Engineering lipid-coated silica nanoparticles as versatile adjuvant delivery platform for the TLR4 agonist MPLA

Area of Science:

• Nanotechnology
• Materials Science
• Immunology

Background:

• Lipid-coated silica nanoparticles (LC-SiNPs and LC-MSNs) offer a modular platform for delivering Toll-like receptor (TLR) agonists.
• Optimizing the incorporation of monophosphoryl lipid A (MPLA), a TLR4 agonist, into the lipid bilayers of these nanoparticles is crucial for enhancing their efficacy.

Purpose of the Study:

• To systematically optimize the formulation of lipid-coated silica nanoparticles for enhanced delivery of the TLR4 agonist MPLA.
• To investigate the influence of cholesterol and anionic lipid (DPPG) content on nanoparticle properties and TLR4 activation.
• To compare the performance of Bayesian optimization (BO) against traditional formulation methods.

Main Methods:

• Development of lipid-coated solid and mesoporous silica nanoparticles (LC-SiNPs and LC-MSNs).
• Systematic variation of cholesterol (15-45%) and DPPG (10-30%) content to optimize MPLA incorporation.
• Application of Bayesian optimization (BO) for refining nanoparticle formulations.
• Evaluation of immunostimulatory activity, membrane fluidity, and particle-cell interactions using macrophages.

Main Results:

• Solid-core MPLA-SiNPs with 45% cholesterol and 20% DPPG showed enhanced immunostimulatory activity.
• Mesoporous MPLA-MSNs required 30% DPPG for comparable activation.
• BO-optimized MPLA-SiNPs achieved a significantly lower EC50 (87 ng/mL) compared to classical methods and unformulated MPLA.
• Macrophages efficiently internalized and processed the optimized nanoparticles.

Conclusions:

• Rational formulation design is critical for optimizing the performance of lipid-coated silica nanoparticles.
• Bayesian optimization significantly enhances the immunostimulatory potential of MPLA-loaded nanoparticles.
• These nanoparticles represent a promising modular platform for vaccine delivery, adaptable for other TLR agonists and antigens.