Lipid Composition Determines Hybrid Nanoparticle Selectivity: Beyond Membrane Mimicry in Cancer Targeting.

Area of Science:

• Biophysics
• Nanotechnology
• Materials Science

Background:

• Lipid-functionalized hybrid nanoparticles (hNPs) offer potential for targeted cancer therapy due to adjustable membrane interactions.
• Key determinants and mechanisms governing selective hNP-membrane recognition remain largely unexplored.

Purpose of the Study:

• To investigate how individual lipid species influence hNP interactions with different membrane compositions.
• To determine if mimicking target membrane composition enhances hNP recognition and selectivity for cancer delivery.

Main Methods:

• Coarse-grained molecular dynamics simulations.
• Umbrella sampling simulations.
• Lipid-specific decomposition analyses across 40 membrane-hNP systems.
• Comparison of hNP interactions with mammalian-like and tumor-like lipid bilayers.

Main Results:

• Cholesterol significantly stabilizes hNPs and drives membrane remodeling in both bilayer types.
• Zwitterionic lipids exhibit weaker interactions, limited insertion, and entropic penalties.
• Tumor-like membranes enhance cholesterol's role, leading to deeper hNP insertion and reorganization.
• Composition-matched hNPs did not show preferential binding; cholesterol-enriched hNPs demonstrated increased affinity and selectivity.

Conclusions:

• Lipid-composition mimicry is not an effective design principle for achieving selective hNP-cancer cell recognition.
• Cholesterol enrichment in hNPs enhances targeting affinity and selectivity for tumor membranes.
• Findings provide a mechanistic foundation for designing hNPs based on lipid complementarity rather than mimicry.