In late 2011, mice with small-cell lung cancer had their tumors reduced by an anti­depressant called imipramine. The basis of the study was the idea that the cancer switches certain genes on, while imipramine turns them off. A neat trick, but no one would have thought to test it if it hadn’t been for analytics developed by Stanford data scientist Atul Butte.

Butte thinks of diseases not in terms of symptoms but of the genes they activate (or deactivate). In that light, conditions that seem unrelated, like heart attack and muscular dystrophy, are kindred, because they show similar genetic patterns. So would heart attack medicine work on muscular dystrophy? Possibly.

Butte and his wife, biologist Gini Deshpande, formed ­NuMedii to find out. The company combs public data to identify drugs and diseases with contrasting gene-expression profiles. “Opposites attract,” Butte says. Two years ago these analytics suggested that the class of anti­depressants that includes ­imipramine might work for small-cell lung cancer. That led to the mouse trials (conducted at Stanford independently from NuMedii), and tests in humans are now under way.

The typical interval between discovery and clini­cal trials is three to six years, but ­NuMedii’s approach—repurposing drugs that have already been proven safe in humans—could propel compounds from hypothesis to human much faster. Which is good for NuMedii—and potentially good for patients too.