Immunai announces a $215M Series B as its ‘immune cell atlas’ matures – TechCrunch

Biotech startup Immunai has been on a roll with regards to funding. The corporate that got down to create an atlas of the human immune system in 2018 had raised about $80 million by February 2021. On Wednesday, the corporate introduced one other considerably bigger spherical: a $215 million sequence B. 

Immunai has been constructing an enormous dataset of scientific immunological data. It combines genetic data, together with different information like epigenetic modifications or proteomics (the examine of proteins), to map out how the immune system features. Then machine studying is utilized to establish what targets may be helpful for drugmakers, what medication would possibly trigger poisonous reactions, and finally predict how a affected person would possibly reply to a possible remedy. 

Immunai claims this dataset, known as the Annotated Multi-omic Immune Cell Atlas, AMICA, is the biggest on the earth.

This spherical, which was led by Koch Disruptive Applied sciences, with participation from Talos VC, 8VC, Alexandria Enterprise Investments, Piedmont, ICON, and others, brings the corporate’s whole funding to $295 million. 

Noam Solomon, Immunai’s co-founder and CEO, instructed TechCrunch this large bounce in funding comes all the way down to a significant change in the kind of insights AMICA has yielded. 

The platform is presently getting used to develop and refine cell therapies for neuroblastoma at the side of the Baylor Faculty of Medication. Solomon additionally says the corporate is working to publish a paper exhibiting it will possibly establish particular gene targets that inform whether or not a affected person will reply to sure therapies. 

Within the meantime, Solomon says, the corporate has been capable of transfer from merely exhibiting correlative information to causative information. 

“In all probability a yr in the past we had been exhibiting robust correlative information – that sure insights we’ve can clarify relationships between sure genes and cells,” he says. “At this time we’ve extra causal inference outcomes. We’re capable of present that issues we’re doing with our practical genomic platform are literally inflicting sure outcomes.” 

Let’s be clear: Immunai is much from the one firm trying to harness cell-level information, and put it into motion. There are many firms, massive and small, enjoying in the identical house. Immunai stands aside, per Solomon, for 2 causes. 

First is the sheer dimension of the dataset Immunai is constructing. Immunai has been collaborating with over 30 firms and educational establishments (Memorial Sloan Kettering, Harvard, Stanford, and the Baylor Faculty of Medication, to call a number of). However the firm has additionally diversified the kinds of organic information it’s amassing, analyzing and managing by two main acquisitions this yr. 

In March, Immunai acquired Dropprint Genomics, an organization engaged on strategies to carry out single-cell sequencing at scale for an undisclosed quantity. Solomon provides Dropprint had made “attention-grabbing progress on autoimmunity.” Over  he summer season, Immunai acquired Nebion, a Swiss firm that had spent 13 years constructing gene expression datasets. In addition they had about 70 exterior partnerships with hospitals and establishments, notes Solomon.

Each acquisitions “actually accelerated the dimensions of the database,” says Solomon. Nonetheless, M&A technique stays to amass complementary applied sciences. Immunai’s information acquisition technique, going ahead, remains to be largely constructed on creating extra partnerships. 

The second cause Solomon believes Immunai stands out comes all the way down to its dealing with of all this data. Solomon calls Immunai an engineering-first firm, as a result of he’s simply as fascinated by constructing the infrastructure to assist the dataset as it’s in regards to the information itself. 

It’s additionally why, he notes, about 50 p.c of the corporate’s 120-person workforce is coming from pure tech or engineering backgrounds.

“I believe there are only a few firms within the house which might be attempting to do greater than create a small dataset and apply subtle machine studying instruments,” he says. “Our method is the other. We imagine we have to construct a strong database that we will feed and develop, with the info engineering instruments to be sure that our algorithms can run on 100,000 samples.” 

This spherical can be used to herald extra staff and to maintain enriching the immunological dataset (and backend infrastructure that may assist it) on the firm’s disposal. 

From a enterprise perspective, it additionally means the corporate is much less depending on up-front funds with future companions. The brand new financing shifts the corporate’s focus.

“We don’t have a reliance on stronger upfront funds. We care way more about success-based funds,” Solomon stated.