FAERS data searching for improved drug safety

Spontaneous adverse event reporting systems, such as the U.S. FDA Adverse Event Reporting System, represent a valuable source of real-world evidence about post-market drug safety.

They allow rapid detection of signals and support an epidemiological approach to identifying adverse events that occur with low frequency, in populations not tested in clinical trials or over longer time periods. They also help to identify adverse events resulting from drug–drug or drug–food interactions.

Introducing new FAERS data search possibilities

With the PharmaPendium FAERS data search functionality, post-market reports can be specifically searched to compare and visualize adverse events reported for a drug or group of drugs and to find instances where a drug is reported as a primary suspect, secondary suspect, concomitant and/or interacting drug. Furthermore, results can be filtered by (for example) secondary suspect drug or type of adverse event.

Choose the right query for your workflow

Summary table, graphical view and direct search

Two types of FAERS search query can be performed for investigating adverse events. To quickly compare adverse events, users can build queries around single drugs or specific, self-defined drug groups and apply filters such as reporter occupation, seriousness of outcome, patient age, or date. Users can also perform a direct FAERS search, which enables the identification of adverse events reported for a drug, drug class or indication and the retrieval of reports on drugs in any role.

Benefit from clear visualizations of the results

Summary table with Adverse Effects Tree(FAERS data)

Results of a comparative FAERS search are displayed in a convenient table or graphic format with links for drilling deeper and the possibility to apply filters.

What FAERS data searching helps you do

What FAERS data searching helps you do

  • Quickly retrieve reports based on co-medications of interest
  • Easily compare the adverse event profile across different drugs or sets of drugs
  • Create subsets of data for comparison across age groups, gender and date
  • Filter data by reporter occupation (i.e., physician, lawyer, consumer, etc.)
  • Filter data by outcome (i.e., serious, non-serious)

This type of detailed searching can provide additional insights into drugs suspected in adverse events, including information on drugs reported as a primary suspect drug and as a secondary suspect drug. It means users can more easily identify co-morbidities or potential DDIs not evident during clinical trials, helping to mitigate risk for new drugs in development and to make drugs safer post-market.

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