Immunotherapy drugs are becoming increasingly popular and successful in treating various diseases. Drugs like Keytruda and Opdivo have been approved by the FDA since 2014 and are generating billions in annual revenue. Amid this growth, a British biotech company called IMU Biosciences is working to make these treatments safer and more effective. IMU was founded in 2021 by King’s College London and the Francis Crick Institute. They have developed a platform to assess how an individual’s immune system influences their response to immunotherapy treatments. To understand how the immune system affects disease progression, treatment response, and patient outcomes, two things were needed. According to IMU CEO John Baker, better instrumentation was required to analyze more parameters, and tools for analyzing vast amounts of data needed to be developed. The rise of artificial intelligence and machine learning has significantly impacted IMU’s ability to develop and run its platform. IMU’s platform analyzes around 70 different proteins on the cell surface of every single immune cell in a blood sample, resulting in the generation of between 200-250 million data points. This information is used to identify immune signatures that can predict disease progression, treatment response, and patient outcomes. Classical immunology is a manual process, but IMU’s team has built algorithms that can determine what is in a sample and classify its value more accurately and efficiently than a human can. The immune system is highly variable between individuals and dynamic within any one individual over time. To understand these factors, IMU has profiled the immunomes of almost 20,000 individuals, including healthy individuals and those with diseases such as cancer, rheumatology, and neurology. Having a baseline of ‘normal’ is essential for understanding what is driving a disease state, an outcome, or a likelihood to respond to a drug. IMU has joined the Manifest consortium, a UK-based program investigating patient response to cancer immunotherapies. The goal is to make these treatments safer and more effective. IMU is leading the immune side of the study, and the data are highly exciting. They have already shown that from a standard blood sample from patients, before they receive their first dose of these drugs, they can predict which patients are going to respond with about 95% accuracy. This is a significant improvement over the previous best accuracy of about 60%. IMU is using the same assay stack and machine learning models for every indication they look at. This makes their offering more readily scalable than next-generation sequencing approaches. They are currently working on programs in solid tumors, blood cancers, and solid organ transplants. In the case of blood cancers, they have some extraordinary data on their ability to improve outcomes in the three nastiest leukemias. This program is currently with the FDA for development track sign-off, and they hope to have it up and running in clinical trials in 2026. Their third program is looking at patients who have received solid organ transplants and monitoring their immune systems to better predict the likelihood of them rejecting or starting to reject those organs. IMU’s deployment strategy depends on the particular disease state being looked at. For example, in hematology oncology, they are working on a central lab type model to deploy in the US and are currently in conversation with the FDA about how to get that moving at pace. Other applications they are developing are likely to be a good fit for more typical hospital type labs, and they are currently working with instrumentation partners in cytometry to demonstrate that they can take signatures that they discover on their platform through into their clinical install bases.