DEPARTMENT OF HOMELAND SECURITY (DHS) SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM FY24

Active
No
Status
Closed
Release Date
November 8th, 2023
Open Date
December 18th, 2023
Due Date(s)
January 18th, 2024
Close Date
January 18th, 2024
Topic No.
DHS241-001

Topic

Agnostic Detection of Synthetic Opioids and Other Illicit Drugs

Agency

Department of Homeland SecurityScience and Technology Directorate

Program

Type: SBIRPhase: Phase IYear: 2024

Summary

The Department of Homeland Security (DHS) Small Business Innovation Research (SBIR) Program for FY24 is seeking proposals for the topic of "Agnostic Detection of Synthetic Opioids and Other Illicit Drugs". The research topic focuses on developing innovative methods to alert field personnel or personnel in a controlled environment to untargeted fentanyl-related substances in the presence of other compounds. The goal is to improve the detection capabilities of field detection devices, which currently rely on predetermined libraries of specific spectral signatures. The proposed solution should include requirements for both a handheld, battery-operated detector (Tier 1) and a portable detector that can be moved by a single person without wheeled assistance (Tier 2). The performance goals include the classification and identification of pure compounds and unknown narcotics, with minimal sample preparation and processing times of no more than 5 minutes for Tier 1 and 1 minute for Tier 2. The detection limits for unknowns should be at least 1% of the sample composition. Software solutions, such as AI/ML algorithms, are also encouraged. The project duration is not specified, and funding specifics can be found on the solicitation agency's website.

Description

In 2021 and 2022, the number of U.S. overdose-related deaths topped 100,000, with about two-thirds of those attributed to synthetic opioids, mainly fentanyl. Fentanyl's chemical scaffold is easily tweaked to alter its spectroscopic signature and evade detection while delivering similar psychoactive effects. Numerous fentanyl analogues have been identified in illicit drug products and associated with fatal overdoses. The Drug Enforcement Administration's temporary scheduling of fentanyl-related substances has mitigated the appearance of new analogues somewhat, but certain derivatives such as fluorofentanyl continue to be widespread. In addition, drug traffickers have turned to other classes of synthetic opioids such as nitazenes which can also be structurally modified into pharmaceutically active, uncontrolled derivatives.

Currently, field detection devices cannot alert users to the presence of novel compounds unless the devices have been appropriately configured. For example, if the device is spectroscopic in nature, detection is based on predetermined libraries of specific spectral signatures associated with those substances. Libraries require updates as substances are identified to incorporate the new spectral information. If the substance has not been previously identified in the field, and has no entry in the library, the end-user will likely not receive any indication that a potentially dangerous fentanyl-related drug is present, until a sample of the seized material has been analyzed by a forensic laboratory. The difficulty is compounded by the fact that most illicit drug products encountered in the field are mixtures of multiple compounds, which may obscure the target compound's signature.

This topic seeks innovative methods to develop the capability to alert field personnel, or personnel in a controlled environment, to untargeted fentanyl-related substances in the presence of other compounds. Note the proposed approach need not be spectroscopic; this was provided as an example of the complexity associated with detecting an unknown compound.

The proposed solution should include the following requirements from a Tier 1 or Tier 2 approach:

Tier 1: Handheld, battery operated detector. • Threshold performance would be classification of pure compounds that might not yet be included in standard libraries (e.g., suspected synthetic opioid, not necessarily the precise ID). Sample preparation should be minimal, no more than dissolution of sample into a solvent. Processing time should be no greater than 5 minutes. • Optimal performance would be classification of suspected narcotics that are present in low-dose forms (e.g., tablet formulations), say less than 1% composition by mass. Minimal sample preparation. Processing time should be no more than 1 minute. • Software solutions applied to existing hardware (such as an algorithm for a Raman detector) would be considered, with the same threshold and optimal requirements.

Tier 2: Portable detector that is moveable by a single person without wheeled assistance. • The weight should be less than 40 pounds, 120 VAC power, and rugged enough to be moved without maintenance (e.g., optical realignment). • Threshold performance would be identification of pure or nearly pure unknowns without a previous library entry. Samples should have minimal preparation beyond dissolving into solution. Processing time should be less than 5 minutes. • Optimal performance would be identification of unknown narcotics, that is, new synthetic opioids, in cut mixtures, such as tablet formulations. Minimal sample prep, processing time should be less than 1 minute. • Detection limits for unknowns should be for at least 1% of the sample composition. Software solutions, such as AI/ML algorithms for existing instruments are also encouraged.

Similar Opportunities

Harnessing Artificial Intelligence and Polypharmacology to Discover Pharmacotherapeutics for Substance Use Disorders (R41/R42 Clinical Trials Not Allowed)
Department of Health and Human Services
The Department of Health and Human Services, specifically the National Institutes of Health, is seeking proposals for the topic of "Harnessing Artificial Intelligence and Polypharmacology to Discover Pharmacotherapeutics for Substance Use Disorders (R41/R42 Clinical Trials Not Allowed)". This solicitation aims to leverage AI/ML tools to identify pharmacotherapeutic development candidates with lower toxicity and higher efficacy for the prevention or treatment of substance use disorders (SUDs). The traditional drug discovery paradigm of single-target-based approaches has limitations in addressing the complex mechanisms and polysubstance use associated with SUDs. Polypharmacology, the study of how drug molecules interact with multiple targets, is emerging as a new paradigm for drug development in multifactorial diseases like SUDs. The use of AI/ML technologies trained in polypharmacology can enhance the discovery and development efforts for SUD pharmacotherapeutics by identifying the best targets, designing effective multi-target directed ligands (MTDLs), and predicting the effects of binding to multiple biological targets. The research objectives include identifying and validating disease targets, screening potential compounds, developing assays, synthesizing novel compounds, and conducting in vitro and in vivo studies. The Small Business Technology Transfer (STTR) program is a phased program, with Phase I focused on establishing technical merit and feasibility, and Phase II aimed at advancing the technology towards commercialization. The Fast Track option allows for the submission and review of Phase I and Phase II grant applications together, expediting the award decisions and funding for projects with high potential for commercialization.