Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine - Ibrahim Shomope MS; Kelly M. Percival BS; Nabil M. Abdel Jabbar PhD; Ghaleb A. Husseini PhD (Technology in Cancer Research & Treatment)
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.