An Accessible and Scalable Pipette-Driven Microfluidic Platform for Lipid Nanoparticle Formulation
Year: 2026
Type: preprint
Abstract: Advances in lipid nanoparticle (LNP) technology have increased the need for accessible and scalable methods that accelerate low-volume formulation screening. Here, we describe a disposable microfluidic mixer actuated by a positive-displacement pipette equipped with dual-chamber pipette tip, providing volumetric control at a fixed flow rate ratio without external microfluidic pumps. Computational fluid dynamics analysis predicted shorter micromixing times in the high-shear spiral channel than in the low-shear design. Across three benchmark lipid formulations, the high-shear mixer reproducibly generated smaller LNPs with higher mRNA encapsulation efficiency than the low-shear mixer or manual pipetting. Orthogonal characterization further revealed higher particle concentrations, structural features consistent with compact particles, more uniform mRNA loading, and a lower empty-particle fraction. LNPs prepared using the selected high-shear mixer mediated functional mRNA delivery in 2D cell cultures, a 3D human liver tissue model, and mice. Integration with a liquid-handling robot enabled automated screening of formulation parameters. Dimensionless mixing-time analysis guided enlargement of the mixer geometry for a separate production system, which produced 75 mL preparations at 150 mL/min with particle size and mRNA encapsulation efficiency comparable to those obtained with the small-scale mixer. This platform provides an accessible, automation-compatible approach to LNP formulation screening and a route to larger-scale production.
Authors: Yuji S. Takeda, Fujun Wang, Xin Gu, James Forster, Haoyi Hou, Parnian Hemmati, Behnam Partopour, Laura Maugeri, Yuriy Kim, Sabrina Apel, Ryan Norton, Joseph Finelli, Parsa Molaei, Shayan Shafiee, Sixuan Li, Jinhan Lin, Chen-wei Chiang, Martha Mayo, Seyoum Ayehunie, Samin Akbari, Hai-Quan Mao, Tza-Huei Wang, Mehdi Dehghani
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225633149
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