Enhancing mRNA Therapeutics Production: A Platform Technology Approach Through IVT Modeling Insights

• Generalizable tool enabling cost-efficient, large-scale mRNA manufacturing • In-house LHS-validated IVT model (100 experiments) for CSP & eGFP (R 2 = 0.87) • Integrates Mg–PPi precipitation, NTP consumption, spermidine & template effects • QbD framework mapping CPPs/CMAs to CQAs for scalable mRNA design spaces • Global Sobol sensitivity reveals key CPPs driving RNA yield variability Rapid, scalable production of high-quality mRNA underpins vaccines, gene therapies, and protein replacement medicines, yet optimisation of the in vitro transcription (IVT) step is often driven by template-specific trial-and-error. We present a first-principles mechanistic IVT bioprocess model—constructed from thermodynamic equilibria, enzyme kinetics, and stoichiometric mass balances—to support Quality-by-Design (QbD) manufacturing. The model is calibrated and validated against > 100 experiments from a Latin Hypercube design that varies nucleotide triphosphate (NTP) concentration, magnesium concentration, T7 RNA polymerase loading, template DNA, spermidine, and reaction time, using two contrasting templates: a SARS-CoV-2 spike construct (CSP) and enhanced green fluorescent protein (eGFP). The model predicts RNA yield with R 2 = 0.85 –0.87 while explicitly tracking individual NTP consumption and magnesium pyrophosphate precipitation. Within a QbD context, the framework links critical process parameters (CPPs) and critical material attributes (CMAs) to product-oriented metrics and manufacturing key performance indicators (KPIs), and identifies a model-derived operating window that reduces reagent cost subject to specified quality constraints. Mechanistic refinements, including inorganic pyrophosphatase kinetics and sequence-aware per-base NTP accounting, support transfer between the two templates studied here and provide a foundation for broader multi-construct validation. Overall, the approach offers a data-efficient route to accelerate IVT process development and reduce consumable spend for emerging mRNA therapeutics.