Application of the perturbed-chain statistical associating fluid theory to predicting drug-polymer miscibility and stability in amorphous solid dispersions: a comprehensive overview

Abstract

Perturbed-chain statistical associating fluid theory has emerged as a powerful thermodynamic framework for predicting drug-polymer miscibility and stability in amorphous solid dispersions. This review provides a comprehensive overview of the theoretical foundations of perturbed-chain statistical associating fluid theory, including its forma, the meanings of key parameters in physics, and common strategies for parameterization. Its applications to solid–liquid and liquid–liquid equilibrium calculations are highlighted, particularly in the construction of phase diagrams and the prediction of phase separation phenomena such as amorphous-amorphous and liquid–liquid phase separation. The utility of perturbed-chain statistical associating fluid theory in amorphous solid dispersions is illustrated through its roles in solubility prediction, stability assessment, drug release mechanism analysis, and rational formulation and process design. In addition, perturbed-chain statistical associating fluid theory is critically compared with alternative predictive methods, including solubility parameter theory, Flory–Huggins models, molecular simulation approaches, and machine learning. Finally, this review outlines the key challenges and future directions for integrating perturbed-chain statistical associating fluid theory with data-driven and multi-scale modeling approaches to advance model-informed amorphous solid dispersion design.