In:
Processes, MDPI AG, Vol. 11, No. 3 ( 2023-03-02), p. 745-
Abstract:
The production of messenger ribonucleic acid (mRNA) and other biologics is performed primarily in batch mode. This results in larger equipment, cleaning/sterilization volumes, and dead times compared to any continuous approach. Consequently, production throughput is lower and capital costs are relatively high. Switching to continuous production thus reduces the production footprint and also lowers the cost of goods (COG). During process development, from the provision of clinical trial samples to the production plant, different plant sizes are usually required, operating at different operating parameters. To speed up this step, it would be optimal if only one plant with the same equipment and piping could be used for all sizes. In this study, an efficient solution to this old challenge in biologics manufacturing is demonstrated, namely the qualification and validation of a plant setup for clinical trial doses of about 1000 doses and a production scale-up of about 10 million doses. Using the current example of the Comirnaty BNT162b2 mRNA vaccine, the cost-intensive in vitro transcription was first optimized in batch so that a yield of 12 g/L mRNA was achieved, and then successfully transferred to continuous production in the segmented plug flow reactor with subsequent purification using ultra- and diafiltration, which enables the recycling of costly reactants. To realize automated process control as well as real-time product release, the use of appropriate process analytical technology is essential. This will also be used to efficiently capture the product slug so that no product loss occurs and contamination from the fill-up phase is 〈 1%. Further work will focus on real-time release testing during a continuous operating campaign under autonomous operational control. Such efforts will enable direct industrialization in collaboration with appropriate industry partners, their regulatory affairs, and quality assurance. A production scale-operation could be directly supported and managed by data-driven decisions.
Type of Medium:
Online Resource
ISSN:
2227-9717
Language:
English
Publisher:
MDPI AG
Publication Date:
2023
detail.hit.zdb_id:
2720994-5
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