Data to be presented at ASCP 2024 demonstrate how NetraAI-generated insights were used to develop the TAP™, a novel measure that can be used to identify placebo responders before randomization, resulting in more efficient trials
Placebo response poses a significant challenge in psychiatric clinical trials, often obscuring the true effectiveness of tested interventions. NetraAI's Sub-Insight Learning approach explaining patient populations, was used to generate the TAP™. Combining TAP™ responses with other clinical data allows the NetraAI to provide insights into drug and placebo responses that can be used by pharmaceutical companies to make critical decisions about future trials. Further, recent innovations in the NetraAI have given more power to clinical trialists as it allows for decision augmentation through an intuitive Large Language Model (LLM)-based conversational process.
"Advancements in AI have increased our understanding of the placebo response phenomenon by identifying key variables derived from clinical scales that can be predictive of placebo response," said
The poster (available here on
Key findings from the NetraAI analysis of the trial data include:
- In the bipolar disorder trial, the NetraAI model correctly predicted placebo responders (PR) 87% of the time and accurately identified 39/44 drug non-responders (DNRs) and falsely identified 5/44 non-responders.
- The key variables emerging from the NetraAI analysis suggest a strong impact of attitudinal variables including treatment attitude, impact of symptoms, and sleep quality on placebo response.
- The NetraAI model identified eight variables that captured 55 of 73 placebo responders along with six other variables linked to drug response, identifying 10 drug non-responders. Of note, Clinical Trial and Site Scale (CTSS) Question 18 (how signing up for the trial made them feel) was a common factor in both placebo and drug response hypotheses, underscoring its significance in distinguishing between drug effects and placebo responses in the anxiety trial.
- The key variables emerging from the NetraAI analysis of the anxiety disorder trial data suggest a strong impact of attitudinal variables, including sentiment towards medication as well as expectations about the trial, on placebo response.
- The TAP™, which was developed based on insights gained from these analyses, incorporates a wide variety of factors that can be used to characterize placebo response, categorized into the following themes:
- Symptom Impact and Severity
- Treatment Perception and Efficacy
- Treatment Management and Behavior
- Patient-Doctor Relationship and Clinical Interaction
- Psychological and Emotional Well-Being
General Health and Lifestyle- Trial Participation History
"Not only did the NetraAI reveal insights that allowed us to construct the TAP™, but the technology helps pharmaceutical companies understand what variables differentiate their drug from placebo," added
In addition to the poster presentation,
Poster Presentation
Title: Introducing the Treatment Attitude Profile (TAP) Scale for Placebo Response Persona Discovery Using Attractor AI Technologies: Applications in Clinical Trial Patient Enrichment
Date and Time:
Location: Salon 4
Poster Board #: W7
Biomarker Panel Discussion / Presentation
Title: Using Machine Learning to Identify Biomarkers for Clinical Trial Enrichment Through the Use of a Sub-Insight Learning Paradigm and Large Language Models
Date and Time:
Location: Salon 2
About NetraAI
In contrast with other AI-based methods, NetraAI is uniquely engineered to include focus mechanisms that separate small datasets into explainable and unexplainable subsets. Unexplainable subsets are collections of patients that can lead to suboptimal overfit models and inaccurate insights due to poor correlations with the variables involved. The NetraAI uses the explainable subsets to derive insights and hypotheses (including factors that influence treatment and placebo responses, as well as adverse events) that can significantly increase the chances of a clinical trial success. Other AI methods lack these focus mechanisms and assign every patient to a class, even when this leads to "overfitting" which drowns out critical information that could have been used to improve a trial's chance of success.
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Forward-Looking Statements
This press release contains "forward-looking information" within the meaning of applicable Canadian securities legislation including statements regarding the potential value of the Company's TAP™ using NetraAI to analyze clinical scale data and help better characterize placebo and drug responders which are based upon
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