Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Chinese hamster ovary (CHO) cells remain the dominant platform for therapeutic antibody and biopharmaceutical production, yet productivity bottlenecks persist, particularly for complex molecules. To identify overarching trends in host cell optimization, a systematic review and quantitative cross-study analysis of 164 publications (2011-2024) reporting CHO cell engineering strategies with effects on titer or specific productivity was conducted. Data from 466 engineered targets were extracted and analyzed by strategy, pathway, and production context. The field - driven largely by antibody production - has evolved from simple overexpression toward CRISPR-mediated knockouts, while combinatorial approaches, and engineering of nuclear, epigenetic, and apoptotic/proliferative targets achieved the greatest gains. Despite technological advances, reported improvement folds remained stable, highlighting the need for pathway-informed, multi-target engineering. Future progress in predictive modeling of engineering strategies will depend on standardized models and structured datasets. This review provides a data-driven framework for rational CHO design to support next-generation biotherapeutic production.

More information Original publication

DOI

10.1080/19420862.2026.2615475

Type

Journal article

Publication Date

2026-12-01T00:00:00+00:00

Volume

18

Keywords

Chinese hamster ovary (CHO), biopharmaceuticals, cell engineering, cross-study analysis, monoclonal antibody, recombinant protein production, systematic review, CHO Cells, Animals, Cricetulus, Cell Engineering, Humans, Antibodies, Monoclonal