For example, the thermodynamic models assume monodisperse NPs with even distributions of ligands over the NP surface

For example, the thermodynamic models assume monodisperse NPs with even distributions of ligands over the NP surface. bound receptors has predicted optimal non-saturating ligand densities for cellular uptake.45 Specifically, thermodynamic modeling revealed that adhesion strength between nanoparticles and cells governs cellular uptake, and the enthalpic and entropic energies governing this process are inherently linked to nanoparticle size and ligand density. When ligand density is increased beyond optimal values, decreases in cellular uptake were predicted for a range of nanoparticle sizes. At high ligand densities, these decreases were partially attributed to a diminishing availability of accessible receptors for nanoparticle binding due to high ratios of receptors bound per nanoparticle. Therefore, over-recruitment and diminishing receptor availability is usually a possible explanation for the observed decrease RN486 in cell binding at high ligand densities. With a limited number of receptors available for binding, over-recruitment via high ligand density nanoparticles could decrease overall cell binding. It should be noted, however, that these predictive models make assumptions that ignore some variability that intrinsically exists with experimental targeting studies. For example, the thermodynamic models assume monodisperse NPs with even distributions of ligands over the NP surface. The NPs used in this study, although relatively uniform in size, were not monodisperse. Further, while the applied conjugation strategy can control for ligand orientation and is expected to produce relatively narrow distributions in ligand density, as discussed above, there will certainly be some variability in ligand density, which is not accounted for in the model. The presence of an RN486 intermediate optimal ligand density does not seem to be limited to the NP platform presented here (i.e. SPIO). An intermediate optimal density of folic acid has been reported on folate receptor-targeted liposomes21, 40 and micellar nanoparticles19, 38, and simulated for general spherical particles.45 Thus, it is our opinion that the ligand density effect is not constrained to specific nanoparticle platforms, but rather is dependent on the surface packing density of functional ligands, the size of the ligands, and the orientation of the surface bound ligand. For example, linking a ligand to a nanoparticle via a bioconjugation technique that lacks chemoselectivity and cannot control for ligand orientation can result in surface ligands with reduced or eliminated functionality, thus altering the perceived functional density of ligands. Additionally, use of inefficient bioconjugation techniques46, 47 or construction of nanoparticles with limited conjugation sites may hinder targeted nanoparticles from achieving optimal ligand densities. Also, as was previously mentioned, ligand densities responsible for decreases in nanoparticle cell binding may not be achievable with larger targeting proteins (i.e. scFvs, diabodies, RN486 antibodies). Notably, this study utilized a short, PEG linker (dPEG4) to connect the nanoparticle surface and targeting ligand. Change in linker lengths and flexibility could increase the availability of targeting ligands and alleviate potential steric hindrances. Computational analysis of the effect of spacer arm length and flexibility for liposomal particles determined that longer linkers increase the area of influence for a nanoparticle thereby decreasing negative steric effects.39 It should be noted, however, that utilizing long spacer arms for ligand attachment could result in an increase in NP size and affect pharmacokinetics. As was noted in Table 1 for this study, no major differences were noted in SPIO nanoparticle hydrodynamic diameters following addition of ligands. Ultimately, the extension of these findings to targeting applications must be considered. Surface functionalization of nanoparticles can effect known pharmacokinetic properties of passively targeted nanoparticle precursors.48 The presence of different targeting ligand surface densities may alter serum protein opsonization patterns and accelerate systemic nanoparticle clearance49. In fact, one study has shown that while cell binding studies were unable to distinguish between targeted nanoparticles with different ligand densities, studies revealed that nanoparticles with ligand densities below the highest conditions tested resulted in an increase in tumoral localization.4 While this result presumably stems from improved nanoparticle pharmacokinetics, it does underscore the multifaceted role that ligand density NOL7 plays Moreover, for popular targeted cancer receptors that are known to also be expressed in healthy tissues (e.g. transferrin receptor, folate receptor, EGFR), maximizing cell binding through ligand density could manifest through off-site nanoparticle accumulation.50 Therefore, the optimal ligand densities for in vivo use may be those which confer the greatest selectivity for the target tissue as oppose to the highest avidity. Actively targeted nanocarriers have emerged as a new tool with the potential to expand the applicability of diagnostic imaging, and improve the selectivity of drug delivery systems. Herein, we have shown that controlling the ligand density on a nanoparticles surface can have a significant impact on target cell binding. Specifically,.