PMID- 32183790
OWN - NLM
STAT- MEDLINE
DCOM- 20210210
LR  - 20210210
IS  - 1472-6947 (Electronic)
IS  - 1472-6947 (Linking)
VI  - 20
IP  - Suppl 2
DP  - 2020 Mar 18
TI  - Mining and visualizing high-order directional drug interaction effects using the 
      FAERS database.
PG  - 50
LID - 10.1186/s12911-020-1053-z [doi]
LID - 50
AB  - BACKGROUND: Adverse drug events (ADEs) often occur as a result of drug-drug 
      interactions (DDIs). The use of data mining for detecting effects of drug 
      combinations on ADE has attracted growing attention and interest, however, most 
      studies focused on analyzing pairwise DDIs. Recent efforts have been made to 
      explore the directional relationships among high-dimensional drug combinations 
      and have shown effectiveness on prediction of ADE risk. However, the existing 
      approaches become inefficient from both computational and illustrative 
      perspectives when considering more than three drugs. METHODS: We proposed an 
      efficient approach to estimate the directional effects of high-order DDIs through 
      frequent itemset mining, and further developed a novel visualization method to 
      organize and present the high-order directional DDI effects involving more than 
      three drugs in an interactive, concise and comprehensive manner. We demonstrated 
      its performance by mining the directional DDIs associated with myopathy using a 
      publicly available FAERS dataset. RESULTS: Directional effects of DDIs involving 
      up to seven drugs were reported. Our analysis confirmed previously reported 
      myopathy associated DDIs including interactions between fusidic acid with 
      simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs 
      leading to increased risk for myopathy, such as the co-administration of 
      zoledronate with different types of drugs including antibiotics (ciprofloxacin, 
      levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). 
      Finally, we visualized directional DDI findings via the proposed tool, which 
      allows one to interactively select any drug combination as the baseline and zoom 
      in/out to obtain both detailed and overall picture of interested drugs. 
      CONCLUSIONS: We developed a more efficient data mining strategy to identify 
      high-order directional DDIs, and designed a scalable tool to visualize high-order 
      DDI findings. The proposed method and tool have the potential to contribute to 
      the drug interaction research and ultimately impact patient health care. 
      AVAILABILITY AND IMPLEMENTATION: http://lishenlab.com/d3i/explorer.html.
FAU - Yao, Xiaohui
AU  - Yao X
AD  - Department of Biostatistics, Epidemiology and Informatics, Perelman School of 
      Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
FAU - Tsang, Tiffany
AU  - Tsang T
AD  - Department of Computer and Information Science, School of Engineering and Applied 
      Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
FAU - Sun, Qing
AU  - Sun Q
AD  - Department of Biostatistics, Epidemiology and Informatics, Perelman School of 
      Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
FAU - Quinney, Sara
AU  - Quinney S
AD  - Department of Obstetrics and Gynecology, School of Medicine, Indiana University, 
      Indianapolis, IN, 46202, USA.
FAU - Zhang, Pengyue
AU  - Zhang P
AD  - Department of Biomedical Informatics, College of Medicine, Ohio State University, 
      Columbus, OH, 43210, USA.
FAU - Ning, Xia
AU  - Ning X
AD  - Department of Biomedical Informatics, College of Medicine, Ohio State University, 
      Columbus, OH, 43210, USA.
FAU - Li, Lang
AU  - Li L
AD  - Department of Biomedical Informatics, College of Medicine, Ohio State University, 
      Columbus, OH, 43210, USA.
FAU - Shen, Li
AU  - Shen L
AD  - Department of Biostatistics, Epidemiology and Informatics, Perelman School of 
      Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. 
      Li.Shen@pennmedicine.upenn.edu.
LA  - eng
GR  - R01 GM104483/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20200318
PL  - England
TA  - BMC Med Inform Decis Mak
JT  - BMC medical informatics and decision making
JID - 101088682
RN  - 0 (Pharmaceutical Preparations)
SB  - IM
MH  - *Data Mining
MH  - Databases, Factual
MH  - *Drug Interactions
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - Humans
MH  - *Pharmaceutical Preparations
PMC - PMC7079342
OTO - NOTNLM
OT  - Apriori
OT  - Directional effect
OT  - FAERS
OT  - High-order drug interaction
OT  - Sunburst
COIS- The authors declare that they have no competing interests.
EDAT- 2020/03/19 06:00
MHDA- 2021/02/11 06:00
PMCR- 2020/03/18
CRDT- 2020/03/19 06:00
PHST- 2020/03/19 06:00 [entrez]
PHST- 2020/03/19 06:00 [pubmed]
PHST- 2021/02/11 06:00 [medline]
PHST- 2020/03/18 00:00 [pmc-release]
AID - 10.1186/s12911-020-1053-z [pii]
AID - 1053 [pii]
AID - 10.1186/s12911-020-1053-z [doi]
PST - epublish
SO  - BMC Med Inform Decis Mak. 2020 Mar 18;20(Suppl 2):50. doi: 
      10.1186/s12911-020-1053-z.