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Sidlik Muskatel R, Nathansohn-Levi B, Reich-Zeliger S, Mark M, Stoler-Barak L, Rosen C, Milman-Krentsis I, Bachar Lustig E, Pete Gale R, Friedman N, Reisner Y. Correction of T-Cell Repertoire and Autoimmune Diabetes in NOD Mice by Non-myeloablative T-Cell Depleted Allogeneic HSCT. Stem Cells Transl Med 2023; 12:281-292. [PMID: 37184893 PMCID: PMC10184699 DOI: 10.1093/stcltm/szad021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 03/03/2023] [Indexed: 05/16/2023] Open
Abstract
The induction of partial tolerance toward pancreatic autoantigens in the treatment of type 1 diabetes mellitus (T1DM) can be attained by autologous hematopoietic stem cell transplantation (HSCT). However, most patients treated by autologous HSCT eventually relapse. Furthermore, allogeneic HSCT which could potentially provide a durable non-autoimmune T-cell receptor (TCR) repertoire is associated with a substantial risk for transplant-related mortality. We have previously demonstrated an effective approach for attaining engraftment without graft versus host disease (GVHD) of allogeneic T-cell depleted HSCT, following non-myeloablative conditioning, using donor-derived anti-3rd party central memory CD8 veto T cells (Tcm). In the present study, we investigated the ability of this relatively safe transplant modality to eliminate autoimmune T-cell clones in the NOD mouse model which spontaneously develop T1DM. Our results demonstrate that using this approach, marked durable chimerism is attained, without any transplant-related mortality, and with a very high rate of diabetes prevention. TCR sequencing of transplanted mice showed profound changes in the T-cell repertoire and decrease in the prevalence of specific autoimmune T-cell clones directed against pancreatic antigens. This approach could be considered as strategy to treat people destined to develop T1DM but with residual beta cell function, or as a platform for prevention of beta cell destruction after transplantation of allogenic beta cells.
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Affiliation(s)
- Rakefet Sidlik Muskatel
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Michal Mark
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Liat Stoler-Barak
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Chava Rosen
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Irit Milman-Krentsis
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Esther Bachar Lustig
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Robert Pete Gale
- Haematology Research Centre, Department of Immunology and Inflammation, Imperial College London, London, UK
| | - Nir Friedman
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Yair Reisner
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- CPRIT Scholar in Cancer Research, Austin, TX, USA
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A Comprehensive Review of the Evolution of Insulin Development and Its Delivery Method. Pharmaceutics 2022; 14:pharmaceutics14071406. [PMID: 35890301 PMCID: PMC9320488 DOI: 10.3390/pharmaceutics14071406] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/09/2022] [Accepted: 06/29/2022] [Indexed: 11/24/2022] Open
Abstract
The year 2021 marks the 100th anniversary of the momentous discovery of insulin. Through years of research and discovery, insulin has evolved from poorly defined crude extracts of animal pancreas to recombinant human insulin and analogues that can be prescribed and administered with high accuracy and efficacy. However, there are still many challenges ahead in clinical settings, particularly with respect to maintaining optimal glycemic control whilst minimizing the treatment-related side effects of hypoglycemia and weight gain. In this review, the chronology of the development of rapid-acting, short-acting, intermediate-acting, and long-acting insulin analogues, as well as mixtures and concentrated formulations that offer the potential to meet this challenge, are summarized. In addition, we also summarize the latest advancements in insulin delivery methods, along with advancement to clinical trials. This review provides insights on the development of insulin treatment for diabetes mellitus that may be useful for clinicians in meeting the needs of their individual patients. However, it is important to note that as of now, none of the new technologies mentioned have superseded the existing method of subcutaneous administration of insulin.
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Armiger R, Reddy M, Oliver NS, Georgiou P, Herrero P. An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms. J Diabetes Sci Technol 2022; 16:29-39. [PMID: 34861785 PMCID: PMC8875066 DOI: 10.1177/19322968211060074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. METHODS The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. RESULTS Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). CONCLUSIONS Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.
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Affiliation(s)
- Ryan Armiger
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Monika Reddy
- Division of Diabetes, Endocrinology & Metabolism, Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Nick S. Oliver
- Division of Diabetes, Endocrinology & Metabolism, Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
- Pau Herrero, PhD, Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
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Choudhary S, Kalra V, Kumar M, Tiwary AK, Sood J, Silakari O. Bio-Inspired Strategies against Diabetes and Associated Complications: A Review. ACTA ACUST UNITED AC 2019; 13:273-282. [PMID: 31884934 DOI: 10.2174/1872211314666191224120145] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/13/2019] [Accepted: 12/16/2019] [Indexed: 01/22/2023]
Abstract
Bio-molecules are the most important target to be considered while designing any drug delivery system. The logic lies in using such bio-sensing or bio-mimicking systems in their formulations that can mimic the active site of those receptors to which the drug is going to bind. Polymers mimicking the active site of target enzymes are regarded as bio-inspired polymers and can be used to ameliorate many diseased conditions. Nowadays, this strategy is also being adopted against diabetes and its complications. Under hyperglycemic conditions, many pathways get activated which are responsible for the progression of diabetes-associated secondary complications viz. retinopathy, neuropathy, and nephropathy. The enzymes involved in the progression of these complications can be mimicked for their effective management. For an instance, Aldose Reductase (ALR2), a rate-limiting enzyme of the polyol pathway (downstream pathway) which gets over-activated under hyperglycemic condition is reported to be mimicked by using polymers which are having same functionalities in their structure. This review aims at critically appraising reports in which target mimicking bio-inspired formulations have been envisaged against diabetes and its complications. The information summarized in this review will provide an idea about the bio-sensing approaches utilized to manage blood glucose level and the utility of bio-inspired polymers for the management of diabetic complications (DC). Such type of information may be beneficial to pharmaceutical companies and academia for better development of targeted drug delivery systems with sustained-release property against these diseased conditions.
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Affiliation(s)
- Shalki Choudhary
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Vinni Kalra
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Manoj Kumar
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Ashok Kumar Tiwary
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Jatin Sood
- Formulation Research and Development Department, Peace Naturals Project Inc. The Cronos Group, Stayner, Ontario, Canada
| | - Om Silakari
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
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Herrero P, El-Sharkawy M, Daniels J, Jugnee N, Uduku CN, Reddy M, Oliver N, Georgiou P. The Bio-inspired Artificial Pancreas for Type 1 Diabetes Control in the Home: System Architecture and Preliminary Results. J Diabetes Sci Technol 2019; 13:1017-1025. [PMID: 31608656 PMCID: PMC6835194 DOI: 10.1177/1932296819881456] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Artificial pancreas (AP) technology has been proven to improve glucose and patient-centered outcomes for people with type 1 diabetes (T1D). Several approaches to implement the AP have been described, clinically evaluated, and in one case, commercialized. However, none of these approaches has shown a clear superiority with respect to others. In addition, several challenges still need to be solved before achieving a fully automated AP that fulfills the users' expectations. We have introduced the Bio-inspired Artificial Pancreas (BiAP), a hybrid adaptive closed-loop control system based on beta-cell physiology and implemented directly in hardware to provide an embedded low-power solution in a dedicated handheld device. In coordination with the closed-loop controller, the BiAP system incorporates a novel adaptive bolus calculator which aims at improving postprandial glycemic control. This paper focuses on the latest developments of the BiAP system for its utilization in the home environment. METHODS The hardware and software architectures of the BiAP system designed to be used in the home environment are described. Then, the clinical trial design proposed to evaluate the BiAP system in an ambulatory setting is introduced. Finally, preliminary results corresponding to two participants enrolled in the trial are presented. RESULTS Apart from minor technical issues, mainly due to wireless communications between devices, the BiAP system performed well (~88% of the time in closed-loop) during the clinical trials conducted so far. Preliminary results show that the BiAP system might achieve comparable glycemic outcomes to the existing AP systems (~73% time in target range 70-180 mg/dL). CONCLUSION The BiAP system is a viable platform to conduct ambulatory clinical trials and a potential solution for people with T1D to control their glucose control in a home environment.
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Affiliation(s)
- Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Mohamed El-Sharkawy
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - John Daniels
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Narvada Jugnee
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Chukwuma N. Uduku
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Monika Reddy
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Nick Oliver
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
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Abstract
Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia 22908
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Güemes A, Herrero P, Bondia J, Georgiou P. Modeling the effect of the cephalic phase of insulin secretion on glucose metabolism. Med Biol Eng Comput 2019; 57:1173-1186. [PMID: 30685858 PMCID: PMC6525153 DOI: 10.1007/s11517-019-01950-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 01/07/2019] [Indexed: 02/05/2023]
Abstract
The nervous system has a significant impact in glucose homeostasis and endocrine pancreatic secretion in humans, especially during the cephalic phase of insulin release (CPIR); that is, before a meal is absorbed. However, the underlying mechanisms of this neural-pancreatic interaction are not well understood and therefore often neglected, despite their significance to achieving an optimal glucose control. As a result, the dynamics of insulin release from the pancreas are currently described by mathematical models that reproduce the behavior of the β cells using exclusively glucose levels and other hormones as inputs. To bridge this gap, we have combined, for the first time, metabolic and neural mathematical models in a unified system to reproduce to a great extent the ideal glucoregulation observed in healthy subjects. Our results satisfactorily replicate the CPIR and its impact during the post-absorptive phase. Furthermore, the proposed model gives insight into the physiological interaction between the brain and the pancreas in healthy people and suggests the potential of considering the neural information for restoring glucose control in people with diabetes. Graphical Abstract (a) Physiological scenario. Diagram of the biological interaction among the most important organs involved in glucose control during meal intake. (b) Scheme of the unified bio-inspired neural-metabolic model. Each of the boxes represents one subsystem of the model. The pink shades boxes depicts the novel subsystems introduced to the current metabolic models (grey shaded boxes). Insulin-related action and mass fluxes (solid black lines) and glucose-related action and mass flux (dotted black lines) are depicted to show the relationship among the blocks. I(t), Ic(t), G(t) and SI related to plasma insulin, plasma cephalic insulin, plasma glucose and insulin sensitivity, respectively.
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Affiliation(s)
- Amparo Güemes
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK.
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK
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Shirin A, Della Rossa F, Klickstein I, Russell J, Sorrentino F. Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon. PLoS One 2019; 14:e0213665. [PMID: 30893335 PMCID: PMC6426249 DOI: 10.1371/journal.pone.0213665] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022] Open
Abstract
The Glucose-Insulin-Glucagon nonlinear model accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.
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Affiliation(s)
- Afroza Shirin
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
- * E-mail:
| | - Fabio Della Rossa
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Isaac Klickstein
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - John Russell
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
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Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 2018; 4:14. [PMID: 32232090 PMCID: PMC7098217 DOI: 10.1186/s42234-018-0015-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/08/2018] [Indexed: 12/28/2022] Open
Abstract
The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, P.O. Box 400888, Charlottesville, VA 22908 USA
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Herrero P, Bondia J, Oliver N, Georgiou P. A coordinated control strategy for insulin and glucagon delivery in type 1 diabetes. Comput Methods Biomech Biomed Engin 2017; 20:1474-1482. [PMID: 28929796 DOI: 10.1080/10255842.2017.1378352] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Type 1 diabetes is an autoimmune condition characterised by a pancreatic insulin secretion deficit, resulting in high blood glucose concentrations, which can lead to micro- and macrovascular complications. Type 1 diabetes also leads to impaired glucagon production by the pancreatic α-cells, which acts as a counter-regulatory hormone to insulin. A closed-loop system for automatic insulin and glucagon delivery, also referred to as an artificial pancreas, has the potential to reduce the self-management burden of type 1 diabetes and reduce the risk of hypo- and hyperglycemia. To date, bihormonal closed-loop systems for glucagon and insulin delivery have been based on two independent controllers. However, in physiology, the secretion of insulin and glucagon in the body is closely interconnected by paracrine and endocrine associations. In this work, we present a novel biologically-inspired glucose control strategy that accounts for such coordination. An in silico study using an FDA-accepted type 1 simulator was performed to evaluate the proposed coordinated control strategy compared to its non-coordinated counterpart, as well as an insulin-only version of the controller. The proposed coordinated strategy achieves a reduction of hyperglycemia without increasing hypoglycemia, when compared to its non-coordinated counterpart.
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Affiliation(s)
- Pau Herrero
- a Centre for Bio-Inspired Technology, Institute of Biomedical Engineering , Imperial College London , London , UK
| | - Jorge Bondia
- b Institut Universitari d'Automàtica i Informàtica Industrial , Universitat Politècnica de València , València , Spain
| | - Nick Oliver
- c Charing Cross Hospital, Imperial College Healthcare NHS Trust , London , UK
| | - Pantelis Georgiou
- a Centre for Bio-Inspired Technology, Institute of Biomedical Engineering , Imperial College London , London , UK
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Herrero P, Bondia J, Adewuyi O, Pesl P, El-Sharkawy M, Reddy M, Toumazou C, Oliver N, Georgiou P. Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:125-131. [PMID: 28688482 PMCID: PMC6522376 DOI: 10.1016/j.cmpb.2017.05.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 04/02/2017] [Accepted: 05/25/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. METHODS In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. RESULTS Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed. CONCLUSION Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.
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Affiliation(s)
- Pau Herrero
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
| | - Jorge Bondia
- Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, València, Spain
| | - Oloruntoba Adewuyi
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Peter Pesl
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Mohamed El-Sharkawy
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Monika Reddy
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Chris Toumazou
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Nick Oliver
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
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Turksoy K, Frantz N, Quinn L, Dumin M, Kilkus J, Hibner B, Cinar A, Littlejohn E. Automated Insulin Delivery-The Light at the End of the Tunnel. J Pediatr 2017; 186:17-28.e9. [PMID: 28396030 DOI: 10.1016/j.jpeds.2017.02.055] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 02/13/2017] [Accepted: 02/20/2017] [Indexed: 12/28/2022]
Affiliation(s)
- Kamuran Turksoy
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL
| | - Nicole Frantz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago, Chicago, IL
| | - Magdalena Dumin
- Biological Sciences Division, University of Chicago, Chicago, IL
| | - Jennifer Kilkus
- Biological Sciences Division, University of Chicago, Chicago, IL
| | - Brooks Hibner
- Biological Sciences Division, University of Chicago, Chicago, IL
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL; Biological Sciences Division, University of Chicago, Chicago, IL; Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL
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Christiansen SC, Fougner AL, Stavdahl Ø, Kölle K, Ellingsen R, Carlsen SM. A Review of the Current Challenges Associated with the Development of an Artificial Pancreas by a Double Subcutaneous Approach. Diabetes Ther 2017; 8:489-506. [PMID: 28503717 PMCID: PMC5446388 DOI: 10.1007/s13300-017-0263-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Indexed: 01/01/2023] Open
Abstract
INTRODUCTION Patients with diabetes type 1 (DM1) struggle daily to achieve good glucose control. The last decade has seen a rush of research groups working towards an artificial pancreas (AP) through the application of a double subcutaneous approach, i.e., subcutaneous (SC) continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion. Few have focused on the fundamental limitations of this approach, especially regarding outcome measures beyond time in range. METHODS Based on insulin physiology, the limitations of CGM, SC insulin absorption, meal challenge, and physical activity in DM1 patients, we discuss the limitations of the double SC approach. Finally, we discuss safety measures and the achievements reported in some recent AP studies that have utilized the double SC approach. RESULTS Most studies show that a double SC AP increases the time in range compared to a sensor-augmented insulin pump and shortens the time in hypoglycemia. Despite these achievements, the proportion of time spent in hyperglycemia is still roughly 20-40%, and hypoglycemia is still present 1-4% of the time. The main factors limiting further progress are the latency of SC CGM (at least 5-10 min) and the slow pharmacokinetics of SC-delivered fast-acting insulin. The maximum blood insulin level is reached after 45 min and the maximum glucose-lowering effect is observed after 1.5-2 h, while the glucose-lowering effect lasts for at least 5 h. CONCLUSIONS Although using a double SC AP leads to significant improvements in glucose control, the SC approach has severe limitations that hamper further progress towards a robust AP.
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Affiliation(s)
- Sverre Christian Christiansen
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
| | - Anders Lyngvi Fougner
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Central Norway Regional Health Authority, Stjørdal, Norway
| | - Øyvind Stavdahl
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Konstanze Kölle
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Central Norway Regional Health Authority, Stjørdal, Norway
| | - Reinold Ellingsen
- Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Sven Magnus Carlsen
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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Huyett LM, Ly TT, Forlenza GP, Reuschel-DiVirgilio S, Messer LH, Wadwa RP, Gondhalekar R, Doyle FJ, Pinsker JE, Maahs DM, Buckingham BA, Dassau E. Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control. Diabetes Technol Ther 2017; 19:331-339. [PMID: 28459617 PMCID: PMC5510043 DOI: 10.1089/dia.2016.0399] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND The artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise. MATERIALS AND METHODS Adolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform. Subjects engaged in twice-daily exercise, including soccer, tennis, and bicycling. Meal size (unrestricted) was estimated and entered into the system by subjects to trigger a bolus, but exercise was not announced. RESULTS Ten adolescents (11.9-17.7 years) completed 72 h of CLC, with data on 95 ± 14 h of sensor-augmented pump (SAP) therapy before CLC as a comparison to usual therapy. The percentage of time with continuous glucose monitor (CGM) 70-180 mg/dL was 71% ± 10% during CLC, compared to 57% ± 16% during SAP (P = 0.012). Nocturnal control during CLC was safe, with 0% (0%, 0.6%) of time with CGM <70 mg/dL compared to 1.1% (0.0%, 14%) during SAP. Despite large meals (estimated up to 120 g carbohydrate), only 8.0% ± 6.9% of time during CLC was spent with CGM >250 mg/dL (16% ± 14% during SAP). The system remained connected in CLC for 97% ± 2% of the total study time. No adverse events or severe hypoglycemia occurred. CONCLUSIONS The use of the ZMPC+HMS algorithms is feasible in the adolescent outpatient environment and achieved significantly more time in the desired glycemic range than SAP in the face of unannounced exercise and large announced meal challenges.
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Affiliation(s)
- Lauren M. Huyett
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California
- William Sansum Diabetes Center, Santa Barbara, California
| | - Trang T. Ly
- Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University, Stanford, California
| | - Gregory P. Forlenza
- Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Suzette Reuschel-DiVirgilio
- Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University, Stanford, California
| | - Laurel H. Messer
- Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - R. Paul Wadwa
- Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Ravi Gondhalekar
- William Sansum Diabetes Center, Santa Barbara, California
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - Francis J. Doyle
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California
- William Sansum Diabetes Center, Santa Barbara, California
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | | | - David M. Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University, Stanford, California
- Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Bruce A. Buckingham
- Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University, Stanford, California
| | - Eyal Dassau
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California
- William Sansum Diabetes Center, Santa Barbara, California
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
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Daskalaki E, Diem P, Mougiakakou SG. Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes. PLoS One 2016; 11:e0158722. [PMID: 27441367 PMCID: PMC4956312 DOI: 10.1371/journal.pone.0158722] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 06/21/2016] [Indexed: 11/23/2022] Open
Abstract
Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI.
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Affiliation(s)
- Elena Daskalaki
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
| | - Peter Diem
- Division of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital “Inselspital”, 3010 Bern, Switzerland
| | - Stavroula G. Mougiakakou
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
- Division of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital “Inselspital”, 3010 Bern, Switzerland
- * E-mail:
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Blauw H, Keith-Hynes P, Koops R, DeVries JH. A Review of Safety and Design Requirements of the Artificial Pancreas. Ann Biomed Eng 2016; 44:3158-3172. [PMID: 27352278 PMCID: PMC5093196 DOI: 10.1007/s10439-016-1679-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 06/13/2016] [Indexed: 01/03/2023]
Abstract
As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components—type 1 diabetes, artificial pancreas, and safety or design—and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.
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Affiliation(s)
- Helga Blauw
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, P.O Box 22660, 1100 DD, Amsterdam, The Netherlands. .,Inreda Diabetic BV, Goor, The Netherlands.
| | - Patrick Keith-Hynes
- TypeZero Technologies, LLC, Charlottesville, VA, USA.,Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | | | - J Hans DeVries
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, P.O Box 22660, 1100 DD, Amsterdam, The Netherlands
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17
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Trevitt S, Simpson S, Wood A. Artificial Pancreas Device Systems for the Closed-Loop Control of Type 1 Diabetes: What Systems Are in Development? J Diabetes Sci Technol 2016; 10:714-23. [PMID: 26589628 PMCID: PMC5038530 DOI: 10.1177/1932296815617968] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND Closed-loop artificial pancreas device (APD) systems are externally worn medical devices that are being developed to enable people with type 1 diabetes to regulate their blood glucose levels in a more automated way. The innovative concept of this emerging technology is that hands-free, continuous, glycemic control can be achieved by using digital communication technology and advanced computer algorithms. METHODS A horizon scanning review of this field was conducted using online sources of intelligence to identify systems in development. The systems were classified into subtypes according to their level of automation, the hormonal and glycemic control approaches used, and their research setting. RESULTS Eighteen closed-loop APD systems were identified. All were being tested in clinical trials prior to potential commercialization. Six were being studied in the home setting, 5 in outpatient settings, and 7 in inpatient settings. It is estimated that 2 systems may become commercially available in the EU by the end of 2016, 1 during 2017, and 2 more in 2018. CONCLUSIONS There are around 18 closed-loop APD systems progressing through early stages of clinical development. Only a few of these are currently in phase 3 trials and in settings that replicate real life.
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Affiliation(s)
- Sara Trevitt
- NIHR Horizon Scanning Research & Intelligence Centre, University of Birmingham, Birmingham, UK
| | - Sue Simpson
- NIHR Horizon Scanning Research & Intelligence Centre, University of Birmingham, Birmingham, UK
| | - Annette Wood
- NIHR Horizon Scanning Research & Intelligence Centre, University of Birmingham, Birmingham, UK
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18
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Reddy M, Herrero P, Sharkawy ME, Pesl P, Jugnee N, Pavitt D, Godsland IF, Alberti G, Toumazou C, Johnston DG, Georgiou P, Oliver NS. Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes: A 24-Hour Randomized Controlled Crossover Study. J Diabetes Sci Technol 2015; 10:405-13. [PMID: 26581881 PMCID: PMC4773972 DOI: 10.1177/1932296815616134] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The Bio-inspired Artificial Pancreas (BiAP) is a closed-loop insulin delivery system based on a mathematical model of beta-cell physiology and implemented in a microchip within a low-powered handheld device. We aimed to evaluate the safety and efficacy of the BiAP over 24 hours, followed by a substudy assessing the safety of the algorithm without and with partial meal announcement. Changes in lactate and 3-hydroxybutyrate concentrations were investigated for the first time during closed-loop. METHODS This is a prospective randomized controlled open-label crossover study. Participants were randomly assigned to attend either a 24-hour closed-loop visit connected to the BiAP system or a 24-hour open-loop visit (standard insulin pump therapy). The primary outcome was percentage time spent in target range (3.9-10 mmol/l) measured by sensor glucose. Secondary outcomes included percentage time in hypoglycemia (<3.9 mmol/l) and hyperglycemia (>10 mmol/l). Participants were invited to attend for an additional visit to assess the BiAP without and with partial meal announcements. RESULTS A total of 12 adults with type 1 diabetes completed the study (58% female, mean [SD] age 45 [10] years, BMI 25 [4] kg/m(2), duration of diabetes 22 [12] years and HbA1c 7.4 [0.7]% [58 (8) mmol/mol]). The median (IQR) percentage time in target did not differ between closed-loop and open-loop (71% vs 66.9%, P = .9). Closed-loop reduced time spent in hypoglycemia from 17.9% to 3.0% (P < .01), but increased time was spent in hyperglycemia (10% vs 28.9%, P = .01). The percentage time in target was higher when all meals were announced during closed-loop compared to no or partial meal announcement (65.7% [53.6-80.5] vs 45.5% [38.2-68.3], P = .12). CONCLUSIONS The BiAP is safe and achieved equivalent time in target as measured by sensor glucose, with improvement in hypoglycemia, when compared to standard pump therapy.
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Affiliation(s)
- Monika Reddy
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Mohamed El Sharkawy
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Peter Pesl
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Narvada Jugnee
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Darrell Pavitt
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Ian F Godsland
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - George Alberti
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Christofer Toumazou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Desmond G Johnston
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Nick S Oliver
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
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