Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project
Bie M. P. Verbista, Günter Klambauerb c, Liesbet Vervoortd e, Willem Talloend e, Ziv Shkedyf, Olivier Thasa, Andreas Benderg h, Hinrich W. H. Göhlmannd e, Sepp Hochreiteri j✉
- aDepartment of Mathematical Modeling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B-9000 Gent, Belgium
- bInstitute of Bioinformatics, Johannes Kepler University, Linz, Austria
- cInstitute of Bioinformatics, Johannes Kepler University Linz, Austria
- dJohnson & Johnson Pharmaceutical Research & Development, Division of Janssen Pharmaceutica, Beerse, Belgium
- eJohnson and Johnson, Pharmaceutical Research and Development, Division of Janssen Pharmaceutica, Beerse, Belgium
- fInstitute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
- gCentre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
- hCentre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- iInstitute of Bioinformatics, Johannes Kepler University, Linz, Austria. Electronic address: hochreit@bioinf.jku.at
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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.