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Assay Drug Dev Technol. 2016 Dec;14(10):557-566. Epub 2016 Sep 15.

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

Federer C1,2, Yoo M1, Tan AC1,3,4.

Author information

1
1 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
2
2 Computational Bioscience Graduate Program, Department of Pharmacology, School of Medicine, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
3
3 Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
4
4 University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus , Aurora, Colorado.

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.

KEYWORDS:

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

PMID:
27631620
PMCID:
PMC5175440
DOI:
10.1089/adt.2016.742
[Indexed for MEDLINE]
Free PMC Article

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