Alltrna Applies Machine Learning to Engineer tRNA Oligonucleotides with Significantly Improved Activity and Demonstrates In Vivo Readthrough of the Two Most Common Premature Termination Codons in Genetic Disease

On May 7, 2024 Alltrna, a Flagship Pioneering company unlocking transfer RNA (tRNA) biology and pioneering tRNA therapeutics to regulate the protein universe and resolve disease, reported the presentation of new data at the American Society of Gene & Cell Therapy (ASGCT) (Free ASGCT Whitepaper) 27th Annual Meeting demonstrating that Alltrna’s platform, enabled by machine learning (ML), can optimize sequences and modifications from natural tRNAs to significantly increase in vivo activity to readthrough premature termination codons (PTCs) caused by nonsense mutations (Press release, Alltrna, MAY 7, 2024, https://www.alltrna.com/press/alltrna-applies-machine-learning-to-engineer-trna-oligonucleotides-with-significantly-improved-activity-and-demonstrates-in-vivo-readthrough-of-the-two-most-common-premature-termination-codons-in-genetic-disease [SID1234646024]).

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"Alltrna’s ML-driven platform explores at an unprecedented scale the expansive combinatorial space of tRNA sequences and modifications, which has the potential to generate more engineered tRNA oligonucleotides than atoms in the universe," said Michelle C. Werner, CEO of Alltrna. "The data demonstrate the power of Alltrna’s platform to identify key combinations of tRNA sequences and modifications and precisely design tRNA oligonucleotides with significantly improved in vivo activity. With optimized engineered tRNAs for the two most prevalent premature termination codons, we are advancing preclinical studies for our first Stop Codon Disease indications."

tRNAs are programmable molecules with a diverse biology of sequences and modifications that are key to their structure, function, and stability. There’s the potential for approximately 10^34 tRNA sequences and more than 120 natural and synthetic modifications for each nucleotide. Using ML-powered screens, Alltrna scientists applied high-throughput sequence optimization to increase engineered tRNA activity by ~100-fold. ML-driven modification optimization further increased engineered tRNA activity.

Data were presented for the optimization of engineered tRNA oligonucleotides to address two different PTCs: Arg-TGA and Gln-TAG. Delivered using a liver-directed lipid nanoparticle (LNP), both optimized tRNAs showed robust in vivo activity in two transgenic mouse models. The first carries a human rare disease gene with a PTC mutation, the second carries a reporter gene with a different PTC as a general in vivo model for Stop Codon Disease. Alltrna previously presented in vitro data on the engineered tRNA for Arg-TGA showing that it can readthrough nonsense mutations regardless of gene or location in 25 disease reporter models, 14 different genes, and seven different mutation locations on a single gene.

"These data confirm that the application of high-throughput sequence and ML-driven modification optimization through Alltrna’s unique platform can significantly increase the in vivo activity of engineered tRNAs," said Stephen W. Eichhorn, Ph.D., Head of Computational and Molecular Biology at Alltrna. "We’ve also demonstrated that we can engineer robust tRNA activity for two different premature termination codons, each of which are highly prevalent in Stop Codon Disease."

About Stop Codon Disease

Stop Codon Disease encompasses thousands of rare and common diseases that stem from premature termination codons (PTC) also called nonsense mutations, where the code for an amino acid has been mutated into a premature "stop" codon. This results in a truncated or shortened protein product with no or altered biological activity that causes disease. Approximately 10% of all people with a genetic disease have Stop Codon Disease, representing approximately 30 million people worldwide. Alltrna is engineering tRNA medicines that can read these PTC mutations and deliver the desired amino acid, thereby restoring the production of the full-length protein.