Adaptive Modular Substance Treatment Analysis Device

What it does

AMSTAD provides a personalized solution for tobacco use disorder (TUD) through a multi-chamber system that controls nicotine dosages based on real-time user data. Integrated with IoT, it addresses both physical and behavioral aspects to reduce dependency.

Your inspiration

Personal experiences with family and friends struggling to quit smoking and vaping inspired this solution. Working with addiction experts revealed limitations of current cessation methods: lack of personalization, fixed dosing, and potential for new dependencies. Research showed individuals transitioning to lower-nicotine products often compensate by increasing consumption, while modern vapes use higher-dose nicotine salts. With 50-70% of global smokers and vapers wanting to quit, we recognized the need for an adaptive system leveraging big data and personalized protocols to overcome existing shortcomings.

How it works

AMSTAD connects to a smartphone via Bluetooth for initial setup. The app creates a personalized profile by collecting initial data, including the user's e-cigarette flavor and nicotine concentration. For the first two weeks, AMSTAD functions like a regular e-cigarette, allowing normal vaping behavior while gathering data on vital signs, inhalation rate, and nicotine consumption to establish a baseline. Once the baseline is set, AMSTAD gradually decreases nicotine concentration, adjusting dilutant levels to compensate for the change, while maintaining flavor concentration. The device continuously monitors vital signs and breathing rates during each vaping session to determine if dosage adjustments are necessary. Haptic feedback would be provided to guide user inhalation. User feedback and medical expert input further tailor the nicotine reduction, improving cessation outcomes. Additionally, AMSTAD's app allows users to visualize their data and gain insights.

Design process

We consulted addiction experts to address limitations in cessation methods, especially fixed nicotine doses. Recognizing flavor's role in addiction, we initially designed a two-cartridge system to modulate nicotine while retaining flavor. Further consultations led to adding a third cartridge to blind nicotine reduction from users. We generated mockups and considered a motorized mechanism like an insulin pump but found it bulky and prone to failure. After reverse engineering e-cigarettes, we adopted a wick and coil design for vaporization, addressing temperature control challenges to fine-tune atomization rates. Focusing on hardware, we balanced cost and functionality to create a closed-loop system adapting to individual vaping habits, using affordable components for prototyping. With 3D printing for rapid iteration and SLA printing for high-tolerance final designs, we optimized the chassis for laminar flow and user data input. With the core system built, custom firmware was developed to collect and visualize data. We are refining a machine learning algorithm to regulate dosages and vaporization rates, using findings from our IRB-approved study that examines how nicotine concentration reduction and flavoring affect nicotine intake, to enhance personalized protocol development.

How it is different

AMSTAD uniquely combines personalization and adaptive technology for nicotine cessation, differentiating it from static methods like patches and gums. Traditional approaches do not account for individual smoking histories or nicotine tolerance, whereas AMSTAD tailors cessation plans through real-time data, enhancing the adaptability to each user's needs and minimizing relapse risks. Unlike devices such as Chrono Therapeutics' transdermal patch that require user input, AMSTAD autonomously adjusts nicotine doses via continuous monitoring of vital signs and inhalation patterns, tackling both neurological and psychological addiction facets. It supports nicotine salts and freebase nicotine, crucial for managing withdrawal effectively. AMSTAD also integrates haptic feedback for precise dosage delivery and an app for progress tracking, setting new standards in smoking cessation technology.

Future plans

Future plans for AMSTAD include completing our IRB-approved study and expanding it cross-regionally. We will apply for an R01 grant and begin writing the study protocol for clinical trials. Next, we aim to package device electronics on a customized system on a chip (SoC) for a more compact design and improve cartridge modularity to address various substance use disorders. This will finalize the design and coincide with our final patent submission, one year after the provisional patent expires. We will also continue refining the machine learning algorithm and begin formalizing the FDA 510K application process.

Awards

UCLA Dean’s Prize for Excellence in Research and Creativity; UCLA Engineering Achievement Award in Student Welfare; acceptance into Babson College's 2024 SUD Sprint Cohort