Nanostructured lipid carriers (NLC) are biocompatible and biodegradable nanoscale systems with extensive application for controlled drug release. However, the development of optimal nanosystems along with a reproducible manufacturing process is still challenging. In this study, a two-step experimental design was performed and databases were successfully modelled using Artificial Intelligence techniques as an innovative method to get optimal, reproducible and stable NLC. The initial approach, including a wide range of values for the different variables, was followed by a second set of experiments with variable values in a narrower range, more suited to the characteristics of the system. NLC loaded with rifabutin, a hydrophobic drug model, were produced by hot homogenization and fully characterized in terms of particle size, size distribution, zeta potential, encapsulation efficiency and drug loading. The use of Artificial Intelligence tools has allowed to elucidate the key parameters that modulate each formulation property. Stable nanoparticles with low sizes and polydispersions, negative zeta potentials and high drug loadings were obtained when the proportion of lipid components, drug, surfactants and stirring speed were optimized by FormRules® and INForm®. The successful application of Artificial Intelligence tools on NLC formulation optimization constitutes a pioneer approach in the field of lipid nanoparticles.
Keywords: Artificial Neural Networks; Genetic algorithms; Hot homogenization; Nanostructured lipid carriers; Neurofuzzy logic.
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