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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

基本信息

批准号：
10460988
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
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-10 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10460988
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 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

项目摘要

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.

项目摘要革兰氏阴性菌中小分子积累的预测模型。耐抗生素的革兰氏阴性细菌感染的发病率正在增加，迫切需要应对这一日益严重的公共卫生威胁。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结构特征的理解不足，渗透和流出。因此，虽然通常可以为新靶点鉴定有效的生化抑制剂，将它们开发成具有全细胞抗菌活性的化合物已被证明具有挑战性。为了解决这个关键问题，我们在此提出了一个全面的，多学科的方法来开发定量模型来预测革兰氏阴性菌中的小分子渗透和流出。我们有开创了一个通用平台，用于系统、定量评估细菌中的小分子积累，使用无标记LC-MS/MS检测和多变量化学信息学分析。我们还开发了野生型、高孔化、外排敲除和双重受损的E.大肠杆菌， P. aeruginosa和A.鲍曼不动杆菌，使我们能够解剖外/内膜的个体贡献，渗透和主动外排净积累，使用动力学模型，准确地概括了现有的实验数据此外，我们还开发了机器学习和神经网络方法来进行QSAR （定量结构-活性关系）药理学性质的建模，现在将用于开发革兰氏阴性蓄积、渗透和外排的预测性化学信息学模型。该项目将由一个多学科SPEAR-GN项目组（小分子渗透和抗生素耐药革兰氏阴性菌的外排，“矛枪”），涉及Derek Tan（MSK，PI），Helen Zgurskaya（P.A.，PI）、布拉德利谢尔本（默克，首席合作者）、Valentin Rybenkov（P.A.，Co-I）、Adam Duerfeldt（默克，合作者）、Carl Balibar（默克，合作者）和大卫麦克拉伦（默克，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、生物物理建模、化学信息学和药物化学。在此，我们将设计和合成具有不同结构和物理化学性质的化学库，特性;分析它们在高通量和高密度下在等基因菌株组中的积累从所得实验数据集中提取渗透和流出的动力学参数; 开发并验证累积、渗透和外排的稳健QSAR模型;并演示实用性这些模型在药物化学运动中开发新的革兰氏阴性抗生素，目标的该项目将在抗菌药物发现领域取得重大进展，提供强大的为科学界提供工具，以应对这一对公共卫生的重大威胁。