Pickering emulsion transport in skeletal muscle tissue: A dissipative particle dynamics simulation approach

Liu, Xuwei; Chen, Wei; Xia, Yufei; Ma, Guanghui; Noda, Reiji; Ge, Wei

doi:10.1016/j.cjche.2024.01.002

Chinese Journal of Chemical Engineering

2024

DOI: 10.1016/j.cjche.2024.01.002

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Pickering emulsion transport in skeletal muscle tissue: A dissipative particle dynamics simulation approach

Xuwei Liu,

Wei Chen,

Yufei Xia

et al.

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Cited by 2 publications

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Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

Gao,

Ciura,

et al. 2024

Advanced Materials

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The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano‐bio interface. To enhance nanocarrier design, scientists employ both physics‐based and data‐driven models. Physics‐based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data‐driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.

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Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

Gao,

Ciura,

et al. 2024

Advanced Materials

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Quantifying the influence of lipophilic carbon chain length on adhesion between emulsified asphalt and acidic aggregates

Kong,

Zhu,

Chen

et al. 2024

International Journal of Pavement Engineering

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Pickering emulsion transport in skeletal muscle tissue: A dissipative particle dynamics simulation approach

Cited by 2 publications

References 53 publications

Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

Quantifying the influence of lipophilic carbon chain length on adhesion between emulsified asphalt and acidic aggregates

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