Dyno launches with gene therapy partnership alongside Novartis

By Ben Hargreaves

- Last updated on GMT

(Image: Getty/SasinParaksa)
(Image: Getty/SasinParaksa)

Related tags Dyno Therapeutics Gene therapy adeno-associated virus Novartis Sarepta Therapeutics

The biotech emerges from stealth mode with two partnerships potentially worth in excess of $2bn.

Dyno Therapeutics announced its existence today and suggested that it offered a platform that was able to better existing adeno-associated virus (AAV) vectors.

According to the US biotech, its CapsidMap platform uses AI to ‘optimize’ AAV capsids to improve targeting ability, payload size, immune evasion and manufacturability.

Dyno is based in Cambridge, Massachusetts, and its platform proved promising enough to snag the interest of Novartis and Sarepta Therapeutics.

There were few details revealed on the nature of these partnerships except that there was a potential figure in excess of $2bn (€1.8bn) on the table for Dyno, depending upon milestones hit.

Dyno will focus on developing gene therapy vectors for ophthalmic, muscle, central nervous system, and liver disease.

With Novartis already possessing Zolgensma (onasemnogene abeparvovec), a treatment for spinal muscular atrophy, and holding the ex-US rights to Luxturna (voretigene neparvovec), a treatment to restore vision in those with inherited retinal disease, there is clear overlap in the areas of interest between the two companies.

For Sarepta, it recently partnered with Roche​ to aid the potential roll out of its gene therapy for Duchenne muscular dystrophy.

The scale of these partnerships shows how quickly the gene therapy field is moving, with Dyno only officially formed in late 2018 with $9m in financing co-led by Polaris Partners and CRV.

The biotech’s technology platform is based on work done at Harvard Medical School, with Dyno holding an exclusive option to enter into license agreement with Harvard University for the technology.

The platform itself is able to design millions of capsid sequences and then use machine learning to determine the AAV capsid ‘fitness landscape’. Dyno suggests that this could lead to optimized capsids that make current gene therapies more effective and could allow for the treatment of new diseases through targeted payload delivery.

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