Systems Pharmacology of Adverse Event Mitigation by Drug Combinations

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Science Translational Medicine  09 Oct 2013:
Vol. 5, Issue 206, pp. 206ra140
DOI: 10.1126/scitranslmed.3006548

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Two Drugs: Better Than One?

Everyone has seen the commercial, where a drug is advertised as the much-awaited treatment for a disease. At the end of the commercial, there is a long list of adverse events (or side effects) that may affect anything from your heart to your eyesight. Surprisingly, the addition of a second drug can mitigate the side effects of the first drug, such that the combination is actually safer for the patient. To search for those mitigating combinations, Zhao and colleagues devised a computational method for scanning the Food and Drug Administration’s Adverse Event Reporting System (FAERS). This database is freely available and contains millions of records of drug-induced adverse events reported by patients, doctors, hospitals, lawyers, and drug companies. Thus, it represents a potentially useful source of beneficial drug combinations.

The authors focused on rosiglitazone—a drug that effectively controls blood glucose levels in diabetic patients, but has been associated with myocardial infarction (MI). By searching through FAERS data, Zhao et al. found that when rosiglitazone was prescribed in combination with exenatide—another drug for treating type 2 diabetes—there were significantly fewer reports of MI as an adverse event. A cell biological interaction network was then developed to look for mechanisms by which rosiglitazone + exenatide affected the heart. The rosiglitazone target PPARγ was plugged into this network, and plasminogen activator inhibitor-1 (PAI-1) was obtained as a potential point of convergence between the two drugs. To test this hypothesis, the authors administered drugs to a mouse model of type 2 diabetes. Mice treated with rosiglitazone alone showed a marked increase in PAI-1 levels, whereas mice treated with the drug combination had significantly decreased levels of PAI-1, similar to those found in untreated mice.

FAERS is self-reported and thus may contain some inaccurate data. Nevertheless, it could be a useful tool for generating meaningful hypotheses from human data and for then testing in vivo in clinically relevant disease models, as shown by Zhao et al. Animal models can provide information about drug mechanism of action, and prospective clinical trials will confirm that such combinations can indeed be translated to people to prevent adverse events.

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