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Title: Effects of cooling rate on structural relaxation in amorphous drugs: elastically collective nonlinear langevin equation theory and machine learning study
Authors: Phan, Anh D.
Wakabayashi, Katsunori
Paluch, Marian
Lamef, Vu D.
Keywords: Amorphous drugs; Machine learning method
Issue Date: 2019
Citation: "RSC Advances" 2019, iss. 69, s. 40214-40221
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-Fulche-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 formulations.
DOI: 10.1039/c9ra08441j
ISSN: 2046-2069
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