Formulation and Characterization of Epalrestat-Loaded Polysorbate 60 Cationic Niosomes for Ocular Delivery.
Axel KattarAna Quelle-RegaldieLaura E SánchezAngel ConcheiroCarmen Alvarez-LorenzoPublished in: Pharmaceutics (2023)
The aim of this work was to develop niosomes for the ocular delivery of epalrestat, a drug that inhibits the polyol pathway and protects diabetic eyes from damage linked to sorbitol production and accumulation. Cationic niosomes were made using polysorbate 60, cholesterol, and 1,2-di-O-octadecenyl-3-trimethylammonium propane. The niosomes were characterized using dynamic light scattering, zeta-potential, and transmission electron microscopy to determine their size (80 nm; polydispersity index 0.3 to 0.5), charge (-23 to +40 mV), and shape (spherical). The encapsulation efficiency (99.76%) and the release (75% drug release over 20 days) were measured with dialysis. The ocular irritability potential (non-irritating) was measured using the Hen's Egg Test on the Chorioallantoic Membrane model, and the blood glucose levels (on par with positive control) were measured using the gluc-HET model. The toxicity of the niosomes (non-toxic) was monitored using a zebrafish embryo model. Finally, corneal and scleral permeation was assessed with the help of Franz diffusion cells and confirmed with Raman spectroscopy. Niosomal permeation was higher than an unencapsulated drug in the sclera, and accumulation in tissues was confirmed with Raman. The prepared niosomes show promise to encapsulate and carry epalrestat through the eye to meet the need for controlled drug systems to treat the diabetic eye.
Keyphrases
- raman spectroscopy
- blood glucose
- drug delivery
- drug release
- wound healing
- type diabetes
- optical coherence tomography
- electron microscopy
- chronic kidney disease
- induced apoptosis
- oxidative stress
- gene expression
- adverse drug
- glycemic control
- adipose tissue
- drug induced
- risk assessment
- end stage renal disease
- cell proliferation
- staphylococcus aureus
- deep learning
- big data
- human health
- skeletal muscle
- artificial intelligence