通过高通量筛选鉴定新型小胶质细胞瘤生长和侵袭的小分子抑制剂。

Identification of novel small-molecule inhibitors of glioblastoma cell growth and invasion by high-throughput screening.

机构信息

Key Laboratory of the Ministry of Education for Experimental Teratology, Department of Histology and Embryology, School of Medicine, Shandong University, Ji'nan, Shandong, China.

出版信息

Biosci Trends. 2012 Aug;6(4):192-200. doi: 10.5582/bst.2012.v6.4.192.

Abstract

Glioblastoma multiforme (GBM) is the most common and lethal type of primary brain tumor with a very poor prognosis. Current therapies for GBM remain palliative and advances made in decades have resulted in only a slight improvement in treatment outcome. Exploring new therapeutic agents for GBM treatment, therefore, is of prime importance. In the present study, we performed a high-throughput screening for GBM cell growth and invasion, with an attempt to identify novel potential anti-GBM agents. An annotated compound library (LOPAC1280) of 1,280 pharmacologically active compounds was screened and ten compounds were validated and identified as inhibitors of GBM cell growth and invasion. Four of them, i.e., 6-nitroso-1,2-benzopyrone, S-(p-azidophenacyl) glutathione, phenoxybenzamine hydrochloride, and SCH-28080 have not been implicated in GBM cell growth and invasion previously, suggesting that they may serve as novel potential therapeutic agents for GBM treatment. In conclusion, novel inhibitors of GBM cell growth and invasion were identified in the present study, which provides a basis for the development of therapies for GBM, and may shed light on the molecular mechanisms underlying GBM cell behavior.

摘要

多形性胶质母细胞瘤(GBM)是最常见和最致命的原发性脑肿瘤,预后极差。目前的 GBM 治疗仍然是姑息性的,几十年来的进展仅导致治疗结果略有改善。因此,探索用于 GBM 治疗的新治疗剂至关重要。在本研究中,我们进行了高通量筛选以检测 GBM 细胞的生长和侵袭,试图鉴定新的潜在的抗 GBM 药物。对注释化合物库(LOPAC1280)中的 1280 种具有药理活性的化合物进行了筛选,验证并鉴定了 10 种化合物,这些化合物可抑制 GBM 细胞的生长和侵袭。其中有 4 种,即 6-亚硝基-1,2-苯并吡喃酮、S-(对叠氮苯甲酰)谷胱甘肽、盐酸酚苄明和 SCH-28080,以前未涉及 GBM 细胞的生长和侵袭,这表明它们可能作为治疗 GBM 的新型潜在治疗剂。总之,本研究鉴定了 GBM 细胞生长和侵袭的新型抑制剂,为 GBM 的治疗提供了基础,并可能揭示 GBM 细胞行为的分子机制。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索