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计算机模拟活性谱分析揭示了在高通量筛选中发现的抗疟药物的作用机制。

In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen.

作者信息

Plouffe David, Brinker Achim, McNamara Case, Henson Kerstin, Kato Nobutaka, Kuhen Kelli, Nagle Advait, Adrián Francisco, Matzen Jason T, Anderson Paul, Nam Tae-Gyu, Gray Nathanael S, Chatterjee Arnab, Janes Jeff, Yan S Frank, Trager Richard, Caldwell Jeremy S, Schultz Peter G, Zhou Yingyao, Winzeler Elizabeth A

机构信息

Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA.

出版信息

Proc Natl Acad Sci U S A. 2008 Jul 1;105(26):9059-64. doi: 10.1073/pnas.0802982105. Epub 2008 Jun 25.

Abstract

The growing resistance to current first-line antimalarial drugs represents a major health challenge. To facilitate the discovery of new antimalarials, we have implemented an efficient and robust high-throughput cell-based screen (1,536-well format) based on proliferation of Plasmodium falciparum (Pf) in erythrocytes. From a screen of approximately 1.7 million compounds, we identified a diverse collection of approximately 6,000 small molecules comprised of >530 distinct scaffolds, all of which show potent antimalarial activity (<1.25 microM). Most known antimalarials were identified in this screen, thus validating our approach. In addition, we identified many novel chemical scaffolds, which likely act through both known and novel pathways. We further show that in some cases the mechanism of action of these antimalarials can be determined by in silico compound activity profiling. This method uses large datasets from unrelated cellular and biochemical screens and the guilt-by-association principle to predict which cellular pathway and/or protein target is being inhibited by select compounds. In addition, the screening method has the potential to provide the malaria community with many new starting points for the development of biological probes and drugs with novel antiparasitic activities.

摘要

目前一线抗疟药物耐药性不断增加,这是一项重大的健康挑战。为了促进新型抗疟药物的发现,我们基于恶性疟原虫(Pf)在红细胞中的增殖,实施了一种高效且可靠的基于细胞的高通量筛选(1536孔板形式)。从大约170万种化合物的筛选中,我们鉴定出了大约6000种小分子的多样化集合,这些小分子由超过530种不同的骨架组成,所有这些小分子都显示出强大的抗疟活性(<1.25 microM)。在此筛选中鉴定出了大多数已知的抗疟药物,从而验证了我们的方法。此外,我们还鉴定出了许多新型化学骨架,它们可能通过已知和新型途径发挥作用。我们进一步表明,在某些情况下,这些抗疟药物的作用机制可以通过计算机化合物活性谱分析来确定。该方法使用来自不相关细胞和生化筛选的大型数据集以及关联推断原则,来预测哪些细胞途径和/或蛋白质靶点被选定的化合物所抑制。此外,这种筛选方法有可能为疟疾研究界提供许多新的起点,用于开发具有新型抗寄生虫活性的生物探针和药物。

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