The CoI‑AI programme, backed by £118 million, is a move to deepen how we understand immune protection against bacterial threats, not just through lab studies, but through human challenge models and data‑driven insights.
What CoI‑AI aims to do
The CoI‑AI (Correlates of Immunity‑Artificial Intelligence) programme is a partnership between Advanced Oxford members, the University of Oxford’s Vaccine Group and the Ellison Institute of Technology. It is focused on bacteria that are major causes of serious disease and resistant to traditional vaccine approaches—E. coli, Streptococcus pneumoniae, Staphylococcus aureus. Researchers will use human challenge studies, where volunteers are exposed under controlled conditions, to see how the body reacts. Alongside, modern immunology and computational tools will help find which immune responses actually predict protection.
Why this matters
Antibiotic resistance is already straining health systems. If we can better design vaccines—ones that anticipate how the immune system reacts—then we reduce reliance on antibiotics and potentially prevent infections before they take hold. CoI‑AI could speed up vaccine development, reduce uncertainty, and focus resources more efficiently.
What’s needed for it to work
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Ethical and safe trials: Human challenge studies must be done to very high safety and ethical standards.
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Reliable data and diverse samples: Immune responses vary across populations. The algorithms must account for that to avoid bias.
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Strong infrastructure: Lab capacity, clinical monitoring, participant recruitment and follow‑up matter. CoI‑AI and Oxford already have good foundations, but scale will test them.
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Translation to wider use: It’s one thing to find immune markers in trial conditions, another to build vaccines usable worldwide.
The broader take‑away
A lot of vaccine research focuses on the trial result: does it protect or not. What CoI‑AI adds is attention to what happens inside immunity—what changes, early signals, patterns that show protection. That kind of clarity makes vaccine design less of a black box.
For Oxfordshire, this is another example of combining world‑leading science, infrastructure investment, and forward‑thinking partnerships. It strengthens the region as a global hub for biomedical innovation.
What to watch next
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Papers or preprints that identify which immune markers are predictive for different bacteria.
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How CoI‑AI handles diversity of volunteers (age, background etc).
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Moves toward designing vaccines (or vaccine candidates) using the insights.
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Regulatory and ethical pathway successes or challenges.
If this works well, we will not just have a new vaccine, we will have a better route to making vaccines that are predictable, resilient, and built on deeper understanding.
