Towards rapid measurement of antibiotics in critical care setting

摘要： 将有效和安全浓度的抗生素(或其他药物)送往不稳定、病情危重的医院 患者是一个令人望而生畏的问题--例如，对于这项工作的重点--所有六种抗生素，药代动力学 研究表明，预测的活性浓度和实际活性浓度之间确实存在很大差异。虽然很重要 在帮助医生做出最有可能帮助、最不可能造成伤害的决定方面，已经取得了很大进展， 这种方法的一个重要基石仍然缺乏：目前还没有经过临床验证的设备可以 快速、准确和精确地测量药物浓度，以解决长期的转折- 以中心实验室为基础的高频治疗药物的周转时间和高昂的成本 监控。我们通过每一步的构建和验证从下到上系统地解决问题 克服这些障碍所需的每一个组成部分。 作为我们的第一个挑战(也是我们的第一个目标)，我们专注于高质量的受体--充分描述和验证 然后可以被其他组织以不同的格式广泛使用和实施，只需 被命令“下架”。我们鉴定了基于寡核苷酸的受体(适配子)就是这样的试剂。 在我们大量初步结果的基础上，我们描绘了一个过程，这将导致分离几组受体， 每个候选对象都以荧光格式单独验证，而不是 患者衍生的液体，在允许这些简单传感器直接应用于混合测量的条件下 格式(例如，透析流出液、超滤血清和标准固相萃取柱提取物)。 我们的第二个挑战，由于从危重病人身上收集的样本高度多变，加剧了这一挑战， 是交叉反应和对标准条件的偏离。我们通过确定成对的 每种抗生素具有正交性质的适配子：其中一种适配子将用于生物传感器模块 (目标2)在严格控制的条件下结合常用的纳米材料(石墨烯)和 在透析流出物和固相萃取柱萃取物上进行验证(在此，生物传感器可以在没有预 纯化)与金标准层析方法进行比较。来自该对的另一个适配子，来自一个不相关的 家庭，将被全面验证为提取模块(目标3)的亲和力组件 商业血清样本和危重患者的实际样本。 通过这种方法，我们将为分析提供严格特征化的标准化组件 多粘菌素、氟喹诺酮、达托霉素、利奈唑胺和β-内酰胺类药物，这将使我们(和其他人)能够 将组件组合成设备，要么在床边使用，要么作为自动化分析仪的墨盒使用。 在任何一种情况下，这些都将促进大型学术医院以外的常规高频药物监测 设置。我们预计在本资金期限结束前展示一种多模块设备的设计。

Summary: Delivering antibiotics (or other drugs) at effective and safe concentrations to unstable, critically ill hospitalized patients is a daunting problem – for example, for all six antibiotics that are the focus of this work, pharmacokinetic studies reveal truly wide discrepancies between predicted and actual active concentrations. While important strides have been made to help physicians make decisions that are most likely to help, and least likely to harm, an important cornerstone for such approaches is still missing: There are no clinically validated devices that can rapidly, accurately and precisely measure concentrations of drugs in a manner that would address the long turn- around-times and prohibitive cost of the central-laboratory-based approach to high-frequency therapeutic drug monitoring. We approach the problem systematically from the bottom up by building and validating at each step each of the components needed to overcome such barriers. As our first challenge (and our first Aim), we focus on high-quality receptors – well characterized and validated on their own – which could then be widely used and implemented by other groups in different formats, by simply being ordered “off the shelf”. We identified oligonucleotide-based receptors (aptamers) as such reagents. Building on our vast preliminary results, we delineate a process that will lead to isolation of sets of receptors, with each candidate validated on its own in a fluorescent format against gold-standard analytical methods in patient-derived fluids, under conditions that allow these simple sensors to be applied directly in mix-and-measure formats (e.g., dialysis effluents, ultrafiltered sera, and extracts from standard SPE columns). Our second challenge, exacerbated by the highly variable nature of samples collected from critically ill patients, is cross-reactivity and deviations from standardized conditions. We address these by identifying pairs of aptamers with orthogonal properties for each antibiotic: One of the aptamers will be used in biosensor modules (Aim 2) under strictly controlled conditions in conjunction with a commonly used nanomaterial (graphene) and validated on dialysis effluents and extracts from SPE columns (here, biosensors can be used without pre- purification) against gold-standard chromatographic methods. The other aptamer from the pair, from an unrelated family, will be comprehensively validated as the affinity component of extraction modules (Aim 3) on spiked commercial samples of sera and actual samples from critically ill patients. Through this approach, we will provide rigorously characterized, standardized components for analysis of polymyxins, fluoroquinolones, daptomycin, linezolid, and beta lactams, which will enable us (and others) to combine components into devices, either to be used at the bedside, or as cartridges in automatized analyzers. In either case, these will facilitate routine high-frequency drug monitoring outside of large, academic hospital settings. We expect to demonstrate one design of multi-modular devices by the end of this funding period.