Rationally engineered polymeric interfaces for selective and dynamic recognition of vesicular agents.

Abstract

It is exceedingly hard to accurately identify and separate nanoscale vesicular entities such as extracellular vesicles (EVs), lipid-enveloped and membrane-associated viral particles, and lipid-based drug delivery nanoparticles. These systems have a membrane-bound structure, but their surface composition, mechanical properties, and molecular structures are diverse. This makes it challenging to differentiate them in complicated biological matrices. Molecularly imprinted polymers (MIPs) are synthetic recognition materials that solve this problem by being selective, stable, and cost effective. This review provides a thorough analysis of rationally engineered MIP interfaces for the targeted detection of vesicular agents. We first analyze the physicochemical properties of vesicular surfaces; including lipid bilayer composition, surface protein and glycan profiles, charge distribution, and membrane mechanical characteristics. We then examine how these attributes guide the strategic design of imprinted recognition interfaces. We critically examine the recognition strategies and supporting material architectures relevant to the molecular imprinting of soft and heterogeneous vesicular templates, including surface and epitope imprinting, as well as core-shell nanoparticle and cryogel-based macroporous architectures. This is done with a specific focus on the design of recognition-site accessibility, membrane-associated template selection, and material architectures adapted to soft vesicular targets at the recognition interface. The integration of vesicle MIPs into analytical and sensing systems is examined across optical, electrochemical, membrane-based, and microfluidic formats, including applications in EV subpopulation profiling, virus detection, and targeting concepts relevant to lipid-based vesicular carriers. Finally, we evaluate the current limitations and potential avenues for next-generation MIPs that provide improved selectivity, sensitivity, and clinical applicability for vesicular biomarker diagnostics.

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