Biotech startup Immunai has been on a roll when it comes to funding. The company that set out to create an atlas of the human immune system in 2018 had raised about $80 million by February 2021. On Wednesday, the company announced another significantly larger round: a $215 million series B.
Immunai has been building a massive dataset of clinical immunological information. It combines genetic information, along with other data like epigenetic changes or proteomics (the study of proteins), to map out how the immune system functions. Then machine learning is applied to identify what targets might be useful for drugmakers, what drugs might cause toxic reactions, and ultimately predict how a patient might respond to a potential treatment.
Immunai claims this dataset, called the Annotated Multi-omic Immune Cell Atlas, AMICA, is the largest in the world.
This round, which was led by Koch Disruptive Technologies, with participation from Talos VC, 8VC, Alexandria Venture Investments, Piedmont, ICON, and others, brings the company’s total funding to $295 million.
Noam Solomon, Immunai’s co-founder and CEO, told TechCrunch this massive jump in funding comes down to a major change in the type of insights AMICA has yielded.
The platform is currently being used to develop and refine cell therapies for neuroblastoma in conjunction with the Baylor College of Medicine. Solomon also says the company is working to publish a paper showing it can identify specific gene targets that tell whether a patient will respond to certain therapies.
In the meantime, Solomon says, the company has been able to move from simply showing correlative data to causative data.
“Probably a year ago we were showing strong correlative data – that certain insights we have can explain relationships between certain genes and cells,” he says. “Today we have more causal inference results. We are able to show that things we are doing with our functional genomic platform are actually causing certain results.”
Let’s be clear: Immunai is far from the only company looking to harness cell-level data, and put it into action. There are plenty of companies, large and small, playing in the same space. Immunai stands apart, per Solomon, for two reasons.
First is the sheer size of the dataset Immunai is building. Immunai has been collaborating with over 30 companies and academic institutions (Memorial Sloan Kettering, Harvard, Stanford, and the Baylor College of Medicine, to name a few). But the company has also diversified the types of biological data it’s collecting, analyzing and managing through two major acquisitions this year.
In March, Immunai acquired Dropprint Genomics, a company working on methods to perform single-cell sequencing at scale for an undisclosed amount. Solomon adds Dropprint had made “interesting progress on autoimmunity.” Over he summer, Immunai acquired Nebion, a Swiss company that had spent 13 years building gene expression datasets. They also had about 70 external partnerships with hospitals and institutions, notes Solomon.
Both acquisitions “really accelerated the size of the database,” says Solomon. However, M&A strategy remains to acquire complementary technologies. Immunai’s data acquisition strategy, going forward, is still largely built on creating more partnerships.
The second reason Solomon believes Immunai stands out comes down to its handling of all this information. Solomon calls Immunai an engineering-first company, because he’s just as interested in building the infrastructure to support the dataset as it is about the data itself.
It’s also why, he notes, about 50 percent of the company’s 120-person workforce is coming from pure tech or engineering backgrounds.
“I think there are very few companies in the space that are trying to do more than create a small dataset and apply sophisticated machine learning tools,” he says. “Our approach is the opposite. We believe we need to build a robust database that we will be able to feed and grow, with the data engineering tools to make sure that our algorithms can run on 100,000 samples.”
This round will be used to bring in more employees and to keep enriching the immunological dataset (and backend infrastructure that can support it) at the company’s disposal.
From a business perspective, it also means the company is less dependent on up-front payments with future partners. The new financing shifts the company’s focus.
“We don’t have a reliance on stronger upfront payments. We care much more about success-based payments,” Solomon said.
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