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Quantitative assessment of binding affinities for nanoparticles targeted to vulnerable plaque.

ABSTRACT: Recent successes in targeted immune and cell-based therapies have driven new directions for pharmaceutical research. With the rise of these new therapies there is an unfilled need for companion diagnostics to assess patients' potential for therapeutic response. Targeted nanomaterials have been widely investigated to fill this niche; however, in contrast to small molecule or peptide-based targeted agents, binding affinities are not reported for nanomaterials, and to date there has been no standard, quantitative measure for the interaction of targeted nanoparticle agents with their targets. Without a standard measure, accurate comparisons between systems and optimization of targeting behavior are challenging. Here, we demonstrate a method for quantitative assessment of the binding affinity for targeted nanoparticles to cell surface receptors in living systems and apply it to optimize the development of a novel targeted nanoprobe for imaging vulnerable atherosclerotic plaques. In this work, we developed sulfated dextran-coated iron oxide nanoparticles with specific targeting to macrophages, a cell type whose density strongly correlates with plaque vulnerability. Detailed quantitative, in vitro characterizations of (111)In(3+) radiolabeled probes show high-affinity binding to the macrophage scavenger receptor A (SR-A). Cell uptake studies illustrate that higher surface sulfation levels result in much higher uptake efficiency by macrophages. We use a modified Scatchard analysis to quantitatively describe nanoparticle binding to targeted receptors. This characterization represents a potential new standard metric for targeted nanomaterials.

SUBMITTER: Tang T

PROVIDER: S-EPMC6066368 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Quantitative assessment of binding affinities for nanoparticles targeted to vulnerable plaque.

Tang Tang T Tu Chuqiao C Chow Sarah Y SY Leung Kevin H KH Du Siyi S Louie Angelique Y AY

Bioconjugate chemistry 20150527 6

Recent successes in targeted immune and cell-based therapies have driven new directions for pharmaceutical research. With the rise of these new therapies there is an unfilled need for companion diagnostics to assess patients' potential for therapeutic response. Targeted nanomaterials have been widely investigated to fill this niche; however, in contrast to small molecule or peptide-based targeted agents, binding affinities are not reported for nanomaterials, and to date there has been no standar ...[more]

PMID: 25970303

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Project description:A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (??G) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (??G < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

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Quantitative assessment of binding affinities for nanoparticles targeted to vulnerable plaque.

Publications

Quantitative assessment of binding affinities for nanoparticles targeted to vulnerable plaque.

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