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ChemBioChem
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https://doi.org/10.26434/chemr...
Article . 2025 . Peer-reviewed
License: CC BY
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PubMed Central
Article . 2025
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Article . 2025
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Model‐Based Optimization of Fed‐Batch In Vitro Transcription

Authors: Nathan Merica Stover; Soroush Ahmadi; Jacob Rosenfeld; Francesco Destro; Allan S. Myerson; Richard D. Braatz;

Model‐Based Optimization of Fed‐Batch In Vitro Transcription

Abstract

Recent developments in RNA vaccines and therapeutics have motivated the need for process engineering strategies to optimize the in vitro transcription (IVT) reaction for RNA synthesis. Specifically, practitioners seek to maximize the production of RNA and the incorporation of the 5‐prime cap to the end of each RNA molecule while minimizing the use of expensive reagents. Fed‐batch IVT is a promising technique for achieving these goals but is difficult to optimize by purely experimental means. Herein, a mechanistic model for fed‐batch IVT is developed and it is used to develop optimized fed‐batch protocols to maximize the formation of RNA while controlling concentrations of nucleoside triphosphates. On a model sequence that has been shown to be sensitive to salt concentrations, this approach can produce twice as much RNA as a heuristic approach. In addition, it is observed and characterized for the first time the formation of magnesium phosphate crystals during the IVT reaction. Strategies informed by thermodynamic modeling are developed to prevent this undesired crystallization during fed‐batch IVT. Finally, co‐transcriptional capping is incorporated into the model‐based optimization approach and a strategy to maximize RNA formation is developed while maintaining a high level of 5‐prime cap incorporation and minimizing the use of cap analogs.

Related Organizations
Keywords

Transcription, Genetic, RNA, Thermodynamics, Research Article

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
hybrid