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Huang Y, Zheng Y, Lu X, Zhao Y, Zhou D, Zhang Y, Liu G. Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine. Bioengineering (Basel) 2023; 10:1404. [PMID: 38135995 PMCID: PMC10741229 DOI: 10.3390/bioengineering10121404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023] Open
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.
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Affiliation(s)
- Yulan Huang
- 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; (Y.H.); (Y.Z.); (G.L.)
| | - Yating Zheng
- 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; (Y.H.); (Y.Z.); (G.L.)
| | - Xiaowei Lu
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361002, China;
| | - Yang Zhao
- Shenzhen Research Institute, Xiamen University, Shenzhen 518000, China;
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, China
| | - Yang Zhang
- 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; (Y.H.); (Y.Z.); (G.L.)
| | - Gang Liu
- 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; (Y.H.); (Y.Z.); (G.L.)
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Rojas A, Sajadian SA, López-de-Dicastillo C, Ardestani NS, Aguila G, Jouyban A. Improving and measuring the solubility of favipiravir and montelukast in SC-CO 2 with ethanol projecting their nanonization. RSC Adv 2023; 13:34210-34223. [PMID: 38020033 PMCID: PMC10664086 DOI: 10.1039/d3ra05484e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/14/2023] [Indexed: 12/01/2023] Open
Abstract
Supercritical carbon dioxide (SC-CO2)-based approaches have become more popular in recent years as alternative methods for creating micro- or nanosized medicines. Particularly, high drug solubility is required in those techniques using SC-CO2 as a solvent. During the most recent pandemic years, favipiravir and montelukast were two of the most often prescribed medications for the treatment of COVID-19. In this study, ethanol at 1 and 3 mol% was utilized as a cosolvent to increase the solubility of both medicines in SC-CO2 by a static approach using a range of temperatures (308 to 338 K) and pressure (12 to 30 MPa) values. The experimentally determined solubilities of favipiravir and montelukast in SC-CO2 + 3 mol% ethanol showed solubility values up to 33.3 and 24.5 times higher than that obtained for these drugs with only SC-CO2. The highest values were achieved in the pressure of 12 MPa and temperature of 338 K. Last but not least, six density-based semi-empirical models with various adjustable parameters were used to perform the modeling of the solubility of favipiravir and montelukast.
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Affiliation(s)
- Adrián Rojas
- Packaging Innovation Center (LABEN), Department of Science and Food Technology, Faculty of Technology, University of Santiago of Chile (USACH) Obispo Umaña 050 Santiago 9170201 Chile
- Center for the Development of Nanoscience and Nanotechnology (CEDENNA) Santiago 9170124 Chile
| | - Seyed Ali Sajadian
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan 87317-53153 Kashan Iran
| | - Carol López-de-Dicastillo
- Packaging Laboratory, Institute of Agrochemistry and Food Technology IATA-CSIC Av. Agustín Escardino 7 46980 Paterna Spain
| | - Nedasadat Saadati Ardestani
- Nanotechnology Research Center, Research Institute of Petroleum Industry (RIPI) P.O. Box: 14857-336 Tehran Iran
| | - Gonzalo Aguila
- Departamento de Ciencias de la Ingeniería, Facultad de Ingeniería, Universidad Andres Bello Antonio Varas 880 Santiago Chile
| | - Abolghasem Jouyban
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences Tabriz Iran
- Pharmaceutical Sciences Research Center, Shahid Beheshti University of Medical Sciences Tehran Iran
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Ansari E, Honarvar B, Sajadian SA, Aboosadi ZA, Azizi M. Experimental solubility of aripiprazole in supercritical carbon dioxide and modeling. Sci Rep 2023; 13:13402. [PMID: 37591914 PMCID: PMC10435544 DOI: 10.1038/s41598-023-40537-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 08/12/2023] [Indexed: 08/19/2023] Open
Abstract
The solubility of compounds in supercritical carbon dioxide (SC-[Formula: see text]) has found crucial significance in the fabrication of micro/nano-scaled drugs. In this research, the solubility of Aripiprazole was measured in SC-[Formula: see text] at various temperatures (308-338 K) and pressures (12-30 MPa). Moreover, the experimental solubility results were correlated with several semi-empirical models (Chrastil, Bartle et al., Kumar & Johnston, Menden-Santiago & Teja, Sodeifian et al., and Jouyban et al.) as well as the modified Wilson model. The molar fraction of the drug in SC-[Formula: see text] varied in the range of [Formula: see text] to [Formula: see text]. The solubility highly depended on the operating pressure and temperature. The Chrastil (0.994), Jouyban et al. (0.993) and Sodeifian et al. (0.992) models showed the highest consistency with the obtained values. Furthermore, self-consistency tests were performed on the solubility of Aripiprazole in SC-[Formula: see text]. The approximate total enthalpy ([Formula: see text]), vaporization enthalpy ([Formula: see text]), and solubility enthalpy ([Formula: see text]) were also calculated.
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Affiliation(s)
- Eslam Ansari
- Department of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
| | - Bizhan Honarvar
- Department of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
| | - Seyed Ali Sajadian
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
- South Zagros Oil and Gas Production, National Iranian Oil Company, Shiraz, 7135717991, Iran
| | - Zahra Arab Aboosadi
- Department of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
| | - Mehdi Azizi
- Department of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
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Ushiki I, Miyajima A, Fujimitsu R, Takishima S. Modeling cobalt (III) acetylacetonate and iron (III) acetylacetonate solubilities in supercritical CO2 with PC-SAFT based on experimentally-determined solid–liquid equilibria in organic solvents. J Supercrit Fluids 2023. [DOI: 10.1016/j.supflu.2023.105882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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Theoretical and experimental study on Chloroquine drug solubility in supercritical carbon dioxide via the thermodynamic, multi-layer perceptron neural network (MLPNN), and molecular modeling. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.104371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Abourehab MA, Alsubaiyel AM, Alshehri S, Alzhrani RM, Almalki AH, Abduljabbar MH, Venkatesan K, Kamal M. Laboratory Determination and Thermodynamic Analysis of Alendronate Solubility in Supercritical Carbon Dioxide. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Alzhrani RM, Almalki AH, Alaqel SL, Alshehri S. Novel Numerical simulation of drug solubility in supercritical CO2 using machine learning technique: Lenalidomide case study. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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Liu W, Zhao R, Su X, Mohamed A, Diana T. Development and validation of machine learning models for prediction of nanomedicine solubility in supercritical solvent for advanced pharmaceutical manufacturing. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.119208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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