Leading biopharma sponsors partner with Unlearn, using digital twins, data, and AI to strengthen each clinical development decision — so trial teams can commit with confidence, and evidence compounds from one decision to the next.
.png)
Unlearn’s methods have been recognized and supported by both U.S. and European regulators.
Our digital twins-based method 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.
Explore how our partners are accelerating their clinical development programs with us.
tumor biopsy records with linked clinical and genomic data behind the pan-cancer foundation model.
of subgroup survival predictions for a Phase III lung cancer trial fell within published confidence intervals, including molecular subgroups, with the model calibrated only to overall trial results.
of the apparent survival gap between two standard pancreatic cancer regimens (FOLFIRINOX and gemcitabine plus nab-paclitaxel) explained by differences in the patients each trial enrolled, not the treatments themselves.
Our technology is built on a foundation of rigorous scientific research across therapeutic areas, from neurodegenerative diseases to oncology and obesity. Explore the full library.
Unlearn has deep expertise in transforming complex clinical data into AI-ready formats. Our technology draws from over 1 million longitudinal clinical study records and spans 20+ indications, including neuroscience, immunology, cardiovascular, and metabolic diseases. This rich data foundation powers scalable, scientifically rigorous disease-specific ML models with exceptional performance.
Alzheimer’s trials demand more from every patient, dataset, and decision. See how the Unlearn Platform connects evidence review, trial simulation, monitoring, and digital twin-powered analysis—and explore retrospective Phase 2 and 3 results showing smaller control arms, lower costs, faster enrollment, and stronger statistical power.
Complete the form to access the white paper.
Patient variability and long timelines make meaningful treatment effects harder to detect in Parkinson’s trials. This case study shows how Unlearn brings evidence, simulation, and digital twins into one clinical development workflow, with validated Phase 2 results and projected Phase 3 gains in sample size, power, cost, and speed.
Complete the form to access the white paper.
Small patient populations and rapid disease progression leave ALS programs little room for inefficient trial design. See how digital twins support stronger decisions from Phase 1b through Phase 3, including a global trial analysis that reduced treatment-effect variance by 18% and could have enrolled 92 fewer participants.
Complete the form to access the white paper.
