ReviewAnalytical and bioanalytical chemistry2026
Challenges and advances in analytical separation techniques for RNA-lipid nanoparticle therapeutics.
Review in Analytical and bioanalytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene therapies are rapidly advancing as drug modalities, serving as a means to treat previously undruggable pathways. The use of small interfering ribonucleic acid (siRNA), messenger RNA (mRNA), and guide RNA (gRNA) in clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing, combined with lipid nanoparticles (LNPs), has demonstrated effective drug delivery. These complex delivery systems often require multiple analytical methodologies to achieve comprehensive characterization, some of which remain underdeveloped or inadequately adapted for RNA-LNP formulations. Commonly used batch-based methods, such as dynamic light scattering (DLS) or the modified RiboGreen assay, are frequently hindered by sample heterogeneity, a limitation that can be addressed through analytical separations. This review discusses the challenges limiting analytical separations for RNA-LNP therapeutics and highlights recent advances in separation science for reliable characterization and quality control. We focus on techniques for RNA, LNPs, and the RNA-LNP complex, emphasizing chromatographic, electrophoretic, and field-based separation techniques.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.