Asimov, the synthetic biology company advancing the design and manufacture of therapeutics, today announced the launch of its fourth generation CHO Edge System. With an increased typical titer range of 5-11 g/L across modalities before any upstream process optimization, the new system has been developed to optimize expression across a breadth of biologic architectures and increase the likelihood of high titer cell lines.
The system’s improved robustness has allowed Asimov to increase its minimum CLD titer guarantee for IgG monoclonal antibodies to 5 g/L. If the lead clone does not hit this benchmark, it is still transferred to the partner, but the service fee and all commercial use license fees are waived.
The 4th generation CHO Edge System updates expression vector architectures, genetic parts selection, and process methodologies. In addition, the new system incorporates a suite of AI models to predict signal peptide cleavage, RNA splicing, and upstream process optimization. Customers can access these advanced capabilities by licensing the CHO Edge System or as part of Asimov’s Cell Line Development Service
Our 4th generation CHO Edge System incorporates learnings from a wide range of molecules, and an array of proprietary data-driven AI models. We have upgraded the vector architectures, genetic parts selection, and predictive modeling across multiple biological processes. While we always target titers in excess of 10 g/L in our CLD process, we can now guarantee a minimum of 5 g/L for IgG molecules. By offering a titer guarantee, we aim to set a new standard for cell line development in the industry and redefine the expectations of therapeutic developers.”
Alec Nielsen, co-founder and CEO, Asimov
The CHO Edge System incorporates a GMP-banked CHO-K1 GS knock-out host (or a GS-Fut8 double knockout), a hyperactive transposase, a library of >1000 characterized genetic parts and advanced computational models. Asimov reliably generates stable cell lines with high titer and product quality by using the CHO Edge System to optimize the expression vector across modalities.