Tenofovir Loaded Poly (Lactide-Co-Glycolide) Nanocapsules: Formulation Optimization by Desirability Functions Approach

ABSTRACT

Background: Nanoparticles made of biodegradable polymers are the most effective colloidal drug delivery systems. The in vitro characteristics as well as in vivo presentation of these nanoparticles are majorly influenced by their formulation parameters. Objectives: The principle objective of the work was to optimize various critical formulation parameters by using statistical tools in order to develop poly (lactide-co-glycolide) (PLGA) nanocapsules loaded with tenofovir disoproxil fumarate (TDF) with effective characteristic properties. Methodology: In this work, biodegradable nanocapsules with PLGA for TDF were prepared by modified double emulsification technique. Concentration of PLGA, concentration of Pluronic F-68 in secondary emulsion and concentration of glycerol in external phase were selected as the formulation factors hence, a 33 full factorial design in response surface methodology was chosen as an experimental design. The prepared nanocapsules were evaluated for various characteristic properties like particle size, surface morphology, entrapment efficiency, drug release studies and DSC studies. The selected formulation parameters were optimized by a mathematical model called desirability functions approach with the set desirability of high entrapment efficiency, low drug release constant and less particle size. Results and Conclusion:Particle size, entrapment efficiency and drug release constant were found in the ranges of 249.3-376.3nm, 20.95-71.35% and 0.039-0.184 hr-1. These selected formulation parameters were found to have significant (at p < 0.05 by ANOVA) influence on various characteristics of prepared nanocapsules. The optimized formulation found to have 63.08% of entrapment efficiency, 284.53 nm of particle size with zeta potential of -26.1 mV and 0.054 h-1 of release rate constant. Key words: Tenofovir, Nanocapsules, Optimization, Response surface methodology, Desirability functions approach.

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• DOI10.5530/ijper.54.2s.79Copy

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