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September 10, 2025Open Access

Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach

WGWei GeBeijing Forestry University RSR. Silva‐GonzálezInternational Iberian Nanotechnology Laboratory YFYanan FanCommonwealth Scientific and Industrial Research Organisation

Key Points

The MLP model effectively predicts solvent effects on drug loading in polymeric nanocarriers using machine learning.
Solubility was identified as the most critical factor among nine descriptors affecting drug incorporation.
Testing five different algorithms revealed the Random Forest and Gradient Boosting models performed well, though MLP excelled.
Using SHAP values, the study highlights essential solvent properties for achieving enhanced drug loading efficiency.

Abstract

ABSTRACT This study aimed to identify solvent characteristics that enhance drug loading in polymeric micelles. Polyethylene glycol‐block‐polystyrene (PEG‐b‐PS) and curcumin were used as model compounds to investigate the impact of 40 different solvent mixtures on drug loading during flow‐based assembly. We tested five algorithms: Random Forest (RF), Gradient Boosting (GP), XGBoost, Support Vector Regression (SVR), and Multilayer Perceptron (MLP), with the MLP model proving to be the most effective among them. To explain the model's predictions, we utilized SHapley Additive exPlanations (SHAP) values to identify solvent properties that contribute to high drug loading. Of the nine descriptors examined—curcumin solubility, polarity, Hildebrand solubility parameters, dipole moment, dielectric constants, viscosity, and Hansen solubility parameters (δD, δP, and δH)—solubility emerged as the most critical factor. Therefore, to achieve optimal drug loading, researchers should prioritize solvents with the highest solubility.

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