Model-Based Optimization of Fed-Batch In Vitro Transcription.

Area of Science:

• Biotechnology
• Biochemical Engineering
• Molecular Biology

Background:

• RNA therapeutics and vaccines require optimized in vitro transcription (IVT) for efficient RNA synthesis.
• Current methods struggle to maximize RNA yield and 5-prime cap incorporation while minimizing reagent costs.
• Fed-batch IVT presents a promising strategy but lacks effective optimization protocols.

Purpose of the Study:

• To develop a mechanistic model for fed-batch in vitro transcription (IVT).
• To optimize fed-batch IVT protocols for increased RNA production and 5-prime cap incorporation.
• To address challenges like reagent cost and magnesium phosphate crystallization.

Main Methods:

• Development of a mechanistic model for fed-batch IVT.
• Model-based optimization of nucleoside triphosphate concentrations and feeding strategies.
• Thermodynamic modeling to predict and prevent magnesium phosphate crystallization.
• Integration of co-transcriptional capping into the optimization framework.

Main Results:

• The model-based approach doubled RNA production compared to heuristic methods for a salt-sensitive sequence.
• Magnesium phosphate crystallization during IVT was observed and characterized for the first time.
• Thermodynamic modeling provided strategies to prevent crystallization.
• Optimized co-transcriptional capping maximized RNA formation while maintaining high cap incorporation and reducing analog usage.

Conclusions:

• Mechanistic modeling offers a powerful tool for optimizing fed-batch IVT processes.
• The developed strategies significantly enhance RNA yield and cap incorporation efficiency.
• Preventing magnesium phosphate crystallization is crucial for robust fed-batch IVT.
• This work advances RNA synthesis for vaccine and therapeutic applications.