Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery
Jonathan Low1, Arunava Chakravartty2, Wayne Blosser1, Michele Dowless1, Christopher Chalfant3, Patty Bragger3, Louis Stancato*, 1
Identifiers and Pagination:Year: 2009
First Page: 13
Last Page: 21
Publisher Id: CCGTM-3-13
Article History:Received Date: 21/10/2008
Revision Received Date: 02/12/2008
Acceptance Date: 04/12/2008
Electronic publication date: 24/3/2009
Collection year: 2009
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/), which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.