Using Data Mining to Predict Safety Actions from FDA Adverse Event Reporting System Data

Purpose: To determine the value of data mining in early identification of drug safety signals from spontaneous reporting databases. Methods: A single data mining algorithm was applied to the 2001–2003 public release of Food and Drug Administration Adverse Event Reporting System (AERS) data for all therapeutic new molecular entities (NMEs) approved in 2001. The list of detected signals was compared with the list of safety-related regulatory actions for those drugs through February 2006. Results: For the 21 NMEs, 73 signals of interest were detected by data mining. In 39 cases, that signal preceded regulatory action. The median time from approval to signal detection was 11.5 months, and the median time from signal detection to action was 21 months. There were 33 actions for which no signal was detected and 34 signals with no corresponding regulatory action. Conclusion: Using AERS data 2–3 years following approval, more than half of FDA actions that occurred in the next 2–4 years were predicted by data mining, and more than half of the signals detected by data mining corresponded to an FDA action. An appropriate data mining procedure can yield meaningful safety information, often well in advance of regulatory action.