A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities
Thanks to the diverse advantages of electrospun nanofibers, multiple drugs have been loaded in these nanoplatforms to be delivered healthily and effectively. Doxorubicin is a drug used in chemotherapy, and its various delivery and efficacy parameters encounter challenges, leading to the seeking of n...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1493194/full |
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| author | Mohammadreza Rostami Mohammadreza Rostami Maliheh Gharibshahian Maliheh Gharibshahian Mehrnaz Mostafavi Ali Sufali Mahsa Golmohammadi Mohammad Reza Barati Reza Maleki Nima Beheshtizadeh Nima Beheshtizadeh |
| author_facet | Mohammadreza Rostami Mohammadreza Rostami Maliheh Gharibshahian Maliheh Gharibshahian Mehrnaz Mostafavi Ali Sufali Mahsa Golmohammadi Mohammad Reza Barati Reza Maleki Nima Beheshtizadeh Nima Beheshtizadeh |
| author_sort | Mohammadreza Rostami |
| collection | DOAJ |
| description | Thanks to the diverse advantages of electrospun nanofibers, multiple drugs have been loaded in these nanoplatforms to be delivered healthily and effectively. Doxorubicin is a drug used in chemotherapy, and its various delivery and efficacy parameters encounter challenges, leading to the seeking of novel delivery methods. Researchers have conducted numerous laboratory investigations on the encapsulation of doxorubicin within nanofiber materials. This method encompasses various parameters for the production of fibers and drug loading, categorized into device-related, material-related, and study design parameters. This study employed a supervised machine-learning analysis to extract the influencing parameters of the input from quantitative data for doxorubicin-loaded electrospun nanofibers. The study also determined the significance coefficient of each parameter that influences the output results and identified the optimum points and intervals for each parameter. Our Support Vector Machine (SVM) analysis findings showed that doxorubicin-loaded electrospun nanofibers could be optimized through employing a machine learning-based investigation on the polymer solution parameters (such as density, solvent, electrical conductivity, and concentration of polymer), electrospinning parameters (such as voltage, flow rate, and distance between the needle tip and collector), and our study parameters, i.e., drug release and anticancer activity, which affect the properties of the drug-loaded nanofibers, such as the average diameter of fiber, anticancer activity, drug release percentage, and encapsulation efficiency. Our findings indicated the importance of factors like distance, polymer density, and polymer concentration, respectively, in optimizing the fabrication of drug-loaded electrospun nanofibers. The smallest diameter, highest encapsulation efficiency, highest drug release percentage, and highest anticancer activity are obtained at a molecular weight between 80 and 474 kDa and a doxorubicin concentration of at least 3.182 wt% with the polymer density in the range of 1.2–1.52 g/cm3, polymer concentration of 6.618–9 wt%, and dielectric constant of solvent more than 30. Also, the optimal distance of 14–15 cm, the flow rate of 3.5–5 mL/h, and the voltage in the range of 20–25 kV result in the highest release rate, the highest encapsulation efficiency, and the lowest average diameter for fibers. Therefore, to achieve optimal conditions, these values should be considered. These findings open up new roads for future design and production of drug-loaded polymeric nanofibers with desirable properties and performances by machine learning methods. |
| format | Article |
| id | doaj-art-069a8f04d742404cb25fcdb96f2b2228 |
| institution | DOAJ |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-069a8f04d742404cb25fcdb96f2b22282025-08-20T02:57:53ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-03-011310.3389/fbioe.2025.14931941493194A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilitiesMohammadreza Rostami0Mohammadreza Rostami1Maliheh Gharibshahian2Maliheh Gharibshahian3Mehrnaz Mostafavi4Ali Sufali5Mahsa Golmohammadi6Mohammad Reza Barati7Reza Maleki8Nima Beheshtizadeh9Nima Beheshtizadeh10Department of Nutrition, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranFood Science and Nutrition group (FSAN), Universal Scientific Education and Research Network (USERN), Tehran, IranNervous System Stem Cells Research Center, Semnan University of Medical Sciences, Semnan, IranDepartment of Tissue Engineering and Applied Cell Sciences, School of Medicine, Semnan University of Medical Sciences, Semnan, IranFaculty of Allied Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranComputational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Tehran, IranDepartment of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran, IranDepartment of Advanced Materials and New Technologies, Iranian Research Organization for Science and Technology (IROST), Tehran, IranDepartment of Chemical Technologies, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran0Department of Tissue Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran1Regenerative Medicine group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, IranThanks to the diverse advantages of electrospun nanofibers, multiple drugs have been loaded in these nanoplatforms to be delivered healthily and effectively. Doxorubicin is a drug used in chemotherapy, and its various delivery and efficacy parameters encounter challenges, leading to the seeking of novel delivery methods. Researchers have conducted numerous laboratory investigations on the encapsulation of doxorubicin within nanofiber materials. This method encompasses various parameters for the production of fibers and drug loading, categorized into device-related, material-related, and study design parameters. This study employed a supervised machine-learning analysis to extract the influencing parameters of the input from quantitative data for doxorubicin-loaded electrospun nanofibers. The study also determined the significance coefficient of each parameter that influences the output results and identified the optimum points and intervals for each parameter. Our Support Vector Machine (SVM) analysis findings showed that doxorubicin-loaded electrospun nanofibers could be optimized through employing a machine learning-based investigation on the polymer solution parameters (such as density, solvent, electrical conductivity, and concentration of polymer), electrospinning parameters (such as voltage, flow rate, and distance between the needle tip and collector), and our study parameters, i.e., drug release and anticancer activity, which affect the properties of the drug-loaded nanofibers, such as the average diameter of fiber, anticancer activity, drug release percentage, and encapsulation efficiency. Our findings indicated the importance of factors like distance, polymer density, and polymer concentration, respectively, in optimizing the fabrication of drug-loaded electrospun nanofibers. The smallest diameter, highest encapsulation efficiency, highest drug release percentage, and highest anticancer activity are obtained at a molecular weight between 80 and 474 kDa and a doxorubicin concentration of at least 3.182 wt% with the polymer density in the range of 1.2–1.52 g/cm3, polymer concentration of 6.618–9 wt%, and dielectric constant of solvent more than 30. Also, the optimal distance of 14–15 cm, the flow rate of 3.5–5 mL/h, and the voltage in the range of 20–25 kV result in the highest release rate, the highest encapsulation efficiency, and the lowest average diameter for fibers. Therefore, to achieve optimal conditions, these values should be considered. These findings open up new roads for future design and production of drug-loaded polymeric nanofibers with desirable properties and performances by machine learning methods.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1493194/fullmachine learninganticancer activityelectrospun nanofiberselectrospinningdoxorubicinartificial intelligence |
| spellingShingle | Mohammadreza Rostami Mohammadreza Rostami Maliheh Gharibshahian Maliheh Gharibshahian Mehrnaz Mostafavi Ali Sufali Mahsa Golmohammadi Mohammad Reza Barati Reza Maleki Nima Beheshtizadeh Nima Beheshtizadeh A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities Frontiers in Bioengineering and Biotechnology machine learning anticancer activity electrospun nanofibers electrospinning doxorubicin artificial intelligence |
| title | A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities |
| title_full | A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities |
| title_fullStr | A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities |
| title_full_unstemmed | A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities |
| title_short | A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities |
| title_sort | supervised machine learning analysis of doxorubicin loaded electrospun nanofibers and their anticancer activity capabilities |
| topic | machine learning anticancer activity electrospun nanofibers electrospinning doxorubicin artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1493194/full |
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