Artificial Pancreas: In Silico Study Shows No Need of Meal Announcement and Improved Time in Range of Glucose with Intraperitoneal vs Subcutaneous Insulin Delivery

Background: The efficacy of the treatment of type 1 diabetes can be markedly improved using artificial pancreas (AP), which is a technology to automatically control blood glucose levels. Aim: In this paper, we propose the construction of a controller for controlling the automated delivery of insulin in AP based on a proportionalintegralderivative (PID) algorithm using intraperitoneal (IP) insulin delivery. Methods: The project used rapid-acting insulin in the IP space when setting up a PID controller with feedback to ensure the safe and efficient delivery of insulin. The controller was configured to satisfy feedback insulin present in blood. Controller check was performed In Silico using the metabolic simulator UVA|Padova T1DMS on 10 virtual patients. Results: The proposed controller design has time to reach 83% within the glycaemic range of 70140 mg/dl (3.97.8 mmol/l), without time spent in hypoglycaemia. Conclusions: In a future study we plan to test this controller in vivo to evaluate its performance in vivo.

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Intraperitoneal insulin delivery provides superior glycaemic regulation to subcutaneous insulin delivery in model predictive control‐based fully‐automated artificial pancreas in patients with type 1 diabetes: a pilot study

Diabetes Obesity and Metabolism ◽

10.1111/dom.12999 ◽

2017 ◽

Vol 19 (12) ◽

pp. 1698-1705 ◽

Cited By ~ 37

Author(s):

Eyal Dassau ◽

Eric Renard ◽

Jérôme Place ◽

Anne Farret ◽

Marie‐José Pelletier ◽

...

Keyword(s):

Type 1 Diabetes ◽

Pilot Study ◽

Model Predictive Control ◽

Predictive Control ◽

Artificial Pancreas ◽

Insulin Delivery ◽

Subcutaneous Insulin ◽

Intraperitoneal Insulin

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Role of Automated Insulin Delivery (Artificial Pancreas) in Islet Transplantation: An In Silico Assessment

OBM Transplantation ◽

10.21926/obm.transplant.1803019 ◽

2018 ◽

Vol 2 (3) ◽

pp. 1-1

Author(s):

Dayu Lv ◽

◽

Jose Garcia-Tirado ◽

Chiara Fabris ◽

◽

...

Keyword(s):

Islet Transplantation ◽

In Silico ◽

Artificial Pancreas ◽

Insulin Delivery

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Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions

Journal of Diabetes Science and Technology ◽

10.1177/1932296819879093 ◽

2019 ◽

Vol 13 (6) ◽

pp. 1026-1034 ◽

Cited By ~ 1

Author(s):

Vanessa Moscardó ◽

José Luis Díez ◽

Jorge Bondia

Keyword(s):

In Silico ◽

Artificial Pancreas ◽

Specific Patient ◽

Quantization Effect ◽

In Silico Study ◽

New Strategy ◽

Parallel Control ◽

Clinically Significant ◽

Mean Glucose ◽

The Impact

Background: An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. Methods: The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. Results: DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). Conclusions: The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.

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How It Started, How It Is Going: The Future of Artificial Pancreas Systems (Automated Insulin Delivery Systems)

Journal of Diabetes Science and Technology ◽

10.1177/19322968211027558 ◽

2021 ◽

pp. 193229682110275

Author(s):

Dana Lewis

Keyword(s):

First Generation ◽

Artificial Pancreas ◽

Insulin Delivery ◽

Delivery Systems ◽

Diabetes Therapy ◽

Unmet Expectations ◽

The Future ◽

Time In Range ◽

Improved Technology

Originally, the future of automated insulin delivery (AID) systems, or artificial pancreas systems (APS), was having them at all, in any form. We’ve learned in the last half dozen years that the future of all artificial pancreas systems holds higher time in range, less work required to manage automated insulin delivery systems to improve quality of life, and the ability to input critical information back into the system itself. The data and user experience stories make it clear: APS works. APS are an improvement over other diabetes therapy methods when they are made available, accessible, and affordable. Understanding the unmet expectations of current users of first generation APS technology may also aid in the development of improved technology and user experiences for the future of APS.

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In Silico Study of Phospholipids as An Oral Insulin Delivery System

Journal of Physics Conference Series ◽

10.1088/1742-6596/1090/1/012053 ◽

2018 ◽

Vol 1090 ◽

pp. 012053

Author(s):

Nanik Rahmawati Fuadah ◽

Rukman Hertadi

Keyword(s):

Delivery System ◽

In Silico ◽

Insulin Delivery ◽

Oral Insulin ◽

In Silico Study ◽

Insulin Delivery System

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The Effect of Glargine As Basal Insulin Support for Recovering Critically Ill and High Dependency Unit Patients: An in Silico Study

7th IFAC Symposium on Modelling and Control in Biomedical Systems ◽

10.3182/20090812-3-dk-2006.00009 ◽

2009 ◽

Author(s):

Lin, Jessica

Keyword(s):

Critically Ill ◽

In Silico ◽

Basal Insulin ◽

High Dependency Unit ◽

High Dependency ◽

In Silico Study

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In Silico Study of Structural and Geometrical Requirements of Natural Sesquiterpene Lactones with Trypanocidal Activity

Mini-Reviews in Medicinal Chemistry ◽

10.2174/13895575113139990066 ◽

2013 ◽

Vol 13 (10) ◽

pp. 1407-1414 ◽

Cited By ~ 10

Author(s):

L. Fabian ◽

V. Sulsen ◽

F. Frank ◽

S. Cazorla ◽

E. Malchiodi ◽

...

Keyword(s):

In Silico ◽

Sesquiterpene Lactones ◽

Trypanocidal Activity ◽

In Silico Study

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In Silico Study of 1, 4 Alpha Glucan Branching Enzyme and Substrate Docking Studies

Current Proteomics ◽

10.2174/1570164616666190401204009 ◽

2020 ◽

Vol 17 (1) ◽

pp. 40-50

Author(s):

Farzane Kargar ◽

Amir Savardashtaki ◽

Mojtaba Mortazavi ◽

Masoud Torkzadeh Mahani ◽

Ali Mohammad Amani ◽

...

Keyword(s):

Phylogenetic Tree ◽

In Silico ◽

Alpha Helix ◽

In Silico Analysis ◽

Glycogen Storage ◽

Docking Studies ◽

Random Coil ◽

Special Topic ◽

Industrial Biotechnology ◽

In Silico Study

Background: The 1,4-alpha-glucan branching protein (GlgB) plays an important role in the glycogen biosynthesis and the deficiency in this enzyme has resulted in Glycogen storage disease and accumulation of an amylopectin-like polysaccharide. Consequently, this enzyme was considered a special topic in clinical and biotechnological research. One of the newly introduced GlgB belongs to the Neisseria sp. HMSC071A01 (Ref.Seq. WP_049335546). For in silico analysis, the 3D molecular modeling of this enzyme was conducted in the I-TASSER web server. Methods: For a better evaluation, the important characteristics of this enzyme such as functional properties, metabolic pathway and activity were investigated in the TargetP software. Additionally, the phylogenetic tree and secondary structure of this enzyme were studied by Mafft and Prabi software, respectively. Finally, the binding site properties (the maltoheptaose as substrate) were studied using the AutoDock Vina. Results: By drawing the phylogenetic tree, the closest species were the taxonomic group of Betaproteobacteria. The results showed that the structure of this enzyme had 34.45% of the alpha helix and 45.45% of the random coil. Our analysis predicted that this enzyme has a potential signal peptide in the protein sequence. Conclusion: By these analyses, a new understanding was developed related to the sequence and structure of this enzyme. Our findings can further be used in some fields of clinical and industrial biotechnology.

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