Most of three years after its founding, biotech startup Immunai has raised $60 million in Series A funding, bringing its total raised to over $80 million. Despite its youth, Immunai has already established the largest database in the world for single cell immunity characteristics, and it has already used its machine learning-powered immunity analysts platform to enhance the performance of existing immunotherapies, but aided by this new funding, it’s now ready to expand into the development of entirely new therapies based on the strength and breadth of its data and ML.
Immunai’s approach to developing new insights around the human immune system uses a ‘multi-omic’ approach – essentially layering analysis of different types of biological data, including a cell’s genome, microbiome, epigenome (a genome’s chemical instruction set) and more. The startup’s unique edge is in combining the largest and richest data set of its type available, formed in partnership with world-leading immunological research organizations, with its own machine learning technology to deliver analytics at unprecedented scale.
“I hope it doesn’t sound corny, but we don’t have the luxury to move more slowly,” explained Immunai co-founder and CEO Noam Solomon in an interview. “Because I think that we are in kind of a perfect storm, where a lot of advances in machine learning and compute computations have led us to the point where we can actually leverage those methods to mine important insights. You have a limit or ceiling to how fast you can go by the number of people that you have – so I think with the vision that we have, and thanks to our very think large network between MIT, and Cambridge to Stanford in the Bay Area, and Tel Aviv, we just moved very quickly to harness people to say, let’s solve this problem together.”
Solomon and his co-founder and CTO Luis Voloch both have extensive computer science and machine learning backgrounds, and they initially connected and identified a need for the application of this kind of technology in immunology. Scientific co-founder and SVP of Strategic Research Danny Wells then helped them refine their approach to focus on improving efficacy of immunotherapies designed to treat cancerous tumors.
Immunai has already demonstrated that its platform can help identify optimal targets for existing therapies, including in a < a href="https://www.nature.com/articles/s41591-020-1074-2"> partnership with the Baylor College of medication where it assisted with a phone therapy product for use in treating neuroblastoma (a type of cancer that develops from immune which, often in the adrenal glands). Dee engineering is now also moving into new sales area with therapies, using its machine acquiring knowledge platform and industry-leading cell system to new therapy discovery : not only identifying and validating beneficial for existing therapies, but in order to create entirely new ones.
“We’re moving from just as observing cells, but actually in which to going and perturbing them, on top of that seeing what the outcome is, ” explained Voloch. This, from the computational side, later allows us to move right from correlative assessments to actually causal tests, which makes our models a lot more forceful. Both on the computational side as well the on the lab side, and is really bleeding edge technologies in reality think we will be the first to really put together again at any kind of real scale. ”
“The next step would be say ‘Okay, now that we understand the human immune profile, can we prepare new drugs? ’, ” understood Solomon. “You can think about it like we’ve been building a Google Maps the immune system of a few years – and we are mapping different roads as well as paths in the in the immune system. Still at some point, we figured out that there are targeted roads or bridges that have not been built yet. And we will withstand support building new roads as new and new bridges, and in addition hopefully leading from current these types of of disease or cities with regards to disease, to building cities within health. ”
