Personalized Antimicrobial Combinations to Combat Resistance

摘要 革兰氏阴性菌(如铜绿假单胞菌、 鲍曼不动杆菌)正在以惊人的速度上升，导致许多(如果不是全部)抗生素在以下情况下无效 单独使用。新药开发的速度不太可能跟上多种药物的增长步伐 抵抗。联合治疗通常在临床上被用作最后的手段。然而，考虑到众多 可能性，联合疗法主要由临床医生根据轶事经验和 直觉。一种可靠的方法来指导合理选择联合治疗将是延迟复发的关键 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。这个 本应用的目的是开发一种新型的精准医学平台(监测装置和数据 处理算法)，这将指导联合治疗的设计。如果短期实验数据能够 用于预测患者特定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疗法。我们计划将应用程序的目标实现为 如下：(1)提高检测能力，以优化临床应用；(2)确定有用的抗生素组合 抗多药耐药细菌；以及(3)最大限度地发挥针对特定目标的药物的增强作用 耐药细菌的耐药机制(如β-内酰胺酶抑制剂)。 在这个应用中，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，拟议的基于模型的系统并不局限于 特定的抗菌剂-病原体组合。它可以推断为其他抗菌剂。 (例如，抗菌药物、抗分枝杆菌药物和抗逆转录病毒药物)具有不同的作用机制，以及 其他具有不同微生物的病原体(如金黄色葡萄球菌、结核病和艾滋病毒) 特点。

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.