(427f) Thermodynamic Modeling of mRNA in Solution to Accelerate the Design of Precipitation-Based Capture and Purification

mRNA therapeutics have recently emerged as an exciting therapeutic modality for a range of indications, most notably as vaccines for SARS-CoV-2. Developing effective separation processes for mRNA is essential for ensuring the safety and efficacy of the therapeutic. Precipitation, via the addition of precipitants e.g., salt / ethanol (EtOH) / polyethylene glycol (PEG) 6K, provides low-cost and scalable means for isolating mRNA from solution. Optimizing the conditions for precipitation can be time-consuming and costly when done experimentally. Thus, being able to predict the solubility of mRNA at various conditions using a thermodynamic model can accelerate process development. In this work, the applicability of the statistical associating fluid theory (SAFT) equation of state (specifically the SAFT- Mie variant) for modeling mRNA solubility across various precipitant and solvent compositions was investigated. Experimental measurements of the solubility of two different mRNA constructs in various conditions (choice of precipitant(s), precipitant concentration, temperature) is performed to evaluate model performance. As primary precipitants, sodium chloride, sodium acetate, and ammonium acetate are chosen, while PEG 6k and EtOH are utilized to aid the precipitation, thus decreasing the salt concentration required. Strategies for predictive thermodynamic modeling of RNA are also discussed.