AI Startup's Bold Bet: Predicting Cancer Drug Combos Before Trials

Jan de Vries · 2026-09-18

What if we could predict which cancer drug combinations will fail—before they ever reach a patient? That's the audacious goal of Big Picture Bio, a London-based biotech startup that just emerged from stealth with a combined funding round of €2.55 million (about $2.8 million). ### The Funding Breakdown The company raised a €1.75 million (roughly $1.9 million) pre-seed round co-led by Kadmos Capital and Exceptional Ventures. They also secured €815,000 (around $900,000) in non-dilutive support from Innovate UK's Investor Partnerships Programme. Gloucester Ventures and angel investor John White joined the pre-seed round. "I had the privilege of working closely with Kerstin and Mark at the very beginning of Deep Science Ventures, and saw first-hand the determination, conviction and sheer resolve they bring to building ambitious, scalable businesses," said Remy Kesrouani, Managing Partner at Kadmos Capital. "Big Picture Bio is the culmination of that journey, applying a genuinely differentiated approach to AI to one of drug development's hardest problems: predicting how complex clinical trials will actually behave." ### A World Model for Cancer Founded by CEO Dr. Kerstin Papenfuss and CTO Dr. Mark Hammond, Big Picture Bio has built a generative "world model" that simulates how tumors, immune cells, and surrounding tissue interact. The goal? Identify which drug combinations—and in what sequence—are most likely to overcome cancer resistance. Before spinning out, both founders spent years at Deep Science Ventures. Papenfuss built and led DSV's therapeutics team, founding 12 therapeutics and enabling-technology companies. Hammond co-founded DSV and grew its portfolio from €174,000 to over €1.16 billion (about $1.3 billion), led its engineering work on agentic scientific discovery, and previously worked in licensing and investment at Imperial College, whose spin-outs during his tenure included Hinge Health, Hark, and Monolith. The pair have worked together for seven years, tackling complex, evolving diseases. They argue that a full pairwise screen of just 100 drugs at 100 doses would require roughly 50 million lab experiments—a scale no physical screen can reach. But their models can search computationally in seconds. ### Early Wins and Validation The company says its predictive approach has already been tested against real clinical data. It correctly forecasted the failure of Regeneron's fianlimab trial and posted 14 predictions ahead of the 2026 ASCO oncology conference, of which 12 proved accurate. "Cancer is not one disease driven by one target, but we still develop drugs as if it were. Combinations are how we beat it—and with more than 900 billion of them possible, no lab on earth can test its way to the right ones," said Dr. Kerstin Papenfuss, CEO and co-founder. "That is the problem we built Big Picture Bio to solve: model the disease as the dynamic system it actually is, then design against it—combinations chosen because they are most likely to work in patients, not because they were the ones we could get to. This funding takes our first designed combinations out of the model and into the lab." Big Picture Bio's advisory team includes senior pharma experts: Dr. Laura Rosenberg (Director of Target Validation at AstraZeneca), Dr. Duncan Young (Head of Search and Evaluation, Oncology Business Development & Licensing at AstraZeneca), Dr. Garry Pairaudeau (former CTO of Exscientia and CEO of DaltonTx), and Dr. Christian Dillon (Chief Scientific Officer at PhoreMost). ### What's Next? The funding will take Big Picture Bio's AI-designed cancer combination therapies from computer models into the wet lab. Initially, the firm will focus on cancer and immune-mediated diseases but aims to move beyond trial-and-error combination testing altogether. If successful, this approach could dramatically speed up drug development, cut costs, and get better treatments to patients faster. For an industry where failure rates are notoriously high, that's a big deal. As the company moves from simulation to reality, all eyes will be on whether their predictions hold up in the lab. If they do, Big Picture Bio could be onto something truly transformative.