Contributions from emerging transcriptomics technologies and computational strategies for drug discovery

Publication type:
Journal article
Metadata:
Autoren
  • Onat Kadioglu
  • Thomas Efferth
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000345142300029&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1007/s10637-014-0081-x
eISSN
1573-0646
Externe Identifier
  • Clarivate Analytics Document Solution ID: AT7UI
  • PubMed Identifier: 24633650
ISSN
0167-6997
Ausgabe der Veröffentlichung
6
Zeitschrift
INVESTIGATIONAL NEW DRUGS
Schlüsselwörter
  • Computational biology
  • Connectivity map
  • Deep sequencing
  • Drug discovery
  • Drug repositioning
  • Genome-wide association studies
  • -omics technologies
  • Systems biology
  • Whole exon sequencing
Paginierung
1316 - 1319
Datum der Veröffentlichung
2014
Status
Published
Titel
Contributions from emerging transcriptomics technologies and computational strategies for drug discovery
Sub types
  • Review
Ausgabe der Zeitschrift
32

Data source: Web of Science (Lite)

Other metadata sources:
Autoren
  • Onat Kadioglu
  • Thomas Efferth
DOI
10.1007/s10637-014-0081-x
eISSN
1573-0646
ISSN
0167-6997
Ausgabe der Veröffentlichung
6
Zeitschrift
Investigational New Drugs
Sprache
en
Online publication date
2014
Paginierung
1316 - 1319
Datum der Veröffentlichung
2014
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1007/s10637-014-0081-x
Datum der Datenerfassung
2023
Titel
Contributions from emerging transcriptomics technologies and computational strategies for drug discovery
Ausgabe der Zeitschrift
32

Data source: Crossref

Abstract
Drug discovery involves various steps and is a long process being even more demanding for complex diseases such as cancer. Tumors are ensembles of subpopulations with different mutations, require very specific and effective strategies. Conventional drug screening technologies may not be adequate and efficient anymore. Drug repositioning is a useful strategy to identify new uses for previously failed drugs. High throughput and deep sequencing technologies provide valuable support by yielding enormous amounts of "-omics" data and contribute to understanding the molecular mechanisms responsible for drug action. Computational methods coupled with systems biology represent a promising step to interpret pharmacogenomic data and establish strong connections with drug discovery. Genomic variations have been found to be linked with differential drug response among individuals. Large genome wide association studies are necessary to identify reliable connections between genomic variations and drug response since personalized medicine has been accepted as an important phenomenon in the drug discovery and development process post approval.
Addresses
  • Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, University of Mainz, Staudinger Weg 5, 55128, Mainz, Germany.
Autoren
DOI
10.1007/s10637-014-0081-x
eISSN
1573-0646
Externe Identifier
  • PubMed Identifier: 24633650
Open access
false
ISSN
0167-6997
Ausgabe der Veröffentlichung
6
Zeitschrift
Investigational new drugs
Schlüsselwörter
  • Computational Biology
  • Databases, Factual
  • Drug Discovery
  • Transcriptome
Sprache
eng
Medium
Print-Electronic
Online publication date
2014
Paginierung
1316 - 1319
Datum der Veröffentlichung
2014
Status
Published
Datum der Datenerfassung
2014
Titel
Contributions from emerging transcriptomics technologies and computational strategies for drug discovery.
Sub types
  • Review
  • Journal Article
Ausgabe der Zeitschrift
32

Data source: Europe PubMed Central

Abstract
Drug discovery involves various steps and is a long process being even more demanding for complex diseases such as cancer. Tumors are ensembles of subpopulations with different mutations, require very specific and effective strategies. Conventional drug screening technologies may not be adequate and efficient anymore. Drug repositioning is a useful strategy to identify new uses for previously failed drugs. High throughput and deep sequencing technologies provide valuable support by yielding enormous amounts of "-omics" data and contribute to understanding the molecular mechanisms responsible for drug action. Computational methods coupled with systems biology represent a promising step to interpret pharmacogenomic data and establish strong connections with drug discovery. Genomic variations have been found to be linked with differential drug response among individuals. Large genome wide association studies are necessary to identify reliable connections between genomic variations and drug response since personalized medicine has been accepted as an important phenomenon in the drug discovery and development process post approval.
Date of acceptance
2014
Autoren
  • Onat Kadioglu
  • Thomas Efferth
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/24633650
DOI
10.1007/s10637-014-0081-x
eISSN
1573-0646
Ausgabe der Veröffentlichung
6
Zeitschrift
Invest New Drugs
Schlüsselwörter
  • Computational Biology
  • Databases, Factual
  • Drug Discovery
  • Transcriptome
Sprache
eng
Country
United States
Paginierung
1316 - 1319
Datum der Veröffentlichung
2014
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2015
Titel
Contributions from emerging transcriptomics technologies and computational strategies for drug discovery.
Sub types
  • Journal Article
  • Review
Ausgabe der Zeitschrift
32

Data source: PubMed

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