In Vitro Comparative Study of Solid Lipid and PLGA Nanoparticles Designed to Facilitate Nose-to-Brain Delivery of Insulin

Abstract

The brain insulin metabolism alteration has been addressed as a pathophysiological factor underlying Alzheimer's disease (AD). Insulin can be beneficial in AD, but its macro-polypeptide nature negatively influences the chances of reaching the brain. The intranasal (IN) administration of therapeutics in AD suggests improved brain-targeting. Solid lipid nanoparticles (SLNs) and poly(lactic-co-glycolic acid) nanoparticles (PLGA NPs) are promising carriers to deliver the IN-administered insulin to the brain due to the enhancement of the drug permeability, which can even be improved by chitosan-coating. In the present study, uncoated and chitosan-coated insulin-loaded SLNs and PLGA NPs were formulated and characterized. The obtained NPs showed desirable physicochemical properties supporting IN applicability. The in vitro investigations revealed increased mucoadhesion, nasal diffusion, and drug release rate of both insulin-loaded nanocarriers over native insulin with the superiority of chitosan-coated SLNs. Cell-line studies on human nasal epithelial and brain endothelial cells proved the safety IN applicability of nanoparticles. Insulin-loaded nanoparticles showed improved insulin permeability through the nasal mucosa, which was promoted by chitosan-coating. However, native insulin exceeded the blood-brain barrier (BBB) permeation compared with nanoparticulate formulations. Encapsulating insulin into chitosan-coated NPs can be beneficial for ensuring structural stability, enhancing nasal absorption, followed by sustained drug release.

Keywords: PLGA nanoparticles; blood-brain barrier permeability; chitosan-coating; insulin; mucoadhesion; nasal mucosa permeability; nose-to-brain delivery; solid lipid nanoparticles.

Figure 1

Encapsulation efficacy (EE) and drug loading (DL) of the prepared nanoparticles: Ins PLGA NPs, Ins C-PLGA NPs, Ins SLNs, and Ins C-SLNs. The ANOVA test was performed to check the significance of the differences between the results of the EE and DL. Measurements were performed in triplicate (n = 3 independent formulations), and data are represented as means ± SD.

Figure 2

SEM images for the prepared nanoparticles. (A) Ins SLNs, (B) Ins C-SLNs, (C) Ins PLGA NPs, and (D) Ins C-PLGA NPs.

Figure 3

Raman spectra of insulin-containing NPs in comparison with native insulin, showing the major spectral regions that are characteristic for the protein structure.

Figure 4

Mucoadhesion assay of Ins PLGA NPs, Ins SLNs, Ins C-PLGA NPs, and Ins C-SLNs. (A) The ZP analysis method; (B) the Turbidity analysis method. The measurements were performed in triplicate (n = 3 independent formulations), and data are represented as means ± SD.

Figure 5

In vitro diffusion of native insulin and insulin-loaded NPs (Ins PLGA NPs, Ins SLNs, Ins C-PLGA NPs, and Ins C-SLNs). The ANOVA test was performed to check the significance of the differences between the diffusion of the native insulin and the prepared NPs, * p < 0.05; ** p < 0.01; *** p < 0.001. Measurements were carried out in triplicate (n = 3 independent formulations), and data are represented as means ± SD.

Figure 6

The dissolution behavior of the native insulin and the insulin loaded nanoparticles (Ins PLGA NPs, Ins SLNs, Ins C-PLGA NPs, and Ins C-SLNs). The ANOVA test was performed to check the significance of the differences between the diffusion of the native insulin and the prepared NPs, * p < 0.05; *** p < 0.001. Measurements were carried out in triplicate (n = 3 independent formulations), and data are represented as means ± SD.

Figure 7

Cell viability of RPMI 2650 nasal epithelial cells after the treatment with insulin, insulin NPs, and HCl measured by impedance. The kinetic curve of cell viability during the 20-h treatment (A) and at the 1-h time point of the treatment (B). Values are presented as means ± SD, n = 6–12. Statistical analysis: ANOVA followed by Dunett’s test. TX-100: Triton X-100. * p < 0.05, ** p < 0.01 compared with the control group.

Figure 8

Cell viability of hCMEC/D3 endothelial cells after the treatment with insulin, Ins NPs, and HCl measured by impedance. The kinetic curve of cell viability during the 20-h treatment (A) and at the 1-h time point of the treatment (B). Values are presented as means ± SD, n = 6–12. Statistical analysis: ANOVA followed by Dunett’s test. TX-100: Triton X-100. *** p < 0.001 compared with the control group.

Figure 9

Apparent permeability coefficients (P_app) for insulin (0.07 mg/mL in all samples) when applied alone or in different formulations measured across RPMI 2650 epithelial cell layers after 1 h of incubation. Values are presented as means ± SD, n = 4. Statistical analysis: ANOVA followed by Bonferroni test. *** p < 0.001 compared with the insulin group, ^### p < 0.01 compared between the indicated groups.

Figure 10

Apparent permeability coefficients (P_app) for insulin (0.07 mg/mL in all samples) when applied alone or in different formulations measured across hCMEC/D3 endothelial cell layers after 1 h of incubation. Values are presented as means ± SD, n = 4. Statistical analysis: ANOVA followed by the Bonferroni test. ** p < 0.01, *** p < 0.001 compared with the insulin group. ^### p < 0.01 compared between the indicated groups. C-: Chitosan coated nanoparticle; NPs: Nanoparticles; PLGA: Poly (lactic-co-glycolic acid; SLN: Solid lipid nanoparticle.