dnet-hadoop/dhp-workflows/dhp-graph-provision/src/test/resources/eu/dnetlib/dhp/oa/provision/edith-demo/10.3390-pr9111967-covid.xml

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<title classid="alternative title" classname="alternative title" schemeid="dnet:dataCite_title"
schemename="dnet:dataCite_title">Digital Twins for Continuous mRNA Production
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<description>The global coronavirus pandemic continues to restrict public life worldwide. An
effective means of limiting the pandemic is vaccination. Messenger ribonucleic acid (mRNA)
vaccines currently available on the market have proven to be a well-tolerated and effective
class of vaccine against coronavirus type 2 (CoV2). Accordingly, demand is presently
outstripping mRNA vaccine production. One way to increase productivity is to switch from the
currently performed batch to continuous in vitro transcription, which has proven to be a crucial
material-consuming step. In this article, a physico-chemical model of in vitro mRNA
transcription in a tubular reactor is presented and compared to classical batch and continuous
in vitro transcription in a stirred tank. The three models are validated based on a distinct and
quantitative validation workflow. Statistically significant parameters are identified as part of
the parameter determination concept. Monte Carlo simulations showed that the model is precise,
with a deviation of less than 1%. The advantages of continuous production are pointed out
compared to batchwise in vitro transcription by optimization of the spacetime yield.
Improvements of a factor of 56 (0.011 µM/min) in the case of the continuously stirred tank
reactor (CSTR) and 68 (0.013 µM/min) in the case of the plug flow reactor (PFR) were found.
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