Mr. Swapan Kakumanu reports

NETRAMARK UNVEILS AI-DISCOVERED TREATMENT-RESPONSIVE SUBGROUPS IN A4 ALZHEIMER'S TRIAL AT AD/PD CONFERENCE

Netramark Holdings Inc. has released new findings illustrating the ability of its proprietary explainable AI (artificial intelligence) platform, NetraAI, to uncover clinically meaningful responder subgroups within the landmark Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) trial.

The results, were presented in a poster titled, "Decoding Heterogeneity in A4: Explainable ML Identifies Solanezumab-Responsive Subgroups in Preclinical AD," which highlight how Netramark's technology has the potential to reveal therapeutic signals that may be obscured in conventional clinical trial analyses.

The poster was presented at the Alzheimer's Disease & Parkinson's Diseases (AD/PD) 2026 International Conference, taking place March 17 to March 21, 2026, in Copenhagen, Denmark, during the poster sessions.

Explainable AI identifies hidden treatment response

Using a dynamical-systems-based explainable machine learning approach, NetraAI analyzed multimodal baseline variables including imaging, cognitive assessments, demographics and biomarkers from participants in the A4 study.

Although the original phase 3 A4 trial showed no statistically significant overall benefit for solanezumab (a humanized monoclonal antibody designed to treat Alzheimer's disease by binding to and clearing soluble amyloid-beta), NetraAI identified two distinct patient subgroups suggesting meaningful treatment effects relative to placebo.

Key findings include:

• Identification of two biologically interpretable responder subgroups characterized by higher regional brain volume and stronger baseline cognitive performance;
• Large treatment effects, within these subgroups, with effect sizes reaching Cohen's d up to 1.52;
• Participants within subgroups showing treatment effects were associated with greater baseline limbic and temporal network integrity, including higher right amygdala or right superior temporal cortex volume, alongside stronger psychomotor speed and attention scores on the Digit Symbol Substitution Test.

These findings suggest that preserved neural reserve may be an important determinant of anti-amyloid treatment response in preclinical Alzheimer's disease.

Implications for Alzheimer's drug development

The results underscore a significant challenge in Alzheimer's clinical development: being patient heterogeneity can mask meaningful drug response within overall trial populations.

By identifying explainable, model-derived subgroups defined by only a small number of baseline variables, NetraAI illustrates how advanced AI can potentially support precision enrichment strategies in future trials.

For the pharmaceutical industry, this approach could:

• Improve trial design by identifying patients most likely to respond to investigational therapies;
• Enable retrospective reanalysis of historical trials to extract new insights;
• Reduce development risk and cost through data-driven patient stratification.

Timely advances for the Alzheimer's research community

The upcoming presentation comes at a pivotal time for the Alzheimer's field, as therapeutic development increasingly focuses on earlier disease stages and precision-guided treatment strategies.

Netramark's explainable AI methodology aligns with this shift by supporting researcher efforts aimed at understanding not only whether a therapy demonstrates an effect, but also which patients may be most likely to benefit and why.

"These findings suggest that patient heterogeneity may be masking treatment effects in Alzheimer's trials, underscoring the need for approaches such as NetraAI that may identify interpretable patient subpopulations most likely to benefit from emerging therapies," said Dr. Joseph Geraci, chief technical officer and founder of Netramark. "Technologies capable of identifying biologically meaningful responder subgroups could fundamentally reshape how Alzheimer's clinical trials are designed."

As the industry continues to explore disease-modifying treatments targeting amyloid and other pathways, technologies capable of interpretable patient segmentation have the potential to play a critical role in unlocking therapeutic signals that traditional analyses fail to detect.

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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