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Optimization of In vitro Transcription Reaction for mRNA Production Using Chromatographic At-Line Monitoring
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In-process analytics for IVT mRNA manufacturing: from process understanding to advanced process control.

Naryeong Kim1, Emily Dong1, Kate Tschudi1

  • 1Cell Therapy Engineering and Development, Genentech, 1 DNA Way, South San Francisco, CA, United States.

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|March 27, 2026
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Summary
This summary is machine-generated.

In-process analytics are crucial for mRNA manufacturing, enhancing process understanding and enabling controls from plasmid DNA preparation to in vitro transcription. These methods support decision-making and lay the groundwork for advanced process control.

Keywords:
IVT mRNAadvanced process controlin-process analyticsprocess analytical technology (PAT)process understanding

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Area of Science:

  • Biotechnology
  • Process Analytical Technology
  • Pharmaceutical Manufacturing

Background:

  • RNA therapeutics manufacturing requires scalable processes to meet clinical demand.
  • Messenger RNA (mRNA) yield and purity depend on plasmid DNA (pDNA) preparation and in vitro transcription (IVT).
  • In-process measurements are vital for both process development and current good manufacturing practice (cGMP) production.

Purpose of the Study:

  • To categorize in-process analytical methods within the FDA's Process Analytical Technology framework based on control intent.
  • To discuss the deployment of these methods during process development and cGMP manufacturing for mRNA production.
  • To identify current limitations and future opportunities for in-process analytics in mRNA manufacturing.

Main Methods:

  • Categorization of analytical methods by control intent: process understanding, in-process controls (IPCs), and advanced/adaptive process control (APC).
  • Analysis of measurements from pDNA preparation (topology, linearization, impurities) and IVT (reactant consumption, product formation).
  • Comparison of at-line, on-line, and in-line approaches, including their strengths, constraints, and validation.

Main Results:

  • Analytical methods are classified by their role in process understanding, IPCs, or APC.
  • Measurements for template readiness (pDNA) and real-time IVT monitoring are highlighted.
  • Key gaps include time-resolved double-stranded RNA quantification and production-ready in-line sensors.

Conclusions:

  • In-process analytics provide immediate value by improving process understanding and supporting IPCs.
  • These analytics are foundational for implementing future advanced or adaptive process control in mRNA manufacturing.
  • Addressing current gaps will enhance the broader adoption of in-process technologies for more robust mRNA production.