Bacterial Drug Susceptibility Identification by Surface Enhanced Raman Microscopy

描述（由申请人提供）：当患者到达急诊室时，其临床症状与血流感染一致，进行血培养并给予经验性抗菌治疗;实验室对病原体的实际鉴定通常需要一天或多天。在缺乏病原体的身份和敏感性的具体数据的情况下，临床医生被迫选择广谱抗菌治疗，以涵盖所有可能的原因，可疑的血流感染。不幸的是，这种经验性的选择有时可能最终是无效的（在抗菌素耐药性的设置）或不必要的广泛（在设置的易感和容易治疗的生物体），潜在地增加发病率，死亡率，和由此产生的医疗保健成本。为了解决这一需求，我们已经开发了一个原型识别系统的基础上表面增强拉曼光谱（Sers）。该检测技术由便携式拉曼显微镜、新型纳米结构基底以及具有精致分析灵敏度（低至单个细菌）和特异性（低至菌株水平，具有区分耐药细菌的能力）的检测算法组成。此外，检测速度超快（约20秒）。为了使这项技术能够用于护理点，我们已经开发出一个初始的原型系统，用于分离和浓缩血液中的少量细菌，并在20分钟内将这些细菌沉积到Sers基底上。在这里，我们建议建立一个下一代样品制备原型，将与我们现有的便携式拉曼显微镜集成。此外，我们将增加拉曼特征库，以包括菌血症的最常见原因，并研究特征的分子基础。最终系统将通过使用人血培养样本和直接从实验菌血症动物中获得的血液进行测试进行优化和验证。为了实现这些目标，我们组建了一支由工程师、基础科学家和临床科学家组成的多学科团队。在这个为期五年的项目结束时，我们将拥有一个经过强化和测试的系统，该系统将准备用于临床研究，以诊断人类菌血症。所提出的系统将能够足够迅速地识别微生物病原体，以通知初始抗微生物药物治疗，从而降低发病率，死亡率，从而降低医疗保健成本。此外，通过对样品处理系统进行微小改变以处理额外的样品类型，该系统可用于解决其他类型的感染。我们相信，完整的系统将彻底改变临床微生物学领域，提供一种新的技术，用于识别细菌，并及时提供基本的敏感性信息，用于初始抗菌治疗。我们建议利用这笔赠款进一步开发的系统将通过使临床医生能够在半小时内识别感染因子及其抗生素耐药性来影响细菌感染的诊断和治疗。然后，医生可以使用这些信息来正确地开出窄谱抗生素，这将改善患者护理并降低医疗成本。

DESCRIPTION (provided by applicant): When a patient arrives in an emergency room with clinical symptoms consistent with bloodstream infection, blood cultures are drawn and empiric antimicrobial therapy is given; the actual identification of the pathogen by the laboratory typically takes one or more days. In the absence of specific data on the identity and susceptibility of the pathogen at the time of presentation, the clinician is forced to choose broad-spectrum antimicrobial therapy to cover all possible causes of the suspected bloodstream infection. Unfortunately, such empiric choices can sometimes end up being either ineffective (in the setting of antimicrobial resistance) or unnecessarily broad (in the setting of a susceptible and easily treated organism), potentially increasing morbidity, mortality, and resultant health care costs To address this need, we have developed a prototype identification system based on surface enhanced Raman spectroscopy (SERS). The detection technology consists of a portable Raman microscope, a novel nanostructured substrate, and detection algorithms that have exquisite analytical sensitivity (down to a single bacterium) and specificity (down to the strain level, with the ability to distinguish drug resistant bacteria). Moreover, detection is ultra-fast (~20 sec). To enable this technology to be used at point of care, we have developed an initial prototype system for isolating and concentrating low numbers of bacteria from blood and depositing those bacteria onto the SERS substrate within ~20 min. Here, we propose to build a next generation sample preparation prototype that will be integrated with our existing portable Raman microscope. Furthermore, we will increase the library of Raman signatures to include the most common causes of bacteremia and study the molecular basis for the signatures. The final system will be optimized and validated by testing with samples from human blood cultures and blood obtained directly from animals with experimental bacteremia. To meet these goals, we have assembled a multi-disciplinary team of engineers, basic scientists, and clinician-scientists. At the conclusion of this five-year project, we will have a hardened and tested system which will be ready for clinical studies to diagnose bacteremia in humans. The proposed system will enable identification of microbial pathogens rapidly enough to inform initial antimicrobial drug therapy, thereby reducing morbidity, mortality, and, thereby, healthcare costs. Moreover, the system can be used to address other types of infections by implementing minor changes to the sample processing system to handle additional sample types. We believe that the complete system will revolutionize the field of clinical microbiology by providing a new technology for identifying bacteria and providing basic susceptibility information in time for initial antimicrobial therapy. The system we propose to develop further with this grant will impact the diagnosis and treatment of bacterial infections by enabling clinicians to identify the infectious agent and its antibiotic resistance within half an hour. The physician can then use this information to correctly prescribe a narrow-spectrum antibiotic, which will result in improved patient care and reduced healthcare costs.