Current Chemical Genomics and Translational Medicine
2009, 3 : 13-21Published online 2009 March 24. DOI: 10.2174/1875397300903010013
Publisher ID: CCGTM-3-13
RESEARCH ARTICLE
Phenotypic Fingerprinting of Small Molecule Cell Cycle Kinase Inhibitors for Drug Discovery
2 Statistics and Information Science, Eli Lilly and Company, Indianapolis, IN 46265, USA
3 Discovery Informatics, Eli Lilly and Company, Indianapolis, IN 46265, USA
* Address correspondence to this author at the Cancer Growth and Translational Genetics Lilly Corporate Center Bldg. 98C, DC0434 Indianapolis, IN 46285, USA; Tel: 317-655-6910; Fax: 317-276-1414; E-mail: stancato_louis@lilly.com
ABSTRACT
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.