Enabling comprehensive diagnosis of sub-acute infection in chronic respiratory disease via high sensitivity next generation sequencing

通过高灵敏度下一代测序实现慢性呼吸道疾病亚急性感染的全面诊断

• 批准号: 10325843
• 负责人: Roland Marcus
• 金额: $ 100万
• 依托单位: PEAK DIAGNOSTIC PARTNERS LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-17 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325843
• 关键词: Acute; Algorithms; Asthma; Automobile Driving; Biological Assay; CLIA certified; Chronic; Chronic Obstructive Airway Disease; Chronic lung disease; Clinical; Clinical Research; Collaborations; Colorado; Computer Systems; Computer software; Computerized Medical Record; Data; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Future; Goals; Gold; Health; Healthcare Systems; Hospitalization; Individual; Infection; Information Systems; Infrastructure; Institution; Knowledge; Laboratories; Lung; Lung diseases; Lung infections; Medical Care Costs; Metagenomics; Methodology; Methods; Microbe; Microbiology; Molecular; Online Systems; Outcome; Pathology; Patients; Pharmaceutical Preparations; Phase; Physicians; Population; Prognosis; Quality of life; Reporting; Research; Research Activity; Respiratory Tract Infections; Risk; Sampling; Secure; Sensitivity and Specificity; Series; Small Business Innovation Research Grant; Source; Symptoms; System; Systems Integration; Technology; Test Result; Testing; Time; Treatment Effectiveness; Validation; Viral; Visualization; Writing; acute infection; base; care costs; clinical database; clinically relevant; cloud based; cost; data integration; design; diagnostic assay; disease phenotype; disorder control; experience; health care service organization; improved; insight; learning algorithm; metagenomic sequencing; microbial; molecular sequence database; next generation sequencing; novel; novel diagnostics; pathogen; pathogenic microbe; personalized medicine; phase 1 study; productivity loss; provider adoption; relational database; research clinical testing; respiratory pathogen; scale up; software systems; statistical learning; targeted sequencing; technological innovation; tool; treatment strategy; web portal

ABSTRACT Sub-acute lung infections are increasingly recognized as drivers of poor symptom control among a subset of individuals with chronic lung disease, estimated to be more than 2 million in the US for Asthma and COPD patients. When these sub-acute infections are diagnosed and treated appropriately, chronic lung disease patients can convert from moderate/severe to a milder disease phenotype, requiring lower medication to achieve better health at a significantly lower cost. Current gold-standard diagnostics for sub-acute infection rely on decades-old technology that can take weeks to complete, have limited sensitivity, and are limited in the type and number of microbes that can be screened by a single test. Thus, a critical gap exists due to the inability of current diagnostics to comprehensively and accurately detect microbial pathogens in low-burden clinical samples, which is a significant barrier to improved clinical outcomes in chronic lung disease. We have thus developed a comprehensive next generation sequencing (NGS) panel for detection and identification of microbes. Our Phase I studies have demonstrated the feasibility of our diagnostic tool for application to subclinical respiratory infections and its superiority to both microbiological and molecular approaches to diagnosis. Our NGS diagnostics (Dx) panel is a significant technological innovation over current methodology; the Dx panel utilizes samples directly from the patient (rather than relying on cultures), provides greater sensitivity than qPCR or meta-genomic sequencing approaches and screens for the presence of tens of thousands other microbes in a single assay. These features are possible due to our Dx panel design in addition to proprietary laboratory and analysis workflows. The long-term goal of this project is to provide novel clinical tools for the detection of low- burden microbial infections driving disease pathology, symptomology, and exacerbations in chronic lung disease populations. In this Phase II, we will develop a data integration system to 1) deploy our diagnostic test in healthcare organizations to drive physician adoption, 2) build data and apply algorithms necessary to expand the impact and value-based reimbursement of our assay. Our Aims are to 1) develop a cloud-based commercial software system for data receipt, storage, analysis and clinical reporting at scale, 2) integrate our software system into the clinical workflow at our clinical partners, and 3) develop a web-based visualization portal for test results and build the infrastructure for use of advanced statistical learning algorithms. This integrated system will drive the development and application of personalized medicine approaches for diagnosis and treatment guidance currently missing in chronic lung disease. The total market for this diagnostic is the set of chronic lung disease patients with uncontrolled symptoms who could be screened for sub-acute infections. Our competitive advantages include improved sensitivity, comprehensive microbe detection, streamlined analysis, and treatment effectiveness insights within a single assay.

摘要