Unlearn's Machine Learning Scientist, Daniele Bertolini, recently partnered with the Multi-Regional Clinical Trial Center (MRCT Center) of Brigham and Women's Hospital and Harvard to host a two-part webinar series on leveraging AI-generated digital twins of study participants and synthetic data in clinical development. 

The sessions offered a comprehensive view of how Unlearn’s technology moves beyond theoretical promise to deliver actionable, regulatory-qualified benefits. Here are the core insights and highlights from the series.

In the first webinar, Daniele establishes why traditional RCTs are fundamentally inefficient: they operate on a "start-from-scratch" principle, systematically ignoring the wealth of historical data available on disease progression. Unlearn’s digital twins are probabilistic computational models that use a patient's baseline data to predict their exact, individual disease progression under standard of care. This prediction provides a critical measure of uncertainty, allowing for highly precise modeling. 

By incorporating the digital twin’s prediction as a "super covariate,” sponsors can effectively explain away a significant portion of the natural variance within the patient population. This leads directly to a more precise estimate of the treatment effect, resulting in powerful, practical clinical development outcomes: either reduced sample sizes or increased power in RCTs. ‍

The second webinar expanded on the foundational concepts to showcase practical applications across the drug development lifecycle, emphasizing the quantitative benefits and regulatory environment. Daniele presents a retrospective analysis across multiple therapeutic areas (including ALS, Alzheimer’s, and Huntington’s) demonstrating how Unlearn’s EMA-qualified and FDA-aligned method for using digital twins in RCTs achieves a variance reduction in the treatment effect estimate of 10% to 20%, translating directly into a potential 20% to 30% reduction in the control arm without sacrificing statistical power. 

The webinar then introduced solutions for challenging trial designs. For indications where randomization is unethical, such as rare diseases or pediatric trials, digital twins provide a robust solution by serving as a synthetic control arm, generating the necessary counterfactual outcome for every treated patient. The session also previewed advanced statistical methods and explored how synthetic data can be leveraged pre-trial to model the impact of different inclusion/exclusion criteria, trial duration, and endpoint choices, enabling faster and cheaper trial design optimization.

Stay tuned for details on the January panel discussion!