Tools4Patient is actively looking for a Junior/Senior Data Mining and Statistical Research Specialist. Look at our open position and stay tune.
Tools4Patient Proves Performance of Novel Technology to Predict Placebo Response in Osteoarthritis Clinical Trials
Belgian startup Tools4Patient recently presented data showing its novel technology Placebell©™ predicted placebo response in a Phase 2 randomized controlled trial (RCT) for osteoarthritis (OA). Placebell is the first robust technology using predictive modeling that is proven to reduce the impact of the placebo response in clinical trials under real-world conditions. Data from the Phase 2 RCT, “Predicting the Placebo Response in OA to Improve the Precision of the Treatment Effect Estimation,” was presented as a late-breaking abstract at the OARSI Connect Virtual World Congress on Osteoarthritis on April 29, 2021. This innovation by Tools4Patient is a major advancement for increasing clinical study success and reducing drug development costs and timelines.
The addition of Delphine to T4P’s expanding team reflects the company’s commitment to building and maintaining a robust quality program, which is central to its goal of being a trusted partner providing predictive tools to biopharmaceutical companies. Delphine works with T4P’s operational and scientific groups to support software and clinical-related activities for programs conducted with sponsors or as part of the company’s R&D activities.
What if you could predict placebo responsiveness of each patient in your clinical trial? Historical strategies to address the placebo response may help but haven’t completely solved the placebo response problem. Until now. Advanced methods like AI and machine learning are uniquely poised to help scientists uncover the full spectrum of patient placebo responsiveness in a clinical trial. Learn more about this approach by attending our webinar, which explains how a solution like Placebell©™ leverages a time-tested predictive algorithm to improve clinical trial assay sensitivity and de-risk drug development.
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