Takács Gergely, Balogh György T, Kiss Róbert
Department of Chemical and Environmental Process Engineering, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics Műegyetem Rakpart 3 Budapest 1111 Hungary.
Mcule.com Kft Bartók Béla út 105-113 Budapest 1115 Hungary
RSC Med Chem. 2025 Apr 22. doi: 10.1039/d4md01051e.
In drug discovery, various approaches exist to find compounds that alter the different states in living organisms. There are two fundamental discovery strategies regarding the mechanism of action: target-based and phenotypic methods. Both have strengths and weaknesses in assay development, target selection, target validation and structure optimization. While phenotypic screening can identify chemical starting points with the desired phenotype, it is typically difficult to carry out efficient, structure-based optimization without confirming the mechanism of action of such hits. It is therefore critical to uncover the targets behind the phenotype. Target deconvolution is typically carried out by a set of highly selective compounds, where each ligand is associated with a particular target. Hits of such a high-selectivity set can provide valuable information on the phenotype's underlying targets and may also enable novel target-based therapeutic strategies. Consequently, there is a continuously high demand for novel highly-selective tool compounds for target deconvolution. In this work, the ChEMBL database, comprising over 20 million bioactivity data, was mined to identify the most selective novel ligands for a diverse set of targets. A novel method for the automated selection of such high-selectivity ligands is presented. Using these high-selectivity compounds in phenotypic screening campaigns can provide a valuable preliminary direction during target deconvolution. 87 representative compounds were purchased and screened against 60 cancer cell lines. Several compounds were found to possess selective inhibition of cell growth of a few distinct cell lines. The phenotypic assay results, along with the nanomolar activities of individual proteins obtained from the ChEMBL database suggest some novel mechanisms of action for anti-cancer drug discovery.
在药物研发中，存在多种方法来寻找能够改变生物体不同状态的化合物。关于作用机制有两种基本的发现策略：基于靶点的方法和表型方法。这两种方法在分析方法开发、靶点选择、靶点验证和结构优化方面都有优缺点。虽然表型筛选可以识别具有所需表型的化学起始点，但如果不确认此类命中靶点的作用机制，通常很难进行高效的基于结构的优化。因此，揭示表型背后的靶点至关重要。靶点反卷积通常通过一组高度选择性的化合物来进行，其中每个配体都与一个特定的靶点相关联。这种高选择性集合的命中靶点可以提供有关表型潜在靶点的有价值信息，还可能促成基于新靶点的治疗策略。因此，对于用于靶点反卷积的新型高选择性工具化合物一直有很高的需求。在这项工作中，对包含超过2000万个生物活性数据的ChEMBL数据库进行挖掘，以识别针对多种靶点的最具选择性的新型配体。提出了一种自动选择此类高选择性配体的新方法。在表型筛选活动中使用这些高选择性化合物可以在靶点反卷积过程中提供有价值的初步方向。购买了87种代表性化合物，并针对60种癌细胞系进行筛选。发现几种化合物对一些不同的细胞系具有选择性抑制细胞生长的作用。表型分析结果以及从ChEMBL数据库获得的单个蛋白质的纳摩尔活性表明了抗癌药物发现的一些新作用机制。
