1. Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project
- Author
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Verbist, Bie, Klambauer, Günter, Vervoort, Liesbet, Talloen, Willem, Shkedy, Ziv, Thas, Olivier, Bender, Andreas, Göhlmann, Hinrich WH, Hochreiter, Sepp, QSTAR Consortium, the, and Clement, Lieven
- Subjects
Drug ,Program evaluation ,Drug-Related Side Effects and Adverse Reactions ,Transcription, Genetic ,media_common.quotation_subject ,Quantitative Structure-Activity Relationship ,Biology ,TRIGLYCERIDE TRANSFER PROTEIN ,Risk Assessment ,Decision Support Techniques ,SUPPORT ,Databases, Genetic ,Drug Discovery ,TYROSINE KINASE INHIBITORS ,Drug approval ,Animals ,Humans ,Drug Approval ,Pharmaceutical industry ,media_common ,Pharmacology ,GENE-EXPRESSION SIGNATURES ,RECEPTOR ,Molecular Structure ,business.industry ,Drug discovery ,CHOLESTEROL ,Gene Expression Profiling ,IN-VITRO ,D optimal ,CANCER ,Biotechnology ,Gene expression profiling ,Chemistry ,Drug development ,Risk analysis (engineering) ,Gene Expression Regulation ,MICROARRAY DATA ,EPIDERMAL-GROWTH-FACTOR ,business ,Program Evaluation - 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.
- Published
- 2014