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

Abstract

Lipid nanoparticles (LNPs) are the most widely investigated delivery systems for nucleic acid-based therapeutics and vaccines. Loading efficiency of nucleic acids may vary with formulation conditions, and it is considered one of the critical quality attributes of LNP products. Current analytical methods for quantification of cargo loading in LNPs often require external standard preparations and preseparation of unloaded nucleic acids from LNPs; therefore, they are subject to tedious and lengthy procedures, LNP stability, and unpredictable recovery rates of the separated analytes. Here, we developed a modeling approach, which was based on locally weighted regression (LWR) of ultraviolet (UV) spectra of unpurified samples, to quantify the loading of nucleic acid cargos in LNPs in-situ. We trained the model to automatically tune the training library space according to the spectral features of a query sample so as to robustly predict the nucleic acid cargo concentration and rank loading capacity with similar performance as the more complicated experimental approaches. Furthermore, we successfully applied the model to a wide range of nucleic acid cargo species, including antisense oligonucleotides, single-guided RNA, and messenger RNA, in varied lipid matrices. The LWR modeling approach significantly saved analytical time and efforts by facile UV scans of 96-well sample plates within a few minutes and with minimal sample preprocessing. Our proof-of-concept study presented the very first data mining and modeling strategy to quantify nucleic acid loading in LNPs and is expected to better serve high-throughput screening workflows, thereby facilitates early-stage optimization and development of LNP formulations.

Re-envisioning the Design of Nanomedicines: Harnessing Automation and Artificial Intelligence

“…This was further demonstrated in a follow-up study using the same automated high-throughput workflow to identify optimal LNP formulations for gene silencing [42]. Similar high-throughput approaches have also been used to screen and prepare LNPs encapsulating mRNA, as well as to improve methods of quantifying the loading of nucleic acid cargos [43,44]. Such experiments highlight the utility of liquid handling robots for advancing the design of experimental methods commonly employed for formulation screening.…”

Cell‐Based Biomaterials for Coronavirus Disease 2019 Prevention and Therapy

“…Currently, lipid nanoparticle (LNP) systems are the lead delivery systems for enabling the bioavailability of nucleic acidbased vaccines. [112,113] Notably, LNP mRNA vaccines have been broadly used in COVID 19 prevention. [114] However, there are still some problems including weak targeting, inefficient uptake by cells, and inability of lysosome escape.…”