Data-Driven Prediction of Drug Effects and Interactions

We present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets

Nicholas P. Tatonetti; Patrick P. Ye; Roxana Daneshjou; Russ B. Altman


Scholarcy highlights

  • Adverse drug events remain a significant source of mortality and morbidity around the world with costs estimated at several billion dollars each year
  • Drugs co-reported with these drugs were 55.8 times as likely to have synthetic associations with the drug events
  • We found that drugs that are disproportionately reported as causing adverse events in males were more likely to be synthetically associated with these events
  • We evaluated the method against three independent silver standards of ADEs: side effects mined from the drug package inserts, adverse events reported to the Food and Drug Administration after our data extraction, and adverse event reports from Canada
  • We found that the drug-event associations reported in OFFSIDES were predictive of known class-wide drug effects, such as the adverse events of the nervous systems associated with antiparasitics and insecticides
  • We found that our classclass interaction predictions were significantly enriched for interactions for which there was evidence in the electronic medical records
  • The methods we present here build upon the foundation of signal detection algorithms developed for drug safety surveillance
  • Methodological detail covering the statistical analysis of the drug and indication case studies, computing the drug-effect association statistics, identifying drug-drug interactions effects and the analytical methods for validating acute, and long-term effects using electronic medical records data can be found in the Supplementary Materials and tables S4, S6, and S7

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