PFI:AIR - TT: Technology Translation: Demonstration and Validation of a Novel Field Drug Test System for Law Enforcement

这个PFI: AIR技术翻译项目的重点是翻译一个识别和检测受控物质的新系统，以满足公众和执法部门对提高准确性和灵活性的需求。这项技术将减少可能严重影响无辜个人生活和家庭的虚假身份的发生率。新指标、手机读取器和专用分析应用程序的组合很重要，因为它提高了识别的准确性，限制了公共执法资源的浪费。该项目将产生一个beta系统，包括一个测试、一个阅读器和一个适合在志愿部门部署的应用程序。这种低成本、易于使用的识别系统与市场上领先的推定化学测试相比，在准确性、数据管理和证据保存方面具有优势。它也比目前的现场部署仪器更具成本效益，这将促进更广泛的采用。该项目解决了从研究发现向商业应用转化过程中在效用、范围和用户安全方面的技术差距。该小组掌握了鉴别非法物质的非常初步的数据。虽然这些结果是有希望的，但这个数据集必须改进。通过与能接触到这些物质以及“街头”毒品的犯罪实验室和学术法医实验室合作，该小组将提高物质鉴定数据库的质量，使其成为一个有效的工具。建立一个可用的数据库的关键是能够识别各种各样的物质。目前的药物指标最适用于含有环胺的化合物，如可卡因、PCP和芬太尼。相对简单的胺，如甲基苯丙胺、MDMA、jwh合成大麻素、LSD和DMT需要不同的指标。有几个候选物质，研究小组将对它们进行评估，同时改进含有环胺的附表物质的数据集。也有可能与被测物质产生不必要的接触。识别大量物质的能力本身并不能降低暴露风险。目前，用户必须通过将小样本应用到测试条上来处理未知。该小组将与志愿部门合作，生产一种新的采样设备，使物质采样对用户的暴露风险降低。此外，该项目的博士后学者将通过志愿执法机构的beta测试和软件开发方向获得技术翻译和管理经验。该项目与IDem系统公司合作，指导商业化方面的工作，并与Softechnologies公司合作，提高移动应用程序的可用性，将这项技术从研究发现转化为商业现实。这项工作还将依赖于与佛罗里达州、佐治亚州、马里兰州、德克萨斯州、弗吉尼亚州和加利福尼亚州的犯罪实验室的合作。

This PFI: AIR Technology Translation project focuses on translating a new system for identifying and detecting controlled substances to meet the needs of the public and law enforcement for enhanced accuracy and flexibility.  This technology will reduce the incidence of false identifications that can seriously impact the lives and families of innocent individuals.  The combination of new indicators, a cell phone enabled reader, and a dedicated analysis app is important because it improves the accuracy of the identification and limits the waste of public law enforcement resources. The project will result in a beta system consisting of a test, a reader, and an app suitable for deployment in volunteer departments. This low-cost, easy-to-use identification system has the advantages of improved accuracy, data management, and evidence preservation when compared to the leading competing presumptive chemical tests in this market space. It is also more cost effective than current field deployable instrumentation, which will facilitate wider adoption. This project addresses technology gaps in utility, scope and user safety as it translates from research discovery toward commercial application. The team has very preliminary data on the identification of illicit substances. Although these results are promising, this data set must be improved. By working with crime labs and academic forensic labs with access to these substances, as well as "street" drugs, the team will improve the quality of the substance identification database to make it an effective tool. Critical to producing a useable database is the ability to identify a wide range of substances. The current drug-indicator works best for compounds with cyclic amines like cocaine, PCP, and fentanyl. Relatively simple amines such as methamphetamine, MDMA, JWH-synthetic cannabinoids, LSD, and DMT require a different indicator. There are several candidates and the team will assess them while improving their dataset for scheduled substances that contain cyclic amines. There is also the possibility of unwanted exposure to the substance under test. The ability to identify a wide range of substances does not, in itself, reduce the exposure risk. Currently users must handle the unknown by applying a small sample onto a test strip. The team will work with volunteer departments to produce a new sampling device that will allow substance sampling with reduced exposure risk to the user. In addition, the post-doctoral scholar in this project will receive technology translation and managerial experiences through beta testing with volunteer law enforcement agencies and the direction of software development. The project engages IDem Systems to guide commercialization aspects and Softechnologies Corporation to improve the mobile app usability in this technology translation effort from research discovery toward commercial reality. This effort will also rely on partnerships with crime labs in Florida, Georgia, Maryland, Texas, Virginia, and California.