Machine learning simulation of pharmaceutical solubility in supercritical carbon dioxide: Prediction and experimental validation for busulfan drug

An artificial intelligence-based predictive model was developed using a support vector machine to investigate the solubility data of the drug Busulfan drug in supercritical carbon dioxide. The data for simulations were collected from literature. The model was trained and implemented in order to dete...

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Bibliographic Details
Published inArabian journal of chemistry Vol. 15; no. 1; p. 103502
Main Authors Sadeghi, Arash, Su, Chia-Hung, Khan, Afrasyab, Lutfor Rahman, Md, Sani Sarjadi, Mohd, Sarkar, Shaheen M.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.01.2022
Elsevier
Subjects
Artificial intelligence
Modeling
Nanomedicine
Pharmaceutics
Simulation
Pharmaceutics
Simulation
Artificial intelligence
Modeling
Nanomedicine
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Summary:An artificial intelligence-based predictive model was developed using a support vector machine to investigate the solubility data of the drug Busulfan drug in supercritical carbon dioxide. The data for simulations were collected from literature. The model was trained and implemented in order to determine the correlation between the solubility values and the input parameters, namely, temperature and pressure. These parameters were used as the inputs as they are known to have a significant effect on the solubility of Busulfan in supercritical carbon dioxide. In the artificial intelligence model, a polynomial model with kernel function was applied to the data, and the model’s findings were compared with measured data for fitting. Good agreement was observed between the model’s outputs and the measured data with coefficient of determination greater than 0.99.
ISSN:1878-5352
1878-5379
DOI:10.1016/j.arabjc.2021.103502