Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data

Guillermo de Anda-Jáuregui, Kai Guo, Junguk Hur

Research output: Contribution to journalArticle

Abstract

The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.

Original languageEnglish (US)
JournalInternational Journal of Molecular Sciences
Volume20
Issue number2
DOIs
StatePublished - Jan 17 2019
Externally publishedYes

Fingerprint

Polypharmacy
Drug-Related Side Effects and Adverse Reactions
Screening
drugs
screening
Throughput
Pharmaceutical Preparations
Drug Combinations
Cells
Polypharmacology
Preclinical Drug Evaluations
Proxy
Libraries

Keywords

  • adverse drug reaction
  • L1000 assay
  • Library of Integrated Network-Based Cellular Signatures
  • LINCS
  • network pharmacology
  • polypharmacology
  • polypharmacy
  • risk prediction

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

Cite this

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data. / de Anda-Jáuregui, Guillermo; Guo, Kai; Hur, Junguk.

In: International Journal of Molecular Sciences, Vol. 20, No. 2, 17.01.2019.

Research output: Contribution to journalArticle

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