Using Machine Learning To Predict the Self-Assembled Nanostructures of Monoolein and Phytantriol as a Function of Temperature and Fatty Acid Additives for Effective Lipid-Based Delivery Systems

Lyotropic liquid crystalline lipid nanomaterials have shown promise as delivery vehicles for small therapeutic drugs, protein, peptides, and in vivo imaging contrast agents. To design effective lipid-based delivery systems, it is important to understand and be able to predict their self-assembly processes. In this study, we utilized a machine learning approach to study the phase behavior of a nanoparticulate system consisting of a base lipid, monoolein, or phytantriol and varied the concentration of saturated and unsaturated fatty acids. The experimental data sets acquired by high throughput characterization techniques were used to train the machine using two separate models, i.e., multiple linear regression (MLR) and Bayesian regularized artificial neural networks (ANNs). The models were accurate (>70%) in predicting the phase behavior for data used to train the neural networks. The ANN model appeared to be more accurate than the MLR model in predicting mesophases. We then used the obtained ANN models to interpolate the phase behavior of various nanoparticles at temperatures not yet tested. Compared to the experimental result, the prediction of phase behavior was interpolated with high accuracy, ranging from 66% to 96% for the different phases. The models were capable of interpolating data for the same fatty acids at temperatures that were not yet tested as well as extrapolating data for new fatty acid structures. We also studied quantitatively the contributions of various factors on the formation of different mesophases and elucidated rules that are useful for future design of advanced lipid systems for therapeutic delivery