An Integrated Tabletop Platform for Rapid Detection of XDR-TB in Clinical Samples

项目摘要/摘要 广泛耐药结核病(XDR-TB)与高达80%的死亡率有关， 在世界许多地区被认为是“几乎无法治愈的”，并正在威胁全球结核病控制。 向世界卫生组织(WHO)报告了来自85个国家的约6000例广泛耐药结核病病例 在2011年。世卫组织估计，由于缺乏全球实验室，这可能是真实病例数量的10%。 诊断广泛耐药结核病的能力。这个项目的目标是生产和交付一个集成的桌面 利用现有凝胶元件在8小时内检测痰中广泛耐药结核病的诊断平台 由我们的行业合作伙伴Akonni BiosSystems开发的微阵列(GEM)技术。 目前还没有FDA批准的广泛耐药结核病的快速诊断，这就是为什么2013年疾控中心的情况说明书 指出，广泛耐药结核病(NIAID C类优先病原体)的诊断目前需要“6至16周”至 完成。美国平民接触广泛耐药结核病的风险在不断增加的潜在名单中增加 传播环境，从接触未被发现的进入美国的广泛耐药结核病感染者，到 飞机飞行，以及休闲和商务时暴露在结核病高发国家的风险增加。美国军队正在 此外，在战术和人道主义任务中遇到的环境中暴露的风险也增加了。 显然，迫切需要一种集成的桌面平台，以实现高灵敏度、高特异性 直接从患者样本中快速诊断耐药结核病。我们建议结合经验， 全球耐药结核病联盟的资源和现有诊断检测流水线 诊断(GCDD-NIAID U01AI082229)与我们的技术创新和行业知识 项目合作伙伴阿科尼生物系统公司开发和测试广泛耐药结核病快速诊断平台。基于我们的 对广泛耐药结核病的遗传基础有丰富的经验，以及Akonni的高保真原型平台 在临床样本中识别特定的单核苷酸多态(SNPs)，我们假设我们将是 能够检测到具有临床相关的异烟肼、RIF、FQ、AMK、Kan和 直接痰标本检测，8小时内敏感性为90-98%，特异性为100%。我们会 通过完成以下三个目标来实现这一目标：1)扩展和优化现有原型 凝胶元件微阵列(GEM)平台检测XDR中发现的所有临床相关耐药等位基因 TB分离；2)在实验室环境中对照300验证扩展的XDR GEM平台性能 来自现有GCDD跨国耐药菌株集合的菌株已经具有 表型药敏试验、测序和全基因组测序；以及3)现场评估 XDR GEM平台检测药物风险患者痰标本中广泛耐药结核分枝杆菌的能力 印度孟买一家高通量临床实验室的耐药结核病。

PROJECT SUMMARY/ABSTRACT Extensively drug-resistant tuberculosis (XDR-TB), which has been associated with up to 80% mortality, is considered "virtually untreatable" in many parts of the world, and is threatening global TB control. Approximately 6,000 cases of XDR-TB from 85 countries were reported to World Health Organization (WHO) in 2011. WHO estimates this is likely <10% of the true number of the cases due to a lack of global laboratory capacity to diagnose XDR-TB. The goal of this project is to produce and deliver an integrated tabletop diagnostic platform for detecting XDR-TB in sputum in less than eight hours using existing Gel Element Microarray (GEM) technology developed by our industry partner, Akonni Biosystems. There is currently no FDA approved rapid diagnostic for XDR-TB, which is why the 2013 CDC fact sheet states that diagnosis of XDR-TB (a NIAID Category C priority pathogen) currently requires "6 to 16 weeks" to complete. US civilians are at increased risk for exposure to XDR-TB from a growing list of potential transmission environments, from exposure to XDR-TB infected persons entering the US undetected, to airplane flights and increased exposure to high TB prevalence countries for leisure and business. US troops are also at increased risk of exposure in environments encountered on tactical and humanitarian missions. There is a clear and critical need for an integrated, table-top platform for high sensitivity, high specificity, rapid diagnosis of drug resistant TB from direct patient samples. We propose to combine the experience, resources and existing diagnostic testing pipeline of the Global Consortium for Drug Resistant Tuberculosis Diagnostics (GCDD - NIAID U01AI082229) with the technological innovation and industry knowledge of our project partner, Akonni Biosystems to develop and test a rapid XDR-TB diagnostic platform. Based on our extensive experience with the genetic basis of XDR-TB, and Akonni's high fidelity prototype platform for identifying specific single nucleotide polymorphisms (SNPs) in clinical samples, we hypothesize that we will be able to detect XDR-TB isolates with clinically relevant phenotypic resistance to INH, RIF, FQ, AMK, KAN, and CAP from a direct sputum sample, with 90-98% sensitivity and ~100% specificity in under eight hours. We will achieve this goal by completing the following three objectives; 1) Expand and optimize an existing prototype Gel Element Microarray (GEM) platform to detect all of the clinically relevant resistant alleles found in XDR- TB isolates; 2) Verify the expanded XDR GEM platform performance in a laboratory setting against 300 isolates from an existing GCDD collection of multinational drug resistant isolates already characterized with phenotypic drug susceptibility testing, sequencing and whole genome sequencing; and 3) Field evaluate the XDR GEM platform ability to detect XDR-TB isolates in sputum samples from patients at risk for drug resistant TB at a high-throughput clinical laboratory in Mumbai, India.