Rapid Identification of Optimal Combination Regimens for Pseudomonas aeruginosa

 描述(申请人提供)：需要呼吸机的医院获得性细菌性肺炎是一种具有相当高死亡率和发病率的疾病过程。耐药性的出现，尤其是铜绿假单胞菌，在单一疗法中很常见，在单一疗法的患者中约为33%-50%。最近的研究表明，粒细胞对细菌细胞的杀伤是饱和的。迅速将细菌负荷降低到低于半饱和点会导致粒细胞介导的细菌杀灭的回归。联合治疗对于获得最大杀伤率和抑制耐药亚群扩大的能力都是谨慎的。确定最佳组合方案是困难和耗时的。这项提议的首要目标是开发一种新的方法来快速而有力地确定将提供最大细胞杀伤力的最佳联合疗法。 带有抵抗抑制功能。快速的细胞杀灭将有助于将细菌负荷降低到半饱和点以下，并使粒细胞恢复正常。我们的目的是：1)开发一种新的快速方法来使用流式细胞术来确定最佳的联合化疗方案；2)在HFIM中使用这种方法得到的测试方案；我们将观察3个等基因菌株，以确定不同的耐药机制对细胞杀伤和耐药产生的影响；我们将使用最先进的数学模型来分析这些实验；然后我们将在小鼠PA肺炎模型中验证这些发现3)定量粒细胞和联合治疗在细胞杀伤和耐药抑制方面的相互作用。与Greco数学模型相结合的流式细胞术的使用将允许在统计上稳健地确定协同作用/相加作用/拮抗作用。在我们的中空纤维感染模型和小鼠铜绿假单胞菌肺炎模型中探索这些组合，将提供通过流动分析确定为最佳或非最佳方案的验证，以预测的方式表现。方案对粒细胞募集的影响将被确定。所有这些实验都将通过最先进的数学模型联系在一起。优化方案可改善预后，抑制耐药扩增，加速粒细胞功能恢复。确定最佳的抗菌药物组合方案将产生几个有益的结果：1)耐药性的出现将被抑制2)快速细菌杀灭将使粒细胞不饱和，每天增加1.0-1.5个额外的细菌杀灭日志3)临床结果和(希望)拔管时间将因杀伤率的提高而缩短。综上所述，总体临床结果将得到改善。

 DESCRIPTION (provided by applicant): Ventilator-Requiring Hospital Acquired Bacterial pneumonia is a disease process with substantial mortality and morbidity. Resistance emergence, particularly with P. aeruginosa is common with monotherapy and is on the order of 33-50% in patients treated with monotherapy. It has been recently demonstrated that granulocytes are saturable for bacterial cell kill. Rapid lowering of the bacterial burden to less than the half-saturation point results in a return of granulocyte-mediated bacterial kill. Combination therapy is prudent for both the ability to obtain maximal kill rate and to suppress amplification of resistant subpopulations. Identifying optimal combination regimens is difficult and time consuming. It is the overarching aim of this proposal to develop a new method to rapidly and robustly identify optimal combination therapy that will provide maximal cell kill along with resistance suppression. The rapid cell kill will help reduce the bacterial burden below the half saturation point and bring the granulocytes "back on line". it is our intent to: 1) Develop a new rapid method to identify optimal combination chemotherapy regimens employing flow cytometry 2) Test regimens resulting from this method in the HFIM; we will look at 3 isogenic strains to ascertain the impact of different resistance mechanisms on cell kill and resistance emergence; we will employ state-of-the art mathematical models to analyze these experiments; we will then validate these findings in the murine PA pneumonia models 3) Quantitate the interaction of granulocytes and combination therapy on cell kill and resistance suppression. The use of flow cytometry, linked with the Greco mathematical model will allow statistically robust determination of synergy/ additivity/ antagonism. Exploration of these combinations in our Hollow Fiber Infection Model and murine P. aeruginosa pneumonia models will provide the validation that the regimens identified by the flow assay as optimal or non-optimal behave in the fashion predicted. The impact of regimen on granulocyte recruitment will be ascertained. All these experiments will be linked by state-of-the-art mathematical models. Optimal regimens will improve outcomes, suppress resistance amplification and speed recovery because of granulyte function return. Defining optimal antimicrobial combination regimens will generate several salutary outcomes: 1] resistance emergence will be suppressed 2] rapid bacterial kill will unsaturate granulocytes, adding 1.0-1.5 extra Logs of bacterial kill per day 3] clinical outcomes and (hopefully) time to extubation will be shortened because of the improved rate of kill. Taken together overall clinical outcomes will be improved.