Loading Efficiency of Doxorubicin-Loaded Beta-1,3- Glucan Nanoparticles: An Artificial Neural Networks Study

Abstract

Purpose: Artificial neural networks (ANNs) were used to optimize a formulation of β-1,3-glucan nanoparticles containing doxorubicin (Dox) through a study of the critical parameters affecting the drug's loading efficiency. Methods: Using an ANNs model, we evaluated the effect of four input variables, involved in preparation of the carrier system, including concentrations of succinic anhydride (Sa), NaOH and polyethyleneimine (PEI) as well as ratio of Dox/Carrier, on loading efficiency of Dox as output parameter, when Dox was conjugated to the carrier (Con-Dox-Glu) or in unconjugated form (Un-Dox-Glu).Results: The model demonstrated that increasing Sa and PEI leads to reduced loading efficiency, while the effect of NaOH on loading efficiency does not appear to be important in both Con-Dox-Glu and Un-Dox-Glu delivery system. Ratio of Dox/Carrier showed complex effects on loading efficiency: while a certain value was required to provide maximum loading efficiency in Con-Dox-Glu, a different critical value was associated with obtaining minimum loading efficiency in Un-Dox-Glu.Conclusion: By defining the effects of each parameter on the loading efficiency of Glu-Dox nanoparticles, this study demonstrated the feasibility of using an ANN model to optimize the conditions for achieving maximum loading efficiency in both conjugated and non-conjugated drug delivery system.