Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning.

Abstract

Liposomes constitute the most exploited drug-nanocarrier with several liposomal drugs on the market. Microfluidic-based preparation methods stand up as a promising approach with high reproducibility and the ability to scale up. In this study, liposomes composed of DOPC, cholesterol, and DSPE-PEG 2000 with different molar ratios were fabricated using a microfluidic system. Process and conditions were optimized by applying design of experiments (DoE) principles. Furthermore, data were used to build an artificial neural network (ANN) model, to predict size and polydispersity index (PDI). Sets of runs were designed by DoE and performed on a micromixer microfluidic chip. Lipids' molar ratio and the process parameters, i.e. total flow rate (TFR) and flow rate ratio (FRR), were found to be the most influential factors on the formation of vesicles with target size and PDI under 100 nm and lower than 0.2, respectively. Size and PDI were predicted by the ANN model for 3 preparations with defined experimental conditions. The results showed no significant difference in size and PDI between the preparations and their values calculated with the ANN. In conclusion, production of optimized liposomes with high reproducibility was achieved by the application of microfluidic manufacturing processes, DoE, and Artificial Intelligence (AI). Microfluidic-based preparation methods assisted by computational tools would enable a faster development and clinical transfer of nanobased medications.