TY - JOUR
T1 - Comprehensive theoretical analysis of the solubility of two benzodiazepine drugs in supercritical solvent; thermodynamic and advanced machine learning approaches
AU - Oghenemaro, Enwa Felix
AU - Alghazali, Tawfeeq
AU - Uthirapathy, Subasini
AU - Doshi, Hardik
AU - Ahmad, Irfan
AU - Raja Praveen, K. N.
AU - Gupta, Anirudh
AU - Verma, Deeksha
AU - Kumar, Abhinav
AU - Hjazi, Ahmed
N1 - Publisher Copyright:
© 2025
PY - 2025/9/15
Y1 - 2025/9/15
N2 - The pharmaceutical industry is showing growing interest in exploring supercritical CO₂ techniques to develop advanced drug delivery systems. In this regard, assessing the solubility of drugs in supercritical CO₂ under different conditions is a vital parameter. In this study, the solubility of two benzodiazepine family drugs, diazepam and alprazolam, in supercritical CO₂ was theoretically assessed using two distinct thermodynamic approaches; the SRK model, which treats supercritical CO₂ as a gas, and the regular solution model, which considers supercritical CO₂ as a liquid and incorporates various expressions for the solubility parameter. Furthermore, machine learning models, including MLP with various training algorithm, GPR and KNN, were employed to predict the solubility of these drugs. The accuracy of the models for each medicine is validated by comparing their results with previously published experimental solubility data. It was demonstrated that both thermodynamic models successfully model the solubility of these drugs in supercritical CO₂. The SRK model yielded mean AARD values of 10.36 for diazepam and 3.15 for alprazolam, while the regular solution model, using a specific expression for the solubility parameter, provided the best fit to the experimental results, with mean AARD values of 4.69 for diazepam and 6.97 for alprazolam. Also, it was demonstrated that all intelligent models achieved the highest performance in predicting the solubility of diazepam and alprazolam in supercritical CO₂.
AB - The pharmaceutical industry is showing growing interest in exploring supercritical CO₂ techniques to develop advanced drug delivery systems. In this regard, assessing the solubility of drugs in supercritical CO₂ under different conditions is a vital parameter. In this study, the solubility of two benzodiazepine family drugs, diazepam and alprazolam, in supercritical CO₂ was theoretically assessed using two distinct thermodynamic approaches; the SRK model, which treats supercritical CO₂ as a gas, and the regular solution model, which considers supercritical CO₂ as a liquid and incorporates various expressions for the solubility parameter. Furthermore, machine learning models, including MLP with various training algorithm, GPR and KNN, were employed to predict the solubility of these drugs. The accuracy of the models for each medicine is validated by comparing their results with previously published experimental solubility data. It was demonstrated that both thermodynamic models successfully model the solubility of these drugs in supercritical CO₂. The SRK model yielded mean AARD values of 10.36 for diazepam and 3.15 for alprazolam, while the regular solution model, using a specific expression for the solubility parameter, provided the best fit to the experimental results, with mean AARD values of 4.69 for diazepam and 6.97 for alprazolam. Also, it was demonstrated that all intelligent models achieved the highest performance in predicting the solubility of diazepam and alprazolam in supercritical CO₂.
KW - Benzodiazepine
KW - Machine learning
KW - Solubility
KW - Supercritical CO
KW - Thermodynamic models
UR - http://www.scopus.com/inward/record.url?scp=105009862583&partnerID=8YFLogxK
U2 - 10.1016/j.molliq.2025.128000
DO - 10.1016/j.molliq.2025.128000
M3 - Article
AN - SCOPUS:105009862583
SN - 0167-7322
VL - 434
JO - Journal of Molecular Liquids
JF - Journal of Molecular Liquids
M1 - 128000
ER -