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PMID: 38135995 Published · epublish English Journal Article Review

Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine.

Bioengineering (Basel, Switzerland) ·Vol. 10 ·No. 12 ·2023-12-08

Huang Y, Zheng Y, Lu X, Zhao Y, Zhou D, Zhang Y, Liu G

Abstract

In recent years, nanomedicines prepared using supercritical technology have garnered widespread research attention due to their inherent attributes, including structural stability, high bioavailability, and commendable safety profiles. The preparation of these nanomedicines relies upon drug solubility and mixing efficiency within supercritical fluids (SCFs). Solubility is closely intertwined with operational parameters such as temperature and pressure while mixing efficiency is influenced not only by operational conditions but also by the shape and dimensions of the nozzle. Due to the special conditions of supercriticality, these parameters are difficult to measure directly, thus presenting significant challenges for the preparation and optimization of nanomedicines. Mathematical models can, to a certain extent, prognosticate solubility, while simulation models can visualize mixing efficiency during experimental procedures, offering novel avenues for advancing supercritical nanomedicines. Consequently, within the framework of this endeavor, we embark on an extensive review encompassing the application of mathematical models, artificial intelligence (AI) methodologies, and computational fluid dynamics (CFD) techniques within the medical domain of supercritical technology. We undertake the synthesis and discourse of methodologies for calculating drug solubility in SCFs, as well as the influence of operational conditions and experimental apparatus upon the outcomes of nanomedicine preparation using supercritical technology. Through this comprehensive review, we elucidate the implementation procedures and commonly employed models of diverse methodologies, juxtaposing the merits and demerits of these models. Furthermore, we assert the dependability of employing models to compute drug solubility in SCFs and simulate the experimental processes, with the capability to serve as valuable tools for aiding and optimizing experiments, as well as providing guidance in the selection of appropriate operational conditions. This, in turn, fosters innovative avenues for the development of supercritical pharmaceuticals.

Keywords
computational fluid dynamics machine learning model particles supercritical fluids
作者与单位
共 7 位作者,点击展开单位 / ORCID
Huang Yulan
State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China.
Zheng Yating
State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China.
Lu Xiaowei
Institute of Artificial Intelligence, Xiamen University, Xiamen 361002, China.
Zhao Yang ORCID
Shenzhen Research Institute, Xiamen University, Shenzhen 518000, China.
Zhou Da ORCID
School of Mathematical Sciences, Xiamen University, Xiamen 361005, China.
Zhang Yang ORCID
State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China.
Liu Gang ORCID
State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China.
Article Info
Journal
Bioengineering (Basel, Switzerland)
Abbr.
Bioengineering (Basel)
ISSN
2306-5354
Published
2023-12-08
电子出版
2023-00-08
Language
English
Country/Region
Switzerland
NLM ID
101676056
基金资助
Major State Basic Research Development Program of China · 2023YFB3810000, 2018YFA0107301
National Natural Science Foundation of China · U22A20333, 81925019, U1705281, and 82202330
Fundamental Research Funds for the Central Universities · 20720190088 and 20720200019
Science Foundation of Fujian Province · 2020Y4003
Program for New Century Excellent Talents in University, China · NCET-13-0502
China Postdoctoral Science Foundation · 2023T160383
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