Valency-affinity mapping of multivalent liposomes for tunable target cell discrimination.

Area of Science:

• Biotechnology
• Nanomedicine
• Molecular Biology

Background:

• Multivalency in ligand-functionalized nanoparticles enhances target cell binding but risks off-target interactions.
• Optimizing nanoparticle binding requires balancing ligand affinity and nanoparticle valency for specific cell targets.

Purpose of the Study:

• To investigate how ligand affinity and liposome valency influence nanoparticle binding performance.
• To develop and validate a Binding Performance Index (BPI) for assessing nanoparticle targeting efficacy.
• To demonstrate the ability to rationally tune nanoparticle binding properties for therapeutic applications.

Main Methods:

• Designed ankyrin repeat proteins (DARPins) with varying HER2-binding affinities were conjugated to PEGylated liposomes at different concentrations.
• Binding performance was evaluated using mixed cell suspensions (HER2-high SKBR3 and HER2-low T47D) and HEK293T cells with heterogeneous HER2-EGFP expression.
• A HER2 expression threshold was varied to determine optimal binding performance and analyze BPI sensitivity.

Main Results:

• High-valency liposomes with high-affinity DARPins achieved the highest BPI (>0.8) in mixed cell suspensions.
• Continuous binding response curves revealed maximum BPI values (>0.85) and optimal HER2 thresholds (HER2_OPT).
• BPI proved more sensitive than traditional selectivity measures to off-target binding and on-target binding efficiency.
• DARPin valency and affinity were shown to be tunable parameters for adjusting HER2_OPT.

Conclusions:

• The Binding Performance Index (BPI) provides a sensitive metric for evaluating nanoparticle targeting efficacy.
• Ligand affinity and nanoparticle valency are critical, tunable parameters for optimizing targeted delivery.
• The developed approach facilitates rapid optimization of nanoparticle ligand properties for specific therapeutic targets.