A foundational in vivo platform for predicting human health outcomes

Authors

Roseberry, T., Krausz, T., Williams, G., Tingley, D.

Abstract

Rodents remain the workhorse of preclinical drug development, yet often fail to predict human clinical outcomes. Existing alternatives are similarly constrained. Cells in culture cannot recapitulate whole-organism physiology, and larger mammals cannot be studied at comparable throughput. Here we present a scalable, information-dense platform that can predict a drug's long-term human clinical outcomes from 24 hours of rodent behavior. A novel home-cage system continuously records behavior, generating thousands of features per hour. Models are trained on human clinical trial data to map these features onto outcomes including gastrointestinal adverse events, cardiac toxicity, neuropsychiatric side effects, and long-term weight loss. In addition to being an order of magnitude faster, the platform provides more accurate clinical predictions than standard long-term preclinical experiments. The approach readily extends to other outcomes, enabling rodents to serve as quantitative models for human clinical prediction.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 10 Aug 2026.

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