Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.
Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.
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DOI：
10.1371/journal.pntd.0003878
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发表时间：
2015
影响因子：
3.8
通讯作者：
Talcott C
中科院分区：
文献类型：
--
作者：
Ekins S;de Siqueira-Neto JL;McCall LI;Sarker M;Yadav M;Ponder EL;Kallel EA;Kellar D;Chen S;Arkin M;Bunin BA;McKerrow JH;Talcott C
查加斯病是由真核寄生虫克氏锥虫引起的一种被忽视的热带疾病。目前的临床和临床前管道T。Cruzi非常稀少，缺乏药物靶点多样性。在本研究中，我们开发了一种计算方法，该方法利用了几个公共的全细胞，表型高通量筛选的数据，这些筛选已经完成。cruzi，包括作为NIH分子库计划的一部分，在寻找化学探针中对超过300，000个分子进行单次筛选。我们还汇编和策划了相关的生物和化学化合物筛选数据，包括（i）文献中的化合物和生物活性数据，（ii）高通量筛选数据集，以及（iii）T的预测代谢物。cruzi代谢途径。这些信息用于帮助我们识别化合物及其潜在目标。我们建立了一个T.克鲁兹此外，我们还开发了贝叶斯机器学习模型，用于虚拟筛选化合物库。选择了97种化合物进行体外试验，发现其中11种的EC 50 < 10μM。我们将五种化合物进展为查加斯病的体内小鼠功效模型，并验证了机器学习模型可以识别不在训练集中的体外活性化合物以及已知的阳性对照。抗疟咯萘啶在急性查加斯病小鼠模型中有效率为85.2%。我们还提出了潜在的目标（为未来的验证），这种化合物的基础上，结构相似的已知化合物的目标在T。克鲁兹我们已经证明了如何结合化学信息学和生物信息学的T。cruzi药物的发现可以将可能被忽视的有趣的体内活性分子带到光中。我们所采取的方法，广泛适用于其他新界东。查加斯病是由真核寄生虫克氏锥虫引起的一种被忽视的热带疾病。该疾病是拉丁美洲的地方病，但在北美和欧洲越来越多地发现，主要是通过移民，这种疾病的传播引起了新的关注，需要新的，安全的，有效的治疗方法来治疗T。克氏感染我们已经使用来自表型筛选的数据来建立贝叶斯模型以预测抗T. Cruzi in Vitro.这些模型用于对各种小分子文库进行评分。我们选择了不到100种化合物进行测试，并发现了体外活性物质，其中一些在体内功效模型中进行了测试。我们确定了抗疟咯萘啶具有体内疗效，并为我们提供了一个新的起点，进一步研究和优化。
Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi. We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs. Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The disease is endemic to Latin America but is increasingly found in North America and Europe, primarily through immigration, and the spread of this disease is bringing new attention to the need for novel, safe, and effective therapeutics to treat T. cruzi infection. We have used data from a phenotypic screen to build Bayesian models to predict anti-parasitic activity against T. cruzi in vitro. These models were used to score various small libraries of molecules. We selected less than 100 compounds for testing and found in vitro actives, some of which were tested in an in vivo efficacy model. We identified the antimalarial pyronaridine as having in vivo efficacy and provides us with a new starting point for further investigation and optimization.
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DOI：
10.1016/0035-9203(80)90010-3
发表时间：
1980-01-01
影响因子：
2.2
作者：
BEVERIDGE, E;CALDWELL, IC;WALDRON, MM
通讯作者：
WALDRON, MM
影响因子：
2.7
作者：
Choy, Jonathan W.;Bryant, Clifford;Renslo, Adam R.
通讯作者：
Renslo, Adam R.
影响因子：
4
作者：
Chandra, M;Shirani, J;Tanowitz, HB
通讯作者：
Tanowitz, HB
影响因子：
3.8
作者：
Andriani, Grasiella;Chessler, Anne-Danielle C.;Rodriguez, Ana
通讯作者：
Rodriguez, Ana
影响因子：
7.3
作者：
Andriani, Grasiella;Amata, Emanuele;Pollastri, Michael P.
通讯作者：
Pollastri, Michael P.