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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.

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


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.

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