Thermally driven dynamic behaviours in polymeric vesicles as biomimetic synthetic cell systems
File(s)
Preprint version
Author(s)
Type
preprint
Abstract
Soft-matter nanoparticles are of great interest in biotechnology, particularly for fields such as therapeutic delivery and in vivo imaging. Underpinning this is their biocompatibility, potential for selective targeting, attractive pharmacokinetic properties, and amenability to downstream functionalisation. Morphological diversity inherent to soft-matter particles can give rise to enhanced functionality. However, this diversity remains underexplored, and only the simplest of particle architectures (spherical lipid vesicles and lipid/polymer nanoparticles (LNPs)) have been exploited at a clinical level. This is largely due to limitations associated with existing particle synthesis methods that rely on poorly controlled bulk mixing conditions and high-energy input. To address this, we have designed a scalable microfluidic hydrodynamic focusing (MHF) technology for the controllable, rapid, and continuous production of lyotropic liquid crystalline (LLC) nanoparticles (both cubosomes and hexosomes); colloidal dispersions of higher-order lipid assemblies with intricate internal structures of 3-D and 2-D symmetry. These particles have been proposed as the next generation of soft-matter nano-carriers, with unique fusogenic and physical properties. Using our platform, we produce stable nanoparticles with varied architectural features. Crucially, our microfluidic method gives unprecedented control over LLC size, a feature we go on to exploit in fusogenic studies with model cell membranes, in which a dependency on particle diameter is evident. We believe our platform has the potential to serve as a tool for future studies around nonlamellar soft nanoparticles, and anticipate it to allow for the rapid prototyping of LLC particles of diverse functionality, paving the way toward their eventual uptake at an industrial level. Keywords Posted on 4 October 2022-CC-BY 4.0-This is a preprint and has not been peer reviewed. Data may be preliminary.-https://
Date Issued
2024-10-08
Citation
Cambridge Open Engage, 2024
Journal / Book Title
Cambridge Open Engage
Copyright Statement
Copyright © 2024 The Author(s). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Identifier
10.26434/chemrxiv-2022-xwq9n-v2