Recursion announces preliminary results from Phase 1b/2 TUPELO trial
Recursion announced preliminary safety and efficacy results from its ongoing Phase 1b/2 TUPELO trial of REC-4881, an investigational, allosteric MEK1/2 inhibitor in development for Familial Adenomatous Polyposis. These data were presented in a late-breaking oral presentation at Digestive Disease Week, DDW, 2025 in San Diego, California. As of the March 17, 2025 data cutoff in the open-label Phase 2 portion of the TUPELO trial, treatment with REC-4881 led to a preliminary median 43% reduction to date in polyp burden at the Week 13 assessment among six efficacy-evaluable patients. Other investigational agents in separate studies have reported 20-30% polyp burden reduction in 6 months1. Five of the six patients experienced reductions in polyp burden ranging from 31% to 82%, while one patient showed a substantial increase of 595% from baseline. Additionally, three of six patients achieved a greater than or equal to1-point reduction in Spigelman stage; one patient had no change, one progressed from Stage II to IV, and one had no baseline stage but was classified as Stage II at Week 13.
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Orthopaedic Institute of North Mississippi merging, rebranding
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AI Algorithm Predicts Transfusion Need in Acute GI Bleeds
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Predictive models based on deep learning and generative AI hold the promise of improving how we predict and treat disease states, but the excitement being generated with studies such as this needs to be balanced with the trade-offs inherent to the current paradigm of deep learning and generative models compared to more traditional regression-based models. These include many of the same 'black box' explainability questions that have risen in the age of convolutional neural networks as well as some method-specific questions due to the continuous and implicit nature of TFM.' Elaborating on that, Glissen Brown said: 'TFM, like many deep learning techniques, raises concerns about explainability that we've long seen with convolutional neural networks — the 'black box' problem, where it's difficult to interpret exactly how and why the model arrives at a particular decision. But TFM also introduces unique challenges due to its continuous and implicit formulation. Since it often learns flows without explicitly defining intermediate representations or steps, it can be harder to trace the logic or pathways it uses to connect inputs to outputs. This makes standard interpretability tools less effective and calls for new techniques tailored to these continuous architectures.' 'This approach could have a real clinical impact,' said Robert Hirten, MD, associate professor of medicine and artificial intelligence, Icahn School of Medicine at Mount Sinai, New York City, who also reviewed the study. 'Accurately predicting transfusion needs and mortality risk in real time could support earlier, more targeted interventions for high-risk patients. While these findings still need to be validated in prospective studies, it could enhance ICU decision-making and resource allocation.' 'For the practicing gastroenterologist, we envision this system could help them figure out when to perform endoscopy in a patient admitted with acute gastrointestinal bleeding in the ICU at very high risk of exsanguination,' Zhang told Medscape Medical News. The approach, the researchers said, will be useful in identifying unique patient characteristics, make possible the identification of high-risk patients and lead to more personalized medicine. Hirten, Zhang, and Shung had no disclosures. Glissen Brown reported consulting relationships with Medtronic, OdinVision, Doximity, and Olympus.
