get funded. So you're on skunkworks. Right. And then there's one guy who. I will, there will remain nameless, but he looks over at me. He takes a sip of his drink and he's like, I would never invest in someone who believes in a funding gap and the whole audience starts laughing and I'm just like, damn it.
And, and my other partner, Chris leans over. He's like Jake, I think he just learned a lot. And I was like, Chris, I'm going to murder you. Like, what do you?
Harry Glorikian: Yeah, well,
Jacob Glanville: But it was good. Cause it was cause it got, I went home and I was like, okay, I need it. Find a way through this to like solve this problem where I don't need to be somebody to accomplish it.
And I started thinking about where animal models, where I could make money before I get to clinic. And I contacted a friend at a, at a veterinary company and he's like, dude, it's it's flu and pigs that's 175 million a year, which has gone up since. And then at that point I was like, you know, there's a, there's a pig industry in Guatemala.
And I contacted the professor and my father and asked him how much does a pig cost in Guatemala? And that's, that's really what started that process. Cause I had labs in the state store on the assays. I could do neutralization assays and stuff up here in theory. Um, but I needed a facility to run it and I needed to be able to enter it iterate quickly and not wait two years for grants, which, which turned out to be a good choice.
Like I got the Gates grant, but it took me three years of politicking to get, get to that point. Where in that period, that same period, we ran four animal studies.
Harry Glorikian: Yeah, no look, I mean, I, I, my last episode was a, I was talking to Rami Ferrari from, uh, Schrodinger and. From where they started to where they are now.
I mean, that, that is not for the faint of heart. I mean, you have got to have a long view and your investors have got to be patient because you're trying to do something that nobody's ever done before. Now. Now let's jump back to where are we with the universal flu vaccine? And do I have to get seven shots?
Yeah, got it. So we're better than seven shots. So, yeah. Um, Sarah arrives is my project lead on this one and we've been working on it steadily for four years. Uh, we first, the first study was just proving the principle. So we showed we can get broad responses. The second study was the seven shots where we got super broad neutralization, uh, future viruses, but you had to give all these, these booster shots to the pigs, which is ridiculous, the newest round of studies we've got it down to three shots. Um, that we think that's a little high, but that's, that's people would tolerate that farmers still, I think would prefer to have less. Um, so where we are right now is that the Gates foundation gave us a larger award and we are now running the studies, not at our facility in Guatemala anymore.
It's being run at the pure bright Institute and the UK animal biosafety research group. And they're going to do a live challenge studies with us. So we're trying an additional panel of, of, uh, And, uh, adjuvants and gel formulations. So ways we can try to give a more vigorous response with less, less shots.
Harry Glorikian: Right.
Jacob Glanville: Uh, and then we're going to do a live challenge, which is basically for the ferrets and pigs received. After they received the vaccine, we let them respond to it for a few weeks. And at that point we spray them in the nose with, with the future flu virus. Uh, and we ask, do they get sick or not? That's the ultimate test of whether the vaccine works.
And that was, that was a study. I could never run in Guatemala because we can never bring live virus down to Guatemala, but it's appear bright. They have these biosafety facilities where they kind of studies.
Harry Glorikian: Although I never thought I, I, you know, I just, I've never thought about it. I'm like, does a ferret get the flu?
It's an interesting question.
Jacob Glanville: They get the flu. Yeah. Weird historical reasons. You don't need to test on primates for flu. You can actually test on ferrets and arguments like, Oh, ferret lung tissue is like very familiar and similar in way respects to humans and like, get the hell out of here. We're just doing ferrets because other people had done ferrets for decades.
Now we have to copy the You have to compare the data.
Harry Glorikian: Right. I love it. I love it. Well, you know, when do you think, you know, if you were to project out into the future, assuming things, you know, Science never goes according to exact plan, but when do you see this, uh, being available for, you know, the average person?
Yeah, sure. So two stages. One is the animal shot and then the human shot. So the animal shot should be ready late 2022 for 2022 flu season, potentially 2023 though. They open unknowns. They're really come around, producing the batches and industrialization of the product. That's 175 million, 180 million market.
Um, per year for flu shots for pigs, that human stuff requires phase trials. So there we're looking at, uh, fall of 2026 is, uh, or 2027 is when our universal flu shot would be available for humans.
Harry Glorikian: All right. So I have a while. Yeah.
Jacob Glanville: You can keep washing your hands?
Harry Glorikian: Yeah. That's, uh, you know, under the last week or so I've, I've washed my hands till they're raw. I mean, it's ridiculous. Um, and I'm usually not paranoid.
Jacob Glanville: Um, I feel like I've always washed my hands wrong now. They're like 20 seconds of handwashing and I'm sitting there being like, I never washed my hands this long.
Harry Glorikian: Yeah. It's the happy birthday song you're supposed to go through, I think. Um, but all right.
So, so now let's jump to like, you know, of obviously the, uh, The, the, you know, what's in the news and driving people crazy. I think, you know, people are going to get coronavirus fatigue here, but tell me, like, I've been trying to read every last damn thing I can get my hands on. And why is this virus spreading? It feels like spreading much faster than I've seen in a lot of other situations.
Jacob Glanville: Yeah. So, I mean, one is we're tracking it. So the flu spreads like crazy, right? We have 50,000 people can easily die from the flu in the United States and a flu season. Um, where I think there's only been something like 3000 people who've died all over the world from the coronavirus. Um, so in terms of like total impact, it's just that we kind of, we don't report as feverously on the flu outbreaks, but there's some differences.
So this, this virus does seem like it spreads, uh, more efficiently than flu. There's this number called RNR, which is it's basically for a given person infected how many new people does that person. In fact, it's, it's like, how, how quickly does the fractal spread?
Harry Glorikian: Yeah.
Jacob Glanville: And, uh, this one, they're still trying to nail it down, but it seems like it's got a pretty hard high or not like at least twice that of flu.
So it's pretty infectious. There's a couple of reasons for that. Um, so the, the current is there. That's like the chassis that SARS and MERS was built on it. It's also a very distant related cousin to certain types of. Uh, cold viruses. Um, but this one's obviously much more dangerous. So those viruses are like the flu and that they, those direct sunlight will kill them.
That'll break up with the RNA, um, being on dry surfaces and warm air, um, will kill them and not being in a body for a while will kill them. So. Uh, the worst case scenario would be like the fish market where it's indoors, everything's cold and wet, and people are pushed together. That's where you're gonna get a lot of infections.
This thing seems like it survives pretty well on surfaces for a bit, and it's pretty infectious. And then the scariest part about this virus is that, which is not usual for most viruses, is that there's a, uh, up to two week period where someone could be infected, not really show any symptoms, but they could infect other people.
So it's like a Trojan horse where that person could travel across countries. Across States into your home, um, touch a bunch of stuff and potentially many other people could be infected silently without realizing. It also means there's like a weird time delay. When you see a couple of like right now, there's about somewhere I think is under 60 cases in the Bay area.
Um, but, but that means that, that, that really the cases that we see is two weeks time delay from the continuous affections that have happened since then. So we're in a much worse position than, than we know at every time point because of the outbreak has, has continued silently. So that's one of the scary things about the, this guy.
The other one is that kids don't get that sick. And you know, kids, you know, every parents know this, they're all going to get sick. So kids don't get that sick. They're going to spread it all to each other. They will still get sick. This is not like a safe disease. It's not like chickenpox where it's relatively minor.
This is dangerous, but the kids won't die, but they will spread it to a lot of their parents. And the people who are really in danger are, uh, grandparents. So people over 80, even anyone over 60 has an elevated risk, but once you're over eighty, so a 15% chance of dying. So I honestly I'm surprised that they have not instituted a national rule to provide quarantine on retirement homes.
Uh, nursing homes and other areas that have concentrated elderly, because those are places where it's going to outbreak quite a bit and there's going to be a lot of deaths.
Harry Glorikian: Well, I want to get back to that, but, but let's, let's step back on, on the actual virus itself. Right? So the, yeah, I was just looking that, you know, the Chinese has now said that there's two different strains.
Um, that they've detected,
Jacob Glanville: they're seeing two different strains and they're claiming one's more virulent than the other. I got to say, I looked at the, I looked at the data, like, they're not that different. There's like a couple of mutations. Um, if that doesn't mean that those mutations couldn't cause differences in virulence, but I'm a little skeptical.
So one of the, one of the good things that's happened here is that scientists all over the world have been sharing information like really efficiently. Um, this is the first uh, you know, one of the first major outbreaks in the kind of the modern digital era, um, and, and sciences have got better in coordinating.
The bad thing is that people are dumping all these, these, um, these articles that look like science, but they have not gone through peer review and to something called the bio archive. And so there's a whole bunch of like pseudoscience has been published and retracted and posted and retracted, which is.
It's really unfortunate. Like I understand why people want to get it out there right away, but there's a fucking reason why we have scientists review each other's work and ask good questions because it reduces, I mean, that's really like one of the majors. If you think about it, it's one of the major successes of the scientific culture and processes that we have peer reviewed multiple.
I mean, really, we should do this in our lives all the time. Anytime you have an idea, I should talk to a couple of your smart friends and be like, let's think about this. Is this a good choice? And science has distilled that, but that, that process is being broken right now, which means I'm always a little skeptical of new data.
That's emerging where I'm like, okay, How much can we trust that information? So like for the case of the two strains, I would want to look carefully and see what exact evidence they're coming up with to make the argument that one of these strains is more virulent than another, as opposed to, like one just happened to outbreak in an area that had like worse medical care or more, more smokers or some other, some other effect.
Harry Glorikian: So let's talk about detection here for a minute, because that seems like a mess. Um, at the moment, at least here, uh, You know, sequencing PCR, other methods. I mean, what do you,
Jacob Glanville: Yeah, so the standard method that you can deploy super fast, right? Is quantitative PCR,
Harry Glorikian: Yeah
Jacob Glanville: Real-time or quantitative PCR. Um, that's the standard protocol that's being used all over.
It doesn't require any special antibody reagents. It just requires the, you know, the sequence of the target and you can produce some, some primers. So that's the technology that's been deployed in China, you know, South Korea. Mostly all over the world, the same kind of kit is being used. There was a decision made in the United States in January when the outbreak was becoming evident to not use that kit to instead they were building a kit that was going to have.
Not just to detect Corona, but also to detect a whole bunch of other viruses. And look, I, I get why that, that made sense, right? They're like, well, this, this way you take someone's sample and you don't just say Corona, not Corona. You're like, okay, this person has flu, right. Or this person has something else.
That's great. But the problem is that they didn't test the kit, that the CDC, there was a decision in 2018 by the administration significantly reduced the funding and the head count or the CDC, including the pandemic preparedness group. And so. I don't know. I think they just are operating with like a skeleton crew, but whatever happened that the testing on the new kit was insufficient.
And so went out to a whole bunch of labs and then, uh, it would start showing these false positives against viruses that were not in the sample. So I don't know exactly what they're testing, but you can imagine someone coming in and being like, all right, let's test you. Okay. It looks like you don't have the coronavirus, you have the flu and also maybe a Bola and you know,so, so they froze it. So they basically said, you know, what, how about nobody does any testing except for like five labs, really thought people were doing it properly, which basically meant that those, I think those labs have policies in place to ignore certain channels. Um, and so they did that. And then they also had a weird policy rule that said only, only the only people that are allowed for testing are people that had just come back from China.
Harry Glorikian: Right? So you're not, yeah. You're not testing the community.
Jacob Glanville: Yeah. It's a really, it's a terrible rule because then you're not detecting the thing you actually care about, which is community outbreak. So over the course of, from January mid January, suddenly into the end of February, there had only been 414 people tested and scientists.
We are all aware of this and we're freaking out because people were going around gloating being like, Oh, America doesn't have this problem. Like, no America has the problem. We're not even, we don't even know. So we have. A complete absence of effective surveillance going on, which means the outbreak has been taking place here.
Potentially for six weeks without people, without any monitoring.
Harry Glorikian: So I feel like there's a disconnect between the science community and non scientists for lack of a better, I mean, I know when I talk to people, I know that literally they're like, ah, this is nothing we don't even need to worry about it.
I'm like, Hold on here. Just, you know, we don't know what we don't know yet. Right. And, and this thing is evolving and, and don't, you know?
Jacob Glanville: Yeah. So the good news is it's, you know, it's not going to destroy society, right. There's not enough people that are going to get sick, that we're going to lose power lights.
Like people come to work, but the bad news, like they need to be realistic. They should look at, go look at live videos, coming out with life is like, and Wuhan in China, what it's like in Korea, what it's like in Iran, like you can actually go like. Use the magic mirror of the internet and peek through people's lives over there.
Like it is highly disruptive
Harry Glorikian: . Um, well, I mean, if this thing got into a, uh, you know, an old age home, I mean, it could, yeah. You know, there's, uh, there's a lot of, uh, problems that could cause let's just, you know, go to that degree. Yeah.
Jacob Glanville: No, it's, I mean, it's going to be honestly, the, if I had any kind of power, I would just, I do implore Government agencies that they should Institute Corona, uh, Institute quarantine on all old folks, home nursing homes and groups that have enriched elderly and tell we have a surveillance in place. And we know which areas of the country are our hotspots, because right now all you need is one person to go in there.
And then that happened up in Washington. Like it was a bunch of people dying at that facility because they're all, they're all immunocompromised, they're old. This thing is super infectious and lethal to, to the elderly. Now the other group that's going to be heavily impacted are going to be medical workers.
So doctors and nurses, um, they're going to the ICU, there's enough people sick that ICU's are going to become over one overrun in any area where there's a hotspot. And that, that is a big risk to the doctors and nurses, both for, because of the exhaustion and lack of sleep. Those things are bad for the immune system and they're going to be exposed to a lot of virus.
So a lot of doctors and nurses end up getting this, and then they ended up dying as well. So their, their mortality rate is elevated and we think that's just from like, overwork and exposure, just huge amounts of this virus.
Harry Glorikian: We saw that with the doctors in, in uh Wuhan, right? Yeah,
Jacob Glanville: that's right. They weren't old, right?
Those are normal.
Harry Glorikian: Oh no, no. They were, you know, of what, what, what is supposed to be healthy age? Right. Um, so what, what the other concept is this whole re-infection. Yeah, I don't, you know, discussion that's been going on. What, what, where, where are you on, on reinfection of somebody that's already had this?
Jacob Glanville: So, you know, immunologist. So I see that in my initial reaction is I need extraordinary proof because the way the immune system normally works is you get infected with something and you produce T-cells and antibodies and adaptive response. So you're protected going forward. Now there have been numerous cases of reports of people who are being reinfected.
So there's a couple of possibilities here. One is that it's actually happening. And I think what I would suspect is it's probably people who are immunocompromised or had some other immune defect. And so, uh, that, you know, we're preferentially observing people who are older and are more at risk. And so they may be, um, didn't have a good immunity response initially.
But the weird thing is like, why did they get better at all? In that case? If, if, cause at some point they were clearing the pathogens. So why did it come back? The other explanations is that they actually never fully cleared the pathogens. So they weren't like they weren't cured yet when they left the hospital.
And so they kind of just came back a bit and the other one is a, is a false positive that their test later was incorrectly labeled as positive when they weren't. Um, so for those reasons, I think I would, I would, it is possible that the virus could do something spooky. Like there are some passages that we know can kind of wipe out the immune memory as they go.
And, but I think. Um, what would be more likely if that was the case, is that people would not be cured. They would have a, it'd be like HIV, where they permanently maintain the virus. And based on how this virus works, we don't think it's doing anything like that. So I suspect these are isolated cases, and I'm still not convinced that they're real.
Harry Glorikian: It always comes back to data, data, data, right? I mean, we've, we've got to have the data to be able to determine. Which one of those three, you suggested that it could be. And I have been skeptical of the testing only because of what I've been hearing through the grapevine from people that I know in. In the world of testing.
Jacob Glanville: Yeah. Um, 'cause, it's, uh, you know, this qPCR tests, um, if you're running a bunch of those in a room, you're going to be creating a bunch of additional DNA amplicon product in that room and, you know, DNA lyophilize. So it dries. And it basically turns into like a little dust in the air. I know this in my lab because whatever we've worked on a whole bunch recently, a little bit of that DNA will show up trace and other projects and that we can detect it when we do deep sequencing.
So. By that same measure. If you're doing a lot of tests in one room, um, you're gonna have to actually use a lot of UV light and careful cleaning to avoid occasional, false, positives, starting to pop up in that room when just like a little like, you know, pixie dust, if that same DNA lands into a little, well of a, of a PCR, um, a PCR reaction.
So I would, I would be cautious about the false-positive rates and hopefully they're running very thorough sets of negative controls every day to be able to assess that.
Harry Glorikian: So, how do you see this? You know, we always talk, you know, develop world versus developing world. I mean, theoretically we're prepared.
I'm not, I'm, I'm not fully sure that our hospital system can handle it, but let's just assume for the moment that everything goes hunky Dory, but most of the rest of the world is not. Designed for this.
Jacob Glanville: Yeah. So again, I don't want to freak people out. Um, the rest of the world is not ready for this. And I would say the United States is not ready for this right now, either.
Um, so the rest of the world, you know, like Guatemala and I have like friends in, in Brazil and many other countries. If you do not have a modern. Um, medical system in place, the capacity to perform modern surveillance in ICU, handling able to handle volume that you're going to have an elevated rate of death because the death rates are assuming you can get people into ICU, do they can be intubated and they can, they can have various corrective medicines applied or at least physical measures to try to drain their lungs.
Um, if, if you have people in waiting room or at home infection rates are going to be much worse and you're going to have people dying in the waiting room. So that, that is a. A real risk. And I think that's a source of great concern. And in the developing world, um, I will say in the United, the United States is, um, Shockingly unprepared right now for the epidemic, which is upon us.
So we can turn around quickly. We have a remarkable resources. We have remarkable medical care, but what's absent is surveillance. So I really I'm. I'm hoping that that is being solved now effectively as we speak, because as soon as we know how broad the problem is, we can start applying a corrective measures and.
Um, and you know, we have the resources, capitalism's the success of capitalism is it's easy to go find stuff that we need and mobilize things to solve problems. We just need the political and personal will to do so.
Harry Glorikian: Pardon me really wants to go down that road with you, but I'm going to let that one go for a moment.
Jacob Glanville: I appreciate that. I deliberately feel like my job right now is that all of us are respective of political opinions, like one our grandparents and our children and ourselves to be safe. And so I want to speak to the common interest that really should cross party lines and share information that everybody can benefit from.
Harry Glorikian: So, no, no, it's not. I it's not even it's I didn't even look at it that way. I look at it as, yeah. There's an understanding of these things and there's, you know, look, I mean, you don't come across it in your daily life. You, you're not reading about it all the time. You didn't go to school for it. Right.
There's misinformation out there or misunderstanding. Right. And when people say, well, it's just like the flu, right. So that is where I'm like, Oh please , you know, help, help me, help me with some level of understanding because I have this conversation with people regularly.
Jacob Glanville: Yeah. Uh, I just try to share accurate information and really get it out there and communicate.
Cause I think that is that's. The other big problem is that when you have an outbreak like this, like it's complicated, there's no hand, nobody gives you a handbook and certain, and you need to be able to have a way that people, they know what the risks are. Cause I had, one of my employees asked me, we did a round table on Monday and we're talking about.
Preparedness. And I was designing policy for my company and someone asked me like, how, how, how scared should we be on a one to 10? And I was like, you should be prepared. You should, you should be aware. You shouldn't have to be scared. And that's, I hope everybody is.
Harry Glorikian: Well, I mean, I, you know, I've actually gotten to the point where there are sometimes I'm like, okay, That's what you believe I'm letting it go.
Right? I there's nothing I can do. Um, so how would you protect predict the continued spread? Because there was, um, the paper that came out of Harvard that said, you know, on a global basis, what was it? 40 to 70% of people. Predicted to be infected. Yeah. It's a big number.
Jacob Glanville: It's a big number. And I think there's a lot of unknowns here.
So I was, you know, I'm, I'm doing modeling right now. Uh, I overestimated the growth of China. So China did a very effective job at suppression for their growth. So they they've kept themselves under a hundred thousand so far. And they did that through, you know, hardcore, um, social distancing measures. They, they quarantined 57 million people.
They. Had, um, broadcasting systems on the street corners telling everyone to stay inside. You know, they just took a bunch of, um, intense measures that their, their governmental structure and system enabled them to do. So I think that's to their, to their credit and really all kind of, all of them, a bit of thanks because they, they, their people, their medical staff suffered so that we didn't have to, um, you know, got out.
If I were to estimate what happens next. I think all the neighboring States are it's outbreaking in around China. That's there just because of human migration. Some of those States are not going to be able to do good containment. So they're going to suffer. The middle East is having a massive outbreak.
Um, Iran being particularly affected it's actually affected their, their, um, their political structure. I think they have 30 people in their upper government that are affected and they've already had one death and that's a category of people which are going to be disproportionally affected because they're older men.
Um, Then you have in Europe, um, Europe, I would guess is going to outbreak very bad in the next couple of months. So then that's not just because they all kiss each other on the cheek, but it's also just because it's, it's, you know, it's, uh, it's hit Italy, it's hit Germany. It's, it's spreading, uh, all the countries are having, um, cases.
And we always know when we see cases that we're two weeks behind the real problem, uh, in the United States, we know the outbreaks are taking place. It looks bad in Seattle and, and in California in particular, but there are lots of States showing, uh, So in cases and like United States, we travel freely across our borders.
There's a lot of movement, um, across the country right now. So I, I think we just need to get that surveillance in place to have a sense of like, we can't even start stopping the problem until you realize the scope of the problem. So if I were to guess over the next month, if we get our shit together, Well realize where the hotspots are.
People will have a little bit of a panic, cause they'll think suddenly there's all these new cases where it's really just the growth of cases that already existed. And then at that point, a series of measures will take place. Um, I am, I am expecting that, you know, the. We will have an absence of certain resources, like toilet paper, like this thing doesn't cause people to have diarrhea, but you'd think so.
Cause you go in and to, you know, a store, you can't find any toilet paper cause everybody's hoarding. And so that is a real concern that like you, you'll not be able to get the things that you want cause people are freaking out. But, um, I think there'll be some like panic exhaustion and you know, the resources we'll just keep filling in every day and eventually people will stock up and we'll be, we'll be okay.
So, uh, If I were to guess going into the summer, I think we're going to have a continued bad outbreak in the United States. And it's not clear to me that the policies are in place to stop it. I don't think we're going to be able to assume this summer is going to stop the outbreak. But most of the modeling suggests that this is going to be the next 12 months.
But, but the optimistic thing is that China succeeded in keeping this under, under a hundred thousand cases. So I think if we're able to identify the problem and apply suppression, we could avoid that 40 to 73% outbreak. I don't think that's a foregone conclusion that's going to happen at this point.
Harry Glorikian: Yeah.But the problem that I see with China is right. It. At some point, you know, he's got to open up his economy to get it running again. Right. You can't keep it close forever. Right. And so the minute, yeah. You open it up and all of a sudden somebody from here or someplace else goes to China and you know, you just started the cycle again.
Jacob Glanville: Yeah. We can't freeze. Look what they did, right. Is they just like froze society. We can't do that permanently. So. So the other thing that could help us is, uh, next month in April, we're going to find out if the HIV antivirals or the, um, the anti Ebola antiviral from Gilly ad or lean were effective. And so all three of those are small molecule drugs that could be used as a treatment.
And if we can, we can find, at least one of those is somewhat effective. Then we have a medicine and that, that could also be used to help, um, at least ameliorate the outbreak.
Harry Glorikian: Right? Well, I mean, you're this, I was going to come back to this, right. You're working on something specifically by. Looking at, um, other antibodies that have, that have been used in the past, I believe for SARS, if I'm not mistaken.
Jacob Glanville: Yeah, that's right. Yeah. So here's sort of my, my assessment of the landscape of medicines. The good news here is that biotechnology has never been more awesome than it is today. So we are really good at making medicines fast. Um, there's sort of a three tier system. I think at the top of that is the small molecules.
Those are the, the antivirals and the chloroquine I was talking about. If that stuff works, uh, next month we have a medicine that's, that's, that's our best thing. Um, my least favorite is actually the vaccine and I do vaccines, but the vaccine work is going to take a long time because vaccine studies are slow.
You have to immunize a bunch of people and then you have to boost them. They have to get booster shots. And then you, and you have to wait three weeks in between these booster shots. You're talking about like nine weeks, maybe before people are protected at least six weeks. And you have to wait a while because you can't give the shots to people who are already sick, because by the time you you're six weeks through, they're either cured or dead before the medicine takes effect.
So you have to give it to healthy people and then monitor them to see if they get sick. Uh, and there's some risks of giving vaccines to medical workers. Who'd be a high risk because there's this risk in, uh, in cat studies of vaccines on cats that the coronavirus is that it actually made them more susceptible in something called antibody dependent enhancement.
And so there's a real risk that the vaccines would sort of blow up in our face. Um, and that's why they're estimating like 18 months before these vaccines could be ready. And they're actually not going to help people who are already sick. Um, Uh, and people at the In, it's unclear how quickly it would be effective for people at the front lines.
So what I'm doing instead is I'm producing an antibody. So what I did is I went back to, I wanted to avoid the long process. It takes to go build new antibodies as drugs.
Harry Glorikian: Right?
Jacob Glanville: I wanted an antibody because if you go up to a patient is already sick, you can't give them a vaccine because it's going to take them six to nine weeks.
Um, you, you can't give them a vaccine because they're already at risk and, uh, It's too late for the vaccine will take six to nine weeks to take effect. Right. But you can give them an antibody. If you give them an antibody, it works within 20 minutes and it provides them like eight weeks of protection. So you can also give antibodies to, um, to doctors and nurses, and it would provide them protection for a couple of months from getting sick.
So it's a great platform. The problem is it takes a long time to go engineer new antibodies. So what I did, it was like, well, let's skip the middleman here. I went back to almost 20 years ago, there were these two, these antibodies sets of antibodies from 2002 that were, um, identified against SARS. And they were really well-characterized.
They found that they were able to neutralize the virus in vitro. They provide protection in vivo. Um, so if SARS ever came back. These would be great drugs. These are neutralizing antibodies. But the problem is that SARS doesn't come back. That's the problem with outbreaks. They always change. But in this case, we got kind of lucky because the new coronavirus is about is a cousin it's about 75% identical, 74% identical to a coronavirus on the part of the virus called the receptor binding domain that you can make an antibody against, and it will neutralize that virus basically blocks it from being able to infect you.
So, um, I had built a computationally optimized technology that lets me take an antibody and it lets me create a. By shuffling in, um, 13 DNA variation from thousands of, or hundreds of healthy people. As well as synthetic DNA variation, I can, I can take an antibody and produce billions of versions of it, um, in two weeks.
And so it's basically a brute force attack. So you take each one of these antibodies. I produced a library of billions of versions of each one, and then we search through them and be like of all the ways I could take this thing. And can I find a version that, that can basically readapt itself to recognize that site it's changed by one out of. 4 residues evolved since 2002, but now we can search with billions of versions of our antibody to find a, kind of like a combination adaptation, which is able to recognize the new virus. And, uh, you know, each one is a gamble that maybe that site is just so perverted and altered that we cannot modify the antibody, but I've got a billion shots to do it with each antibody and I've got five different antibodies.
So it gets back to my strategy of building multiple bridges across the river. And I think my odds are good. So I'm basically taking like, you know, five, 5 billion spaghetti's and throwing them against the wall and seeing what sticks. And we're going to have that answer. Um, and, uh, the week of April 6th, so in about a month, and the benefit of that is that that antibody, if you can manage to adapt it, we can basically piggyback on three years of research done on each one of those antibodies.
They're super well characterized. We know they work, we just need to adapt them to the new virus. And then we can basically not have to do all that research all over again. We can save ourselves the years of research and jump immediately super quickly into a clinical test and then into clinical trials.
Harry Glorikian: So, so the, okay. That, that brings me to the next, you know, item. Right. Which is, you know, I have people going, Oh my , you know, we need a vaccine tomorrow. We need, I'm like, okay, hold on, hold on. We do not, I mean, we're moving fast. We're moving faster than I've ever experienced before, but we don't move that fast.
Jacob Glanville: So here's the awesome thing about an antibody compared to a vaccine. So with a vaccine, like I said, Your trials take a long time because you have to do six, maybe eight, nine weeks of waiting. And then, then you have to wait longer to evaluate safety and efficacy, whereas, and you can't give them to patients.
You have to give it to healthy people and see whether they get sick. Is that, that makes things take a long time. With an antibody what you do is you go up to, uh, for patients. So you treat it the same way. They do those small molecule studies. You go and you give the antibody, you infusion infuse it into patients.
Harry Glorikian: Right
Jacob Glanville: And then, uh, within two weeks, you know, whether or not the drug works, like did the group who received it versus those that got placebo fair, better. Um, the other thing that we're going to do is we're taking advantage of rapid GMP manufacturer with a company called Swiss scale. They're working with them to create this rapid bacterial-based ability to scale up something called single train of VFC fusions.
So there are type of antibody, and then there's also a company called. Um, Angstrom that we're talking to that has this rapid, like Cho base transient transfection, GMP manufacturer. There's two different ways we could produce this stuff, like super fast, like in an under a month, as opposed to having to go through the traditional 18 month process.
Um, when you get into the phase trials, normally you do a phase one in human, which is just testing safety. Then you do a phase two trial and human, which is efficacy with a small group for financial reasons. And then you do a phase three trial was, is a massive study. Um, and then you get approval. But here, there are some opportunities to make that happen way faster.
Um, first you don't give a shit about approval. Your goal is to treat patients. So the strategy, the strategy is different. So what you do is in phase one, normally people might give 10 to 20 people, the drug, technically you only need to give a couple. Um, and you're doing safety, but the oncology researchers for years now have done this thing where they call a phase one slash two, where they're like, if you're giving the drug anyway for safety, you might as well give it to case cancer patients who are at huge risk anyway, and then you might learn something in the process.
So you spend a little more money. Um, and what we do is instead of just giving it to healthy people, you'd go give it to. Instead of 10 to 20 healthy people, you give it to 200 to 600 patients and you do it in a rolling way. So you give it to six people the first day, 24 people the next day. So if there's a major problem with it, you stop
Harry Glorikian: Yeah you catch it along the way.
Jacob Glanville: Yeah. But, but the effect is you get, you get in that first study in the first two weeks, you don't just learn as it's safe, but you also learn, is it effective? That's a phase one class two study. It costs more, but their resources, I don't think are a problem here. It's having an effective medicine. So then the next one is the phase two slash three. So normally that's where you do like 200 to 600 patients. But here, if you showed effectiveness in phase one, you could create a rolling protocol for your phase two phase three, where you could treat millions of patients. So really you're releasing the drug in the phase two to all the people who need it.
And that gets you in a position. Now, if you looked at my numbers, we're not talking about 18 months or multiple years, you're talking about that being a couple months process, you could actually release a therapeutic to the people who need it. And you're actually the goal is that we're not trying to make a therapeutic.
That would become a product. What you're trying to do is protect people from dying and that's a mechanism to go accomplish it so that these are the sets of strategic acts that some of the. The later one I just described that was applied in the Ebola outbreak and that's called compassionate use. And if you have extra resources, you can do it.
And if you have appropriate demonstrations of efficacy, you can justify it. And then the one in phase one, that's just through the cancer group. Those guys have hacked the FDA for us to go solve that the combination phase one patch to the class two. And it's really just new technology advantages in GMP.
Um, that allows us to do these rapid GMP manufacturers faster. So you take all that, all that stuff together, and it no longer takes multiple years to bruise a drug. This we're talking about being able to do something more like stuff like basically it could be widespread treatment by September.
Harry Glorikian: And this is all based on the, uh, I believe the tumbler technology that you guys have created, right?
Jacob Glanville: Yeah. So Tumblr was. That was basically me going in and getting kind of impatient and annoyed with how antibody discovery's normally done. So there's like in computer science, there's these like three virtues or the three virtues of a programmer or inpatients, uberous and laziness. And some of that applies to biotech.
So the way normally people engineer antibodies is they have these like six loops and the loops all kind of come together like the, like a Rose or the looking down at a Rose or an artichoke, or like the suckers on a jellyfish or something. Um, So, or an octopus. So that's kind of how like, uh, in antibody goes and binds a target.
And what people do is they typically go and they mutate one mutation at a time on each little one, each one of those loops and they kind of search for a single change. And then they kind of search around for a single, another change. And from an algorithm perspective, that's a very inefficient way of searching for changes to, from one sequence to another.
And you're going to miss a synergistic combinations of residue changes that happen across different loops for. For technical reasons, you search one loop at a time as opposed to trying all single and double combinations historically. So I looked at that and I'm like, I hate this. This takes forever. It's super serial.
And you're going to miss, um, like synergistic coordinated benefits of like constellations of beneficial interdependent mutation changes. So what I built instead was was tumbler with searches through diversity. It all succeeded ours at once. And when I initially proposed it, people were like, Jake, you can't do that because you're going to run out of sequence diversity.
So the, the idea is that each position can be mutated. There's 20 amino acids. So each position could be mutated to 19 alternative immuno acids. Right. So you have 20 amino acid changes at a single site. You know, you have, uh, 400 to two sites and you know, that that number keeps growing 8,000 and so forth.
So very quickly, even across 10 sites, you run, you run out of the amount of space that you can search using the physical, like DNA attached to protein technologies. We, as an, a laboratory, we can search about 10 billion things conveniently. Either either the nine 30, sorry. Either the 10. Um, so what I did was I said, well, I don't want to search everything evenly.
The numbers you just described to me are incorrect because they're assuming equal frequency of all the possible components, but I actually have something in mind in the sequence space. There's antibody that I pretty much want. I just want to explore single mutations, double mutations, triple mutations, quadruple mutations, but I don't want to search off into like the massively mutated combinatorial inches use-case space.
Of mutating, most of those molecules at once. Cause the more you mutate to get antibody, the less likely it is to fold up and work anymore. You're sort of like kind of going down a mountain of, as you get farther away from your starting cloud and you're going closer and closer into the valleys of death on all sides of the mountain where the molecule doesn't work anymore.
So my way of dealing with that is when I build these libraries, I deliberately add in strategic frequent by frequency bias and the DNA composition of the diversity in each one of those loops. So. I make the starting clown would be the highest frequency that might be 30% of each of those six loops. Um, and so that means that, uh, the rest of the 70% is exploring variation.
And that's also biased, but you can imagine that if you multiply 0.3 by point by 0.3 to the sixth, that means the parental clown is present at a sufficient abundance that it's in the library. And then any given CDR, if you can't change it, it's present there. And you're sort of drifting down this frequency landscape of single and double and triple mutations.
And that has the effect when you build it, this delightful commentorial chemistry consequence. That that tumbler library is actually exploring all the local space producing lots of molecules that look pretty similar to the starting clone, not wasting too much space on things that are so radically different that are basically alien, um, and, and searching all six CDRs at once.
So that, that is tumbler and it's extremely powerful. And that's, we do, we do this commercially for a bunch of pharmaceutical partners that come to us and ask, and they ask us to optimize their antibodies. Um, but here, I think it provided a unique opportunity to mutate. That surface where you don't know a priority, which residues need to change across which CDRs.
So the correct answer is permeate all of them simultaneously and brute force the solution.
Harry Glorikian: You know, we need a, we need more people thinking out of the box, Jake, you know, it, uh, biggest breakthroughs seem to come when people are not dogmatically, uh, held in position. I mean, I had this conversation with, uh, the CEO of Schrodinger on the last episode.
He's like, you know, certain age of chemist, just. You know, would come at the problem in a certain way. And now we have computational capabilities where the new chemist can come at it from a, you know, a completely different view and drive to a problem faster. And I think what you're talking about again, 10 years ago, I don't think computationally, we could have done it.
Jacob Glanville: Yeah. I think, you know, there was sort of a cost thing that also happens in immunology, right? For immunology. The problem is we don't have all the data and the experiments were expensive. And so. Uh, new research tends to be governed by precedent rather than by empiricism. And that's sort of one of. One of the things I try to break and try to say, you know, we're like we exist in this like magical Candyland golden age of amazing technologies that make it ever easier than ever before, to deep sequence what we're looking at to deep synthesize things that we want.
And so we should be willing to re-ask the questions of, okay. Just because the technologies have been done this this way so far, um, has there been a state change? That means that the way those old ways are actually obsolete and, and that's what we do at distributed by a lot, we do a lot of. We do paired innovation.
So the rule is always, you do it the way we've already done it. And if you have another idea, you run that thing in parallel. And if we can just, we can demonstrate that significantly better than we switch over. And so it's sort of like modular replacement and optimizing components, but proof driven and, you know, some things are there for a reason we can't switch over, but increasingly we find that the new technologies allow kind of the synapse of shortcuts that have led us, optimize our timelines and let us accomplish.
What was previously impossible. So producing hits against like GPCRs and ion channels, peptide, MHC complexes. Uh, we made antibodies, uh, broadly neutralizing antibody's against HIV and under four weeks in a collaboration with Peter Kim. And that's just a consequence of the advances of the technologies.
Harry Glorikian: Yeah. I wish they taught this in school. Right. They, um, they need to teach people to be willing to challenge the, uh, The way you do it. Uh, otherwise we won't have breakthroughs. Um, but, well, uh, this was fantastic. Uh, I want you to go back and keep working on that antibody. Cause I have a feeling that, you know, we have a long way to go before this, uh, The situation that we currently have, uh, dies down.
I mean, I, I, I pray I'm wrong, but, uh,
Jacob Glanville: I think you're, I think you're right. So yeah, we're working on it. We're going to know April 6th where they're not one or more of those antibodies we're trying to optimize across over at that point. You're definitely going to hear more about it. Cause we're, uh, we're moving really quickly with Swift scale, um, with.
Angstrom, um, and with the military. So we, we were, uh, DARPA's familiar with what we're up to and we have partnerships in place with, um, USM red, rare and, uh, MRC. Um, so we're kind of keeping those guys up to date on where we are, and we're just trying to be, you know, cautiously optimistic, but we want to push it forward because this thing could still go wrong.
And in which case we don't have anything, but, but we're. If we have a success against any one of those antibodies at that point, we have a therapeutic and we can mobilize it pretty quick, pretty rapidly.
Harry Glorikian: Well, it is science. You never know where it's going to take you. Yep. That's right. Well, good having you on the show.
Jacob Glanville: Yeah. Likewise. Thanks for having me on
Harry Glorikian: And that's it for this episode. If you enjoyed Moneyball medicine, please head over to iTunes to subscribe, rate. And leave a review. It is greatly appreciated. Hope you join us next time until then farewell.