Preparation and Characterization of Fluconazole Topical Nanosponge Hydrogel

Abstract

Objective: The study aimed to develop a polymeric nanosponge-based hydrogel system for enhanced topical application of fluconazole, an antifungal drug. Methods: Nanosponges were formulated using the emulsion solvent diffusion method using various polymers like hydroxypropyl methylcellulose, ethylcellulose and Eudragit RS 100. Polyvinyl alcohol and ethanol were used to prepare the aqueous and dispersed phases. Nanosponges were dispersed in an appropriate amount of gelling agent Carbopol 940 to get nanosponge gel. Drug–polymer interaction has been carried out by FTIR spectroscopy. The prepared nanosponges were evaluated for various tests like production yield, drug entrapment efficiency, compatibility and SEM studies. The nanosponge hydrogel was tested for pH, drug content, spreadability, in vitro diffusion and kinetic studies. Results: The drug entrapment efficiency of fluconazole nanosponges was found in the range of 52.3±0.84% to 80.8±0.36% for all formulations, respectively. The spreadability of prepared nanosponges gel formulation was in the range between 5.20±0.19 to 7.187±0.85. Particle size analysis showed that the average particle size of fluconazole nanosponges formulated using ethyl cellulose (F5) was found to be 334 nm. The zeta potential was found to be-10.4 mV, indicating the formulated fluconazole nanosponges (F5) had moderate stability. FTIR and DSC studies of pure drug and nanosponges suggested that the formulations were stable and there was no chemical interaction with polymer and other excipients. The optimised fluconazole topical nanosponge hydrogel (FG5) released 90.90% drug in 8 h. Conclusion: Fluconazole topical nanosponge hydrogel could be successfully prepared by emulsion solvent diffusion method. Fluconazole topical nanosponge hydrogel showed promising results under in vitro condition and thus, there exists a scope for evaluation of the developed nanosponge hydrogel for further pharmacokinetic studies, using appropriate test models.