Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.
Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.
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DOI：
10.1371/journal.pone.0063240
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Bunin BA
中科院分区：
文献类型：
--
作者：
Ekins S;Reynolds RC;Franzblau SG;Wan B;Freundlich JS;Bunin BA
全细胞高通量筛选 (HTS) 被广泛用于寻找具有抗结核分枝杆菌 (Mtb) 活性的化合物，从而进一步开发新的结核病 (TB) 药物。这些筛选的命中率通常在 10 至 25 µM 浓度下进行，范围通常从低于 1% 到低个位数。迫切需要从过去的筛选数据中学习提高命中识别效率的新方法。制药行业多年来一直利用计算方法来优化体外测试的化合物库，但学术实验室在寻找新的结核病药物时并未完全接受这种做法。采用这些经过验证的方法，我们最近建立并验证了贝叶斯机器学习模型，用于根据结核病抗菌药物采购协调设施公开的大规模 HTS 数据来预测具有抗 Mtb 活性的化合物。我们现在展示了迄今为止最大规模的前瞻性验证，其中我们使用这些贝叶斯模型计算筛选了 82,403 个分子，在体外分析了总共 550 个分子，并鉴定了 124 种针对 Mtb 的活性物质。不同数据集的个体命中率从 15% 到 28% 不等。我们已经确定了几种 FDA 批准的后期临床候选激酶抑制剂，具有抗 Mtb 活性，这可能代表进一步优化的起点。本文开发的计算模型以及由它们衍生的市售分子现在可供任何追求 Mtb 药物发现的团体使用。
High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15–28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
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影响因子：
5.6
作者：
Cavazos, Alexandra;Prigozhin, Daniil M.;Alber, Tom
通讯作者：
Alber, Tom
DOI：
10.4049/jimmunol.1201538
发表时间：
2012-10-15
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
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通讯作者：
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作者：
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通讯作者：
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影响因子：
3.7
作者：
Ekins, Sean;Williams, Antony J.
通讯作者：
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影响因子：
3.7
作者：
Ekins, Sean;Freundlich, Joel S.
通讯作者：
Freundlich, Joel S.