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Data Is Critical to Engineering Antibodies to Block COVID-19


Episode Summary

Distributed Bio aims to use its computational antibody engineering platform to identify antibodies that protect against SARS and optimize them to block the SARS-CoV2 coronavirus. This week Harry gets a progress update from three key Distributed Bioscientists.

Episode Notes

Building on his March 2020 interview with Jake Glanville, the founding partner and CEO of South San Francisco-based computational antibody engineering startup Distributed Bio, Harry speaks with three company scientists in the trenches: JP Buerckert, director of computational immunology, and Shahrad Daraekia and Jack Wang, both senior scientists. Together they’re working on projects such as engineering existing human antibodies to the SARS virus so that they’ll also work against the novel coronavirus, SARS-CoV2.

The company’s special sauce lies in its computational algorithms for analyzing antibody gene sequences and generating billions of new candidate antibodies against different pathogens. “We have a very strong wet lab team that is generating data for us and then we have a very strong data team that is sorting through these data” to help scientists decide which antibody leads to move forward with, Buerckert explains.

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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 talked to people behind the trends to find out where data is making the biggest difference.

In this next discussion, I decided to mix it up a bit and talk to some of the people in the trenches. If you remember my last episode, I spoke to Dr. Jacob Glanville, who is one of the founders of distributed bio, and we discussed many topics surrounding the diagnosis and treatment of Corona virus. After that conversation with him, I decided.

Why not talk to some of the people who drive all the computational immunology and anybody engineering. They’re a distributed bio. And that’s exactly what I did in this discussion. We talked to three key people at the company to understand what they do, how they do it and what they hope they will produce when all the work is done.

Please welcome to the show. JP, who is the director of computational immunology, Shirad and Jack, both senior scientists I will let them describe who they are and what they do at company.

JP Buerckert: So my name is Jean-Phillipe Buerckert, uh, I go the by JP, usually in the United States because of the name is a little bit complicated to pronounce.

And I’m the director of computational immunology at Distributed Bio and therefore heading the entire data analysis squad that we have. So I joined Distributed Bio last year in October, I’ve been on and off a little before that for some consulting work. And if Ive known  Jake since, I think now seven years and we’ve like worked together in the same field we did a PhD in the same fields, and this is how we circled each other for a longer time. And then around, uh, August, um, two years ago, we started having a conversation, whether I should like join Distributed Bio. And, uh, this is where we are right now.

Harry Glorikian: Excellent. And you’re located in Belgium, I think you said.

JP Buerckert: Right now by force. Yes. Um, so I flew over, I flew over here too, sent my brother’s wedding. Um, that was exactly when, when all these shutdowns happened, um, you get married on March the eighth. Um, and you know, right before we could turn back and return to the United States, uh, every, all the borders closed down into flights were canceled.

So I’m stuck here now for, well, I guess at least a couple of months more, but luckily, as I said before, I’m the data guy. So I can work from wherever I am. I don’t have to go to the lab actually. And probably if I would be in San Francisco right now, I wouldn’t be going to the lab anyway. Um, just, you know, to minimize exposures as much as you can.

Harry Glorikian: Yeah. Well, congratulations to your brother and what a honeymoon ha there they’re locked up together for, for quite some time. Yes.

JP Buerckert: They should have booked some kind of vacation beforehand and fly over there. I’ve seen some people posting pictures, they were stranded in some paradise in Mexico. Um, laying on the beach all day, you know, and they can’t go home.

Harry Glorikian: Yeah. Yeah. I’m sure there’s some bad stories about that too. Like they really want to get home and they can’t, um, Shahrad, you want to give us, give us an idea of what  you know, what you do at distributed bio. And,

Shahrad Daraekia: um, I am a scientist currently at Distributed Bio. I do a lot of the antibody engineering and the antibody library engineering at Distributed Bio.

Um, a lot of it pertains to taking an antibody or two and making it stronger interaction or display better affinity towards a selected a target. Um, in this case of the Corona virus, we took a few of the starting antibodies and we kind of re-engineered them to make them have a better affinity from, from SARS to Corona virus.

Um, I’ve been working on building libraries for about eight months now at Distributed Bio. And before that I met Jake at my master’s program where he had some hands-on projects. Um, from Distributed Bio with the USF biotech program where I was doing my masters and that’s where we kind of got to meet.

And I believe I started in Distributed Bio as an intern back in January of 2018 and became a full-time after that and have been with the company ever since.

Harry Glorikian: Oh yeah. It actually like working with like Lego blocks and trying to make these things do things you want them to do.

Shahrad Daraekia: Exactly. I think the first time I met Jake and we were working on a project for him at USC SFE described engineering and molecular biology as building Legos and at the whole it’s very true in a degree.

So, um, yeah, you can definitely make that connection there.

Harry Glorikian: Well, we want everybody who’s listening to sort of understand some of these concepts that we’re talking about. So Jack, please feel free to introduce yourself and tell us what you do at Distributed Bio.

Jack Wang: Of course. Um, so my name is Jack Wayne. Um, I’m a senior scientist at the Ditributed Bio.

Currently I’m working, um, Both antibody and protein engineering, trying to produce protein now be used for, um, selections against higher affinity and also target that people are interested in going after  also work along the. Um, I’ll say centre back of the company where we’re interested in introducing a bio neutralizing, um, vaccine platform that will be useful for many different viral infections, including some of the work that JP’s working on for HIV and also the current COVID-19 crisis.

Harry Glorikian: So look, I’m glad to have you guys on the, on the show, JP, it sounds like you were the conductor of the orchestra in a sense of doing all the computational work and, and maybe identifying changes and modifications that need to be made. And then these two gentlemen, then hopefully can. Get the tinker toys and the Lego blocks to do what they want.

What, uh, matches up against that. Is that a decent analogy?

JP Buerckert: I think yeah. Can put it like that. So Distributed Bio in general is a company that is driven by computational algorithms. Um, our slogan underneath the low-risk algorithms for life. And this is what we’re, what we’re doing with all of our projects.

So we have a very, very strong, wet lab team that is generating data for us. And then we have a very strong. Data team at the back that sort of sorting through these datas and helping the scientists were making the different decisions and choices to move forward with the most promising leads in our antibody discovery.

Harry Glorikian: So this, this whole show is, is typically about how data and it’s changing how we do things. And it’s funny because I have a lot of friends that are like, can’t we do this faster. And, um, I’m cracking up cause I’m like, if you knew how fast we’re doing this compared to, you know, five or 10 or 15 years ago, this is mind blowing at how quickly this whole thing is moving.

And so give me a sense of like, you know, if you were trying to do this five years ago versus today, and then what sort of data are you guys reliant on getting in that starts the process and helps you move forward. Maybe Jean- Phillip, you, you can start..

JP Buerckert: Yeah, sure  Um, so there were a couple of game changers and the past, let’s say 10, 15 years in the molecular biology industry, one, including high throughput, DNA sequencing, and we have high throughput kinetics, and all of these technologies are used by us to like speed up our process of antibody discovery.

For example, whereas in the, in the past, you would have to manually sequence every single antibody sequence and you can maybe do like a thousand or 2000 sequences in a quite good throughput. And that is what we’re still doing today. For most of our projects, you know, have a high throughput sequencing platform with which we can generate like 30 million sequences, in a matter of three days. And that just gives us a completely different perspective at all the, the different, um, antibiotics that we have in our platforms. So it’d be a very good  and detail about this. They’re very good in generating billions and billions of antibodies, but then we need the data to support us, to sort through them and decide which ones of these are actually good enough to move forward with.

Harry Glorikian: Right. So I was, I was actually, uh, you know, thinking, as you were saying, it I’m like. That’s a lot of data. And so you need to have all the downstream analytics to sort and identify now, is that done manually automated? How do you guys, I’m assuming that’s part of the secret sauce.

JP Buerckert: Yeah, that is correct. Um, so most of that part, um, involves from my team and my work.

Um, we have like developed algorithms and software, uh, that like takes all the data that comes from our high throughput sequencing machine and just parses it through, um, all the necessary quality control metrics and, um, like steps to assure that we are having a good quality of sequences. And then we can use algorithms on these sequences to decide, okay, this seems to be a configuration that is particularly.

I’m enriched in an hour samples and therefore most likely, um, like a good candidate to move forward with. We can look at the sequences in detail and can say, Hey, this sequence house, um, liabilities that have in the past been shown in, in like drug trials that are not really beneficial for an antibody, so we can remove them.

And all of these checks and balances, we’ve implemented them in algorithms and a nice standalone platform or Genesis data analysis software. Um, um, that is basically our procedure period, like defining a new algorithm and then we automated so that the team can use it, uh, by themselves.

Harry Glorikian: So then you gentlemen actually take that data and.

Do what with it?

Shahrad Daraekia: Well, a lot of the data this starts with, uh, like JP mentioned, we do look at algorithms, but in the case of developing the, uh, the antibodies we did for this COVID 19 project, we looked at a lot of structural data of these antibodies in relationship to where they bind, uh, to SARS, because we knew that.

They were SARS, binders. And then we ran like a computational scan, um, on similarities between the SARS protein and the coronavirus protein. And based on those similarities that we saw on the computational level, we were able to start developing antibodies in the lab, um, that would somehow be able to be re-engineered to mimic the structure of the new SARS. Or excuse me, in the new Corona virus. Um, so I think the computational algorithm not only lays the groundwork for what we do in the lab, but it kind of helps clean the things up we do in the lab, as well as JP mentioned, we do generate billions of antibodies, but you know, of those billions, there’s maybe a good handful that we want.

And in order to start selecting for those, we do start at the computational level. Does it, like you mentioned to see which sequences look the best, which ones would look better as a human therapeutic? Um, so the algorithm kind of sandwiches, I believe the work that’s done in the lab where it lays the groundwork in the beginning, and then it kind of helps tidy and Polish things up in the end. And in many aspects, it doesn’t guide the lab work along too, because we are, we have a platform where we’re able to upload all of our data and all of our sequences, and it gives us feedback and metrics and all these different parameters on how our antibodies look.

And, um, gives us the data that we really need to analyze their antibodies. Um, cause as JP said, a lot of this is sequence level and we can’t really see that when we’re working with antibodies in the test tube. So it’s nice to have some kind of computational aspect, um, that gives us the sequences we want as well as all the other metrics are how well an antibody could potentially do, um, based on a computational level.

Harry Glorikian: So let’s, let’s face it. I mean, this is not all happening, like in silico, right? So, or, or, you know, in a computer all by itself, right. Somebody is actually going to put hands on something and actually put it to work. So there is a still, I hate saying it, there is a, still a human rate limiting step of actual testing that goes on. And so some of that may never change theoretically. I’m not sure if the computation is going to get perfectly, good to make the final judgment. JP is smiling. So I can, I can see, he’s probably thinking no one day we’ll crack that code, but when you guys are in the lab and you’re getting this data sort of, what are the first steps that you need to think about when implementing your testing?

And then getting it in the right sort of situation to, to sandwich it on that other end that you were talking about in cleaning up.

Shahrad Daraekia: So I think it’s, um, it’s important to realize that, like in the case of the Corona virus, uh, antibodies, we were doing developing, uh, we went on off the, the computational basis that there were some antibodies that look like they would bind to the protein, current, the COVID protein.

There were also very similar to SARS. Now some bound in areas that had very conserved regions that we knew that we can maybe maintain that kind of affinity from SARS to coronavirus. And based on the structural modeling, some antibodies had, um, binding on very non conserved regions. So areas where there was mutations between the SARS virus in 2003 and the Corona virus of 2019.

So the antibodies that displayed binding in the areas that were heavily mutated were ones that we didn’t really try working on. Because we figured, you know, we have a much lower chance of success, so we can at least put all of our eggs in the basket of a few antibodies that look like they bind to the more conserved regions and the less mutated sites.

Um, so that was the first guiding step. And, um, I think Jack can also jump in here because he did develop a lot of the, the current virus proteins based on what we learned too.

Harry Glorikian: Jack, you want to add.

Jack Wang: Sure. So as Shahrad mentioned, there are sites of the protein that in the COVID-19 as come conserved throughout the different family Scully causing such as the SARS

So, um, in the beginning, we did have to look into a lot of structured data, identifying particular antibody that has high specificity and also perhaps a potential to actually. Could be repurposed in a way so that you will still maintain this neutralization of a potential for the new COVID-19 protein. So looking at on instructors and also with a lot of the design work,

Um, creative for generating billions of variants that we believe are, could be a potential solution for those and helping with, uh, more high throughput work that we’re doing a wet lab combination with the technology in the computational side to help really mining to that big data really allow us to achieve things in such a short amount of time.

Harry Glorikian: So now some data got released. How do you guys feel about where you are in the current process? JP. You want to, you want to take that?

JP Buerckert: You mean as in, um, how, how well we are like situated and like finishing this problem or in terms of how other people are doing?,

Harry Glorikian: No. No you’re, you’re how, where you guys are in the process.

I’ve been trying to keep up with it and it looks like, you know, the data is looking good. What do you guys need next? Where are you in the process and how do we put this into production?

JP Buerckert: Sure. So the data’s looking extremely promising. Um, that is true. So we have, like, we finished the big hurdle in the beginning to see if we can actually mutate these antibodies as Shahrad  lined out.

Can we mutate them to, to cross from the original SARS COV one with the new SARS COV two variant, which is currently causing us COVID 19 pandemic and the data, um, like proof. Right? So, so we, we made it, we actually have antibodies that can cross from SARS, one to SARS two. And we even have a couple off that seem to be crossing to (inaudible), but we have to evaluate that a little further on.

So the next step from, from here on Jack can go into a very much detail about that would be to, um, to test these antibodies for safety. Um, we are, we’re trying to find partners, uh, at the moment. Um, we’re looking into partnering with the US military, but also with, um, the, uh, COVID consortium, which is a consortium led by, um, the Bill and Melinda Gates foundation that is working on COVID 19.

And a couple of other partners that we’ve have identified that could help us forward with the different aspects of making it an actual drug so that we can give it to people.

Harry Glorikian: Yeah, it’s interesting because I keep hearing so much about the vaccines and sometimes I think that we may need to shift some of the attention to.

These more immediate therapies that could bridge us to when we get a vaccine. But, but Jack, can you tell us about where you are in the process of, of moving this into safety testing?

Jack Wang: Currently we’re as really the end stage of the discovery where we have tried to narrow down a handful of candidates, I would believe could be a neutralizer to the COVID-19 proteins. And so, as JP mentioned, we are interested in finding partners who are able to perform those assets for us so that we can finally narrow down to a more, um, established pool of antibody that we believe could be used for actual human and treatments, and therefore being able to achieve that one as for how we are looking for, um, a lot of, I would say manufacturing facilities, and also people who work on cell line development to TA.

To help us produce these kind of high-quality graded, uh, GMP or GLP graded material that we can eventually put it into animal for first checking the in-vitro protections, preimpulse infection or the buyers as well as putting into co-working with a child’s ladder. Charles river laboratory in establishing toxicology study, looking at whether or not, if we put this animal, uh, antibody into animal, would it still be safe for us in order to the applications that we see to against fighting against the COVID-19?

Harry Glorikian: Got it, got it. So assuming this works, uh, I think I saw. September as a timeframe to, um, have this potentially available for use in patients?

Jack Wang: Um, I would say, yeah, that was the expected timeline in a way that we’re doing multiple. Um, experiments in parallel. So it’s a lot of time people usually like um, advocacy model and then move on

Harry Glorikian: Yep

Jack Wang: =To safety and eventually going into big scale up manufacturing and because of the kind of viruses and everyday counts.

And so because of that, we are moving approach to accelerate, this kind of process, and, um, to do that, we need more resources and that’s why we’re seeking out partnerships as well as government funding to help us. Uh, I mean this kind of bottleneck

Harry Glorikian:. Yeah. I don’t think the way that we did it in the past is applying right now to this current.

You can’t shut down a whole country forever. I think the cure would be eventually worse than the disease, depending on how long you keep everything shut down. You know, my next question I guess, would be, is let’s put COVID to the side for a minute and say this capability that you guys have. Can you guys talk about other areas where you think it has applicability.

And then what would a normal timeline look like versus say what you guys have done with the, uh, COVID or the SARS antibodies and the modifications.

Shahrad Daraekia: So this application for, are you talking about like other targets

Harry Glorikian: in general? Yeah. Yeah. Just, just, you know, you’ve got a platform here and driven by computational capabilities that are enabled because of all the advancements that we’ve had either in cloud computing, uh, you know, micro processing capabilities, sequencing capabilities that, you know, you are able to feed the system. I mean, it’s a perfect storm of things coming together. It’s not necessarily just one thing. That’s driving this. And so this platform will enable other opportunities. And where do you guys see those opportunities?

Shahrad Daraekia: So as you mentioned, all those technologies, we have, we currently implemented Distributed Bio as we are kind of a client research organization, contract research organization, where we take pretty much everything you described from the sequencing to the library, building to the screening, um, and all the computational analysis.

We do for our clients and developing antibodies against, you know, very common immuno oncology targets, uh, difficult receptors. Um, we have discovered targets or excuse me, antibodies in the past that can bind to two different targets altogether. Through this process as well. And currently we have a long extensive list of clients who come to us with a target in mind, whether it’s for cancer, whether it’s for Alzheimer’s, um, whether it’s for, um, you know, controlling some kind of signal cascade within the body.

And they want us to find antibodies to these particular targets and pathways. Um, and we implement exactly the same process that we did for the coronavirus, um, for our clients. And we develop antibodies for them down the road and the whole process. I Distributed Bio techs from six to eight. Depending on the challenge of the target itself and the extra steps that need to be done and normally generate the antibodies for the clients.

Um, and so nothing that we get for the coronavirus is pertained specifically. To that target in the, in the way of how our processes work. We just took, you know, the same typical processes that we do for our clients, but we do it for the Corona virus, but at more of a supercharged rate, because we realized that we need to develop the therapeutic much faster in this case, because, um, We are, the world is kind of that standstill, as you mentioned with countries being shut down, you know, no one’s able to leave their homes.

So, um, what we usually typically try to achieve for our clients in six to eight months, we have achieved that in a couple months, I would say, just because we have a great team at Distributed Bio and everyone has offered to work weekends nights. Um, we have. We had for awhile shifts of two or three teams that would do am and PM runs just to keep the experiments going and just to keep the process going.

Um, and that has kind of enabled us to get to where we want to be much faster than we had originally anticipated. Um, but everything we did for coronavirus and developing and finding these therapeutics mimics what we do in the lab on a day-to-day basis, the way Distributed Bios usually operates

Harry Glorikian: Well, 24 seven, isn’t that the definition of a startup.

Especially, uh, you know, sort of, I feel like IT focused startups seem to be much more 24 seven. So can you imagine one day that the software allows this to be almost self serve or do you need all this special capabilities behind the, uh, behind the software?

Shahrad Daraekia: So. JP, maybe you can correct me if I’m wrong or let me know if this is too far fetched of an idea, but I’m wondering if one day, you know, machine learning will get us to the point where we can identify sequences of antibodies that we think will bind to a particular target, or we’ll be good candidates to binding to a particular target, rather than going through the process of building these extensive libraries with billions of members.

Selecting for them over a week or two, and then screening them. Um, I believe machine learning is maybe the wave of the future when it comes to identifying particular antibody sequences for a target

Harry Glorikian: J P you’re you’re smiling there. So I can see like this, this idea’s going through your head. So what, what, what, what do you, what do you, what are you going to tell us that it’s, this is the wave of the future.

JP Buerckert: Yeah. I mean, I wouldn’t be lying if, uh, if I wouldn’t say we haven’t tried yet. Um, and, and the point with machine learning is that, um, whatever you’re developing can only be as good as the data that you throw at it on a Distributed Bio. We have this fantastic situation that we have generated millions and millions of sequences against so many different targets.

So we can start thinking about algorithms that say, Hey, How do you, um, how does an antibody have to look to bind an alpha helix, for example? Right. We can ask these questions because we have the data for it. And we have, um, implemented certain algorithms already that help us first guiding through the data.

That we have, like which ones are the best ones to move forward with as we already explained. And then we have developed the tool actually with our, uh, computational immunologists who was just 17 years old. Aishani Aatresh. Um, she has put together a tool that enables to predict sequences that would be even better than the ones we found in the seat, in our dataset.

Harry Glorikian: I love it.

JP Buerckert: That’s fantastic. So, so you’re currently testing this. Um, we have a few candidates also for, uh, COVID 19 for SaaS cough, too, that we have predicted with, um, archetype as a tool is called. I just got the data in this morning and we’re currently reviewing it, which is like super exciting because we are, we are having sequences in front of us and we didn’t fish out of the pool that we have.

We just told the computer, Hey, give us a prediction of what would be best at this particular position of the antibody. And it tells us that. And so we will of course include a couple of these antibodies in our set for screening to see if they work. And that is the other part that comes with, um, this consecutive improvement of involving machine learning is you always like you run, you run a screen, you run it a tool or an algorithm, and then you generate data and you need to test that in the wet lab. And step-by-step, we will be moving forward, improving sequences at first discovering new sequences that we haven’t had yet. And then maybe in the future, even designing sequences um, from scratch, but I think that that’s still a long way to go.

Harry Glorikian: So two, two things, I mean, to, to, uh, thoughts come to mind, is, is it just straight machine learning or is there sort of a. Rules-based on top of it based on, you know, experiences.

JP Buerckert: So, um, archetype that I just described and maybe, uh, Aishani can jump on a podcast with you as well and more detailed because it’s really her invention, um, that works with a statistical regression.

So we are performing statistical tests on the individual positions and see based on the data that we have, which positions. Would be better off having a different amino acid at that particular position, just based on the data that’s existing. Okay. So there no like Denovo, uh, generation of, of something, um, that, that isn’t in the data already.

And then later on, um, machine learning algorithms, I mean, there’s, there’s different ways how to perform machine learning.

Harry Glorikian: Yep.

JP Buerckert: We can, we can have like completely different, uh, set ups for that. One very, very, um, promising tool in this particular guard is forming new neural networks. And I have seen publications from other groups who have successfully used neural networks.

Um, actually several layers of neural networks to optimize a paratope. So the part of the antibody that is interacting with the antigen

Harry Glorikian: Yep

JP Buerckert: And that seemed to have worked tremendously well, at least in their publication. And against the single target. So I guess here we’re, we’re hitting also at that point.

And I don’t want to like go into too much detail on the technical part, but, um, every target and for an antibody is, is different in design. That is usually the beauty of it because we can define an antibody specifically to bind that target into nothing else. But this also poses a problem for, for our machine learning approaches, because we would have to like set something up for each individual target.

Harry Glorikian: Yeah. I mean, I’m thinking about this and I’m, I mean, just from a technological software and approach perspective, you’ve gotta be constantly willing to make changes because the technology is not standing still. I mean, this isn’t like a classical experiment and that’s just the way we do it. It’s an ever evolving.

This might be a slightly better way to do it. Well, no, this might be a slightly better way to do it and you’ve gotta be willing to play with that. To see which one gets you to the optimal results

JP Buerckert: And at the same time, keep going with what you already have. Right. And instead of just every time there is a, is a new fancy car coming around the corner you immediately jump on it, then you never drive a single mile. Right. So you have to keep something going at the same time. And that is the way we doing it at Distributed Bio.

Harry Glorikian: Yeah. Well it’s software development, right? You’ve got to get the product to a certain point and then you keep iterating until you’re ready to introduce the next iteration of it.

But it also makes me believe that what you said. If we have enough data in a particular area, the computational capabilities should be able to generate a much faster and more specific answer than we would do traditionally. And therefore that the machine could probably do it on its own at some point, without needing to do wet lab experiments.

JP Buerckert: I think that particular part we will, um, Probably never hit or if that’s the case then like in the very distant future, because, um, right now, and we talked about it briefly with, with COVID-19, we will hit the stage where we have our set of, um, and just to add some numbers to this, we started with five antibodies and we generated like 2.5 to 5 billion variants of that.

We, um, screened around. Around 2000. Right. And we have selected down to about 48 lead candidates. So that’s like the data flow that we, that we’ve generated and we can’t move forward with all 48. That’s impossible. So we have to narrow it down further, but then there will always be the point where we have to decide, can this go into a human or can it not?

And I, I don’t know whether we will ever want to make that decision using a machine learning algorithm, if we could just, you know, test it in an actual. In vivo system first. Harry

Glorikian: Well, that’s good. Then that means that human beings will never lose their jobs. This is always a good thing. Um, everybody always worries about that when we’re talking about this stuff.

So you guys are looking for partners right now, and I don’t want to ask how that’s going. Hopefully, you know, the, the people are knocking on the door or at least interesting in helping move this forward. And, and, you know, am I going to see a bag hanging next to me that says Distributed Bio on it? When it’s, I’m getting my antibody dose. That’s going to protect me. Who do you think is going to get this first? If it’s a, if it goes through the process

JP Buerckert: in terms of the drug itself? Yes. Once we, once we’ve moved through, through the testing phase and produced enough that we can actually distribute it, I think we will, we will work in a similar way as it does currently discussed with, uh, um, anti-malarial drugs.

And it’s been repurposed already are trying to repurpose it. You would first give it to people that are really, really badly sick. Um, and basically where you have this scenario that you have a one-off chance. Okay. So either that patient is going to die anyway, or are we going to give them the drug and see if he gets better with that?

I think that will be the first application for it. And then once we have made clear that this is working, it’s efficacious, it’s safe to take. And I think the first doses will be distributed to the frontline workers. So all the nurses and healthcare workers that are currently battling this every day in our, you know, they’re in an environment where infection is highly likely.

And even though they are keeping, um, extreme safety measures and, and like content decontamination protocols, we still hear news about nurses and doctors getting sick and eventually dying off of COVID-19. And if that part of our system collapses. Then I will be absolutely faithful because then we have nobody anymore to take care of patients.

Harry Glorikian: Right. All right. So it’s it right now. What we see is it’s a, it provides a curative, hopefully in the L patient and a protective in someone who has not gotten it yet. So can you guys describe what the antibody is doing per se, that that would provide that curative slash protective capability

Jack Wang: A lot of the already neutralizing anti-SARS antibiotic is blocking a interaction to a human receptor called ACE2, which is very essential for the viral entry and similarly to COVID-19, which is a closely of close family of SARS

They bind to the same partners. And therefore, um, we tried to engineer antibody that mimic the same interaction to block that interaction between the ACE2 and the COVID-19 proteins, so that we can prevent the interaction of that leading which to the downstream and viral entry into the cells for infectivity.

Harry Glorikian: So basically it’s, it’s, uh, stopping the spikes from. Adhering to the human cell. Sorry. I have to make it accessible, right. To make sure everybody, including my wife who listens to this, you know, gets it. Well, this was great guys. I mean, is there anything else you guys want to communicate on, on the existing programs that you’re doing or, or any highlights that I might’ve missed in this conversation?

Shahrad Daraekia: Oh,  sure. Well, I will say if anyone is curious and they want more information about not just the coronavirus vaccine, but other work that, uh, we are doing, um, they can visit, uh, or There’s a whole list of all the current cancer targets, you know, ONC targets, difficult receptors.

We’re working on a universal flu vaccine, incentive X. There is a legal clonal for, um, in a venom. Um, antibody, if someone is bitten by a snake, they could administer this dose of an antivenom. Um, therepeutic. Um, there’s a lot of cool work being done. And, um, you know, we’re always looking for, um, people to partner with, whether it’s for continuing lab operations or for taking the current virus therapeutic through IMD.

Um, if they need any more information, all of that should be on or

Harry Glorikian: you know, for everybody’s sake. I hope you guys are. Truly successful on all fronts to all of you in all of your geographies. I hope you guys are safe and sheltered at home, enjoying your wine at the end of the day.

Thanks for the time gentlemen.

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.


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