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.
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