Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligence
This research focuses on investigating the solubility of tolfenamic acid in SC-CO2 (supercritical carbon dioxide) and the density of SC-CO2 solvent via theoretical artificial intelligence method. The study involves analyzing the relationship between temperature, pressure, and the corresponding menti...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23008936 |
| _version_ | 1827794099075284992 |
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| author | Abdulrahman Sumayli Wael A. Mahdi Jawaher Abdullah Alamoudi |
| author_facet | Abdulrahman Sumayli Wael A. Mahdi Jawaher Abdullah Alamoudi |
| author_sort | Abdulrahman Sumayli |
| collection | DOAJ |
| description | This research focuses on investigating the solubility of tolfenamic acid in SC-CO2 (supercritical carbon dioxide) and the density of SC-CO2 solvent via theoretical artificial intelligence method. The study involves analyzing the relationship between temperature, pressure, and the corresponding mentioned outputs for the process. Three different predictive models, namely Multi-layer Perceptron (MLP), Polynomial Regression (PR), and Extra Trees (ET) are utilized to forecast the solubility of drug and the density of solvent (SC–CO2). The models are fine-tuned with hyper-parameters using the Dragonfly Algorithm (DA) to ensure accurate predictions. The solubility prediction is remarkably accurate using the MLP model, showing a high score of 0.98329 in terms of R-squared. The maximum error is 0.2474, and the MAE is 0.1095, demonstrating the model's high precision in estimating tolfenamic acid's solubility in SC-CO2. The PR model demonstrates exceptional accuracy, yielding a score of 0.99844 by R-squared metric, a maximum error of 0.068, and an MAE of 0.0314. The ET model also performs well, with an R-squared score of 0.90977, a maximum error of 0.445, and an MAE of 0.1665. Regarding density prediction, MLP outperforms the other techniques, achieving a significant R2 parameter of 0.99919, an MSE of 12.663, and a mean MAPE of 0.0037. |
| first_indexed | 2024-03-11T18:28:54Z |
| format | Article |
| id | doaj.art-65fb7ef6e09944c3b9465b677d15a188 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-11T18:28:54Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-65fb7ef6e09944c3b9465b677d15a1882023-10-13T13:53:57ZengElsevierCase Studies in Thermal Engineering2214-157X2023-11-0151103587Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligenceAbdulrahman Sumayli0Wael A. Mahdi1Jawaher Abdullah Alamoudi2Department of Mechanical Engineering, College of Engineering, Najran University, Najran, Saudi Arabia; Corresponding author.Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi ArabiaDepartment of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi ArabiaThis research focuses on investigating the solubility of tolfenamic acid in SC-CO2 (supercritical carbon dioxide) and the density of SC-CO2 solvent via theoretical artificial intelligence method. The study involves analyzing the relationship between temperature, pressure, and the corresponding mentioned outputs for the process. Three different predictive models, namely Multi-layer Perceptron (MLP), Polynomial Regression (PR), and Extra Trees (ET) are utilized to forecast the solubility of drug and the density of solvent (SC–CO2). The models are fine-tuned with hyper-parameters using the Dragonfly Algorithm (DA) to ensure accurate predictions. The solubility prediction is remarkably accurate using the MLP model, showing a high score of 0.98329 in terms of R-squared. The maximum error is 0.2474, and the MAE is 0.1095, demonstrating the model's high precision in estimating tolfenamic acid's solubility in SC-CO2. The PR model demonstrates exceptional accuracy, yielding a score of 0.99844 by R-squared metric, a maximum error of 0.068, and an MAE of 0.0314. The ET model also performs well, with an R-squared score of 0.90977, a maximum error of 0.445, and an MAE of 0.1665. Regarding density prediction, MLP outperforms the other techniques, achieving a significant R2 parameter of 0.99919, an MSE of 12.663, and a mean MAPE of 0.0037.http://www.sciencedirect.com/science/article/pii/S2214157X23008936Machine learningPharmaceuticsGreen processingExtra TreesTemperature effect |
| spellingShingle | Abdulrahman Sumayli Wael A. Mahdi Jawaher Abdullah Alamoudi Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligence Case Studies in Thermal Engineering Machine learning Pharmaceutics Green processing Extra Trees Temperature effect |
| title | Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligence |
| title_full | Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligence |
| title_fullStr | Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligence |
| title_full_unstemmed | Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligence |
| title_short | Analysis of nanomedicine production via green processing: Modeling and simulation of pharmaceutical solubility using artificial intelligence |
| title_sort | analysis of nanomedicine production via green processing modeling and simulation of pharmaceutical solubility using artificial intelligence |
| topic | Machine learning Pharmaceutics Green processing Extra Trees Temperature effect |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23008936 |
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