Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.
Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.
复制标题
结合命中率优化的计算方法在结核分枝杆菌中的铅优化。
DOI：
10.1007/s11095-013-1172-7
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发表时间：
2014-02
影响因子：
3.7
通讯作者：
Reynolds, Robert C.
中科院分区：
文献类型：
--
作者：
Ekins, Sean;Freundlich, Joel S.;Hobrath, Judith V.;White, E. Lucile;Reynolds, Robert C.
关键词：
结核病治疗需要缩短疗程，并克服耐药性。我们先前针对结核分枝杆菌（Mtb）的大规模表型高通量筛选已经鉴定了737种活性化合物和数千种非活性化合物。我们已经使用这些数据来建立计算模型，作为一种方法，以尽量减少测试的化合物的数量。使用化学信息学聚类方法，然后使用贝叶斯机器学习模型（基于公开可用的Mtb筛选数据）来说明将这些模型应用于筛选集选择可以丰富命中率。为了探索从我们先前的Mtb筛选获得的剂量-反应命中的活性簇支架周围的化学多样性，选择了一组1924种市售分子，并使用Vero、THP-1和HepG 2细胞系分别以4.3%、4.2%和2.7%的命中率评价了抗结核活性和细胞毒性。我们证明，与随机选择相比，结合了非洲绿猴肾细胞抗结核和细胞毒性数据的模型可以显着丰富无毒活性物质的选择。在所有细胞系中，分子文库小分子库（MLSMR）和细胞毒性模型在筛选的前1%中鉴定了约10%的命中（>10倍富集）。我们还发现，来自不同学术发表研究的9种Mtb活性化合物中有7种，来自药物筛选（GSK）的11种Mtb活性化合物中有8种将通过这些贝叶斯模型进行鉴定。结合聚类和贝叶斯模型是一个有用的策略，化合物的优先级和命中导致抗结核药物的优化。
Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate. In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models. Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.
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DOI：
10.1073/pnas.1214188109
发表时间：
2012-10-02
影响因子：
11.1
作者：
Gold, Ben;Pingle, Maneesh;Nathan, Carl F.
通讯作者：
Nathan, Carl F.
影响因子：
3.7
作者：
Ekins, Sean;Freundlich, Joel S.
通讯作者：
Freundlich, Joel S.
影响因子：
5.6
作者：
Klon, Anthony E.;Lowrie, Jeffrey F.;Diller, David J.
通讯作者：
Diller, David J.
影响因子：
64.8
作者：
Gamo, Francisco-Javier;Sanz, Laura M.;Garcia-Bustos, Jose F.
通讯作者：
Garcia-Bustos, Jose F.
影响因子：
3.8
作者：
Lin, Zhiwu;Will, Yvonne
通讯作者：
Will, Yvonne