A comparison between solvent casting and electrospinning methods for the fabrication of neem extract-containing buccal films

JOURNAL OF INDUSTRIAL TEXTILES(2022)

引用 9|浏览2
暂无评分
摘要
In the present study a double layer mucoadhesive buccal film containing nanocarriers encapsulated with neem extract was fabricated through electrospinning and solvent casting techniques for dental therapeutic applications. The morphological, physical and mucoadhesive properties of the resulting electrospun and solvent cast oral films were mutually compared, and their drug release behavior and antibacterial activity were further investigated. Chitosan/poly(vinylalcohol) (PVA) as a mucoadhesive component and phenylalanine amino acid nanotubes (PhNTs)-containing neem extract as a drug nanocarrier were used to fabricate oral films. A poly(caprolactone) (PCL) layer was used as an impermeable backing layer to protect the mucoadhesive component from tongue movement and drug loss. The results indicated an interconnected porous and fully filled solid structures for electrospun and solvent cast films, respectively. The physicomechanical parameters of the samples such as pH, weight, thickness, folding endurance and tensile strength were also evaluated. The crosslinked electrospun buccal film indicated better swelling and mucoadhesive properties compared to the solvent cast film. In addition, the drug loading capacity and encapsulation efficiency of the solvent cast film showed lower experimental values than those of electrospun oral film. On the other hand, the electrospun oral film had a well-controlled release of neem extract up to 82% at oral pH, which is best fitted to the Weibull model, and demonstrated the highest antibacterial properties against S. mutans bacteria with high biocompatibility on L929 fibroblast cells. Generally, the synthesized electrospun mucoadhesive film has a better potential for oral therapeutic applications than the solvent cast film.
更多
查看译文
关键词
Electrospinning, solvent casting, mucoadhesive buccal film, neem extract, dental problems
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要