In-line monitoring of Bordetella pertussis cultivation using fluorescence spectroscopy.

Fluorescence spectroscopy is a non-invasive and highly sensitive method for bioprocess monitoring. The use of fluorescence spectroscopy is not very well established in the industry for in-line monitoring. In the present work, a 2-D fluorometer with two excitation lights (365 and 405 nm) and emission spectra in the range of 350-850 nm were used for in-line monitoring of two strains of Bordetella pertussis cultivation operated in batch and fed batch. A Partial Least Squares (PLS) based regression model was used for the estimation of cell biomass, amino acids (glutamate and proline) and antigen (Pertactin) produced. It was observed that accurate predictions were achieved when models were calibrated separately for each cell strain and nutrient media formulation. Also, prediction accuracy was improved when dissolved oxygen, agitation and culture volume are added as additional features in the regression model. The proposed approach of combining in-line fluorescence and other online measurements is shown to have good potential for in-line monitoring of bioprocesses.

Modeling the effect of oxidative stress on Bordetella pertussis fermentations.

A mathematical model is proposed for Bordetella pertussis with the main goal to better understand and describe the relation between cell growth, oxidative stress and NADPH levels under different oxidative conditions. The model is validated with flask experiments conducted under different conditions of oxidative stress induced by high initial glutamate concentrations, low initial inoculum and secondary culturing following exposure to starvation. The model exhibited good accuracy when calibrated and validated for the different experimental conditions. From comparisons of model predictions to data with different model mechanisms, it was concluded that intracellular reactive oxidative species only have an indirect effect on growth rate by reacting with NADPH and thereby reducing the amount of NADPH that is available for growth.

Applications of flow cytometry sorting in the pharmaceutical industry: A review.

The article reviews applications of flow cytometry sorting in manufacturing of pharmaceuticals. Flow cytometry sorting is an extremely powerful tool for monitoring, screening and separating single cells based on any property that can be measured by flow cytometry. Different applications of flow cytometry sorting are classified into groups and discussed in separate sections as follows: (a) isolation of cell types, (b) high throughput screening, (c) cell surface display, (d) droplet fluorescent-activated cell sorting (FACS). Future opportunities are identified including: (a) sorting of particular fractions of the cell population based on a property of interest for generating inoculum that will result in improved outcomes of cell cultures and (b) the use of population balance models in combination with FACS to design and optimize cell cultures.

Development of new media formulations for cell culture operations based on regression models.

The paper discusses modelling and optimization of multi-component cell culture medium. The specific productivity (Qp) was considered a function of the medium components and possible interactions described by linear factors, two-way interactions and squared terms that results in a high dimensional problem where the number of variables p (represented by the medium components and their interactions) is much larger than the number of observations n. Principal Components Regression (PCR), Partial Least Squares (PLS), Lasso and Elastic Net regressions were compared as modelling tools to deal with a high dimensional [Formula: see text] problem. PCR and PLS regression models resulted in better prediction results and were used for robust optimization of the medium composition by a nonlinear optimization. The case studies show that it is possible to formulate new media that result in higher Qp than the ones provided by the initial media experiments available. Also, the multivariate statistical approach permitted us to select media that is most informative about the optimum thus permitting modelling and optimization with a reduced set of initial experiments.

Investigation of the effects of oxidative stress-inducing factors on culturing and productivity of Bordetella pertussis.

The stress response of Bordetella pertussis during fermentation was assessed by means of fluorescence-based techniques. During the manufacturing of vaccines, B. pertussis is subjected to stress during adaptation to a new environment and operating conditions in the bioreactor, which can have harmful consequences on growth and protein yield. In this study, stress was imposed by varying the percentage of dissolved oxygen (DO) and inoculum size, and by adding rotenone and hydrogen peroxide. In this study, fluorescence spectroscopy is used as a tool for measuring oxidative stress. High levels of DO during fed-batch operation had no detrimental effect on growth, but the specific productivity of pertactin (PRN) decreased. Cultures that were started with an inoculum size that was 10 times smaller than the control resulted in significantly less PRN as compared to controls where reduction was more significant in flasks as compared to bioreactors. A comparison of filtered to heat-sterilized media revealed that filtered media offered a protective effect against H2 O2 . Heat sterilization of the media might result in the destruction of components that offer protection against oxidative stress. Nonetheless, filter sterilization on its own would be insufficient for large-scale manufacturing. It should be emphasized that the effects of these stressors while investigating for other microorganisms have not been studied for B. pertussis.

A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis.

Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough).

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets.

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images.

Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

A semi-empirical glycosylation model of a camelid monoclonal antibody under hypothermia cell culture conditions.

The impact of cell culture environment on the glycan distribution of a monoclonal antibody (mAb) has been investigated through a combination of experiments and modeling. A newly developed CHO DUXB cell line was cultivated at two levels of initial Glutamine (Gln) concentrations (0, 4 mM) and incubation temperatures of (33 and 37 °C) in batch operation mode. Hypothermia was applied either through the entire culture duration or only during the post-exponential phase. Beyond reducing cell growth and increasing productivity, hypothermia significantly altered the galactosylation index profiles as compared to control conditions. A novel semi-empirical dynamic model was proposed for elucidating the connections between the extracellular cell culture conditions to galactosylation index. The developed model is based on a simplified balance of nucleotides sugars and on the correlation between sugars' levels to the galactosylation index (GI). The model predictions were found to be in a good agreement with the experimental data. The proposed empirical model is expected to be useful for controlling the glycoprofiles by manipulating culture conditions.

Identification of active constraints in dynamic flux balance analysis.

This study deals with the calibration of dynamic metabolic flux models that are formulated as the maximization of an objective subject to constraints. Two approaches were applied for identifying the constraints from data. In the first approach a minimal active number of limiting constraints is found based on data that are assumed to be bounded within sets whereas, in the second approach, the limiting constraints are found based on parametric sensitivity analysis. The ability of these approaches to finding the active limiting constraints was verified through their application to two case studies: an in-silico (simulated) data-based study describing the growth of E. coli and an experimental data-based study for Bordetella pertussis (B. pertussis). © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:26-36, 2017.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

Monitoring of an antigen manufacturing process.

Fluorescence spectroscopy in combination with multivariate statistical methods was employed as a tool for monitoring the manufacturing process of pertactin (PRN), one of the virulence factors of Bordetella pertussis utilized in whopping cough vaccines. Fluorophores such as amino acids and co-enzymes were detected throughout the process. The fluorescence data collected at different stages of the fermentation and purification process were treated employing principal component analysis (PCA). Through PCA, it was feasible to identify sources of variability in PRN production. Then, partial least square (PLS) was employed to correlate the fluorescence spectra obtained from pure PRN samples and the final protein content measured by a Kjeldahl test from these samples. In view that a statistically significant correlation was found between fluorescence and PRN levels, this approach could be further used as a method to predict the final protein content.

Intrinsic fluorescence-based at situ soft sensor for monitoring monoclonal antibody aggregation.

Intrinsic fluorescence spectroscopy, in conjunction with partial least squares regression (PLSR), was investigated as a potential technique for online quality control and quantitative monitoring of Immunoglobulin G (IgG) aggregation that occurs following exposure to conditions that emulate those that can occur during protein downstream processing. Initially, the impact of three stress factors (temperature, pH, and protein concentration) on the degree of aggregation determined using size exclusion chromatography data, was investigated by performing a central composite designexperiment and applying a fitting response surface model. This investigation identified the influence of the factors as well as the operating regions with minimum propensity to induce protein aggregation. Spectral changes pertinent to the stressed samples were also investigated and found to corroborate the high sensitivity of the intrinsic fluorescence to conformational changes of the proteins under study. Ultimately, partial least squares regression was implemented to formulate two fluorescence-based soft sensors for quality control--product classification--and quantitative monitoring--concentration of monomer. The resulting regression models exhibited accurate prediction ability and good potential for in situ monitoring of monoclonal antibody downstream purification processes.

Development of a soft-sensor based on multi-wavelength fluorescence spectroscopy and a dynamic metabolic model for monitoring mammalian cell cultures.

A soft-sensor based on an Extended Kalman Filter (EKF) that combines data obtained using a fluorescence-based soft-sensor with a dynamic mechanistic model, was investigated as a tool for continuous monitoring of a Chinese hamster ovary (CHO) cell cultivation process. A standalone fluorescence based soft-sensor, which uses a combination of an empirical multivariate statistical model and measured spectra, was designed for predicting key culture variables including viable and dead cells, recombinant protein, glucose, and ammonia concentrations. The standalone fluorescence sensor was then combined with a dynamic mechanistic model within an EKF framework, for improving the prediction accuracy and generating predictions in-between sampling instances. The dynamic model used for the EKF framework was based on a structured metabolic flux analysis and mass balances. In order to calibrate the fluorescence-based empirical model and the dynamic mechanistic model, cells were grown in batch mode with different initial glucose and glutamine concentrations. To mitigate the uncertainty associated with the model structure and parameters, non-stationary disturbances were accounted for in the EKF by parameter-adaptation. It was demonstrated that the implementation of the EKF along with the dynamic model could improve the accuracy of the fluorescence-based predictions at the sampling instances. Additionally, it was shown that the major advantage of the EKF-based soft-sensor, compared to the standalone fluorescence-based counterpart, was its capability to track the temporal evolution of key process variables between measurement instances obtained by the fluorescence-based soft-sensor. This is crucial for designing control strategies of CHO cell cultures with the aim of guaranteeing product quality.

Fluorescence-based soft sensor for at situ monitoring of Chinese hamster ovary cell cultures.

Multi-wavelength fluorescence spectroscopy was investigated as a potential tool for use in monitoring key process variables that include: viable and dead cells, recombinant protein, glucose, and ammonia concentrations for Chinese hamster ovary (CHO) cells during cultivation.For the purpose of calibrating the fluorescence-based empirical model, cells were grown in batch mode with different initial glucose and glutamine concentrations.Spectrofluorometer settings were optimized to ensure reproducibility and accuracy of the acquired spectra. With the purpose of gaining qualitative insight into the evolution of the spectra, the trajectories of individual fluorophore peaks were studied during the cultivation process. Spectral changes related to biomass and secreted proteins were investigated by comparing the spectra at various stages during the downstream processing. A partial least square regression (PLSR) was used to formulate empirical models that related the input data set, i.e., the fluorescence excitation-emission matrix, to the actual state of the system including viable cell and dead cells and recombinant protein, glucose, and ammonia concentrations. The models exhibited accurate prediction ability for the process variables of interest.

Effects of nutrient levels and average culture pH on the glycosylation pattern of camelid-humanized monoclonal antibody.

The impact of operating conditions on the glycosylation pattern of humanized camelid monoclonal antibody, EG2-hFc produced by Chinese hamster ovary (CHO) cells has been evaluated by a combination of experiments and modeling. Cells were cultivated under different levels of glucose and glutamine concentrations with the goal of investigating the effect of nutrient depletion levels and ammonia build up on the cell growth and the glycoprofiles of the monoclonal antibody (Mab). The effect of average pH reduction on glycosylation level during the entire culture time or during a specific time span was also investigated. The relative abundance of glycan structures was quantified by hydrophilic interaction liquid chromatography (HILIC) and the galactosylation index (GI) and the sialylation index (SI) were determined. Lower initial concentrations of glutamine resulted in lower glucose consumption and lower cell yield but increased GI and SI levels when compared to cultures started with higher initial glutamine levels. Similarly, reducing the average pH of culture resulted in lower growth but higher SI and GI levels. These findings indicate that there is a tradeoff between cell growth, resulting Mab productivity and the achievement of desirable higher glycosylation levels. A dynamic model, based on a metabolic flux analysis (MFA), is proposed to describe the metabolism of nutrients, cell growth and Mab productivity. Finally, existing software (GLYCOVIS) that describes the glycosylation pathways was used to illustrate the impact of extracellular species on the glycoprofiles.

Modeling of cell culture damage and recovery leads to increased antibody and biomass productivity in CHO cell cultures.

The development of an efficient and productive cell-culture process requires a deep understanding of intracellular mechanisms and extracellular conditions for optimal product synthesis. Mathematical modeling provides an effective strategy to predict, control, and optimize cell performance under a range of culture conditions. In this study, a mathematical model is proposed for the investigation of cell damage of a Chinese hamster ovary cell culture secreting recombinant anti-RhD monoclonal antibody (mAb). Irreversible cell damage was found to be correlated with a reduction in pH. This irreversible damage to cellular function is described mathematically by a Tessier-based model, in which the actively growing fraction of cells is dependent on an intracellular metabolic product acting as a growth inhibitor. To further verify the model, an offline model-based optimization of mAb production in the cell culture was carried out, with the goal of minimizing cell damage and thereby enhancing productivity through intermittent refreshment of the culture medium. An experimental implementation of this model-based strategy resulted in a doubling of the yield as compared to the batch operation and the resulting biomass and productivity profiles agreed with the model predictions.

Modeling the coupled extracellular and intracellular environments in mammalian cell culture.

The regulation of metabolism in mammalian cell culture is closely linked to the process of apoptosis-programmed cell death. Apoptosis negatively impacts culture viability, product yield, and quality. An improved understanding of the interaction between apoptosis and metabolism will give rise to better control over the culture process, and thus improvements in product yield. This study presents a mathematical model that describes both the metabolic fluxes involving the extracellular metabolites and the progression of apoptosis in terms of intracellular caspases, and thus highlights the interactions between these two processes. The model is trained and validated against experimental observations of Chinese Hamster Ovary cell culture producing monoclonal antibody. Importantly, the model describes the continued production of monoclonal antibody in post exponential phase by incorporating different rates of antibody production for separate sub-populations within the culture. A parameter estimability test was applied on the combined model to assess the confidence in parameter estimates.

A dynamic metabolic flux balance based model of fed-batch fermentation of Bordetella pertussis.

A mathematical model based on a dynamic metabolic flux balance (DMFB) is developed for a process of fed-batch fermentation of Bordetella pertussis. The model is based on the maximization of growth rate at each time interval subject to stoichiometric constraints. The model is calibrated and verified with experimental data obtained in two different bioreactor experimental systems. It was found that the model calibration was mostly sensitive to the consumption or production rates of tyrosine and, for high supplementation rates, to the consumption rate of glutamate. Following this calibration the model correctly predicts biomass and by-products concentrations for different supplementation rates. Comparisons of model predictions to oxygen uptake and carbon emission rates measurements indicate that the TCA cycle is fully functional.

Characterizing natural colloidal/particulate-protein interactions using fluorescence-based techniques and principal component analysis.

Characterization of the interactions between natural colloidal/particulate- and protein-like matter is important for understanding their contribution to different physiochemical phenomena like membrane fouling, adsorption of bacteria onto surfaces and various applications of nanoparticles in nanomedicine and nanotoxicology. Precise interpretation of the extent of such interactions is however hindered due to the limitations of most characterization methods to allow rapid, sensitive and accurate measurements. Here we report on a fluorescence-based excitation-emission matrix (EEM) approach in combination with principal component analysis (PCA) to extract information related to the interaction between natural colloidal/particulate- and protein-like matter. Surface plasmon resonance (SPR) analysis and fiber-optic probe based surface fluorescence measurements were used to confirm that the proposed approach can be used to characterize colloidal/particulate-protein interactions at the physical level. This method has potential to be a fundamental measurement of these interactions with the advantage that it can be performed rapidly and with high sensitivity.