Machine learning algorithms for prediction of entrapment efficiency in nanomaterials

Abstract

Exploitation of machine learning in predicting performance of nanomaterials is a rapidly growing dynamic area of research. For instance, incorporation of therapeutic cargoes into nanovesicles (i.e., entrapment efficiency) is one of the critical parameters that ensures proper entrapment of drugs in the developed nanosystems. Several factors affect the entrapment efficiency of drugs and thus multiple assessments are required to ensure drug retention, and to reduce cost and time. Supervised machine learning can allow for the construction of algorithms that can mine data available from earlier studies to predict performance of specific types of nanoparticles. Comparative studies that utilize multiple regression algorithms to predict entrapment efficiency in nanomaterials are scarce. Herein, we report on a detailed methodology for prediction of entrapment efficiency in nanomaterials (e.g., niosomes) using different regression algorithms (i.e., CatBoost, linear regression, support vector regression and artificial neural network) to select the model that demonstrates the best performance for estimation of entrapment efficiency. The study concluded that CatBoost algorithm demonstrated the best performance with maximum R² score (0.98) and mean square error (< 10^-4). Among the various parameters that possess a role in entrapment efficiency of drugs into niosomes, the results obtained from CatBoost model revealed that the drug:lipid ratio is the major contributing factor affecting entrapment efficiency, followed by the lipid:surfactant molar ratio. Hence, supervised machine learning may be applied for future selection of the components of niosomes that achieve high entrapment efficiency of drugs while minimizing experimental procedures and cost.

Keywords: Artificial neural network; CatBoost; Entrapment efficiency; Machine learning; Nanoparticles; Niosomes.