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Predicting lipid nanoparticle size in continuous antisolvent precipitation: Influence of micro-mixing time, flow rate ratio and lipid concentration
Date
2026-05-01
Abstract
Lipid nanoparticles (LNPs) have emerged as a promising platform for delivering a wide range of therapeutics, including nucleic acids such as siRNA and mRNA. Among various synthesis routes, continuous anti-solvent precipitation has been widely adopted due to its simplicity, scalability and ability to achieve consistent product quality. However, the rational design and optimization of LNP formation remain challenging because of the complex interplay between mixing, nucleation, and post-nucleation processes such as growth or self-assembly dynamics of LNPs. While population balance models (PBMs) have been successfully used for anti-solvent crystallization systems, their application to LNPs is hindered by the lack of relevant thermodynamic and kinetic data. In this work, we propose a simplified mechanistic model for predicting the mean size of LNPs formed at a steady state of continuous anti-solvent precipitator. The model is based on two competing mechanisms viz. flux controlled and re-arrangement-controlled precipitation of LNPs and leads to a closed-form analytical expression for the mean LNP size. System specific parameters governing the minimum stable LNP size and intrinsic rearrangement rate of LNPs are estimated by fitting the experimental data obtained across a wide range of micromixing time, residence time, flow rate ratio and lipid concentration. The model qualitatively and quantitatively captured the observed trends and predicted LNP sizes over a wide operating space. The validated model was used to develop design guidelines to achieve target particle size at desired concentration of LNPs in the product stream. This simplified yet predictive framework represents, to our knowledge, the first analytical model capable of quantitatively linking process conditions to LNP size in continuous anti-solvent precipitation. The model will facilitate process development and optimisation of continuous LNP manufacturing.
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Description
Publisher
Elsevier
Citation
Chemical Engineering Journal Advances 101194
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Khan_2026_Predicting.pdf
Adobe PDF, 2.57 MB
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Funding Information
Sustainable Development Goals
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Attribution-NonCommercial-ShareAlike 4.0 International