Collaboration to chart AI-generated map of the immune system

By Jenni Spinner

- Last updated on GMT

(Design Cells/iStock via Getty Images Plus)
(Design Cells/iStock via Getty Images Plus)

Related tags Immune system immunity Genomics Artificial intelligence machine learning

A partnership between Immunai and 10x Genomics will use artificial intelligence to create a map of the immune system to help accelerate drug development.

Immunai, a company specializing in comprehensive mapping of the human immune system, is joining forces with 10x Genomics. The latter will leverage its single-cell technologies to map hundreds of cell types and states. By applying its artificial intelligence (AI) and machine learning (ML) algorithms, Immunai supports biomarker discovery and insight generation to help power new therapeutic discoveries and accelerate drug development.

Outsourcing-Pharma (OSP) discussed the partnership with Luis Voloch (LV), CTO and co-founder of Immunai, and how the map generated through the collaborative effort stands to benefit drug developers.

OSP: Please tell us a bit about Immunai.

LV: Immunai is comprehensively mapping the immune system to power new therapeutic discoveries, accelerate drug development, and improve patient outcomes. Leveraging single-cell technologies to profile cells and machine learning to map incoming data to hundreds of cell types and states, Immunai supports biomarker discovery and insight generation to better detect, diagnose, and treat disease.

The immune system is an incredibly complex, distributed system that researchers have been trying to understand with limited success for years. Immunai is the first company to fully map the immune system, generating the largest proprietary database for immunology.

We’re disrupting legacy companies by analyzing 10,000 times more data from each cell than they are. No one is doing exactly what we’re doing.

OSP: How did you come to partner with 10x Genomics?

LV: There is an undeniable fit between the goals and capabilities of our two companies. At Immunai, we want to use AI to identify and understand novel elements within hundreds of different cell types to inform drug development, and we have been leveraging 10x’s products to do that at a granular level from the start.

Through our initial work together, we identified even more mutually beneficial applications of our technologies for pharma companies and academic institutions alike. So we most recently applied to 10x’s Certified Service Provider Program to give 10x’s customers access to our advanced immune profiling solutions.

OSP: What does each of you bring to the table in this partnership, and how will the collaboration work?

LV: With this collaboration, we will pair our immune cell atlas with the phenotypic clinical data that hospitals, biopharma, and biotech companies derive from 10x’s technology. With Immunai’s end-to-end computational AI pipeline customized for single-cell methods, researchers at pharmaceutical and cell therapy companies can better understand how immune cells operate with both granularity and scale. In turn, we will help 10x’s customers answer clinical and translational questions related to the immune response to therapies.

OSP: Could you please talk a bit about the evolution of AI and how drug discovery professionals have made use of it to date?

LV: An analysis published earlier this year in the Journal of the American Medical Association found that the median cost of R&D for a new drug in the years between 2009 and 2018 was $985 million. This ever-increasing cost forces pharma companies to search for innovative means to create efficiencies in drug development.

Pharma companies are catching on to what Immunai already knows: AI can maximize our ability to layer data points, uncover deep insights, and advance research.

We envision AI—in conjunction with human intelligence—as the major component to understanding and curing cancer. AI will increasingly have a tremendous impact on pharma. Pharma has traditionally had to experiment by testing out different compounds in a dish or in animals.

With more biological data available, AI provides a partial alternative to this that allows us to predict (without actual experiments) the impact of compounds in different biological systems. This ability has increased the speed in which we can profile and improve compounds.

OSP: What is particularly novel and noteworthy about this project—what do you hope to accomplish that hasn’t been accomplished before?

LV: Until now, no one has been able to uncover the complexities of the immune system in the way that Immunai has. Current single-cell approaches generally operate at the scale of small academic studies because they suffer from the problem of batch effects, where noise from variation in biological samples quickly washes out any real biological signal as scale grows.

Immunai’s end-to-end platform is designed to manage batch effects through both proprietary lab methods and advanced AI, allowing us to build a large multi-omic single-cell database that we pair with clinical context. We train our proprietary neural network models on this data to surface insights about immune responses and facilitate the development of better therapies.

This lack of understanding of the immune system contributes to inefficiencies in drug R&D. Developing immunotherapies based on information provided by only two cells doesn’t give researchers a view of the entire picture.

We believe that this collaboration will help to drastically improve the development of therapies and answer some of the biggest questions about cancer.

OSP: Is there anything you’d like to add that we didn’t touch upon?

LV: Our work with 10x is the second official collaboration we’ve announced over the past few months. In November, we announced a collaboration with Baylor College of Medicine to drive forward the development of novel NKT cell therapies. As our database continues to grow with these partnerships, we can apply learnings around immune response across different diseases from cancer to autoimmune disorders to cardiovascular diseases as well.

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