Abstract
In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients’ lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.
| Original language | English |
|---|---|
| Article number | 795225 |
| Journal | Frontiers in Endocrinology |
| Volume | 13 |
| DOIs | |
| State | Published - 22 Apr 2022 |
Bibliographical note
Funding Information:Authors acknowledge the French National Agency for Research (DIABLO ANR-18-CE17-0005-01), ECOSNord (M18M01) and SEP-CONACYT-ECOS-ANUIES under Grant 296692. We gratefully acknowledge the contribution of Dr Fanny Lebreton (Medical Faculty Diabetes Center, University of Geneva, Switzerland) to the experimental data corpus of islet recordings.
Funding Information:
The authors wish to thank various Funding agencies. ANR (HyBiopacs to JL and SR, Isletchip to BC, JL, and SR, DIABLO to SR, JL, BC, and DH), Région d’Aquitaine (to BC, SR, and JL), French Ministery of Research (to MR). More precisely, this work has been supported by the French National Agency for Research (DIABLO ANR-18-CE17-0005-01), ECOSNord (M18M01) and SEP-CONACYT-ECOS-ANUIES under Grant 296692. This research has also been supported by the Fonds Européen de Développement Régional (FEDER) under the grant agreement DIAGLYC N°3538519.
Funding Information:
Authors acknowledge the French National Agency for Research (DIABLO ANR-18-CE17-0005-01), ECOSNord (M18M01) and SEP-CONACYT-ECOS-ANUIES under Grant 296692. We gratefully acknowledge the contribution of Dr Fanny Lebreton (Medical Faculty Diabetes Center, University of Geneva, Switzerland) to the experimental data corpus of islet recordings.
Funding Information:
Building on promising results of the previously developed and patented glucose bio-device, which integrates multiple physiological signal information (, ), a consortium has been created in 2019 to assess the possibility to integrate this islet-based biosensor in closed-loop therapies for patients with T1D. This consortium started the collaboration in a national project named DIABLO, supported by the French National Agency for Research (ANR). Preliminary work () provided guidelines for the controller tuning with an in silico methodology based on clinically-relevant criterion: a meta-heuristic method (genetic algorithm (GA)-based optimization technique) is used with the BG risk index (). The core element of the GA-based protocol is the UVA/Padova T1DM Simulator (T1DMS - v3.2) (). This computer model of the human metabolic system simulates the glucose-insulin dynamics in T1D patients, and is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of insulin therapies, including closed-loop algorithms (). Using the T1D adult cohort of the simulator, a first comparison between two AP systems (a biosensor-based one and a CGM-based one) was presented in (). Thanks to individualised controller parameters, satisfactory performance was achieved with the biosensor-based AP system, even with a simple proportional-derivative controller associated to continuous basal infusion (PD). This regulation scheme was as efficient as standard treatments with unannounced meals (no bolus strategy was implemented). BASAL
Publisher Copyright:
Copyright © 2022 Olçomendy, Cassany, Pirog, Franco, Puginier, Jaffredo, Gucik-Derigny, Ríos, Ferreira de Loza, Gaitan, Raoux, Bornat, Catargi, Lang, Henry, Renaud and Cieslak.
Keywords
- artificial pancreas
- biosensor
- closed-loop simulation
- insulin therapy
- micro-electrode array
- pancreatic islets
- type 1 diabetes