Solubility of Nifedipine and Lacidipine in Supercritical CO2: Measurement and Correlation

Wang, Yanlou; Meng, Tiantian; Jia, Dianzeng; Sun, Yuan; Nai-xuan, LI

doi:10.1007/s10953-016-0550-2

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Comparison of SF-CO 2 solubility results obtained for nifedipine using the static SSV–SFC method with those obtained using the static method used by Knez et al and the dynamic method of Wang et al …”

Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

“…To the best of our knowledge only two other reports have previously provided SF-CO 2 solubility data for nifedipine. , The static method used by Knez et al involved trapping an aliquot of SF-CO 2 nifedipine solution in ethanol prior to quantitation using off-line UV spectroscopy. In contrast, Wang et al have described the use of dynamic solubility apparatus that involved collecting the nifedipine dissolved in a volume of SF-CO 2 that generated about 4–6 L of gaseous carbon dioxide during the decompression stage. After decompression, Wang et al used a 5 mL aliquot of methanol through which the 4–6 L of decompressed carbon dioxide was percolated to trap the nifedipine, and this was then combined with further methanol used to wash the dynamic system transfer lines.…”

Section: Resultsmentioning

confidence: 99%

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Determining the Solubility of Nifedipine and Quinine in Supercritical Fluid Carbon Dioxide Using Continuously Stirred Static Solubility Apparatus Interfaced with Online Supercritical Fluid Chromatography

Guo

Ramsey

2017

J. Chem. Eng. Data

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A high pressure vessel equipped with continuous magnetic stirring has been interfaced directly online with supercritical fluid chromatography (SFC). The interface consists of an oven housing a recirculation pump that maintains a constant flow of supercritical fluid solution from the vessel through a sample injection valve for online SFC analysis. The method involves increasing the vessel pressure in controlled steps to produce a series of progressively more saturated supercritical fluid carbon dioxide solutions for online SFC solubility measurements. The integrated static solubility system provides a convenient means to ensure equilibrium is established prior to performing online SFC solubility measurements and also facilitates a self-validation accuracy check procedure. For nifedipine, solubility data was obtained at 333.15 and 353.15 K through the pressure range from 12.5 to 27.5 MPa using 2.5 MPa steps. For quinine, solubility data was obtained at 323.15 and 343.15 K through the pressure range from 12.5 to 27.5 MPa using 3 MPa steps. All sets of experimental solubility data for each compound were correlated using the Mendéz-Santiago and Teja model.

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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Determining the Solubility of Nifedipine and Quinine in Supercritical Fluid Carbon Dioxide Using Continuously Stirred Static Solubility Apparatus Interfaced with Online Supercritical Fluid Chromatography

Guo

Ramsey

2017

J. Chem. Eng. Data

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“…The experimental data on the solubility of solid solutes in supercritical carbon dioxide was collected from a wide range of literature sources. ,,− …”

Section: Methodsmentioning

confidence: 99%

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

Makarov,

Kalikin,

Budkov

2024

Ind. Eng. Chem. Res.

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Supercritical carbon dioxide (scCO 2 ) plays an essential role in various technological procedures, making the solubility of drugs in scCO 2 a crucial aspect of the drug formulation process. This study focuses on utilizing theoretical approaches to predict the solubility of drug-like compounds in scCO2 in order to select the optimum parameters for subsequent experimental procedures. Several machine learning models were developed and compared with a previously established theoretical approach based on classical density functional theory (cDFT). The CatBoost model, utilizing alvaDesc descriptors, demonstrated reasonably accurate predictions for the solubility of 187 drugs (AARD = 1.8%). Meanwhile, the CatBoost model, incorporating CDK descriptors and melting points of drugs as input parameters, exhibited satisfactory accuracy (AARD = 14.3%) in extrapolating predictions for new compounds. Comparing the results between the machine learning approach and the cDFT-based one revealed, on average, a higher accuracy and faster prediction speed for the former. However, cDFT demonstrated a more physical behavior of solubility isotherms compared with the machine learning models. This was particularly evident when the ML models struggled to accurately extrapolate solubility values beyond the experimental range of parameters in the supercritical state. Model CatBoost/CDK is freely accessible at http://chem-predictor.isc-ras.ru/ individual/scco/.

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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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Solubility of Nifedipine and Lacidipine in Supercritical CO2: Measurement and Correlation

Cited by 8 publications

References 23 publications

Determining the Solubility of Nifedipine and Quinine in Supercritical Fluid Carbon Dioxide Using Continuously Stirred Static Solubility Apparatus Interfaced with Online Supercritical Fluid Chromatography

Determining the Solubility of Nifedipine and Quinine in Supercritical Fluid Carbon Dioxide Using Continuously Stirred Static Solubility Apparatus Interfaced with Online Supercritical Fluid Chromatography

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

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

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