Spectroscopy-Based Local Modeling Method for High-Throughput Quantification of Nucleic Acid Loading in Lipid...

Area of Science:

• Biotechnology
• Analytical Chemistry
• Pharmaceutical Sciences

Background:

• Lipid nanoparticles (LNPs) are key delivery systems for nucleic acid therapeutics and vaccines.
• Accurate quantification of nucleic acid loading efficiency in LNPs is crucial for product quality.
• Existing methods for LNP cargo quantification are often complex, time-consuming, and prone to variability.

Purpose of the Study:

• To develop a novel, rapid, and robust method for quantifying nucleic acid loading in LNPs.
• To establish a data mining and modeling strategy for high-throughput screening of LNP formulations.
• To reduce analytical time and sample preprocessing for LNP development.

Main Methods:

• Development of a modeling approach based on locally weighted regression (LWR) of ultraviolet (UV) spectra.
• In-situ quantification of nucleic acid cargo concentration without preseparation.
• Automatic tuning of the training library space based on query sample spectral features.

Main Results:

• The LWR model accurately predicts nucleic acid cargo concentration and ranks loading capacity.
• The method was successfully applied to various nucleic acid types (ASOs, sgRNA, mRNA) and lipid matrices.
• Significant reduction in analytical time and effort through rapid UV scans of unpurified samples.

Conclusions:

• The developed LWR modeling approach offers a streamlined and efficient method for quantifying nucleic acid loading in LNPs.
• This strategy facilitates high-throughput screening and accelerates early-stage optimization of LNP formulations.
• This represents a novel data mining and modeling strategy for LNP analysis.