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: 10226047
• 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/10226047
• 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.

项目总结