PMID- 27631620
OWN - NLM
STAT- MEDLINE
DCOM- 20171003
LR  - 20240326
IS  - 1557-8127 (Electronic)
IS  - 1540-658X (Print)
IS  - 1540-658X (Linking)
VI  - 14
IP  - 10
DP  - 2016 Dec
TI  - Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.
PG  - 557-566
AB  - 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.
FAU - Federer, Callie
AU  - Federer C
AD  - 1 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of 
      Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical 
      Campus , Aurora, Colorado.
AD  - 2 Computational Bioscience Graduate Program, Department of Pharmacology, School 
      of Medicine, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
FAU - Yoo, Minjae
AU  - Yoo M
AD  - 1 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of 
      Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical 
      Campus , Aurora, Colorado.
FAU - Tan, Aik Choon
AU  - Tan AC
AD  - 1 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of 
      Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical 
      Campus , Aurora, Colorado.
AD  - 3 Department of Biostatistics and Informatics, University of Colorado Anschutz 
      Medical Campus , Aurora, Colorado.
AD  - 4 University of Colorado Cancer Center, University of Colorado Anschutz Medical 
      Campus , Aurora, Colorado.
LA  - eng
GR  - P30 CA046934/CA/NCI NIH HHS/United States
GR  - P50 CA058187/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20160915
PL  - United States
TA  - Assay Drug Dev Technol
JT  - Assay and drug development technologies
JID - 101151468
SB  - IM
MH  - Clinical Trials as Topic/methods/*statistics & numerical data
MH  - Data Mining/methods/*statistics & numerical data
MH  - Databases, Factual/*statistics & numerical data
MH  - Drug-Related Side Effects and Adverse Reactions/*epidemiology
MH  - Humans
MH  - *Registries
PMC - PMC5175440
OTO - NOTNLM
OT  - adverse events
OT  - big data mining
OT  - bioinformatics
OT  - clinical drug trials
OT  - pattern analysis
COIS- Statement No competing financial interests exist.
EDAT- 2016/09/16 06:00
MHDA- 2017/10/04 06:00
PMCR- 2016/12/01
CRDT- 2016/09/16 06:00
PHST- 2016/09/16 06:00 [pubmed]
PHST- 2017/10/04 06:00 [medline]
PHST- 2016/09/16 06:00 [entrez]
PHST- 2016/12/01 00:00 [pmc-release]
AID - 10.1089/adt.2016.742 [pii]
AID - 10.1089/adt.2016.742 [doi]
PST - ppublish
SO  - Assay Drug Dev Technol. 2016 Dec;14(10):557-566. doi: 10.1089/adt.2016.742. Epub 
      2016 Sep 15.