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. 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

Bie Verbist  1 , Günter Klambauer  2 , Liesbet Vervoort  3 , Willem Talloen  3 ; QSTAR Consortium; Ziv Shkedy  4 , Olivier Thas  1 , Andreas Bender  5 , Hinrich W H Göhlmann  3 , Sepp Hochreiter  6
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Free article

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

Bie Verbist et al. Drug Discov Today. 2015 May.
Free article

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.

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