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Insights into siRNA Duplex Stability by UV Spectrophotometry Absorbance and Melting Temperature Analyses
Molly S Blevins1, Steven Chin1, Natali M Lelieur1
1Synthetic Molecule Analytical Chemistry, Genentech Inc., South San Francisco, California 94080, United States.
None:
The non-covalent nature of therapeutic siRNA duplexes necessitates advanced analytical characterization, which is often complicated by the dynamic interplay between the siRNA structure and its surrounding environment. Here, we utilize UV Visible (UV-Vis) absorbance and thermal melt (T m) analyses to systematically benchmark the structural stability of a model siRNA duplex across key conditions relevant for analytical method development and drug formulation, including: siRNA concentration, cation identity and content, and solvent composition. Our results highlight the sensitivity of UV-Vis absorbance and T m analyses for distinguishing changes in siRNA structural populations (i.e., single-stranded vs. double-stranded populations) via the hyperchromicity effect. These experiments reveal the effects of formulation, sample concentration, and dilution on siRNA structure. Furthermore, by isolating structural effects from molar absorptivity changes using nucleotide monophosphate controls, we are able to track absorbance changes arising from duplex dissociation vs. those arising from solvatochromism effects, and are further able to evaluate thermodynamic parameters for stable duplex conditions. These findings showcase UV-Vis as a powerful and sensitive tool for evaluating siRNA structural dynamics, providing novel and quantitative data that can inform and guide analytical methods, support formulation strategies, and illuminate the structural consequences of siRNA environment throughout the drug development cycle. Critically, this work also highlights an array of critical variables for method development, including diluent composition, dilution factors, and siRNA concentration, and how these sample preparation and experimental methods and conditions can impact the resulting data.
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