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

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

Research output: Contribution to journalShort surveypeer-review

75 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)505-513
Number of pages9
JournalDrug Discovery Today
Volume20
Issue number5
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Keywords

  • 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

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