(471a) Development of a Segregated Cell Growth Model Structured By Dynamic Flux Balance Analysis | AIChE

(471a) Development of a Segregated Cell Growth Model Structured By Dynamic Flux Balance Analysis


Barbosa, R. - Presenter, Imperial College of London
Kontoravdi, C., Imperial College London
Diaz-Fernandez, P., GSK Medicines Research Centre
Skeene, K., GSK Medicines Research Centre
Finka, G., GSK Medicines Research Centre
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Barbosa, Rodrigo Normal Barbosa, Rodrigo 2 15 2019-04-12T10:09:00Z 2019-04-12T10:09:00Z 1 308 1756 Imperial College 14 4 2060 16.00

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font-family:" calibri>Development of a Segregated
Cell Growth Model Structured By Dynamic Flux Balance Analysis 

text-indent:36.0pt;background:white"> font-family:" calibri>Barbosa R1, Skeene " calibri> K2, Diaz-Fernandez P2, Finka G 2Kontoravdi

background:white">1Department of Chemical Engineering, Imperial College London

background:white">2GlaxoSmithKline, Stevenage Gunnels Wood Road SG1 2NY


background:white">Fed-batch Chinese Hamster Ovary (CHO) cell systems are the most
efficient processes for large-scale manufacturing of Monoclonal Antibodies (mAbs), introduced as cancer biotherapeutics. CHO cells
undergo changes in their internal controls in response to intracellular and
extracellular conditions affecting their growth and the quality of mAbs they secrete. Predictive mathematical modelling can be
applied as a basis for optimisation and control of often expensive, time
consuming and hard-to-analyse biological and process parameters that affect
cell proliferation and product titer. The cellular
transition through different metabolic states displays random and deterministic
features, which can be integrated into a more refined modelling approach.
Despite recent advances in mathematical bioprocess engineering, most cell
growth model structures lack the intrinsic balance among the series of dynamic
behaviours under different metabolic states. To address this issue, we have
designed a novel segregated kinetic model describing by the transitions in
metabolic behaviour, which are linked to cell cycling activity. We also
integrate in the model formulation exercise the results of Flux Balance
analysis (FBA) as a means to determine model structure
and introduce reasonable constraints for metabolic parameter values and
therefore expedite the parameter estimation exercise. Simulation results show a
better agreement with the observed data than previous unsegregated models as
the co-existing cycling and resting cell population dynamics are incorporated
and validated. This modelling approach further aids systematic experimental
design through quantitative and qualitative closed loop analysis, with the aim
of predicting, controlling and optimizing the manufacturing of mAbs."