Should strong placebo responders be excluded from clinical trials?

November 16, 2021

Using Predictive Modeling to understand the impact on assay sensitivity The placebo response is a heavily studied and historically challenging phenomenon for drug developers. Strong placebo effect diminishes the ability to distinguish efficacy of an experimental drug, leading to phase II and III trial failures1– even for otherwise effective drugs.  Researchers have long devised strategies

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How much of the measured treatment response in clinical trials is due to the placebo response?

September 17, 2021

Clinical trials measure efficacy of experimental therapies by comparing outcomes in patients receiving therapeutic interventions (treatment response) with patients receiving placebo (placebo response).

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The Importance of Patient Psychology in Health and Clinical Research

July 9, 2021

When we think about patient characteristics that influence health, disease, and clinical research, we tend to think about things like vital statistics, medical history and genetic makeup – while patient personality or psychology is often overlooked. In reality, the importance of personality has been under scrutiny for centuries and dates back to Greek and Roman times

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The placebo response in drug development. Part 4: Irritable Bowel Disease

June 17, 2021

As in many other diseases (e.g. pain, osteoarthritis, rheumatoid arthritis, Parkinson’s Disease, etc ), patients participating in clinical trials for IBD treatments may experience a pronounced placebo effect or placebo response. As IBD is a chronic disease, efficacy is comprised of improvement rates as well as remission rates; unfortunately, both are influenced by placebo response, albeit at different rates.

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Minimizing Evaluation Error in Osteoarthritis Pain

May 17, 2021

Osteoarthritis trials have relatively a rate of failure due to many factors, including disease heterogeneity, a disconnect between pain improvement and structural improvement, a strong placebo response, and high variability/inaccuracy in patient reporting of pain.

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Placebell©™ Predicts Placebo Response in Initial Study in Parkinson’s Disease

April 19, 2021

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, as it affects 1-2 per 1000 of the population at any time(1). Although it is primarily a disease of the elderly, individuals have developed PD in their 30s and 40s(2). Gender differences pertaining to the incidence of PD are reflected in a 3:2 ratio of males to females, with a delayed onset in females attributed to the neuroprotective effects of estrogen on the nigrostriatal dopaminergic system(3,4).

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Prediction of placebo response in Osteoarthritis improves estimation of the treatment effect: Impact on drug development

March 11, 2021

Placebell©™ can be used in OA and similar diseases in which efficacy is characterized using patient-reported outcomes to reduce the interference of the placebo effect and improve assessment of the drug effect. The performance and applicability of PlacebellI©™ has recently been demonstrated in a Phase 2 RCT conducted by a biotech sponsor in subjects with moderate to severe painful knee OA.

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The placebo response – a phenomenon related to the placebo group or to the individual patient?

February 8, 2021

Historically, interpretation of clinical trials relies on “assay sensitivity”, or the sensitivity to detect clinically meaningful differences between endpoints measured in the group of patients given active drug compared to the group of patients given placebo. Assay sensitivity can be influenced by many factors, including the study design, specific endpoints selected, number of clinical sites and, of course, the magnitude of the placebo response.

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From covariates to confounding factors: the danger of having too many covariates

October 20, 2020

Clinical trials typically evaluate efficacy of experimental therapies in heterogeneous patient populations, as patient characteristics vary significantly. These patient characteristics might be prognostic factors that ultimately induce variability in clinical trial data. An imbalance in these factors between treatment groups at baseline will increase variability of the estimated treatment effect, ultimately compromising study power and decreasing

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Regulatory Guidelines for the Use of Baseline Covariates to Increase Clinical Trial Study Power

October 15, 2020

Clinical trials data analyses can employ baseline covariates to control for factors that may impact measurement of outcomes – particularly to describe individual patient characteristics that may or may not relate to treatment response. For example, patient age may be used as a baseline covariate to reduce data variability resulting from this factor. The use of baseline

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