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

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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.