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Optimal multivalent targeting of membranes with many distinct receptors

Accepted version
Peer-reviewed

Type

Article

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Abstract

Cells can often be recognized by the concentrations of receptors expressed on their surface. For better (targeted drug treatment) or worse (targeted infection by pathogens), it is clearly important to be able to target cells selectively. A good targeting strategy would result in strong binding to cells with the desired receptor profile and barely binding to other cells. Using a simple model, we formulate optimal design rules for multivalent particles that allow them to distinguish target cells based on their receptor profile. We find the following: (i) It is not a good idea to aim for very strong binding between the individual ligands on the guest (delivery vehicle) and the receptors on the host (cell). Rather, one should exploit multivalency: High sensitivity to the receptor density on the host can be achieved by coating the guest with many ligands that bind only weakly to the receptors on the cell surface. (ii) The concentration profile of the ligands on the guest should closely match the composition of the cognate membrane receptors on the target surface. And (iii) irrespective of all details, the effective strength of the ligand–receptor interaction should be of the order of the thermal energy kBT, where T is the absolute temperature and kB is Boltzmann’s constant. We present simulations that support the theoretical predictions. We speculate that, using the above design rules, it should be possible to achieve targeted drug delivery with a greatly reduced incidence of side effects.

Description

Keywords

multivalency, Monte Carlo simulations, drug delivery, endocytosis, statistical mechanics

Journal Title

Proceedings of the National Academy of Sciences of the United States of America

Conference Name

Journal ISSN

0027-8424
1091-6490

Volume Title

114

Publisher

National Academy of Sciences
Sponsorship
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (674979)
European Research Council (227758)