Skip to main content

Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov

  • Protocol
  • First Online:
Computational Methods for Drug Repurposing

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1903))

Abstract

The protocol below describes an in silico method for drug repositioning (drug repurposing). The data source is ClinicalTrials.gov, which contains about a quarter of a million clinical studies. Mining such rich and clean clinical summary data could be helpful to many health-related researches. Described here is a method that utilizes serious adverse event data to identify potential new uses of drugs and dietary supplements (repositioning).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hodos RA, Kidd BA, Shameer K, Readhead BP, Dudley JT (2016) In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med 8:186–210. https://doi.org/10.1002/wsbm.1337

    Article  PubMed  PubMed Central  Google Scholar 

  2. Mullen J, Cockell SJ, Tipney H, Woollard PM, Wipat A (2016) Mining integrated semantic networks for drug repositioning opportunities. PeerJ 4:e1558. https://doi.org/10.7717/peerj.1558

    Article  PubMed  PubMed Central  Google Scholar 

  3. Coelho ED, Arrais JP, Oliveira JL (2016) Computational discovery of putative leads for drug repositioning through drug-target interaction prediction. PLoS Comput Biol 12:e1005219. https://doi.org/10.1371/journal.pcbi.1005219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zheng C et al (2015) Large-scale direct targeting for drug repositioning and discovery. Sci Rep 5:11970. https://doi.org/10.1038/srep11970

    Article  PubMed  PubMed Central  Google Scholar 

  5. Su EW, Sanger TM (2017) Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov. PeerJ 5:e3154. https://doi.org/10.7717/peerj.3154

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by Eli Lilly and Company.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eric Wen Su .

Editor information

Editors and Affiliations

1 Electronic Supplementary Material

Supplementary Material

: Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov (DOCX 17 kb)

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Su, E.W. (2019). Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov. In: Vanhaelen, Q. (eds) Computational Methods for Drug Repurposing. Methods in Molecular Biology, vol 1903. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8955-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8955-3_4

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8954-6

  • Online ISBN: 978-1-4939-8955-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics