Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models
Production of solid-dosage drug nanoparticles was assessed by theoretical models to investigate the possibility of drug treatment via supercritical green processing. Nanonization can enhance drug solubility and consequently its bioavailability which is of great importance for pharmaceutical industry...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23004070 |
| _version_ | 1797808420741447680 |
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| author | Amr S. Abouzied Saad M. Alshahrani Umme Hani Ahmad J. Obaidullah Ahmed Abdullah Al Awadh Ahmed A. Lahiq Halah Jawad Al-fanhrawi |
| author_facet | Amr S. Abouzied Saad M. Alshahrani Umme Hani Ahmad J. Obaidullah Ahmed Abdullah Al Awadh Ahmed A. Lahiq Halah Jawad Al-fanhrawi |
| author_sort | Amr S. Abouzied |
| collection | DOAJ |
| description | Production of solid-dosage drug nanoparticles was assessed by theoretical models to investigate the possibility of drug treatment via supercritical green processing. Nanonization can enhance drug solubility and consequently its bioavailability which is of great importance for pharmaceutical industry. This research presents a comparative study of three different regression models including Gaussian process regression, k-nearest neighbors, and multi-layer perceptron for predicting solvent density and solubility of Hyoscine drug. The models optimized using political optimizer (PO) algorithm. The results showed that all three optimized methods were able to predict density and solubility with high accuracy. PO-GPR achieved the highest R2 score for solubility (0.9984) and same for density (0.9999). The PO-MLP model achieved the high R2 score for density (0.9997) and the second-highest score for solubility (0.9945). PO-KNN also showed good performance for density (R2 = 0.9557) and solubility (R2 = 0.9783) but was outperformed by the other two models. In terms of RMSE and AARD%, PO-GPR and PO-MLP achieved lower error rates compared to PO-KNN. Overall, the results suggest that PO-GPR and PO-MLP are promising methods for predicting density and solubility of values. The models were useful for the application of drug nanonization and can be used to optimize the process. |
| first_indexed | 2024-03-13T06:37:13Z |
| format | Article |
| id | doaj.art-986fa9ea3d834f9dbac116c7ac27bb12 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-13T06:37:13Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-986fa9ea3d834f9dbac116c7ac27bb122023-06-09T04:28:06ZengElsevierCase Studies in Thermal Engineering2214-157X2023-07-0147103101Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning modelsAmr S. Abouzied0Saad M. Alshahrani1Umme Hani2Ahmad J. Obaidullah3Ahmed Abdullah Al Awadh4Ahmed A. Lahiq5Halah Jawad Al-fanhrawi6Department of Pharmaceutical Chemistry, College of Pharmacy, University of Ha'il, Hail, 81442, Saudi Arabia; Department of Pharmaceutical Chemistry, National Organization for Drug Control and Research (NODCAR), Giza, 12553, Egypt; Corresponding author. Department of Pharmaceutical Chemistry, College of Pharmacy, University of Ha'il, Hail, 81442, Saudi Arabia.Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi ArabiaDepartment of Pharmaceutics, College of Pharmacy, King Khalid University (KKU), Abha, 62529, Saudi ArabiaDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi ArabiaDepartment of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Najran University, 1988, Najran, 61441, Saudi ArabiaDepartment of Pharmaceutics, College of Pharmacy, Najran University, Najran, 66262, Saudi ArabiaResearch and Studies Unit, Al-Mustaqbal University College, 51001, Hillah, Babylon, IraqProduction of solid-dosage drug nanoparticles was assessed by theoretical models to investigate the possibility of drug treatment via supercritical green processing. Nanonization can enhance drug solubility and consequently its bioavailability which is of great importance for pharmaceutical industry. This research presents a comparative study of three different regression models including Gaussian process regression, k-nearest neighbors, and multi-layer perceptron for predicting solvent density and solubility of Hyoscine drug. The models optimized using political optimizer (PO) algorithm. The results showed that all three optimized methods were able to predict density and solubility with high accuracy. PO-GPR achieved the highest R2 score for solubility (0.9984) and same for density (0.9999). The PO-MLP model achieved the high R2 score for density (0.9997) and the second-highest score for solubility (0.9945). PO-KNN also showed good performance for density (R2 = 0.9557) and solubility (R2 = 0.9783) but was outperformed by the other two models. In terms of RMSE and AARD%, PO-GPR and PO-MLP achieved lower error rates compared to PO-KNN. Overall, the results suggest that PO-GPR and PO-MLP are promising methods for predicting density and solubility of values. The models were useful for the application of drug nanonization and can be used to optimize the process.http://www.sciencedirect.com/science/article/pii/S2214157X23004070Drug nanoparticlesMachine learningPharmaceutical manufactureArtificial intelligenceGreen technology |
| spellingShingle | Amr S. Abouzied Saad M. Alshahrani Umme Hani Ahmad J. Obaidullah Ahmed Abdullah Al Awadh Ahmed A. Lahiq Halah Jawad Al-fanhrawi Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models Case Studies in Thermal Engineering Drug nanoparticles Machine learning Pharmaceutical manufacture Artificial intelligence Green technology |
| title | Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models |
| title_full | Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models |
| title_fullStr | Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models |
| title_full_unstemmed | Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models |
| title_short | Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models |
| title_sort | assessment of solid dosage drug nanonization by theoretical advanced models modeling of solubility variations using hybrid machine learning models |
| topic | Drug nanoparticles Machine learning Pharmaceutical manufacture Artificial intelligence Green technology |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23004070 |
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