Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine
The type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. In recent years, Machine Learning (ML) has shown potential for modeling complex drug delivery systems and predicting drug release dynamics with a greater degree of precision. In t...
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/25715 |
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| _version_ | 1864513442100019200 |
|---|---|
| author | Shomope, Ibrahim |
| author2 | Percival, Kelly M. Abdel-Jabbar, Nabil Husseini, Ghaleb |
| author2_role | author author author |
| author_facet | Shomope, Ibrahim Percival, Kelly M. Abdel-Jabbar, Nabil Husseini, Ghaleb |
| author_role | author |
| dc.creator.none.fl_str_mv | Shomope, Ibrahim Percival, Kelly M. Abdel-Jabbar, Nabil Husseini, Ghaleb |
| dc.date.none.fl_str_mv | 2024-11-18T08:45:50Z 2024-11-18T08:45:50Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Shomope I, Percival KM, Abdel Jabbar NM, Husseini GA. Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine. Technology in Cancer Research & Treatment. 2024;23. doi:10.1177/15330338241296725 1533-0338 https://hdl.handle.net/11073/25715 10.1177/15330338241296725 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Sage |
| dc.relation.none.fl_str_mv | https://doi.org/10.1177/15330338241296725 |
| dc.subject.none.fl_str_mv | Artificial intelligence Calcein Drug delivery Drug release Machine learning Power density Random forest Support vector machine Ultrasound |
| dc.title.none.fl_str_mv | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. In recent years, Machine Learning (ML) has shown potential for modeling complex drug delivery systems and predicting drug release dynamics with a greater degree of precision. In this regard, Random Forest (RF) and Support Vector Machine (SVM) are two ML algorithms that have been extensively applied in various biomedical and drug delivery contexts. Yet, direct comparisons of their predictive accuracy in modeling ultrasound-triggered drug release from liposomes remain limited. Existing studies predominantly focus on drug release under static conditions or with limited external stimuli rather than the dynamic, nonlinear responses observed under ultrasound exposure.Objective: This study presents a comparative analysis of RF and SVM for predicting calcein release from ultrasound-triggered, targeted liposomes under varied low-frequency ultrasound (LFUS) power densities (6.2, 9, and 10 mW/cm²). Methods: Liposomes loaded with calcein and targeted with seven different moieties (cRGD, estrone, folate, Herceptin, hyaluronic acid, lactobionic acid, and transferrin) were synthesized using the thin-film hydration method. The liposomes were characterized using Dynamic Light Scattering and Bicinchoninic Acid assays. Extensive data collection and preprocessing were performed. RF and SVM models were trained and evaluated using mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²), and the a20 index as performance metrics. Results: RF consistently outperformed SVM, achieving R² scores above 0.96 across all power densities, particularly excelling at higher power densities and indicating a strong correlation with the actual data. Conclusion: RF outperforms SVM in drug release prediction, though both show strengths and apply based on specific prediction needs. |
| format | article |
| id | aus_dfbc03b2b60f84bf34371b362f61b535 |
| identifier_str_mv | Shomope I, Percival KM, Abdel Jabbar NM, Husseini GA. Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine. Technology in Cancer Research & Treatment. 2024;23. doi:10.1177/15330338241296725 1533-0338 10.1177/15330338241296725 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25715 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Sage |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector MachineShomope, IbrahimPercival, Kelly M.Abdel-Jabbar, NabilHusseini, GhalebArtificial intelligenceCalceinDrug deliveryDrug releaseMachine learningPower densityRandom forestSupport vector machineUltrasoundThe type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. In recent years, Machine Learning (ML) has shown potential for modeling complex drug delivery systems and predicting drug release dynamics with a greater degree of precision. In this regard, Random Forest (RF) and Support Vector Machine (SVM) are two ML algorithms that have been extensively applied in various biomedical and drug delivery contexts. Yet, direct comparisons of their predictive accuracy in modeling ultrasound-triggered drug release from liposomes remain limited. Existing studies predominantly focus on drug release under static conditions or with limited external stimuli rather than the dynamic, nonlinear responses observed under ultrasound exposure.Objective: This study presents a comparative analysis of RF and SVM for predicting calcein release from ultrasound-triggered, targeted liposomes under varied low-frequency ultrasound (LFUS) power densities (6.2, 9, and 10 mW/cm²). Methods: Liposomes loaded with calcein and targeted with seven different moieties (cRGD, estrone, folate, Herceptin, hyaluronic acid, lactobionic acid, and transferrin) were synthesized using the thin-film hydration method. The liposomes were characterized using Dynamic Light Scattering and Bicinchoninic Acid assays. Extensive data collection and preprocessing were performed. RF and SVM models were trained and evaluated using mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²), and the a20 index as performance metrics. Results: RF consistently outperformed SVM, achieving R² scores above 0.96 across all power densities, particularly excelling at higher power densities and indicating a strong correlation with the actual data. Conclusion: RF outperforms SVM in drug release prediction, though both show strengths and apply based on specific prediction needs.American University of SharjahSage2024-11-18T08:45:50Z2024-11-18T08:45:50Z2024Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfShomope I, Percival KM, Abdel Jabbar NM, Husseini GA. Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine. Technology in Cancer Research & Treatment. 2024;23. doi:10.1177/153303382412967251533-0338https://hdl.handle.net/11073/2571510.1177/15330338241296725en_UShttps://doi.org/10.1177/15330338241296725oai:repository.aus.edu:11073/257152024-11-18T13:04:34Z |
| spellingShingle | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine Shomope, Ibrahim Artificial intelligence Calcein Drug delivery Drug release Machine learning Power density Random forest Support vector machine Ultrasound |
| status_str | publishedVersion |
| title | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine |
| title_full | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine |
| title_fullStr | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine |
| title_full_unstemmed | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine |
| title_short | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine |
| title_sort | Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine |
| topic | Artificial intelligence Calcein Drug delivery Drug release Machine learning Power density Random forest Support vector machine Ultrasound |
| url | https://hdl.handle.net/11073/25715 |