Artificial intelligence driven platform to aid experimental design of optimised plasmid DNA for in vivo expression of biologics

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DNA-based in vivo expression of therapeutic molecules is an emerging platform aiming to deliver biologic compounds to the patient via administration of viral or non-viral DNA. It is now known that the presence of particular sequences (such as cruciform, hairpins, palindromes, micro-RNA targets, cryptic splice sites) within the plasmid DNA can have a strong impact on the structure of the DNA, can affect the quality of the DNA produced, and the performance of expression elements. Additionally, there is a complex relationship between the presence of particular DNA elements and downstream effects on structure, expression levels, immunostimulatory effects and plasmid production. Artificial intelligence-driven bioinformatics can aid the development of novel computation tools for the automated analysis of DNA elements and, combined with literature, used to guide the design of optimised plasmid systems. Synthetic biology can offer a route for characterisation of novel designs and their output for incorporation of structure considerations into nucleic acid-based therapeutics. In this project we will optimise plasmid-based therapeutic design by adopting iterative experimental and computational approaches in addition to leveraging advanced machine learning algorithms. Following this systematic design approach our aim is to understand how to successfully transfer industrially-relevant therapeutic systems design within in vitro and, possibly, in vivo studies. Achieving an understanding of how to translate motif design rules into mammalian cell lines and in vivo systems will be a major achievement enabling us to revolutionise therapeutic production in the future.