The use of enhanced analytical pipelines for the characterization of poly(A) and poly(A)-LNP formulation critical quality attributes

The number of nucleic acid therapeutics is set to grow within the pharmaceutical industry sector, deploying nanocarrier-based delivery systems as drug products. Poly(A) is a widely used model sequence used in lipid nanoparticle (LNP) formulations for which there are no reported critical quality attributes (CQAs) such as molecular weight, chain length, and impurity profile. In this study, we analyze Poly(A) from three different vendors to measure any existing differences in their CQAs. Poly(A) from these brands was encapsulated in SM102 LNPs using microfluidics to produce three branded Poly(A)-based LNPs. We utilized an orthogonal analytical pipeline approach for both Poly(A) drug substance and LNP drug product CQA evaluation, which included a combination of dynamic light scattering and flow field flow fractionation multiplexed with inline UV, dynamic, and multiangle light-scattering detectors. Similar purity (260/280) values of >3 were obtained across all three brands of Poly(A); however, distinct differences in molecular weight and chain length distributions were identified across Poly(A) brands, with this study representing the first to apply EAF4 methodology for in-depth characterization of model RNA drug substances. Brand A produced a smaller and broader molecular weight distribution, followed by Brand B, and then Brand C produced the largest molecular weight species and the most uniform molecular weight distribution. On encapsulation in LNPs, differences seen in Poly(A) CQAs did not translate to CQA differences in resultant LNPs. We show that a deeper understanding of drug substance CQAs and their subsequent impact on resultant overall drug product characteristics is needed on a case-by-case basis. We show correlations between analytical pipelines, with future work investigating the impact of RNA molecular weight in LNP formulations with different lipid compositions and using these correlations in AI or machine learning to further enhance our knowledge of the correlation between drug substance and resultant drug product CQAs.