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Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery
Phenotypic drug discovery, primarily abandoned in the 1980’s in favor of targeted approaches to drug development, is once again demonstrating its value when used in conjunction with new technologies. Phenotypic discovery has been brought back to the fore mainly due to recent advances in the field of...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Bentham Open
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2793401/ https://www.ncbi.nlm.nih.gov/pubmed/20161832 http://dx.doi.org/10.2174/1875397300903010013 |
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author | Low, Jonathan Chakravartty, Arunava Blosser, Wayne Dowless, Michele Chalfant, Christopher Bragger, Patty Stancato, Louis |
author_facet | Low, Jonathan Chakravartty, Arunava Blosser, Wayne Dowless, Michele Chalfant, Christopher Bragger, Patty Stancato, Louis |
author_sort | Low, Jonathan |
collection | PubMed |
description | Phenotypic drug discovery, primarily abandoned in the 1980’s in favor of targeted approaches to drug development, is once again demonstrating its value when used in conjunction with new technologies. Phenotypic discovery has been brought back to the fore mainly due to recent advances in the field of high content imaging (HCI). HCI elucidates cellular responses using a combination of immunofluorescent assays and computer analysis which increase both the sensitivity and throughput of phenotypic assays. Although HCI data characterize cellular responses in individual cells, these data are usually analyzed as an aggregate of the treated population and are unable to discern differentially responsive subpopulations. A collection of 44 kinase inhibitors affecting cell cycle and apoptosis were characterized with a number of univariate, bivariate, and multivariate subpopulation analyses demonstrating that each level of complexity adds additional information about the treated populations and often distinguishes between compounds with seemingly similar mechanisms of action. Finally, these subpopulation data were used to characterize compounds as they relate in chemical space. |
format | Text |
id | pubmed-2793401 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Bentham Open |
record_format | MEDLINE/PubMed |
spelling | pubmed-27934012010-02-16 Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery Low, Jonathan Chakravartty, Arunava Blosser, Wayne Dowless, Michele Chalfant, Christopher Bragger, Patty Stancato, Louis Curr Chem Genomics Article Phenotypic drug discovery, primarily abandoned in the 1980’s in favor of targeted approaches to drug development, is once again demonstrating its value when used in conjunction with new technologies. Phenotypic discovery has been brought back to the fore mainly due to recent advances in the field of high content imaging (HCI). HCI elucidates cellular responses using a combination of immunofluorescent assays and computer analysis which increase both the sensitivity and throughput of phenotypic assays. Although HCI data characterize cellular responses in individual cells, these data are usually analyzed as an aggregate of the treated population and are unable to discern differentially responsive subpopulations. A collection of 44 kinase inhibitors affecting cell cycle and apoptosis were characterized with a number of univariate, bivariate, and multivariate subpopulation analyses demonstrating that each level of complexity adds additional information about the treated populations and often distinguishes between compounds with seemingly similar mechanisms of action. Finally, these subpopulation data were used to characterize compounds as they relate in chemical space. Bentham Open 2009-03-24 /pmc/articles/PMC2793401/ /pubmed/20161832 http://dx.doi.org/10.2174/1875397300903010013 Text en © Low et al.; Licensee Bentham Open. http://creativecommons.org/licenses/by-nc/3.0/ This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited. |
spellingShingle | Article Low, Jonathan Chakravartty, Arunava Blosser, Wayne Dowless, Michele Chalfant, Christopher Bragger, Patty Stancato, Louis Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery |
title | Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery |
title_full | Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery |
title_fullStr | Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery |
title_full_unstemmed | Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery |
title_short | Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery |
title_sort | phenotypic fingerprinting of small molecule cell cycle kinase inhibitors for drug discovery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2793401/ https://www.ncbi.nlm.nih.gov/pubmed/20161832 http://dx.doi.org/10.2174/1875397300903010013 |
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