Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models
Abstract In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO2. This important role is the first stage for assessing each...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87383-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862286300741632 |
|---|---|
| author | Gholamhossein Sodeifian Ratna Surya Alwi Reza Derakhsheshpour Nedasadat Saadati Ardestani |
| author_facet | Gholamhossein Sodeifian Ratna Surya Alwi Reza Derakhsheshpour Nedasadat Saadati Ardestani |
| author_sort | Gholamhossein Sodeifian |
| collection | DOAJ |
| description | Abstract In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO2. This important role is the first stage for assessing each SCF technology. A statistical method was developed for the first time and employed to determine 5-fluorouracil (5-Fu) solubility. The measurements were performed at different pressures (120–270 bar) and temperatures (308–338 K) through UV-vis spectrophotometry, for the first time. The solubility was obtained between 0.0024 and 0.0176 g/L. The 5-Fu mole fraction at constant temperature, increases with an increase in pressure. Whereas, a crossover point has been seen. Three models with different approaches were applied to correlate and model the experimental data set: (i) seven density-based models, (ii) PR equations of state (vdW2 mixing rule), and (iii) machine learning-based models, namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, and Kernel Ridge. All tested models successfully correlate and model the solubility data within an acceptable accuracy. Meanwhile, the empirical model suggested by Sodeifian model 2, is superior with the lowest AARD% (AARD = 4.12%). Finally, total, solvation, and vaporization enthalpies of the drug/Sc-CO2 binary system were determined using semi-empirical correlations, for the first time. |
| format | Article |
| id | doaj-art-2812f7fe479940228231a7c3a0cb6670 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2812f7fe479940228231a7c3a0cb66702025-02-09T12:32:39ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-87383-zDetermination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning modelsGholamhossein Sodeifian0Ratna Surya Alwi1Reza Derakhsheshpour2Nedasadat Saadati Ardestani3Department of Chemical Engineering, Faculty of Engineering, University of KashanResearch Center for Computing, Research Organization of Electronics and Informatics, Cibinong Science Center, National Research and Innovation Agency (BRIN)Department of Chemical Engineering, Faculty of Engineering, University of KashanModeling and Simulation Centre, Faculty of Engineering, University of KashanAbstract In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO2. This important role is the first stage for assessing each SCF technology. A statistical method was developed for the first time and employed to determine 5-fluorouracil (5-Fu) solubility. The measurements were performed at different pressures (120–270 bar) and temperatures (308–338 K) through UV-vis spectrophotometry, for the first time. The solubility was obtained between 0.0024 and 0.0176 g/L. The 5-Fu mole fraction at constant temperature, increases with an increase in pressure. Whereas, a crossover point has been seen. Three models with different approaches were applied to correlate and model the experimental data set: (i) seven density-based models, (ii) PR equations of state (vdW2 mixing rule), and (iii) machine learning-based models, namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, and Kernel Ridge. All tested models successfully correlate and model the solubility data within an acceptable accuracy. Meanwhile, the empirical model suggested by Sodeifian model 2, is superior with the lowest AARD% (AARD = 4.12%). Finally, total, solvation, and vaporization enthalpies of the drug/Sc-CO2 binary system were determined using semi-empirical correlations, for the first time.https://doi.org/10.1038/s41598-025-87383-z5-fluorouracil anti-cancer drugSolubilitySodeifian model 2Supercritical carbon dioxideMachine learning method |
| spellingShingle | Gholamhossein Sodeifian Ratna Surya Alwi Reza Derakhsheshpour Nedasadat Saadati Ardestani Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models Scientific Reports 5-fluorouracil anti-cancer drug Solubility Sodeifian model 2 Supercritical carbon dioxide Machine learning method |
| title | Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models |
| title_full | Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models |
| title_fullStr | Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models |
| title_full_unstemmed | Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models |
| title_short | Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models |
| title_sort | determination of 5 fluorouracil anticancer drug solubility in supercritical co 2 using semi empirical and machine learning models |
| topic | 5-fluorouracil anti-cancer drug Solubility Sodeifian model 2 Supercritical carbon dioxide Machine learning method |
| url | https://doi.org/10.1038/s41598-025-87383-z |
| work_keys_str_mv | AT gholamhosseinsodeifian determinationof5fluorouracilanticancerdrugsolubilityinsupercriticalco2usingsemiempiricalandmachinelearningmodels AT ratnasuryaalwi determinationof5fluorouracilanticancerdrugsolubilityinsupercriticalco2usingsemiempiricalandmachinelearningmodels AT rezaderakhsheshpour determinationof5fluorouracilanticancerdrugsolubilityinsupercriticalco2usingsemiempiricalandmachinelearningmodels AT nedasadatsaadatiardestani determinationof5fluorouracilanticancerdrugsolubilityinsupercriticalco2usingsemiempiricalandmachinelearningmodels |