Predictive Models for Small-Molecule Accumulation in Gram-Negative Bacteria

革兰氏阴性细菌中小分子积累的预测模型

• 批准号: 9982190
• 负责人: DEREK S TAN
• 金额: $ 123.93万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-10 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9982190
• 关键词: Acinetobacter baumannii; Address; Algorithmic Software; Anti-Bacterial Agents; Antibiotic Resistance; Antibiotics; Architecture; Bacteria; Biochemical; Biochemistry; Biological Assay; Biological Availability; Cells; Chemicals; Communities; Data; Data Set; Detection; Development; Effectiveness; Escherichia coli; Gram-Negative Bacteria; Gram-Negative Bacterial Infections; Human; Incidence; Individual; Infection; Interdisciplinary Study; Kinetics; Knock-out; Label; Lead; Libraries; Machine Learning; Mammalian Cell; Mass Spectrum Analysis; Measurement; Measures; Membrane; Microbiology; Modeling; Oral; Partner in relationship; Penetration; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacology; Property; Pseudomonas aeruginosa; Public Health; Quantitative Evaluations; Quantitative Structure-Activity Relationship; Role; Structure; Testing; Variant; analog; base; biophysical model; cell envelope; cheminformatics; combat; computerized tools; density; design; drug discovery; efflux pump; high throughput screening; improved; inhibitor/antagonist; interdisciplinary approach; kinetic model; lead optimization; learning network; multidisciplinary; neural network; novel; predictive modeling; programs; prospective; public health relevance; screening; small molecule; small molecule libraries; success; tool

PROJECT SUMMARY Predictive Models for Small-Molecule Accumulation in Gram-Negative Bacteria. Antibiotic-resistant Gram-negative bacterial infections are increasing in incidence and novel antibiotics are urgently needed to combat this growing threat to public health. A major roadblock to the development of novel antibiotics is our poor understanding of the structural features of small molecules that correlate with bacterial penetration and efflux. As a result, while potent biochemical inhibitors can often be identified for new targets, developing them into compounds with whole-cell antibacterial activity has proven challenging. To address this critical problem, we propose herein a comprehensive, multidisciplinary approach to develop quantitative models to predict small-molecule penetration and efflux in Gram-negative bacteria. We have pioneered a general platform for systematic, quantitative evaluation of small-molecule accumulation in bacteria, using label-free LC-MS/MS detection and multivariate cheminformatic analysis. We have also developed unique isogenic strain sets of wild-type, hyperporinated, efflux-knockout, and doubly-compromised E. coli, P. aeruginosa, and A. baumannii that allow us to dissect the individual contributions of outer/inner membrane penetration and active efflux to net accumulation, using a kinetic model that accurately recapitulates available experimental data. Moreover, we have developed machine learning and neural network approaches to QSAR (quantitative structure–activity relationship) modeling of pharmacological properties that will now be used to develop predictive cheminformatic models for Gram-negative accumulation, penetration, and efflux. This project will be carried out by a multidisciplinary SPEAR-GN Project Team (Small-molecule Penetration & Efflux in Antibiotic-Resistant Gram-Negatives, “speargun”) involving the labs of Derek Tan (MSK, PI), Helen Zgurskaya (OU, PI), Bradley Sherborne (Merck, Lead Collaborator), Valentin Rybenkov (OU, Co-I), Adam Duerfeldt (OU, Co-I), Carl Balibar (Merck, Collaborator), and David McLaren (Merck, Collaborator), comprising extensive combined expertise in organic and diversity-oriented synthesis, biochemistry, microbiology, high- throughput screening, mass spectrometry, biophysical modeling, cheminformatics, and medicinal chemistry. Herein, we will design and synthesize chemical libraries with diverse structural and physicochemical properties; analyze their accumulation in the isogenic strain sets in both high-throughput and high-density assay formats; extract kinetic parameters for penetration and efflux from the resulting experimental datasets; develop and validate robust QSAR models for accumulation, penetration, and efflux; and demonstrate the utility of these models in medicinal chemistry campaigns to develop novel Gram-negative antibiotics against three targets. This project will provide a major advance in the field of antibacterial drug discovery, providing powerful enabling tools to the scientific community to address this major threat to public health.

项目概要 革兰氏阴性细菌中小分子积累的预测模型。 耐抗生素革兰氏阴性细菌感染的发病率正在增加，新型抗生素正在出现 迫切需要应对这种对公共卫生日益严重的威胁，这是小说发展的主要障碍。 抗生素是我们对与细菌相关的小分子的结构特征了解甚少 因此，虽然通常可以确定新靶标的有效生化抑制剂， 事实证明，将它们开发成具有全细胞抗菌活性的化合物具有挑战性。 为了解决这个关键问题，我们提出了一种全面的、多学科的方法来开发 我们有预测革兰氏阴性细菌中小分子渗透和流出的定量模型。 开创了系统评估细菌中小分子积累定量的通用平台， 我们还开发了使用无标记 LC-MS/MS 检测和多变量化学信息分析。 野生型、超孔化、外排敲除和双重受损大肠杆菌的独特同基因菌株组， 铜绿假单胞菌和鲍曼不动杆菌使我们能够剖析外膜/内膜的个体贡献 渗透和主动流出到净积累，使用准确概括可用的动力学模型 此外，我们还开发了 QSAR 的机器学习和神经网络方法。 （定量结构-活性关系）药理特性建模，现在将用于 开发革兰氏阴性菌积累、渗透和流出的预测化学信息学模型。 该项目将由多学科 SPEAR-GN 项目团队（小分子渗透和 抗生素耐药革兰氏阴性菌的外流（“鱼枪”）涉及 Derek Tan（MSK、PI）、Helen 的实验室 Zgurskaya（OU，PI）、Bradley Sherborne（默克，首席合作者）、Valentin Rybenkov（OU、Co-I）、Adam Duerfeldt（OU，Co-I）、Carl Balibar（默克公司，合作者）和 David McLaren（默克公司，合作者），包括 在有机和多样性导向的合成、生物化学、微生物学、高 通量筛选、质谱分析、生物物理建模、化学信息学和药物化学。 在这里，我们将设计和合成具有不同结构和理化性质的化学库 特性；分析它们在高通量和高密度的同基因菌株组中的积累 测定格式；从所得实验数据集中提取渗透和流出的动力学参数； 开发和验证强大的 QSAR 累积、渗透和流出模型并演示其实用性； 这些模型在药物化学运动中的应用，旨在开发针对三种的新型革兰氏阴性抗生素 该项目将在抗菌药物发现领域取得重大进展，提供强大的支持。 为科学界提供解决这一公共卫生重大威胁的工具。