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Advances in DNA, RNA, Protein Printing, and Automation Technologies to Drive Adoption of Synthetic Biology in the Next Three Years
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The conventional drug discovery and diagnostics technologies are unable to cope up with the current disease management practices. To this end, researchers have explored possibilities beyond life sciences. Specifically, they have figured out that the use of engineering principles to biological systems can revolutionize the drug discovery and diagnostics field. This is when synthetic biology has started gathering interest. Synthetic biology involves technologies like DNA/RNA/protein/tissue printing, gene regulatory circuits, Clustered Regularly Interspersed Short Palindromic Repeats (CRISPR)?Cas, xenonucleic acids (XNA), and branched DNA. Most of these technologies are enabled by well-established technologies of machine learning, artificial intelligence, automation, and microfluidics.DNA printing technology is at the heart of synthetic biology based drug discovery as it can design a library of thousands of DNA molecule in a very short time. This technology can help build various genetic circuits to synthetic viral genomes. Genetic circuits can act as either the drug molecules or biosensors. The DNA printers can be modified to print RNA/XNA/protein molecules. This technology is enabled by microfluidics, which can be modified to print tissues and organs, thereby producing various disease models for toxicity testing. CRISPR?Cas technology can serially characterize multiple genes, thereby helping in drug target identification. Alternatively, CRIPR?Cas based genetic circuits can serve as good diagnostic tools to detect viruses, bacteria, as well as cancers. XNA or smart nucleotide bases and branched DNA technologies leverage diagnostics to the level above next generation sequencing both in terms of time and cost. The enablers serve as icing on a cake. Biofoundries expedite the Design, Build, Test, and Learn (DBTL) cycle; automation brings in the continuity and accuracy in the experimentation; and machine learning helps reduce the number of iteration of experiments. Altogether, these technologies accomplish the best outcomes in drug discovery and diagnostics.This report describes each of these technologies in detail and portrays the true potential of them. It also discusses the applications of synthetic biology as well as the drivers and challenges in the adoption of synthetic biology in drug discovery and diagnostics. Further, the report enlightens about the funding opportunities, the types of funding in this area, key companies, and investors. Overall, we believe that synthetic biology has evolved drug discovery for high success rate and diagnostics for early detection with the overall diminished healthcare burden.Key questions:• What are the driving factors for research and adoption of synthetic biology in drug discovery and diagnostics?• What challenges and impediments remain to the adoption of synthetic biology?• What initiatives are industry participants undertaking to accelerate adoption?• What are the specialized synthetic biology platforms expected business outcomes compared to conventional diagnostics and drug discovery?
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