Advancing Amorphous Solid Dispersions Design: Insights into Dissolution Kinetics via Thermodynamic Descriptor and Machine Learning

Amorphous solid dispersions (ASD) are an effective strategy
for
enhancing the solubility and bioavailability of poorly soluble drugs.
However, designing and optimizing ASD formulations often rely on extensive in vitro dissolution experiments without sufficient theoretical
guidance. To address this, a machine learning approach for rapidly
and reliably predicting the ASD dissolution kinetics was proposed.
A comprehensive data set comprising 616 dissolution profiles was collected
from the “Web of Science” database, and a correlation
analysis was performed to optimize input feature selection. Among
the ten evaluated machine learning algorithms, lightGBM demonstrated
superior predictive performance. Improvement strategies were implemented
to enhance the accuracy and interpretability of the model. The improved
lightGBM model achieves commendable predictive performance on commercially
available ASD products, successfully quantifying the relationship
between ASD formulations and the dissolution behavior. This work reduces
the necessity for extensive experimental efforts and provides valuable
insights into optimizing ASD formulations, thus advancing pharmaceutical
formulation strategies through machine learning.