| Literature DB >> 31430872 |
Muhammad Mustafa Abeer1, Anand Kumar Meka1, Naisarg Pujara1, Tushar Kumeria1,2, Ekaterina Strounina3, Rute Nunes4,5, Ana Costa4,5, Bruno Sarmento4,5,6, Sumaira Z Hasnain2,7, Benjamin P Ross1, Amirali Popat8,9.
Abstract
Type 2 diabetes makes up approximately 85% of all diabetic cases and it is linked to approximately one-third of all hospitalisations. Newer therapies with long-acting biologics such as glucagon-like peptide-1 (GLP-1) analogues have been promising in managing the disease, but they cannot reverse the pathology of the disease. Additionally, their parenteral administration is often associated with high healthcare costs, risk of infections, and poor patient adherence associated with phobia of needles. Oral delivery of these compounds would significantly improve patient compliance; however, poor enzymatic stability and low permeability across the gastrointestinal tract makes this task challenging. In the present work, large pore dendritic silica nanoparticles (DSNPs) with a pore size of ~10 nm were prepared, functionalized, and optimized in order to achieve high peptide loading and improve intestinal permeation of exenatide, a GLP-1 analogue. Compared to the loading capacity of the most popular, Mobil Composition of Matter No. 41 (MCM-41) with small pores, DSNPs showed significantly high loading owing to their large and dendritic pore structure. Among the tested DSNPs, pristine and phosphonate-modified DSNPs (PDSNPs) displayed remarkable loading of 40 and 35% w/w, respectively. Furthermore, particles successfully coated with positively charged chitosan reduced the burst release of exenatide at both pH 1.2 and 6.8. Compared with free exenatide, both chitosan-coated and uncoated PDSNPs enhanced exenatide transport through the Caco-2 monolayer by 1.7 fold. Interestingly, when a triple co-culture model of intestinal permeation was used, chitosan-coated PDSNPs performed better compared to both PDSNPs and free exenatide, which corroborated our hypothesis behind using chitosan to interact with mucus and improve permeation. These results indicate the emerging role of large pore silica nanoparticles as promising platforms for oral delivery of biologics such as exenatide.Entities:
Keywords: anti-diabetic peptides; exenatide; large pore silica nanoparticles; oral delivery
Year: 2019 PMID: 31430872 PMCID: PMC6723263 DOI: 10.3390/pharmaceutics11080418
Source DB: PubMed Journal: Pharmaceutics ISSN: 1999-4923 Impact factor: 6.321
Figure 1(a–d) TEM images of pristine DSNPs, PDSNPs, ADSNPs, and SDSNPs, respectively, showing dendritic morphology with pores radiating from the center; (e) particle size distribution showing that ADSNPs follow the trend of aggregation and remaining negatively charged DSNPs exhibit particle size at pH 7.4, agreeable with that of TEM imaging; (f) TGA of pristine and functionalized DSNPs.
Hydrodynamic size, poly dispersity index (PDI), ζ-potential, and BET surface area of DSNPs and derivatives. (SD: standard deviation; BET: Brunauer–Emmett–Teller).
| Dynamic Light Scattering | Nitrogen Sorption Analysis | ||||
|---|---|---|---|---|---|
| Particles | PDI (AU ± SD) | Particle Size (Mean Diameter nm ± SD) | ζ-Potential (mV) | BET Surface Area (m2/g) | Pore Volume (cm3/g) |
| DSNPs | 0.34 ± 0.05 | 226 ± 9 | −14.3 | 486 | 1.14 |
| PDSNPs | 0.26 ± 0.03 | 254 ± 7 | −30.5 | 305 | 0.88 |
| ADSNPs | 0.45 ± 0.04 | 742 ± 13 | 11.5 | 191 | 0.60 |
| SDSNPs | 0.15 ± 0.01 | 248 ± 23 | −23.2 | 83 | 0.39 |
Figure 2(a) ζ-potential profile of exenatide acetate at different pH confirming pI. (b) Loading of exenatide on different DSNPs and MCM-41, using pH 5.0 MES buffer, observed as maximum for pristine DSNPs. (c) Loading of exenatide on PDSNPs at different pH. (d) Effect of coating, chitosan in 0.1 M acetic acid solution (pH 3.8), strategies on exenatide loading. t = 0 and t = 90 shows the time interval at which chitosan was introduced into the exenatide loading solution. t = 90, PL (post loading) stands for the technique in which loaded particles were separated before the coating step. (All data are n = 3, Mean ± SD).
Figure 3(a,b) The hydrodynamic sizes of uncoated and chitosan-coated PDSNPs behaved oppositely, indicating different assembly of CDSNPs, as the latter tended to aggregate as pH increased. (c) ζ-potential profile of the particles. (All data are n = 3, mean ± SD). (d) TEM image of CPDSNPs; radial pore network was clearly masked upon coating with chitosan.
Figure 4In vitro release profile of exenatide from PDSNPs and CPDSNPs at pH 1.2 and 6.8 for 6 and 8 h, respectively (all data are n = 3, mean ± SD).
Figure 5(a) Cumulative amount of exenatide transported through the Caco-2 monolayer at 0.5, 1, and 2 h, where monolayers were incubated with 40 µg/mL exenatide solution and equivalent exenatide-loaded nanoparticles (all data are n = 3, mean ± SD, and analyzed by one-way ANOVA, post-hoc Tukey’s test, * p < 0.05, **** p < 0.0001) (b) Cumulative amount of exenatide transported through the triple co-culture model at 2 h, where the cellular barrier was incubated with 40 µg/mL exenatide solution and equivalent exenatide-loaded nanoparticles. (c) Apparent permeability coefficient (Papp) of exenatide from PDSNPs and CPDSNPs at 2 h in the Caco-2 monolayer (all data are n = 3, mean ± SD, and analyzed by one-way ANOVA, post-hoc Tukey’s test, * p < 0.05). (d) Apparent permeability coefficient (Papp) of exenatide from PDSNPs and CPDSNPs at 2 h in (d) the triple co-culture model (all data are n = 3, mean ± SD).