title: Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.
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.
authors.affiliation: 1 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus , Aurora, Colorado.|2 Computational Bioscience Graduate Program, Department of Pharmacology, School of Medicine, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
authors.forename: Callie
authors.initials: C
authors.lastname: Federer
authors.affiliation: 1 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
authors.forename: Minjae
authors.initials: M
authors.lastname: Yoo
authors.affiliation: 1 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus , Aurora, Colorado.|3 Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, Colorado.|4 University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
authors.forename: Aik Choon
authors.initials: AC
authors.lastname: Tan
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D012042:Registries
keywords: adverse events; big data mining; bioinformatics; clinical drug trials; pattern analysis