Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV-Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV-Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 10(5) ± 1.90 10(5) cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV-VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.
Monitoring mammalian cell culture with UV–vis spectroscopy has not been widely explored. The aim of this work was to calibrate Partial Least Squares (PLS) models from off-line UV–vis spectral data in order to predict some nutrients and metabolites, as well as viable cell concentrations for mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Spectra of samples taken from batches performed at different dissolved oxygen concentrations (10, 30, 50, and 70% air saturation), in two bioreactor configurations and with two strategies to control pH were used to calibrate and validate PLS models. Glutamine, glutamate, glucose, and lactate concentrations were suitably predicted by means of this strategy. Especially for glutamine and glucose concentrations, the prediction error averages were lower than 0.5060.10 mM and 2.2160.16 mM, respectively. These values are comparable with those previously reported using near infrared and Raman spectroscopy in conjunction with PLS. However, viable cell concentration models need to be improved. The present work allows for UV–vis at-line sensor development, decrease cost related to nutrients and metabolite quantifications and establishment of fed-batch feeding schemes.
This work focused on determining the effect of dissolved oxygen concentration (DO) on growth and metabolism of BHK-21 cell line (host cell for recombinant proteins manufacturing and viral vaccines) cultured in two stirred tank bioreactors with different aeration-homogenization systems, as well as pH control mode. BHK-21 cell line adapted to single-cell suspension was cultured in Celligen without aeration cage (rotating gas-sparger) and Bioflo 110, at 10, 30 and 50 % air saturation (impeller for gas dispersion from sparger-ring). The pH was controlled at 7.2 as far as it was possible with gas mixtures. In other runs, at 30 and 50 % (DO) in Bioflo 110, the cells grew at pH controlled with CO2 and NaHCO3 solution. Glucose, lactate, glutamine, and ammonium were quantified by enzymatic methods. Cell concentration, size and specific oxygen consumption were also determined. When NaHCO3 solution was not used, the optimal DOs were 10 and 50 % air saturation for Celligen and Bioflo 110, respectively. In this condition maximum cell concentrations were higher than 4 × 10(6) cell/mL. An increase in maximum cell concentration of 36 % was observed in batch carried out at 30 % air saturation in a classical stirred tank bioreactor (Bioflo 110) with base solution addition. The optimal parameters defined in this work allow for bioprocess developing of viral vaccines, transient protein expression and viral vector for gene therapy based on BHK-21 cell line in two stirred tank bioreactors with different agitation-aeration systems.
This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions.
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