Using Digital Twins to Improve Efficiency and Accuracy in Clinical Research

In a recent interview with Applied Clinical Trials, Jon Walsh, founder and chief scientific officer of Unlearn, discussed how AI-designed therapies and digital twin technologies are reshaping the clinical trial landscape. While the fundamental principles of clinical testing remain consistent, Walsh explained that AI-discovered drugs often warrant deeper attention to specific biomarkers and safety signals in early development. He also highlighted how digital twins—virtual models of trial participants—can significantly improve trial efficiency, reduce control group sizes, and enhance the precision of treatment effect estimates. As regulatory agencies like the FDA and EMA begin to offer clear frameworks for integrating AI into drug development, Walsh emphasized the importance of transparency, reproducibility, and interpretability in model design and implementation.

ACT: How are AI-designed therapies fundamentally different from traditionally developed treatments when they enter clinical trials?

Walsh: At Unlearn, we help sponsors run faster, more efficient trials, and we’re agnostic to the type of treatment. What we do is build digital twins of clinical trial participants—models that simulate how patients would respond to standard of care.

That said, when you're working with an AI-designed therapy in a trial, there are often specific considerations. For example, the compound may have been designed to target a particular mechanism of action, so you might pay closer attention to biomarker signals in early-phase studies than you would with a traditionally developed drug. Additionally, these AI-designed therapies may benefit from more advanced computational safety models, allowing for a greater focus on efficacy even in early trials.

In those scenarios, we use our technology to help detect early signals of efficacy and enable faster decision-making. This helps sponsors determine more quickly whether a compound is viable in terms of both safety and efficacy.

Full Interview Summary: AI-designed therapies can present unique characteristics in clinical trials compared to traditionally developed treatments, often requiring closer scrutiny of biomarker signals and safety data early on. However, the fundamental process of clinical testing remains consistent. Digital twin technology helps make these trials more efficient and insightful by modeling individual trial participants under standard of care, offering a virtual comparator without needing additional enrollees.

Digital twins work by creating a probabilistic model of how a specific patient would respond to standard treatments over time. Rather than enrolling new participants, sponsors gain deeper insights into existing ones, improving the precision of treatment effect estimates and boosting trial power. In randomized controlled trials, this added statistical confidence can reduce participant numbers or accelerate timelines. In open-label studies, digital twins can serve as virtual control arms, allowing meaningful comparisons when a real control group is not feasible.

Regulatory agencies like the FDA and EMA are providing clear guidance on the use of AI and digital twins in drug development. They emphasize defining the model’s context of use, evaluating its risks, and proving its reliability. The companies building digital twins must ensure model development is transparent, reproducible, and interpretable—tracking how models were built, and which data were used, while safeguarding against unblinding.

Looking ahead, digital twins are expected to become a foundational part of the clinical trial landscape, particularly in neuroscience and CNS trials. They offer the promise of more adaptive, efficient, and patient-centric designs that can accelerate drug development and reduce reliance on traditional control groups, while maintaining—or even improving—the quality and reliability of clinical evidence. As adoption grows, digital twins could reshape how sponsors design trials and regulators evaluate new therapies.