Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

Assay Drug Dev Technol. 2016 Dec;14(10):557-566. doi: 10.1089/adt.2016.742. Epub 2016 Sep 15.


Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from ( ), a database of clinical studies around the world. By extracting drug and AE information from and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from . Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.

Keywords: adverse events; big data mining; bioinformatics; clinical drug trials; pattern analysis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clinical Trials as Topic / methods
  • Clinical Trials as Topic / statistics & numerical data*
  • Data Mining / methods
  • Data Mining / statistics & numerical data*
  • Databases, Factual / statistics & numerical data*
  • Drug-Related Side Effects and Adverse Reactions / epidemiology*
  • Humans
  • Registries*