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Polarity- and Sequence-Dependent Ionization of Therapeutic Antibody-siRNA Conjugates: Enabling Intact Multi-attribute
Hao Liu1, Jamie L Veltri1, P Clayton Gough1
1Bioproduct Research and Development, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285, United States.
Abstract:
Antibody-siRNA conjugates (ARCs) represent a promising approach for delivering small interfering ribonucleic acids (siRNAs) to targeted cells, tissues, or organs. The complexity of the molecules poses great analytical challenges in identifying, characterizing, and monitoring their critical quality attributes (CQAs) during process development and manufacturing release. We developed a novel approach, intact multi-attribute method (iMAM), for ARC characterization using native size exclusion chromatography mass spectrometry (SEC-MS). The iMAM provides a simple and effective approach for monitoring CQAs such as identity, purity, higher molecular weight species (HMWS), lower molecular weight species (LMWS), N-glycosylation, modifications on unconjugated cysteine, linker hydrolysis, and drug-to-antibody ratio (DAR). The method was evaluated and qualified in a Good Manufacturing Practice (GMP) environment as an ARC drug substance (DS) and drug product (DP) identity release assay. During the method development, we found that ionization profiles of ARCs depended on the mass spectrometry operating polarity. Under positive polarity, more duplex siRNAs are preserved on the ARC molecules, whereas more ARC molecules with single-stranded RNA are present under negative polarity. Meanwhile, the presence of the antibody in ARCs provides a certain level of protection for the siRNA duplex during the ionization compared with siRNA alone. The ratio of the siRNA duplex on the ARC molecules during mass spectrometry detection was correlated with its GC content or melting temperature (Tm). These findings provide a fundamental understanding of the relationship between siRNA and the antibody during ARC ionization and facilitate the development of effective methods for ARC characterization.
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