World Cancer Day’s “United by Unique” campaign reminds us that cancer is more than a diagnosis. Behind every case is a person with a distinct biology, context, and set of needs. But clinical trials still optimise for the average; in rare cancers and early development, where single-arm studies and small cohorts are common, the “average” can obscure whether a specific patient has benefited.
Regulatory thinking is pushing in the same direction. The FDA’s 2025 draft guidance on novel combination regimens reinforces the need to demonstrate “contribution of effect”: separating what each component adds to the overall outcome, and discussing the use of real-world data approaches where standard multi-arm trials are not feasible.
This is especially important when a trial can’t easily randomise patients to isolate the effect of a novel agent on top of a backbone therapy. If we can’t clearly separate what’s attributable to standard-of-care (SOC) versus the investigational treatment, we risk wasting programmes and enrolling patients who never had a realistic chance of benefit.
At Concr, we use molecular digital twins and in silico clinical trials to help close that gap. In a single-arm SOC + novel therapy setting, our Bayesian AI models can predict each participant’s expected outcome on SOC alone as a patient-specific synthetic control, then isolate where the novel therapy appears to be adding benefit.
This shifts the question from “did the curve move?” to “who benefited and why?”
The result is more patient-centred trial decisions powered by:
identifying likely responders earlier,
sharpening inclusion/exclusion for expansion, and
running smaller, more informative studies, particularly in rare indications where every participant matters.
This World Cancer Day, we’re united by a shared goal: better outcomes for people living with cancer. But progress will come from embracing what makes each patient unique and designing systems that give every patient a fighter’s chance.
Get in touch to learn how Concr’s digital twins can help you design smarter trials and de-risk development decisions.
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