5.4.22-Startup-Ecosystem-Immunai

ELIZAVETA FREINKMAN: Good morning and thanks for the opportunity to speak. As Ariadna mentioned, I'm Lisa Freinkman here on behalf of Immunai. Imunnai is a tech bio startup based in New York and Tel Aviv. We're about 3 and 1/2 years old. And both our CEO and CTO are MIT Alumni.

OK, so in broad strokes, the challenge that we're looking to address at Imunnai is the high cost and slow timeline of new drug development, which is in large part due to just a higher failure rate at every stage of the process from drug target discovery all the way through late stage clinical trials. On the other hand, the opportunity that we see is specifically through the impact of the immune system on all aspects of human health, not just in infectious disease but also in oncology, inflammatory disease, and beyond. And the way that our platform specifically is addressing this opportunity and challenge is through single cell multiomic profiling.

So that's a bit of a mouthful. So I will walk you through what that means. Basically traditional ways of looking at the immune system pool all the cells together into a single sample per patient. But that really glosses over the huge diversity of cell types and cell states that can play really different roles, and even opposing roles, in a disease state.

With single cell technology, we're able to collect really granular information on individual cells, collecting tens or even hundreds of thousands of data points for each patient, and including gene expression levels as well as cell surface markers and even what a specific cell is recognizing as foreign. As you can imagine, this generates a lot of data. So we use machine learning to clean, filter, and annotate these complex data streams and ultimately zero in on the most important differences among samples, for example, between patients that do or don't respond to a certain therapy.

And this is the technology that has enabled us to build AMICA, our multiomic immune cell atlas. AMICA consists of millions of data points or individual cells from thousands of patients across a wide variety of diseases. And importantly, these are all unified together in a single annotation framework, which enables us to look across data sets and generate uniquely powered therapeutic hypotheses.

Importantly, though, AMICA is not just an observational database, because it also includes readouts from our functional genomics stalls. And what this means is that we're using CRISPR technology to disrupt individual genes in human derived cells. We call these cells knockouts. And when we compare these knockout cells to the clinical data that I just described in the previous slide, this allows us to pinpoint individual genes that we can target with new drugs.

So here's an example of how this works in practice. As many of us are aware, in the last decade immunotherapies have really revolutionized cancer treatment by unleashing a patient's own immune system, especially T-cells to kill tumor cells. However, most patients do not derive durable benefit from immunotherapies. So it's really important to figure out what differentiates those who do from those who don't respond, and devise better treatments for the latter category of patients.

So we were able to use our AMICA database to pinpoint specific molecular differences between patients who do, versus don't, derive benefit from one of the most common immunotherapies called PD-1. And when we compared this specific molecular signature of response to our collection of T-cell knockouts, we found specific genes that really differentiated the two. So when we knock out these genes, they make the T-cells look much more like T-cells from patients who respond to therapy.

And so the hypothesis would be that targeting these genes in an animal model or patient should make the immune system work better to fight tumors. And in fact, we found that, for several of these genes, there was already data in the literature showing just that. So in each of these cases, the investigators found that disrupting a target gene that we had rediscovered actually improves survival or improves how the T-cells are able to kill tumor cells.

And most importantly, the same held true for one of the novel genes that we had discovered, that hadn't been appreciated before, showing that our platform is able to discover new drug targets that improve T-cell ability to kill cancers. This ability to discover new drug targets is only one of the ways that we're using signal cell multiomics to revolutionize the entire drug discovery pipeline.

Other areas of active work include developing better in vitro models that are informed by clinical data as well as indication selection and biomarker development, to inform clinical strategy. And we're eager to use these approaches to deliver value for former partners as well as to power our internal therapeutic development pipeline.

Thank you very much for your attention. And I look forward to interacting later today.

[APPLAUSE]