Personalized Antimicrobial Combinations to Combat Resistance

对抗耐药性的个性化抗菌药物组合

基本信息

批准号：
10448308
负责人：
VINCENT H TAM
金额：
$ 70.1万
依托单位：
UNIVERSITY OF HOUSTON
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：
2018-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10448308
关键词：
Acinetobacter baumannii Address Algorithms Animal Model Anti-Bacterial Agents Anti-Retroviral Agents Antibiotics Antimicrobial Resistance Antimycobacterial Agents Bacteria Bacterial Antibiotic Resistance Bacterial Infections Cessation of life Characteristics Clinical Collaborations Combined Antibiotics Combined Modality Therapy Data Development Devices Distant Dose Drug resistance Foundations Future Goals Gram-Negative Bacteria Grant Growth HIV Hospitals Infection Intuition Investigational Therapies Klebsiella pneumoniae Lactamase Mathematics Methods Microbiology Modeling Multi-Drug Resistance Multiple Bacterial Drug Resistance Outcome Patients Persons Prediction of Response to Therapy Predisposition Prevalence Pseudomonas aeruginosa Regimen Research Research Personnel Resistance Resort Risk Staphylococcus aureus System Testing Time Treatment outcome Tuberculosis United States National Institutes of Health antibiotic design antimicrobial antimicrobial drug bacterial resistance base beta-Lactamase clinical application clinical investigation clinically relevant combat computerized data processing design drug development drug resistant bacteria effective therapy experience experimental study immune function in vitro Model inhibitor innovation mathematical model model building monitoring device multidisciplinary new combination therapies novel novel therapeutics pathogen patient response pre-clinical precision medicine predicting response predictive modeling resistance mechanism response targeted agent tool treatment strategy

项目摘要

Abstract The prevalence of multidrug-resistance in Gram-negative bacteria (e.g., Pseudomonas aeruginosa, Acinetobacter baumannii), is rising at an alarming rate, rendering many (if not all) antibiotics ineffective when used alone. The rate of new drug development is unlikely to keep pace with the increase in multidrug resistance. Combination therapy is often used clinically as a last resort. However, considering the numerous possibilities, combination therapy are selected by clinicians mostly based on anecdotal experience and intuition. A robust method to guide rational selection of combination therapy would be crucial to delay returning to the pre-antibiotic era. Our long-term goal is to optimize clinical use of antibiotics to combat the emergence of resistance. The objective of this application is to develop a novel precision medicine platform (monitoring device and data processing algorithm) that will guide the design of combination therapy. If short-term experimental data can be used to predict the response of patient-specific bacteria to clinically relevant antibiotic exposures, effective treatment strategies could be formulated rationally by identifying the best possible combination, thus guiding clinicians in the selection of combination therapy. We plan to accomplish the objective of the application as follows: (1) enhance testing throughput for optimal clinical application; (2) identify useful antibiotic combinations against multidrug resistant bacteria; and (3) maximize the potentiating effect of agents targeting specific mechanisms of resistance (e.g., β-lactamase inhibitors) in drug-resistant bacteria. In this application, the proposed approach will be illustrated by experimental data with P. aeruginosa, A. baumannii and Klebsiella pneumoniae. However, the proposed model-based system is not confined to a specific antimicrobial agent - pathogen combination. It could be extrapolated to other antimicrobial agents (e.g., antibacterials, antimycobacterials and antiretrovirals) with different mechanisms of action, as well as to other pathogens (e.g., Staphylococcus aureus, tuberculosis and HIV) with different microbiological characteristics.

抽象的革兰氏阴性细菌中多药抗性的患病率（例如，铜绿假单胞菌， baumannii），以惊人的速度上升，使许多（如果不是全部）抗生素无效独自使用。新药物开发的速度不太可能与多饮用物的增加保持同步反抗。联合疗法在临床上通常被用作最后的手段。但是，考虑到很多可能性，联合疗法是由临床医生选择的，主要是基于轶事经验和直觉。指导合理选择组合疗法的一种强大方法对于延迟回报至关重要到抗生素前时代。我们的长期目标是优化抗生素的临床使用以抵抗抗药性的出现。这该应用的目的是开发一个新颖的精密医学平台（监视设备和数据处理算法）将指导组合疗法的设计。如果短期实验数据可以是用于预测患者特异性细菌对临床相关抗生素暴露的反应，有效可以通过确定最佳组合来合理地制定治疗策略，从而指导临床医生在选择组合疗法中。我们计划实现应用程序的目标以下内容：（1）增强测试吞吐量以进行最佳临床应用；（2）确定有用的抗生素组合针对多药耐药细菌；（3）最大化针对特定的代理的增强作用耐药细菌中耐药机制（例如β-内酰胺酶抑制剂）。在此应用中，建议的方法将通过铜绿假单胞菌。 Baumannii和Klebsiella肺炎。但是，提出的基于模型的系统不仅限于特异性抗菌剂 - 病原体组合。它可以推断到其他抗菌剂（例如，抗菌，抗菌细菌和抗逆转录病毒）具有不同的作用机制，以及其他病原体（例如金黄色葡萄球菌，结核病和HIV）具有不同的微生物学特征。

项目成果

期刊论文数量（26）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Case Commentary: Novel Therapy for Multidrug-Resistant Acinetobacter baumannii Infection.

病例评论：多重耐药鲍曼不动杆菌感染的新疗法。

DOI：
10.1128/aac.01996-21
发表时间：
2022
期刊：
Antimicrobial agents and chemotherapy
影响因子：
4.9
作者：
Eales,BriannaM;Tam,VincentH
通讯作者：
Tam,VincentH

Rapid In Vitro Assessment of Antimicrobial Drug Effect Bridging Clinically Relevant Pharmacokinetics: A Comprehensive Methodology.

DOI：
10.3390/pharmaceutics15061671
发表时间：
2023-06-07
期刊：
Pharmaceutics
影响因子：
5.4
作者：
Nikolaou M;Tam VH
通讯作者：
Tam VH

In vitro activity of tigecycline and proteomic analysis of tigecycline adaptation strategies in clinical Enterococcus faecalis isolates from China.

中国临床粪肠球菌替加环素的体外活性和替加环素适应策略的蛋白质组学分析。

DOI：
10.1016/j.jgar.2022.04.022
发表时间：
2022
期刊：
Journal of global antimicrobial resistance
影响因子：
4.6
作者：
Bai,Bing;Chen,Chengchun;Zhao,Yuxi;Xu,Guangjian;Yu,Zhijian;Tam,VincentH;Wen,Zewen
通讯作者：
Wen,Zewen

Hydrolytic activity of KPC-producing Klebsiella pneumoniae clinical isolates.