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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