Mr. Swapan Kakumanu reports

NETRAMARK POSITIONED TO ADVANCE PSYCHEDELIC CLINICAL TRIALS FOLLOWING U.S. EXECUTIVE ORDER, SUPPORTED BY PEER-REVIEWED RESEARCH AND ACTIVE PROGRAMS

Netramark Holdings Inc. today underscored its differentiated capability to support psychedelic clinical trials in a manner distinguishable from conventional analytical approaches, potentially advancing the development of psychedelic therapies following the recent United States executive order aimed at accelerating innovation in this emerging field.

While such policy momentum may help accelerate development timelines, it does not reduce the evidentiary bar required for regulatory decision making. Central nervous system (CNS) and psychedelic trials remain among the most analytically demanding in drug development due to patient heterogeneity, subjective and multidimensional endpoints, and elevated placebo and expectancy effects. In this context, Netramark believes that improved trial design, combined with transparent and auditable analytics, is essential to reliably distinguish true treatment effects from noise.

Netramark's NetraAI platform is designed to address these challenges by seeking to identify model-derived subgroups (MDS), compact, clinically interpretable patterns across multiple variables, that can be used to support hypothesis generation and prospectively evaluable stratification and enrichment strategies.

Peer-reviewed scientific foundation in CNS and psychedelic-relevant research

Netramark's approach is grounded in peer-reviewed scientific validation within complex central nervous system (CNS) data sets, an area directly relevant to psychedelic drug development.

• In npj Digital Medicine (Nature Portfolio), NetraAI was applied to a completed phase II ketamine study supported by the National Institute of Mental Health (NIMH), demonstrating an explainable approach to identifying clinically meaningful patient subgroups from small, high-dimensional data sets. The publication emphasizes interpretability and reports results as hypothesis generating.
• In Frontiers in Pharmacology, Netramark's scientific team contributed to a peer-reviewed, hypothesis-driven article exploring a falsifiable mechanistic model relevant to psychedelic pharmacology, including proposed quantum-level interactions involving Posner molecules. This work is intended to stimulate testable research directions.

Furthermore, Netramark is currently engaged in continuing clinical trial programs involving psychedelic compounds with a leading sponsor in the field, reinforcing the company's role as both a scientific contributor and an active partner in the clinical development of next-generation therapies.

"Acceleration initiatives are meaningful, but they don't change what regulators must ultimately see: credible evidence that is robust, reproducible and clinically interpretable," said Dr. Luca Pani, chief innovation and regulatory officer of Netramark. "In CNS, and particularly in psychedelic trials, the core challenge is separating true treatment signal from heterogeneity, expectancy effects and placebo response. NetraAI is designed to generate compact, explainable subpopulation definitions, and quantify differential treatment and placebo effects within those strata, to help sponsors move faster while staying regulator ready."

Why psychedelic trials challenge traditional approaches

Psychedelic clinical trials represent one of the most analytically challenging areas in drug development:

• Patient populations are highly heterogeneous, often with complex symptom profiles;
• End points are often subjective and multidimensional;
• Trials exhibit elevated placebo and expectancy effects;
• Data sets are typically small, limiting statistical power.

Netramark believes conventional analyses, including many machine learning approaches, may be limited in this context because they:

• Analyze variables in isolation or in small combinations;
• Do not consider the exceptional complexity found in patient populations;
• Optimize for average population effects, diluting true treatment signals;
• Fail to distinguish drug responders from placebo responders in a meaningful way;
• Struggle to generalize from small, noisy data sets.

Netramark's differentiated approach: learning across the full structure of the trial population

NetraAI was designed specifically to address these limitations. Rather than analyzing variables in isolation, NetraAI learns across the full combinatorial structure of clinical trial data, aiming to identify MDS, precisely defined patient populations characterized by specific combinations of variables that drive treatment response.

This approach enables Netramark to:

• Identify who responds to a therapy and why, not just whether a drug works on average;
• Detect high-effect-size subgroups that are invisible to conventional methods;
• Separate true drug response from placebo-driven outcomes, a critical need in psychedelic trials;
• Generate clinically explainable and regulator-aligned insights, rather than black-box predictions;
• Operate effectively on small, complex data sets, typical of breakthrough therapy programs.

Unlike traditional machine learning, which often requires large data sets and produces opaque models, NetraAI is built to extract actionable, explainable insights from the exact type of data that defines psychedelic trials.

Enabling the next phase of psychedelic drug development

With increasing regulatory support for psychedelic therapies, including the recent United States executive order, there is a growing need for technologies that can improve trial design, reduce failure risk and support regulatory decision making.

Netramark's platform is purpose built to:

• Enhance patient stratification within protocols and statistical analysis plans;
• Improve signal detection in subjective end point studies;
• Support more efficient phase 2 and phase 3 trial design;
• Provide evidence-based insights for regulators, clinicians and sponsors.

"Across psychiatric and neurological disorders, clinical trial data is inherently heterogeneous, and meaningful treatment effects are often masked by variability and noise," said Joseph Geraci, founder and chief scientific and technical officer of Netramark. "NetraAI is designed to identify compact, explainable combinations of variables that define patient subpopulations with reproducible patterns of response. This approach is particularly relevant for emerging therapeutic areas such as psychedelics, where variability in response is a defining feature of the clinical challenge."

As regulatory momentum is expected to accelerate in support of psychedelic therapies, Netramark remains focused on enabling sponsors to generate more precise, interpretable and clinically actionable insights from complex trial data -- addressing one of the most critical barriers to success in this emerging field.

About NetraAI

In contrast to other artificial-intelligence-based methods, NetraAI is designed with mechanisms that separate data sets into subsets where consistent, explainable relationships can be identified versus subsets where signal is weak or inconsistent. This approach is intended to reduce overfitting risk in small, heterogeneous data sets and to support hypothesis generation, such as factors associated with treatment response, placebo response or adverse events, that can be prospectively evaluated.

About Netramark Holdings Inc.

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 data sets and accurately segment diseases into different types, as well as accurately classify patients for sensitivity to drugs and/or efficacy of treatment.

We seek Safe Harbor.