Personalized Antimicrobial Combinations to Combat Resistance

摘要 革兰氏阴性菌中多重耐药的流行率（例如，铜绿假单胞菌、 鲍曼不动杆菌）正以惊人的速度上升，当 单独使用。新药开发的速度不太可能跟上多药治疗的增长速度。 阻力联合治疗通常在临床上作为最后手段使用。然而，考虑到众多 可能性，临床医生主要根据轶事经验选择联合治疗， 直觉一个强大的方法来指导合理选择联合治疗将是至关重要的延迟返回 回到抗生素出现之前的时代。 我们的长期目标是优化抗生素的临床使用，以对抗耐药性的出现。的 本申请的目的是开发一种新型的精密医学平台（监测设备和数据 处理算法），其将指导组合疗法的设计。如果短期实验数据可以 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 通过确定可能的最佳组合，可以合理地制定治疗策略，从而指导 临床医生在选择联合治疗。我们计划实现申请的目标， 如下：（1）提高测试通量以获得最佳临床应用;（2）鉴定有用的抗生素组合 针对多药耐药细菌;和（3）最大化靶向特异性靶向药物的增强作用。 抗性机制（例如，β-内酰胺酶抑制剂）。 在本申请中，所提出的方法将通过铜绿假单胞菌、A. 鲍曼不动杆菌和肺炎克雷伯菌。然而，所提出的基于模型的系统并不局限于 特异性抗微生物剂-病原体组合。它可以外推到其他抗菌剂 (e.g.,抗菌药物、抗分枝杆菌药物和抗逆转录病毒药物），以及 其它病原体（例如，金黄色葡萄球菌，结核病和艾滋病毒）与不同的微生物 特色

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.