eprintid: 18293
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/82/93
datestamp: 2024-06-04 14:10:28
lastmod: 2024-06-04 14:10:28
status_changed: 2024-06-04 14:02:14
type: article
metadata_visibility: show
creators_name: Safdar, R.
creators_name: Thanabalan, M.
title: Preparation of Chitosan-Tripolyphosphate Formulated Insulin Microparticles, Their Characterization, ANN Prediction, and Release Kinetics
ispublished: pub
keywords: chitosan; drug carrier; insulin; microparticle; tripolyphosphate; unclassified drug, Article; artificial neural network; concentration (parameter); differential scanning calorimetry; dispersity; drug delivery system; drug formulation; encapsulation; energy dispersive X ray spectroscopy; evaluation study; field emission scanning electron microscopy; Fourier transform infrared spectroscopy; particle size; pH; sustained drug release; zeta potential
note: cited By 1
abstract: Purpose: Insulin is highly sensitive to an acidic pH medium and degrades faster. The encapsulation of insulin with chitosan (CS) particulates could partially solve this issue. Since the formation of particles involves different parameters, the unoptimized parameters and formulation result in unstable particles that exhibit a burst release in an acidic medium (pH = 1.20). On the other hand, the CS is poorly or partially soluble in PBS medium (pH = 7.40) and results in a limited insulin release. Therefore, the current study was conducted to prevent the insulin from degrading in an acidic medium and provide a sustained release in a PBS medium. Methods: In this research, the chitosan-tripolyphosphate (CS-TPP) microparticles (MPs) were prepared and characterized in terms of size, PDI, and zeta potential. Furthermore, the effects of different parameters on the size, PDI, and zeta potential were determined. The experimental data for all these properties were validated using the artificial neural network (ANN) technique. The best conditions and formulation were used for insulin encapsulation and prepared MPs were characterized by FTIR, DSC, FESEM, and EDX. Afterwards, insulin release experiments were conducted in acidic (pH = 1.2) and PBS medium (pH = 7.4) for 8 h and release kinetics was conducted to estimate the release rate and mechanism of insulin release from different formulations. Results: The MPs prepared with optimized conditions such as CS concentration = 0.20 w/v, CS pH = 5.30, CS:TPP volume ratio = 3:1, mixing time = 1 h, and stirring speed = 1100 rpm possessed a particle size of 2.90 ± 0.33 μm and a high zeta potential of 55.65 ± 0.68 mV. These optimum conditions were further used for the formation of CS-insulin-TPP MPs that possessed a zeta potential of 25.41 ± 1.64 mV, 1.92 ± 0.07 μm size. Among different CS:TPP volume ratios, the MPs formed with 3:1 exhibited a cumulative insulin release of 55.78 ± 1.42 in 0.10 N HCl medium (pH = 1.20) and 60.79 ± 0.71 in the PBS medium (pH = 7.40). The insulin release occurred due to both diffusion and erosion mechanisms. The predominance of the mechanism for insulin release varied with time from diffusion to polymer chain relaxation. Conclusions: Current findings revealed that MPs prepared with optimized formulation and operating conditions protected the insulin from burst release in the acidic medium and provided a controlled release in the PBS medium. These MPs sufficiently controlled the insulin release rate and can be its good carrier. Graphical Abstract: Figure not available: see fulltext. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146554664&doi=10.1007%2fs12247-023-09707-8&partnerID=40&md5=6ef043acd4dade401ec5e2ef20e1a721
id_number: 10.1007/s12247-023-09707-8
full_text_status: none
publication: Journal of Pharmaceutical Innovation
volume: 18
number: 3
pagerange: 1047-1064
refereed: TRUE
citation:   Safdar, R. and Thanabalan, M.  (2023) Preparation of Chitosan-Tripolyphosphate Formulated Insulin Microparticles, Their Characterization, ANN Prediction, and Release Kinetics.  Journal of Pharmaceutical Innovation, 18 (3).  pp. 1047-1064.