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ORIGINAL ARTICLE
Year : 2015  |  Volume : 6  |  Issue : 3  |  Page : 88-96

Application of quality by design approach to optimize process and formulation parameters of rizatriptan loaded chitosan nanoparticles


1 Department of Pharmacy, JJTU, Rajasthan, India
2 Department of Quality Assurance, Padm. Dr. D. Y. Patil Institute of Pharmaceutical Science and Research, Pimpri, Pune, Maharashtra, India

Correspondence Address:
Ajinath Eknath Shirsat
Department of Pharmacy, JJT University, Jhunjhunu, Rajasthan - 333 001
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2231-4040.157983

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The purpose of present study was to optimize rizatriptan (RZT) chitosan (CS) nanoparticles using ionic gelation method by application of quality by design (QbD) approach. Based on risk assessment, effect of three variables, that is CS %, tripolyphosphate % and stirring speed were studied on critical quality attributes (CQAs); particle size and entrapment efficiency. Central composite design (CCD) was implemented for design of experimentation with 20 runs. RZT CS nanoparticles were characterized for particle size, polydispersity index, entrapment efficiency, in-vitro release study, differential scanning calorimetric, X-ray diffraction, scanning electron microscopy (SEM). Based on QbD approach, design space (DS) was optimized with a combination of selected variables with entrapment efficiency > 50% w/w and a particle size between 400 and 600 nm. Validation of model was performed with 3 representative formulations from DS for which standard error of − 0.70-3.29 was observed between experimental and predicted values. In-vitro drug release followed initial burst release 20.26 ± 2.34% in 3-4 h with sustained drug release of 98.43 ± 2.45% in 60 h. Lower magnitude of standard error for CQAs confirms the validation of selected CCD model for optimization of RZT CS nanoparticles. In-vitro drug release followed dual mechanism via, diffusion and polymer erosion. RZT CS nanoparticles were prepared successfully using QbD approach with the understanding of the high risk process and formulation parameters involved and optimized DS with a multifactorial combination of critical parameters to obtain predetermined RZT loaded CS nanoparticle specifications.


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