![]() ![]() A less studied area in high-throughput (HT) polymeric nanoparticle methodology ( 13– 16) is the facile incorporation of diverse chemical groups into polymers with defined architectures. Although combinatorial methods have driven small molecule drug discovery ( 6), they have been less explored for the discovery of polymers ( 7– 12) in part due to these inherent challenges. A key challenge in nanoparticle development is the need to perform distinct synthesis, characterization, and formulation steps before performance can be evaluated. However, it is difficult to predict the optimal chemical and physical properties for delivery of a specific drug or biomolecule. The ability to tune the chemical nature of the core and shell may afford utility of these materials in additional applications.Ĭhemically diverse nanoparticles form the basis of a number of emerging applications in materials science ( 1– 3), including drug and gene delivery vehicles ( 4, 5). Covalent cholesterol attachment allowed for transfection in vivo to liver hepatocytes in mice. Cross-linkers optimally possessed tertiary dimethylamine or piperazine groups and potential buffering capacity. Analysis revealed structure-function relationships and beneficial design guidelines, including a higher reactive block weight fraction, stoichiometric equivalence between epoxides and amines, and thin hydrophilic shells. Using robotic automation, epoxide-functionalized block polymers were combinatorially cross-linked with a diverse library of amines, followed by measurement of molecular weight, diameter, RNA complexation, cellular internalization, and in vitro siRNA and pDNA delivery. This enabled elucidation of complexation, internalization, and delivery trends that could only be learned through evaluation of a large library. Analogous to an assembly line, we employed a modular design for the high-throughput study of 1,536 structurally distinct nanoparticles with cationic cores and variable shells. ![]()
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