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Unlearn’s digital twins are being used by some of the world’s largest pharmaceutical companies and most innovative biotechs—backed by global regulators and supported by cutting-edge machine learning research.
Oncology trials are among the most complex, costly, and failure-prone in drug development.
Sponsors hold extensive datasets across multiple oncology indications, but these remain fragmented and difficult to access in a unified framework, limiting their ability to inform trial design and make strategic evidence-based decisions.
Without strong evidence upfront, teams can spend months aligning on endpoints, biomarkers, and eligibility-driving delays, inflating costs, and weakening confidence in study design.
Portfolio decisions are often made mid-trial or with limited data, and the lack of context around early results slows go/no-go calls and raises strategic risk.
A unified AI platform to accelerate planning, improve cross-functional alignment, and increase confidence in oncology development, while helping reduce costs, compress timelines, and lower risk across the clinical lifecycle:
All designed from the ground up for oncology-specific challenges and regulatory demands.
Standardize structured and unstructured historical data assets into a searchable, query-ready format. Give clinical development teams self-serve analytics to explore subpopulations, patient-level outcomes, and cross-trial patterns to make past studies a resource for trial design and label expansion.
Design smarter trials from the outset. Simulate patient cohorts, eligibility, endpoints, biomarkers, sample sizes, and treatment effects to help sponsors reduce protocol amendments, speed study activation, and strengthen regulatory justification.
We've already qualified AI-driven solutions with FDA and EMA in other therapeutic areas. We're seeking a small number of strategic partners to help us continue our expansion into oncology with a next-generation platform.
Gain competitive advantage through early access.
Validate tools on your own historical oncology studies.
Co-publish with us and be recognized as a category leader in oncology trial innovation.