Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.
Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.
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DOI：
10.1371/journal.pone.0141076
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Freundlich JS
中科院分区：
文献类型：
--
作者：
Ekins S;Madrid PB;Sarker M;Li SG;Mittal N;Kumar P;Wang X;Stratton TP;Zimmerman M;Talcott C;Bourbon P;Travers M;Yadav M;Freundlich JS
结核分枝杆菌（Mtb）的综合计算方法有助于识别可能导致未来结核病（TB）药物的新分子。我们的方法使用来自TBCyc途径和基因组数据库的信息，协同药物发现TB数据库结合3D药效团和全细胞活性和缺乏细胞毒性的双事件贝叶斯模型。我们已经优先考虑了大量的分子，这些分子可能作为结核代谢组中底物和代谢物的模拟物。我们使用基于Mtb的底物和代谢物的66个药效团计算搜索了超过200，000个商业分子，并使用贝叶斯模型进一步过滤。我们最终在体外测试了110种化合物，得到了两种感兴趣的化合物，BAS 04912643和BAS 00623753（MIC分别为2.5和5 μg/mL）。这些分子被用作命中到铅优化的起点。最有前途的一类被证明是喹喔啉二-N-氧化物，证明了转录谱诱导mRNA水平扰动最接近已知的质子载体。其中，SRI 58对结核分枝杆菌的MIC = 1.25 μg/mL，在Vero细胞中的CC 50>40 μg/mL，同时具有良好的Caco-2 A-B渗透性（2.3 x 10−6 cm/s）、动力学溶解度（PBS中pH 7.4时为125 μM）和小鼠代谢稳定性（与小鼠肝微粒体孵育1小时后剩余63.6%）。尽管证明了组合的生物信息学/化学信息学方法如何提供具有有希望的体外特征的小分子，但我们发现SRI 58在小鼠中没有表现出可定量的血液水平。
Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10−6 cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.