Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes

Technological advancements in glucose monitoring and artificial pancreas systems for shaping diabetes care

The management of diabetes mellitus has undergone remarkable progress with the introduction of cutting-edge technologies in glucose monitoring and artificial pancreas systems. These innovations have revolutionized diabetes care, offering patients more precise, convenient, and personalized management solutions that significantly improve their quality of life. This review aims to provide a comprehensive overview of recent technological advancements in glucose monitoring devices and artificial pancreas systems, focusing on their transformative impact on diabetes care. A detailed review of the literature was conducted to examine the evolution of glucose monitoring technologies, from traditional invasive methods to more advanced systems. The review explores minimally invasive techniques such as continuous glucose monitoring (CGM) systems and flash glucose monitoring (FGM) systems, which have already been proven to enhance glycemic control and reduce the risk of hypoglycemia. In addition, emerging non-invasive glucose monitoring technologies, including optical, electrochemical, and electro-mechanical methods, were evaluated. These techniques are paving the way for more patient-friendly options that eliminate the need for frequent finger-prick tests, thereby improving adherence and ease of use. Advancements in closed-loop artificial pancreas systems, which integrate CGM with automated insulin delivery, were also examined. These systems, often referred to as "hybrid closed-loop" or "automated insulin delivery" systems, represent a significant leap forward in diabetes care by automating the process of insulin dosing. Such advancements aim to mimic the natural function of the pancreas, allowing for better glucose regulation without the constant need for manual interventions by the patient. Technological breakthroughs in glucose monitoring and artificial pancreas systems have had a profound impact on diabetes management, providing patients with more accurate, reliable, and individualized treatment options. These innovations hold the potential to significantly improve glycemic control, reduce the incidence of diabetes-related complications, and ultimately enhance the quality of life for individuals living with diabetes. Researchers are continually exploring novel methods to measure glucose more effectively and with greater convenience, further refining the future of diabetes care. Researchers are also investigating the integration of artificial intelligence and machine learning algorithms to further enhance the precision and predictive capabilities of glucose monitoring and insulin delivery systems. With ongoing advancements in sensor technology, connectivity, and data analytics, the future of diabetes care promises to deliver even more seamless, real-time management, empowering patients with greater autonomy and improved health outcomes.

Extracellular electrophysiology on clonal human β-cell spheroids

Background

Pancreatic islets are important in nutrient homeostasis and improved cellular models of clonal origin may very useful especially in view of relatively scarce primary material. Close 3D contact and coupling between β-cells are a hallmark of physiological function improving signal/noise ratios. Extracellular electrophysiology using micro-electrode arrays (MEA) is technically far more accessible than single cell patch clamp, enables dynamic monitoring of electrical activity in 3D organoids and recorded multicellular slow potentials (SP) provide unbiased insight in cell-cell coupling.

Objective

We have therefore asked whether 3D spheroids enhance clonal β-cell function such as electrical activity and hormone secretion using human EndoC-βH1, EndoC-βH5 and rodent INS-1 832/13 cells.

Methods

Spheroids were formed either by hanging drop or proprietary devices. Extracellular electrophysiology was conducted using multi-electrode arrays with appropriate signal extraction and hormone secretion measured by ELISA.

Results

EndoC-βH1 spheroids exhibited increased signals in terms of SP frequency and especially amplitude as compared to monolayers and even single cell action potentials (AP) were quantifiable. Enhanced electrical signature in spheroids was accompanied by an increase in the glucose stimulated insulin secretion index. EndoC-βH5 monolayers and spheroids gave electrophysiological profiles similar to EndoC-βH1, except for a higher electrical activity at 3 mM glucose, and exhibited moreover a biphasic profile. Again, physiological concentrations of GLP-1 increased AP frequency. Spheroids also exhibited a higher secretion index. INS-1 cells did not form stable spheroids, but overexpression of connexin 36, required for cell-cell coupling, increased glucose responsiveness, dampened basal activity and consequently augmented the stimulation index.

Conclusion

In conclusion, spheroid formation enhances physiological function of the human clonal β-cell lines and these models may provide surrogates for primary islets in extracellular electrophysiology.

Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility

Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.

Islets-on-Chip: A Tool for Real-Time Assessment of Islet Function Prior to Transplantation

Islet transplantation improves metabolic control in patients with unstable type 1 diabetes. Clinical outcomes have been improving over the last decade, and the widely used beta-score allows the evaluation of transplantation results. However, predictive pre-transplantation criteria of islet quality for clinical outcomes are lacking. In this proof-of-concept study, we examined whether characterization of the electrical activity of donor islets could provide a criterion. Aliquots of 8 human donor islets from the STABILOT study, sampled from islet preparations before transplantation, were characterized for purity and split for glucose-induced insulin secretion and electrical activity using multi-electrode-arrays. The latter tests glucose concentration dependencies, biphasic activity, hormones, and drug effects (adrenalin, GLP-1, glibenclamide) and provides a ranking of CHIP-scores from 1 to 6 (best) based on electrical islet activity. The analysis was performed online in real time using a dedicated board or offline. Grouping of beta-scores and CHIP-scores with high, intermediate, and low values was observed. Further analysis indicated correlation between CHIP-score and beta-score, although significance was not attained (R = 0.51, p = 0.1). This novel approach is easily implantable in islet isolation units and might provide means for the prediction of clinical outcomes. We acknowledge the small cohort size as the limitation of this pilot study.

From islet of Langerhans transplantation to the bioartificial pancreas

Type 1 diabetes is a disease resulting from autoimmune destruction of the insulin-producing beta cells in the pancreas. When type 1 diabetes develops into severe secondary complications, in particular end-stage nephropathy, or life-threatening severe hypoglycemia, the best therapeutic approach is pancreas transplantation, or more recently transplantation of the pancreatic islets of Langerhans. Islet transplantation is a cell therapy procedure, that is minimally invasive and has a low morbidity, but does not display the same rate of functional success as the more invasive pancreas transplantation because of suboptimal engraftment and survival. Another issue is that pancreas or islet transplantation (collectively known as beta cell replacement therapy) is limited by the shortage of organ donors and by the need for lifelong immunosuppression to prevent immune rejection and recurrence of autoimmunity. A bioartificial pancreas is a construct made of functional, insulin-producing tissue, embedded in an anti-inflammatory, immunomodulatory microenvironment and encapsulated in a perm-selective membrane allowing glucose sensing and insulin release, but isolating from attacks by cells of the immune system. A successful bioartificial pancreas would address the issues of engraftment, survival and rejection. Inclusion of unlimited sources of insulin-producing cells, such as xenogeneic porcine islets or stem cell-derived beta cells would further solve the problem of organ shortage. This article reviews the current status of clinical islet transplantation, the strategies aiming at developing a bioartificial pancreas, the clinical trials conducted in the field and the perspectives for further progress.