Prozomix, and Ginkgo Biowork announced a new partnership. Together, Ginkgo and Prozomix aim to build out the production of next generation enzyme plates for active pharmaceutical ingredient (API) manufacturing. This collaboration aims to leverage Ginkgo's Enzyme Services and AI/ML models along with Prozomix's existing enzyme libraries and deep experience manufacturing enzyme plates.

This agreement also marks Prozomix's entry into the Ginkgo Technology Network, a ecosystem of cutting-edge technology partners dedicated to driving innovation in customer R&D programs. Ginkgo's Technology Network brings together a diverse array of partners, spanning AI, genetic medicines, biologics, and manufacturing, with the aim of integrating their capabilities to provide customers with robust end-to-end solutions for successful R&D outcomes. With Prozomix now in the Technology Network, Ginkgo customers will have access to Prozomix's scalable contract manufacturing services, including enzyme samples from mg to kg scale. For several decades, demands for both improved supply chain sustainability and reduction of costs of goods sold has driven the pharma industry towards the adoption of biocatalysts in commercial API manufacturing.

Existing enzyme plates offer users an opportunity to rapidly screen potential candidates early in development to identify and de-risk the use of biocatalysts capable of supporting specific reactions in API manufacturing routes. As such, biocatalyst adoption largely depends on the diversity and performance of the enzymes available in these plates. Prozomix and Ginkgo are partnering to usher in a new generation of biocatalysts built off of sequences and activity data from previous enzyme libraries.

Ginkgo will build class-specific AI models informed by enzyme sequences and data from its own massive metagenomic database as well as Prozomix's enzyme libraries and associated screening data. These models can then be used to discover novel functional enzyme sequences. Prozomix intends to then use next-gen enzyme libraries, designed by these models, to manufacture novel enzyme plates.

Together, the partners expect these next-gen enzyme plates to have a diversity and performance that traditional plates lack, potentially unlocking biocatalytic opportunities where previous plates have failed. These plates will be freely available to all pharma process chemistry groups, provided that screening data is shared back with Ginkgo to drive further refinement of the Ginkgo AI/ML models.