Innovate UK Biomedical Catalyst Accelerator - Future Medicines

LYVA Labs, in partnership with Innovate UK and a consortium of internationally recognised scientific, technical, and commercial experts, is proud to deliver the Innovate UK Biomedical Catalyst Accelerator - Future Medicines.
The Biomedical Catalyst is Innovate UK’s flagship grant funding mechanism, designed to accelerate innovation and help UK companies bring products to market more efficiently.
As part of the Innovate UK Biomedical Catalyst, the Future Medicines Accelerator supports SMEs, spinouts, and academic innovators working in therapeutics to develop and test innovative health and care solutions. The cohort receive tailored support to refine their ideas, explore market viability, and prepare for clinical or commercial validation. Alongside this participants are also trained in investment readiness, grant writing and have access to funded technical support with our programme partners.
Partners
2026 Accelerator Cohort

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Decentralising radionuclide production to make nuclear medicine more reliable and accessible worldwide.

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Revolutionising organ repair with an innate immunotherapy that offers a less-invasive alternative to liver transplantation.

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Advancing safer, more accessible cell therapies to improve outcomes for cancer patients.

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Turning the promise of genetic medicine into cures for peripheral nerve diseases, starting with Neurofibromatosis type 1.

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Developing a data-driven discovery platform that unlocks new Therapy-induced Novel Antigens to power the next generation of targeted cancer therapeutics.

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Discovered and optimised first-in-class compounds across oncology and neurological disease, including a novel dual-action immuno-oncology programme designed to activate anti-tumour immunity while directly destroying tumour cells.

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Advancing next-generation CAR T-cell therapies to tackle the unmet needs of patients with sold tumours.

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Pioneering a child-first cancer gene therapy that reprogrammes tumour cells to trigger powerful immune responses.

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Using advanced computational models of bacteria to develop first-in-class antibacterial drugs.






