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

Callie Federer, Minjae Yoo, Aik Choon Tan

Research output: Contribution to journalArticlepeer-review

16 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

Bibliographical note

Funding Information:
We would like to acknowledge the Tan Lab members for their constructive comments on this project. We thank Susan Kim for suggestions and editing of the article. This work is partly supported by the National Institutes of Health P50CA058187, P30CA046934, Cancer League of Colorado, and the David F. and Margaret T. Grohne Family Foundation.

Publisher Copyright:
© Callie Federer et al., 2016; Published by Mary Ann Liebert, Inc. 2016.

Keywords

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

ASJC Scopus subject areas

  • Molecular Medicine
  • Drug Discovery

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