Cambridge is building a £20m hub to speed up drug development using AI and lab-grown organs

[QuantumToken65]

The Medical Research Council has announced £20 million in funding for a new Pre-clinical Translational Models Hub, led by the Cambridge Stem Cell Institute, aimed at transforming how new medicines get developed and tested. The hub combines AI powered approaches with advanced human based lab models, including organoids, lab grown mini organs derived from stem cells, rather than relying primarily on animal testing.

The core idea is building a national UK accelerator platform that turns promising human based models into validated, scalable, industry ready technology. Professor Matthias Zilbauer and Professor Bertie Göttgens, who lead the hub, say the aim is more effective, personalised treatments while cutting the time and cost of drug development and reducing reliance on animal models.

The hub will be based on the Cambridge Biomedical Campus. Working alongside partners including the Wellcome Sanger Institute, the MRC Laboratory of Molecular Biology, and Royal Papworth Hospital NHS Foundation Trust, with the goal of building a national network that gives researchers, clinicians and companies across the UK access to human based testing platforms that better reflect actual human biology than animal models do.

Organoids have been a major research focus in stem cell biology since the field really took off back in 2009. But building genuinely reliable, reproducible models that regulators and drug companies actually trust for decision making has remained a real bottleneck, which is specifically the gap this hub is aiming to close by integrating those experimental models with AI and other computational approaches.

If it works as intended, the pitch is a new generation of predictive tools that can flag which drug candidates are actually worth pursuing much earlier in the development pipeline. Before they reach expensive and ethically fraught animal or human trials

Plans for £20m hub in Cambridge to develop new medicines using AIwww.bbc.com