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

描述(申请人提供)：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“几乎无法治疗的”，并正在威胁全球结核病控制。2011年，向世界卫生组织(世卫组织)报告了来自85个国家的约6000例广泛耐药结核病病例。世卫组织估计，由于缺乏诊断广泛耐药结核病的全球实验室能力，这可能是真实病例数量的10%。该项目的目标是利用我们的行业合作伙伴Akonni BiosSystems开发的现有凝胶元件微阵列(GEM)技术，在不到8小时的时间内生产和提供一个集成的桌面诊断平台，用于检测痰中的广泛耐药结核病。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”才能完成。在越来越多的潜在传播环境中，美国平民接触广泛耐药结核病的风险增加，从接触未被发现的广泛耐药结核病感染者进入美国，到乘坐飞机，以及为了休闲和商务而更多地暴露在结核病高发国家。美军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，迫切需要一个集成的桌面平台，用于从直接患者样本中快速诊断耐药结核病的高敏感性、高特异性。我们建议将全球耐药结核病诊断联盟(GCDD-NIAID U01AI082229)的经验、资源和现有诊断检测流水线与我们的项目合作伙伴Akonni BiosSystems的技术创新和行业知识相结合，开发和测试快速XDR-TB诊断平台。基于我们在XDR-TB遗传基础方面的丰富经验，以及Akonni用于在临床样本中识别特定单核苷酸多态(SNP)的高保真原型平台，我们假设我们将能够在8小时内从直接痰样本中检测出对异烟肼、RIF、FQ、AMK、Kan和CAP具有临床相关表型耐药性的XDR-TB分离株，敏感度为90-98%，特异度为~100%。我们将通过完成以下三个目标来实现这一目标：1)扩展和优化现有的原型凝胶元件微阵列(GEM)平台，以检测XDR-TB分离株中发现的所有临床相关耐药等位基因；2)在实验室环境中验证扩展的XDR GEM平台对来自已进行表型药敏测试、测序和全基因组测序的跨国耐药菌株集合中的300株菌株的性能；3)现场评估XDR GEM平台检测印度孟买高通量临床实验室患者痰标本中XDR-TB菌株的能力。

DESCRIPTION (provided by applicant): 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, rapd 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.