Researchers at Columbia University have developed AwareDX—Analysing Women At Risk for Experiencing Drug toXicity—a machine learning algorithm that identifies and predicts differences in adverse drug effects between men and women by analyzing 50 years’ worth of reports in an FDA database. The algorithm, described September 22 in the journal Patterns, automatically corrects for the biases in these data that stem from an overrepresentation of male subjects in clinical research trials.
Though men and women can have different responses to medications—the sleep aid Ambien, for example, metabolizes more slowly in women, causing next-day grogginess—even doctors may not know about these differences because most clinical trial data itself are biased toward men. This trickles down to impact prescribing guidelines, drug marketing, and ultimately, patients’ health.
“Pharma has a history of ignoring complex problems. Traditionally, clinical trials have not even included women in their studies. The old-fashioned way used to be to get a group of healthy guys together to give them the drug, make sure it didn’t kill them, and you’re off to the races. As a result, we have a lot less information about how women respond to drugs than men,” says Nicholas Tatonetti, an associate professor of biomedical informatics at Columbia University and a co-author on the paper. “We haven’t had the ability to evaluate these differences before, or even to quantify them.”
Tatonetti teamed up with one of his students—Payal Chandak, a senior biomedical informatics major at Columbia University and the other co-author on the paper. Together they developed AwareDX. Because it is a machine learning algorithm, AwareDX can automatically adjust for sex-based biases in a way that would take concerted effort to do manually.
“Machine learning is definitely a buzzword, but essentially the idea is to correct for these biases