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

Arabian Journal of Chemistry

Shaheen M. Sarkar¹  Afrasyab Khan¹  Chia-Hung Su²  Mohd Sani Sarjadi³  Md Lutfor Rahman⁴  Arash Sadeghi⁵ 

[1] Corresponding authors.;Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan;Faculty of Science and Natural Resources, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia;Research Institute of Mechanical Engineering, Department of Vibration Testing and Equipment Condition Monitoring, South Ural State University, Lenin prospect 76, Chelyabinsk 454080, Russian Federation;Research and Development Department, Pars Alcohol Company, Eghlid, Fars, Iran;

关键词: Artificial intelligence;    Simulation;    Modeling;    Pharmaceutics;    Nanomedicine;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown