Microfluidic synthesis offers precise control over liposome self-assembly, however the transfer of optimized formulations between different chip geometries still remains limited by geometry-dependent hydrodynamics. This work presents for the first time a Computational Fluid Dynamics (CFD) guided framework enabling predictive translation of DSPC:Cholesterol liposome production across microfluidic platforms without empirical reformulation. CFD simulations reproduced the local solvent exchange and mixing environments governing liposomes formation during ethanol dilution. Transferable hydrodynamic descriptors were extracted to quantitatively link flow conditions with liposome formation dynamics and morphological evolution. Experimental validation using dynamic light scattering and transmission electron microscopy demonstrated consistent size distributions and morphologies across different device geometries after optimal BCs selection. Using curcumin as a model cargo, we demonstrated that CFD-guided translation effectively identifies processes that yield drug-loaded liposomes with nearly identical features. Although absolute size prediction is not yet achieved, the proposed methodology enables rational comparison of microfluidic designs, reduces trial-and-error optimization, and supports scalable liposome manufacturing. The framework provides a general strategy for CFD-assisted translation of liposome self-assembly processes in microfluidic systems. • Computational optimization protocol for the definition of liposome formulation. • Brand new CFD-based descriptors for the translation of liposome synthesis across microfluidic geometries. • Experimental validation confirmed computational driven liposome manufacturing across devices. • Numerical approach reduces empirical optimization and supports scalable microfluidic manufacturing.
Bellotti et al. (Sun,) studied this question.