A Novel Device for Rapid and Noninvasive Volatile Metabolite-based Screening and Diagnosis of Multiple Disease States

项目摘要/摘要：人类排放一系列挥发性有机化合物（VOC）， 正常的新陈代谢在许多疾病状态中存在代谢变化，并且具有高度敏感性的动物 嗅觉系统可以被训练成基于患者的特征气味来识别患有某些疾病的患者。 我们试图将这些独特的VOC通过皮肤散发的检测转化为更强大的， 标准化和机械化的平台，易于适用于筛查，诊断和监测 具有不同病理生理学的多种人类疾病状态。我们假设全身代谢 许多高负荷感染性、炎症性、代谢性、恶性、精神性和 神经系统疾病可以通过皮肤发射来识别。我们将采用一种新颖的便携式气相色谱仪- 差示迁移率光谱仪（GC-DMS）设备，可直接快速检测挥发性样品， 的护理评估这些皮肤挥发性代谢物签名。这个装置非常灵敏， 对人类挥发性排放景观进行全面和生物代表性评估， 比许多其它气体传感装置更能抵抗混杂和环境因素， 在各种现实世界的传感应用中的成功使用记录，并已在进行商业化 发展，这将大大促进其快速发展，为皮肤挥发性有机化合物为基础的诊断，这20 疾病状态。我们建议进一步发展和严格评估这种基于气味的诊断 （1）整合额外的过程分析技术（PAT），以确保 通过重复、高容量患者，样本和器械之间的仪器准确度和精密度 随着时间的推移进行测试并修改用于皮肤挥发物分析的装置入口，（2）识别和验证GC- DMS签名和相应的皮肤挥发性代谢物组，区分个体与 如果没有使用机器学习方法的这20种疾病状态中的每一种， GC-DMS设备上的这些特征，以及（3）开发用于至少一种疾病的可穿戴传感器， 简单的挥发性签名，确定这个可穿戴诊断设备的测试特性。缺乏 重现性是挥发性代谢物分析领域的一个主要问题，通常是由于混淆和 研究设计缺陷。我们将尽一切努力尽量减少和消除任何混淆、偏见和 当我们开发和评估这种基于气味的诊断方法时， 每个疾病状态的签名。最终，成功完成这些目标将产生快速，非侵入性的 皮肤挥发性代谢物测定用于筛查、诊断和监测这些疾病中的每一种， 明确的二元（是/否）评估个体是否有一种或多种这些疾病的证据， 有助于在这些患者中早期给予适当的治疗剂，并减轻临床 这些疾病的后果。

Project Summary/Abstract: Humans emit an array of volatile organic compounds (VOCs) as part of normal metabolism. There are metabolic shifts in many disease states, and animals with highly sensitive olfactory systems can be trained to identify patients with certain diseases based on their characteristic scent. We seek to translate detection of these unique VOCs emanating through the skin to a more robust, standardized, and mechanized platform, readily adaptable for the screening, diagnosis, and monitoring of a variety of human disease states with distinct pathophysiologies. We hypothesize that systemic metabolic derangements in many high burden infectious, inflammatory, metabolic, malignant, psychiatric, and neurologic diseases can be identified via skin emissions. We will adapt a novel, portable gas chromatography- differential mobility spectrometry (GC-DMS) device that rapidly examines volatile samples directly at the point of care for the assessment of these skin volatile metabolite signatures. This device is highly sensitive, allowing a comprehensive and biologically representative assessment of the landscape of human volatile emissions, is more robust to confounding and environmental factors than many other gas sensing devices, has a long track record of successful use in various real-world sensing applications, and is already undergoing commercial development, which will greatly facilitate its rapid development for the skin VOC-based diagnosis of these 20 disease states. We propose further development and rigorous evaluation of this scent-based diagnostic approach to these disease states, (1) integrating additional process analytical technologies (PATs) to ensure instrumental accuracy and precision between samples and devices through repeated, high-volume patient testing over time and modifying the device inlet for skin volatile analysis, (2) identifying and validating GC- DMS signatures and the corresponding set of skin volatile metabolites that distinguish individuals with and without each of these 20 disease states using machine learning methods, integrating automated detection of these signatures on the GC-DMS device, and (3) developing a wearable sensor for at least one disease with a simple volatile signature, determining the test characteristics of this wearable diagnostic device. A lack of reproducibility is a major issue plaguing the field of volatile metabolite analysis, often due to confounding and study design flaws. We will make every effort to minimize and eliminate any sources of confounding, bias, and extraneous variability as we develop and evaluate this scent-based diagnostic approach, to yield generalizable signatures for each disease state. Ultimately, successful completion of these aims will yield rapid, noninvasive skin volatile metabolite assays for the screening, diagnosis, and monitoring of each of these diseases, with a clear, binary (yes/no) assessment of whether the individual has evidence of one or more of these diseases, facilitating early administration of appropriate therapeutics in these patients and mitigating the clinical consequences of each of these diseases.