CHO Edge System
Data-driven CHO cell line development achieving 8-12 g/L titers across antibody and bispecific modalities.
Overview
The CHO Edge Expression System, developed by Asimov, is a biologics expression platform designed for biologics developers working across discovery, cell line development (CLD), and GMP manufacturing. It combines a high-performance CHO host, vector design tools, and computational modeling to routinely achieve titers of 8–12 g/L before process development, across multiple modalities including standard mAbs, fusion proteins, and multi-chain bispecifics.
The system is positioned as an end-to-end solution, covering lead optimization through process and analytical development. It is available either as a licensed system or through Asimov's CLD service.
Rapid Pools
- Addresses the limitation of transient expression systems, which often fail to predict how a molecule will perform in stable cell line manufacturing at scale.
- Uses the same CHO host, vectors, and media from the stable CLD process to generate characterized pools in two weeks.
- Provides manufacturability data early in development, enabling identification of problematic candidates before they advance further in the pipeline.
Cell Line Development
- 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.
- Uses Asimov's Kernel design software to optimize vector variants for the specific architecture of each molecule.
- Delivers a high-titer Research Cell Bank in 14 weeks, with a guaranteed timeline.
Process Development
- Pairs the CHO Edge host with hybrid physics-informed machine learning models to co-optimize the clone and process together.
- Designed to support consistent performance from screening through 2,000L GMP production.
- Aims to ensure that high-performing clones translate reliably to manufacturing scale.
Asimov is based in Boston, MA, and offers access to the CHO Edge System through licensing or as a CLD service. The platform integrates genetic tools with data-driven models, with Kernel serving as the underlying computational design software for vector optimization.