Rapid Identification of Optimal Combination Regimens for Pseudomonas aeruginosa

快速鉴定铜绿假单胞菌的最佳组合方案

• 批准号: 9186485
• 负责人: George Louis Drusano
• 金额: $ 72.99万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9186485
• 关键词: Back; Bacteria; Bacterial Pneumonia; Biological Assay; Chemotherapy-Oncologic Procedure; Clinical; Clinical Trials; Combination Drug Therapy; Combined Modality Therapy; Data; Disease; Dose; Dose Fractionation; Drug Combinations; Drug Interactions; Evaluation; Fiber; Flow Cytometry; Hospitals; Hour; Infection; Investigation; Link; Literature; Lung; Mediating; Methods; Modeling; Morbidity - disease rate; Mus; Nature; Nosocomial pneumonia; Organism; Outcome; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pneumonia; Process; Pseudomonas aeruginosa; Pseudomonas aeruginosa pneumonia; Recovery; Recruitment Activity; Regimen; Resistance; Schedule; Site; Speed; Testing; Time; Validation; Ventilator; Work; antimicrobial; cell killing; experimental study; granulocyte; improved; improved outcome; in vivo; insight; killings; mathematical model; mortality; mouse model; novel; pathogen; public health relevance; rapid technique; resistance mechanism; survivorship; synergism; virtual; Back; Bacteria; Bacterial Pneumonia; Biological Assay; Chemotherapy-Oncologic Procedure; Clinical; Clinical Trials; Combination Drug Therapy; Combined Modality Therapy; Data; Disease; Dose; Dose Fractionation; Drug Combinations; Drug Interactions; Evaluation; Fiber; Flow Cytometry; Hospitals; Hour; Infection; Investigation; Link; Literature; Lung; Mediating; Methods; Modeling; Morbidity - disease rate; Mus; Nature; Nosocomial pneumonia; Organism; Outcome; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pneumonia; Process; Pseudomonas aeruginosa; Pseudomonas aeruginosa pneumonia; Recovery; Recruitment Activity; Regimen; Resistance; Schedule; Site; Speed; Testing; Time; Validation; Ventilator; Work; antimicrobial; cell killing; experimental study; granulocyte; improved; improved outcome; in vivo; insight; killings; mathematical model; mortality; mouse model; novel; pathogen; public health relevance; rapid technique; resistance mechanism; survivorship; synergism; virtual

 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.

 描述（由应用提供）：呼吸机征收医院获得的细菌性肺炎是一种具有重大死亡率和发病率的疾病过程。耐药性出现，尤其是铜绿假单胞菌的耐药性很常见，单一疗法的命令为33-50％。最近已证明粒细胞可用于细菌细胞杀死。细菌燃烧的迅速降低至半饱和点导致粒细胞介导的细菌杀死的恢复。联合治疗对于获得最大杀伤率和抑制耐药亚群的扩增的能力是审慎的。识别最佳组合方案是困难且耗时的。这项提案的总体目的是开发一种新方法来快速，稳健地识别最佳组合疗法，从而提供最大的细胞杀伤 抑制阻力。快速细胞杀死将有助于将细菌燃烧在半饱和点以下，并将粒细胞“重新恢复”。我们的目的是：1）开发一种新的快速方法，以使用流式细胞仪2）在HFIM中通过该方法引起的测试方案确定最佳组合化疗方案；我们将研究3种等源性菌株，以确定不同耐药机制对细胞杀死和耐药出现的影响；我们将就这些实验进行先进的数学模型来分析这些实验。然后，我们将在鼠PA肺炎模型中验证这些发现3）量化粒细胞和组合疗法在细胞杀伤和耐药性抑制中的相互作用。流式细胞术的使用与Greco数学模型相关联，将允许对协同/添加性/拮抗作用的统计确定确定。在我们的中空纤维感染模型和鼠铜绿假单胞菌肺炎模型中对这些组合的探索将提供以下方式以预测的方式鉴定为流动测定法将其识别为最佳或非优势行为的验证。将确定方案对颗粒细胞募集的影响。所有这些实验将通过最新的数学模型链接。最佳方案将改善结果，抑制阻力放大和由于Granulyte功能返回而恢复速度。定义最佳抗微生物组合方案将产生几种有益的结果：1]抗药性出现将被抑制2]快速细菌杀死将不饱和粒细胞，增加1.0-1.5个细菌每天额外的细菌杀死的日志3]临床结束和（希望）延长的时间会增加，因为杀死的速度会延长。共同完成整体临床结果将得到改善。