Mr. Swapan Kakumanu reports

NETRAMARK-AUTHORED, PEER-REVIEWED ARTICLE SUGGESTS PSYCHEDELICS MIGHT NOT ONLY AFFECT BRAIN CHEMISTRY, THEY MAY ALSO INVOLVE QUANTUM-LEVEL PROCESSES INSIDE THE BRAIN

Netramark Holdings Inc. has published a new peer-reviewed article, "Psychedelics and the Quantum Brain: A Falsifiable Hypothesis on Posner Molecules and Spin-Dependent Pharmacology," in Frontiers in Pharmacology.

The publication was led by Netramark's founder and chief scientific officer, Dr. Joseph Geraci, PhD, and co-authored by Netramark's chief innovation and regulatory officer, Luca Pani, MD, alongside other academic collaborators. The publication presents a speculative but mechanistically grounded and experimentally testable framework for understanding psychedelic drug response.

The publication's proposed hypothesis explores whether psychedelic treatment effects in clinical trials may arise from complex, multiscale biological processes involving calcium signaling, phosphate metabolism and potentially spin-dependent molecular interactions. Importantly, it examines potential connections between these mechanistic layers and a central but unresolved issue in drug development: substantial interindividual variability in treatment response.

A key contribution of the publication is its framing of this variability as structured biological heterogeneity, rather than statistical noise. The publication in Frontiers in Pharmacology outlines a translational pathway from:

• Mechanistic biological processes;
• To measurable patient-level variability;
• To biomarker-informed clinical trial design and patient stratification.

This framework directly reflects the type of structured biological heterogeneity that Netramark's platform is designed to address.

Netramark's NetraAI platform is designed to identify explainable, model-derived patient subpopulations within complex clinical data sets. By analyzing relationships across all collected variables simultaneously, NetraAI is designed to uncover structured patterns that define responders, non-responders and placebo-sensitive groups -- supporting prospective incorporations of these insights into clinical trial design and regulatory strategy.

The publication supports a broader potential industry shift: clinical trial success, particularly in central nervous system disorders and emerging areas such as psychedelic therapeutics, may increasingly depend on the ability to identify and act on patient-level heterogeneity, rather than relying on population averages.

For investors, this highlights a growing opportunity tied to technologies that can potentially translate biological complexity into actionable clinical trial strategy.

As pharmaceutical companies are expected to continue to face rising development costs, high failure rates and increasing regulatory expectations, there is a structural need for technologies that can:

• Improve signal detection in small and complex data sets;
• Reduce trial risk through better patient selection;
• Support more precise, data-driven development strategies.

Netramark's approach focuses on this intersection -- aiming to bridge advanced biological insight, artificial-intelligence-driven analytics and regulatory-aligned clinical trial design.

While the paper introduces a testable hypothesis at the mechanistic level, its broader implication is independent of that hypothesis: future clinical trials stand to benefit from being designed around patient-level variability.

"What this work shows is that variability in clinical trials isn't a problem -- it's the key to unlocking better outcomes," said Joseph Geraci. "If you can identify which patients are driving response and why, you can potentially design smarter, more successful trials. That's the shift we're focused on potentially enabling with NetraAI."

Netramark believes this publication further strengthens the role of explainable AI and precision analytics as essential components of next-generation drug development.

About NetraAI

In contrast to other AI-based methods, NetraAI is uniquely engineered to include focus mechanisms that separate small data sets 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. NetraAI uses explainable subsets to derive insights and hypotheses (including factors that influence treatment and placebo responses and adverse events), potentially increasing the likelihood of a clinical trial's success. Many other AI methods lack these focus mechanisms and assign every patient to a class, often leading to overfitting, which drowns out critical information that could have been used to improve a trial's chance of success.

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.

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