Advancing RNA delivery with Ionizable lipid nanoparticles: the roles of microfluidics and machine learning

Ionizable lipid nanoparticles (iLNPs) have revolutionized Ribonucleic acid (RNA) therapeutics by enabling precise and efficient delivery of nucleic acids. However, their clinical translation remains challenged by batch-to-batch variability, complex lipid - RNA interactions, and stringent regulatory requirements. This review highlights how advanced microfluidic technologies address these issues by providing precise control over iLNP fabrication through engineered mixer geometries, optimized flow dynamics, and pH-dependent self-assembly. Comparative analyses of hydrodynamic flow focusing (HFF), and staggered herringbone mixers (SHM) demonstrate their distinct influence on particle size, polydispersity index (PDI), and encapsulation efficiency. Furthermore, the integration of design-of-experiments (DoE) methodologies, computational fluid dynamics (CFD) modeling, and machine learning (ML)-assisted optimization enables predictive formulation design and adaptive process control, enhancing reproducibility and scalability. Collectively, this review underscores microfluidics and ML as synergistic technologies that bridge laboratory innovation with Good Manufacturing Practice (GMP)-compliant, large-scale production paving the way for the next generation of intelligent, personalized RNA nanomedicines.