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On the Trail of Drug Risks

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That’s how it is for most attempts to improve post-approval drug monitoring. Though there are many promising possibilities, few have yet proved they can quickly detect serious problems so that the FDA can decide whether to beef up its warnings on a therapy or pull it off the market.

To detect post-approval drug events reliably and at an early stage will almost certainly require combining several different data sources, and the potential of a multipronged approach was demonstrated in a study published in Science Translational Medicine in 2012. Researchers at Stanford University School of Medicine used a computer algorithm to sort through more than 1.8 million reports in the FAERS database from 2004 through 2009, as well as to look at a smaller Canadian database of 300,000 adverse event reports and chemical databases used to correlate drug side effects. Those sources enabled them to identify 47 pairs of previously unsuspected drug-to-drug interactions. The algorithm compared FAERS data from reports filed by patients taking a single drug and experiencing side effects against a separate database, a control group of one or more patients with the same condition and other matching factors, such as sex and age, but who weren‘t taking the same drug. “It sounds obvious, but if a lot more people on the drug reported side effects than those not taking the drug, the medication is the likely cause,” says Nicholas Tatonetti, a lead researcher on the study and now an assistant professor at Columbia University.

In a universe of 1,332 drugs, Tatonetti and his colleagues found an average of 329 new adverse events for each medicine‐dwarfing the 69, on average, that are listed on drug labels. They then applied the same algorithm to mine FAERS for drug-to-drug interactions against another database of off-label side effects. That produced 1,301 adverse events from an analysis of 59,220 pairs of drugs. Those discoveries were then tested against “real patients” by mining lab test results in the electronic records of patients at Stanford Hospital & Clinics. That final step confirmed 47 previously unknown drug-to-drug combinations that seemed particularly likely to cause problems. The worst involved diuretics called thiazides, often prescribed to treat high blood pressure, and selective serotonin reuptake inhibitors, used for depression. Patients who took both drugs were significantly more likely to develop a heart condition known as prolonged QT, associated with increased risk of irregular heartbeats and sudden death. The FDA is evaluating those findings to see whether updated drug labeling is warranted.

Online conversations about drugs and their side effects could add still another useful source of intelligence that might speed detection of serious problems. But before that can happen, there will need to be further testing and refining of computational tools, and regulatory and legal issues will also have to be addressed. “In digitizing data, we’re looking at a paradigm shift and a new business model as these tools get validated,” says Pfizer’s Ibara.

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1. “Novel Data-Mining Methodologies for Adverse Drug Event Discovery and Analysis,” by Rave Harpaz et al. Clinical Pharmacology & Therapeutics, June 2012. A leading researcher in biomedical informatics and colleagues discuss the potential and challenges of developing data mining tools and using diverse data sources.

2. “The U.S. Food and Drug Administration’s Mini-Sentinel Program: Status and Direction,” by Richard Platt et al. Pharmacoepidemiology and Drug Safety, January 2012. A report on the FDA’s drug surveillance pilot, using electronic health records of 100 million people, by the lead investigator at the Harvard Pilgrim Health Care Institute.

3. “Serious Adverse Drug Events Reported to the Food and Drug Administration, 1998–2005”, by Thomas Moore et al., JAMA Internal Medicine, Sept. 10, 2007. The first analysis using the FDA’s adverse event reporting database uncovered a number of disturbing trends during the time period, including a nearly threefold increase in reported serious injuries, disability and death associated with drug therapy, suggesting inadequacies in the existing surveillance system.

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