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
“Sensory Profiles and stratification of neuropathic pain patients based on the neuropathic pain symptom inventory (NPSI)” presented at the 2019 European Pain Federation EFIC Conference.
This study aimed to identify specific sensory phenotypes of peripheral neuropathic pain patients that could predict treatment response based on pain symptoms assessed by the Neuropathic Pain Symptom Inventory (NPSI).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
Identification Of Peripheral Neuropathic Pain Sensory Phenotypes Based On Specific Combinations Of Symptoms Identified With The NPSI (Neuropathic Pain Symptom Inventory)
Scientific Presentation made on September 2019 at the European Pain Federation (EFIC), Valencia, Spain. 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. Recent results have suggested that quantitative sensory testing (QST) could be useful, but these
Leveraging Historical Data For High-dimensional Covariate-adaptive Randomization, A Machine Learning Approach.
here 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.
Often, the primary endpoint of RCTs is defined as a change from baseline of a continuous outcome. In
such cases, regulators recommend including the outcome’s baseline value as a covariate in the
statistical analysis. Regression to the mean can explain the benefits of this procedure.
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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