Rapid and responsive development of "spice" sensors using a novel recognition scaffold

项目总结 在过去的十年里，旨在模仿大麻使用效果的合成大麻类化合物已经出现 作为一类重要的新型精神活性物质(NPSS)，在品牌下被作为“合法高”产品销售 “辣妹”、“黑曼巴”和“湮灭”之类的名字。这些化合物效力更强，也更危险 与大麻中发现的激动剂相比，它们造成了各种不利的健康影响，包括 精神病、住院治疗和死亡。作为回应，DEA已将43种合成大麻素 附表一分类；然而，新的合成大麻素成分定期产生并释放给 这两家公司都规避了法律限制，并提供了一种避免通过常规药物筛查测试发现的产品。 虽然GC/LC-MSN可用于全面检测这些病原体，但绝大多数法医和 由于简单和成本高，临床实验室依赖免疫分析进行药物测试。加速发展的新方法 发展对不断变化的滥用物质的诊断将是有用的，这两者都有助于解决 合成大麻素问题和作为一项通用技术。我们建议通过以下方式应对这一挑战 开发一种与免疫分析功能垂直的快速分析开发新平台。去做 为此，我们建议使用定向进化来重新编程植物PYR1-PP2C胁迫感受到的配体 荷尔蒙感应系统。这种传感器通过自然发生的化学诱导的二聚化作用发挥作用。 (Cid)PYR1识别配体与形成稳定的PYR1-PP2C复合体的机制； 这一特征有助于对识别新配体的受体进行基因选择实验。我们假设 我们的平台将实现新诊断的快速开发周期。为了证明这一假设，这个 探索性研究将(1)通过以下途径开发选择性识别合成大麻素的受体 定向进化，(2)利用蛋白质片段整合目标识别和信号输出 互补试验，以及(3)建立和验证合成大麻素检测系统 生物基质。传感器将被设计成检测多种合成大麻素，优先考虑 2020年流行的合成大麻素(5F-MDMB-PICA)，目前临床尚未检测到 免疫分析。这项建议的可行性得到了来自PI实验室的大量初步数据的支持， 这表明PYR1-PP2C系统可以用于进化高亲和力的合成大麻素 传感器。我们的长期目标是建立这一新系统，以快速开发诊断工具和 提供能够快速和选择性地检测合成大麻素的试剂 标本。这些使能工具将对合成的生物医学和临床分析有价值 大麻类药物控制药物滥用，改善中毒的治疗和诊断，以确定其特征 药理和健康效应，并作为快速发展的新诊断分析的通用平台。

PROJECT SUMMARY Over the past decade, synthetic cannabinoids that intend to mimic the effects of cannabis use have emerged as an important group of New psychoactive substances (NPSs) sold as “legal high” products under brand names such as “Spice”, “Black Mamba”, and “Annihilation”. These compounds are more potent and dangerous agonists than those found in cannabis and they have caused a variety of adverse health effects, including psychoses, hospitalizations, and deaths. In response, the DEA has placed 43 synthetic cannabinoids under Schedule I classification; however, new synthetic cannabinoid ingredients are regularly created and released to both evade legal restrictions and provide a product that avoids detection by routine drug screening tests. Although GC/LC-MSn can be used to comprehensively detect these agents, the vast majority of forensic and clinical labs rely on immunoassays for their drug tests due to simplicity and cost. New methods that accelerate the development of diagnostic for the ever-changing substances of abuse would be useful, both for addressing the synthetic cannabinoid problem and as a general technology. We propose to address this challenge by developing a new platform for rapid assay development that functions orthogonally to immunoassays. To do this, we propose to use directed evolution to reprogram the ligands sensed by the plant PYR1-PP2C stress hormone sensing system. This sensor functions through a naturally occurring chemical-induced dimerization (CID) mechanism that couples ligand recognition by PYR1 to the formation of a stable PYR1-PP2C complex; this feature facilitates genetic selection experiments for receptors that recognize new ligands. We hypothesize that our platform will enable rapid development cycles for new diagnostics. To prove this hypothesis, this exploratory study will (1) Develop receptors for selective recognition of synthetic cannabinoids through directed evolution, (2) Integrate target recognition and signal output using protein fragment complementation assays, and (3) Establish and validate synthetic cannabinoid detection systems in biological matrices. Sensors will be designed to detect multiple synthetic cannabinoids, prioritizing the most prevalent synthetic cannabinoid of 2020 (5F-MDMB-PICA), which is not detected by current clinical immunoassays. The feasibility of this proposal is backed by extensive preliminary data from the PI’s laboratory, which demonstrates that the PYR1-PP2C system can be used to evolve high-affinity synthetic cannabinoid sensors. Our long-term goal is to establish this new system for the rapid development of diagnostic tools and deliver reagents that enable fast and selective detection of synthetic cannabinoids in low volumes of biological specimens. These enabling tools will be valuable for biomedical and clinical analyses of synthetic cannabinoids to control drug abuse, improve treatment and diagnosis of intoxication, to characterize their pharmacological and health effects, and as a general platform for rapidly evolving new diagnostic assays.