Improving Cancer Targeting: A Study on the Effect of Dual-Ligand Density on Targeting of Cells Having Differential Expression of Target Biomarkers

提高癌症靶向性:双配体密度对靶向表达差异性靶向生物标志物的细胞的影响研究

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作者:Nayer Sultana ,Allan E David

Abstract

Silica nanoparticles with hyaluronic acid (HA) and folic acid (FA) were developed to study dual-ligand targeting of CD44 and folate receptors, respectively, in colon cancer. Characterization of particles with dynamic light scattering showed them to have hydrodynamic diameters of 147-271 nm with moderate polydispersity index (PDI) values. Surface modification of the particles was achieved by simultaneous reaction with HA and FA and results showed that ligand density on the surface increased with increasing concentrations in the reaction mixture. The nanoparticles showed minimal to no cytotoxicity with all formulations showing ≥ 90% cell viability at concentrations up to 100 µg/mL. Based on flow cytometry results, SW480 cell lines were positive for both receptors, the WI38 cell line was positive for CD44 receptor, and Caco2 was positive for the folate receptor. Cellular targeting studies demonstrated the potential of the targeted nanoparticles as promising candidates for delivery of therapeutic agents. The highest cellular targeting was achieved with particles synthesized using folate:surface amine (F:A) ratio of 9 for SW480 and Caco2 cells and at F:A = 0 for WI38 cells. The highest selectivity was achieved at F:A = 9 for both SW480:WI38 and SW480:Caco2 cells. Based on HA conjugation, the highest cellular targeting was achieved at H:A = 0.5-0.75 for SW480 cell, at H:A = 0.75 for WI38 cell and at H:A = 0.5 for Caco2 cells. The highest selectivity was achieved at H:A = 0 for both SW480:WI38 and SW480:Caco2 cells. These results demonstrated that the optimum ligand density on the nanoparticle for targeting is dependent on the levels of biomarker expression on the target cells. Ongoing studies will evaluate the therapeutic efficacy of these targeted nanoparticles using in vitro and in vivo cancer models.

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