Dyno Therapeutics: Disrupting Gene Therapy R&D with AI

October 15, 20200

Pairing biology with Artificial Intelligence (AI), Dyno Therapeutics has emerged as a disruptive start-up in the gene therapy segment. For a start-up that is less than a year old, Dyno has been creating buzz amidst the big pharma and has inked mega-deals that could fetch it more than USD 3 billion in the near future through upfront and milestone payments.  

Dyno now struck a deal with Roche and its subsidiary Spark Therapeutics to develop next-generation adeno-associated viral (AAV) vectors for central nervous system and liver-directed gene therapies. As part of the collaboration and licensing agreement, Dyno will apply its proprietary CapsidMap platform to design superior viral vectors and validate them using its AI model, while Roche and Spark will be responsible for taking the vectors through preclinical, clinical and commercialization milestones. The aggregate value of the agreement is pegged to be close to USD 1.8 billion, which includes an undisclosed upfront payment and milestone based payments linked to R&D, clinical development, commercialization and royalties on sales of the gene therapy once commercialized. This marks one of the largest collaborations till date for Dyno.  

Dyno formally unveiled itself in May this year, out of George Church’s Harvard Lab with two big deals in its bag for developing therapies for ocular diseases with Novartis for muscular diseases with Sarepta. Total revenue from both the deals was pegged to be close to USD 2 billion in upfront and future milestone payments if the gene therapies are successful.  

Dyno is addressing one of the major gaps in currently available adeno-associated gene therapies – developing effective and safe gene therapies with good payload capacities. Powered with artificial intelligence, Dyno’s proprietary platform can design capsids that confer superior functional properties to the vectors and overcome the limitations associated with the current gene therapies, namely, targeted delivery, payload capability, immune response, manufacturing etc. The AI model applies DNA libraries synthesis and sequencing to simultaneously measure and optimize capsid properties for therapeutic efficacy and success. Machine learning algorithms and massive quantities of experimental data further allow it to build a comprehensive database of AAV capsid sequences and accelerate discovery and optimization of synthetic AAV capsids. Experts have touted it as one of the best applications of AI-ML in biology and it is evidently transforming upstream development in gene therapy. It has rightly positioned itself at the intersection of biology and artificial intelligence and opened a treasure trove of possibilities in next-generation gene therapies. We are excited by the pace of development in the gene therapy space and are optimistic that the future promises safer and more effective gene therapies.  

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