An official website of the United States government
Abstract Type 1 and advanced type 2 diabetes treatment involves daily injections or continuous infusion of exogenous insulin aimed at regulating blood glucose levels in the normoglycemic range. However, current options for insulin therapy are limited by the risk of hypoglycemia and are associated with suboptimal glycemic control outcomes. Therefore, a range of glucose‐responsive components that can undergo changes in conformation or show alterations in intermolecular binding capability in response to glucose stimulation has been studied for ultimate integration into closed‐loop insulin delivery or “smart insulin” systems. Here, an overview of the evolution and recent progress in the development of molecular approaches for glucose‐responsive insulin delivery systems, a rapidly growing subfield of precision medicine, is presented. Three central glucose‐responsive moieties, including glucose oxidase, phenylboronic acid, and glucose‐binding molecules are examined in detail. Future opportunities and challenges regarding translation are also discussed.
more »
« less
Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management
Abstract The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well‐poised for the next generation of closed‐loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on‐tooth tattoos, skin‐mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real‐time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self‐regulating closed‐loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management.
more »
« less
- Award ID(s):
- 1944480
- PAR ID:
- 10449054
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Healthcare Materials
- Volume:
- 10
- Issue:
- 17
- ISSN:
- 2192-2640
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor blood glucose and administer insulin. This technology allows subjects to easily track their blood glucose levels with early intervention without the need for hospital visits. The data collected from these sensors is an excellent candidate for the application of machine learning algorithms to learn patterns and predict future values of blood glucose levels. In this study, we developed artificial neural network algorithms based on the OhioT1DM training dataset that contains data on 12 subjects. The dataset contains features such as subject identifiers, continuous glucose monitoring data obtained in 5 minutes intervals, insulin infusion rate, etc. We developed individual models, including LSTM, BiLSTM, Convolutional LSTMs, TCN, and sequence-to-sequence models. We also developed transfer learning models based on the most important features of the data, as identified by a gradient boosting algorithm. These models were evaluated on the OhioT1DM test dataset that contains 6 unique subject’s data. The model with the lowest RMSE values for the 30- and 60-minutes was selected as the best performing model. Our result shows that sequence-to-sequence BiLSTM performed better than the other models. This work demonstrates the potential of artificial neural networks algorithms in the management of Type 1 diabetes.more » « less
-
Abstract Diabetes is one of the most pressing healthcare challenges facing society. Dysfunctional insulin signaling causes diabetes, leading to blood glucose instability and many associated complications. While the administration of exogenous insulin is then essential for achieving glucose control, issues with dosing accuracy and timing remain. Hydrogel‐based drug delivery systems have been broadly explored for controlled protein release, including for applications in long‐lasting and oral insulin delivery. More recently, efforts have focused on injectable hydrogels with glucose‐directed controlled release of insulin and glucagon, aiming for more autonomous and biomimetic approaches to blood glucose control. These materials typically use protein‐based sensing mechanisms or glucose binding by synthetic aryl boronates for glucose‐directed release. Despite advancements in this area, there remains a need for more precise timing of therapeutic availability to afford healthy blood glucose homeostasis, providing an opportunity for further research and innovation. This review summarizes the current state of hydrogel‐based delivery of insulin and glucagon, with insights into the potential benefits, future directions, and challenges that must be overcome to achieve clinical impact.more » « less
-
Neural networks present a useful framework for learning complex dynamics, and are increasingly being considered as components to closed loop predictive control algorithms. However, if they are to be utilized in such safety-critical advisory settings, they must be provably "conformant" to the governing scientific (biological, chemical, physical) laws which underlie the modeled process. Unfortunately, this is not easily guaranteed as neural network models are prone to learn patterns which are artifacts of the conditions under which the training data is collected, which may not necessarily conform to underlying physiological laws. In this work, we utilize a formal range-propagation based approach for checking whether neural network models for predicting future blood glucose levels of individuals with type-1 diabetes are monotonic in terms of their insulin inputs. These networks are increasingly part of closed loop predictive control algorithms for "artificial pancreas" devices which automate control of insulin delivery for individuals with type-1 diabetes. Our approach considers a key property that blood glucose levels must be monotonically decreasing with increasing insulin inputs to the model. Multiple representative neural network models for blood glucose prediction are trained and tested on real patient data, and conformance is tested through our verification approach. We observe that standard approaches to training networks result in models which violate the core relationship between insulin inputs and glucose levels, despite having high prediction accuracy. We propose an approach that can learn conformant models without much loss in accuracy.more » « less
-
Glucose-responsive hydrogel systems are increasingly explored for insulin delivery, with dynamic-covalent crosslinking interactions between phenylboronic acids (PBA) and diols forming a key glucose-sensing mechanism. However, commonly used PBA and diol chemistries often have limited responsiveness to glucose under physiological concentrations. This is due, in part, to the binding of PBA to the commonly used diol chemistries having higher affinity than for PBA to glucose. The present study addresses this challenge by redesigning the diol chemistry in an effort to reduce its binding affinity to PBA, thereby enhancing the ability of glucose to compete with these redesigned PBA–diol crosslinks at its physiological concentration, thus improving responsiveness of the hydrogel network. Rheological analyses support enhanced sensitivity of these PBA–diol networks to glucose, while insulin release likewise improves from networks with reduced crosslink affinities. This work thus offers a new molecular design approach to improve glucose-responsive hydrogels for insulin delivery in diabetes management.more » « less
