Run quick and confident
‍clinical trials with digital twins

Digital twins are AI-generated forecasts of an individual trial participant’s control outcomes. By forecasting clinical outcomes at every future time point with unparalleled precision, they serve as the powering technology for a more rigorous clinical analysis.

Digital Twin Application

Increase signal.
Reduce noise.

Randomized Controlled Trials

Increase power while maintaining sample sizes, or reduce control arm size while preserving power. This methodology improves sensitivity across primary and secondary endpoints for a clearer signal of efficacy.

Early-Stage Trials and Rare Diseases

Generate participant-level synthetic control arms to enable credible treatment comparisons when randomization is infeasible. Strengthen high-stakes go/no-go decisions through improved statistical sensitivity.

Interim and Retrospective Looks

Improve sensitivity in interim looks and subgroup analyses to catch signals that traditional methods may miss. Re-evaluate historical trial data using regulatory-aligned methods to support learning across programs.

Regulatory Acceptance

Paving the regulatory path for AI in clinical trials

Unlearn’s methods have been recognized and supported by both U.S. and European regulators.

PROCOVA was officially qualified by the European Medicines Agency for use in Phase 2 and 3 trials with continuous outcomes.

U.S. FDA provided positive feedback on PROCOVA, supporting its use in covariate-adjusted analyses across clinical development.

FDA recommends that sponsors adjust for covariates that are anticipated to be most strongly associated with the outcome of interest…it may be useful to use previous studies to select prognostic covariates or form prognostic indices.

In a trial that uses covariate adjustment, the sample size and power calculations can be based on adjusted or unadjusted methods.

CHMP qualifies PROCOVA and that the proposed procedures could enable increases in power and/or decreases in sample size in phase 2 and 3 in clinical trials with continuous outcomes.

Digital twin technology

Disease-specific ML models trained on extensive historical clinical data generate digital twins for each trial participant using only their baseline data.