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

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...

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Autores principales: Hoseini, Benyamin, Jaafari, Mahmoud Reza, Golabpour, Amin, Momtazi-Borojeni, Amir Abbas, Karimi, Maryam, Eslami, Saeid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590434/
https://www.ncbi.nlm.nih.gov/pubmed/37865639
http://dx.doi.org/10.1038/s41598-023-43689-4
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author Hoseini, Benyamin
Jaafari, Mahmoud Reza
Golabpour, Amin
Momtazi-Borojeni, Amir Abbas
Karimi, Maryam
Eslami, Saeid
author_facet Hoseini, Benyamin
Jaafari, Mahmoud Reza
Golabpour, Amin
Momtazi-Borojeni, Amir Abbas
Karimi, Maryam
Eslami, Saeid
author_sort Hoseini, Benyamin
collection PubMed
description 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.
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spelling pubmed-105904342023-10-23 Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles Hoseini, Benyamin Jaafari, Mahmoud Reza Golabpour, Amin Momtazi-Borojeni, Amir Abbas Karimi, Maryam Eslami, Saeid Sci Rep Article 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. Nature Publishing Group UK 2023-10-21 /pmc/articles/PMC10590434/ /pubmed/37865639 http://dx.doi.org/10.1038/s41598-023-43689-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Hoseini, Benyamin
Jaafari, Mahmoud Reza
Golabpour, Amin
Momtazi-Borojeni, Amir Abbas
Karimi, Maryam
Eslami, Saeid
Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles
title Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles
title_full Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles
title_fullStr Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles
title_full_unstemmed Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles
title_short Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles
title_sort application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590434/
https://www.ncbi.nlm.nih.gov/pubmed/37865639
http://dx.doi.org/10.1038/s41598-023-43689-4
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