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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-11-01
|
| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338241296725 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850191240534949888 |
|---|---|
| author | Ibrahim Shomope MS Kelly M. Percival BS Nabil M. Abdel Jabbar PhD Ghaleb A. Husseini PhD |
| author_facet | Ibrahim Shomope MS Kelly M. Percival BS Nabil M. Abdel Jabbar PhD Ghaleb A. Husseini PhD |
| author_sort | Ibrahim Shomope MS |
| collection | DOAJ |
| 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/cm 2 ). 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 R 2 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 | doaj-art-36f42de8d4cd424e89aa33f0d584cbb3 |
| institution | OA Journals |
| issn | 1533-0338 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj-art-36f42de8d4cd424e89aa33f0d584cbb32025-08-20T02:14:59ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382024-11-012310.1177/15330338241296725Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector MachineIbrahim Shomope MS0Kelly M. Percival BS1Nabil M. Abdel Jabbar PhD2Ghaleb A. Husseini PhD3 Department of Chemical and Biological Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates Department of Chemical and Biological Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates Department of Chemical and Biological Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates Materials Science and Engineering Program, College of Arts and Sciences, American University of Sharjah, Sharjah PO Box 26666, United Arab EmiratesThe 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/cm 2 ). 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 R 2 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.https://doi.org/10.1177/15330338241296725 |
| spellingShingle | Ibrahim Shomope MS Kelly M. Percival BS Nabil M. Abdel Jabbar PhD Ghaleb A. Husseini PhD Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine Technology in Cancer Research & Treatment |
| 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 |
| url | https://doi.org/10.1177/15330338241296725 |
| work_keys_str_mv | AT ibrahimshomopems predictingcalceinreleasefromultrasoundtargetedliposomesacomparativeanalysisofrandomforestandsupportvectormachine AT kellympercivalbs predictingcalceinreleasefromultrasoundtargetedliposomesacomparativeanalysisofrandomforestandsupportvectormachine AT nabilmabdeljabbarphd predictingcalceinreleasefromultrasoundtargetedliposomesacomparativeanalysisofrandomforestandsupportvectormachine AT ghalebahusseiniphd predictingcalceinreleasefromultrasoundtargetedliposomesacomparativeanalysisofrandomforestandsupportvectormachine |