Immunai, a pioneering startup dedicated to constructing a foundational model of the human immune system, has announced a significant expansion of its oncology collaboration with pharmaceutical giant AstraZeneca. This marks the third time the two entities have deepened their partnership, underscoring the critical value Immunai’s AMICA-OS platform brings to AstraZeneca’s extensive clinical development pipeline. Under the terms of the new agreement, Immunai is poised to receive up to $37.5 million over the course of 2026 and 2027, further solidifying a relationship that began in earnest in late 2022 but whose roots trace back to the transformative period of the global pandemic.
The enduring nature of this collaboration highlights a growing trend in the pharmaceutical industry: the integration of advanced artificial intelligence and machine learning platforms to dismantle longstanding bottlenecks in drug discovery and development. For companies like AstraZeneca, which manages a colossal portfolio of over 100 Phase 3 clinical studies across diverse therapeutic areas including oncology, rare diseases, cardiovascular and metabolic medicine, and respiratory and immunology, optimizing every stage of development is paramount. Immunai’s approach, which CEO Noam Solomon metaphorically describes as "high-end plumbing," aims to fix the complex, costly infrastructure issues that traditionally impede the journey of a new drug from lab to patient.
A Deepening Strategic Alliance: A Chronology of Collaboration
The relationship between Immunai and AstraZeneca is a testament to the power of sustained scientific synergy. Immunai CEO Noam Solomon revealed in a recent interview that his team has been acquainted with AstraZeneca’s researchers for approximately five years, indicating a period of mutual observation and engagement that predates formal agreements. The initial collaboration, formalized in late 2022, primarily focused on specific oncology clinical programs, a critical area given the immense unmet needs in cancer treatment.
This foundational work quickly proved its worth, leading to a substantial expansion in October 2025. This pivotal moment saw the partnership extend beyond oncology into Inflammatory Bowel Disease (IBD), a complex and debilitating condition that represents another significant therapeutic focus for AstraZeneca. Solomon articulated the strategic importance of this move, stating, "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 ambition to apply its immune system understanding across a broad spectrum of diseases where immune dysregulation plays a central role.
The current, third expansion further embeds Immunai’s AMICA-OS platform within AstraZeneca’s clinical development, signifying a deeper integration and a broader application of Immunai’s capabilities across the pharmaceutical giant’s R&D efforts. This progression from an initial oncology focus to a multi-indication, systemic approach reflects the versatility of Immunai’s technology and AstraZeneca’s strategic commitment to leveraging cutting-edge AI for precision medicine. Such sustained, expanding partnerships are increasingly vital in an industry where the average cost to bring a new drug to market for top pharmaceutical companies can exceed $2.67 billion, according to estimates by Deloitte. The sheer scale of AstraZeneca, with roughly 95,000 employees globally, necessitates highly coordinated, efficient external collaborations to maintain its competitive edge and accelerate innovation. Solomon noted the operational intensity, explaining, "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."
Immunai’s "Digital Plumbing": Unlocking the Immune System’s Secrets
At the heart of Immunai’s value proposition is its unique approach to understanding the human immune system, which it frames as building a "foundation model" – a concept gaining significant traction in AI for its ability to generalize across diverse tasks and datasets. Unlike many AI pharma companies that primarily apply algorithms to existing, often heterogeneous datasets, Immunai takes a more integrated approach, commencing with the meticulous generation of high-resolution data directly from patient samples.
"The signal already exists, but it’s hidden in the clinical patient samples sitting in your biobanks," Solomon emphasized. Immunai’s process begins by receiving these precious biological specimens from clinical trials at its state-of-the-art laboratory in New York. The first crucial step involves transforming these biological samples into digital data through single-cell multi-omic profiling of the patient’s immune system. This advanced technique allows for an unprecedented level of detail, examining individual cells rather than bulk tissue, and simultaneously analyzing multiple layers of biological information – genomics, transcriptomics, proteomics, and epigenomics – from each cell.
Specifically, for every patient, Immunai generates an "immune MRI" at single-cell, multi-omic resolution, typically taken both before and after therapeutic intervention. Each profile is a data-rich matrix comprising approximately 10,000 cells. For each individual cell, Immunai measures around 37,000 gene expressions, approximately 75 surface proteins, and performs VDJ sequencing, which deciphers the unique genetic rearrangements in T-cell and B-cell receptors. This comprehensive data allows researchers to track minute changes in the immune system over weeks and months post-treatment.
The critical advantage of this granular data lies in its ability to uncover subtle yet profound immunological features. By correlating these immune surrogate endpoints with clinical outcomes such as progression-free survival or overall survival, Immunai’s platform can distill clinically meaningful insights. These insights are instrumental in addressing some of the most pressing questions in drug development: identifying biomarkers for patient stratification in clinical trials, predicting toxic events, determining optimal combination therapies for improved efficacy, and refining dosing schedules.
The Power of Single-Cell Resolution and Foundation Models
Immunai’s AMICA database currently houses over 300,000 samples, with approximately 50,000 analyzed at single-cell resolution. Solomon argues that this distinction between resolution and scale is where many competitors fall short. He likens low-resolution data to scaling black-and-white photographs: "You’ll never be able to see the difference between green and blue. If that’s the distinction you need to make, you’re stuck." This analogy powerfully conveys why depth of data is often more critical than sheer volume when it comes to discerning complex biological signals.
The foundation model architecture further amplifies Immunai’s capabilities, particularly when dealing with the relatively small patient cohorts often seen in early-stage clinical trials. While traditional AI models struggle with limited data, a foundation model trained on vast, high-resolution datasets can effectively "compound" new, smaller cohorts against its existing knowledge base. "When you get a new cohort, you can resolve the signal," Solomon explained, meaning even a small group of 20 patients can yield significant insights when processed through a model pre-trained on hundreds of thousands of diverse immune profiles. This allows for more robust biomarker discovery and patient stratification even in early clinical development, accelerating decision-making and de-risking later-stage trials.
This capability was demonstrated in April 2025 when Immunai, in collaboration with the Parker Institute for Cancer Immunotherapy, announced the assembly of what they described as the largest single-cell dataset for real-world immunotherapy research. This monumental dataset, derived from 3,700 blood samples across 1,070 patients treated with immune checkpoint inhibitors (ICI), provides an unparalleled resource for understanding response, resistance, and toxicity to these transformative cancer therapies. Further validating its platform, Immunai also secured a separate multi-year partnership with Bristol Myers Squibb in January 2026, focused on analyzing clinical immune data to clarify mechanisms of action, identify patient subgroups, and guide development decisions.
Broader Implications for Pharmaceutical R&D and Precision Medicine
The ongoing success and expansion of the Immunai-AstraZeneca partnership carry significant implications for the broader pharmaceutical industry. It underscores a fundamental shift towards more data-driven, precise approaches to drug development. For AstraZeneca, a company that invested approximately $10.7 billion in R&D in 2023, leveraging external innovation from agile biotech firms like Immunai is a strategic imperative. Partnerships of this nature allow large pharma companies to access cutting-edge technologies and specialized expertise without the overhead of building such capabilities entirely in-house.
The move beyond oncology into IBD, cardiovascular inflammation, neuroinflammation, obesity, and diabetes reflects a recognition that the immune system is a central player in a vast array of human diseases. Conditions like IBD affect millions globally, often requiring chronic management and exhibiting varied responses to existing therapies. Cardiovascular inflammation is increasingly understood as a key driver of heart disease, while neuroinflammation is implicated in neurodegenerative disorders like Alzheimer’s and Parkinson’s. Even metabolic diseases such as obesity and diabetes have significant immunological components. By applying its deep immune profiling across these diverse areas, Immunai positions itself as a crucial partner in unlocking novel therapeutic avenues and developing more effective, targeted treatments.
This collaboration also signifies the maturity of AI and machine learning in drug discovery. The days of simply "applying AI" to existing, often disparate data are evolving into a more sophisticated paradigm where AI is integrated into the very process of data generation and interpretation. Immunai’s emphasis on generating its own high-resolution, multi-omic single-cell data, combined with its foundation model approach, represents a leading edge in this evolution. It promises to reduce the high failure rates in clinical trials, which currently stand at around 90% across all phases, and significantly shorten the decade-plus timeline typically required to bring a new drug to market.
As pharmaceutical companies increasingly pivot towards precision medicine – tailoring treatments to individual patient characteristics – the ability to deeply profile a patient’s immune system at single-cell resolution becomes indispensable. Immunai’s "digital plumbing" is not just about fixing existing issues; it’s about building a more robust, efficient, and intelligent infrastructure for the future of drug discovery, one where the intricate language of the immune system can be fully understood and leveraged for therapeutic benefit. The continued commitment from a global leader like AstraZeneca serves as a powerful validation of this vision and a harbinger of more such transformative partnerships across the industry.
