Effects of Cooling Rate on Structural Relaxation in Amorphous Drugs: Elastically Collective Nonlinear Langevin Equation Theory and Machine Learning Study

TL;DR

This study combines theoretical modeling and machine learning to analyze how cooling rates affect molecular mobility and relaxation in amorphous drugs, providing insights for pharmaceutical development.

Contribution

It introduces a coupled theoretical framework and machine learning approach to predict relaxation dynamics and glass transition properties in amorphous pharmaceuticals.

Findings

Theoretical calculations align well with experimental data.

Machine learning uncovers a linear relation between glass transition and melting points.

Predictive models aid in drug formulation development.

Abstract

Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs. We describe the structural relaxation of a tagged molecule as a coupled process of cage-scale dynamics and collective molecular rearrangement beyond the first coordination shell. The coupling between local and non-local dynamics behaves distinctly in different substances. Theoretical calculations for the structural relaxation time, glass transition temperature, and dynamic fragility are carried out over twenty-two amorphous drugs and polymers. Numerical results have a quantitatively good accordance with experimental data and the extracted physical quantities using the Vogel-Fulcher-Tammann fit function and machine learning. The machine learning method reveals the linear relation between the glass transition temperature and the melting point, which is a key factor for pharmaceutical solubility. Our predictive approaches are reliable tools for developing drug formulation.