HIBT Lab! Immunai: Noam Solomon

Episode Summary

Executive Summary: Noam Solomon explains how Immuni is building a large-scale, AI-driven atlas of the human immune system by analyzing cellular data from clinical trials and hospital partnerships. The goal is to identify why patients respond differently to therapies, improve trial design, and accelerate development of more effective, personalized treatments—especially in cancer and immune-related diseases.

Main Topics: Founding story and scientific pivot (Priority: 5/5): Solomon moved from academia in math/computer science into biotech after seeing how data science might improve therapy selection, especially after learning about a cancer patient who benefitted from immunotherapy but suffered severe side effects. Mapping the immune system (Priority: 5/5): Immuni aims to create a 'Google Maps' for the immune system by profiling cells at single-cell resolution and understanding how immune cells interact across diseases and treatments. AI and data infrastructure in drug discovery (Priority: 5/5): The company uses massive computing power and machine learning to analyze one terabyte of data per patient sample and build a database (Amica) to detect patterns that humans cannot see manually. Clinical trial and pharmaceutical impact (Priority: 5/5): Immuni works with pharma, hospitals, and universities to analyze trial samples, identify responder/non-responder signatures, and improve clinical trial design and drug approval odds. Business model and partnerships (Priority: 4/5): The company already generates revenue from biopharma partnerships by producing data and insights, while maintaining a long-term, high-risk scientific mission supported by substantial fundraising. Future of personalized and preventive medicine (Priority: 4/5): Solomon argues that deeper immune mapping could enable more personalized treatment, better care for rare diseases, and eventually preventive health insights that keep people healthier before disease advances. Scientific culture of uncertainty (Priority: 4/5): A core theme is that 'not knowing' is treated as productive: asking questions, embracing multidisciplinary collaboration, and letting data reveal counterintuitive therapeutic possibilities.

Key Arguments: The immune system is too complex to understand through trial-and-error alone, so large-scale data and AI are needed to identify therapeutic patterns. Single-cell technologies now make it possible to measure cell types, genes, and proteins at unprecedented granularity, enabling immune-system mapping that was not feasible 5-20 years ago. Mapping thousands of patients will reveal recurring immune profiles, allowing patients with similar cancers or diseases to be grouped and matched to better therapies. The biggest bottleneck in drug development is predicting what will happen inside a real human patient, not just in mice or lab dishes. Clinical trial data can be used to distinguish responders from non-responders, and those subtle differences may point to new drug targets. A sufficiently large and contextualized immune database can improve trial design, reduce inefficiency, and increase the likelihood that promising drugs succeed. The company’s collaborative model with more than 30 partnerships helps it gather diverse biological samples and accelerate its learning loop. The long-term goal is not just treatment optimization but preventive and personalized medicine based on immune profiling.

Data Points: Founding year: 2018 - Immuni was launched in 2018 by Noam Solomon and co-founders. Company funding raised: Over $200 million - The transcript says Immuni has raised over $200 million for its work. Company size: About 150 people - Solomon says the company has around 150 employees across multiple cities. Offices: New York, Tel Aviv, Europe; plus San Francisco, Zurich, Prague mentioned in team locations - The company has an international footprint across offices and teams. Data per patient sample: 1 terabyte - Solomon says each sample from a patient can generate about one terabyte of information. Database size: Over 100,000 patients - Immuni’s Amica database spans more than 100,000 patients. Disease indications: Over 500 - The database includes more than 500 disease indications. Studies: Thousands - Amica includes data from thousands of studies. Clinical trial/drug development cost: More than $2.6 billion - Solomon cites the cost to develop a new immunotherapy. Drug development timeline: More than 10 years - He says bringing a new immunotherapy to market takes over a decade. Drug failure rate: More than 90% - He notes that more than 90% of drugs fail to get FDA approval. Partnership count: Over 30 partnerships - Solomon says Immuni has more than 30 partnerships across academia and industry.

Pivotal Quotes: "We can cure every type of cancer in mice." — A great scientist (as quoted by Noam Solomon): Used to illustrate the gap between animal-model success and real human-patient outcomes. "Not knowing is a methodology." — Noam Solomon: Describes Immuni’s culture of asking questions and embracing uncertainty as a driver of discovery. "I think that the world is going into a more personalized way to understand our health." — Noam Solomon: Explains the long-term vision for immune profiling, prevention, and individualized treatment.

Implications: If Immuni succeeds, drug discovery could shift from broad trial-and-error to data-driven, patient-specific development. That could improve trial success rates, uncover new cancer targets, and eventually enable more preventive, personalized care.