# Novel methods and devices to deliver substitution therapy in addiction

> **NIH NIH R43** · ATMAN THERAPEUTICS CO. · 2021 · $253,882

## Abstract

PROJECT SUMMARY
Current treatment options for addiction do not sufficiently address the clinical needs of patients. Due to low
efficacy, misaligned treatment objectives, or lack of accessibility, patients are not able to durably abstain from
substances or reduce their use to safer levels. Substitution therapy (ST) is a proven treatment approach and has
resulted in massive global public health benefits for tobacco use disorder and opioid use disorder; however, the
potential impact of ST is limited by concerns for causing more widespread harms. Thus substitution therapy is
either underutilized or underexplored in other addictions due to risks of abuse, misuse, and diversion. Through
implementing safe prescribing systems, health systems have solved for the provision of existing ST like
methadone and buprenorphine. Nevertheless, there remains significant potential for broader access to current
and novel ST treatments by utilizing advancement in software and hardware technology.
Atman Therapeutics is an early-stage company seeking to develop novel therapeutics in addictive disorders. We
are developing a platform to enable increased access to as well as novel development of ST. To achieve this
objective, our platform uses cutting-edge machine learning methods, patient-centered research, and
sophisticated hardware technology. Our technology will also enable the concurrent delivery of contingency
management services, thus creating safe, engaging, and durably effective treatments for these complex
neurobehavioral disorders.
This Small Business Innovation and Research (SBIR) project aims to establish the feasibility of machine learning
and integrated sensor technologies to confirm medication adherence in a diverse sample of patients. We will
develop a deep-learning algorithm utilizing a feed-forward convolutional neural network (CNN), such as ResNet-
50, U-NET, PCU-Net, to enact classification of medication ingestion events. We will also demonstrate the
feasibility of integrating a hardware microcontroller unit with multi-dimensional MEMS sensors into a device that
can classify ingestion events in a highly sensitive and specific manner. These efforts will demonstrate the early
feasibility of the Atman platform and form the basis for regulated development efforts of ST-enabling products
and services.

## Key facts

- **NIH application ID:** 10325846
- **Project number:** 1R43AA029964-01A1
- **Recipient organization:** ATMAN THERAPEUTICS CO.
- **Principal Investigator:** Michael Easton
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $253,882
- **Award type:** 1
- **Project period:** 2021-09-10 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10325846

## Citation

> US National Institutes of Health, RePORTER application 10325846, Novel methods and devices to deliver substitution therapy in addiction (1R43AA029964-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10325846. Licensed CC0.

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