Repository logo
 

A Multi Clone Kinetic Model for characterizing Chinese hamster ovary cell line variability.

Published version
Peer-reviewed

Repository DOI


Change log

Abstract

This paper presents the Multi Clone Kinetic Model (MCKM), a novel generalized kinetic mechanistic model for fed-batch cultivations of diverse Chinese hamster ovary (CHO) cell lines, producing different recombinant monoclonal antibodies (mAbs). Unlike traditional kinetic models requiring multiple cultures for one parameter regression, MCKM derives a complete set of 13 kinetic parameters from a single fed-batch cell line culture of 49 data points. This enables per-cell-line metabolic characterization during cell line development, as well as direct comparisons of kinetics across clones, passages, and different recombinant mAbs. To enable MCKM to be broadly applicable across many cell lines and mAbs, and to address the high-dimensional challenge of estimating 13 kinetic parameters from a small number of datapoints, the model uniquely incorporates a mechanistic growth constraint, a glucose-dependent lactate switch, and automated parameter balancing. MCKM demonstrated successful regression of 656 fed-batch culture runs of 157 unique CHO cell lines across four passage generations, recombinant for three different mAbs, achieving high accuracy in biomass and mAb titre (average ${\rm{\bar{R}}}{{{{\rm{X}}}{\rm{v}}}}^2$ ≈ 0.96 ± 0.07 and ${\rm{\bar{R}}}_{\rm{P}}^2$ ≈ 0.97 ± 0.05, respectively). MCKM could facilitate automated cell line selection, identification of critical process parameters and biomarkers, guide media and feeding strategies, predict metabolite profiles, and support scale-up and quality-by-design studies, delivering overall reduction of experimental workload. One-Sentence Summary: This paper presents a novel kinetic model that derives distinct parameter sets from a single fed-batch run, enabling characterization of individual CHO clones across different mAb targets.

Description

Acknowledgements: This study was co-funded by BBSRC and GlaxoSmithKline via the CASE Award programme. Furthermore, an acknowledgement goes to Ruth Rowland-Jones and Paloma Diaz-Fernandez within GSK for collecting and sharing data used in this study.


Funder: Biotechnology and Biological Sciences Research Council; doi: https://doi.org/10.13039/501100000268

Journal Title

J Ind Microbiol Biotechnol

Conference Name

Journal ISSN

1367-5435
1476-5535

Volume Title

52

Publisher

Oxford University Press (OUP)

Rights and licensing

Except where otherwised noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/