Comparison of statistical design of experiments methods for optimizing cationic solid lipid nanoparticles in gene delivery

Abstract

Aim: To optimize the production process of cationic solid lipid nanoparticles (SLNs) using statistical experimental design methods and response surface methodology, and to develop an effective carrier system capable of overcoming biological barriers.

Methods: Screening and optimization of formulations were performed using Central Composite design, Box–Behnken design, and Taguchi design. Subsequently, the physicochemical properties of SLNs were characterized. The optimal SLN formulation was evaluated in terms of plasmid DNA complexation capacity, transfection efficiency, nuclease protection potential, cytotoxicity profile, and storage stability.

Results: Response surface analyses demonstrated that the Box–Behnken model was significant for particle size and zeta potential. The interactions among the independent variables were determined, and optimization was performed using second-order polynomial equations. The optimal formulations were found to be nanosized, positively charged, and exhibited low polydispersity index (PDI) values, indicating a monodisperse structure. The cytotoxicity of the optimal formulation was found acceptable for further studies.

Conclusion: This study demonstrated that factorial design and response surface methodology are effective approaches for developing cationic solid nanoparticle-based gene delivery systems. The optimized formulations obtained herein are considered promising candidates for safe, stable, and efficient gene delivery.