Immunai, a pioneering startup dedicated to constructing a comprehensive foundation model of the human immune system, has significantly deepened its strategic collaboration with pharmaceutical giant AstraZeneca. This marks the third expansion of their oncology partnership, underscoring the critical value Immunai’s AMICA-OS platform brings to AstraZeneca’s extensive clinical development pipeline. Under the terms of this latest agreement, Immunai is eligible to receive up to $37.5 million over the years 2026 and 2027, reflecting the growing integration and reliance on its advanced analytical capabilities.
The relationship between Immunai and AstraZeneca first formally commenced in late 2022, though its roots trace back to initial engagements during the pandemic era. According to Immunai CEO Noam Solomon, the teams have been acquainted for approximately five years, fostering a strong foundation for their evolving partnership. Initially, the collaboration was primarily centered on specific oncology clinical programs, a crucial area given the global burden of cancer and the complexities of developing effective treatments. However, the scope has since broadened considerably, demonstrating the versatility and applicability of Immunai’s technology across diverse therapeutic landscapes.
Deepening the Partnership: A Chronology of Expansion
The trajectory of this collaboration highlights a clear pattern of increasing trust and expanding application. The initial focus on oncology clinical programs was a strategic entry point, leveraging Immunai’s expertise in immune system analysis, which is particularly relevant for understanding cancer progression and response to immunotherapies. As the partnership matured, its success paved the way for significant diversification.
In October 2025, a pivotal expansion was announced, extending Immunai’s platform into the realm of Inflammatory Bowel Disease (IBD). This move was significant, signaling a deliberate shift beyond oncology and into immunology and inflammation, which represents another major department and therapeutic area for AstraZeneca. Solomon emphasized this expansion as a reflection of Immunai’s burgeoning interest in applying its technology across multiple indications. He noted, "We started in immune oncology, expanded to other oncology areas, then into immunology and inflammation, and now we’re moving into cardiovascular inflammation, neuroinflammation, and even obesity and diabetes. The common thread is the immune system." This statement encapsulates Immunai’s vision of its platform as a universal tool for any disease where the immune system plays a significant role, which, given the pervasive influence of immune responses, is a vast and growing domain.
The latest expansion, covering 2026 and 2027, further solidifies this multi-indication strategy, embedding Immunai’s AMICA-OS platform even deeper into AstraZeneca’s clinical development efforts across these expanded therapeutic areas. This progressive integration underscores AstraZeneca’s confidence in Immunai’s ability to deliver actionable insights that can accelerate drug discovery and development.
Navigating the Scale: AstraZeneca’s Ecosystem and Drug Development Challenges
AstraZeneca operates on an immense scale, employing approximately 95,000 people globally and running more than 100 Phase 3 clinical studies across a wide array of therapeutic areas including oncology, rare diseases, cardiovascular and metabolic medicine, and respiratory and immunology. This sheer organizational size and operational complexity present unique challenges for coordination, even for internal teams, let alone for a startup partner.
Solomon elaborated on the intricate nature of this collaboration, stating, "Over the years, there are many dozens of people on their side and dozens on our side collaborating. We work with multiple groups: people on the AI and data science side, people in translational medicine, people in clinical development. Each group covers different indications and therapeutic areas." This cross-functional engagement is testament to the deep integration of Immunai’s platform, which serves as a connective tissue across various departments within AstraZeneca, streamlining data flow and analytical processes that might otherwise be siloed.
The operational intensity of coordinating with such a large organization highlights the "plumbing" metaphor that Immunai CEO Noam Solomon frequently employs. He describes Immunai’s role as fixing "very expensive plumbing issues" in drug development. This analogy speaks to the profound infrastructure bottlenecks that traditionally slow down the arduous and costly process of bringing new drugs to market. The average cost to develop a new drug for top 20 pharmaceutical companies is estimated at a staggering $2.67 billion, according to a recent Deloitte report. This figure underscores the immense financial pressure on pharmaceutical companies to optimize every stage of development, minimize failure rates, and accelerate time to market. Clinical trial failures, particularly in late stages, contribute significantly to these costs, often due to an insufficient understanding of drug mechanisms, patient response variability, or unforeseen toxicities. Immunai aims to mitigate these risks by providing deeper, more precise insights into the immune system’s role.
Immunai’s "Plumbing": Addressing Bottlenecks with AI and Multi-Omics
At the core of Immunai’s "plumbing" lies its sophisticated approach to data manipulation at scale and the generation of high-resolution biological insights. Solomon outlines a two-pronged strategy: "First, generating a large volume of data from thousands of samples, creating a digital twin of the patients. Then applying our immune profiling and finding the clinical covariates manifesting in the immune system, so our platform can distill clinically meaningful insights from that."
The concept of a "digital twin" in this context refers to a comprehensive computational model of a patient’s immune system, built from their biological samples. This model captures the intricate details of immune cell states, interactions, and responses, providing a dynamic representation that can be analyzed and queried to predict outcomes or identify key biological drivers.
Immunai’s partnerships typically originate from pharmaceutical companies grappling with complex clinical questions that their existing infrastructure struggles to resolve. These challenges are often critical determinants of a drug’s success or failure. Solomon lists common issues: "Usually those questions involve finding a better way to stratify patients for a clinical trial, identifying a biomarker for a toxic event, determining the optimal combination agent because a monotherapy isn’t producing the right efficacy results, or finding the right dose and schedule."
For example, patient stratification—identifying which patients are most likely to respond positively to a particular treatment—is crucial for improving trial success rates and developing precision medicines. Similarly, early identification of biomarkers for adverse events can prevent serious side effects, while optimizing combination therapies, dosing, and scheduling can significantly enhance a drug’s efficacy and safety profile. These are precisely the "plumbing issues" that, if left unaddressed, can lead to costly trial failures or suboptimal drug performance.
The Power of Single-Cell Multi-Omics: Beyond Traditional Approaches
Immunai differentiates itself in the crowded AI pharma market by not merely applying AI to existing, often aggregated, data. Instead, it begins by generating novel, high-resolution data from the ground up. Solomon explains, "The signal already exists, but it’s hidden in the clinical patient samples sitting in your biobanks. So in every collaboration, the starting point is the same: send us all the samples you have from the clinical trials, to our lab at 430 East 29th Street in New York. The first step is translating those biological specimens into digital data using single-cell multi-omic profiling of the patient’s immune system."
This methodological distinction is paramount. Traditional bulk sequencing or less granular profiling methods average signals across millions of cells, potentially obscuring critical information from rare cell types or subtle cellular states. Single-cell multi-omics, conversely, provides an unprecedented level of detail by analyzing individual cells. This allows Immunai to create what Solomon likens to an "immune MRI" for each patient, profiling their immune system at single-cell, multi-omic resolution, both before and after therapeutic intervention.
Each such profile is a complex data matrix: approximately 10,000 cells per patient, with each cell providing around 37,000 gene expressions, roughly 75 surface proteins, and VDJ sequencing data.
- Gene expression (transcriptomics): Measures which genes are active in each cell and to what extent, indicating cellular function and state.
- Surface proteins (proteomics): Identifies markers on the cell surface, crucial for cell identification, communication, and interaction.
- VDJ sequencing: Provides highly specific information about the T-cell and B-cell receptors, revealing the diversity and specificity of a patient’s adaptive immune response, which is vital for understanding how the immune system recognizes and targets diseases like cancer or infections.
This extraordinary resolution enables Immunai’s team to meticulously track immune system changes weeks and months post-treatment. By correlating these high-fidelity immune surrogate endpoints with clinical outcomes—such as progression-free survival or overall survival—Immunai can pinpoint the specific immunological features linked to treatment efficacy, resistance, toxicity, and optimal dosing. This granular understanding is critical for developing more targeted and effective therapies.
Immunai’s AMICA database currently houses an impressive collection of over 300,000 samples, with approximately 50,000 of these processed at single-cell resolution. Solomon argues that this distinction between resolution and scale is where many competitors fall short. He illustrates this with an analogy: "A lot of big numbers in this field don’t actually lead to better decisions or better insights because the data was collected without depth. You’ll never be able to see the difference between green and blue if you’re scaling black-and-white photographs. If that’s the distinction you need to make, you’re stuck." This highlights that merely having a large volume of data is insufficient; the data must possess the necessary depth and detail to extract meaningful biological insights.
Foundation Models and Precision Medicine
The application of a foundation model architecture is another cornerstone of Immunai’s approach. This advanced AI model is trained on vast, diverse datasets to understand complex biological systems, making it highly adaptable to new, smaller datasets. This capability is particularly advantageous when dealing with the relatively small patient cohorts often available in early-stage clinical trials, sometimes as few as 20 patients.
"If you’ve built a foundation model on large-scale data, every new cohort compounds against the others," Solomon explains. "When you get a new cohort, you can resolve the signal." This means that even limited new data can be interpreted within the rich context of the pre-trained foundation model, allowing Immunai to extract robust and reliable signals that might otherwise be missed by models trained solely on small, isolated datasets. This ability to generalize and learn from diverse immune system data makes Immunai’s platform incredibly powerful for accelerating precision medicine initiatives.
Broader Industry Impact and Future Outlook
Immunai’s success with AstraZeneca is not an isolated event but part of a broader trend of significant collaborations. In April 2025, Immunai partnered with the Parker Institute for Cancer Immunotherapy to assemble what they described as the largest single-cell dataset for real-world immunotherapy research. This monumental effort drew from 3,700 blood samples across 1,070 patients treated with immune checkpoint inhibitors, a class of drugs that has revolutionized cancer treatment by harnessing the body’s own immune system. This collaboration further solidified Immunai’s position at the forefront of immune data generation and analysis in oncology.
Following this, in January 2026, Bristol Myers Squibb (BMS), another pharmaceutical titan, signed a separate multi-year partnership with Immunai. This agreement specifically focuses on analyzing clinical immune data to clarify mechanisms of action, identify patient subgroups who would benefit most from specific treatments, and guide critical development decisions for BMS’s pipeline. These partnerships collectively validate Immunai’s platform and approach, signaling a growing industry-wide recognition of the necessity for high-resolution immune system profiling and AI-driven insights in drug development.
The repeated and expanding collaborations with AstraZeneca, coupled with other high-profile partnerships, position Immunai as a critical enabler in the era of precision medicine. By providing unparalleled insights into the immune system, Immunai is helping pharmaceutical companies overcome some of the most persistent and costly bottlenecks in drug discovery and development. The financial commitments, expanding therapeutic scopes, and the depth of integration indicate a strong belief in Immunai’s technology as a transformative force. As the biopharmaceutical industry continues its quest for more effective, safer, and personalized treatments, the "digital plumbing" provided by companies like Immunai will undoubtedly play an increasingly pivotal role in shaping the future of medicine. The trajectory of Immunai’s partnership with AstraZeneca exemplifies how cutting-edge AI and multi-omics are not just buzzwords but are becoming indispensable tools for accelerating scientific discovery and bringing life-changing therapies to patients faster.
