Common Drug Combo Results In Dangerous Side Effect
Unexpected increases in blood sugar levels may be caused by the combination use of an anti-depressant medication and a cholesterol lowering drug, according to researchers.
A study conducted at the Stanford University School of Medicine, Vanderbilt University and Harvard Medical School discovered that the antidepressant drug marketed as Paxil, when taken together with Pravachol, a cholesterol-lowering medication, resulted in a spike in blood glucose level, even though the similar side effect does not occur when the drugs are taken separately.
The interaction between the two drugs was uncovered when researchers analyzed voluntary adverse-event reporting database methods (AERS) maintained by the U.S. Food and Drug Administration as well as sophisticated electronic medical records used by each of the three medical institutions.
Researchers’ use of this “data-mining” technique helped to “identify certain patterns of associations in large populations that would not be readily apparent to physicians treating individual patients,” the study says.
Latent signal detection was used in the study to identify random pairs of drugs that caused diabetes-related symptoms such as altered blood sugar levels.
In order to do this, researchers first looked in the AERS for any individual drugs known to cause side effects resembling that of diabetes, then a profile was created of symptoms related to hyperglycemia, which included fever and fatigue occurring in patients receiving these drugs, the study reports.
"We were able to create a symptomatic ‘fingerprint’ to predict glucose-altering drugs," says Russ Altman, MD, PhD, professor of bioengineering, of genetics and of medicine at Stanford.
"We then looked for that fingerprint in people who were receiving pairs of drugs not known to affect blood sugar levels."
As a result, four pairs of drugs were found by researchers that seemed to cause such symptoms, but only in combination.
Researchers then focused their study on Paxil and Pravachol, since they are so commonly prescribed, with about 13 to 15 million people in the U.S. having prescriptions to these two drugs, says the study.
Altman says, "By extrapolating from the electronic medical records at Stanford and elsewhere, we can predict that between 500,000 and 1 million people are taking them simultaneously."
However, none of the patients in AERS who were taking the two drugs together were directly reporting hyperglycemia. Researchers had to further rely on electronic medical records at the three participating institutions to demonstrate a direct connection.
The results found that 135 non-diabetic people who were prescribed with both drugs experienced an average increase in random blood glucose levels of 19 mg/dl after beginning treatment.
In addition, 104 people who were diabetic had an even greater average increase of 48 mg/dl after being given both drugs.
Patients with fasting blood glucose levels of 126 mg/dl or higher are considered diabetic, and those with levels between 100 and 125 mg/dl are considered to have impaired fasting glucose levels, which puts them in the pre-diabetic category, according to the report.
“Understanding and mitigating the effect this pair of medications has on blood sugar could allow a person with diabetes to better control his or her glucose levels, or even prevent someone who is pre-diabetic from crossing that threshold into full-blown diabetes," says Altman.
Researchers then took their theory into the lab by testing the drug combination in laboratory mice that were first fed a high-fat, high-calorie diet that would put them in a state of pre-diabetes and made them insulin resistant.
After treating the pre-diabetic mice with the two drug combination for about three weeks, the study found that their blood glucose level soared to 193 mg/dl from 128 mg/dl.
When taken alone, neither drug had such an effect.
“It’s not uncommon for medications to have effects together that they don’t display alone,” the study says.
"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," says Altman.
The study underlines the importance of data-mining.
"It’s very exciting because we were led to this conclusion by mining data that already exists, but of which many people were skeptical," Altman says.
"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."
Bioinformatics studies of the databases allowed Altman and his colleagues to be very focused and targeted in their mice experiments, therefore saving time and expense compared to traditional drug screening studies in animals.
"Post-marketing surveillance of drugs has traditionally been very difficult," says Altman.
"The FDA is doing the best it can, but it may be time to embrace some new bioinformatics methods. This study shows that we can identify previously unsuspected issues that may affect hundreds of thousands of people around the world."
The study can be found online in the May 25 issue of the publication Clinical Pharmacology and Therapeutics.
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