"This is especially important in the case of drug-drug interactions where the effect may not appear until a very large cohort of patients has been exposed," Stanford University biomedical informatics graduate student Nicholas Tatonetti recently said in an article that appeared in the Journal of the American Medical Association.
Tatonetti, a PhD candidate in 2012, was lead author of a study that appeared in May in the online version of the journal Clinical Pharmacology and Therapeutics that found an interaction between the antidepressant paroxetine (Paxil) and the cholesterol-lowering drug pravastatin (Pravachol) that could put patients at risk of developing diabetes. Taking the two drugs together, Tatonetti and fellow researchers from Stanford, Vanderbilt University, and Harvard University reported, could cause blood glucose levels to spike.
They noticed this after applying a specially developed algorithm to the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (AERS), analyzing "latent signals" to detect diabetes-related side effects rather than directly reported side effects. "We found an intriguing suggestion that pravastatin and paroxetine taken together may affect glucose homeostasis. Neither drug is typically associated with significant changes in glucose levels," the original article said. And neither the public nor the FDA had been aware of this interaction prior to Tatonetti's analysis.
The researchers then looked at electronic records of patients at three academic medical centers to corroborate their discovery. "EMRs were really critical in our study because they allowed us to validate our FDA-derived predictions at minimal cost," corresponding author Dr. Ross Altman, a Stanford University professor of bioengineering, genetics, and medicine, is quoted as saying in the JAMA article.
They also tested their theory that the combination of pravastatin and paroxetine can increase blood sugar by studying laboratory rats fed a high-calorie, high-fat diet. Only when the rodents were given both drugs for three weeks did their blood glucose levels rise significantly.
"Random data mining of spontaneous reports often leads to blind alleys, but this creative study used the approach to generate a wholly unexpected hypothesis that the authors were then able to confirm in observational studies of patients' glucose levels in multiple sites, as well as in an animal experiment," Brigham and Women's Hospital pharmaco-epidemiology and pharmaco-economics chief Dr. Jerry Avorn said in the JAMA account." Avorn was not involved in the study, although he is on the Harvard faculty.
Tatonetti expressed optimism that his strategy for identifying other potentially damaging drug interactions could easily be replicated. "Perhaps best of all, it doesn’t require a supercomputer to do this type of research. Any modern laptop can run these algorithms in minutes," he told JAMA.
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