Prioritizing hits from phenotypic high-content screens.
In the past decade, advances in the field of high-content screening (HCS) have provided researchers with a powerful new screening tool to observe treatment effects on multiple experimental parameters. While extremely useful, HCS has resulted in the collection of large datasets of increased complexity that require intensive analysis. Recently, approaches have been developed to analyze multi-parametric HCS data more completely and, when used in conjunction with RNA interference, target-based biochemistry and structural analysis, these approaches have begun to unlock the potential of this screening format in aiding drug discovery. This review illustrates how the combination of these technologies has been used to successfully drive the drug discovery process.
Low
Jonathan
J
Department of Cancer Growth and Translational Genetics, Eli Lilly & Co, Indianapolis, IN 46285, USA.
Stancato
Louis
L
Lee
Jonathan
J
Sutherland
Jeffrey J
JJ
D000818:Animals; D001681:Biological Assay; D016000:Cluster Analysis; D019295:Computational Biology; D003196:Computer Graphics; D017076:Computer-Aided Design; D015195:Drug Design; D006801:Humans; D015394:Molecular Structure; D010641:Phenotype; D011487:Protein Conformation; D034622:RNA Interference; D012015:Reference Standards; D013329:Structure-Activity Relationship; D013678:Technology, Pharmaceutical / Q000379:methods*