Predictive modeling of oleuropein release from double nanoemulsions: An analytical study comparing intelligent models and Monte Carlo simulation

Abstract

The objective of this study was to evaluate the release of oleuropein (OLP) from double nanoemulsions stabilized with polymeric complexes. Initially, W1/O nano-emulsions loaded with OLP were prepared and re-emulsified into an aqueous phase (W2), which included a complex of whey protein concentrate (WPC)/pectin, to form W1/O/W2 emulsions. The microstructure of the final double emulsions was analyzed by scanning electron microscopy (SEM), and particles with smooth, comparatively spherical, and somewhat asymmetrical surfaces with a size range of 100–200 nm were observed, which were compatible with dynamic light scattering (DLS) data. The release trend of OLP was determined by fitting it to several empirical models including zero order, first order, Higuchi, Hixson-Crowell, Korsemeyer-Peppas, Baker-Lonsdale, and utilizing intelligent modeling techniques such as Fuzzy Logic (FL) and Artificial Neural Networks (ANNs). Among the mathematical models, the zero order equation had the highest coefficient of determination (R2 = 0.988), while the first order equation had the lowest root-mean-square error (RMSE = 0.0176) and sum of squared errors (SSE = 0.0009) for the goodness of fit of the model, when considering the release trend of OLP. FL and ANNs proved effective in modeling controlled release of OLP-loaded nanocarriers, achieving high R2 values. Additionally, Monte Carlo (MC) simulation showed potential for evaluating the release process when compared to other methods.

Keywords

• Monte Carlo simulation
• Controlled release
• Oleuropein
• Double emulsions
• Polymeric complexes
• Intelligent modeling