Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media
This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifed...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Pharmaceuticals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8247/15/11/1405 |
| _version_ | 1797464225272037376 |
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| author | Sait Senceroglu Mohamed Arselene Ayari Tahereh Rezaei Fardad Faress Amith Khandakar Muhammad E. H. Chowdhury Zanko Hassan Jawhar |
| author_facet | Sait Senceroglu Mohamed Arselene Ayari Tahereh Rezaei Fardad Faress Amith Khandakar Muhammad E. H. Chowdhury Zanko Hassan Jawhar |
| author_sort | Sait Senceroglu |
| collection | DOAJ |
| description | This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future. |
| first_indexed | 2024-03-09T18:04:40Z |
| format | Article |
| id | doaj.art-6ea3cdd8a4fa4af58916513848ef133a |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-09T18:04:40Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-6ea3cdd8a4fa4af58916513848ef133a2023-11-24T09:35:13ZengMDPI AGPharmaceuticals1424-82472022-11-011511140510.3390/ph15111405Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric MediaSait Senceroglu0Mohamed Arselene Ayari1Tahereh Rezaei2Fardad Faress3Amith Khandakar4Muhammad E. H. Chowdhury5Zanko Hassan Jawhar6Faculty of Pharmacy, Ege University, Izmir 35040, TurkeyDepartment of Civil and Architectural Engineering, Qatar University, Doha 2713, QatarNeuroscience Research Center, Shiraz University of Medical Sciences, Shiraz 71348, IranDepartment of Business, Data Analysis, The University of Texas Rio Grande Valley (UTRGV), Edinburg, TX 78539, USADepartment of Electrical Engineering, Qatar University, Doha 2713, QatarDepartment of Electrical Engineering, Qatar University, Doha 2713, QatarDepartment of Medical Laboratory Science, College of Health Science, Lebanese French University, Erbil 44001, Kurdistan Region, IraqThis study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future.https://www.mdpi.com/1424-8247/15/11/1405solubility temperaturedrugpolymersupport vector regressionkernel typetuning techniques |
| spellingShingle | Sait Senceroglu Mohamed Arselene Ayari Tahereh Rezaei Fardad Faress Amith Khandakar Muhammad E. H. Chowdhury Zanko Hassan Jawhar Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media Pharmaceuticals solubility temperature drug polymer support vector regression kernel type tuning techniques |
| title | Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media |
| title_full | Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media |
| title_fullStr | Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media |
| title_full_unstemmed | Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media |
| title_short | Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media |
| title_sort | constructing an intelligent model based on support vector regression to simulate the solubility of drugs in polymeric media |
| topic | solubility temperature drug polymer support vector regression kernel type tuning techniques |
| url | https://www.mdpi.com/1424-8247/15/11/1405 |
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