Psychiatry faces a persistent core challenge: treatments are largely selected without objective biological guidance, and as a result many patients experience inadequate benefit, prolonged trial-and-error, and avoidable side effects. This lack of patient stratification also contributes to the difficulty of traditional neuropsychiatric drug development, where effect sizes frequently appear small and outcomes show high variability, often reflecting substantial heterogeneity in underlying patient biology rather than an absence of true drug activity.

Alto Neuroscience is advancing a precision psychiatry approach designed to address this problem directly. Our approach emphasizes characterizing drug activity using pharmacodynamic biomarkers early in development, while concurrently discovering and prospectively replicating objective biomarkers that can identify patient subpopulations most likely to respond. By integrating these elements prior to advancing programs, we aim to increase signal detection, reduce biological noise, and ultimately improve both clinical outcomes and development efficiency.

Central to this strategy is Alto’s suite of scalable biomarkers intended to segment patients in ways that are clinically meaningful and commercially feasible. Leveraging proprietary tools and an expanding, multi-diagnosis, multi-treatment, multi-modal longitudinal database of biomarker and clinical data, Alto is building an evidence-driven foundation to support biomarker-informed decision-making across programs.

This talk will highlight two illustrative examples. First, a pharmacodynamic biomarker approach with ALTO-101 demonstrates dose-dependent effects in EEG measures, supporting measurable target engagement and drug activity. Second, a biomarker stratification approach with ALTO-300 for major depressive disorder uses a scalable EEG-based machine learning strategy to identify a unique resting-state EEG signal that was prospectively replicated as a predictor of ALTO-300 response. Together, these examples illustrate how biomarker-driven development can help move psychiatry from unguided treatment selection toward more precise, biologically anchored care.