A toolbox to optimize and scale up microfluidic production of lipid-based nanoparticles

The use of nanoparticles (NPs) for delivery, particularly for nucleic acid-based therapeutics has become a central determinant of therapeutic efficacy. To ensure safety, potency, and manufacturability across drug development stages, the physicochemical properties of NPs need to be precisely controlled. Microfluidic (MF) synthesis enables great control over the NPs formation process and maintains physicochemical properties across different production scales. MF offers economic advantages for high-throughput screening of large formulation libraries, while delivering superior reproducibility and scalability to meet regulatory requirements. In this review, we map the end-to-end workflow of MF based NPs synthesis and present an integrated "toolbox" of technologies to optimize this process for successful drug development. We highlight advances in chip fabrication methods and the critical performance tests required to ensure robust NPs production. We then detail the underlying principles of NPs formation in MF systems and highlight emerging computational and simulation approaches to model and predict NPs assembly. Finally, we examine the incorporation of machine learning tools to establish predictive relationships between process parameters and NPs properties. We anticipate that strategic selection of MF chip design, integrated with chip fabrication technologies, simulation-based approaches, and machine learning tools, can greatly boost experimental optimization, process control, and predictability, ultimately accelerating the clinical translation of NPs-based therapies.