Immunai Raises $215M Series B to Advance Immune Cell Atlas

Immunai Secures $215 Million in Series B Funding

Biotechnology firm Immunai has experienced substantial success in attracting investment. Established in 2018 with the goal of mapping the human immune system, the company had already garnered approximately $80 million in funding by February of 2021. This week, Immunai announced a further, considerably larger funding round: a $215 million Series B.

Building a Comprehensive Immunological Dataset

Immunai is focused on constructing an extensive dataset of clinical immunological data. This involves integrating genetic information with other data types, such as epigenetic modifications and proteomics – the study of proteins – to comprehensively understand immune system functionality. Subsequently, machine learning techniques are employed to pinpoint potential drug targets, predict adverse drug reactions, and forecast patient responses to treatments.

The company asserts that its dataset, known as the Annotated Multi-omic Immune Cell Atlas (AMICA), represents the largest of its kind globally.

Funding Details and Investors

Led by Koch Disruptive Technologies, with contributions from Talos VC, 8VC, Alexandria Venture Investments, Piedmont, ICON, and others, this funding round elevates Immunai’s total funding to $295 million.

A Shift in Data Insights

According to Noam Solomon, Immunai’s co-founder and CEO, the significant increase in funding is attributable to a pivotal evolution in the insights generated by AMICA. He explained to TechCrunch that the platform is currently being utilized to develop and refine cell therapies for neuroblastoma, in collaboration with the Baylor College of Medicine.

Solomon also indicated that the company is preparing to publish research demonstrating its ability to identify specific gene targets predictive of patient responsiveness to particular therapies.

From Correlation to Causation

Immunai has transitioned from identifying correlative data to establishing causative relationships. “Around a year ago, we were presenting compelling correlative data – demonstrating how our insights could explain connections between genes and cells,” Solomon stated. “Currently, we possess more evidence of causal inference. We can now demonstrate that our functional genomic platform is actively driving specific outcomes.”

Immunai’s Competitive Advantage

While numerous companies are exploring the application of cell-level data, Immunai distinguishes itself through two key factors, as per Solomon.

Firstly, the sheer size of the dataset Immunai is compiling is unparalleled. The company has established collaborations with over 30 organizations, including Memorial Sloan Kettering, Harvard, Stanford, and the Baylor College of Medicine. Furthermore, Immunai has expanded the scope of biological data collected and analyzed through two strategic acquisitions this year.

Strategic Acquisitions

In March, Immunai acquired Dropprint Genomics, a company specializing in scalable single-cell sequencing, for an undisclosed sum. Solomon highlighted Dropprint’s advancements in autoimmunity research. Subsequently, over the summer, Immunai acquired Nebion, a Swiss company with 13 years of experience in building gene expression datasets and a network of approximately 70 partnerships with hospitals and institutions.

These acquisitions have significantly expanded the database. However, Immunai’s M&A strategy continues to prioritize acquiring technologies that complement its existing capabilities, with a continued emphasis on forging new partnerships for data acquisition.

Engineering-First Approach

Secondly, Immunai’s approach to data management sets it apart. Solomon characterizes Immunai as an “engineering-first” company, emphasizing the importance of building the infrastructure to support the dataset as much as the data itself.

This is reflected in the company’s workforce composition, with approximately 50% of its 120 employees possessing backgrounds in technology or engineering.

“Few companies in this field are attempting to do more than create a limited dataset and apply advanced machine learning tools,” Solomon noted. “Our strategy is the reverse. We believe in constructing a robust database that can be continuously expanded, coupled with the data engineering tools necessary to run our algorithms on a large scale – 100,000 samples and beyond.”

Future Plans and Business Model

The new funding will be allocated to expanding the workforce and further enriching the immunological dataset and its supporting infrastructure. From a business standpoint, this financing also reduces the company’s reliance on substantial upfront payments from partners.

“We are moving away from a heavy dependence on large upfront payments and are prioritizing success-based payments,” Solomon concluded.