“Modeling of Peripheral Neuropathic Pain and Osteoarthritis Placebo Response” presented at the International Society for CNS Clinical Trials and Methodology
Dr. Dominique Demolle, CEO of Tools4Patient, recently presented data at the 16th Annual Scientific Meeting of the International Society for CNS Clinical TrialsView
The epidemic opioid crisis is in stark contrast to the fact that more than 50 million adults (20.4%) in the US is living with chronic pain, including nearly 20 million individuals dealing with high impact chronic pain (persistent pain that reduces life or work activities). As a result of this dichotomy, the FDA has challenged the biopharmaceutical industry to accelerate development of non-opioid analgesics, along with abuse-deterrent opioid formulations and/or solid dosage forms.Read More
As 2019 came to a close and we begin a new decade, it’s an excellentRead More
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
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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