Preparation and optimization of PIT solid lipid nanoparticles via statistical factorial design

Area of Science:

• Nanotechnology
• Biomedical Engineering
• Pharmaceutical Sciences

Background:

• Solid lipid nanoparticles (SLN) are versatile drug delivery systems.
• Cationic SLN are being explored for gene therapy applications.
• Optimization of SLN formulation is crucial for efficacy and safety.

Purpose of the Study:

• To prepare and characterize cationic solid lipid nanoparticles (SLN) for gene therapy.
• To statistically optimize SLN formulation using a factorial design.
• To evaluate the physico-chemical properties and cytotoxicity of optimized SLN.

Main Methods:

• Preparation of SLN using the Phase Inversion Temperature (PIT) method.
• Screening of cationic lipids (CTAB, DDAB, DOTAP) for optimal SLN properties.
• Application of a 2(2) full factorial experimental design to optimize DOTAP amount and lipid matrix concentration.
• Physico-chemical characterization including particle size and zeta potential measurements.
• In vitro cytotoxicity assessment on DU-145 cancer cells and rat astrocytes.

Main Results:

• DOTAP was selected as the optimal cationic lipid, yielding SLN with higher zeta potential and smaller particle size.
• Factorial design identified significant influences of DOTAP amount and lipid concentration on particle size and zeta potential.
• Optimized SLN (7% cutina CP, 1% DOTAP) exhibited a particle size of 462.9 nm and zeta potential of 50.8 mV.
• In vitro studies indicated minimal cytotoxicity of the optimized SLN on both cancer and astrocyte cell lines.

Conclusions:

• The PIT method combined with factorial design is effective for optimizing cationic SLN.
• DOTAP-containing SLN demonstrate favorable physico-chemical characteristics for gene delivery.
• Optimized SLN show low cytotoxicity, supporting their potential for safe and effective gene therapy applications.