title: Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS).
abstract: OBJECTIVE
  Targeted drugs dramatically improve the treatment outcomes in cancer patients; however, these innovative drugs are often associated with unexpectedly high cardiovascular toxicity. Currently, cardiovascular safety represents both a challenging issue for drug developers, regulators, researchers, and clinicians and a concern for patients. While FDA drug labels have captured many of these events, spontaneous reporting systems are a main source for post-marketing drug safety surveillance in 'real-world' (outside of clinical trials) cancer patients. In this study, we present approaches to extracting, prioritizing, filtering, and confirming cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS).
  
  
  METHODS
  The dataset includes records of 4,285,097 patients from FAERS. We first extracted drug-cardiovascular event (drug-CV) pairs from FAERS through named entity recognition and mapping processes. We then compared six ranking algorithms in prioritizing true positive signals among extracted pairs using known drug-CV pairs derived from FDA drug labels. We also developed three filtering algorithms to further improve precision. Finally, we manually validated extracted drug-CV pairs using 21 million published MEDLINE records.
  
  
  RESULTS
  We extracted a total of 11,173 drug-CV pairs from FAERS. We showed that ranking by frequency is significantly more effective than by the five standard signal detection methods (246% improvement in precision for top-ranked pairs). The filtering algorithm we developed further improved overall precision by 91.3%. By manual curation using literature evidence, we show that about 51.9% of the 617 drug-CV pairs that appeared in both FAERS and MEDLINE sentences are true positives. In addition, 80.6% of these positive pairs have not been captured by FDA drug labeling.
  
  
  CONCLUSIONS
  The unique drug-CV association dataset that we created based on FAERS could facilitate our understanding and prediction of cardiotoxic events associated with targeted cancer drugs.
authors.affiliation: Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, United States. Electronic address: rxx@case.edu.
authors.forename: Rong
authors.initials: R
authors.lastname: Xu
authors.affiliation: ThinTek, LLC, Palo Alto, CA, United States.
authors.forename: Quanqiu
authors.initials: Q
authors.lastname: Wang
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D000970:Antineoplastic Agents / Q000737:chemistry*; D002318:Cardiovascular Diseases / Q000139:chemically induced* / Q000453:epidemiology*; D057225:Data Mining; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D001330:Electronic Data Processing; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy*; D014481:United States; D014486:United States Food and Drug Administration
chemical_list: D000970:Antineoplastic Agents
keywords: Cardiotoxicity; Data mining; Personalized medicine; Post-market drug safety surveillance; Targeted cancer therapy