Three major pharma partnerships in six months is an unusual position for a startup to be in. For Immunai, a 190-person company building what it describes as one of the world's largest clinically annotated single-cell immune datasets, it is beginning to look like a pattern.
In January 2026, the company signed a collaboration with Bristol Myers Squibb to apply its platform across oncology clinical development programs. In May, AstraZeneca expanded its partnership with Immunai for the third consecutive time since 2022, in a deal worth up to $37.5 million. Last month, Boehringer Ingelheim signed on for a discovery collaboration valued at up to $15 million, focused on identifying novel T cell targets across immuno-oncology and autoimmune disease. The AstraZeneca relationship alone has now spanned oncology, inflammatory bowel disease, and a multi-year extension through 2027.
The question worth asking is what a company of this size keeps solving that eight of the top 20 pharmaceutical companies cannot solve internally. Noam Solomon, CEO of Immunai, told DDN that his answer is straightforward: the data.
The asset that cannot be recreated quickly
"The immune system sits at the center of almost every disease and therapeutic response, yet it is still measured surprisingly sparsely in most clinical trials," Solomon said. Immunai has spent several years building a dataset of single-cell immune profiles generated directly from patients in pharmaceutical clinical trials and linked to real clinical outcomes. The company's database now spans 50,000 single-cell samples from patients who did and did not respond to different treatments, alongside healthy controls.
The differentiation, according to Solomon, is not in the sequencing technology itself, which is broadly available. "Our advantage comes from the scale, consistency, and clinical annotation of the data. Every sample is connected to rich information about the patient, the treatment they received, and how they ultimately responded. Building that resource requires years of prospective studies across thousands of patients and many therapeutic programs. It cannot simply be recreated in a single R&D cycle."
That data feeds proprietary foundation models, explainability tools, and clinical benchmarks that continuously validate and refine model outputs. The data makes the models possible, Solomon told DDN, and the models make the data increasingly valuable over time.
Why the same partners keep expanding
The AstraZeneca relationship is the clearest illustration of how Immunai's model compounds in practice. What started as a collaboration in oncology has since expanded to inflammatory bowel disease and then to a multi-year extension covering broader programs. Three consecutive expansions with the same partner across different therapeutic areas is a different kind of signal than a single deal.
"To me, that progression demonstrates that the value is not tied to a single dataset or indication," Solomon said. "It is the ability to repeatedly generate biological insights that improve drug development across different therapeutic areas."
What has surprised him most about how the partnerships have developed, he said, is how transferable the underlying immune biology has proven to be. "Oncology and inflammatory bowel disease are completely different diseases clinically, but many of the immune programs that determine treatment response are shared. Once you can read the immune system at sufficient depth, you begin to see common biological principles that extend well beyond individual diseases."
What single-cell immune profiling at scale reveals
Conventional biomarker approaches typically focus on a small number of measurements — asking whether a marker is present or absent. That has produced important advances, but, as Solomon noted, biology is rarely driven by a single signal.
"Drug response emerges from the coordinated behavior of millions of immune cells interacting over time," he said. By profiling the immune system at single-cell resolution across large patient cohorts, Immunai's platform aims to reconstruct the immune state underlying response or resistance, rather than relying on a single proxy marker. The goal is not only to predict which patients are more likely to respond, but to understand why they respond, or why they do not. "Those mechanistic insights can help identify new biomarkers, optimize dosing strategies, select combination therapies, and ultimately improve the design of future clinical trials," Solomon explained.
The Boehringer Ingelheim collaboration is structured around exactly that logic. T cell dysfunction is implicated in both cancer and autoimmune diseases, but research in the two areas has largely proceeded independently. The collaboration is designed to build a shared data foundation across both disease areas and apply Immunai's platform to identify patterns of dysfunction that might point to novel targets, with promising findings moving into wet lab validation before feeding into Boehringer's drug discovery programs.
Beyond drug discovery
Looking ahead, Solomon said that the company’s long-term ambition extends beyond pharmaceutical partnerships. Immunai's research is exploring whether detailed immune cell profiles can be used to estimate biological immune age, a measure of how well the immune system is functioning relative to a person's chronological age.
"Chronological age tells you how long you have been alive. Your immune system does not necessarily age at the same pace," Solomon added. Models that estimate immune age from single-cell profiles could eventually help identify people at elevated risk before disease becomes clinically apparent, monitor whether interventions are improving immune health, and enable medicine to move toward a more preventive model.
"Drug discovery remains our core business today," he said, "but the long-term vision is much broader. Understanding the immune system at this level has the potential to change not only how we develop medicines, but also how we measure and maintain health."
