Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility
Determination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learnin...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23003118 |
| _version_ | 1797832721417895936 |
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| author | Bader Huwaimel Tareq Nafea Alharby |
| author_facet | Bader Huwaimel Tareq Nafea Alharby |
| author_sort | Bader Huwaimel |
| collection | DOAJ |
| description | Determination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learning nature. These parameters are molded based on temperature and pressure input features due to the significant variability of drug solubility with the temperature and pressure. The experimental data have been collected and inputted the models to train them and used the data for testing the machine learning models. The results are useful for production of nanomedicine with enhanced solubility in solvents. Decision Tree (DT), Extra Trees (ET), and Gradient Boosting (GB) models are used and optimized using SCA algorithm to obtain more robust models for prediction of the drug solubility in the solvent. So, the developed models are called SCA-DT, SCA-ET, and SCA-GB in this study and have R2-scores of 0.932, 0.951, and 0.997, respectively. The SCA-DT model has an RMSE error rate of 0.0948, this rate is 0.0822 for SCA-ET, and 0.0203 for SCA-GB. So, the SCA-GB is introduced as the best model of this research for prediction of Lenalidomide solubility in the solvent. |
| first_indexed | 2024-04-09T14:13:28Z |
| format | Article |
| id | doaj.art-c382b96e336a47fe8481cbe87bcfe032 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-04-09T14:13:28Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-c382b96e336a47fe8481cbe87bcfe0322023-05-06T04:38:13ZengElsevierCase Studies in Thermal Engineering2214-157X2023-05-0145103005Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubilityBader Huwaimel0Tareq Nafea Alharby1Department of Pharmaceutical Chemistry, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi Arabia; Medical and Diagnostic Research Centre, University of Ha'il, Hail, 55476, Saudi Arabia; Corresponding author. Department of Pharmaceutical Chemistry, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi Arabia.Department of Clinical Pharmacy, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi ArabiaDetermination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learning nature. These parameters are molded based on temperature and pressure input features due to the significant variability of drug solubility with the temperature and pressure. The experimental data have been collected and inputted the models to train them and used the data for testing the machine learning models. The results are useful for production of nanomedicine with enhanced solubility in solvents. Decision Tree (DT), Extra Trees (ET), and Gradient Boosting (GB) models are used and optimized using SCA algorithm to obtain more robust models for prediction of the drug solubility in the solvent. So, the developed models are called SCA-DT, SCA-ET, and SCA-GB in this study and have R2-scores of 0.932, 0.951, and 0.997, respectively. The SCA-DT model has an RMSE error rate of 0.0948, this rate is 0.0822 for SCA-ET, and 0.0203 for SCA-GB. So, the SCA-GB is introduced as the best model of this research for prediction of Lenalidomide solubility in the solvent.http://www.sciencedirect.com/science/article/pii/S2214157X23003118PharmaceuticsModelingMachine learningGradient boosting |
| spellingShingle | Bader Huwaimel Tareq Nafea Alharby Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility Case Studies in Thermal Engineering Pharmaceutics Modeling Machine learning Gradient boosting |
| title | Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility |
| title_full | Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility |
| title_fullStr | Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility |
| title_full_unstemmed | Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility |
| title_short | Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility |
| title_sort | development of computational intelligence models for assessment of drug nanonization using green chemistry technique improvement of drug solubility |
| topic | Pharmaceutics Modeling Machine learning Gradient boosting |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23003118 |
| work_keys_str_mv | AT baderhuwaimel developmentofcomputationalintelligencemodelsforassessmentofdrugnanonizationusinggreenchemistrytechniqueimprovementofdrugsolubility AT tareqnafeaalharby developmentofcomputationalintelligencemodelsforassessmentofdrugnanonizationusinggreenchemistrytechniqueimprovementofdrugsolubility |