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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. [ABSTRACT FROM AUTHOR]

Ultrasonic velocities have been measured for different binary mixtures of common choline chloride-based deep eutectic solvents (DESs), namely, reline, glyceline and ethaline with water, in the range of temperature: 303.15-353.15 K. The experimental data measurements with different mole fractions of DES-water solutions were utilised in fitting four different models for speed of sound. These models correlate the speed of sound with some physical properties such as molar volumes, molar components sound velocity, densities, molecular weights, collision factors etc. A close match was obtained with these models with an average relative error of less than 4% for all data points used. It was observed that the ultrasonic velocity increases with the concentration of DES solvent and decreases with temperature. Moreover, this study roughly indicated that the intermolecular interactions in reline and ethaline aqueous mixtures exist in the form of disruption of dipole-dipole interactions (that varies considerably as a function of DES mixture composition and solution temperature). On the other hand, the interactions on the molecular level in glyceline aqueous solution are mainly due to dipole-dipole intermolecular forces. [ABSTRACT FROM AUTHOR]