# ADAPTIVE MOTIF-BASED CONTROL (AMBC): A FUNDAMENTALLY NEW APPROACH TO AUTOMATED TREATMENT OPTIMIZATION FOR TYPE 1 DIABETES

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2024 · $654,373

## Abstract

PROJECT SUMMARY
Adaptive Motif-Based Control (AMBC): A Fundamentally New Approach to Automated Treatment
Optimization for Type 1 Diabetes
Automated Insulin Delivery (AID) has transitioned to the clinical practice and one of the most advanced systems
to date –Control-IQ (Tandem, Inc.)– is based on the UVA-AID, developed and tested by our team. Following two
pivotal trial in adults and children with type 1 diabetes (T1D), both published in the New England J of Medicine,
the system was cleared by the FDA and European regulatory agencies, and is now in use worldwide. With this
clinical translation now accomplished, our academic objective is to design and test next-generation AID solutions.
 The key innovative concept behind the new Adaptive Motif-Based Control (AMBC) class of AID algorithms
 proposed here, is the ability to learn from a user’s past glycemic-control patterns and from population patterns,
and optimize this user’s treatment in real time; thus, unlike any other AID system, the AMBC will utilize both a
person’s own history and the history of others, to forecast glycemic changes and adapt AID action accordingly.
To achieve its goals, the AMBC will employ: (i) a newly discovered fundamental structure underlying the multitude
of possible daily continuous glucose monitoring (CGM) profiles in diabetes, which allows classification of these
profiles into a finite number of basic “motifs”, and (ii) a new Adaptation-to-Profile treatment optimization process.
To test the AMBC, we propose a pilot study, followed by a randomized cross-over trial enrolling 90 participants
with T1D and Control-IQ experience, to compare the UVA-AID (as built in Control-IQ) to 3 treatment modalities:
AMBC with meal and exercise announcements; AMBC-A without meal or exercise announcements, i.e. a “full
closed-loop,” and an intermediate AMBC-EA which will have meal but no exercise announcement. Participants
will be randomized to two groups following different sequences of treatment modalities: UVA-AID-->AMBC-
A-->AMBC-EA-->AMBC and AMBC-->AMBC-EA-->AMBC-A-->UVA-AID. Each treatment modality will continue for
5 weeks. This time-tested design enables four crossover comparisons, which will test the following hypotheses:
(1) AMBC with meal/exercise announcements will be superior to UVA-AID in terms of time in the range 70-
 180mg/dl and reduced incidence of hypoglycemia (measured by CGM), and technology acceptance;
(2) AMBC-A without meal/exercise announcements will be non-inferior to UVA-AID in terms of time >180mg/dL
 during the day, incidence of hypoglycemia during and after exercise, and postprandial glucose variability;
(3) Deescalating AMBC-->AMBC-EA-->AMBC-A vs escalating AMBC-A-->AMBC-EA-->AMBC deployment of
 meal and exercise announcements will have no influence on the outcome within each treatment modality.
Overall, we affirm that reliable technology has been developed and sufficient data accumulated to warrant the
development of a new class of AID algorithms – AMBC –...

## Key facts

- **NIH application ID:** 10898733
- **Project number:** 5R01DK133148-03
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** SUE A BROWN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $654,373
- **Award type:** 5
- **Project period:** 2022-08-17 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898733

## Citation

> US National Institutes of Health, RePORTER application 10898733, ADAPTIVE MOTIF-BASED CONTROL (AMBC): A FUNDAMENTALLY NEW APPROACH TO AUTOMATED TREATMENT OPTIMIZATION FOR TYPE 1 DIABETES (5R01DK133148-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10898733. Licensed CC0.

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