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Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size
[Image: see text] Introduction: Optimization of filgrastim (G-CSF) (granulocyte colony stimulating factor) liposomes formulation prepared by the method of film hydration was the aim of this research. Methods: To study the independent variables effects in the development of filgrastim (G-CSF) liposom...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Tabriz University of Medical Sciences
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5326667/ https://www.ncbi.nlm.nih.gov/pubmed/28265535 http://dx.doi.org/10.15171/bi.2016.26 |
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author | Kiafar, Farhad Siahi Shadbad, Mohammad Reza Valizadeh, Hadi |
author_facet | Kiafar, Farhad Siahi Shadbad, Mohammad Reza Valizadeh, Hadi |
author_sort | Kiafar, Farhad |
collection | PubMed |
description | [Image: see text] Introduction: Optimization of filgrastim (G-CSF) (granulocyte colony stimulating factor) liposomes formulation prepared by the method of film hydration was the aim of this research. Methods: To study the independent variables effects in the development of filgrastim (G-CSF) liposomes, method of factorial design was applied. The molar ratio of dipalmitoyl phophatidylcholine (DPPC) per cholesterol (Chol.) and hydration time were chosen as two independent factors. The dependent variables were encapsulation efficiency percent (EE %) and particle size (PS). Ultrafiltration method was applied for separation of un-encapsulated protein. RP-HPLC method was employed for analysis of G-CSF. Results: Application of response surface methodology (RSM) in formulation of filgrastim liposomes and the obtained results for responses including particle size and EE % showed that the main effective independent variable was DPPC/Chol molar ratio. Different impacts of influencing parameters including interaction and individual effects were checked employing a mathematical method for obtaining desired liposomes. Optimum liposomal formulations were established using this method for enhancing their characteristics. Average percent errors (APEs) were 3.86% and 3.27% for predicting EE % and PS, respectively which reflect high model ability in this regard. Conclusion: It is concluded that observed and predicted values regarding PS and EE % were consistent and this model is efficient enough in prediction of the mentioned characteristics while preparing filgrastim (G-CSF) liposomes. |
format | Online Article Text |
id | pubmed-5326667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Tabriz University of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-53266672017-03-06 Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size Kiafar, Farhad Siahi Shadbad, Mohammad Reza Valizadeh, Hadi Bioimpacts Original Research [Image: see text] Introduction: Optimization of filgrastim (G-CSF) (granulocyte colony stimulating factor) liposomes formulation prepared by the method of film hydration was the aim of this research. Methods: To study the independent variables effects in the development of filgrastim (G-CSF) liposomes, method of factorial design was applied. The molar ratio of dipalmitoyl phophatidylcholine (DPPC) per cholesterol (Chol.) and hydration time were chosen as two independent factors. The dependent variables were encapsulation efficiency percent (EE %) and particle size (PS). Ultrafiltration method was applied for separation of un-encapsulated protein. RP-HPLC method was employed for analysis of G-CSF. Results: Application of response surface methodology (RSM) in formulation of filgrastim liposomes and the obtained results for responses including particle size and EE % showed that the main effective independent variable was DPPC/Chol molar ratio. Different impacts of influencing parameters including interaction and individual effects were checked employing a mathematical method for obtaining desired liposomes. Optimum liposomal formulations were established using this method for enhancing their characteristics. Average percent errors (APEs) were 3.86% and 3.27% for predicting EE % and PS, respectively which reflect high model ability in this regard. Conclusion: It is concluded that observed and predicted values regarding PS and EE % were consistent and this model is efficient enough in prediction of the mentioned characteristics while preparing filgrastim (G-CSF) liposomes. Tabriz University of Medical Sciences 2016 2016-12-29 /pmc/articles/PMC5326667/ /pubmed/28265535 http://dx.doi.org/10.15171/bi.2016.26 Text en © 2016 The Author(s) This work is published by BioImpacts as an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/). Non-commercial uses of the work are permitted, provided the original work is properly cited. |
spellingShingle | Original Research Kiafar, Farhad Siahi Shadbad, Mohammad Reza Valizadeh, Hadi Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size |
title | Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size |
title_full | Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size |
title_fullStr | Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size |
title_full_unstemmed | Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size |
title_short | Filgrastim (G-CSF) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size |
title_sort | filgrastim (g-csf) loaded liposomes: mathematical modeling and optimization of encapsulation efficiency and particle size |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5326667/ https://www.ncbi.nlm.nih.gov/pubmed/28265535 http://dx.doi.org/10.15171/bi.2016.26 |
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