Format

Send to

Choose Destination
Drug Discov Today. 2015 May;20(5):505-13. doi: 10.1016/j.drudis.2014.12.014. Epub 2015 Jan 10.

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

Author information

1
Department of Mathematical Modeling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B-9000 Gent, Belgium.
2
Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.
3
Johnson & Johnson Pharmaceutical Research & Development, Division of Janssen Pharmaceutica, Beerse, Belgium.
4
Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium.
5
Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
6
Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. Electronic address: hochreit@bioinf.jku.at.

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.

PMID:
25582842
DOI:
10.1016/j.drudis.2014.12.014
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center