Issue 32, 2016

Intelligent semi-IPN chitosan–PEG–PAAm hydrogel for closed-loop insulin delivery and kinetic modeling

Abstract

A successful approach to designing an intelligent insulin delivery system could facilitate diabetic patients’ lives especially for type 1 diabetes. The aim of the present investigation was to develop an intelligent closed-loop insulin delivery system for implantation. Glucose-responsive semi-IPN hydrogels were synthesized from free radical polymerization of chitosan (CS), acrylamide (AAm) and polyethylene glycol (PEG). Glucose oxidase (GOx) and catalase (CAT) along with insulin were immobilized and loaded into the hydrogels in order to make an intelligent drug carrier, which is able to play the role of an artificial pancreas. The designed glucose responsive hydrogel acts as a self-regulating insulin delivery system and the insulin release rate is associated with the blood glucose level. The loaded hydrogels were characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), high performance liquid chromatography (HPLC), and thermal analysis (TGA/DTG). Cell culture tests with fibroblast cells were conducted to perform biocompatibility testing for the drug carrier systems. The effect of the incorporated PEG on the swelling ratio (SR), drug loading capacities (DLC), and entrapment efficiency (EE) of the intelligent semi-IPN hydrogels was investigated using HPLC and UV-vis spectroscopy. Optimization of the hydrogel was also investigated using a full factorial design and by changing the amount of PEG. Kinetic studies were performed using different kinetic models to investigate the insulin release rate.

Graphical abstract: Intelligent semi-IPN chitosan–PEG–PAAm hydrogel for closed-loop insulin delivery and kinetic modeling

Article information

Article type
Paper
Submitted
31 des. 2015
Accepted
22 feb. 2016
First published
24 feb. 2016

RSC Adv., 2016,6, 26590-26598

Intelligent semi-IPN chitosan–PEG–PAAm hydrogel for closed-loop insulin delivery and kinetic modeling

B. V. Farahani, H. Ghasemzaheh and S. Afraz, RSC Adv., 2016, 6, 26590 DOI: 10.1039/C5RA28188A

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