POS3-0314
Automated In-situ Characterization of Lipid Nanoparticles via Computer Vision-based Flow RiboGreen Assay
When and Where
Nov 30, -0001
12:00am - 12:00am
Presenter(s)
Gi-Su Na (POSTECH)
Co-Author(s)
Abstract
Lipid nanoparticles (LNPs) have become the leading non-viral delivery platform for nucleic acid therapeutics, enabling the clinical success of mRNA vaccines and gene therapies. While recent advances in automated formulation and continuous purification technologies have substantially accelerated LNP production, physicochemical characterization remains largely dependent on conventional off-line analytical methods, creating a critical bottleneck in formulation optimization. Among the key quality attributes of LNPs, encapsulation efficiency (EE) is particularly important as it directly reflects the proportion of nucleic acid successfully incorporated into nanoparticles and serves as a primary metric for formulation screening, process development, and quality control. Here, we present an Automated Flow Encapsulation Efficiency Calculator (AFEC), a computer vision-based analytical platform that enables rapid and continuous determination of LNP encapsulation efficiency. In AFEC, RiboGreen reagent is continuously mixed with either intact or Triton X-treated LNPs within a microfluidic channel, and the resulting fluorescence signal is captured using a simple optical setup consisting of blue-light illumination and a camera. Acquired fluorescence images are automatically processed through region-of-interest selection, image segmentation, and pixel-based intensity analysis, enabling quantitative RNA measurement without the need for a conventional fluorescence microplate reader.
By combining continuous-flow fluorescence detection with automated image analysis, AFEC transforms the traditionally batch-based RiboGreen assay into a rapid, low-volume, and real-time analytical workflow. This platform addresses a major characterization bottleneck in LNP development and provides a practical strategy for integrating EE analysis with emerging automated and high-throughput nanoparticle manufacturing systems.
By combining continuous-flow fluorescence detection with automated image analysis, AFEC transforms the traditionally batch-based RiboGreen assay into a rapid, low-volume, and real-time analytical workflow. This platform addresses a major characterization bottleneck in LNP development and provides a practical strategy for integrating EE analysis with emerging automated and high-throughput nanoparticle manufacturing systems.
