A simple correlation to predict drug solubility in supercritical carbon dioxide

Amooey, Ali Akbar

doi:10.1016/j.fluid.2014.05.025

Cited by 17 publications

(12 citation statements)

References 31 publications

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“…Eventually other correlations that do not belong to the above-cited groups can be found in the literature. − Among these, the Yu and Gordillo approaches are not density-based models because they do not establish an explicit dependence of the solute solubility on the solvent density.…”

Section: Correlation Of Solubility Datamentioning

confidence: 99%

“…The experimental data of this work were correlated with all the above cited models, − which are among the most popular correlations available in the literature. The models were fitted to the experimental data through nonlinear regression by minimizing the average absolute relative deviation between the calculated and experimental values.…”

Section: Correlation Of Solubility Datamentioning

confidence: 99%

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Solubility of Tolbutamide and Chlorpropamide in Supercritical Carbon Dioxide

Manna

Banchero

2018

J. Chem. Eng. Data

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Tolbutamide and chlorpropamide are oral hypoglycemic drugs that are used to treat diabetic patients. In the context of employing these drugs in green pharmaceutical applications that make use of supercritical fluids, the solubility of tolbutamide and chlorpropamide in supercritical CO2 has been measured at 313.15, 333.15, and 353.15 K and in the pressure range of 10–30 MPa. A semiflow apparatus equipped with a continuous solvent-dilution device of the depressurization line, which avoids the solubility data to be underestimated, was employed. The solubility of tolbutamide is in the range of 1.66 × 10–5 to 40.5 × 10–5 mole fraction while that of chlorpropamide is in the range of 2.29 × 10–6 to 72.2 × 10–6 mole fraction, which indicates that the first drug has higher solubility in the supercritical fluid than the latter, probably due to its higher hydrophobicity. The results were successfully correlated with the most popular empirical and semiempirical models reported in the literature. The Sparks model provided the best correlation for chlorpropamide with an adjusted absolute average percent deviation (AARD%) of 3.5%, while the Keshmiri model provided the best correlation for tolbutamide, with an AARD% of 4.8%. The self-consistency of the experimental data was checked through the Méndez-Santiago and Teja model.

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Section: Correlation Of Solubility Datamentioning

confidence: 99%

Section: Correlation Of Solubility Datamentioning

confidence: 99%

Solubility of Tolbutamide and Chlorpropamide in Supercritical Carbon Dioxide

Manna

Banchero

2018

J. Chem. Eng. Data

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“…They are used to modify and to control numerous particle properties, including various sizes, shapes, crystallinity structures, polymorphism, processability, and solubilities. [1][2][3][4][5] Among the advantages of the SCF used are their potential regulating properties with temperature and pressure. Also, SCF are known to be non-toxic, non-flammable, and can achieve high mass and heat transfer rates.…”

Section: Introductionmentioning

confidence: 99%

“…Also, SCF are known to be non-toxic, non-flammable, and can achieve high mass and heat transfer rates. [1][2][3][4][5][6] Current literature on applications of SCF technology applications in particle engineering, including crystal and polymorph modification, complex formation, solid dispersion, and other methods, used to improve the solubility of API, the bioavailability and processing capacity. [7] Carbon dioxide is considered the most common solvent for SFT applications in the pharmaceutical industries (98 % of applications).…”

Section: Introductionmentioning

confidence: 99%

QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide

Euldji

Si‐Moussa

Hamadache

et al. 2022

Molecular Informatics

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Quantitative structure–property relationship (QSPR) modeling was investigated to predict drug and drug‐like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug‐like compounds, accounting for 3971 experimental data points (EDPs), was collected and used for modelling the relationship between selected molecular descriptors and solubility fraction data achieved by a nonlinear approach (Artificial neural network, ANN) based on molecular descriptors. Experimental solubility data for a given drug were published as a function of temperature and pressure. In the present study, 11 significant PaDEL descriptors (AATS3v, MATS2e, GATS4c, GATS3v, GATS4e, GATS3 s, nBondsM, AVP‐0, SHBd, MLogP, and MLFER_S), the temperature and the pressure were statistically proved to be sufficient inputs. The architecture of the optimized model was found to be {13,10,1}. Several statistical metrics, including average absolute relative deviation (AARD=3.7748 %), root mean square error (RMSE=0.5162), coefficient of correlation (r=0.9761), coefficient of determination (R2=0.9528), and robustise (Q2=0.9528) were used to validate the obtained model. The model was also subjected to an external test by using 143 EDPs. Sensitivity analysis and domain of application were examined. The overall results confirmed that the optimized ANN‐QSPR model is suitable for the correlation and prediction of this property.

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“…Numerous solubility models have been proposed as prediction tools for SC-CO 2 extraction process where it could save energy, time and cost of operation. The models are classified as theoretical equations of state and density-based approach [11]. Due to simplicity of the application, the density-based models were widely implemented for solubility modelling where the models only need available independent variables such as pressure, temperature and density of pure fluids [12].…”

Section: Introductionmentioning

confidence: 99%

Solubility Modelling pf Butterfly Wing Leaves (Christia Vespertilionis) in Supercritical Carbon Dioxide

Hamid

Ismail²,

Mustapa³

et al. 2018

IJET

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Medicinal herb Christia vespertilionis oil has been claimed to possess anti-cancer and anti-plasmodial properties and show interest in food and pharmaceutical industries. Being an important alternative medicine plant, solubility data of Christia vespertilionis oil is demanded in order to understand the separation process and is crucial for designing purposes. In this work, extraction and determination of the oil's solubility were carried out using a green technique of supercritical carbon dioxide at a temperature range of 40 to 60℃ and 276 to 414 bar of pressure. The results demonstrated that the highest solubility was obtained at the highest temperature and pressure of 60℃ and 414 bar, respectively. For solubility prediction, experimental data were modelled using four empirical density-based models: Chrastil, del Valle and Aguilera, Adachi and Lu, and Sparks et al. models. In general, all the models were able to predict the solubility of Christia vespertilionis oil in supercritical carbon dioxide. The best fitting showed that Adachi and Lu model gave the best correlation with the lowest %AARD value of 1.61%.

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A simple correlation to predict drug solubility in supercritical carbon dioxide

Cited by 17 publications

References 31 publications

Solubility of Tolbutamide and Chlorpropamide in Supercritical Carbon Dioxide

Solubility of Tolbutamide and Chlorpropamide in Supercritical Carbon Dioxide

QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide

Solubility Modelling pf Butterfly Wing Leaves (Christia Vespertilionis) in Supercritical Carbon Dioxide

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