NYU Langone and Sage Bionetworks Awarded $25 Million NIH Grant to Launch National Data Hub for Animal Testing Alternatives

NYU Langone and Sage Bionetworks establish a national NIH data hub to advance human-based alternatives to animal research, including 3D organoids and AI models.

By: AXL Media

Published: Mar 19, 2026, 7:26 AM EDT

Source: Information for this report was sourced from NYU Langone

Revolutionizing the Benchmark for Human Biomedical Research

The National Institutes of Health (NIH) has taken a significant step toward modernizing drug and toxicology testing by awarding a five-year, $25 million grant to NYU Langone Health and Sage Bionetworks. The funding establishes a central data hub for the "Complement-ARIE" program, a high-impact initiative focused on New Approach Methodologies (NAMs). These advanced lab- and computer-based systems are designed to model human biology with greater precision than traditional animal models, potentially shortening the path from laboratory discovery to clinical application.

Standardizing the Language of Synthetic Biology

A primary challenge in replacing animal models is the vast diversity of data generated by different synthetic platforms, ranging from 3D organoids to complex AI simulations. The NYU-Sage NDHCC will address this through a "FUSION" framework—a Unified Schema for Interoperability of Ontologies. This system integrates disparate biomedical knowledge maps into a common data model, ensuring that molecular tests from one lab are interoperable with the computational models of another. This standardization is essential for creating a "FAIR" (Findable, Accessible, Interoperable, and Reusable) data environment that can adapt as the NAMs field evolves.

AI-Augmented Data Curation and Cloud Architecture

Under the leadership of Dr. Gustavo A. Stolovitzky, the hub will serve as the technical backbone for the entire Complement-ARIE consortium. Utilizing a sophisticated cloud architecture, the center will provide researchers with AI-augmented tools for data curation and metadata management. By centralizing the consortium’s code and computational models, the hub allows scientists to benchmark their findings against standardized population outcomes, effectively creating a virtual laboratory where digital and biological datasets can be analyzed in tandem.