Dr. Dr. Joseph Geraci reports

NETRAMARK ACHIEVES MAJOR FDA MILESTONE

Netramark Holdings Inc. has completed its Critical Path Innovation Meeting (CPIM) with the United States Food and Drug Administration (FDA). During this scientific exchange, FDA provided feedback on NetraAI-Netramark's explainable AI/ML (artificial intelligence/machine learning) platform -- and discussed its potential application as an enrichment methodology in clinical trial design. During the discussion, FDA suggested that Netramark consider exploring the agency's Model-Informed Drug Development (MIDD) Paired Meeting Program as a potential avenue for further regulatory dialogue.

Important disclaimer: CPIM discussions are non-regulatory, drug product-independent and non-binding on both FDA and CPIM requesters. CPIM meetings are scientific forums for early stage discussion and are not substitutions for formal regulatory meetings. The CPIM held between FDA and Netramark did not constitute FDA's endorsement of the NetraAI platform, or any product or service provided by Netramark.

Key takeaways from the FDA CPIM:

• Scientific discussion of NetraAI's approach and predictive enrichment strategy: FDA provided feedback on NetraAI's approach to prespecified, alpha-controlled predictive enrichment and discussed considerations for identifying responder-enriched subgroups while maintaining appropriate control of Type I error, in the context of FDA's enrichment guidance.
• Discussion of the MIDD Paired Meeting Program: FDA suggested that Netramark consider the MIDD Paired Meeting Program as a potential pathway for further regulatory dialogue alongside a pharmaceutical sponsor, noting the program's selective nature with limited quarterly acceptance.
  • Discussion of NetraAI's differentiation -- FDA discussed how NetraAI differs from complex adaptive designs, Bayesian methods or computer simulation-based approaches that are excluded from MIDD eligibility.
• Potential applications discussed: FDA discussed potential applications where NetraAI could be considered by sponsors, including:
  • Targeted inclusion/exclusion criteria;
  • Prespecified stratification in the statistical analysis plan (SAP);
  • Trial simulations to assess power, effect size and design robustness.

Dr. Joseph Geraci, founder and chief scientific and technology officer, stated: "NetraAI was built from the ground up to address one of the most persistent challenges in clinical research: identifying the true biological and clinical signatures that govern patient response, even within small, heterogeneous data sets. Our platform leverages a fundamentally different mathematical substrate -- rooted in dynamical systems and long-range memory mechanisms -- to reveal stable, clinically interpretable subgroups that traditional machine learning often misses. FDAs engagement in scientific discussion about our approach reinforces our belief that explainability, reproducibility and scientific rigor are the foundations upon which next-generation AI for drug development must be built. We are excited to continue exploring regulatory pathways and helping sponsors design trials that are not only more efficient, but meaningfully more aligned with underlying biology."

Dr. Luca Pani, chief regulatory and innovation officer, commented: "This milestone reflects nearly three years of disciplined work to ensure that NetraAI meets the highest standards of regulatory science, quality and methodological transparency. From the outset, our focus has been to align NetraAI with FDA guidance on enrichment strategies, model risk and the credibility of AI/ML frameworks. The CPIM provided valuable feedback on NetraAI's framework and offered insights into potential regulatory pathways. Exploring the MIDD program will allow us to formalize this dialogue within an active drug development program and demonstrate how explainable AI -- used appropriately -- can derisk pivotal trials and meaningfully improve the probability of success for sponsors and patients."

George Achilleos, chief executive officer, added: "This CPIM discussion is an important step forward for Netramark. The FDA's engagement in a scientific discussion about NetraAI's capabilities and their suggestion to explore the MIDD Paired Meeting Program is very valuable. In practical terms, this means sponsors can consider NetraAI to target the right patients, improve statistical power and reduce trial risk. The CPIM and the MIDD now become a regulatory reference point across all our engagements -- an important strategic asset for the company."

With the completion of this CPIM, Netramark has taken an important initial step forward in engaging the FDA and advancing the company's understanding of regulatory considerations for AI in clinical development. This scientific discussion with the FDA illuminated potential pathways for sponsors to benefit from and utilize NetraAI's explainable enrichment capabilities. We believe this may help partners to design stronger, more predictable phase 2 and phase 3 programs and unlock greater value from their clinical assets.

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. The 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 can parse patient data sets into subsets of people who are strongly related across 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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