Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application
Abstract A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. The release of medication was measured using Raman spectroscopy and the data was used for estimation of drug delivery using machine...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-06227-y |
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| author | Anupam Yadav B. Jayaprakash Laith Hussein Jasim Mayank Kundlas Maan Younis Anad Ankur Srivastava M. Janaki Ramudu B. Bharathi Prabhat Kumar Sahu |
| author_facet | Anupam Yadav B. Jayaprakash Laith Hussein Jasim Mayank Kundlas Maan Younis Anad Ankur Srivastava M. Janaki Ramudu B. Bharathi Prabhat Kumar Sahu |
| author_sort | Anupam Yadav |
| collection | DOAJ |
| description | Abstract A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. The release of medication was measured using Raman spectroscopy and the data was used for estimation of drug delivery using machine learning models. Raman data was used along with some inputs including coating type, medium, and release time to estimate the drug release as the sole target. The study explores predictive modeling for the release variable in a dataset including 155 samples with over 1500 spectral variables. Partial least squares (PLS) was applied for dimensionality reduction, and models such as AdaBoost with linear regression, multilayer perceptron (MLP), and Theil-Sen regression were utilized, achieving the highest predictive performance with the AdaBoost-MLP model (R2 = 0.994, MSE = 0.000368). Uniquely, this work integrates glowworm swarm optimization (GSO) for model hyperparameter tuning, enhancing model accuracy and efficiency. The results suggest that spectral characteristics combined with environmental and compositional factors provide a comprehensive foundation for modeling release dynamics in evaluation of targeted colonic delivery formulations. |
| format | Article |
| id | doaj-art-d303fda20c1d4b34a7090de080c43a70 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d303fda20c1d4b34a7090de080c43a702025-08-20T03:45:28ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-06227-yImplementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery applicationAnupam Yadav0B. Jayaprakash1Laith Hussein Jasim2Mayank Kundlas3Maan Younis Anad4Ankur Srivastava5M. Janaki Ramudu6B. Bharathi7Prabhat Kumar Sahu8Department of Computer Engineering and Application, GLA UniversityDepartment of Computer Science & IT, School of Sciences, JAIN (Deemed to be University)Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic UniversityCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Cybersecurity, College of Engineering Technology, Alnoor UniversityDepartment of CSE, Chandigarh Engineering College, Chandigarh Group of Colleges-JhanjeriDepartment of CSE, Raghu Engineering CollegeDepartment of Computer Science and Engineering, Sathyabama Institute of Science and TechnologyDepartment of Computer Science and Information Technology, Siksha ’O’ Anusandhan (Deemed to be University)Abstract A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. The release of medication was measured using Raman spectroscopy and the data was used for estimation of drug delivery using machine learning models. Raman data was used along with some inputs including coating type, medium, and release time to estimate the drug release as the sole target. The study explores predictive modeling for the release variable in a dataset including 155 samples with over 1500 spectral variables. Partial least squares (PLS) was applied for dimensionality reduction, and models such as AdaBoost with linear regression, multilayer perceptron (MLP), and Theil-Sen regression were utilized, achieving the highest predictive performance with the AdaBoost-MLP model (R2 = 0.994, MSE = 0.000368). Uniquely, this work integrates glowworm swarm optimization (GSO) for model hyperparameter tuning, enhancing model accuracy and efficiency. The results suggest that spectral characteristics combined with environmental and compositional factors provide a comprehensive foundation for modeling release dynamics in evaluation of targeted colonic delivery formulations.https://doi.org/10.1038/s41598-025-06227-yDrug deliveryMachine learningLinear regressionMultilayer perceptronTheil-Sen regression |
| spellingShingle | Anupam Yadav B. Jayaprakash Laith Hussein Jasim Mayank Kundlas Maan Younis Anad Ankur Srivastava M. Janaki Ramudu B. Bharathi Prabhat Kumar Sahu Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application Scientific Reports Drug delivery Machine learning Linear regression Multilayer perceptron Theil-Sen regression |
| title | Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application |
| title_full | Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application |
| title_fullStr | Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application |
| title_full_unstemmed | Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application |
| title_short | Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application |
| title_sort | implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application |
| topic | Drug delivery Machine learning Linear regression Multilayer perceptron Theil-Sen regression |
| url | https://doi.org/10.1038/s41598-025-06227-y |
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