Data Mining Strategy to Prevent Adverse Drug Events: The Cases of Rosiglitazone and COVID-19 Vaccines

Abstract

This chapter analyzes how a simple strategy of early detection of safety signals using data mining can prevent the potential risk of adverse events with new or former drugs. We first present the case of an active antidiabetic ingredient, rosiglitazone. The capability of the strategy to detect the risk of heart failure among the data reported during the first 8 years of commercialization was demonstrated 2 years before rosiglitazone was withdrawn from the market in 2020 due to that risk. Ten years later, agility in obtaining safety signals after marketing a drug was put to the test with COVID-19 vaccines. Among adverse events notified during only 2 months of follow-up, we early detected thrombosis following COVID-19 vaccines. Several weeks after, these events were in the spotlight of the vaccination campaign and defined changes in the type of vaccine administered according to susceptible age groups. This early analysis strategy of suspected adverse drug reactions reported can provide useful information in making decisions in a faster way than the standard data mining methodology.