Unlearn.ai found that scaling digital twins from CNS to oncology isn't about parameter changes. Radically different data structures—like oncology's hierarchy of rare diseases and complex treatment histories—demand entirely new modeling approaches, unlike the more siloed data found in CNS trials.
Unlearn.ai strategically avoids diseases where a single biomarker determines progression. Instead, they focus on complex, systematic diseases where many variables each have a small impact on the outcome. These are the areas where sophisticated, multi-variable modeling provides the most significant advantage over standard statistical adjustment.
Instead of the high-risk approach of replacing a trial's control arm with digital twins, Unlearn.ai adds counterfactual data to every participant. This method increases a trial's statistical power, allowing for smaller control arms or a higher chance of success, while satisfying regulatory constraints for pivotal trials.
A significant part of Unlearn.ai's value is not just its advanced generative models, but its painstaking data harmonization work. The company builds internal machine learning tools to unify complex, disparate data sources like clinical trials and real-world data, which is the essential foundation for creating powerful models.
Unlearn.ai's method for late-phase trials (PROCOVA) is acceptable to regulators because it's designed to statistically correct for any bias in the digital twin model. This ensures the model's inaccuracy doesn't affect the trial's final decision procedure or error rate, a critical feature distinguishing it from simply replacing the control arm.