Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/18723
Title: | Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project | Authors: | Verbist, Bie Klambauer, Günter Vervoort, Liesbet TALLOEN, Willem SHKEDY, Ziv THAS, Olivier Bender, Andreas Göhlmann, Hinrich W. H. Hochreiter, Sepp BIGIRUMURAME, Theophile |
Corporate Authors: | QSTAR Consortium | Issue Date: | 2015 | Source: | DRUG DISCOVERY TODAY, 20 (5), p. 505-513. | 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. | Notes: | 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 Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom 6 Wolfson Research Institute, Durham University, Durham, UK 7 OpenAnalytics, Jupiterstraat 20, 2600 Antwerp, Belgium 8 Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14611, United States of America 9 CMAST, Eikenlaan11, 9150 Bazel, Belgium | Keywords: | bioactivity; biomarkers; chemical Structure; integration; joint model; transcriptomic. | Document URI: | http://hdl.handle.net/1942/18723 | ISSN: | 1359-6446 | e-ISSN: | 1878-5832 | DOI: | 10.1016/j.drudis.2014.12.014 | ISI #: | 000355714600003 | Rights: | © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2016 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S1359644614004851-main.pdf | Published version | 2.13 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
41
checked on Sep 2, 2020
WEB OF SCIENCETM
Citations
60
checked on Oct 13, 2024
Page view(s)
206
checked on Sep 6, 2022
Download(s)
242
checked on Sep 6, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.