Unlearn

Unlearn is a clinical development intelligence platform that leverages artificial intelligence and digital twin technology to transform the design and execution of clinical trials. At its core, Unlearn generates personalized digital twins of clinical trial participants—virtual replicas that simulate how a patient would have progressed under a control condition (e.g., placebo or standard of care). These digital twins are derived from historical patient data, real-world evidence, and advanced machine learning models. By comparing actual treatment outcomes against these virtual control arms, trial teams can reduce the number of patients needed in the control group, often by 30% or more, which significantly cuts enrollment time and costs while maintaining statistical power. The platform covers the entire clinical trial lifecycle through three integrated modules: Plan, Monitor, and Analyze. In the Plan phase, users design smarter trials by simulating different enrollment scenarios, estimating required sample sizes, and selecting optimal endpoints using digital twin predictions. The Monitor module provides real-time dashboards that track patient trajectories against their digital twins, flagging early signals of efficacy or safety issues. During the Analyze phase, the system applies causal inference and Bayesian methods to interpret results, generate regulatory-ready reports, and visualize evidence across heterogeneous subgroups. Unlearn also maintains a growing library of evidence from completed studies, allowing teams to benchmark their own findings against historical data without re-hosting raw datasets. Unlearn specializes in therapeutic areas where small effect sizes and slow disease progression make traditional trials inefficient, such as Alzheimer’s disease, Parkinson’