2021•Current Drug TherapyRequires access

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

Akshat Shah, Asha Patel, Abhay T. Dharamsi

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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 was 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 dependent variables were Particle Size and Poly Dispersity Index. Result: 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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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 was 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 dependent variables were Particle Size and Poly Dispersity Index. Result: 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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Available 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 was 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 dependent variables were Particle Size and Poly Dispersity Index. Result: 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.

Key concepts: Solid lipid nanoparticle, Zeta potential, Dispersity, Particle size, Pulmonary surfactant, Materials science, Nanoparticle, Box–Behnken design

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