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

Abstract

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 ...