1. Orthogonal analysis reveals inconsistencies in cargo loading of extracellular vesicles
- Author
-
Lowe, Neona M, Mizenko, Rachel R, Nguyen, Bryan B, Chiu, Kwan Lun, Arun, Vishalakshi, Panitch, Alyssa, and Carney, Randy P
- Subjects
Medical Biotechnology ,Biomedical and Clinical Sciences ,Bioengineering ,Generic health relevance ,bioengineering ,drug delivery ,exosomes ,therapeutics - Abstract
Since extracellular vesicles (EVs) have emerged as a promising drug delivery system, diverse methods have been used to load them with active pharmaceutical ingredients (API) in preclinical and clinical studies. However, there is yet to be an engineered EV formulation approved for human use, a barrier driven in part by the intrinsic heterogeneity of EVs. API loading is rarely assessed in the context of single vesicle measurements of physicochemical properties but is likely administered in a heterogeneous fashion to the detriment of a consistent product. Here, we applied a suite of single-particle resolution methods to determine the loading of rhodamine 6G (R6G) surrogate cargo mimicking hydrophilic small molecule drugs across four common API loading methods: sonication, electroporation, freeze-thaw cycling and passive incubation. Loading efficiencies and alterations in the physical properties of EVs were assessed, as well as co-localization with common EV-associated tetraspanins (i.e., CD63, CD81 and CD9) for insight into EV subpopulations. Sonication had the highest loading efficiency, yet significantly decreased particle yield, while electroporation led to the greatest number of loaded API particles, albeit at a lower efficiency. Moreover, results were often inconsistent between repeated runs within a given method, demonstrating the difficulty in developing a rigorous loading method that consistently loaded EVs across their heterogeneous subpopulations. This work highlights the significance of how chosen quantification metrics can impact apparent conclusions and the importance of single-particle characterization of EV loading.
- Published
- 2024