During this season of joy and reflection, it is our pleasure at Tools4Patient to offer our warmest wishes for a safe, happyView
Tools4Patient’s Placebell©™ technology has been selected as a finalist in the 2019 Clinical Trials Innovation Challenge sponsored by IQVIA, Lundbeck, and LEO Innovation Lab.View
Learn more about the development of the MPsQ and the its use as part of the Placebell©™ technology.Read More
A recent study published in Nature Human Behavior developed a methodology to understand and assess the role of physician expectation on the placebo response. Read more about this study here.Read More
Tools4Patient is committed to our mission of developing predictive tools to optimize the evaluation of clinical-stage drugs. Along with our recent launch of Placebell©™ – a predictive algorithm focused on the placebo response – and our recent fundraising to support commercialization of Placebell©™ and further R&D, T4P is entering a new phase as a company.Read More
Drug developers have been struggling with the placebo response for decades, as a strong placebo effect diminishes the ability to distinguish pharmacologic treatment efficacy of an experimental drug.Read More
Modelling of PNP and OA Placebo Response: Working towards a unique model of the placebo response in chronic pain?
In analgesia randomized clinical trials (RCTs), the magnitude and the variability of the placebo response negatively impacts the ability to demonstrate superiority of active compounds compared to placebo. The first objective of this analysis was to investigate parameters influencing the placebo response in PNP as a way to control for this major confounding factor.
Identification Of Peripheral Neuropathic Pain Sensory Phenotypes Based On Specific Combinations Of Symptoms Identified With The NPSI (Neuropathic Pain Symptom Inventory)
One way to better personalized the treatment of peripheral neuropathic pain (PNP) would be to identify specific sensory phenotypes of patients responding to different classes of drugs.
Leveraging Historical Data For High-dimensional Covariate-adaptive Randomization, A Machine Learning Approach.
There is a continuous growth in data collected in clinical trials. Many of those patient’s characteristics are potential confounding factors. Ideally, these factors should be accounted for in the randomization process to balance study arms and reduce the variability of the estimated treatment effect.
Interview with Erica Smith, VP of Business Development at Biotech Showcase, San Francisco (January 2019).
Interview with Daniel Levine and Erica Smith, VP of Business Development on The Bio Report podcast (June 2019).
The Characterization of Individual Patient Placebo Response: Impact on the Clinical Study Power
A sophisticated method to identify placebo responders and reduce data variability due to the placebo response, providing drug developers with a tool to manage the placebo response without excluding high placebo responders.
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