NetraMark Presents AI-Driven Advances in Precision Psychiatry to Enhance Clinical Trial Designs at Joint Autumn Conference
Rhea-AI Summary
NetraMark (OTCQB: AINMF) presented AI-driven analyses at ISCTM and ECNP on Oct 29, 2025 demonstrating NetraAI’s ability to identify responder subgroups and improve prediction in major depressive disorder trials.
Key readouts: ketamine responder Personas diverged from placebo by infusion 2 (separation 1.0) and ketamine responder cohesion rose from 25% to ~67%. In CAN-BIND escitalopram data NetraAI reduced clinical features to 8 key variables, isolated a highly predictive responder subgroup, and reported 91% accuracy for that subgroup using hypomethylation at three gene sites.
Positive
- Ketamine responder vs placebo separation reached 1.0 by infusion 2
- Ketamine responder cohesion increased from 25% to ~67%
- Clinical feature set reduced to 8 variables in CAN-BIND
- 91% predictive accuracy for HPRs using 3-site hypomethylation signature
Negative
- Traditional ML on 718 variables showed only modest accuracy
- Large unknown class identified (124 unknowns) in escitalopram analysis
- Methylation results based on 169 patients, limiting cohort size
TORONTO, Oct. 29, 2025 (GLOBE NEWSWIRE) -- NetraMark Holdings Inc. (the “Company” or “NetraMark”) (CSE: AIAI) (OTCQB: AINMF) (Frankfurt: PF0) a premier artificial intelligence (AI) company that is transforming clinical trials with AI-powered precision analytics in the pharmaceutical industry, presented two significant applications of its technology at the International Society for CNS Clinical Trials and Methodology (ISCTM) Autumn conference and the European College of Neuropsychopharmacology (ECNP) Congress. These presentations (available on NetraMark’s website) showcase the power of NetraAI in major depressive disorder (MDD) clinical trials.
NetraAI Personas Identify Distinct Separation Between Patient Responder Subgroups
NetraMark’s first presentation, “Leveraging ML-derived patient personas to assess functional unblinding in ketamine trials for major depressive disorder”, leveraged NetraAI technology to analyze data from a randomized, crossover trial of ketamine in treatment-resistant depression.
The analysis revealed that ketamine responders had distinct baseline characteristics compared to placebo responders. Importantly, the separation between groups became clearer with repeated dosing, while within-group consistency also strengthened. These findings indicate that ketamine’s efficacy is not simply the result of functional unblinding and that distinct patient subtypes were driving response. The results point to new opportunities for persona-guided trial enrichment, stratified randomization, and patient selection strategies in clinical practice.
Ketamine & Functional Unblinding
In a Phase II ketamine trial, NetraAI revealed that responder subgroups diverged completely from placebo responders by the second infusion, while responder profiles became twice as cohesive, demonstrating ketamine’s true pharmacologic signal beyond expectancy effects.
- Cross-Group Divergence: Separation between ketamine and placebo responder Personas increased from 0.75 (moderate) after the first infusion to 1.0 (complete separation) by the second infusion.
- Within-Group Cohesion: For ketamine responders, cohesion rose from
25% after infusion 1 to ~67% after infusion 2, showing that responders became more internally consistent with repeated treatment. - Interpretation: These shifts demonstrate that ketamine responders are biologically and clinically distinct from placebo responders, not simply influenced by expectancy effects.
NetraAI Boosts Predictive Accuracy of Subpopulations In Heterogeneous MDD Trials
In NetraMark’s second presentation, “Sub-insight learning disentangles heterogeneous MDD trial data, boosting predictive accuracy and patient stratification for escitalopram response,” NetraMark introduced its novel algorithm to data from the CAN-BIND trial of escitalopram.
Traditional machine learning approaches struggled with prediction accuracy in this heterogeneous population. However, NetraMark’s purpose-built NetraAI identified a compact, reproducible feature set related to anhedonia and mood that improved the prediction of treatment response. It also uncovered a highly predictive responder subgroup defined by hypomethylation at three gene sites, offering biological insights into why certain patients respond more robustly to treatment. These findings demonstrate the algorithm’s potential to boost predictive accuracy, improve trial interpretability, and advance precision psychiatry by defining clinically meaningful subgroups.
Escitalopram & NetraAI (CAN-BIND Trial)
In the CAN-BIND escitalopram dataset, NetraAI reduced the amount of clinical variables, boosted prediction accuracy, and identified a subgroup of highly predictive responders.
- Clinical Scale Data: Traditional machine learning (ML) applied to 718 variables showed only modest accuracy. NetraAI refined the feature set to 8 key variables (covering anhedonia, daily functioning, appetite, and negative thinking). With the “unknown” class introduced, 26 non-responders, 23 responders, and 124 unknowns were identified, leading to improved classifier performance.
- Methylation Data: Among 169 patients with ~68,600 methylation variables each, NetraAI identified 33 of 117 responders as Highly Predictive Responders (HPRs).
- Genetic Signature: Hypomethylation at 3 gene sites (LINC01580, STK24, ATXN7L3) linked to neuroplasticity, predicted HPRs whose treatment success could be forecast with
91% accuracy when traditional classifiers were retrained on these features. - Mechanistic Insight: These genes support synaptic remodeling, chromatin plasticity, and hippocampal neurogenesis, offering a biological explanation for treatment response.
Together, these presentations highlight the power of NetraAI to overcome long-standing barriers in CNS drug development. By separating signals from noise, uncovering responder subtypes, and mitigating bias, NetraAI can enable more efficient trials, sharper insights into drug efficacy, and better strategies for matching patients with treatments.
"The ability to distinguish true pharmacologic effects from placebo and to predict treatment response with explainable subgroups represents a meaningful advancement," said Josh Spiegel, President at NetraMark. "These findings demonstrate how our technology can transform the way psychiatric and CNS trials are designed, interpreted, and translated into clinical practice."
Readers are referred to the presentations on NetraMark’s website for full information regarding the study protocols and research results, including any limitations thereof.
The Future of CNS Trial Designs Guided by AI-Powered Insights
NetraMark’s AI-driven methodologies demonstrate how explainable machine learning can reshape CNS clinical research. By enabling sharper patient stratification, reducing placebo-related noise, and improving predictive modeling, these approaches support more targeted and efficient clinical trials. As precision medicine becomes central to drug development, NetraMark’s innovations provide pharmaceutical companies and researchers with a powerful toolkit to uncover meaningful patient subgroups, accelerate development timelines, and gain deeper insight into psychiatric disorders and treatment response.
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.
About NetraMark
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+).
Forward-Looking Statements
This press release contains "forward-looking information" within the meaning of applicable Canadian securities legislation including statements regarding the Company's ability to enhance clinical trial designs; the potential applications of NetraMark's technology in identifying patient subgroups and improving treatment response prediction; expectations regarding the adoption of precision medicine in drug development; the ability of NetraMark's methodologies to enable more efficient trials and improved patient stratification; and the potential for NetraMark's technology to transform psychiatric and CNS trial design and interpretation. 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, 2024. These risk factors and other factors could cause actual events or results to differ materially from those described in any forward-looking information.
The CSE does not accept responsibility for the adequacy or accuracy of this release.
Contact Information:
Swapan Kakumanu - CFO | swapan@netramark.com | 403-681-2549
FAQ
What did NetraMark announce on Oct 29, 2025 about AINMF and precision psychiatry?
How did NetraAI perform in the ketamine trial presented by NetraMark (AINMF)?
What predictive accuracy did NetraMark report for escitalopram responders (AINMF) using methylation markers?
What feature reduction did NetraAI achieve in the CAN-BIND escitalopram dataset reported by NetraMark?
What limitations did NetraMark disclose about the presented analyses for AINMF?
How could NetraMark’s NetraAI findings affect clinical trial design for AINMF clients?
