Background: Postmarket drug safety surveillance largely depends on spontaneous
reports by patients and healthcare providers, hence less common adverse drug
reactions—especially those caused by long-term exposure, multidrug treatments, or
specific to special populations—often elude discovery. Objective: Here we propose
an ultra-low-cost fully automated method for continuous monitoring of adverse drug
reactions in single drugs and in combinations thereof, and demonstrate the
discovery of heretofore unknown ones. Materials and Methods: We use aggregated
search data of large populations of Internet users to extract information related
to drugs and adverse reactions to them, and correlate these data over time. We
further extend our method to identify adverse reactions to combinations of drugs.
Results: We validate our method by showing high correlation of our findings with
known adverse drug reactions (ADRs). However, while acute, early-onset drug
reactions are more likely to be reported to regulatory agencies, we show that less
acute, later-onset ones are better captured in Web search queries. Conclusions: Our
method is advantageous in identifying previously unknown adverse drug reactions.
These ADRs should be considered as candidates for further scrutiny by medical
regulatory authorities, e.g., through Phase IV trials.