期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2017
卷号：114
期号：28
页码：7210-7215
DOI：10.1073/pnas.1704226114
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：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 k B T , where T is the absolute temperature and k B 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.
关键词：multivalency ; Monte Carlo simulations ; drug delivery ; endocytosis ; statistical mechanics
