27th July 2026 – (London) Scientists seeking practical ways to extend healthy years have turned to an off‑the‑shelf medicine after screening thousands of approved drugs through a new gene‑network approach. The framework, developed by researchers at Northeastern University and Harvard Medical School and reported in Nature Ageing, links medicines to clusters of genes and proteins tied to recognised hallmarks of ageing, such as declining cellular energy and impaired intercellular communication.
The team began with 1,250 genes previously associated with one or more ageing hallmarks and plotted them on the human interactome, a large map of protein–protein relationships numbering more than half a million connections. Rather than being scattered randomly, ageing‑related genes formed tight neighbourhoods around distinct hallmarks. That topology, the authors say, makes it possible to pinpoint drugs most likely to influence several hallmarks at once.
From a set of 6,442 licensed pharmaceuticals, the researchers prioritised compounds by assessing both whether they targeted these gene–protein clusters and whether their known effects were consistent with slowing, rather than accelerating, ageing biology. Among the leads was oxymetazoline, a widely used decongestant available as nasal sprays, eye drops and topical creams, which their analyses suggest may bolster communication between cells — a function that wanes with age.
The shortlist also included familiar agents such as aspirin, which has already attracted interest in longevity research. The authors stress that their study neither cures ageing nor proves any drug will lengthen human life. Instead, it offers a roadmap to select the most promising candidates for experimental testing in cells, animal models and, in time, clinical studies.
Given the many processes that drive ageing and age‑related disease, effective interventions are likely to act on multiple hallmarks simultaneously. By marrying network science with pharmacology, the researchers argue, the field can move more efficiently from data‑driven hypotheses to trials that determine whether any of today’s medicines can meaningfully slow biological time.
