NETRAMARK HOLDINGS INC. - NetraMark's NetraAI is tasked with identifying inclusion and exclusion criteria that are predicted to drive larger effect and smaller placebo responses in future clinical trials -
- Potential to demonstrate statistically significant efficacy results in future trials using fewer patients and reduce the time and cost needed for product approval -
"We recently launched the
Under the scope of this project, the Sponsor has provided NetraMark with data from patients who participated in a completed Phase 2 clinical trial of a specific therapy and NetraMark applied its proprietary NetraAI clinical solution to the data to achieve five key objectives:
- characterizing the drivers of placebo and treatment responses;
- feature significance testing;
- extracting personas that characterize placebo responders/treatment non-responders (PRTNR) and placebo non-responders/treatment responders (PNRTR);
- creating probability density distribution maps of key factors across all patients in placebo and treatment arms, in order to enable enrichment criteria for future trials; and
- establishing a data environment that enables continued exploration of the data
NetraMark will identify key inclusion / exclusion criteria that are predicted with high statistical confidence. Use of these criteria in future trials is expected to increase the number of PNRTR subjects while reducing the number of PRTNR subjects, enabling the trial to potentially demonstrate significant efficacy in a smaller, better-defined patient population.
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.
For further details on the Company please see the Company's publicly available documents filed on the System for Electronic Document Analysis and Retrieval (SEDAR).
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