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I'll start from the executive summary.
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We made or we formed the company about a year ago to solve an existing problem, which is the suboptimal state of certain therapeutics in the late stage of clinical development or even those that are approved.
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And some of them have the dozing issues, some of them have not convenient route of administration, some of them have safety signal or even insufficient efficacy.
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And our solution to that is to develop a function first platform that turns suboptimal antibodies into best in class.
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So our secret sauce is function first.
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I'm going to talk about that for the rest for the remaining 9 minutes. And I started, I usually start with the latest progress.
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We had our wet lab space in the incubator for about 12 months.
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In this 12 months, we got 2 proprietary programmes, 1 achieved the in vivo POC, another is going into in vivo POC on Monday.
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The first thing was dosed on Monday.
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Each took about nine months to develop, and we have commercial validation of our platform as we use as we provide our function first data to bio AI companies that have the synthetic library design and need the refinement, need the data for refinement.
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We also work with the technology company I can disclose it that aims that intends to generate an antibody prediction tool.
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So what do we do?
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This is our design, express, test, refine cycle and we start usually with the focused libraries that are either reverse folded or AI designed or maybe rationally designed.
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We express them in the mammalian cells and here what normally would be a hit identification stage, we actually combine hit ID and lead optimization.
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Here we express antibodies in mammalian cells.
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So we know the expression.
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We look at developability properties such as polyreactivity.
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We look at the binding to the target or several targets of interest and it in our secret sauce, which is the function first assay.
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And this method is compatible with pretty much any mammalian reporter cell.
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We sort the cells of interest, we sequence them, we feedback to the proprietary data analysis tool, and we can refine that system is compatible with multiple IG formats.
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And these are the four programmes we have.
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I'm going to talk about the first two.
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It's the neutralised antibody to the RSV virus and the active in receptor to AB antagonist, which is aimed for muscle mass preservation during the weight loss.
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So a brief background on RSV.
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It's a pretty bad respiratory virus.
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Every fifth person who has RSV, one out of 20 people like that gets into the hospital.
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And the estimated economic burden in the US alone is $28 billion.
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The good news is that vaccines work for adults, for immuno competent adults.
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The bad news is that they don't really work for immunocompromised adults, and they also don't really work for newborns.
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There are three approved neutralising antibodies.
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Nirsevimab is the best antibody out of three, and even nirsevimab only lasts for five up to five months in the six months RSV season.
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So the newborns usually receive a neutralising antibody shot, but if they are born outside of the RSV season they need to come back to the hospital to get a shot, insurances don't like it.
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Parents tend to forget about that.
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Doctors don't administer.
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So there is all possible problems.
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We wanted to improve the antibody.
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We took the took nirsevimab and ran it through the optimization cycle just twice actually got 4.9x improvement and when brought it to the gold standard RSV prophylaxis model in cotton rats.
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And here we are comparing nirsevimab to our antibody.
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You can see that a 2 mg/kg dose in rats.
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One rat still had a breakthrough infection in lungs.
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This is the viral titre in lungs at 5 days post infection, whereas our antibody provided complete protection.
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At sub optimal dose, this is the nirsevimab performance.
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This is our antibody performance.
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You can see that nirsevimab barely made it to the significance and these error bars are overlapping with the control.
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That's what we think will be the protection curve.
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We need to add more data here.
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The second story is muscle mass preservation antibody.
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You heard about GLP-1, you probably heard about GLP-1 agonist by now.
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A lot of people are taking the agonist to lose weight.
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Problem is that when people lose weight, 40% of that is the muscle mass.
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It's not the fat mass.
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When patients stop taking the drug and the weight rebounds, that actually is pure fat.
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So what happened?
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The patient just lost 40% of their muscle mass for good.
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Eli Lily has a solution.
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They thought they have a solution for that.
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There is a drug called bimagrumab intended for muscle mass preservation taken with the GLP-1 agonist.
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It actually doesn't work as monotherapy and they just finished a phase 2B study, I think it's called EVOLVE.
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Data were published two to three months ago.
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When taken with GLP-1 in combination it led to 2.5% increase in muscle mass.
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Long story short, the antibody is safe but not very efficacious.
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We took the antibody ran through 2 optimization cycles.
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This is not even our final candidate.
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We're still working on additional candidates, and we immediately 3.8x improvement in the functional cell based assay.
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And if you look at the SPR data, this is the Bima binding to active interceptor IIA and IIB.
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It binds to the IIB pretty well and then stays bound to the target.
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However, it dissociates from Activin receptor IIA pretty fast.
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This is our antibody.
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It's balanced.
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We actually improved the binding to both A and B.
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And we believe that this antibody will not only increase muscle mass preservation, will increase the reduction of fat mass and reduce the side effects.
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We think that this antibody will be efficacious as a monotherapy, which is the deal break in this case.
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We have a very experienced team.
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Scott Dessain is our academic Co-founder.
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OCMS Bio just like Immunome where I used to work before our spin outs from the same lab of Doctor Scott Dessain.
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We have high hopes for the company because the technology is really cool.
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If you have any questions and if you would like to learn more about that, please come find me.
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We have a Booth H in the startup zone and happy to take your questions now or later.
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Thank you.