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

Callie Federer, Minjae Yoo, Aik-Choon Tan

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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 ClinicalTrials.gov (https://clinicaltrials.gov/), a database of clinical studies around the world. By extracting drug and AE information from ClinicalTrials.gov 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 ClinicalTrials.gov. Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from ClinicalTrials.gov 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.

Original languageEnglish
Pages (from-to)557-566
Number of pages10
JournalAssay and Drug Development Technologies
Volume14
Issue number10
DOIs
Publication statusPublished - 2016 Dec 1
Externally publishedYes

Keywords

  • adverse events
  • big data mining
  • bioinformatics
  • clinical drug trials
  • pattern analysis

ASJC Scopus subject areas

  • Medicine(all)
  • Molecular Medicine
  • Drug Discovery

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