2.28-29.24-Ethics-Quantiscope

KEVIN CHRISTOPHER: All right, a huge thanks to the ILP team for allowing us to have this opportunity to meet potential partners and share a little bit about our vision. I am Kevin Christopher, and my opportunity today is to tell you a little bit about QUANTiSCOPT, which launched out of the Broad Institute, and is developing enterprise platforms for image-based intelligence tools with applications in drug discovery, bio manufacturing, and agtech.

So generally, what we're tackling is pulling out the treasure trove of information in image data within biological experiments and assays. And so this image in the left corner here is a Brightfield image from one of our pilots. And we like to say that an image is worth a 1,000 data points. There's actually about 100,000 data points in this image.

And what we do is we automate the process of extracting that information and making comparisons to provide qualitative phenotypic measurements in terms of variations between controls, positive and negative controls, and then also quantitative insights, when you're looking at, for instance, large libraries and drug screening. And I will say, as a reformed patent attorney, that this type of process can enable a company to add one year of patent life, for instance, by shrinking and de-risking the preclinical stage.

But also, we're in an ethics conference. So the true impact here for us as a public benefit corporation is being able to scale these resources, which may open up global teams to combat pandemics, which could open up the potential for more rare disease research by eliminating some of the cost and time barriers in the regulation scale.

The different types of applications that we're looking at are drug screening of large libraries. We're complementary to a lot of AI technologies, which are actually producing larger and larger initial data sets. So whereas a company might start historically with 1,000 potential compounds to screen, to move the few into animal and then human studies that could be allowed, based on cost and time constraints, we're now seeing companies who are starting with 100,000 or a million data set libraries. And they need to funnel that down.

So this type of approach can be used for funneling, also for novel design for insights into mechanism of action. In the biomanufacturing space, a lot of the adverse effects from the COVID vaccines, which were mRNA vaccines, were actually attributable to batch inconsistencies in manufacturing. So this same type of approach can be used as an image-based sampling among batches to be able to detect inconsistencies and measure why. And then also in climate, the same approach for therapeutic screening can be used for environmental agent screening, looking at the impact in cellular perturbations among different types of agents-- say, for instance, in wildfire restoration.

At a high level, what we're doing is looking at high-content image fields, segmenting what's happening there, comparing among hundreds of different features that we extract for, and then modeling those and creating signatures that can be compared on a large scale, so that you can truly understand at a holistic level and a more data-rich environment what's happening to a target cellular population beyond just a single data point, for instance, suppression of a protein. But what we're wanting to do is really scale this type of approach.

And so we're building a team-based dashboard environment for this, which we think is really important. Some of the different types of companies who've been able to access these types of resources are using them for a very, very limited and customized applications. And by scaling this technology through a distributed, team-based environment, we're really able to make the impact that we want to make and that we've promised.

What's our traction? So we launched in late 2022 through a BARDA accelerator. BARDA is a federal agency, biodefense, focused on pandemic response; disclosed some IP to the Broad Institute office; launched bootstrapped our MVP, moved to the Silicon Slopes of Salt Lake City in an incubator from recursion, which is also a Broad Institute launch. And now we are developing downstream innovation and looking for additional partners.

So as we are in a partnering forum, we are looking for beta testers of our initial MVP'S around screening fluorescent and Brightfield images and then, also, downstream partners in the innovation space. We're looking ultimately, to, How does this translate to quantum infrastructure and learning about the insights into quantum image analysis and development as well? So thank you very much for your time.