Advanced Artificial Pancreas Systems to Enable Fully Automated Glycemic Control in Type 1 Diabetes Mellitus

PROJECT ABSTRACT
Despite advancements in automated insulin delivery systems that demonstrate superior glycemic
outcomes, challenging clinical issues remain with the continued need for accurate carbohydrate
counting and meal announcement combined with slow-acting subcutaneous insulin that
contribute to repeated occurrences of postprandial hyperglycemia. This clinical gap in glycemic
control is addressed by the proposed project. We intend to leverage our existing advancements
in pattern recognition and anticipation to refine and test a fully automated closed-loop system that
does not require meal announcements. This system is unique in that it relies on pattern
recognition, particularly surrounding meals, in an anticipatory manner so that insulin action is
taken in advance of an expected meal rather than the predominantly reactive solutions that are
often proposed. We intend to further test and tune this system within the context of accelerated
insulins to further improve glycemic outcomes and remove barriers to use of these systems by
lowering burden of disease management.
We have already developed the foundational tools needed for a fully automated closed-loop and
will plan for further refinement by leveraging data from our accumulated clinical studies database.
We plan to conduct 2 pilots studies followed by a larger, 7-month main study to evaluate the
following specific aims of this proposal:
1) We hypothesize that novel faster acting insulin analogs can be safely used to provide more
effective fully automated closed-loop control. Our first pilot will test the differences between Fiasp
and a novel formulation of aspart (AT247) in a supervised trial. This trial results will inform the
selection of the insulin used in the subsequent trials.
2) We hypothesize that, with a design adapted to these new insulin formulations, fully automated
closed-loop control is a valid clinical alternative to hybrid closed-loop systems, the current state-
of-the-art systems.
3) Lastly, we hypothesize that further adding glycemic disturbance anticipation (e.g. generally
surrounding meals) to this fully automated closed-loop control system will further improve
glycemic outcomes compared with a fully automated closed-loop system without disturbance
anticipation.

Grant Number: 5R01DK129553-05
NIH Institute/Center: NIH
Principal Investigator: MARC BRETON