Experimental measurement and thermodynamic modeling of solubility of Riluzole drug (neuroprotective agent) in supercritical carbon dioxide

Abadian, Mohammadamin; Sodeifian, Gholamhossein; Razmimanesh, Fariba; Mahmoudabadi, Samane Zarei

doi:10.1016/j.fluid.2022.113711

Cited by 27 publications

(9 citation statements)

References 72 publications

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“…The reliability and validity of the solubility system and experimental outcomes were assessed in a recent article which evaluated the solubility of Riluzole in SC-CO 2 56 – 58 . Examination of the methods and equipment utilized in the experiments requires evaluation of the solubility of a "prototype solute" 59 .…”

Section: Resultsmentioning

confidence: 99%

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2

Nateghi

Sodeifian

Razmimanesh

et al. 2023

Sci Rep

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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.

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Section: Resultsmentioning

confidence: 99%

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2

Nateghi

Sodeifian

Razmimanesh

et al. 2023

Sci Rep

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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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“…When talking about CO 2 , topics such as greenhouse gases, global warming and the relevant environmental considerations maybe occur to someone. However, the magic effects of its supercritical state on the development of numerous fields are undeniable, including extraction of organic compounds [7][8][9][10][11] , drug solubility measurement [12][13][14][15][16][17] , particle production [18][19][20] , power generation 21 , polymer synthesis and processing 22,23 , impregnation process 24 , tissue engineering 25 and food industry 26 . Moreover, low toxicity, safety, low environmental impacts and accessible critical conditions make CO 2 a special and the most frequently used supercritical solvent, both on the laboratory scale and in the industrial level processes.…”

Section: Solubility Of Palbociclib In Supercritical Carbon Dioxide Fr...mentioning

confidence: 99%

Solubility of palbociclib in supercritical carbon dioxide from experimental measurement and Peng–Robinson equation of state

Sodeifian

Hsieh

Tabibzadeh

et al. 2023

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Palbociclib is a poorly water-soluble medicine which acts against metastatic breast cancer cells. Among various techniques to improve the solubility of this medicine, applying supercritical technologies to produce micro- and nano-sized particles is a possible option. For this purpose, extraction of solubility data is required. In this research, the solubility of palbociclib in supercritical carbon dioxide (ScCO2) at different equilibrium conditions was measured at temperatures between 308 and 338 K and pressures within 12–27 MPa, for the first time. The minimum and maximum solubility data were found to be 8.1 × 10–7 (at 338 K and 12 MPa) and 2.03 × 10–5 (at 338 K and 27 MPa), respectively. Thereafter, two sets of models, including ten semi-empirical equations and three Peng–Robinson (PR) based integrated models were used to correlate the experimental solubility data. Bian’s model and PR equation of state using van der Waals mixing rules (PR + vdW) showed better accuracy among the examined semi-empirical and integrated models, respectively. Furthermore, the self-consistency of the obtained data was confirmed using two distinct semi-empirical models. At last, the total and vaporization enthalpies of palbociclib solubility in ScCO2 were calculated from correlation results of semi-empirical equations and estimated to be 40.41 and 52.67 kJ/mol, respectively.

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Experimental measurement and thermodynamic modeling of solubility of Riluzole drug (neuroprotective agent) in supercritical carbon dioxide

Cited by 27 publications

References 72 publications

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2

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

Solubility of palbociclib in supercritical carbon dioxide from experimental measurement and Peng–Robinson equation of state

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