Immunai Secures $60 Million in Series A Funding

Biotech firm Immunai has successfully completed a $60 million Series A funding round just two years following its establishment, increasing its total funding to over $80 million. Despite being a relatively new company, Immunai has already compiled the world’s most extensive database detailing single cell immunity characteristics.

The company’s machine learning-driven immunity analysis platform has already been utilized to improve the effectiveness of current immunotherapies. This new financial injection will facilitate the expansion of Immunai into the creation of novel therapies, leveraging the strength and scope of its data and machine learning capabilities.

A Multiomic Approach to Immune System Understanding

Immunai’s strategy for uncovering new insights into the human immune system employs a “multiomic” methodology. This involves integrating analyses of diverse biological data types, including a cell’s genome, microbiome, and epigenome – the chemical instructions within the genome.

The startup’s competitive advantage lies in its combination of the largest and most comprehensive dataset of its kind, developed in collaboration with leading immunological research institutions, and its proprietary machine learning technology. This allows for analytics to be performed at an unprecedented level of scale.

Rapid Advancement Driven by Collaboration

“We recognize the urgency and don’t have the option of proceeding at a slower pace,” stated Immunai co-founder and CEO Noam Solomon in a recent interview. “Advances in machine learning and computational power have converged, enabling us to effectively utilize these methods to extract significant insights.”

Solomon continued, explaining that traditional research is limited by manpower, but Immunai’s extensive network spanning MIT, Cambridge, Stanford, and Tel Aviv has allowed for rapid progress in addressing this challenge.

Founders’ Backgrounds and Focus on Immunotherapy

Both Solomon and co-founder/CTO Luis Voloch possess strong backgrounds in computer science and machine learning. They initially identified the potential for applying these technologies to the field of immunology.

Scientific co-founder and SVP of Strategic Research, Danny Wells, subsequently assisted in refining their focus towards enhancing the efficacy of immunotherapies designed for cancer treatment.

Demonstrated Platform Capabilities and New Therapeutic Avenues

Immunai has already proven its platform’s ability to pinpoint optimal targets for existing therapies. This was demonstrated through a collaboration with the Baylor College of Medicine, where it contributed to the development of a cell therapy product for neuroblastoma treatment.

The company is now venturing into new areas of therapy development, utilizing its machine learning platform and leading-edge cell database for novel therapy discovery. This includes not only identifying targets for existing treatments but also creating entirely new therapeutic approaches.

From Observation to Perturbation: Advancing Causal Assessments

“We are transitioning from simply observing cells to actively manipulating them and analyzing the resulting effects,” explained Voloch. “This shift allows us to move from correlative assessments to causal assessments, significantly enhancing the power of our models.”

He emphasized that both the computational and laboratory aspects of this work represent cutting-edge technologies that Immunai is uniquely positioned to integrate at a substantial scale.

Building a “Google Maps” for the Immune System

“Our next objective is to determine if we can develop new drugs, now that we have a comprehensive understanding of the human immune profile,” Solomon stated. “We’ve been essentially creating a ‘Google Maps’ for the immune system, mapping its various pathways and routes.”

Solomon concluded by saying that they have identified gaps in the current immunological landscape – “roads or bridges that haven’t been built yet” – and aim to construct these missing connections, ultimately guiding patients from states of disease towards improved health.