Integrating machine learning and physics-based modeling for predictive design of gemcitabine-loaded nanocomposites.

This study aims to develop a machine learning framework to predict loading efficiency and encapsulation efficiency in gemcitabine-loaded nanocomposites, thereby overcoming the limitations of purely experimental approaches. A curated dataset of 59 experimental formulations, augmented with 200 physics-informed synthetic data points, was used to train and compare multiple machine learning algorithms. A Physics-Informed Machine Learning algorithm incorporated interactions between drugs and polymers, as well as kinetic release. Model performance was evaluated using the coefficient of determination, root mean square error, and mean absolute error, along with SHapley Additive exPlanations values to assess the influence of individual variables. The XGBoost algorithm yielded the highest value of prediction accuracy, with a coefficient of determination of 0.89 for Loading Efficiency and 0.91 for Encapsulation Efficiency. Nanoparticle size and zeta potential emerged as important features. Physics-Informed Machine Learning allowed for increased interpretability and generability of models. A suitable design space that led to good performance was determined to be in the range of 80-150 nm for size and + 15 to + 25 mV for zeta potential. This work presents a new machine learning / Physics-Informed Machine Learning framework that is a valuable asset when designing nanocarriers in a rational manner. This framework enables one to accelerate research and decrease costs related to experimentation. Overall, this work presents a promising in silico framework that can guide the rational improvement of nanomedicine formulations, pending experimental validation.