A quantitative view of strategies to engineer cell-selective ligand binding

Abstract

A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.

Mixed IgG Fc immune complexes exhibit blended binding profiles and refine FcR affinity estimates

“…1c). This trend is expected since, although complexes of both valencies can bind densely with high-affinity units, only high-valent complexes compensate for low affinity through avidity 4 . Therefore, while high-affinity complexes result in greater binding, low-affinity complexes have greater intervalency binding ratios.…”

“…1c). This trend is expected since, although complexes of both valencies can bind densely with highaffinity units, only high-valent complexes compensate for low affinity through avidity 4 .…”

“…SPR accurately assesses protein-protein binding kinetics, but many antibody-Fc receptor interactions are weak enough to fall outside the assay’s quantitative range when assessed in monovalent form. Clustering leads to avidity effects, wherein even weak interactions can cooperatively lead to strong binding 4 . Indeed, avidity is widely employed in natural and engineered systems to promote binding through low-affinity interactions 5 .…”

Multivalent, asymmetric IL-2–Fc fusions show enhanced selectivity for regulatory T cells

“…Our previous work shows that specificity is conferred by markers expressed at a high ratio between target and off-target cells (Fig. 2J) (32). As measures of difference, we calculated the Wasserstein distance and Kullback-Leibler divergence of each surface marker abundance and expression between T reg populations and all off-target PBMCs.…”

Hsp70‐Targeting and Size‐Tunable Nanoparticles Combine with PD‐1 Checkpoint Blockade to Treat Glioma

“…[16][17][18][19] The over-expressed receptors in tumor cells could provide targets for nanoparticles, but off-target effects frequently occur due to the expression on the normal cells. [20][21][22][23] The heat shock protein 70 (Hsp70) serves as a molecular chaperone to support the correct folding of peptides, repair damaged protein, and protect cells from apoptosis. [24][25][26] Hsp70 is usually located in the cytoplasm of normal cells, but it would be highly expressed on the membrane of malignantly transformed cells under stress such as hypoxia, glucose deprivation, and cytotoxicity pressure.…”