Explainable AI-driven precision clinical trial enrichment: demonstration of the NetraAI platform with a phase II depression trial.
Authors
Affiliations (8)
Affiliations (8)
- NetraMark Corp., Toronto, ON, Canada.
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
- Tandem Centre for Pharmacogenetics, Molecular Brain Science Department, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Arthur C. Clarke Centre for Human Imagination, School of Physical Sciences, University of California San Diego, San Diego, CA, USA.
- NetraMark Corp., Toronto, ON, Canada. [email protected].
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, USA.
- Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Abstract
Clinical trial failures are frequently driven by patient heterogeneity and limited sample sizes that obscure treatment effects by diluting statistical power. We introduce NetraAI, a novel explainable artificial intelligence (AI) platform that integrates dynamical-systems modeling, evolutionary long-range memory feature selection, and large-language model (LLM)-generated insights, to discover high-effect-size patient subpopulations ("Personas") from high-dimensional clinical data. In a Phase II ketamine trial for treatment-resistant depression (n = 63), NetraAI analyzed psychiatric scale data (175/patient) and MRI-derived features (185/patient). NetraAI outperformed traditional machine learning (ML) models in predicting treatment outcomes, improving predictive accuracy by approximately 25-30% and achieving higher sensitivity and specificity in detecting responders. NetraAI identified a 10-clinical variable model that improved predictive AUC by 0.32 over standard machine learning (ML) models and an 8-MRI feature model achieving 95% accuracy and 100% specificity. These findings demonstrate that an explainable dynamical AI approach can leverage small but rich datasets to uncover hidden clinically meaningful subgroups. NetraAI's precision enrichment strategy has the potential to improve trial success rates and enable personalized medicine by prospectively identifying patients most likely to benefit from a given therapy in oncology, psychiatry, neurodegeneration, and for other disorders.