1
|
Alamoudi JA. Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO 2: a machine learning perspective. Front Med (Lausanne) 2024; 11:1467289. [PMID: 39286644 PMCID: PMC11402729 DOI: 10.3389/fmed.2024.1467289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 08/20/2024] [Indexed: 09/19/2024] Open
Abstract
Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.
Collapse
Affiliation(s)
- Jawaher Abdullah Alamoudi
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| |
Collapse
|
2
|
Sodeifian G, Alwi RS, Sodeifian F, Amraee S, Rashidi-Nooshabadi M, Razmimanesh F. Determination of Regorafenib monohydrate (colorectal anticancer drug) solubility in supercritical CO 2: Experimental and thermodynamic modeling. Heliyon 2024; 10:e29049. [PMID: 38681600 PMCID: PMC11052913 DOI: 10.1016/j.heliyon.2024.e29049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 05/01/2024] Open
Abstract
In this study, the solubilities of Regorafenib monohydrate (REG), a widely used as a colorectal anticancer drug, in supercritical carbon dioxide (ScCO2) were measured under various pressures and temperature conditions, for the first time. The minimum value of REG in mole fraction was determined to be 3.06×10-7, while the maximum value was found to be 6.44×10-6 at 338 K and 27 MPa. The experimental data for REG were correlated through the utilization of two types of models: (1) a set of 25 existing empirical and semi-empirical models that incorporated 3-8 parameters according to functional dependencies, (2) a model that relied on solid-liquid equilibrium (SLE) and the newly improved association models. All of the evaluated models were capable of generating suitable fits to the solubility data of REG, however, the average absolute relative deviation (AARD) of Gordillo et al. model (AARD=13.2%) and Reddy et al. model (AARD=13.5%) indicated their superiority based on AARD%. Furthermore, solvation and sublimation enthalpies of REG drug were estimated for the first time.
Collapse
Affiliation(s)
- Gholamhossein Sodeifian
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran
- Laboratory of Supercritical Fluids and Nanotechnology, University of Kashan, 87317-53153, Kashan, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran
| | - Ratna Surya Alwi
- Research Centre for Computing, National Research and Innovation Agency (BRIN), Jl, Raya Jakarta-Bogor KM 46 Cibinong, Indonesia
| | | | - Solmaz Amraee
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran
- Laboratory of Supercritical Fluids and Nanotechnology, University of Kashan, 87317-53153, Kashan, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran
| | | | - Fariba Razmimanesh
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran
- Laboratory of Supercritical Fluids and Nanotechnology, University of Kashan, 87317-53153, Kashan, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, 87317-53153, Kashan, Iran
| |
Collapse
|
3
|
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.
Collapse
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.)
| |
Collapse
|
4
|
Sodeifian G, Garlapati C, Arbab Nooshabadi M, Razmimanesh F, Roshanghias A. Studies on solubility measurement of codeine phosphate (pain reliever drug) in supercritical carbon dioxide and modeling. Sci Rep 2023; 13:21020. [PMID: 38030705 PMCID: PMC10687273 DOI: 10.1038/s41598-023-48234-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023] Open
Abstract
In this study, the solubilities of codeine phosphate, a widely used pain reliever, in supercritical carbon dioxide (SC-CO2) were measured under various pressures and temperature conditions. The lowest determined mole fraction of codeine phosphate in SC-CO2 was 1.297 × 10-5 at 308 K and 12 MPa, while the highest was 6.502 × 10-5 at 338 K and 27 MPa. These measured solubilities were then modeled using the equation of state model, specifically the Peng-Robinson model. A selection of density models, including the Chrastil model, Mendez-Santiago and Teja model, Bartle et al. model, Sodeifian et al. model, and Reddy-Garlapati model, were also employed. Additionally, three forms of solid-liquid equilibrium models, commonly called expanded liquid models (ELMs), were used. The average solvation enthalpy associated with the solubility of codeine phosphate in SC-CO2 was calculated to be - 16.97 kJ/mol. The three forms of the ELMs provided a satisfactory correlation to the solubility data, with the corresponding average absolute relative deviation percent (AARD%) under 12.63%. The most accurate ELM model recorded AARD% and AICc values of 8.89% and - 589.79, respectively.
Collapse
Affiliation(s)
- Gholamhossein Sodeifian
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
- Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran.
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
| | - Chandrasekhar Garlapati
- Department of Chemical Engineering, Puducherry Technological University, Puducherry, 605014, India
| | - Maryam Arbab Nooshabadi
- Bolvar Ghotbe Ravandi, Kashan Branch, Islamic Azad University, Ostaadan Street, Kashan, 87159-98151, Iran
| | - Fariba Razmimanesh
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
- Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
| | - Armin Roshanghias
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
- Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
| |
Collapse
|
5
|
Nateghi H, Sodeifian G, Razmimanesh F, Mohebbi Najm Abad J. A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO 2. Sci Rep 2023; 13:12906. [PMID: 37558797 PMCID: PMC10412577 DOI: 10.1038/s41598-023-40231-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 08/07/2023] [Indexed: 08/11/2023] Open
Abstract
Nilotinib hydrochloride monohydrate (NHM) is an anti-cancer drug whose solubility was statically determined in supercritical carbon dioxide (SC-CO2) for the first time at various temperatures (308-338 K) and pressures (120-270 bar). The mole fraction of the drug dissolved in SC-CO2 ranged from 0.1 × 10-5 to 0.59 × 10-5, corresponding to the solubility range of 0.016-0.094 g/L. Four sets of models were employed to evaluate the correlation of experimental data; (1) ten empirical and semi-empirical models with three to six adjustable parameters, such as Chrastil, Bartle, Sparks, Sodeifian, Mendez-Santiago and Teja (MST), Bian, Jouyban, Garlapati-Madras, Gordillo, and Jafari-Nejad; (2) Peng-Robinson equation of state (Van der Waals mixing rule, had an AARD% of 10.73); (3) expanded liquid theory (modified Wilson model, on average, the AARD of this model was 11.28%); and (4) machine learning (ML) algorithms (random forest, decision trees, multilayer perceptron, and deep neural network with respective R2 values of 0.9933, 0.9799, 0.9724 and 0.9701). All the models showed an acceptable agreement with the experimental data, among them, the Bian model exhibited excellent performance with an AARD% of 8.11. Finally, the vaporization (73.49 kJ/mol) and solvation (- 21.14 kJ/mol) enthalpies were also calculated for the first time.
Collapse
Affiliation(s)
- Hassan Nateghi
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
- Laboratory of Supercritical Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
| | - Gholamhossein Sodeifian
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
- Laboratory of Supercritical Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran.
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
| | - Fariba Razmimanesh
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
- Laboratory of Supercritical Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
| | - Javad Mohebbi Najm Abad
- Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, 9479176135, Iran
| |
Collapse
|
6
|
Sodeifian G, Garlapati C, Arbab Nooshabadi M, Razmimanesh F, Tabibzadeh A. Solubility measurement and modeling of hydroxychloroquine sulfate (antimalarial medication) in supercritical carbon dioxide. Sci Rep 2023; 13:8112. [PMID: 37208371 DOI: 10.1038/s41598-023-34900-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/09/2023] [Indexed: 05/21/2023] Open
Abstract
A supercritical fluid, such as supercritical carbon dioxide (scCO2) is increasingly used for the micronization of pharmaceuticals in the recent past. The role of scCO2 as a green solvent in supercritical fluid (SCF) process is decided by the solubility information of the pharmaceutical compound in scCO2. The commonly used SCF processes are the rapid expansion of supercritical solution (RESS) and supercritical antisolvent precipitation (SAS). To implement micronization process, solubility of pharmaceuticals in scCO2 is required. Present study is aimed at both measuring and modeling of solubilities of hydroxychloroquine sulfate (HCQS) in scCO2. Experiments were conducted at various conditions (P = 12 to 27 MPa and T = 308 to 338 K), for the first time. The measured solubilities were found to be ranging between (0.0304 × 10-4 and 0.1459 × 10-4) at 308 K, (0.0627 × 10-4 and 0.3158 × 10-4) at 318 K, (0.0982 × 10-4 and 0.4351 × 10-4) at 328 K, (0.1398 × 10-4 and 0.5515 × 10-4) at 338 K. To expand the usage of the data, various models were tested. For the modelling task existing models (Chrastil, reformulated Chrastil, Méndez-Santiago and Teja (MST), Bartle et al., Reddy-Garlapati, Sodeifian et al., models) and new set of solvate complex models were considered. Among the all models investigated Reddy-Garlapati and new solvate complex models are able to fit the data with the least error. Finally, the total and solvation enthalpies of HCQS in scCO2 were calculated with the help of model constants obtained from Chrastil, reformulated Chrastil and Bartle et al., models.
Collapse
Affiliation(s)
- Gholamhossein Sodeifian
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
- Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran.
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
| | - Chandrasekhar Garlapati
- Department of Chemical Engineering, Puducherry Technological University, Puducherry, 605014, India
| | - Maryam Arbab Nooshabadi
- Bolvar Ghotbe Ravandi, Kashan Branch, Islamic Azad University, Ostaadan Street, Kashan, 87159-98151, Iran
| | - Fariba Razmimanesh
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
- Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
| | - Amirmuhammad Tabibzadeh
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
- Laboratory of Supercriritcal Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran
- Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran
| |
Collapse
|
7
|
Sodeifian G, Usefi MMB. Solubility, Extraction, and Nanoparticles Production in Supercritical Carbon Dioxide: A Mini‐Review. CHEMBIOENG REVIEWS 2022. [DOI: 10.1002/cben.202200020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Gholamhossein Sodeifian
- University of Kashan Faculty of Engineering, Department of Chemical Engineering 87317-53153 Kashan Iran
- University of Kashan Laboratory of Supercritical Fluids and Nanotechnology 87317-53153 Kashan Iran
| | - Mohammad Mahdi Behvand Usefi
- University of Kashan Faculty of Engineering, Department of Chemical Engineering 87317-53153 Kashan Iran
- University of Kashan Laboratory of Supercritical Fluids and Nanotechnology 87317-53153 Kashan Iran
| |
Collapse
|
8
|
Experimental solubility and thermodynamic modeling of empagliflozin in supercritical carbon dioxide. Sci Rep 2022; 12:9008. [PMID: 35637271 PMCID: PMC9151729 DOI: 10.1038/s41598-022-12769-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/16/2022] [Indexed: 11/22/2022] Open
Abstract
The solubility of empagliflozin in supercritical carbon dioxide was measured at temperatures (308 to 338 K) and pressures (12 to 27 MPa), for the first time. The measured solubility in terms of mole faction ranged from 5.14 × 10–6 to 25.9 × 10–6. The cross over region was observed at 16.5 MPa. A new solubility model was derived to correlate the solubility data using solid–liquid equilibrium criteria combined with Wilson activity coefficient model at infinite dilution for the activity coefficient. The proposed model correlated the data with average absolute relative deviation (AARD) and Akaike’s information criterion (AICc), 7.22% and − 637.24, respectively. Further, the measured data was also correlated with 11 existing (three, five and six parameters empirical and semi-empirical) models and also with Redlich-Kwong equation of state (RKEoS) along with Kwak-Mansoori mixing rules (KMmr) model. Among density-based models, Bian et al., model was the best and corresponding AARD% was calculated 5.1. The RKEoS + KMmr was observed to correlate the data with 8.07% (correspond AICc is − 635.79). Finally, total, sublimation and solvation enthalpies of empagliflozin were calculated.
Collapse
|
9
|
Euldji I, SI-MOUSSA C, HAMADACHE M, BENKORTBI O. QSPR Modelling of The Solubility of Drug and Drug‐Like Compounds in Supercritical Carbon Dioxide. Mol Inform 2022; 41:e2200026. [DOI: 10.1002/minf.202200026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/03/2022] [Indexed: 11/05/2022]
|
10
|
Abstract
The growing emission of carbon dioxide (CO2), combined with its ecotoxicity, is the reason for the intensification of research on the new technology of CO2 management. Currently, it is believed that it is not possible to eliminate whole CO2 emissions. However, a sustainable balance sheet is possible. The solution is technologies that use carbon dioxide as a raw material. Many of these methods are based on CO2 methanation, for example, projects such as Power-to-Gas, production of fuels, or polymers. This article presents the concept of using CO2 as a raw material, the catalytic conversion of carbon dioxide to methane, and consideration on CO2 methanation catalysts and their design.
Collapse
|