Using Digital Twins to Decrease Enrollment and Increase Statistical Power in Alzheimer's Disease Trials (CTAD 2020)

Background: Drug development for Alzheimer’s disease (AD) is increasingly expensive and time-consuming.  To decrease the high failure rate of these trials, it will be necessary to improve clinical trial design by reducing total trial size and/or recruitment time.  Randomized controlled trials (RCTs) have long been the gold-standard among clinical trial designs, even though they can be very inefficient. The volume of clinical trials provides an opportunity to improve the efficiency of AD trials, which has been highlighted by the FDA in a number of communications. With data collected from the control groups of many prior AD trials and state-of-the-art statistical methods, we have developed machine learning (ML) technology to comprehensively model the progression of control subjects. Our model can generate digital twins, which are digital subject records generated from the baseline data of actual subjects in a trial. These digital twins show the potential outcomes of individual subjects had they received a placebo.

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Using AI-based Prognostic Models to Design Efficient, Unbiased Clinical Trials


Part 3: Innovation in Clinical Research: AI-based Drug Development Tools and the Regulatory Landscape‍


Part 2: Faster, More Efficient Trials: Novel Trial Designs using Digital Twins‍

Watch an overview of specific use cases for Digital Twins and learn how novel trial designs with Digital Twins enable smaller trials that maintain their power.
Watch a panel discussion on the regulatory landscape where experts share perspectives on the future of AI-based drug development tools like Digital Twins.
Statistical principles of clinical trials with Digital Twins