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Application of Response Surface Methodology for the Technological Improvement of Solid Lipid Nanoparticles

机译:响应面方法在固体脂质纳米粒子的技术改进中的应用

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Solid lipid nanoparticles (SLN) are colloidal particles consisting of a matrix composed of solid (at room and body temperatures) lipids dispersed in aqueous emulsifier solution. During manufacture, their physicochemical properties may be affected by several formulation parameters, such as type and concentration of lipid, proportion of emulsifiers and amount of solvent. Thus, the aim of this work was to study the influence of these variables on the preparation of SLN. A D- optimal Response Surface Methodology design was used to establish a mathematical model for the optimization of SLN. A total of 30 SLN formulations were prepared using the ultrasound method, and then characterized on the basis of their physicochemical properties, including particle size, polydispersity index (PI) and Zeta Potential (s). Particle sizes ranged between 107 and 240 nm. All SLN formulations showed negative s and PI values below 0.28. Prediction of the optimal conditions was performed using the desirability function targeting the reduction of all responses. The optimized SLN formulation showed similar theoretical and experimental values, confirming the sturdiness and predictive ability of the mathematical model for SLN optimization.
机译:固体脂质纳米颗粒(SLN)是由由分散在水性乳化剂溶液中的固体(室温和体温)脂质组成的基质组成的胶体颗粒。在制造过程中,它们的物理化学性质可能受几种配方参数的影响,例如脂质的类型和浓度,乳化剂比例和溶剂的量。因此,这项工作的目的是研究这些变量对SLN的制备的影响。 D-最佳响应面方法设计用于建立SLN优化的数学模型。使用超声方法制备总共30个SLN制剂,然后基于它们的物理化学性质,包括粒度,多分散指数(PI)和Zeta电位。粒度范围在107到240nm之间。所有SLN配方均显示负数S和PI值低于0.28。使用靶向所有响应减少的期望函数进行最佳条件的预测。优化的SLN配方显示了类似的理论和实验值,确认SLN优化数学模型的稳定性和预测能力。

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