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

Bie Verbist; Günter Klambauer; Liesbet Vervoort; Willem Talloen; QSTAR Consortium; Ziv Shkedy; Olivier Thas; Andreas Bender; Hinrich W H Göhlmann; Sepp Hochreiter

doi:10.1016/j.drudis.2014.12.014

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

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

Authors

Bie Verbist¹, Günter Klambauer², Liesbet Vervoort³, Willem Talloen³; QSTAR Consortium; Ziv Shkedy⁴, Olivier Thas¹, Andreas Bender⁵, Hinrich W H Göhlmann³, Sepp Hochreiter⁶

Affiliations

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.

PMID: 25582842
DOI: 10.1016/j.drudis.2014.12.014

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Databases, Genetic
Decision Support Techniques
Drug Approval*
Drug Discovery / methods*
Drug-Related Side Effects and Adverse Reactions / genetics*
Gene Expression Profiling*
Gene Expression Regulation / drug effects
Humans
Molecular Structure
Program Evaluation
Quantitative Structure-Activity Relationship
Risk Assessment
Transcription, Genetic / drug effects*