Show Notes 179: From Dark Matter to Digital Twins: How Concr is Revolutionising Cancer Treatment Prediction

Episode 179 of the Cambridge Tech Podcast delivers a fascinating deep-dive into one of the most innovative applications of cross-disciplinary technology we've heard in ages. If you're building a deep tech company or investing in life sciences, this one's essential listening.

The episode kicks off with a roundup of Cambridge innovation headlines, and there's plenty to get excited about:

• £3 billion investment unlocked for Cambridge Biomedical Campus expansion

• NuQuantum opening a trapped ion networking lab to accelerate distributed quantum computing

• Gearset hitting 37% revenue growth with 400+ new customers

• Luminance (Cambridge's legal AI champion) landing a major deal with Ajinomoto Europe

Concr's Astrophysics-Powered Cancer Solution

Hosts Faye Holland and James Parton sit down with Irina Barbina (CEO) and Matthew Griffiths (CTO) to unpick how Concr is using predictive modelling and digital twins to transform cancer drug development.

The Problem They're Solving

Cancer data is fragmented. Clinical trials, pre-clinical research, and real-world patient data exist in silos. There's no unified way to predict how individual patients will respond to specific therapies, until now.

As Matthew explains:

"The core problem that Concr was founded to address is this idea that in cancer, the way data and models are used is very siloed and fragmented."

The Breakthrough: Astrophysics Meets Oncology

Here's where it gets genuinely clever. Concr's technology borrows from astrophysics, specifically, how scientists model dark matter using gravitational lensing. The parallel is striking:

Astrophysicists can't directly observe dark matter, so they build complex simulations to infer its distribution. Concr can't directly know why a drug worked for a patient, so they build digital twin simulations to predict outcomes.

Key innovations:

• Bayesian inference at scale to handle messy, incomplete cancer data

• Hierarchical modelling that learns from shared biology across cancer types

• 94% prediction accuracy on retrospective clinical trial data

• Prospective validation underway with NHS partners and pharma companies

Why This Matters

For biotech founders: Concr dramatically reduces the cost and complexity of clinical trials. Irina notes:

"The ability to predict how individual patients are going to respond to a given cancer therapy is really powerful, both in healthcare settings and in drug development."

For VCs: The commercial model is proven. They're already working with biotech companies lacking Big Pharma's resources, using digital twins to inform trial design and drug development decisions.

For the ecosystem: Irina's journey from academic geneticist to VC-backed entrepreneur exemplifies how deep domain expertise + business acumen creates category-defining companies.

The Ask

Concr is fundraising their growth round with strong early backing from pharmaceutical, oncology, and tech investors. They're targeting UK, Europe, US, and Australian investors.

If you're interested in backing frontier deep tech in life sciences or you're a clinician, regulator, or researcher with feedback on their product. They're actively seeking ecosystem engagement.

Final Thought

This episode brilliantly illustrates why Cambridge is a global innovation hub. It's not just about brilliant science, it's about brilliant people from different disciplines colliding, recognising patterns, and building companies that matter.

Listen now and find out why digital twins might just transform how we treat cancer.

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