# Adherence Monitor for Substance Use Disorder Treatment

> **NIH NIH R44** · YELLOWSTONE SCIENTIFIC INSTRUMENTS · 2020 · $397,991

## Abstract

Project Summary
The aim of the present SBIR program is to develop a new noninvasive, point-of-care testing
device for patients undergoing treatment for substance use disorder. The new device will be
capable of monitoring the progress and verifying patient adherence to prescribed treatment by
detecting the presence and level of a compounded adherence marker released in the urine,
providing for the first time a device capable of at-site monitoring of medication adherence for
treatment outcome in clinical trials and patient treatment programs. Our program to develop
this new point-of-care approach uses surface-enhanced Raman spectroscopy (SERS) for
detecting and quantifying the medication adherence tracer, utilizing the Company's unique
nanoparticle surface-coating chemistry to detect the adherence biomarker in a patient's urine
sample. A significant advantage of our surface-enhanced Raman technique on a properly
prepared nanoparticle surface -- relying on the uniqueness of the molecular vibrational
signature for differentiation and identification of the adherence biomarkers present in urine -- is
that it requires no sample preparation or chemical manipulation to detect the biomarker in the
urine sample.

## Key facts

- **NIH application ID:** 9881262
- **Project number:** 5R44DA045409-03
- **Recipient organization:** YELLOWSTONE SCIENTIFIC INSTRUMENTS
- **Principal Investigator:** Richard H Clarke
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $397,991
- **Award type:** 5
- **Project period:** 2018-03-01 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9881262

## Citation

> US National Institutes of Health, RePORTER application 9881262, Adherence Monitor for Substance Use Disorder Treatment (5R44DA045409-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9881262. Licensed CC0.

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