Development and machine-learning optimization of mucoadhesive nanostructured lipid carriers loaded with fluconazole for treatment of oral candidiasis.

The aim of this work was to prepare and optimize mucoadhesive nanostructured lipid carrier (NLC) impregnated with fluconazole for better management of oral candidiasis. The NLCs were fabricated using an emulsification/sonication technique. The nanoparticles consisted of stearic acid, oleic acid, Pluronic F127, and lecithin. Box-Behnken design, artificial neural networking, and variable weight desirability were employed to optimize the joint effect of drug concentration in the drug/lipid mixture, solid lipid concentration in the solid/liquid lipid mixture, and surfactant concentration in the total mixture on size and entrapment. The optimized NLCs were coated with chitosan. The nanoparticles were characterized by surface charge, spectroscopic, thermal, morphological, mucoadhesion, release, histopathological, and antifungal properties. The nanoparticles are characterized by a particle size of 335 ± 13.5 nm, entrapment efficiency of 73.1 ± 4.9%, sustained release, minor histopathological effects on rabbit oral mucosa, and higher fungal inhibition efficiency for an extended period of time compared with fluconazole solution. Coating the nanoparticles with chitosan increased its adhesion to rabbit oral buccal mucosa and improved its anti-candidiasis activity. It is concluded that mucoadhesive lipid-based nanoparticles amplify the effect of fluconazole on . This finding warrants pre-clinical and clinical studies in oral candidiasis disease models to corroborate findings.