Optimization of Solid Lipid Nanoparticles and Nanostructured Lipidic Carriers as Promising Delivery for Gefitinib: Characterization and Invitro Evaluation

Title:Optimization of Solid Lipid Nanoparticles and Nanostructured Lipidic Carriers as Promising Delivery for Gefitinib: Characterization and Invitro Evaluation

VOLUME: 16 ISSUE: 2

Author(s):Akshat Shah , Asha Patel* and Abhay Dharamsi

Affiliation:Department of Pharmaceutics, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat, 391760, Department of Pharmaceutics, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat, 391760, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat, 391760

Keywords:Response surface methodology, central composite design, box benkhen design, solid lipid nanoparticles, nanostructured lipid carriers, gefitinib.

Abstract:

Background: Response surface methodology is a unique tool for the optimization of Solid lipid Nanoparticles and Nanostructured lipid carriers by developing the relationship between dependent and independent variables and exploring their interactions.

Methods: Central Composite Design and Box Benkhen Design were used to develop optimized formulations of Gefitinib [GEF] Solid Lipid Nanoparticles [SLN] and Nanostructured Lipidic Carriers [NLC]. In the design matrix, the independent variables chosen were the amount of Solid Lipid, Liquid Lipid, and Surfactant and the dependent variables were Particle Size and Poly Dispersity Index.

Results: The GEF-SLN under optimized conditions gave rise to Particle size (187.9 nm ± 1.15), PDI (0.318 ± 0.006), %EE (95.38%±0.14), Zeta Potential (-8.75 mv ±0.18) and GEF-NLC under optimized conditions gave rise to Particle size (188.6 nm± 1.12), PDI (0.395± 0.004), %EE (97.46%± 0.33), Zeta Potential (-5.72 mv± 0.04) respectively. SEM of the Freeze-dried optimized lipidic carriers showed spherical particles. The in vitro experiments proved that Gefitinib in the lipidic carriers is released gradually throughout 24 h.

Conclusion: This study showed that the response surface methodology could be efficiently applied for the modeling of GEF-SLN & GEF-NLC.

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