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A recent study published in Cell Reports presents an integrated drug-discovery platform designed to identify pharmacological interventions capable of extending lifespan across multiple species. By combining automation, machine learning and standardized experimental workflows, the researchers screened more than 400 compounds in yeast, nematodes, fruit flies, killifish and mice, creating a scalable framework for prioritizing candidate longevity interventions with translational potential.
The platform incorporates automated imaging and deep learning-based survival analysis in invertebrate models, enabling the evaluation of thousands of drug-dose conditions while improving reproducibility and reducing manual workload. Through this approach, the authors identified several compounds that extended lifespan across multiple species and highlighted conserved biological pathways, particularly those related to PI3K-AKT-mTOR signaling, nutrient sensing, and stress response.
A key component of the rodents studies was the integration of continuous behavioral phenotyping using our DVC® (Digital Ventilated Cage) technology. In addition to survival outcomes, mice were monitored continuously for spontaneous locomotor activity, allowing researchers to quantify age-related changes and assess the impact of candidate geroprotective interventions on circadian and behavioral patterns. The data showed that several treatments produced measurable effects on activity profiles, demonstrating the value of longitudinal home-cage monitoring as a complementary functional endpoint in aging studies.
Overall, the study illustrates how the combination of high-throughput screening, artificial intelligence and home-cage monitoring can accelerate the identification of promising longevity interventions while improving the robustness and translational relevance of preclinical research.
Find the study here (Cell Reports, 2026)
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