Unlearn

Unlearn is an AI-powered platform designed to revolutionize clinical trials by creating Digital Twins of participants. Its core value proposition lies in accelerating drug development, reducing trial costs, and improving the probability of success by generating synthetic control arms and enabling more powerful, efficient study designs. By leveraging advanced machine learning on historical patient data, it provides researchers with a powerful simulation environment to predict individual patient outcomes and optimize trial protocols before they begin.

Key features: The platform enables the creation of prognostic digital twins for each enrolled patient, which serve as sophisticated statistical controls. This allows for the implementation of innovative trial designs like TwinRCTs, where each participant is matched with their own AI-generated twin. Specific capabilities include simulating trial outcomes under various conditions, optimizing patient enrollment criteria, and providing real-time insights during an ongoing study to support adaptive decision-making. For example, it can model how different inclusion/exclusion criteria might affect statistical power or predict the likely trajectory of a placebo group.

What sets Unlearn apart is its rigorous, science-first approach validated through publications and collaborations with regulatory bodies. Unlike simpler predictive modeling tools, its digital twins are built using deep learning models trained on vast, diverse datasets of patient-level historical trial data, ensuring robustness and generalizability. The platform is designed to integrate seamlessly with existing clinical trial management systems and electronic data capture (EDC) software, operating both as a cloud service and offering on-premises solutions for data-sensitive pharmaceutical companies, ensuring compliance with strict data governance requirements like GDPR and HIPAA.

Ideal for biopharmaceutical companies, clinical research organizations (CROs), and academic medical centers conducting interventional trials, particularly in neurology, immunology, and oncology. Specific use cases include designing more efficient Phase II and Phase III studies, strengthening evidence for regulatory submissions by creating enhanced control arms, and rescuing trials that are struggling with patient recruitment or high placebo response rates. It is also valuable for biostatisticians and clinical development teams tasked with de-risking expensive and lengthy clinical programs.

The platform operates on a freemium model, offering basic features for exploration. For full-scale commercial use, pricing is typically customized based on trial scope and size, with enterprise contracts being the standard for large pharmaceutical partners, reflecting the high-value nature of the service in multi-million dollar development programs.