Main Highlights:
- On Wednesday, Immunai announced a $215 million Series B capital round.
- According to Noam Solomon, Immunai’s CEO and co-founder, the unexpected surge in funding is due to AMICA’s many forms of insights.
- This technology is now being used to develop and improve a cell treatment for neuroblastoma in partnership with Baylor College of Medicine.
- Immunai collects a lot of data about its users. Immunai has worked with over 30 businesses, and universities reprint Genomics for an unknown price in March. The company was working on large-scale single-cell sequencing.
Immunai, a biotech company, has had a great deal of success raising capital. By the end of February 2021, the company that set out to create an atlas of the human Immunai system had garnered approximately $80 million in funding. On Wednesday, the company announced a new, far larger round of funding: a $215 million investment in series B financing.
A vast amount of clinical immunology data has taken Immunai years to collect and arrange, and this has taken years longer than anticipated. Scientists combine genetic data with other sources of information to better understand how the Immunai system functions. Examples of such sources of information include epigenetic changes and proteomics. Machine learning makes it possible to uncover which targets would be helpful for drugmakers, which drugs might cause hazardous reactions, and eventually predict how a patient may respond to a given treatment.
Immunai’s aim:
According to Immunai, the Annotated Multi-omic Immunai Cell Atlas, or AMICA, is the largest of its kind in the world. Up to this point, the company has attracted $295 million in venture capital, with Koch Disruptive Technologies serving as the lead investor and participation from additional investors such as Talos VC, 8VC, Alexandria Venture Investments, Piedmont, and ICON.
Currently, cell therapy for neuroblastoma is being developed and enhanced in collaboration with the Baylor College of Medicine, and this platform is being used in this process. According to Solomonccording to Solomon, a report demonstrating that the company can identify specific gene targets that predict whether or not a patient would respond to particular treatments is also in the works. Solomon expresses himself.
According to Noam Solomon, the company can deliver causative data rather than just correlational data for the time being. He goes on to say that they were demonstrating substantial correlation evidence about a year ago, indicating that some of the insights they’d gathered may be used to explain connections between genes and cells.
As a result of scientific advancements, there are more causal inference outcomes available now than ever before. Through their functional genomics platform, they will demonstrate that their efforts are producing specific products.
While Immunai is one business aiming to use cell-level data, it is by no means the only one in this field of endeavor. In the same market, there are many enterprises of varied sizes competing against one another. Immunai is distinguished in Solomon’s view by two characteristics.
To begin, Immunai is accumulating a massive amount of information about its users. Immunai has collaborated with more than 30 companies and academic organizations. However, this year’s purchases have increased the types of biological data that the company may collect and manage due to two essential investments made by the company.
Immunai purchased Immunai acquired Dropprint Genomics in March for an undisclosed sum. The company was engaged in the development of technologies for large-scale single-cell sequencing. According to Solomon, dropping has made “interesting progress on autoimmunity,” and he credits the company for this. Immunai purchased Nebion, a 13-year-old Swiss company specializing in creating gene expression data sets, this summer. As well as internal collaborations, according to Solomon, they have 70 external relationships with hospitals and universities.
According to Solomon, both acquisitions “substantially hastened the expansion of the database.” To acquire complementary technology, however, the M&A approach remains unchanged… As time goes on, Immunai’s data collecting strategy will be heavily influenced by the formation of new collaborations.
Immunai’s distinctiveness, according to Solomon, stems from the company’s approach to handling sensitive data. This is because Solomon is equally concerned with developing the infrastructure to manage the data set as he is with the data itself, rather than with labeling Immunai as an engineering-first company, as others have done.
According to him, around half of the company’s 120 employees come from purely technical or engineering backgrounds. Following the completion of this financing, the company wants to hire additional employees and continue to expand its immunological data library (as well as the supporting back-end infrastructure). From a business sense, it also means that the organization is less reliant on upfront payments from prospective partners than it was previously. As a result of the extra finance, the company’s emphasis has moved slightly.
