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

PFI:AIR - TT：技术翻译：用于执法的新型现场药物测试系统的演示和验证

• 批准号: 1701917
• 负责人: Richard Blair
• 金额: $ 20万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701917&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Technology; Translation

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.

该PFI：AIR技术翻译项目的重点是翻译一个新的系统，用于识别和检测受控物质，以满足公众和执法部门对提高准确性和灵活性的需求。该技术将减少可能严重影响无辜个人生活和家庭的错误识别的发生率。新指标的组合，一个手机功能的阅读器，一个专门的分析应用程序很重要，因为它提高了识别的准确性，并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者部门部署的应用程序。这种低成本，易于使用的识别系统具有提高准确性，数据管理和证据保存相比，领先的竞争假定化学测试在这个市场空间的优势。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时在实用性、范围和用户安全方面的技术差距。该小组掌握了关于查明非法物质的非常初步的数据。 虽然这些结果是有希望的，但这一数据集必须加以改进。 通过与犯罪实验室和能够接触这些物质以及“街头”毒品的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物质。 目前的药物指示剂最适合含有环胺的化合物，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需要不同的指示剂。 有几个候选人，该小组将评估他们，同时改进他们的数据集，含有环胺的附表物质。 也有可能不必要地暴露于待测物质。识别各种物质的能力本身并不能降低接触风险。目前，用户必须通过将小样本应用到测试条上来处理未知情况。该小组将与志愿部门合作，生产一种新的取样装置，使物质取样能够降低用户的接触风险。 此外，该项目的博士后学者将通过与志愿执法机构的beta测试和软件开发方向获得技术翻译和管理经验。该项目聘请了IDM Systems来指导商业化方面的工作，并聘请了Softechnologies Corporation来提高移动的应用程序的可用性，以实现从研究发现到商业现实的技术转化。这项工作还将依靠与佛罗里达、格鲁吉亚、马里兰州、得克萨斯州、弗吉尼亚州和加州的犯罪实验室的合作。