Imperial College London
Portrait Picture

Professor Cleo Kontoravdi

Faculty of Engineering, Department of Chemical Engineering

Professor of Biological Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6655cleo.kontoravdi98 Website

 
 
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Location

 

310ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Antonakoudis:2021:10.1016/j.compchemeng.2021.107471,
author = {Antonakoudis, A and Strain, B and Barbosa, R and Jimenez, del Val I and Kontoravdi, K},
doi = {10.1016/j.compchemeng.2021.107471},
journal = {Computers and Chemical Engineering},
pages = {1--11},
title = {Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells},
url = {http://dx.doi.org/10.1016/j.compchemeng.2021.107471},
volume = {154},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In-process quality control of biotherapeutics, such as monoclonal antibodies, requires computationally efficient process models that use readily measured process variables to compute product quality. Existing kinetic cell culture models can effectively describe the underlying mechanisms but require considerable development and parameterisation effort. Stoichiometric models, on the other hand, provide a generic, parameter-free means for describing metabolic behaviour but do not extend to product quality prediction. We have overcome this limitation by integrating a stoichiometric model of Chinese hamster ovary (CHO) cell metabolism with an artificial neural network that uses the fluxes of nucleotide sugar donor synthesis to compute the profile of Fc N-glycosylation, a critical quality attribute of antibody therapeutics. We demonstrate that this hybrid framework accurately computes glycan distribution profiles using a set of easy-to-obtain experimental data, thus providing a platform for process control applications.
AU  - Antonakoudis,A
AU  - Strain,B
AU  - Barbosa,R
AU  - Jimenez,del Val I
AU  - Kontoravdi,K
DO  - 10.1016/j.compchemeng.2021.107471
EP  - 11
PY  - 2021///
SN  - 0098-1354
SP  - 1
TI  - Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells
T2  - Computers and Chemical Engineering
UR  - http://dx.doi.org/10.1016/j.compchemeng.2021.107471
UR  - https://www.sciencedirect.com/science/article/pii/S0098135421002490?via%3Dihub
UR  - http://hdl.handle.net/10044/1/90764
VL  - 154
ER  -
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