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

Contemporary Artificial Pancreas (AP) consists of a subcutaneous (SC) glucose sensor, a SC insulin pump and a control algorithm. Even the most advanced systems are far from optimal, in particular due to the non-physiologic nature of SC route. While SC insulin delivery is convenient and minimally invasive, it introduces delays to insulin action that make tight control difficult, particularly during meals. In addition frequent patient interventions are needed, e.g., at mealtime. The intraperitoneal (IP) insulin delivery could address this major challenge since it exhibits a faster pharmacokinetics/pharmacodynamics, hence making easier to quickly respond to glycemic disturbances. A 1-day hospital closed-loop study has shown significant improvements of IP glucose control vs SC AP, and that meal announcement is not necessary. However, the IP AP has not been tested in more realistic everyday life conditions. In this work we have performed an in silico study of 14 days of an IP AP by using the UVA/Padova simulator which includes intra- and inter-day variability of insulin sensitivity and several real life scenarios. We show superiority of IP AP vs SC AP in terms of quality of glucose control (time in range 87% IP vs 80% SC) without the need of a meal announcement

Implantable medical devices for drug and cell release

This work is focused on the research on how to leverage 3D printing technology in the field of cell transplantation. More specifically, the study of an artificial organ for hormone replacement therapies thanks to the close collaboration between the Methodist Hospital Research Institute, Houston, Texas and Politecnico di Torino, Turin, Italy. Cell transplantation offers an attractive therapeutic approach for many endocrine deficiencies. Transplanted endocrine cells or engineered cells encapsulated in the here presented 3D printed device, can act as biological sensors detecting changes in hormonal levels and secrete molecules in response to maintain homeostasis. The major advantage of this technology is that patients affected by endocrine disorder could potentially avoid the need of frequent hormone injections, such as insulin or testosterone, resulting in an improved quality of life and lower chronic side effects associated to external hormone supplementations. This implant was extensively tested both in vitro and in vivo condition, providing remarkable results that lead to several publications. The cell encapsulation system was fabricated via 3D printing technology adopting an FDA approved polymeric material. The structure, composed by an array of micro and macro channels, was specifically designed in order to allow vasculature formation within the device and for housing cells while avoiding cell clustering. Over the course of the Ph.D., the technology was designed, fabricated and tested for the encapsulation of several cell lines and for small and large animal models. According to the in vivo results, we demonstrated that our 3D printed device exemplifies a clinically translatable strategy for preserving viability and function of transplanted cells. Currently, is ongoing an experiment in Non-Human Primates (data not shown), last pre- clinical study before the possibility to move to the clinical development in humans. The pre-vascularization approach to achieve an ideal intra-device milieu prior to transplantation, transcutaneous cell loading and refilling capabilities, as well as the potential for rapid device retrievability, addresses current challenges in transplantation. This technology may offer exciting potential for clinical adoption in relevant medical areas of diabetes, hypogonadism, hypothyroidism, cancer, and neurological diseases among others

Emerging Treatment Strategies for Diabetes Mellitus and Associated Complications: An Update

The occurrence of diabetes mellitus (DM) is increasing rapidly at an accelerating rate worldwide. The status of diabetes has changed over the last three generations; whereas before it was deemed a minor disease of older people but currently it is now one of the leading causes of morbidity and mortality among middle-aged and young people. High blood glucose-mediated functional loss, insulin sensitivity, and insulin deficiency lead to chronic disorders such as Type 1 and Type 2 DM. Traditional treatments of DM, such as insulin sensitization and insulin secretion cause undesirable side effects, leading to patient incompliance and lack of treatment. Nanotechnology in diabetes studies has encouraged the development of new modalities for measuring glucose and supplying insulin that hold the potential to improve the quality of life of diabetics. Other therapies, such as β-cells regeneration and gene therapy, in addition to insulin and oral hypoglycemic drugs, are currently used to control diabetes. The present review highlights the nanocarrier-based drug delivery systems and emerging treatment strategies of DM

Patented novelties in immunoisolation for the treatment of endocrine disorders

Immunoisolation is based on the principle that transplanted tissue is protected for the host immune system by an artificial membrane. During the past decades a number of different approaches of immunoisolation have been described. The approaches include (i) intravascular devices, which are anatomized to the vascular system, (ii) extravascular macrocapsules, which are mostly diffusion chambers transplanted at different sites, and (iii) extravascular microcapsules. Many reviews describing the advantages and pitfalls of the different approaches of immunoisolation have been described during recent years. Almost none of these reviews however describe the technical advances and (pre)clinical results described in the numerous patents on the subject. Therefore this review presents the recent novelties described in patents related to immunoisolation of tissue

Study of Insulin Attached onto Magnetic Nanoparticles

Glucose regulation is compromised in diabetic patients and hence diabetes is characterized by accumulation of glucose in blood. As a standard practice diabetic patients usually self-administer subcutaneous insulin injections daily, which are usually associated with pain, tissue necrosis, microbial contamination and nerve damage to local areas. Glucose-responsive implantable devices have provided a hope for a brighter future of diabetes management. However, one of the limitations of such devices is their refill requirement, which often requires surgical procedures leading to lower patient compliance. To overcome this limitation led to the idea of reusing insulin after it has been in the body circulation system and later becomes residues. To make this idea work, the first step proposed in this thesis is to tag insulin with magnetic nanoparticles and then to use a magnetic guidance system to bring it back the residue insulin to the implanted device before it can go to the clearance sites. Obviously, the precondition for the foregoing idea to work is to make sure that insulin’s conformation is not affected by the attachment with magnetic nanoparticles. This thesis was designed to study this precondition. The hypothesis is that the insulin’s conformation will not be affected by the attachment with the magnetic nanoparticles. Two specific objectives are: (1) assessment of the feasibility of potential capturing techniques and analysis of the attachment of insulin onto the magnetic nanoparticles to confirm the attachment; (2) measurement of the insulin’s conformation before and after it is attached with the magnetic nanoparticles. The spectroscopy techniques, including Fourier transform infrared, circular dichroism, absorbance and fluorescence spectroscopy, were used to conduct data collection and analysis. All four of these spectroscopies provide important information concerning the research objectives of this thesis. The results from the fluorescence and absorbance spectroscopy confirm the attachment of insulin onto the magnetic nanoparticles, hence the achievement of Objective 1. The results from the CD and FTIR spectroscopy show that insulin’s conformation is unchanged before and after its attachment onto magnetic nanoparticles, hence the achievement of Objective 2. The general conclusion of the study is that the insulin’s conformation will not be affected by the attachment of it with magnetic nanoparticles

Design and Implementation of an Integrated Biosensor Platform for Lab-on-a-Chip Diabetic Care Systems

Recent advances in semiconductor processing and microfabrication techniques allow the implementation of complex microstructures in a single platform or lab on chip. These devices require fewer samples, allow lightweight implementation, and offer high sensitivities. However, the use of these microstructures place stringent performance constraints on sensor readout architecture. In glucose sensing for diabetic patients, portable handheld devices are common, and have demonstrated significant performance improvement over the last decade. Fluctuations in glucose levels with patient physiological conditions are highly unpredictable and glucose monitors often require complex control algorithms along with dynamic physiological data. Recent research has focused on long term implantation of the sensor system. Glucose sensors combined with sensor readout, insulin bolus control algorithm, and insulin infusion devices can function as an artificial pancreas. However, challenges remain in integrated glucose sensing which include degradation of electrode sensitivity at the microscale, integration of the electrodes with low power low noise readout electronics, and correlation of fluctuations in glucose levels with other physiological data. This work develops 1) a low power and compact glucose monitoring system and 2) a low power single chip solution for real time physiological feedback in an artificial pancreas system. First, glucose sensor sensitivity and robustness is improved using robust vertically aligned carbon nanofiber (VACNF) microelectrodes. Electrode architectures have been optimized, modeled and verified with physiologically relevant glucose levels. Second, novel potentiostat topologies based on a difference-differential common gate input pair transimpedance amplifier and low-power voltage controlled oscillators have been proposed, mathematically modeled and implemented in a 0.18μm [micrometer] complementary metal oxide semiconductor (CMOS) process. Potentiostat circuits are widely used as the readout electronics in enzymatic electrochemical sensors. The integrated potentiostat with VACNF microelectrodes achieves competitive performance at low power and requires reduced chip space. Third, a low power instrumentation solution consisting of a programmable charge amplifier, an analog feature extractor and a control algorithm has been proposed and implemented to enable continuous physiological data extraction of bowel sounds using a single chip. Abdominal sounds can aid correlation of meal events to glucose levels. The developed integrated sensing systems represent a significant advancement in artificial pancreas systems

Impact of Sensing and Actuation Characteristics on Artificial Pancreas Design

Type 1 diabetes mellitus (T1DM) is a chronic disease characterized by the body’s inability to produce insulin, leading to chronically high blood glucose (BG) concentrations. T1DM is treated by frequent self-administration of insulin based on BG measurements; however, there is a fine line between too little and too much insulin, and an overdose can lead to a dangerous drop in BG. The artificial pancreas (AP), consisting of a glucose sensor, an insulin pump, and a feedback control algorithm, will replace self-treatment by automatically calculating and delivering insulin dosages based on continuous glucose measurements. Many iterations of the AP utilize commercially available subcutaneous (SC) insulin pumps and glucose sensors, but these devices introduce physiological limitations that make control difficult. In this work, we present a clinical evaluation of an AP that uses SC devices, as well as an investigation of the intraperitoneal (IP) space as an alternative site for insulin delivery and glucose sensing to improve AP performance. Our results show that glucose sensors placed in the IP space have a lower time constant than SC sensors, allowing the controller to respond more quickly to BG disturbances. Similarly, insulin delivered through the IP space has faster pharmacokinetic and pharmacodynamic characteristics than SC insulin. Based on models of the sensing and actuation dynamics, a proportional-integral-derivative control algorithm with anti-reset windup protection was designed for the IP-IP route and evaluated on 10 simulated T1DM subjects. Using the IP-IP route led to a more robust controller that provided excellent control during the simulation studies. Our results support the development of a fully implantable AP that will operate within the IP space to safely and effectively control BG levels

The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient