NETRAMARK HOLDINGS INC. — Results presented in two posters at the
"The presentation and posters presented at ISCTM reveal how the AttractorAI technologies on which NetraAI is based are able to deal with the very complex patient populations found in psychiatric disorders," said Dr.
Poster Presentations
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. Independent variables driving these responses were extracted from initial patient assessments through scales and other variables, while dependent variables were based on trial outcomes. NetraAI has the capability to learn which patients in these trials are explainable and which are unexplainable with the provided variables, and the explainable subsets are used to generate "personas" characterized by 3-12 factors, from which enrichment criteria for a next trial are derived. Applying this approach to the active and placebo arms of the trials distinguishes between drug and placebo effects and identifies the degree of efficacy relative to placebo. Key findings from this study include:
- In a 378-patient bipolar disorder trial in which the study drug did not show separation from placebo, a 115-patient training data set enabled the identification of 71 explainable patients in a placebo response subpopulation. The model was tested on 239 independent participants (i.e., not included in the training data set), and correctly predicted 87% of placebo responders. The model also accurately identified 39/44 drug non-responders.
- Application of NetraAI to a failed, 332-patient Phase 3 anxiety trial identified 8 variables that were drivers of placebo response and 6 variables that were drivers of drug non-response. Importantly, this analysis showed that the drug response was very poor and similar to placebo in all but a small class of patients. Based on these findings, the efficacy of this drug is unlikely to be improved in this patient population.
- Application of NetraAI to a 135-patient Phase 2a schizophrenia trial identified 4 variables driving placebo response and 3 variables driving drug non-response. 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 described 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. A model based on these variables and insights gained from the initial analysis showed 94% accuracy for predicting response, non-response, and a no-call subset in cross-validation studies. When the insights weren't included in the model, the cross-validation was only 65% accurate.
- Three of the significant variables form an inclusion/exclusion persona that can be used to enhance the number of escitalopram responders and decrease the number of escitalopram non-responders without excluding a large proportion of patients.
- The same methods used to enrich for escitalopram responders can also be used to mitigate placebo response.
Panel Discussion
Towards precision medicine in CNS disorders: Progress and challenges
In addition to the new results presented in the posters,
"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
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.
NetraMark is a company focused on being a leader in the development of Generative Artificial Intelligence (Gen AI)/Machine Learning (ML) solutions targeted at the Pharmaceutical industry. Its product offering uses a novel topology-based algorithm that has the ability to parse patient data sets into subsets of people that are strongly related according to several variables simultaneously. This allows NetraMark to use a variety of ML methods, depending on the character and size of the data, to transform the data into powerfully intelligent data that activates traditional AI/ML methods. The result is that NetraMark can work with much smaller datasets and accurately segment diseases into different types, as well as accurately classify patients for sensitivity to drugs and/or efficacy of treatment.
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