AI

Postmarket Drug Surveillance Without Trial Costs: Discovery of Adverse Drug Reactions Through Large-Scale Analysis of Web Search Queries

Abstract

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.