P505 Accelerating Crohn's Disease clinical trials using generative artificial intelligence

Abstract

Abstract Background At Unlearn.AI, we expedite decision making and address challenges of lengthy and costly Crohn’s Disease (CD) trial enrollment by introducing deep learning models that create digital twins of trial participants, which are comprehensive forecasts of health outcomes. These generative predictions are incorporated into standard linear and logistic regression analyses, in addition to more advanced methods, in clinical trials. Our TwinRCT technology, which aligns with regulatory guidance, augments standard RCTs for easy integration into clinical programs. We demonstrate the expected benefit of these solutions in a subset of IBD Plexus data that were not previously involved in the training of the generative model, showcasing its potential to transform future CD trials. Methods We developed a generative deep learning model using data from approximately 4,500 Inflammatory Bowel Disorder (IBD) participants in observational studies and randomized controlled trial (RCT) control arms from the IBD Plexus program of the Crohn’s & Colitis Foundation. The trained model was used to forecast, entirely from each participant’s baseline data, the control outcomes of participants in a held out set of 300 participants. The resultant forecasts, or digital twins, represent comprehensive longitudinal trajectories across a range of outcomes, laboratory measures, and vital signs. We evaluated the expected benefits of incorporating the digital twins in linear and logistic regression models, analyzing the change from baseline to follow-up (Weeks 12 through 52) on sCDAI total score, change in daily bowel movement count, change in abdominal pain score and proportion of patients achieving clinical remission (sCDAI < 150; sCDAI is very closely related to CDAI). Results Digital twins provided substantial attainable benefit across outcomes and time points, enabling up to 21% overall sample size reduction or 34% reduction in the control arm only. Conclusion TwinRCTs offer a strategic enhancement to traditional RCTs by minimizing the required sample size needed to identify significant treatment effects. Aligning with existing FDA and EMA guidelines, TwinRCTs can be effectively utilized in Phase 2 and Phase 3 CD studies for faster decision making, as well as reduced timelines and costs, without increasing regulatory risk over standard approaches.