title: Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis.
abstract: BACKGROUND
  Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task.
  
  
  OBJECTIVE
  To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events.
  
  
  RESULTS
  The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis.
  
  
  CONCLUSIONS
  The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection.
authors.affiliation: Department of Biomedical Informatics, Columbia University Medical Center, New York, New York 10032, USA.
authors.forename: Santiago
authors.initials: S
authors.lastname: Vilar
authors.forename: Rave
authors.initials: R
authors.lastname: Harpaz
authors.forename: Herbert S
authors.initials: HS
authors.lastname: Chase
authors.forename: Stefano
authors.initials: S
authors.lastname: Costanzi
authors.forename: Raul
authors.initials: R
authors.lastname: Rabadan
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D000465:Algorithms; D057225:Data Mining; D016208:Databases, Factual; D003661:Decision Support Techniques; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D006801:Humans; D015394:Molecular Structure; D060735:Pharmacovigilance; D012206:Rhabdomyolysis / Q000139:chemically induced*