TORONTO, Feb. 15, 2024 /CNW/ - NetraMark Holdings Inc. (the "Company" or "NetraMark") (CSE: AIAI) (OTCQB: AINMF) (Frankfurt: 8TV) a generative AI software leader in clinical trial analytics, announces that it will present new data supporting the potential of its proprietary NetraAI clinical trial solution to de-risk clinical trials for neurology therapies by identifying the key variables that drive clinical trial success and providing testable hypotheses to guide clinical trial design. The data will be presented at the International Society for CNS Clinical Trials and Methodology (ISCTM) 20th Annual Meeting, which is taking place February 21-23 in Washington, DC.

Dr. Joseph Geraci, PhD, Founder and Chief Scientific Officer of NetraMark will participate in a panel discussion on patient enrichment strategies in central nervous system (CNS) trials and new data will be reported in two poster presentations.

"There is an urgent need for novel therapies to treat a wide array of CNS diseases, but innovation can't succeed if the biopharmaceutical industry continues to utilize the same clinical trial strategies that result in an 88% trial failure rate," said Dr. Geraci. "We are generating a robust body of evidence demonstrating our expertise in precision medicine and the power of NetraAI in de-risking CNS and other neurological diseases. We look forward to participating in this impactful session with many of the important stakeholders in this industry."

Session 2: Towards Precision Medicine in CNS Disorders: Progress and Challenges

Title: Biomarker identification for patient enrichment strategies in CNS clinical trials: Alternative approaches and challenges

Date and Time: Thursday, February 22, 2024, 11:45am-12:15pm

Poster Presentation Information
Title: Evaluating drug efficacy: Leveraging machine learning insights from placebo response modeling

Date and Time: Thursday, February 22, 2024, 5:45pm

Title: Identifying efficacy variables for use of escitalopram in mild major depression disorder (MDD): Implications for treatment-resistant MDD trials

Date and Time: Thursday, February 22, 2024, 5:45pm

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).

This press release contains "forward-looking information" within the meaning of applicable Canadian securities legislation including statements regarding the potential improvements and success arising from NetraAI and the Company's position to empower pharmaceutical companies, provide them with critical insights and the possible improvement of patient outcomes and operational results, which are based upon NetraMark's current internal expectations, estimates, projections, assumptions and beliefs, and views of future events. Forward-looking information can be identified by the use of forward-looking terminology such as "expect", "likely", "may", "will", "should", "intend", "anticipate", "potential", "proposed", "estimate" and other similar words, including negative and grammatical variations thereof, or statements that certain events or conditions "may", "would" or "will" happen, or by discussions of strategy. Forward-looking information includes estimates, plans, expectations, opinions, forecasts, projections, targets, guidance, or other statements that are not statements of fact. The forward-looking statements are expectations only and are subject to known and unknown risks, uncertainties and other important factors that could cause actual results of the Company or industry results to differ materially from future results, performance or achievements. Any forward-looking information speaks only as of the date on which it is made, and, except as required by law, NetraMark does not undertake any obligation to update or revise any forward-looking information, whether as a result of new information, future events, or otherwise. New factors emerge from time to time, and it is not possible for NetraMark to predict all such factors.

When considering these forward-looking statements, readers should keep in mind the risk factors and other cautionary statements as set out in the materials we file with applicable Canadian securities regulatory authorities on SEDAR at www.sedarplus.ca including our Management's Discussion and Analysis for the year ended September 30, 2022. These risk factors and other factors could cause actual events or results to differ materially from those described in any forward-looking information.

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