Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.
The intent of this work is to speed up drug repurposing, which is not a new concept—think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic strategy to reduce the appearance of wrinkles.
But getting to those new uses typically involves a mix of serendipity and time-consuming and expensive randomized clinical trials to ensure that a drug deemed effective for one disorder will be useful as a treatment for something else.
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