Open AccessCUSTOM DESIGN PERSPECTIVE IN THE PROCESS PARAMETER OPTIMIZATION OF NANO LIPID CARRIERS
Author(s) -
M Ashwini,
Preethi Sudheer,
Bharani S. Sogali
Publication year - 2020
Publication title -
international journal of applied pharmaceutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.238
H-Index - 15
ISSN - 0975-7058
DOI - 10.22159/ijap.2020v12i6.39565
Subject(s) - homogenization (climate) , dispersity , materials science , sonication , particle size , response surface methodology , dispersion (optics) , composite material , chromatography , chemical engineering , chemistry , physics , biodiversity , ecology , optics , polymer chemistry , engineering , biology
Objective: Nanostructured lipid carrier is an emerging lipid-derived delivery system that is rapidly gaining popularity due to the simplicity of the manufacturing technique. The primary task in formulating nanoparticles is to optimize the parameters that are involved in the process. The rationale behind this study is to optimize the process parameters for the preparation of nanostructured lipid carriers.
Methods: The optimization of selected techniques hot homogenization with ultra-sonication and melt dispersion technique, was carried out via statistical analysis software JMP version 13 Pro using custom design approaches. Sonication time, homogenization speed, stirring rate, and cooling temperature were selected as factors for hot homogenization. Stirring speed, stirring time, and dilution volume were the factors deliberated for melt dispersion. The impact of these factors on the responses, particle size, and polydispersity index were studied. The nanoparticles were prepared according to the ten experimental runs generated by the design. Based on the responses, the design space and optimum framework were selected.
Results: The prediction profiler indicated maximum desirability at 81% and 80% for hot homogenization and melt dispersion respectively. The actual versus predicted plot of particle size indicated a regression coefficient (R2) of 0.98, and a p-value of 0.0001 for hot homogenization and for melt dispersion the corresponding values were 0.95 and 0.0003. For the response polydispersity index, these values were 0.92 and 0.0052 for hot homogenization and 0.90 and 0.0024 for melt dispersion.
Conclusion: The endorsing results indicated the authenticity of the model in predicting the significant processing parameters for NLC.