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Using transcriptomics to guide lead optimization in drug discovery projects : lessons learned from the QSTAR project

Bie Verbist, Günter Klambauer, Liesbet Vervoort, Willem Talloen, Ziv Shkedy, Olivier Thas UGent, Andreas Bender, Hinrich WH Göhlmann, Sepp Hochreiter, the QSTAR Consortium, et al. (2015) DRUG DISCOVERY TODAY. 20(5). p.505-513
abstract
The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (review)
publication status
published
subject
keyword
GENE-EXPRESSION SIGNATURES, RECEPTOR, TRIGLYCERIDE TRANSFER PROTEIN, TYROSINE KINASE INHIBITORS, EPIDERMAL-GROWTH-FACTOR, IN-VITRO, CANCER, MICROARRAY DATA, CHOLESTEROL, SUPPORT
journal title
DRUG DISCOVERY TODAY
Drug Discov. Today
volume
20
issue
5
pages
505 - 513
Web of Science type
Review
Web of Science id
000355714600003
JCR category
PHARMACOLOGY & PHARMACY
JCR impact factor
5.625 (2015)
JCR rank
13/253 (2015)
JCR quartile
1 (2015)
ISSN
1359-6446
DOI
10.1016/j.drudis.2014.12.014
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
A1
copyright statement
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
id
5814401
handle
http://hdl.handle.net/1854/LU-5814401
date created
2015-01-22 13:22:09
date last changed
2017-02-22 12:05:17
@article{5814401,
  abstract     = {The pharmaceutical industry is faced with steadily declining R\&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making.},
  author       = {Verbist, Bie and Klambauer, G{\"u}nter and Vervoort, Liesbet and Talloen, Willem and Shkedy, Ziv and Thas, Olivier and Bender, Andreas and G{\"o}hlmann, Hinrich WH and Hochreiter, Sepp and QSTAR Consortium, the and Clement, Lieven},
  issn         = {1359-6446},
  journal      = {DRUG DISCOVERY TODAY},
  keyword      = {GENE-EXPRESSION SIGNATURES,RECEPTOR,TRIGLYCERIDE TRANSFER PROTEIN,TYROSINE KINASE INHIBITORS,EPIDERMAL-GROWTH-FACTOR,IN-VITRO,CANCER,MICROARRAY DATA,CHOLESTEROL,SUPPORT},
  language     = {eng},
  number       = {5},
  pages        = {505--513},
  title        = {Using transcriptomics to guide lead optimization in drug discovery projects : lessons learned from the QSTAR project},
  url          = {http://dx.doi.org/10.1016/j.drudis.2014.12.014},
  volume       = {20},
  year         = {2015},
}

Chicago
Verbist, Bie, Günter Klambauer, Liesbet Vervoort, Willem Talloen, Ziv Shkedy, Olivier Thas, Andreas Bender, et al. 2015. “Using Transcriptomics to Guide Lead Optimization in Drug Discovery Projects : Lessons Learned from the QSTAR Project.” Drug Discovery Today 20 (5): 505–513.
APA
Verbist, Bie, Klambauer, G., Vervoort, L., Talloen, W., Shkedy, Z., Thas, O., Bender, A., et al. (2015). Using transcriptomics to guide lead optimization in drug discovery projects : lessons learned from the QSTAR project. DRUG DISCOVERY TODAY, 20(5), 505–513.
Vancouver
1.
Verbist B, Klambauer G, Vervoort L, Talloen W, Shkedy Z, Thas O, et al. Using transcriptomics to guide lead optimization in drug discovery projects : lessons learned from the QSTAR project. DRUG DISCOVERY TODAY. 2015;20(5):505–13.
MLA
Verbist, Bie, Günter Klambauer, Liesbet Vervoort, et al. “Using Transcriptomics to Guide Lead Optimization in Drug Discovery Projects : Lessons Learned from the QSTAR Project.” DRUG DISCOVERY TODAY 20.5 (2015): 505–513. Print.