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建立用于肿瘤学先导化合物优化的高通量自动化癌细胞增殖检测板。

Establishing a high-throughput and automated cancer cell proliferation panel for oncology lead optimization.

作者信息

Lei Ming, Ribeiro Humberto, Kolodin Garrett, Gill James, Wang Yu-Sun, Maloney Daniel, Fan Yi, Li Sha, Myer Larnie, Beluch Michael, Zhang Litao, Schweizer Liang

机构信息

1Department of Lead Evaluation and Mechanistic Biochemistry, Bristol-Myers Squibb, Princeton, NJ, USA.

出版信息

J Biomol Screen. 2013 Oct;18(9):1043-53. doi: 10.1177/1087057113491825. Epub 2013 Jun 3.

Abstract

Tumor cell proliferation assays are widely used for oncology drug discovery, including target validation, lead compound identification, and optimization, as well as determination of compound off-target activities. Taking advantage of robotic systems to maintain cell culture and perform cell proliferation assays would greatly increase productivity and efficiency. Here we describe the establishment of automated systems for high-throughput cell proliferation assays in a panel of 13 human tumor cell lines. These cell lines were selected from various types of human tumors containing a broad range of well-characterized mutations in multiple cellular signaling pathways. Standard procedures for cell culture and assay performance were developed and optimized in each cell line. Moreover, in-house developed software (i.e., Toolset, Curvemaster, and Biobars) was applied to analyze the data and generate data reports. Using tool compounds, we have shown that results obtained through this panel exhibit high reproducibility over a long period. Furthermore, we have demonstrated that this panel can be used to identify sensitive and insensitive cell lines for specific cancer targets, to drive cellular structure-activity relationships, and to profile compound off-target activities. All those efforts are important for cancer drug discovery lead optimization.

摘要

肿瘤细胞增殖测定广泛应用于肿瘤学药物研发,包括靶点验证、先导化合物鉴定与优化,以及化合物脱靶活性的测定。利用机器人系统维持细胞培养并进行细胞增殖测定将大大提高生产力和效率。在此,我们描述了在13种人类肿瘤细胞系中建立用于高通量细胞增殖测定的自动化系统。这些细胞系选自各种类型的人类肿瘤,在多个细胞信号通路中含有广泛的、特征明确的突变。针对每个细胞系制定并优化了细胞培养和测定操作的标准程序。此外,应用内部开发的软件(即Toolset、Curvemaster和Biobars)分析数据并生成数据报告。使用工具化合物,我们已经表明通过该细胞系面板获得的结果在很长一段时间内具有高度可重复性。此外,我们已经证明该细胞系面板可用于识别针对特定癌症靶点的敏感和不敏感细胞系,以推动细胞结构-活性关系研究,并分析化合物的脱靶活性。所有这些努力对于癌症药物研发的先导优化都很重要。

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