Getting Beyond the Bias

March 22, 2021 2:09 pm Published by
Clinical Trials

Scientific research isn’t perfect. In fact, some argue that a lot of the clinical studies out there are unreliable, due to small sample sizes, insignificant effects and biases that can sway the outcome of a study.

This can be troubling because evidence-based medicine is an important tool for healthcare professionals to help them decide which treatments work and which do not.

And, while improving health outcomes is the ultimate goal, you first need credible clinical research to guide the path forward.

Part of the issue is that behavioural studies are highly vulnerable to response bias. Researchers judge the efficacy of a particular treatment based on whether they witness behavioural changes and/or the subject indicates they experience a positive or negative effect. 

In order to get beyond the inherent biases that can exist with this technique, Nova is taking a unique approach and will be utilizing statistically objective diagnostic tools to measure psychedelic treatment response, instead of relying on behavioural methods alone.

We recently announced that we’ve launched an Autism Microbiome Study in the United States, with a goal of discovering more precise ways to diagnose and treat autism spectrum disorder (ASD). We’re aiming to recruit at least 300 participants across the U.S.: 200+ with moderate/severe ASD and 100+ neurotypical controls.

We’ll be assessing the gut microbiome across various subtypes of ASD (including conditions with ASD symptoms like fragile X syndrome) via genetic analysis of fecal samples and comparing bacterial profiles with those from age-matched neurotypical controls.

This research is significant because shifts in gut bacteria contribute to the pathophysiology of GI disorders, which are four times more common among children with ASD and are associated with worsening of behavioural symptoms, including anxiety, hypersensitivity and rigid-compulsive behaviours.

By examining differences in the microbiome profile and evaluating the possibility of serotonergic synergies, we may be able to produce clinical biomarkers that could be used to determine if patients respond to therapy with psilocybin or other entheogen therapy.

To analyze the data, we will use a computational approach known as machine learning – a way to aggregate collected data and compare small differences between groups.

Dr. Kyle Ambert, who is currently Director of Data Science at Nike, Inc., is assisting us in designing preclinical and clinical ASD studies and analyzing their results. He has extensive experience in data analytics, machine learning, artificial intelligence, and applied analytics.

His techniques will segment the patient population into groups based on microbiome characteristics, genomics, inflammatory biomarkers, and performance on behavioural tests. Our findings will improve diagnostic methods and allow us to quantify disease severity.

The large-scale design of this study, as well as inclusion of multidimensional datasets and proprietary machine learning systems, aims to resolve inconsistencies in prior clinical studies regarding the role of gut microbiota in ASD, particularly with regard to diagnostics and treatment – currently an unmet medical need.

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