Department of Physicochemistry, Chemistry Institute, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City, 04510, Mexico.
Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City, 04510, Mexico.
Mol Inform. 2020 Dec;39(12):e2000061. doi: 10.1002/minf.202000061. Epub 2020 May 27.
High-throughput screening data of compounds consistently tested against the same panel of cell lines is a rich source of information for interrogating cell-selectivity of a compound. Nevertheless, there is a high risk of false positives for these rapid-testing strategies. Then, a single cell-inactive compound can be mistakenly labeled as highly cell-selective if a false positive occurs in any of the cell assays. More interesting would be the case of a series of analogs, which are structurally related compounds, that have a trend to be active only against a small number of cells. To this end, it is herein proposed a proof-of-concept of a method for finding consistent cell-selective analog series of chemical compounds through analysis of high-throughput cell-compound screening data systematically obtained. Furthermore, statistics for quantifying cell-promiscuity and consistency of an analog series are presented.
针对同一细胞系panel 进行反复测试的化合物高通量筛选数据是探究化合物细胞选择性的丰富信息来源。然而，这些快速筛选策略存在很高的假阳性风险。如果在任何细胞测定中出现假阳性，那么单个非细胞活性化合物可能会被错误地标记为高度细胞选择性。更有趣的情况是一系列类似物，它们是结构相关的化合物，只有在少数细胞中才有活性的趋势。为此，本文提出了一种通过系统地分析高通量细胞-化合物筛选数据来寻找一致的细胞选择性类似物系列的方法的概念验证。此外，还提出了用于量化化合物混杂性和类似物系列一致性的统计方法。
