表型筛选
计算生物学
药物发现
高含量筛选
表型
鉴定(生物学)
药物开发
小分子
高通量筛选
生物
计算机科学
药品
生物信息学
细胞
药理学
遗传学
基因
植物
作者
Hua Tang,Shannon Duggan,Paul L. Richardson,Violeta L. Marin,Scott E. Warder,Shaun M. McLoughlin
标识
DOI:10.1177/1087057115622431
摘要
The pharmaceutical industry has been continually challenged by dwindling target diversity. To obviate this trend, phenotypic screens have been adopted, complementing target-centric screening approaches. Phenotypic screens identify drug leads using clinically relevant and translatable mechanisms, remaining agnostic to targets. While target anonymity is advantageous early in the drug discovery process, it poses challenges to hit progression, including the development of backup series, retaining desired pharmacology during optimization, discovery of markers, and understanding mechanism-driven toxicity. Consequently, significant effort has been expended to elaborate the targets and mechanisms at work for promising screening hits. Affinity capture is commonly leveraged, where the compounds are linked to beads and targets are abstracted from cell homogenates. This technique has proven effective for identifying targets of kinase, PARP, and HDAC inhibitors, and examples of new targets have been reported. Herein, a three-pronged approach to target deconvolution by affinity capture is described, including the implementation of a uniqueness index that helps discriminate between bona fide targets and background. The effectiveness of this approach is demonstrated using characterized compounds that act on known and noncanonical target classes. The platform is subsequently applied to phenotypic screening hits, identifying candidate targets. The success rate of bead-based affinity capture is discussed.
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