A machine learning framework for predicting entrapment efficiency in niosomal particles

Abstract

Niosomes are vesicles formed mostly by nonionic surfactant and cholesterol incorporation as an excipient. The drug entrapment efficiency of niosomal vesicles is particularly important and depends on many parameters. Changing the effective parameters to have maximum entrapment efficiency in the laboratory is time-consuming and costly. In this study, a machine learning framework was proposed to address these problems. In order to find the most critical parameter affecting the entrapment efficiency and its optimal value in a specific experiment, data were first extracted from articles of the last decade using keywords of niosome and thin-film hydration method. Then, deep neural network (DNN), linear regression, and polynomial regression models were trained with four cost functions. Afterward, the most influential parameter on entrapment efficiency was determined using the sensitivity experiment. Finally, the optimal point of the most influential parameter was found by keeping the other parameters constant and changing the most influential parameter. The veracity of this test was evaluated by entrapment efficiency results of 7 niosomal formulations containing doxycycline hyclate prepared in the laboratory. The best model was DNN, which yielded root mean square error (RMSE) of 13.587 ± 2.61, mean absolute error (MAE) of 10.17 ± 1.421, and R-squared (R2) of 0.763 ± 0.1 evaluated by 5-fold cross-validation. The hydrophilic-lipophilic balance (HLB) was identified as the most influential parameter, and the entrapment efficiency change curve was plotted versus the HLB value. This study uses machine learning methods to synthesize niosomal systems with optimal entrapment efficiency at a lower cost and time.