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Recent Techniques to Improve Amorphous Dispersion Performance with Quality Design, Physicochemical Monitoring, Molecular Simulation, and Machine Learningopen access

Authors
Bhatta, Hari PrasadHan, Hyo-KyungMaharjan, RaviJeong, Seong Hoon
Issue Date
Sep-2025
Publisher
MDPI
Keywords
amorphous solid dispersion; stability; thermodynamics; machine learning; simulation; drug-polymer interaction; drug release
Citation
Pharmaceutics, v.17, no.10, pp 1 - 33
Pages
33
Indexed
SCIE
SCOPUS
Journal Title
Pharmaceutics
Volume
17
Number
10
Start Page
1
End Page
33
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/61938
DOI
10.3390/pharmaceutics17101249
ISSN
1999-4923
1999-4923
Abstract
Amorphous solid dispersions (ASDs) represent a promising formulation strategy for improving the solubility and bioavailability of poorly water-soluble drugs, a major challenge in pharmaceutical development. This review provides a comprehensive analysis of the physicochemical principles underlying ASD stability, with a focus on drug-polymer miscibility, molecular mobility, and thermodynamic properties. The main manufacturing techniques including hot-melt extrusion, spray drying, and KinetiSol (R) dispersing are discussed for their impact on formulation homogeneity and scalability. Recent advances in excipient selection, molecular modeling, and in silico predictive approaches have transformed ASD design, reducing dependence on traditional trial-and-error methods. Furthermore, machine learning and artificial intelligence (AI)-based computational platforms are reshaping formulation strategies by enabling accurate predictions of drug-polymer interactions and physical stability. Advanced characterization methods such as solid-state NMR, IR, and dielectric spectroscopy provide valuable insights into phase separation and recrystallization. Despite these technological innovations, ensuring long-term stability and maintaining supersaturation remain significant challenges for ASDs. Integrated formulation design frameworks, including PBPK modeling and accelerated stability testing, offer potential solutions to address these issues. Future research should emphasize interdisciplinary collaboration, leveraging computational advancements together with experimental validation to refine formulation strategies and accelerate clinical translation. The scientists can unlock the full therapeutic potential with emerging technologies and a data-driven approach.
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