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化合物选择性的多途径细胞分析

Multi-pathway cellular analysis of compound selectivity.

作者信息

Hancock Michael K, Lebakken Connie S, Wang Jun, Bi Kun

机构信息

Discovery Assays and Services, Invitrogen, Part of Life Technologies, Madison, WI 53719, USA.

出版信息

Mol Biosyst. 2010 Oct;6(10):1834-43. doi: 10.1039/c003669b. Epub 2010 Jun 30.

DOI:10.1039/c003669b
PMID:20593073
Abstract

Signaling pathways and their protein target constituents (e.g. kinases) have become important therapeutic targets in many disease areas. Traditional selectivity profiling for kinase inhibitors has relied upon screening panels of recombinant enzymes in biochemical assay formats. Recent studies have highlighted the importance of using cellular assays to better approximate true biological selectivity. We have developed a portfolio of CellSensor beta-lactamase transcriptional reporter gene assays that can be used to screen for perturbagens of various endogenous signaling pathways. Here we describe a multi-pathway profiling approach for generating compound-pathway selectivity maps. To demonstrate the utility of this approach, we have screened 32 known compounds across a diverse panel of 12 key signaling pathways and generated the first comprehensive cellular pathway selectivity profiles of several clinically approved kinase and other well-known bioactive inhibitors. Selectivity score comparisons identified several kinase inhibitors that were more promiscuous than predicted by traditional biochemical profiling methods. For example, we identified effects of sorafenib on the JAK/STAT pathway and demonstrated the potential therapeutic indication of sorafenib in treating leukemia/myeloproliferative disorder patients harboring TEL-JAK2 or JAK2V617F mutations. Our results indicate that multi-pathway profiling can efficiently characterize both on and off-pathway compound activities, revealing potential novel pathways and opportunities for drug repositioning purposes and/or safety liabilities in one profiling campaign.

摘要

信号通路及其蛋白质靶标成分(如激酶)已成为许多疾病领域重要的治疗靶点。传统的激酶抑制剂选择性分析依赖于在生化分析形式中筛选重组酶的面板。最近的研究强调了使用细胞分析来更好地模拟真实生物学选择性的重要性。我们开发了一系列CellSensorβ-内酰胺酶转录报告基因分析方法,可用于筛选各种内源性信号通路的干扰物。在此,我们描述了一种用于生成化合物-通路选择性图谱的多通路分析方法。为了证明这种方法的实用性,我们在12条关键信号通路的不同面板上筛选了32种已知化合物,并生成了几种临床批准的激酶和其他知名生物活性抑制剂的首个全面细胞通路选择性图谱。选择性评分比较确定了几种激酶抑制剂比传统生化分析方法预测的更具 promiscuous性。例如,我们确定了索拉非尼对JAK/STAT通路的影响,并证明了索拉非尼在治疗携带TEL-JAK2或JAK2V617F突变的白血病/骨髓增殖性疾病患者中的潜在治疗指征。我们的结果表明,多通路分析可以有效地表征化合物的通路内和通路外活性,在一次分析活动中揭示潜在的新通路以及药物重新定位目的和/或安全风险的机会。

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引用本文的文献

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Oncotarget. 2017 Jul 28;8(35):57948-57963. doi: 10.18632/oncotarget.19678. eCollection 2017 Aug 29.
2
A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib.一种用于识别预测临床结果的通路基因生物标志物的数据挖掘方法:以厄洛替尼和索拉非尼为例的案例研究。
PLoS One. 2017 Aug 8;12(8):e0181991. doi: 10.1371/journal.pone.0181991. eCollection 2017.
3
The use of novel selectivity metrics in kinase research.
激酶研究中新型选择性指标的应用。
BMC Bioinformatics. 2017 Jan 5;18(1):17. doi: 10.1186/s12859-016-1413-y.
4
A facile method for simultaneously measuring neuronal cell viability and neurite outgrowth.一种同时测量神经元细胞活力和神经突生长的简便方法。
Curr Chem Genom Transl Med. 2015 Feb 27;9:6-16. doi: 10.2174/2213988501509010006. eCollection 2015.