Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Abstract

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics.

Doxorubicin-Based Ionic Nanomedicines for Combined Chemo-Phototherapy of Cancer

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Skin Tolerability of Oleic Acid Based Nanovesicles Designed for the Improvement of Icariin and Naproxen Percutaneous Permeation

“…In fact, the average size of ca. 160 nm allows the permeation of oleosomes through the skin, while the PDI values less than or equal to 0.2 demonstrated a narrow size distribution …”

“…160 nm allows the permeation of oleosomes through the skin, 32 while the PDI values less than or equal to 0.2 demonstrated a narrow size distribution. 33 The presence of Npx or Ica, or both drugs, in oleosomes did not significantly modify the size or PDI, thus endorsing the lack of physical destabilization due to the drugs' loading. The zeta potential of oleosomes was below −50 mV for all the resulting nanovesicles due to the presence of the oleic acid carboxylic moiety on their surface, thus suggesting a proper electrostatic repulsion that stabilized oleosomes and prevented their aggregation in the dispersion medium.…”

A Study on Prediction of Size and Morphology of Ag Nanoparticles Using Machine Learning Models for Biomedical Applications

“…To measure the variance amongst the actual and expected outcomes for each observation, the mean square error, or MSE, is employed. The model's predicted values and actual values are quantified by the mean absolute error (MAE), which measures the average absolute difference between them [6,39].…”

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