CHO Edge System

A state-of-the-art biologics expression system that routinely achieves 8-12 g/L across modalities before process development

A new approach to biologicS EXPRESSION

A smarter way to make biologics

Applications

Cell line development has always involved a lot of guesswork. CHO Edge replaces that with data-driven engineering.

Our system covers every stage from discovery through GMP manufacturing, combining computational design with a high-performance CHO host and vector design tools. The goal: predictable, high-titer results with fewer surprises at scale.

An end-to-end solution for biologics developers

We offer integrated solutions for your biologics expression needs, from lead optimization, to CLD, to process & analytical development:

The CHO Edge System

Rapid Pools

Transient expression systems often don't predict how a molecule will actually perform when manufactured at scale using stable cell lines. That can mean selecting leads based on data that won't hold up later.

Rapid Pools addresses this by using the same CHO host, vectors, and media from our stable CLD process to generate characterized pools in two weeks. You get manufacturability data early, which helps you identify problematic candidates before they cost you time and money.

Cell Line Development

CHO Edge routinely produces clones at 8-12 g/L before process development across modalities: standard mAbs, fusion proteins, 2-chain, 3-chain, and 4-chain bispecifics.

We use our Kernel design software to optimize vector variants for your specific molecule, whatever its architecture. The result is a high-titer Research Cell Bank delivered in 14 weeks guaranteed.

Process Development

A great clone means nothing if it doesn't perform at scale.

We pair our CHO Edge host with hybrid physics-informed machine learning models to co-optimize the clone and process together. This approach is designed to support consistent performance from screening through 2,000L GMP production.

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CONTACT US

We're hiring at the intersection of biology, engineering, and machine learning.

Asimov team at their Boston office
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