In this paper, the application of a robust nonlinear model predictive control (NMPC) framework to mammalian cell cultures is proposed, dealing with possible large kinetic parameter uncertainties. Industrial constraints formulated in view of good manufacturing practice and quality-by-design approach are also considered, namely the assurance that all state trajectories are contained within a corridor defined by lower and upper safety bounds. The latter are assimilated to the well-known tube-based paradigm which is used to formulate the corresponding robust NMPC problem. Both classical and tube-based NMPC performances are assessed in numerical simulations where specific key-species are regulated while dealing with an uncertain plant model. The capability of the tube-based method to reduce the impact of the parameter variations on the state trajectories and the violation of the constraints is highlighted, suggesting the transfer of the method on a real pharmaceutical process.

Even if the performances of bioprocesses can be significantly improved by model-based control, there often remains a tradeoff between model complexity and control robustness. This paper proposes an original data -driven strategy for fast design of dynamic bioprocess models with minimal complexity (i.e., minimal number of bioreactions). Maximum likelihood principal component analysis (MLPCA) is applied to infer the minimal reaction scheme from a 25-state mammalian cell culture database. Then, a systematic algorithm is used to provide a continuous kinetic model formulation assuming all rates to occur simultaneously, which may be far from true cell metabolic conditions sometimes presenting discontinuous metabolic switches. A robust model predictive formulation is therefore adopted to reduce the impact of model structural uncertainty on the process performances. Additional numerical results show that the proposed strategy presents excellent performances in presence of unexpected metabolic switches.

Cultivating Chinese hamster ovary (CHO) cells in microtiter plates (MTPs) with time-resolved monitoring of the oxygen transfer rate (OTR) is highly desirable to provide process insights at increased throughput. However, monitoring of the OTR in MTPs has not been demonstrated for CHO cells, yet. Hence, a CHO cultivation process was transferred from shake flasks to MTPs to enable monitoring of the OTR in each individual well of a 48-well MTP. For this, the cultivation of an industrially relevant, antibody-producing cell line was transferred from shake flask to MTP based on the volumetric oxygen mass transfer coefficient (kLa). Culture behavior was well comparable (deviation of the final IgG titer less than 10%). Monitoring of the OTR in 48-well MTPs was then used to derive the cytotoxicity of dimethyl sulfoxide (DMSO) based on a dose–response curve in a single experiment using a second CHO cell line. Logistic fitting of the dose–response curve determined after 100 h was used to determine the DMSO concentration that resulted in a cytotoxicity of 50% (IC50). A DMSO concentration of 2.70% ± 0.25% was determined, which agrees with the IC50 previously determined in shake flasks (2.39% ± 0.1%). Non-invasive, parallelized, and time-resolved monitoring of the OTR of CHO cells in MTPs was demonstrated and offers excellent potential to speed up process development and assess cytotoxicity.

The increased use of biopharmaceuticals calls for improved means of bioprocess monitoring. In this work, capillary electrophoresis (CE) and microchip electrophoresis (MCE) methods were developed and applied for the analysis of amino acids (AAs) in cell culture supernatant. In samples from different days of a Chinese hamster ovary cell cultivation process, all 19 proteinogenic AAs containing primary amine groups could be detected using CE, and 17 out of 19 AAs using MCE. The relative concentration changes in different samples agreed well with those measured by high-performance liquid chromatography (HPLC). Compared to the more commonly employed HPLC analysis, the CE and MCE methods resulted in faster analysis, while significantly lowering both the sample and reagent consumption, and the cost per analysis. 

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed "generic" models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Raman spectrum based predictive models provide a process analytical technology (PAT) tool for monitoring and control of culture parameters in bioprocesses. Steady-state perfusion cultures generate a relatively stable metabolite profile, which is not conducive to modeling due to the absence of variations of culture parameters. Here we present an approach where different steady-states obtained by variation of the cell specific perfusion rate (CSPR) between 10 and 40 pL/(cell * day) with cell densities up to 100 × 106 cells/mL during the process development provided a dynamic culture environment, favorable for the model calibration. The cell density had no effect on the culture performance at similar CSPR, however a variation in the CSPR had a strong influence on the metabolism, mAb productivity and N-glycosylation. Predictive models were developed for multiple culture parameters, including cell density, lactate, ammonium and amino acids; and then validated with new runs performed at multiple or single steady-states, showing high prediction accuracy. The relationship of amino acids and antibody N-glycosylation was modeled to predict the glycosylation pattern of the product in real time. The present efficient process development approach with integration of Raman spectroscopy provides a valuable PAT tool for later implementation in steady-state perfusion production processes.

The biopharmaceutical market has been rapidly growing in recent years, creating a highly competitive arena where R&D is critical to strike a balance between clinical safety and profitability. Toward process optimization, the recent development and adoption of new process analytical technologies (PAT) highlight the dynamic complexity of mammalian/human cell culture processes, as well as the importance of fine-tuning and modeling key metabolites and proteins. In this context, simple, rapid, and cost-effective devices allowing routine at-line monitoring of specific proteins during process development and production are currently lacking. Here, we report the development of a versatile microfluidic protein analysis cartridge allowing the multiplexed bead-based immunodetection of specific proteins directly from complex mixtures with minimal hands-on time. Colorimetric quantification of Chinese hamster ovary (CHO) host cell proteins as key impurities, monoclonal antibodies as target biopharmaceuticals, and lactate dehydrogenase as a marker of cell viability was achieved with limits of detection in the 1-10 ng/mL range and analysis times as short as 30 min. The device was further demonstrated for the monitoring of a Rituximab-producing CHO cell bioreactor over the course of 8 days, providing comparable recoveries to standard enzyme-linked immunosorbent assay (ELISA) kits. The high sensitivity combined with robustness to matrix interference highlights the potential of the device to perform at-line measurements spanning from the bioreactor to the downstream processing.

Mathematical modelling can provide precious tools for bioprocess simulation, prediction, control and optimization of mammalian cell-based cultures. In this paper we present a novel method to generate kinetic models of such cultures, rendering complex metabolic networks in a poly-pathway kinetic model. The model is based on subsets of elementary flux modes (EFMs) to generate macro-reactions. Thanks to our column generation-based optimization algorithm, the experimental data are used to identify the EFMs, which are relevant to the data. Here the systematic enumeration of all the EFMs is eliminated and a network including a large number of reactions can be considered. In particular, the poly-pathway model can simulate multiple metabolic behaviors in response to changes in the culture conditions. We apply the method to a network of 126 metabolic reactions describing cultures of antibody-producing Chinese hamster ovary cells, and generate a poly-pathway model that simulates multiple experimental conditions obtained in response to variations in amino acid availability. A good fit between simulated and experimental data is obtained, rendering the variations in the growth, product, and metabolite uptake/secretion rates. The intracellular reaction fluxes simulated by the model are explored, linking variations in metabolic behavior to adaptations of the intracellular metabolism.