Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18723
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dc.contributor.authorVerbist, Bie-
dc.contributor.authorKlambauer, Günter-
dc.contributor.authorVervoort, Liesbet-
dc.contributor.authorTALLOEN, Willem-
dc.contributor.authorSHKEDY, Ziv-
dc.contributor.authorTHAS, Olivier-
dc.contributor.authorBender, Andreas-
dc.contributor.authorGöhlmann, Hinrich W. H.-
dc.contributor.authorHochreiter, Sepp-
dc.contributor.authorBIGIRUMURAME, Theophile-
dc.date.accessioned2015-04-16T11:15:36Z-
dc.date.available2015-04-16T11:15:36Z-
dc.date.issued2015-
dc.identifier.citationDRUG DISCOVERY TODAY, 20 (5), p. 505-513.-
dc.identifier.issn1359-6446-
dc.identifier.urihttp://hdl.handle.net/1942/18723-
dc.description.abstractThe 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.-
dc.description.sponsorshipThe authors wish to thank the many lab technicians and scientists at Janssen Research & Development who collected and produced the data. We thank the scientific community for the numerous tools (e.g. R, BioConductor, CDK, jCompoundMapper, ChEMBL, Connectivity Map) without which this project would not have been possible. A.B. thanks Unilever and the European Research Commission for support (Starting Grant ERC-2013-StG 336159 MIXTURE). The whole QSTAR consortium gratefully acknowledges the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT) for providing us with the O&O grant 100988: QSTAR - quantitative structure transcriptional activity relationship.-
dc.language.isoen-
dc.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/).-
dc.subject.otherbioactivity; biomarkers; chemical Structure; integration; joint model; transcriptomic.-
dc.titleUsing transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project-
dc.typeJournal Contribution-
dc.identifier.epage513-
dc.identifier.issue5-
dc.identifier.spage505-
dc.identifier.volume20-
local.bibliographicCitation.jcatA1-
dc.description.notes1 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-
local.contributor.corpauthorQSTAR Consortium-
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local.type.refereedRefereed-
local.type.specifiedReview-
dc.identifier.doi10.1016/j.drudis.2014.12.014-
dc.identifier.isi000355714600003-
item.contributorVerbist, Bie-
item.contributorKlambauer, Günter-
item.contributorVervoort, Liesbet-
item.contributorTALLOEN, Willem-
item.contributorSHKEDY, Ziv-
item.contributorTHAS, Olivier-
item.contributorBender, Andreas-
item.contributorGöhlmann, Hinrich W. H.-
item.contributorHochreiter, Sepp-
item.contributorBIGIRUMURAME, Theophile-
item.fullcitationVerbist, Bie; Klambauer, Günter; Vervoort, Liesbet; TALLOEN, Willem; SHKEDY, Ziv; THAS, Olivier; Bender, Andreas; Göhlmann, Hinrich W. H.; Hochreiter, Sepp & BIGIRUMURAME, Theophile (2015) Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project. In: DRUG DISCOVERY TODAY, 20 (5), p. 505-513..-
item.accessRightsOpen Access-
item.fulltextWith Fulltext-
item.validationecoom 2016-
crisitem.journal.issn1359-6446-
crisitem.journal.eissn1878-5832-
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