Towne Danli L, Nicholl Emily E, Comess Kenneth M, Galasinski Scott C, Hajduk Philip J, Abraham Vivek C
Abbott Laboratories, Global Pharmaceutical Research & Development, Lead Discovery Technologies, Abbott Park, IL 60064, USA.
J Biomol Screen. 2012 Sep;17(8):1005-17. doi: 10.1177/1087057112450050. Epub 2012 Jun 15.
Efficient elucidation of the biological mechanism of action of novel compounds remains a major bottleneck in the drug discovery process. To address this need in the area of oncology, we report the development of a multiparametric high-content screening assay panel at the level of single cells to dramatically accelerate understanding the mechanism of action of cell growth-inhibiting compounds on a large scale. Our approach is based on measuring 10 established end points associated with mitochondrial apoptosis, cell cycle disruption, DNA damage, and cellular morphological changes in the same experiment, across three multiparametric assays. The data from all of the measurements taken together are expected to help increase our current understanding of target protein functions, constrain the list of possible targets for compounds identified using phenotypic screens, and identify off-target effects. We have also developed novel data visualization and phenotypic classification approaches for detailed interpretation of individual compound effects and navigation of large collections of multiparametric cellular responses. We expect this general approach to be valuable for drug discovery across multiple therapeutic areas.
高效阐明新型化合物的生物学作用机制仍然是药物发现过程中的一个主要瓶颈。为满足肿瘤学领域的这一需求,我们报告了一种单细胞水平的多参数高内涵筛选分析方法的开发,以大幅加速大规模了解细胞生长抑制化合物的作用机制。我们的方法基于在同一实验中,通过三种多参数分析测量与线粒体凋亡、细胞周期紊乱、DNA损伤和细胞形态变化相关的10个既定终点。综合所有测量数据,有望帮助增进我们目前对靶蛋白功能的理解,限制使用表型筛选鉴定出的化合物的可能靶标列表,并识别脱靶效应。我们还开发了新颖的数据可视化和表型分类方法,用于详细解释单个化合物的效应以及对大量多参数细胞反应进行分类导航。我们预计这种通用方法对多个治疗领域的药物发现具有重要价值。