Jon Walsh, founder, chief scientific officer, Unlearn, explains how AI and digital twins are helping clinical trials become more efficient, patient-centric, and capable of supporting innovative study designs over the next decade.
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: Looking ahead, how do you envision the clinical trial landscape evolving in the next 5–10 years with digital twins and AI more deeply embedded in the process?
Walsh: One key aspect is the broader adoption of these technologies in clinical studies. We’re currently working with a number of sponsors, primarily in oncology and CNS, but also in other therapeutic areas. We’d like to see this grow, with more organizations using digital twins and AI to improve clinical trials.
In randomized studies, these tools can help make trials more efficient and patient centric. For example, they can increase patients’ chances of receiving treatment by reducing the size of control arms, while maintaining the quality of evidence. It’s essential that sponsors, patients, clinicians, and regulators can all trust the results generated with digital twins.
Another major shift is the ability to run more innovative trial designs. This could include adaptive designs or open-label studies earlier in the development process, supported by digital twins. These approaches can help drugs reach the market faster, give more patients access to treatments sooner, and still allow for high-quality decision-making. Overall, we hope to see continued adoption of these methods across the industry.
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.
