An optimization study on solid lipid nanoparticles using artificial neural network

Abstract

Common use of supportive programs in finding the best in R&D studies provides positive results and thus ensures benefits to companies in terms of cost and time. The aim of this work was to develop, evaluate and optimize solid lipid nanoparticles (SLNs) formulations by applying the artificial neural network (ANN) programme to achieve the best combination of materials. SLNs have been produced by high-pressure homogenization, and the formulations have been characterized for their mean particle size, polydispersity index and zeta potential. SLN formulations were evaluated with INForm V5.1 program to optimize the best-fit formulation. According to ANN evaluation, S-PT8 formulation including 50% Compritol 888 ATO, 38% Poloxamer 188 and 12% Tween 80 mixture was found to be the most promising formulation in terms of parameters tested. It has been shown that artificial intelligence could be used to improve our understanding of the critical quality parameters that contribute to the overall quality of the drug product.