کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1985669 | 1540229 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Methotrexate loading in chitosan nanoparticles at a novel pH: Response surface modeling, optimization and characterization
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
زیست شیمی
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چکیده انگلیسی
The aim of this study was to assess the feasibility of employing a novel but critical formulation pH (6.2) to encapsulate an anionic model drug (methotrexate, MTX) into chitosan(Cs)-tripolyphosphate nanoparticles(NPs). A response surface methodology using a three-level full factorial design was applied studying the effects of two independent variables namely; Cs concentration and MTX concentration. The responses investigated were the entrapment efficiency (EE%), mean hydrodynamic particle size (PS), polydispersity index (PDI) and zeta potential (ZP). In order to simultaneously optimize the series of models obtained, the desirability function approach was applied with a goal to produce high percent of MTX encapsulated into highly charged Cs-TPP NPs of homogenous optimum PS. MTX-loaded CsNPs were successfully prepared at the novel pH applied. The suggested significant models were found quadratic for EE, PS and ZP responses, while 2-factor interaction model for PDI. The optimization overlay graph showed that the maximum global desirability, DÂ =Â 0.856, was reached when the conditions were set at high Cs and MTX concentration. Thus, the use of such optimized conditions, at this novel pH, achieved a maximum drug EE% (73.38%) into NPs characterized by optimum PS (232.6Â nm), small PDI value (0.195) and highly surface charged (+18.4Â mV).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Biological Macromolecules - Volume 91, October 2016, Pages 630-639
Journal: International Journal of Biological Macromolecules - Volume 91, October 2016, Pages 630-639
نویسندگان
Rania A. Hashad, Rania A.H. Ishak, Ahmed S. Geneidi, Samar Mansour,