Modeling Intraperitoneal Insulin Absorption in Patients with Type 1 Diabetes

Engineered nanoparticles in non-invasive insulin delivery for precision therapeutics of diabetes

Diabetes mellitus is a chronic disease leading to the death of millions a year across the world. Insulin is required for Type 1, Type 2, and gestational diabetic patients, however, there are various modes of insulin delivery out of which oral delivery is noninvasive and convenient. Moreover, factors like insulin degradation and poor intestinal absorption play a crucial role in its bioavailability and effectiveness. This review discusses various types of engineered nanoparticles used in-vitro, in-vivo, and ex-vivo insulin delivery along with their administration routes and physicochemical properties. Injectable insulin formulations, currently in use have certain limitations, leading to invasiveness, low patient compliance, causing inflammation, and side effects. Based on these drawbacks, this review emphasizes more on the non-invasive route, particularly oral delivery. The article is important because it focuses on how engineered nanoparticles can overcome the limitations of free therapeutics (drugs alone), navigate the barriers, and accomplish precision therapeutics in diabetes. In future, more drugs could be delivered with a similar strategy to cure various diseases and resolve challenges in drug delivery. This review significantly describes the role of various engineered nanoparticles in improving the bioavailability of insulin by protecting it from various barriers during non-invasive routes of delivery.

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.

An Intelligent Model-Based Effective Approach for Glycemic Control in Type-1 Diabetes

Type-1 diabetes mellitus (T1DM) is a challenging disorder which essentially involves regulation of the glucose levels to avoid hyperglycemia as well as hypoglycemia. For this purpose, this research paper proposes and develops control algorithms using an intelligent predictive control model, which is based on a UVA/Padova metabolic simulator. The primary objective of the designed control laws is to provide an automatic blood glucose control in insulin-dependent patients so as to improve their life quality and to reduce the need of an extremely demanding self-management plan. Various linear and nonlinear control algorithms have been explored and implemented on the estimated model. Linear techniques include the Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR), and nonlinear control strategy includes the Sliding Mode Control (SMC), which are implemented in this research work for continuous monitoring of glucose levels. Performance comparison based on simulation results demonstrated that SMC proved to be most efficient in terms of regulating glucose profile to a reference level of 70 mg/dL compared to the classical linear techniques. A brief comparison is presented between the linear techniques (PID and LQR), and nonlinear technique (SMC) for analysis purposes proving the efficacy of the design.

Intraperitoneal Insulin Delivery: Evidence of a Physiological Route for Artificial Pancreas From Compartmental Modeling

BACKGROUND

Intraperitoneal insulin delivery has proven to safely overcome a major limit of subcutaneous delivery-meal announcement-and has been able to optimize glycemic control in adults under controlled experimental conditions. In addition, intraperitoneal delivery avoids peripheral hyperinsulinemia resulting from the subcutaneous route and restores a physiological liver gradient.

METHODS

Relying on a unique data set of intraperitoneal closed-loop insulin delivery obtained with a Model Predictive Controller (MPC), we develop a compartmental model of intraperitoneal insulin kinetics, which, once included in the UVa/Padova T1D simulator, will facilitate the investigation of various control strategies, for example, the simpler Proportional Integral Derivative controller versus MPC.

RESULTS

Intraperitoneal insulin kinetics can be described with a 2-compartment model including liver and plasma.

CONCLUSION

Intraperitoneal insulin transit is fast enough to render irrelevant the addition of a peritoneal compartment, proving the peritoneum being a virtual-not actual-transit space for insulin delivery.