Application of metabolic modeling for targeted optimization of high seeding density processes.

Process intensification by application of perfusion mode in pre-stage bioreactors and subsequent inoculation of cell cultures at high seeding densities (HSD) has the potential to meet the increasing requirements of future manufacturing demands. However, process development is currently restrained by a limited understanding of the cell's requirements under these process conditions. The goal of this study was to use extended metabolite analysis and metabolic modeling for targeted optimization of HSD cultivations. The metabolite analysis of HSD N-stage cultures revealed accumulation of inhibiting metabolites early in the process and flux balance analysis led to the assumption that reactive oxygen species (ROS) were contributing to the fast decrease in cell viability. Based on the metabolic analysis an optimized feeding strategy with lactate and cysteine supplementation was applied, resulting in an increase in antibody titer of up to 47%. Flux balance analysis was further used to elucidate the surprisingly strong synergistic effect of lactate and cysteine, indicating that increased lactate uptake led to reduced ROS formation under these conditions whilst additional cysteine actively reduced ROS via the glutathione pathway. The presented results finally demonstrate the benefit of modeling approaches for process intensification as well as the potential of HSD cultivations for biopharmaceutical manufacturing.

Pre-stage perfusion and ultra-high seeding cell density in CHO fed-batch culture: a case study for process intensification guided by systems biotechnology.

Process intensification strategies are needed in the field of therapeutic protein production for higher productivities, lower cost of goods and improved facility utilization. This work describes an intensification approach, which connects a tangential-flow-filtration (TFF) based pre-stage perfusion process with a concentrated fed-batch production culture inoculated with an ultra-high seeding density (uHSD). This strategy shifted biomass production towards the pre-stage, reaching up to 45 × 106 cells/mL in perfusion mode. Subsequently, production in the intensified fed-batch started immediately and the product titer was almost doubled (1.9-fold) in an equivalent runtime and with comparable product quality compared to low-seeded cultures. Driven by mechanistic modelling and next-generation sequencing (NGS) the process had been optimized by selecting the media composition in a way that minimized cellular adaptation between perfusion and production culture. As a main feature, lactate feeding was applied in the intensified approach to promote cell culture performance and process scalability was proven via transfer to pilot-scale i.e., 20 L pre-stage perfusion and 80 L production reactor. Moreover, an earlier shift from a growth associated to a production stage associated gene expression pattern was identified for uHSD cultures compared to the reference. Overall, we showed that the described intensification strategy yielded in a higher volumetric productivity and is applicable for existing or already approved molecules in common, commercial fed-batch facilities. This work provides an in-depth molecular understanding of cellular processes that are detrimental during process intensification.

Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective.

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism.

Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing.

The arrival of next-generation sequencing (NGS) technologies has led to novel opportunities for expression profiling and genome analysis by utilizing vast amounts of short read sequence data. Here, we demonstrate that expression profiling in organisms lacking any genome or transcriptome sequence information is feasible by combining Illumina's mRNA-seq technology with a novel bioinformatics pipeline that integrates assembled and annotated Chinese hamster ovary (CHO) sequences with information derived from related organisms. We applied this pipeline to the analysis of CHO cells which were chosen as a model system owing to its relevance in the production of therapeutic proteins. Specifically, we analysed CHO cells undergoing butyrate treatment which is known to affect cell cycle regulation and to increase the specific productivity of recombinant proteins. By this means, we identified sequences for >13,000 CHO genes which added sequence information of approximately 5000 novel genes to the CHO model. More than 6000 transcript sequences are predicted to be complete, as they covered >95% of the corresponding mouse orthologs. Detailed analysis of selected biological functions such as DNA replication and cell cycle control, demonstrated the potential of NGS expression profiling in organisms without extended genome sequence to improve both data quantity and quality.

CHO gene expression profiling in biopharmaceutical process analysis and design.

Increase in both productivity and product yields in biopharmaceutical process development with recombinant protein producing mammalian cells can be mainly attributed to the advancements in cell line development, media, and process optimization. Only recently, genome-scale technologies enable a system-level analysis to elucidate the complex biomolecular basis of protein production in mammalian cells promising an increased process understanding and the deduction of knowledge-based approaches for further process optimization. Here, the use of gene expression profiling for the analysis of a low titer (LT) and high titer (HT) fed batch process using the same IgG producing CHO cell line was investigated. We found that gene expression (i) significantly differed in HT versus LT process conditions due to differences in applied chemically defined, serum-free media, (ii) changed over the time course of the fed batch processes, and that (iii) both metabolic pathways and 14 biological functions such as cellular growth or cell death were affected. Furthermore, detailed analysis of metabolism in a standard process format revealed the potential use of transcriptomics for rational media design as is shown for the case of lipid metabolism where the product titer could be increased by about 20% based on a lipid modified basal medium. The results demonstrate that gene expression profiling can be an important tool for mammalian biopharmaceutical process analysis and optimization.

Towards robust cell culture processes - Unraveling the impact of media preparation by spectroscopic online monitoring.

Biopharmaceutical manufacturing processes can be affected by variability in cell culture media, e.g. caused by raw material impurities. Although efforts have been made in industry and academia to characterize cell culture media and raw materials with advanced analytics, the process of industrial cell culture media preparation itself has not been reported so far. Within this publication, we first compare mid-infrared and two-dimensional fluorescence spectroscopy with respect to their suitability as online monitoring tools during cell culture media preparation, followed by a thorough assessment of the impact of preparation parameters on media quality. Through the application of spectroscopic methods, we can show that media variability and its corresponding root cause can be detected online during the preparation process. This methodology is a powerful tool to avoid batch failure and is a valuable technology for media troubleshooting activities. Moreover, in a design of experiments approach, including additional liquid chromatography-mass spectrometry analytics, it is shown that variable preparation parameters such as temperature, power input and preparation time can have a strong impact on the physico-chemical composition of the media. The effect on cell culture process performance and product quality in subsequent fed-batch processes was also investigated. The presented results reveal the need for online spectroscopic methods during the preparation process and show that media variability can already be introduced by variation in media preparation parameters, with a potential impact on scale-up to a commercial manufacturing process.

Metabolic flux analysis in Escherichia coli by integrating isotopic dynamic and isotopic stationary 13C labeling data.

The novel concept of isotopic dynamic 13C metabolic flux analysis (ID-13C MFA) enables integrated analysis of isotopomer data from isotopic transient and/or isotopic stationary phase of a 13C labeling experiment, short-time experiments, and an extended range of applications of 13C MFA. In the presented work, an experimental and computational framework consisting of short-time 13C labeling, an integrated rapid sampling procedure, a LC-MS analytical method, numerical integration of the system of isotopomer differential equations, and estimation of metabolic fluxes was developed and applied to determine intracellular fluxes in glycolysis, pentose phosphate pathway (PPP), and citric acid cycle (TCA) in Escherichia coli grown in aerobic, glucose-limited chemostat culture at a dilution rate of D = 0.10 h(-1). Intracellular steady state concentrations were quantified for 12 metabolic intermediates. A total of 90 LC-MS mass isotopomers were quantified at sampling times t = 0, 91, 226, 346, 589 s and at isotopic stationary conditions. Isotopic stationarity was reached within 10 min in glycolytic and PPP metabolites. Consistent flux solutions were obtained by ID-13C MFA using isotopic dynamic and isotopic stationary 13C labeling data and by isotopic stationary 13C MFA (IS-13C MFA) using solely isotopic stationary data. It is demonstrated that integration of dynamic 13C labeling data increases the sensitivity of flux estimation, particularly at the glucose-6-phosphate branch point. The identified split ratio between glycolysis and PPP was 55%:44%. These results were confirmed by IS-13C MFA additionally using labeling data in proteinogenic amino acids (GC-MS) obtained after 5 h from sampled biomass.

Integrated sampling procedure for metabolome analysis.

Metabolome analysis, the analysis of large sets of intracellular metabolites, has become an important systems analysis method in biotechnological and pharmaceutical research. In metabolic engineering, the integration of metabolome data with fluxome and proteome data into large-scale mathematical models promises to foster rational strategies for strain and cell line improvement. However, the development of reproducible sampling procedures for quantitative analysis of intracellular metabolite concentrations represents a major challenge, accomplishing (i) fast transfer of sample, (ii) efficient quenching of metabolism, (iii) quantitative metabolite extraction, and (iv) optimum sample conditioning for subsequent quantitative analysis. In addressing these requirements, we propose an integrated sampling procedure. Simultaneous quenching and quantitative extraction of intracellular metabolites were realized by short-time exposure of cells to temperatures < or =95 degrees C, where intracellular metabolites are released quantitatively. Based on these findings, we combined principles of heat transfer with knowledge on physiology, for example, turnover rates of energy metabolites, to develop an optimized sampling procedure based on a coiled single tube heat exchanger. As a result, this sampling procedure enables reliable and reproducible measurements through (i) the integration of three unit operations into a one unit operation, (ii) the avoidance of any alteration of the sample due to chemical reagents in quenching and extraction, and (iii) automation. A sampling frequency of 5 s(-)(1) and an overall individual sample processing time faster than 30 s allow observing responses of intracellular metabolite concentrations to extracellular stimuli on a subsecond time scale. Recovery and reliability of the unit operations were analyzed. Impact of sample conditioning on subsequent IC-MS analysis of metabolites was examined as well. The integrated sampling procedure was validated through consistent results from steady-state metabolite analysis of Escherichia coli cultivated in a chemostat at D = 0.1 h(-)(1).

A global RNA-seq-driven analysis of CHO host and production cell lines reveals distinct differential expression patterns of genes contributing to recombinant antibody glycosylation.

Boehringer Ingelheim uses two CHO-DG44 lines for manufacturing biotherapeutics, BI-HEX-1 and BI-HEX-2, which produce distinct cell type-specific antibody glycosylation patterns. A recently established CHO-K1 descended host, BI-HEX-K1, generates antibodies with glycosylation profiles differing from CHO-DG44. Manufacturing process development is significantly influenced by these unique profiles. To investigate the underlying glycosylation related gene expression, we leveraged our CHO host and production cell RNA-seqtranscriptomics and product quality database together with the CHO-K1 genome. We observed that each BI-HEX host and antibody producing cell line has a unique gene expression fingerprint. CHO-DG44 cells only transcribe Fut10, Gfpt2 and ST8Sia6 when expressing antibodies. BI-HEX-K1 cells express ST8Sia6 at host cell level. We detected a link between BI-HEX-1/BI-HEX-2 antibody galactosylation and mannosylation and the gene expression of the B4galt gene family and genes controlling mannose processing. Furthermore, we found major differences between the CHO-DG44 and CHO-K1 lineages in the expression of sialyl transferases and enzymes synthesizing sialic acid precursors, providing a rationale for the lack of immunogenic NeuGc/NGNA synthesis in CHO. Our study highlights the value of systems biotechnology to understand glycoprotein synthesis and product glycoprofiles. Such data improve future production clone selection and process development strategies for better steering of biotherapeutic product quality.

Advancing biopharmaceutical process development by system-level data analysis and integration of omics data.

Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential. Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.

In vivo dynamics of glycolysis in Escherichia coli shows need for growth-rate dependent metabolome analysis.

Metabolomics emerges to become an important profiling technique in bio(techno)logical systems. In addition to intracellular metabolite concentrations at (quasi) stationary conditions, stimulus-response experiments provide information on the dynamic behavior of metabolic pathways. These data are relevant for bioprocess analysis on the level of metabolism and for application of metabolic engineering principles aiming at a metabolic redesign of producer cells. However, even for the well-studied bacteria Escherichia coli only limited growth-rate dependent intracellular metabolite information is currently available, thereby impeding comprehensive metabolome analysis. Here, we present intracellular metabolite concentration data of representative glycolytic intermediates in E. coli cultivated in glucose-limited chemostats, (i) at systematic variation of growth-rate (D = 0.1, 0.2, 0.3, and 0.4 h(-1)) and (ii) at both steady-state and after a glucose pulse applying a recently introduced integrated sampling procedure and LC-MS analytical method. Whereas intracellular steady-state concentrations of upper part glycolytic intermediates FBP and DHAP+GAP increased 2.3-fold, respectively 2.8-fold, when specific growth-rate is raised from micro = 0.1 h(-1) to micro = 0.4 h(-1), the opposite trend was observed for 2PG+3PG and PEP pools with a decrease by a factor of 2.1, respectively 1.9. In glucose pulse experiments FBP and DHAP+GAP showed a 3.3 (1.8)-fold, respectively 2.8 (2.0)-fold, increase relative to the steady-state level at micro = 0.1 (0.4) h(-1). Also, the dynamics changed with growth-rate for these two metabolite pools. In contrast, 2PG+3PG and PEP were characterized by decreased concentrations in response to a glucose pulse and the relative changes related to steady-state values were significantly smaller compared with FBP and DHAP+GAP. The observed growth-rate dependency of our data clearly indicates the necessity for metabolome studies covering a broader range of physiological growth conditions.