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.


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*