CTAD Lessons for 2020: More Phase 2 Trials, More Diversity

"Aaron Smith of Unlearn.AI, a startup company that uses machine learning to facilitate trials, suggested another way digital technology could cut enrollment. Machines can use data from the control arms of past trials to develop predictions for how disease will progress in a person depending on his or her baseline characteristics, he said. In effect, computers can generate a digital twin for each enrollee, describing what would likely happen to them without treatment. These digital twins can supplement the physical placebo group of a trial. This would lower the number of participants needed for a given trial, and allow a greater proportion of participants to receive drug rather than placebo. The chance of ending up in the placebo group deters many a potential participant."

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Applying Machine Learning to Increase Clinical Trial Efficiency: A Regulatory Journey

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Unlearn Closes $50 Million Series B Funding to Advance the Use of Its Machine Learning-Powered TwinRCTs™ in Clinical Trials

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Unlearn Signs Multi-Year Collaboration with Merck KGaA, Darmstadt, Germany to Accelerate Immunology Trials using Twintelligent RCTs™

Collaborators will leverage AI-generated Digital Twins to enable smaller, more efficient pivotal clinical trials.
Led by Insight Partners, financing builds on the company’s momentum in working with leading biopharmaceutical companies to improve clinical trial efficiency.
Learn about the evolution of ideas that led to our TwinRCT™ solution for smaller, more efficient clinical trials and recent EMA draft qualification opinion.