In vitro analysis of XLAsp-P2 peptide loaded cellulose acetate nanofiber for wound healing

Recently, nanofiber-based wound dressings are currently a viable strategy to expedite the healing of wounds by providing a suitable microenvironment for tissue growth with active ingredients. This research study subjects the development of electrospun cellulose acetate (CA) nanofibers loaded with the XLAsp-P2, an antimicrobial peptide (AMP) that holds great potential for enhanced wound healing as a therapeutic agent. The synthesized XLAsp-P2-loaded CA nanofibers were fabricated via three loading percentages, 0.1 %, 0.2 %, and 0.3 % w/w, and characterized and evaluated their antimicrobial potential with MTT assay and Agar overlay methods as an alternative strategy. FT-IR analysis confirmed the compatibility of the peptide-loaded CA nanocomposite, showing distinct peaks corresponding to the constituent materials. Scanning electron microscopy (SEM) analysis was employed to characterize the morphology of electrospun peptide CA nano- composites and illustrate the fiber's size at the nanoscale. The in vitro release study during the 24 hr, 87 % of the peptide was released which was approximately 5.2 mg; which was closer matched to the square root model of Higuchi at room temperature. MTT assay presented sensitive results towards Gram-positive bacteria compared to Gram Negative bacteria; which corresponded to the inhibition zones of the Agar overlay method proving that Escherichia coli (ATCC 25922) 17.66 f 0.38 mm and Pseudomonas aeruginosa (ATCC 27853) 17.44 f 0.38 mm exhibited moderate susceptibility, while Staphylococcus aureus (ATCC 25923) 19.89 f 0.69 mm and Bacillus cereus (ATCC 11778) 23.00 f 0.33 mm showed promising responses. Collectively, The study's findings indicate that the XLAsp-P2 incorporated CA mat possesses an opportunity to function as an efficient platform for delivering therapeutic peptides. (c) 2024 American Pharmacists Association. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.