2022-Korea-Showcase-Modulus-Discovery

SPEAKER: [SPEAKING KOREAN]

ROY KIMURA: Hello. Well, first of all, my name is Roy Kimura, Co-founder and CEO of Modulus Discovery. And I want to thank you all for spending the afternoon with us today. I want to, especially, thank the MIT ILP corporate members and prospective members for all the support in making this possible. Today, I'd like to give you a brief overview of our company, our technology platform, and our drug discovery programs.

Let's see. How does this work? Just a little bit of background of our company. The Modulus Discovery team, we're all from major pharmaceutical firms. And we all recognized, prior to starting Modulus, right around 2012 or 2013 was the time that we saw that computational technology for drug discovery had finally reached a critical inflection point.

That is, the accuracy of the predictions coming out of the technology became high enough to be used in the industry. And yet, as you know, computational costs continue to drop, such that we could imagine using simulation at a large scale to significantly impact drug discovery. However, through our experiences working in big pharma, we saw that this technology wasn't being leveraged to its fullest potential, especially in established companies because of legacy processes.

So in 2017, we saw this as an opportunity. And the team came together with a shared vision in mind. And that shared vision was to build a brand-new organization from the ground up with a repeatable, cost-effective and scalable R&D process that enables us to leverage the best simulation technology of today to discover the much-needed medicines of tomorrow and dramatically improve patient lives.

So that's reflected in our mission. We are dedicated to accelerating the discovery of new medicines for patients and their families through the innovative synthesis of computational intelligence, biological inspiration, and global integration.

Since 2017, our founding, we have built and validated our proprietary drug discovery methodology and platform, using both state-of-the-art available software as well as internally-developed software. And using those, we've created a repeatable, scalable, internal process that could reduce R&D timelines by up to 50%. Using this platform, we've developed novel small molecule drug candidates that are currently being evaluated in preclinical studies.

And we've, so far, established collaborations with PeptiDream, Astellas Pharma, Nissan Chemical Corporation, and Fujitsu Limited. We believe that much of our success in our collaborations with Japanese firms owes a lot to our cultural fit.

We are headquartered in Tokyo with an office in Kendall Square. And also, logistically and geographically, it's very convenient for our partners. And we also believe the same could be true in Korea. And we're very interested in forming collaborations here. In the funding side, we've so far raised approximately $50 million USD from leading life-science investors.

So the way our simulation-driven drug discovery platform works is as follows. We start off with seed assets. This could be a novel compound or a new target in the body that's validated or validation data. And we take this seed asset and look at the risk benefits of starting an actual drug discovery program around it.

And once we decide to launch the program, then we fire up our platform, consisting of large-scale simulation capabilities, project-specific machine-learning models, as well as experimental tools that synergize with our computational platform. Using this, we generate a very large number of candidate compounds in silico. We simulate them in our platform and score them.

And every top-scoring compound coming out of this simulation is actually, then, experimentally synthesized and screened and tested. Eventually, all of our lead molecules are evaluated industry standard-level drug discovery assay tiers. So that means your usual in vitro, cellular, in vivo assays. And we are able to generate data packages that are necessary for licensing discussions.

Our platform consists mainly of these six modules. We have ModSim and ModScale, our simulation protocols, that enables us to run a very large number of simulations very efficiently. We have ModScreen, which is our ultra large-scale virtual screening protocols. ModOpt which is our simulation protocols that enables very efficient lead optimization.

ModSolve is our technology to identify and characterize new pockets on drug discovery targets. And finally, the latest addition to our platform is ModBind which is a fast and accurate proprietary method to predict ligand efficacy, using a fundamentally unique approach.

We just actually announced our collaboration with Fujitsu which is focused on implementation and optimization of ModBind. Together with Fujitsu, we hope to fully make our new technology scalable and usable in the actual drug discovery setting. And we are actually starting to use it within our programs.

Just a little bit about ModBind. It is a proprietary simulation-based predictor of compound efficacy or activity. It is an absolute and not a relative predictor, meaning we don't need data for a reference compound in order to predict activities of new compounds. It is simpler to set up. It's faster to run than other simulation methods. For instance, we've demonstrated we can run it about 20 times faster than existing methods.

And with Fujitsu, we intend to take this up to about 100-fold speed up, enabling about a 10,000 plus compound throughput per week, which opens up completely new discovery workflow possibilities. In terms of accuracy of the predictions, we believe we have competitive performance with the leading methods. And so you can actually get the benefits with the faster speed and also the absolute predictions with the same accuracy.

This is how our current pipeline looks like. We have 10 programs running, mostly in oncology and chronic inflammation. Three of these programs are now approaching the clinical-trial stage. Just a little bit about the projects. MOD-A is our most advanced project, where we have potent and selective cathepsin C inhibitor.

We've identified the clinical candidate. And we've already demonstrated in multiple disease models proof of concept efficacy, including a significant improvement in lung micro-bleeding and kidney histopathology in the diseased models. And this data was presented at EULAR 2022.

MOD-B is our highly-selective cKIT inhibitor with selectivity against 350 other kinases, showing no activity in all the other kinases, except our kinase. This is in contrast with, say, a compound already on the market, imatinib, which hits a lot of other kinases, which translates into a lot of adverse events or side effects. And we've also demonstrated in vivo efficacy in animal models.

And MOD-C is our potential, first in class, PARG inhibitor that shows tumor regression, or tumor growth inhibition, in a sensitive cancer cell line as monotherapy. We have multiple proprietary crystal structures, multiple lead compounds. And we're currently working out the translational medicine to be able to come up with a clinical strategy for our compound.

So as I said before, we're actively seeking strategic partnerships worldwide. And especially in Korea, we're open to discussing possibilities for a ModBind Platform Drug Discovery Alliance, R&D collaborations, and, of course, licensing of our lead assets.

Thank you very much. And if you have any questions for me, I'll be available next door. And also, you can contact me here. Thank you.

[APPLAUSE]