Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of tunable parameters makes it difficult to identify optimal design "sweet spots" without guiding principles. Here, we combine superselectivity theory with soft matter physics into a unified theoretical framework and we prove its validity using blood brain barrier cells as target. We apply our approach to polymersomes functionalized with targeting ligands to identify the most selective combination of parameters in terms of particle size, brush length and density, as well as tether length, affinity, and ligand number. We show that the combination of multivalent interactions into multiplexed systems enable interaction as a function of the cell phenotype, that is, which receptors are expressed. We thus propose the design of a "bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies.

中文翻译：

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精密纳米医学的设计。


严格控制纳米颗粒与生物系统相互作用的选择性对于靶向治疗的开发至关重要。然而，大量的可调参数使得在没有指导原则的情况下很难确定最佳设计“最佳点”。在这里，我们将超选择性理论与软物质物理结合成一个统一的理论框架，并以血脑屏障细胞为目标证明了其有效性。我们将我们的方法应用于用靶向配体功能化的聚合物囊泡，以确定在粒径、刷长度和密度以及系链长度、亲和力和配体数量方面最具选择性的参数组合。我们证明，将多价相互作用组合到多重系统中，可以使相互作用成为细胞表型（即表达哪些受体）的函数。因此，我们建议设计一种“条形码”靶向方法，该方法可以针对独特的细胞群量身定制，从而实现个性化治疗。