Effects of packing density and adsorption conditions on extracellular vesicle dynamic binding capacities for capillary-channeled polymer (C-CP) fiber columns.

Extracellular vesicles (EVs) are membrane-bound nanoparticles (50-1000 nm) secreted by all cell types and play critical roles in various biological processes. Among these, exosomes, a smaller subset of EVs, have attracted considerable interest due to their potential applications in diagnostics and therapeutics. However, conventional EV isolation methods are often limited by inefficiencies in processing time, recovery, and scalability. Hydrophobic interaction chromatography utilizing capillary-channeled polymer (CCP) fiber stationary phases offers a promising alternative, enabling rapid (<15 min), cost-effective (∼$5 per column) EV isolation with high loading capacities (∼1010-10¹² particles) and minimal sample pre-processing. Despite these advantages, achieving high-throughput EV isolation for larger-scale applications using the CCP fiber platform is the present challenge. To this end, further optimization of stationary phase packing and adsorption conditions is necessary to maximize the available binding surface area in the current microbore column format. This study systematically investigates the influence of interstitial fraction (i.e. packing density) in polyester (PET) CCP fiber columns on the dynamic binding capacity (DBC) of EVs isolated from human urine using a high-performance liquid chromatography platform. Microbore columns (0.76 mm i.d. × 300 mm) packed with PET CCP fibers in both an eight-channel (PET-8) and a novel trilobal (PET-Y) configuration were evaluated using breakthrough curves and frontal analysis. The results reveal that lower packing densities correlate with higher mass- and surface area-based EV binding capacities, with a maximum DBCs of 2.86 × 10¹³ EVs g-1 fiber and 1.22 × 10¹⁴ EVs m⁻² fiber achieved in <2 min of sample loading. Under optimum conditions, surface utilization of >50 % is realized. These results establish a framework for optimizing CCP fiber-based platforms to enhance EV capture efficiency, facilitating the development of scalable EV isolation techniques for biomedical research and therapeutic applications.