NetraMark Holdings Inc. announced the presentation of new data demonstrating that the application of its NetraAI clinical solution to data sets of fewer than 400 patients can identify variables that predict efficacy and placebo responses in psychiatric clinical trials with high statistical significance. They also show that application of models based on these variables to independent patient populations correctly predicts efficacy and placebo responses, providing a novel approach to de-risk clinical trials for psychiatric therapies. The data were presented on February 22 at the ISCTM 20th Annual Meeting in Washington DC.

Evaluating drug efficacy: Leveraging machine learning insights from placebo response modeling (Poster #15) This poster described the results of a study designed to leverage machine learning (ML) algorithms to identify characteristics of drug and placebo response across clinical trials in bipolar disorder, anxiety, and schizophrenia. NetraAI, based on Attractor AI methods, was used to analyze efficacy, demographics, and safety data for predicting placebo responses. Use of these variables in determining inclusion/exclusion criteria is expected to greatly improve the statistical significance for future trials of the therapy evaluated in the Phase 2a trial.

Identifying efficacy variables for the use of escitalopram in mild major depression disorder (MDD): Implications for treatment-resistant MDD trials (Poster #24) This poster describes the results of a study designed To determine if NetraAI can identify unique subpopulations in MDD clinical trials with varying responses to escitalopram for the treatment of depression. The study used a 172-patient data set from the exploratory escitalopram arm of a MDD trial. Key findings from this study include: NetraAI identified an escitalopram response subpopulation of 110 patients characterized by 7 variables.

In addition to the new results presented in the posters, Dr. Geraci also presented previously reported results demonstrating the power of AttractorAI in a presentation, titled "Biomarker identification for patient enrichment strategies in CNS clinical trials: Alternative approaches and challenges," that underscored NetraAI's ability to discover subpopulations of clinical trial participants for which causal factors for response are present in combination, and to transform insights from these participants into tunable parameters that can be used to improve clinical trial outcomes. This included data from a schizophrenia clinical trial use case showing that NetraAI delivered insights regarding variables driving placebo and drug responses. Notably, while only 30% of the subpopulation identified by these variables was explainable, the application of these variables was explainable, The application of these variables to a model of a larger trial is predicted to have a substantial impact on statistical significance - a reduction in p-value from 0.04 to 0.0019.

" Traditional ML methods can be effective when they can be trained on large amounts of data and when the objects they are trained to recognize are clearly distinct from one another," added Dr. Geraci. As shown in the schizophrenia use case described in the presentation, models based on variables with high statistical significance in a subset of the total population can be extremely powerful and can drive the significant improvements in p-values that the biopharmaceutical industry needs to improve its clinical trial success rate." 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 treatment and placebo responses.