DESIGN OF EXPERIMENTS MODEL FOR THE OPTIMIZATION OF SILK FIBROIN BASED NANOPARTICLES

Duy Toan Pham; Nuttawut Saelim; Waree Tiyaboonchai

doi:10.22159/ijap.2018v10i5.28139

Authors

Duy Toan Pham Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok 65000, Thailand http://orcid.org/0000-0002-8693-3367
Nuttawut Saelim Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok 65000, Thailand http://orcid.org/0000-0001-5085-2998
Waree Tiyaboonchai Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok 65000, Thailand The Center of Excellence for Innovation in Chemistry (PERCH-CIC), Commission on Higher Education, Ministry of Education, Bangkok, Thailand The Center of Excellence in Medical Biotechnology, Naresuan University, Phitsanulok 65000, Thailand http://orcid.org/0000-0002-8570-0465

DOI:

https://doi.org/10.22159/ijap.2018v10i5.28139

Keywords:

design of experiments, DOE, D-optimal, optimization, validation, fibroin, nanoparticles

Abstract

Objective: Silk fibroin based nanoparticles have been utilized extensively in biomedical fields. Amongst many preparation methods, desolvation is a favorable one. However, this method yields nanoparticles with unpredictable parameters. Thus, this investigation aimed to systematically study the effects of three independent variables including fibroin concentration (% w/v, X₁), volume ratio between fibroin solution and ethanol (X₂), formulation time (h, X₃) on three main responses, particle size (nm, Y₁), polydispersity index (Y₂), zeta potential (mV, Y₃).

Methods: Fibroin was extracted from degummed Bombyx mori silk. The fibroin calibration curve was constructed by UV-spectrophotometer at 276 nm. The nanoparticles were prepared using the desolvation method of aqueous fibroin solution in ethanol. Design Expert^Â® software was used to design the model. The mean particle size, polydispersity index and zeta potential were determined using ZetaPALS^Â®analyzer.

Results: By using D-optimal design with the quadratic model, the results showed that all X₁, X₂, and X₃ variables had significant impacts on the fibroin nanoparticles characteristics Y₁, Y₂, and Y₃. The generated model was also validated and demonstrated to be solid and reliable. The obtained optimal nanoparticles possessed Y₁ of 238.1 nm, Y₂ of 0.12, and Y₃of-21.78 mV, which were in agreement with the predicted values, 224.8 nm, 0.13 and-19.31 mV, respectively. The optimal actual and theoretical particle characteristics were correlated with a desirable value of R² = 0.8770.

Conclusion: The D-optimal design proved its effectiveness in the prediction and optimization of fibroin nanoparticle properties.

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