Integration of Multivariate Data Analysis and composite desirability index to identify process and optimize formulation components in development of gel containing aceclofenac loaded nanoparticles

Abstract

The purpose of the research was to identify the method of preparation and the composition of aceclofenac based nanoparticles using various multivariate data analysis methods. Top down and bottom up approaches were used for the preparation of the nanoparticles and polymers like hydroxylpropyl methylcellulose and polyvinyl pyrrolidone, surfactants like sodium lauryl sulphate and Tween 80 were screened for the purpose of formulation. Different combinations of these factors were used for designing of the experiments and the obtained products were evaluated for particle size, poly dispersibility index, zeta potential, drug content and entrapment efficiency. Principal component analysis, agglomerative hierarchical clustering, composite desirability index aided to identify the best possible combination. Nanoprecipitation along with sonication as process and poly vinyl pyrrolidone as stabilizer gave the highest values of desirability index. The formulation composition of nanosuspension was oscillated around the settings for the zeroing in on optimum quantitative formulation. The nanosuspension was gelled using Carbopol. The gel was studied for in vitro and ex vivo permeation characteristics. The optimized formulation was found to be stable and had acceptable levels of irritation as studied in wistar rats. Thus various data analysis methods could help identify the right combination of process and formulation composition.