Developing and Up-Scaling RNA-Based Vaccines and Therapeutics Production Using Big Data and Generative AI
Beyond its potential for infectious disease prophylaxis, RNA technology has emerged as one of the most promising techniques for the rapid development of treatments envisaging a wide range of pathologies ranging from cancer to cardiovascular, autoimmune, or rare diseases.
While conventional vaccines and pharmaceuticals rely on the active use of cell cultures, RNA manufacturing is based on a relatively simple, scalable, and affordable cell-free production line. Furthermore, RNA-based technology is known to hold the major advantage of being disease-agnostic, hence exhibiting high versatility in terms of therapeutic scope. Nevertheless, a decisive factor in the successful design of such innovative therapeutics for personalized medicine is the use of big data and AI technologies. These account not only for the fast RNA sequencing and proteomics data analysis but also for the identification and selection of viable and relevant RNA codes with low immunogenicity, maximum potency, and superior production yields. Multi-omics data integration helps molecule design and discovery, ensuring drug connectivity analysis and drug response prediction.
This presentation will showcase how to harness the potential of a disease-agnostic RNA platform for personalized medicine by making use of custom-designed big data architecture, generative AI, and deep learning but also biotech digital twins and software sensors, all within the framework of Quality by Digital Design (QbDD) with fast and interactive visualizations provided by the solution’s designated dashboard. All these have led to the development and up-scaling of the existing production processes both in terms of volume as well as timelines at unprecedented speed.
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