Methotrexate loading in chitosan nanoparticles at a novel pH: Response surface modeling, optimization and characterization

Hashad, Rania A.; Aziz Ishak, Rania; Geneidi, Ahmed S.; Mansour, Samar;

Abstract


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, D = 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 (232.6 nm), small PDI value (0.195) and highly surface charged (+18.4 mV).


Other data

Title Methotrexate loading in chitosan nanoparticles at a novel pH: Response surface modeling, optimization and characterization
Authors Hashad, Rania A.; Aziz Ishak, Rania ; Geneidi, Ahmed S.; Mansour, Samar
Keywords Chitosan;Methotrexate;Nanoparticles;Optimization;PH value;Response surface methodology
Issue Date 1-Oct-2016
Publisher ELSEVIER SCIENCE BV
Journal International Journal of Biological Macromolecules 
Volume 91
Start page 630
End page 639
ISSN 01418130
DOI 10.1016/j.ijbiomac.2016.06.014
PubMed ID 27283234
Scopus ID 2-s2.0-84974794914
Web of science ID WOS:000382339200073

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Citations 10 in pubmed
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