The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV–vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4H-pyrido[1,2-a]pyrimidin-4-one.

中文翻译：

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机器学习模型识别新德里金属-β-内酰胺酶抑制剂


金属-β-内酰胺酶 (MBL)，特别是新德里金属-β-内酰胺酶-1 (NDM-1) 在全球范围内的传播，正在威胁着 β-内酰胺的功效，而β-内酰胺是目前最有效、最处方的一类抗生素。诊所。目前，尽管在抑制剂开发中使用了许多策略，包括定量高通量筛选（qHTS）、基于片段的药物发现（FBDD）和分子对接，但临床上仍缺乏 FDA 批准的 MBL 抑制剂。本文描述了一种基于机器学习的预测工具，该工具是使用大型化学库的 HTS 结果和之前发布的抑制数据生成的。然后使用预测工具对 NIH Genesis 文库进行虚拟筛选，随后使用 qHTS 进行筛选。一种新型 MBL 抑制剂被发现并被证明可以降低美罗培南对一组表达 NDM-1 的大肠杆菌和肺炎克雷伯菌临床分离株的最低抑制浓度 (MIC)。利用平衡透析与金属分析、自然态电喷雾电离质谱、紫外可见分光光度法和分子对接探讨了这种新型支架的抑制机制。未发现的抑制剂化合物72922413被证明是9-羟基-3-[(5-羟基-1-氧杂-9-氮杂螺[5.5]十一碳-9-基)羰基]-4H-吡啶并[1,2- a]嘧啶-4-酮。