Accelerating attribute-focused process and product development through the development and deployment of autonomous process analytical technology platform system.

Enabling real-time monitoring and control of the biomanufacturing processes through product quality insights continues to be an area of focus in the biopharmaceutical industry. The goal is to manufacture products with the desired quality attributes. To realize this rigorous attribute-focused Quality by Design approach, it is critical to support the development of processes that consistently deliver high-quality products and facilitate product commercialization. Time delays associated with offline analytical testing can limit the speed of process development. Thus, developing and deploying analytical technology is necessary to accelerate process development. In this study, we have developed the micro sequential injection process analyzer and the automatic assay preparation platform system. These innovations address the unmet need for an automatic, online, real-time sample acquisition and preparation platform system for in-process monitoring, control, and release of biopharmaceuticals. These systems can also be deployed in laboratory areas as an offline analytical system and on the manufacturing floor to enable rapid testing and release of products manufactured in a good manufacturing practice environment.

Engineering protein glycosylation in CHO cells to be highly similar to murine host cells.

Since 2015 more than 34 biosimilars have been approved by the FDA. This new era of biosimilar competition has stimulated renewed technology development focused on therapeutic protein or biologic manufacturing. One challenge in biosimilar development is the genetic differences in the host cell lines used to manufacture the biologics. For example, many biologics approved between 1994 and 2011 were expressed in murine NS0 and SP2/0 cell lines. Chinese Hamster ovary (CHO) cells, however, have since become the preferred hosts for production due to their increased productivity, ease of use, and stability. Differences between murine and hamster glycosylation have been identified in biologics produced using murine and CHO cells. In the case of monoclonal antibodies (mAbs), glycan structure can significantly affect critical antibody effector function, binding activity, stability, efficacy, and in vivo half-life. In an attempt to leverage the intrinsic advantages of the CHO expression system and match the reference biologic murine glycosylation, we engineered a CHO cell expressing an antibody that was originally produced in a murine cell line to produce murine-like glycans. Specifically, we overexpressed cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) and N-acetyllactosaminide alpha-1,3-galactosyltransferase (GGTA) to obtain glycans with N-glycolylneuraminic acid (Neu5Gc) and galactose-α-1,3-galactose (alpha gal). The resulting CHO cells were shown to produce mAbs with murine glycans, and they were then analyzed by the spectrum of analytical methods typically used to demonstrate analytical similarity as a part of demonstrating biosimilarity. This included high-resolution mass spectrometry, biochemical, as well as cell-based assays. Through selection and optimization in fed-batch cultures, two CHO cell clones were identified with similar growth and productivity criteria to the original cell line. They maintained stable production for 65 population doubling times while matching the glycosylation profile and function of the reference product expressed in murine cells. This study demonstrates the feasibility of engineering CHO cells to express mAbs with murine glycans to facilitate the development of biosimilars that are highly similar to marketed reference products expressed in murine cells. Furthermore, this technology can potentially reduce the residual uncertainty regarding biosimilarity, resulting in a higher probability of regulatory approval and potentially reduced costs and time in development.

Investigation of a failed DNA assay leads to identification of an unexpected contaminant in a commonly used chromatography buffer.

For mammalian cell-derived recombinant biotherapeutics, controlling host cell DNA levels below a threshold is a regulatory requirement to ensure patient safety. DNA removal during drug substance manufacture is accomplished by a series of chromatography-based purification steps and a qPCR-based analytical method is most used to measure DNA content in the purified drug substance to enable material disposition. While the qPCR approach is mature and its application to DNA measurement is widespread in the industry, it is susceptible to trace levels of process-related contaminants that are carried forward. In this study, we observed failures in spike recovery studies that are an integral component of the qPCR-based DNA testing, suggesting the presence of an inhibitory compound in the sample matrix. We generated hypotheses around the origin of the inhibitory compound and generated multiple sample matrices and deployed a suite of analytical techniques including Raman and NMR spectroscopy to determine the origin and identity of the inhibitory compound. The caustic wash step and depth filter extractables were ruled out as root causes after extensive experimentation and DNA testing. Subsequently, 2-(N-morpholino)ethanesulfonic acid (MES), a buffer used in the chromatography unit operations, was identified as the source of the contaminant. A 500-fold concentration followed by Raman and NMR spectroscopy analysis revealed the identity of the inhibitory compound as polyvinyl sulfone (PVS), an impurity that originates in the MES manufacturing process. We have implemented PVS concentration controls for incoming MES raw material, and our work highlights the need for rigor in raw material qualification and control.

State-of-the-art and emerging trends in analytical approaches to pharmaceutical-product commercialization.

The biopharmaceutical landscape continues to evolve rapidly, and associated modality complexity and the need to improve molecular understanding require concomitant advances in analytical approaches used to characterize and release the product. The Product Quality Attribute Assessment (PQAA) and Quality Target Product Profile (QTPP) frameworks help catalog and translate molecular understanding to process and product-design targets, thereby enabling reliable manufacturing of high-quality product. The analytical target profile forms the basis of identifying best-fit analytical methods for attribute measurement and continues to be successfully used to develop robust analytical methods for detailed product characterization as well as release and stability testing. Despite maturity across multiple testing platforms, advances continue to be made, several with the potential to alter testing paradigms. There is an increasing role for mass spectrometry beyond product characterization and into routine release testing as seen by the progress in multi-attribute methods and technologies, applications to aggregate measurement, the development of capillary zone electrophoresis (CZE) coupled with mass spectrometry (MS) and capillary isoelectric focusing (CIEF) with MS for measurement of glycans and charged species, respectively, and increased application to host cell protein measurement. Multitarget engaging multispecific modalities will drive advances in bioassay platforms and recent advances both in 1- and 2-D NMR approaches could make it the method of choice for characterizing higher-order structures. Additionally, rigorous understanding of raw material and container attributes is necessary to complement product understanding, and these collectively can enable robust supply of high-quality product to patients.

A high-resolution multi-attribute method for product characterization, process characterization, and quality control of therapeutic proteins.

During the manufacturing of therapeutic proteins, Critical Quality Attributes (CQAs) have been monitored by conventional methods, such as cation exchange chromatography (CEX), reduced capillary electrophoresis-sodium dodecyl sulfate (rCE-SDS), and 1,2-diamino-4,5-methylenedioxybenzene (DMB) labelling method. The conventional methods often generate individual peaks that contain multiple components, which may obscure the detection and the quantification of individual critical quality attributes (CQAs). Alternatively, Multi-Attribute Method (MAM) enables detection and quantification of specific CQAs. A high resolution MAM has been developed and qualified to replace several conventional methods in monitoring product quality attributes, such as oxidation, deamidation, clipping, and glycosylation. The qualified MAM was implemented in process characterization, as well as release and stability assays in quality control (QC). In combination with a design-of-experiments (DoE), the MAM method identified multivariate process parameter ranges that yield acceptable CQA level, which provides operational flexibility for manufacturing.

Enabling development, manufacturing, and regulatory approval of biotherapeutics through advances in mass spectrometry.

Rapid technological advances have significantly improved the capability, versatility, and robustness of mass spectrometers which has led to them playing a central role in the development, characterization, and regulatory filings of biopharmaceuticals. Their application spans the entire continuum of drug development, starting with discovery research through product development, characterization, and marketing authorization and continues well into product life cycle management. The scope of application extends beyond traditional protein characterization and includes elements like clone selection, cell culture physiology and bioprocess optimization, investigation support, and process analytical technology. More recently, advances in the MS-based multi-attribute method are enabling the introduction of MS in a cGMP environment for routine release and stability testing. While most applications of MS to date have been for monoclonal antibodies, the successes and learnings should translate to the characterization of next-gen biotherapeutics where modalities like multispecifics could be more prevalent. In this review, we describe the most significant advances in MS and correlate them to the broad spectrum of applications to biotherapeutic development. We anticipate rapid technological improvements to continue that will further accelerate widespread deployment of MS, thereby elevating our overall understanding of product quality and enabling attribute-focused product development.

Methods for Estimating the Probability of Clonality in Cell Line Development.

Mammalian cell banks for biopharmaceutical production are usually derived from a single progenitor cell. Different methods to estimate the probability that the cell banks are clonally derived, or the probability of clonality (PoC), associated with various cloning workflows have been reported previously. In this review, a systematic analysis and comparison of the methods used to calculate the PoC are provided. As the single cell deposition and high-resolution imaging technologies continue to advance and the cloning workflow evolves, an aligned understanding and best practice on estimating the PoC is necessary to compare different cloning workflows adopted across the biopharmaceutical industry and it will help to accelerate regulatory acceptance.

A Comparative Transcriptomics Workflow for Analyzing Microarray Data From CHO Cell Cultures.

Microarray-based comparative transcriptomics analysis is a powerful tool to understand therapeutic protein producing mammalian cell lines at the gene expression level. However, an integrated analysis workflow specifically designed for end-to-end analysis of microarray data for CHO cells, the most prevalent host for commercial recombinant protein production, is lacking. To address this gap, an automated data analysis workflow in R that leverages public domain analysis modules is developed to analyze microarray based gene expression data. In addition to testing the global transcriptome differences of CHO cells at different conditions, the workflow identifies differentially expressed genes and pathways with intuitive visualizations as the outputs. The utility of this automated workflow is demonstrated by comparing the transcriptomic profiles of recombinant protein expressing CHO cells with and without a temperature shift. Statistically significant differential expression at the gene, pathway, and global transcriptome levels are identified and visualized. An automated workflow like the one developed in this study will enable rapid translation of CHO culture microarray data into biologically relevant information for mechanism-driven cell line optimization and bioprocess development.

Accelerating patient access to novel biologics using stable pool-derived product for non-clinical studies and single clone-derived product for clinical studies.

Cell cloning and subsequent process development activities are on the critical path directly impacting the timeline for advancement of next generation therapies to patients with unmet medical needs. The use of stable cell pools for early stage material generation and process development activities is an enabling technology to reduce timelines. To successfully use stable pools during development, it is important that bioprocess performance and requisite product quality attributes be comparable to those observed from clonally derived cell lines. To better understand the relationship between pool and clone derived cell lines, we compared data across recent first in human (FIH) programs at Amgen including both mAb and Fc-fusion modalities. We compared expression and phenotypic stability, bioprocess performance, and product quality attributes between material derived from stable pools and clonally derived cells. Overall, our results indicated the feasibility of matching bioprocess performance and product quality attributes between stable pools and subsequently derived clones. These findings support the use of stable pools to accelerate the advancement of novel biologics to the clinic. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1476-1482, 2017.

Evaluation of two public genome references for chinese hamster ovary cells in the context of rna-seq based gene expression analysis.

RNA-Seq is a powerful transcriptomics tool for mammalian cell culture process development. Successful RNA-Seq data analysis requires a high quality reference for read mapping and gene expression quantification. Currently, there are two public genome references for Chinese hamster ovary (CHO) cells, the predominant mammalian cell line in the biopharmaceutical industry. In this study, we compared these two references by analyzing 60 RNA-Seq samples from a variety of CHO cell culture conditions. Among the 20,891 common genes in both references, we observed that 31.5% have more than 7.1% quantification differences, implying gene definition differences in the two references. We propose a framework to quantify this difference using two metrics, Consistency and Stringency, which account for the average quantification difference between the two references over all samples, and the sample-specific effect on the quantification result, respectively. These two metrics can be used to identify potential genes for future gene model improvement and to understand the reliability of differentially expressed genes identified by RNA-Seq data analysis. Before a more comprehensive genome reference for CHO cells emerges, the strategy proposed in this study can enable more robust transcriptome analysis from CHO cell RNA-Seq data. Biotechnol. Bioeng. 2017;114: 1603-1613. © 2017 Wiley Periodicals, Inc.

An automated RNA-Seq analysis pipeline to identify and visualize differentially expressed genes and pathways in CHO cells.

Recent advances in RNA-Seq based comparative transcriptomics have opened up a unique opportunity to understand the mechanisms of different phenotypes in bioprocessing-related cell lines including Chinese hamster ovary (CHO) cells. However, simple and powerful tools are needed to translate large data sets into biologically relevant information that can be leveraged for genetic engineering and cell culture medium and process development. While tools exist to perform specific tasks associated with transcriptomics analysis, integrated end to end solutions that span the entire spectrum of raw data processing to visualization of gene expression changes on canonical pathways are rare. Additionally, these are not automated and require substantial user intervention. To address this gap, we have developed an automated RNA-Seq analysis pipeline in R which leverages the latest public domain statistical advances in transcriptomics data analysis. This pipeline reads RNA-Seq gene count data, identifies differentially expressed genes and differentially expressed pathways, and provides multiple intuitive visualizations as outputs. By using two publicly available CHO RNA-Seq datasets, we have demonstrated the utility of this pipeline. Subsequently, this pipeline was used to demonstrate transcriptomic similarity between laboratory- and pilot-scale bioreactors, helping make a case for the suitability of the lab-scale bioreactor as a scaled-down model. Automated end to end RNA-Seq data analysis approaches such as the one presented in this study will shorten the time required from acquiring sequencing data to biological interpretation of the results and can help accelerate the adoption of RNA-Seq analysis and thus mechanism-driven approaches for cell line and bioprocess optimization.

Elucidating the role of copper in CHO cell energy metabolism using (13)C metabolic flux analysis.

(13)C-metabolic flux analysis was used to understand copper deficiency-related restructuring of energy metabolism, which leads to excessive lactate production in recombinant protein-producing CHO cells. Stationary-phase labeling experiments with U-(13)C glucose were conducted on CHO cells grown under high and limiting copper in 3 L fed-batch bioreactors. The resultant labeling patterns of soluble metabolites were measured by GC-MS and used to estimate metabolic fluxes in the central carbon metabolism pathways using OpenFlux. Fluxes were evaluated 300 times from stoichiometrically feasible random guess values and their confidence intervals calculated by Monte Carlo simulations. Results from metabolic flux analysis exhibited significant carbon redistribution throughout the metabolic network in cells under Cu deficiency. Specifically, glycolytic fluxes increased (25%-79% relative to glucose uptake) whereas fluxes through the TCA and pentose phosphate pathway (PPP) were lower (15%-23% and 74%, respectively) compared with the Cu-containing condition. Furthermore, under Cu deficiency, 33% of the flux entering TCA via the pyruvate node was redirected to lactate and malate production. Based on these results, we hypothesize that Cu deficiency disrupts the electron transport chain causing ATP deficiency, redox imbalance, and oxidative stress, which in turn drive copper-deficient CHO cells to produce energy via aerobic glycolysis, which is associated with excessive lactate production, rather than the more efficient route of oxidative phosphorylation.

An evaluation of public genomic references for mapping RNA-Seq data from Chinese hamster ovary cells.

While RNA-Seq is increasingly used as the method of choice for transcriptome analysis of mammalian cell culture processes, no universal genomic reference for mapping RNA-Seq reads from CHO cells has been reported. In previous publications, de novo transcriptomes assembled using these RNA-Seq reads were subsequently used for mapping. Potential caveats with this approach include the incomplete coverage and the non-universal nature of the de novo assemblies, leading to challenges in comparing results across studies. In order to facilitate future RNA-Seq studies in CHO cells, we performed a comprehensive evaluation of four public genomic references for CHO cells hosted by the NCBI Reference Sequence Database (RefSeq), including two annotated genomes released in 2012 and 2014 and their accompanying transcriptomes. Each genome showed significantly higher mapped rates compared to its accompanying transcriptome. Furthermore, higher mapped rates in deep intra-genic regions, especially within exons, were observed for the more recent genome release (2014) compared to the older one (2012), indicating that the 2014 genome was the preeminent reference among the four. Sequential addition of human and mouse genomes increased the total mapped rate to 87.3 and 89.7%, respectively, from 73.5% using the 2014 Chinese hamster genome alone. Thus, the sequential combination of the 2014 RefSeq Chinese hamster genome, the Ensembl human genome (h38), and the Ensembl mouse genome (m38) was suggested as the most effective strategy for mapping RNA-Seq data from CHO cells.

Characterization of intrinsic variability in time-series metabolomic data of cultured mammalian cells.

In an attempt to rigorously characterize the intrinsic variability associated with Chinese Hamster Ovary (CHO) cell metabolomics studies, supernatant and intracellular samples taken at 5 time points from duplicate lab-scale bioreactors were analyzed using a combination of gas chromatography (GC)- and liquid chromatography-mass spectrometry (LC-MS) based metabolomics. The intrinsic variability between them was quantified using the relative standard deviation (RSD), and the median RSD was 9.4% and 12.4% for supernatant and intracellular samples, respectively. When exploring metabolic changes between lab- and pilot-scale bioreactors, a high number of metabolites (65-105) were significantly different when no corrections were made for this intrinsic variability. This distinction also extended to principal component and metabolic pathway analysis. However, when intrinsic variability was taken into account, the number of metabolite with significant changes reduced substantially (20-25) as did the separation in principal component and metabolic pathway analysis, suggesting a much smaller change in physiology across bioreactor scale. Our results also suggested the contribution of biological variability to the total variability across replicates (∼0.4%) was significantly lower than that from technical variability (∼9-12%). Our study highlights the need for understanding and accounting for intrinsic variability in CHO cell metabolomics studies. Failure to do so can result in incorrect biological interpretation of the observations which could ultimately lead to the identification of a suboptimal set of targets for genetic engineering or process development considerations.

Evaluating the impact of high Pluronic® F68 concentrations on antibody producing CHO cell lines.

Pluronic® F68 (P-F68) is an important component of chemically-defined cell culture medium because it protects cells from hydrodynamic and bubble-induced shear in the bioreactor. While P-F68 is typically used in cell culture medium at a concentration of 1 g/L (0.1%), higher concentrations can offer additional shear protection and have also been shown to be beneficial during cryopreservation. Recent industry experience with variability in P-F68-associated shear-protection has opened up the possibility of elevated P-F68 concentrations in cell culture media, a topic that has not been previously explored in the context of industrial cell culture processes. Recognizing this gap, we first evaluated the effect of 1-5 g/L P-F68 concentrations in shake flask cultures over ten 3-day passages for cell lines A and B. Increase in terminal cell density and cell size was seen over time at higher P-F68 concentrations but protein productivity was not impacted. Results from this preliminary screening study suggested no adverse impact of high P-F68 concentrations. Subsequently fed-batch bioreactor experiments were conducted at 1 and 5 g/L P-F68 concentrations with both cell lines where cell growth, viability, metabolism, and product quality were examined under process conditions reflective of a commercial process. Results from these bioreactor experiments confirmed findings from the preliminary screen and also indicated no impact of elevated P-F68 concentration on product quality. If additional shear protection is desired, either due to raw material variability, cell line sensitivity, or a high-shear cell culture process, our results suggest this can be accomplished by elevating the P-F68 concentration in the cell culture medium without impacting cell culture performance and product quality.

Recent advances in the understanding of biological implications and modulation methodologies of monoclonal antibody N-linked high mannose glycans.

With the prevalence of therapeutic monoclonal antibodies (mAbs) in the biopharmaceutical industry, the use of mammalian cell culture systems, particularly Chinese hamster ovary (CHO) cells, has become the main method for the production of therapeutics. Despite their similarity to human cells, one major challenge of mammalian cell based biopharmaceutical production is controlling aberrant glycosylation, especially glycans with five to nine mannose residues-high mannose glycoforms. Glycosylation plays a critical role in determining the therapeutic profile of therapeutic glycoproteins; high mannose glycoforms in particular have been shown to have a significant impact on clinical efficacy and pharmacokinetics. Thus, producing glycoform profiles with consistent levels of high mannose is necessary to reduce batch-to-batch therapeutic variability and to meet regulatory standards. Studies have shown that high mannose glycoforms can be modulated through the genetic engineering of cell lines, addition of inhibitors to key enzymes in the glycosylation pathways, and varying cell culture conditions. Focusing on these three types of techniques, this review will examine and critically assess current methods for high mannose glycosylation control and future developments in this area.

Exploring the transcriptome space of a recombinant BHK cell line through next generation sequencing.

Baby Hamster Kidney (BHK) cell lines are used in the production of veterinary vaccines and recombinant proteins. To facilitate transcriptome analysis of BHK cell lines, we embarked on an effort to sequence, assemble, and annotate transcript sequences from a recombinant BHK cell line and Syrian hamster liver and brain. RNA-seq data were supplemented with 6,170 Sanger ESTs from parental and recombinant BHK lines to generate 221,583 contigs. Annotation by homology to other species, primarily mouse, yielded more than 15,000 unique Ensembl mouse gene IDs with high coverage of KEGG canonical pathways. High coverage of enzymes and isoforms was seen for cell metabolism and N-glycosylation pathways, areas of highest interest for biopharmaceutical production. With the high sequencing depth in RNA-seq data, we set out to identify single-nucleotide variants in the transcripts. A majority of the high-confidence variants detected in both hamster tissue libraries occurred at a frequency of 50%, indicating their origin as heterozygous germline variants. In contrast, the cell line libraries' variants showed a wide range of occurrence frequency, indicating the presence of a heterogeneous population in cultured cells. The extremely high coverage of transcripts of highly abundant genes in RNA-seq enabled us to identify low-frequency variants. Experimental verification through Sanger sequencing confirmed the presence of two variants in the cDNA of a highly expressed gene in the BHK cell line. Furthermore, we detected seven potential missense mutations in the genes of the growth signaling pathways that may have arisen during the cell line derivation process. The development and characterization of a BHK reference transcriptome will facilitate future efforts to understand, monitor, and manipulate BHK cells. Our study on sequencing variants is crucial for improved understanding of the errors inherent in high-throughput sequencing and to increase the accuracy of variant calling in BHK or other systems.

Transcriptomics as a tool for assessing the scalability of mammalian cell perfusion systems.

DNA microarray-based transcriptomics have been used to determine the time course of laboratory and manufacturing-scale perfusion bioreactors in an attempt to characterize cell physiological state at these two bioreactor scales. Given the limited availability of genomic data for baby hamster kidney (BHK) cells, a Chinese hamster ovary (CHO)-based microarray was used following a feasibility assessment of cross-species hybridization. A heat shock experiment was performed using both BHK and CHO cells and resulting DNA microarray data were analyzed using a filtering criteria of perfect match (PM)/single base mismatch (MM) > 1.5 and PM-MM > 50 to exclude probes with low specificity or sensitivity for cross-species hybridizations. For BHK cells, 8910 probe sets (39 %) passed the cutoff criteria, whereas 12,961 probe sets (56 %) passed the cutoff criteria for CHO cells. Yet, the data from BHK cells allowed distinct clustering of heat shock and control samples as well as identification of biologically relevant genes as being differentially expressed, indicating the utility of cross-species hybridization. Subsequently, DNA microarray analysis was performed on time course samples from laboratory- and manufacturing-scale perfusion bioreactors that were operated under the same conditions. A majority of the variability (37 %) was associated with the first principal component (PC-1). Although PC-1 changed monotonically with culture duration, the trends were very similar in both the laboratory and manufacturing-scale bioreactors. Therefore, despite time-related changes to the cell physiological state, transcriptomic fingerprints were similar across the two bioreactor scales at any given instance in culture. Multiple genes were identified with time-course expression profiles that were very highly correlated (> 0.9) with bioprocess variables of interest. Although the current incomplete annotation limits the biological interpretation of these observations, their full potential may be realized in due course when richer genomic data become available. By taking a pragmatic approach of transcriptome fingerprinting, we have demonstrated the utility of systems biology to support the comparability of laboratory and manufacturing-scale perfusion systems. Scale-down model qualification is the first step in process characterization and hence is an integral component of robust regulatory filings. Augmenting the current paradigm, which relies primarily on cell culture and product quality information, with gene expression data can help make a substantially stronger case for similarity. With continued advances in systems biology approaches, we expect them to be seamlessly integrated into bioprocess development, which can translate into more robust and high yielding processes that can ultimately reduce cost of care for patients.

Metabolic flux estimation in mammalian cell cultures.

Metabolic flux analysis with its ability to quantify cellular metabolism is an attractive tool for accelerating cell line selection, medium optimization, and other bioprocess development activities. In the stoichiometric flux estimation approach, unknown fluxes are determined using intracellular metabolite mass balance expressions and measured extracellular rates. The simplicity of the stoichiometric approach extends its application to most cell culture systems, and the steps involved in metabolic flux estimation by the stoichiometric method are presented in detail in this chapter. Specifically, overdetermined systems are analyzed since the extra measurements can be used to check for gross measurement errors and system consistency. Cell-specific rates comprise the input data for flux estimation, and the logistic modeling approach is described for robust-specific rate estimation in batch and fed-batch systems. A simplified network of mammalian cell metabolism is used to illustrate the flux estimation procedure, and the steps leading up the consistency index determination are presented. If gross measurement errors are detected, a technique for determining the source of gross measurement error is also described. A computer program that performs most of the calculation described in this chapter is presented, and references to flux estimation software are provided. The procedure presented in this chapter should enable rapid metabolic flux estimation in any mammalian cell bioreaction network by the stoichiometric approach.