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通过体外筛选和计算分析确定的 N-1-磺酰基胞嘧啶衍生物的非典型细胞抑制机制。

Atypical cytostatic mechanism of N-1-sulfonylcytosine derivatives determined by in vitro screening and computational analysis.

作者信息

Supek Fran, Kralj Marijeta, Marjanović Marko, Suman Lidija, Smuc Tomislav, Krizmanić Irena, Zinić Biserka

机构信息

Division of Electronics, Laboratory for Information Systems, Ruder Bosković Institute, Bijenicka cesta 54, 10002, Zagreb, Croatia.

出版信息

Invest New Drugs. 2008 Apr;26(2):97-110. doi: 10.1007/s10637-007-9084-1. Epub 2007 Sep 27.

Abstract

We have previously shown that N-1-sulfonylpyrimidine derivatives have strong antiproliferative activity on human tumor cell lines, whereby 1-(p-toluenesulfonyl)cytosine showed good selectivity with regard to normal cells and was easily synthesized on a large scale. In the present work we have used an interdisciplinary approach to elucidate the compounds' mechanistic class. An augmented number of cell lines (11) has allowed a computational search for compounds with similar activity profiles and/or mechanistic class by integrating our data with the comprehensive DTP-NCI database. We applied supervised machine learning methodology (Random Forest classifier), which offers information complementary to unsupervised algorithms commonly used for analysis of cytostatic activity profiles, such as self-organizing maps. The computational results taken together with cell cycle perturbation and apoptosis analysis of the cell lines point to an unusual mechanism of cytostatic action, possibly a combination of nucleic acid antimetabolite activity and a novel molecular mechanism.

摘要

我们之前已经表明,N-1-磺酰基嘧啶衍生物对人肿瘤细胞系具有很强的抗增殖活性,其中1-(对甲苯磺酰基)胞嘧啶对正常细胞表现出良好的选择性,并且易于大规模合成。在本研究中,我们采用了一种跨学科方法来阐明这些化合物的作用机制类别。通过将我们的数据与全面的DTP-NCI数据库相结合,增加的细胞系数量(11个)使得能够通过计算搜索具有相似活性谱和/或作用机制类别的化合物。我们应用了监督式机器学习方法(随机森林分类器),该方法提供了与常用于分析细胞生长抑制活性谱的无监督算法(如自组织映射)互补的信息。计算结果与细胞系的细胞周期扰动和凋亡分析一起表明了一种不同寻常的细胞生长抑制作用机制,可能是核酸抗代谢物活性和一种新的分子机制的组合。

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