Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center

肺炎治疗 (SCRIPT) 系统生物学中心成功的临床反应

• 批准号: 10551461
• 负责人: RICHARD G WUNDERINK
• 金额: $ 250.61万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-17 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551461
• 关键词: 2019-nCoV; Admission activity; Alveolar; Bioinformatics; Blood specimen; Bronchoalveolar Lavage; COVID-19; COVID-19 pandemic; COVID-19 pneumonia; Caring; Cessation of life; Clinical; Clinical Data; Clinical Trials; Collaborations; Complex; Critical Illness; Curettage procedure; DNA; Data; Data Set; Databases; Direct Costs; Disease model; Distal; Dose; Drug Kinetics; Enrollment; Epidemiology; Etiology; Evaluation; Flow Cytometry; Funding; Generations; Grant; Hospitals; Immune response; Inbred Mouse; Infection; Intervention; Intubation; Investigational Therapies; Klebsiella; Laboratories; Lead; Lung; Lung Transplantation; Mechanical ventilation; Metagenomics; Modeling; Modification; Morbidity - disease rate; Multiomic Data; Names; Nasopharynx; Nature; Nose; Nosocomial pneumonia; Outcome; Pathogenesis; Patient Care; Patients; Pharmacodynamics; Phase; Phase II Clinical Trials; Pneumonia; Procedures; Pseudomonas aeruginosa; Publishing; Research Personnel; Respiratory Mucosa; Risk; Sampling; Space Models; Staphylococcus aureus; Systems Biology; Technology; Testing; Therapeutic; Time; United States; Validation; Viral; Work; clinical care; clinical phenotype; clinically actionable; co-infection; cohort; community acquired pneumonia; data management; high dimensionality; humanized mouse; improved; influenza epidemic; inhibitor; innovation; longitudinal analysis; microbial; microbiome; microbiome therapeutics; molecular modeling; mortality; mouse model; multidimensional data; multiple omics; novel therapeutics; participant enrollment; pathogen; pharmacologic; phenome; pneumonia model; pneumonia treatment; predictive modeling; randomized trial; response; seasonal influenza; severe COVID-19; single-cell RNA sequencing; superinfection; therapeutic target; translational impact; treatment response; ventilation; ventilator-associated pneumonia

PROJECT SUMMARY/ABSTRACT – Overall Pneumonia, including both community-acquired pneumonia (CAP) and hospital-acquired/ventilator-associated pneumonia (HAP/VAP), causes almost 80% of deaths from infections in the US. As evidenced by the ongoing COVID-19 pandemic, severe pneumonia represents an important cause of morbidity and mortality. Our U19 Systems Biology Center, Successful Clinical Response In Pneumonia Therapy (SCRIPT), which we rename Super-SCRIPT (SCRIPT2) for this renewal, leverages bronchoalveolar lavage (BAL), nasal curettage, and blood sampling paired with cutting-edge multi-omics technologies—including multiparameter flow cytometry, single- cell RNA-sequencing, deep pathogen sequencing, DNA metagenomics, and deep clinical phenotyping—to develop models of pneumonia pathogenesis. In SCRIPT, we generated a detailed systems biology model of SARS-CoV-2 pathobiology that supported a novel therapy for severe SARS-CoV-2 pneumonia that was efficacious in a phase II clinical trial. In SCRIPT2, we will determine whether models generated using high- dimension longitudinal data describing the host response, pathogen, microbiome, and clinical phenome in patients with severe CAP (Project 1) and HAP/VAP (Project 2) can predict favorable or unfavorable clinical transitions over the course of a pneumonia episode. We test the hypothesis that multi-omics inputs will create actionable models that identify favorable and unfavorable clinical transitions/outcomes for both severe CAP and severe HAP/VAP. Aim 1/Project 1. To generate actionable models to improve the clinical course of patients with severe CAP using multi-omics analysis of longitudinal samples from the distal lung and nasopharynx. Aim 2/Project 2. To generate actionable models to improve the clinical course of patients with severe HAP/VAP using a multi-omics analysis of longitudinal samples from the distal lung. Three scientific cores support the projects. The Technology Core developed cutting edge multi-omics approaches for the analysis of bronchoalveolar lavage samples collected as part of clinical care. The Data Management and Bioinformatics Core weaves together clinical data extracted from our own EHR and those from other centers. The Modeling Core works seamlessly with the Projects and Cores to integrate, validate and iteratively improve latent space models of disease. Clinician scholars who provide direct care to patients with pneumonia lead or contribute to each project and core, enhancing the translational impact of our findings.

项目总结/摘要-总体 肺炎，包括社区获得性肺炎（CAP）和医院获得性/呼吸机相关性肺炎 肺炎（HAP/VAP）导致美国近80%的感染死亡。正如正在进行的 2019冠状病毒病大流行，严重肺炎是发病率和死亡率的重要原因。我们的U19 系统生物学中心，肺炎治疗的成功临床反应（CCLT），我们将其重新命名为 用于这种更新的Super-BRICT（BRICT 2）利用支气管肺泡灌洗（BAL）、鼻刮术和血液 采样与尖端的多组学技术配对，包括多参数流式细胞术，单 细胞RNA测序、深层病原体测序、DNA宏基因组学和深层临床表型分析， 建立肺炎发病机制的模型。在CBT中，我们生成了一个详细的系统生物学模型， SARS-CoV-2病理生物学支持一种治疗严重SARS-CoV-2肺炎的新疗法， 在II期临床试验中有效。在CITT2中，我们将确定是否使用高- 描述宿主反应、病原体、微生物组和临床表型的纵向数据 严重CAP（项目1）和HAP/VAP（项目2）患者可以预测有利或不利的临床 在肺炎发作过程中的转变。我们测试的假设，多组学输入将 创建可操作的模型，识别有利和不利的临床过渡/结果， 重度CAP和重度HAP/VAP。 目标1/项目1。生成可操作的模型，以改善重度 CAP使用来自远端肺和鼻咽的纵向样本的多组学分析。 目标2/项目2。生成可操作的模型，以改善重度 使用来自远端肺的纵向样本的多组学分析的HAP/VAP。 三个科学核心支持这些项目。技术核心开发了尖端的多组学 作为临床护理的一部分收集的支气管肺泡灌洗样品的分析方法。数据 管理和生物信息学核心将从我们自己的EHR和来自 其他中心。建模核心与项目和核心无缝协作， 迭代地改进疾病潜在空间模型。临床医生学者谁提供直接照顾病人， 肺炎导致或有助于每个项目和核心，增强我们的研究结果的转化影响。