In-process analytics for IVT mRNA manufacturing: from process understanding to advanced process control

RNA therapeutics are expanding rapidly, driving demand for manufacturing processes that can keep pace with clinical translation. Because mRNA yield and impurity profiles are jointly influenced by upstream plasmid DNA (pDNA) preparation and the in vitro transcription (IVT) reaction, in-process measurements are increasingly applied across the product lifecycle, albeit with distinct objectives in process development versus current good manufacturing practice (cGMP) production. Within the U.S. Food and Drug Administration's Process Analytical Technology framework, we categorize in-process analytical methods by control intent: (1) measurements that build process understanding and define operating windows; (2) in-process controls (IPCs) that support predefined stop/go, forward-processing, and endpoint decisions; and (3) measurements that could enable advanced or adaptive process control (APC) through closed-loop feedback. We discuss how each category is deployed during process development and in cGMP manufacturing. Following the workflow from pDNA preparation through IVT, we highlight analytical measurements that establish template readiness-such as plasmid topology, linearization completeness, and co-purifying impurities that can propagate into transcription performance and complicate downstream processing-as well as time-resolved measurements during IVT that track reactant consumption and product formation to inform endpoint selection, feed timing, and deviation triage under predefined decision rules. We compare the strengths, implementation constraints, and validation considerations of at-line, on-line, and in-line approaches, and identify key gaps that currently limit broader adoption, including practical time-resolved quantification of double-stranded RNA and the availability of production-ready in-line sensing technologies. Collectively, these in-process analytics deliver near-term value by enabling process understanding and IPC-based decision support, while establishing the foundation required for future APC in mRNA manufacturing.