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Stanford University School of Medicine

Unexpected drug interactions identified by Stanford data mining

Like many people in this country, I have sometimes taken two or more prescription drugs at one time to treat different conditions. What I didn't realize, though, is that doctors lack a good way to predict how different drugs will interact. Stanford bioengineer Russ Altman, MD, PhD, and his colleagues are devising ways to use existing medical databases to suss out some of the more unexpected, and dangerous, interactions. The findings described in our release are alarming:

A widely used combination of two common medications may cause unexpected increases in blood glucose levels, according to a study conducted at the Stanford University School of Medicine, Vanderbilt University and Harvard Medical School. Researchers were surprised at the finding because neither of the two drugs -- one, an antidepressant marketed as Paxil, and the other, a cholesterol-lowering medication called Pravachol --has a similar effect alone.

The increase is more pronounced in people who are diabetic, and in whom the control of blood sugar levels is particularly important. It's also apparent in pre-diabetic laboratory mice exposed to both drugs. The researchers speculate that between 500,000 and 1 million people in this country may be taking the two medications simultaneously.

The research was published this morning in Clinical Pharmacology and Therapeutics.

The scientists used computerized data mining techniques to look for patterns in blood glucose levels in people taking the two drugs simultaneously. But when they found something interesting, graduate student and lead author Nick Tatonetti took the unusual (in the bioinformatics field) step of testing his theory in laboratory mice:

Although informatics models can provide novel insight and lead to fascinating new discoveries, I think it's important to acknowledge that they can only take you so far. At some point it becomes time to roll up your sleeves and head into the lab.

When I was measuring the fasting glucose levels in the combination therapy mice, I honestly couldn't believe the values that were coming out of the instrument. It was an incredibly exciting moment, and while I understand that many informatics researchers don't want to do wet lab work, there is nothing that beats that feeling of validating your own hypothesis in the lab.

The work is an elegant example of how the emerging field of bioinformatics can influence public health, said Altman:

These kinds of drug interactions are almost certainly occurring all of the time, but, because they are not part of the approval process by the Food and Drug Administration, we can only learn about them after the drugs are on the market [...]. It's very exciting because we were led to this conclusion by mining data that already exists, but of which many people were skeptical. Physicians tend to think of electronic medical records as ways to better track data about single patients, but there's another really important component to them -- their utility in looking at population effects. The information is there to change health-care practice in a meaningful, substantial way.

Previously: Do electronic health records improve health? It's complicated

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