Statistical methods in media optimization for batch and fed-batch animal cell culture

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

Optimization of Multiparameters for Increased Yields of Cytochrome B5 in Bioreactors

The production of recombinant proteins is gaining increasing importance as the market requests high quality proteins for several applications. However, several process parameters affect both the growth of cells and product yields. This study uses high throughput systems and statistical methods to assess the influence of fermentation conditions in lab-scale bioreactors. Using this methodology, it was possible to find the best conditions to produce cytochrome b5 with recombinant cells of Escherichia coli. Using partial least squares, the height-to-diameter ratio of the bioreactor, aeration rate, and PID controller parameters were found to contribute significantly to the final biomass and cytochrome concentrations. Hence, we could use this information to fine-tune the process parameters, which increased cytochrome production and yield several-fold. Using aeration of 1 vvm, a bioreactor with a height-to-ratio of 2.4 and tuned PID parameters, a production of 72.72 mg/L of cytochrome b5 in the culture media, and a maximum of product to biomass yield of 24.97 mg/g could be achieved.

Expression and purification of a novel single-chain diabody (scDb-hERG1/β1) from Pichia pastoris transformants

In the last decades, protein engineering has developed particularly in biotechnology and pharmaceutical field. In particular, the engineered antibody subclass has arisen. The single chain diabody format (scDb), conjugating small size with antigen specificity, offers versatility representing a gold standard for a variety of applications, spacing from research to diagnostics and therapy. Along with such advantages, comes the challenge of optimizing their production, improving expression systems, purification procedures and stability. All such parameters are detrimental for protein production in general and above all for recombinant antibody expression, which has to be fine-tuned, choosing a proper protein-expression host and adjusting expression protocols accordingly. In the present paper, we present data regarding the production and purification of a single chain diabody directed against the macromolecular complex hERG1/β1 integrin. We focus on the expression of clones deriving from the transformation of Pichia pastoris yeast cells. In particular, we compare two different clones arose from two separate transformation processes, demonstrating that both are suitable for proper protein expression. Moreover, we have set up an expression protocol and compared the yields obtained using two purification machines: Akta Pure and Akta Start, with a positive outcome.

Yeast synthetic biology for the production of recombinant therapeutic proteins

The production of recombinant therapeutic proteins is one of the fast-growing areas of molecular medicine and currently plays an important role in treatment of several diseases. Yeasts are unicellular eukaryotic microbial host cells that offer unique advantages in producing biopharmaceutical proteins. Yeasts are capable of robust growth on simple media, readily accommodate genetic modifications, and incorporate typical eukaryotic post-translational modifications. Saccharomyces cerevisiae is a traditional baker's yeast that has been used as a major host for the production of biopharmaceuticals; however, several nonconventional yeast species including Hansenula polymorpha, Pichia pastoris, and Yarrowia lipolytica have gained increasing attention as alternative hosts for the industrial production of recombinant proteins. In this review, we address the established and emerging genetic tools and host strains suitable for recombinant protein production in various yeast expression systems, particularly focusing on current efforts toward synthetic biology approaches in developing yeast cell factories for the production of therapeutic recombinant proteins.

A framework for the systematic design of fed-batch strategies in mammalian cell culture

A methodology to calculate the required amount of amino acids (a.a.) and glucose in feeds for animal cell culture from monitoring their levels in batch experiments is presented herein. Experiments with the designed feeds on an antibody-producing Chinese hamster ovary cell line resulted in a 3-fold increase in titer compared to batch culture. Adding 40% more nutrients to the same feed further increases the yield to 3.5 higher than in batch culture. Our results show that above a certain threshold there is no linear correlation between nutrient addition and the integral of viable cell concentration. In addition, although high ammonia levels hinder cell growth, they do not appear to affect specific antibody productivity, while we hypothesize that high extracellular lactate concentration is the cause for the metabolic shift towards lactate consumption for the cell line used. Overall, the performance of the designed feeds is comparable to that of a commercial feed that was tested in parallel. Expanding this approach to more nutrients, as well as changing the ratio of certain amino acids as informed by flux balance analysis, could achieve even higher yields.

Screening and optimization of chemically defined media and feeds with integrated and statistical approaches

The majority of therapeutic proteins are expressed in mammalian cells, predominantly in Chinese Hamster Ovary cells. While cell culture media and feed supplements are crucial to protein productivity, medium optimization can be labor intensive and time-consuming. In this chapter, we describe some basic concepts in medium development and introduce a rational and rapid workflow to screen and optimize media and feeds. The major goal of medium screening is to select a base formulation as the foundation for further optimization, but ironically, the most conventional screening method may actually rule out ideal chemically defined medium candidates. Appropriate cell adaptation is the key to identifying an optimal base medium, particularly when cells were originally cultured in serum-free medium containing recombinant proteins and/or undefined hydrolysates. The efficient workflow described herein integrates the optimization of both medium and feed simultaneously using a Design-of-Experiment (DOE) approach. The feasibility of the workflow is then demonstrated with a case study, in which chemically defined medium and feed were optimized in a single fed-batch study using a high-throughput microbioreactor system (SimCell™), which resulted in improving protein titers three- to sixfold.

Identification of growth phases and influencing factors in cultivations with AGE1.HN cells using set-based methods

Production of bio-pharmaceuticals in cell culture, such as mammalian cells, is challenging. Mathematical models can provide support to the analysis, optimization, and the operation of production processes. In particular, unstructured models are suited for these purposes, since they can be tailored to particular process conditions. To this end, growth phases and the most relevant factors influencing cell growth and product formation have to be identified. Due to noisy and erroneous experimental data, unknown kinetic parameters, and the large number of combinations of influencing factors, currently there are only limited structured approaches to tackle these issues. We outline a structured set-based approach to identify different growth phases and the factors influencing cell growth and metabolism. To this end, measurement uncertainties are taken explicitly into account to bound the time-dependent specific growth rate based on the observed increase of the cell concentration. Based on the bounds on the specific growth rate, we can identify qualitatively different growth phases and (in-)validate hypotheses on the factors influencing cell growth and metabolism. We apply the approach to a mammalian suspension cell line (AGE1.HN). We show that growth in batch culture can be divided into two main growth phases. The initial phase is characterized by exponential growth dynamics, which can be described consistently by a relatively simple unstructured and segregated model. The subsequent phase is characterized by a decrease in the specific growth rate, which, as shown, results from substrate limitation and the pH of the medium. An extended model is provided which describes the observed dynamics of cell growth and main metabolites, and the corresponding kinetic parameters as well as their confidence intervals are estimated. The study is complemented by an uncertainty and outlier analysis. Overall, we demonstrate utility of set-based methods for analyzing cell growth and metabolism under conditions of uncertainty.

Application of statistical techniques for elucidating flow cytometric data of batch and fed-batch cultures

The objective of this work is to develop structured, segregated stochastic models for bioprocesses using time-series flow cytometric (FC) data. To this end, mammalian CHO cells were grown in both batch and fed-batch cultures, and their viable cell numbers (VCDs), monoclonal antibody (MAb), cell cycle phases, mitochondria membrane potential/mitochondria mass, Golgi apparatus, and endoplasmic reticulum (ER) were analyzed. For the fed-batch mode, soy hydrolysate was introduced at 24-H intervals. The cytometric data were analyzed for early indicators of growth and productivity by multiple linear regression analysis, which involved taking into account multicollinearity diagnostics, Durbin-Watson statistics, and Houston tests to determine and refine statistically significant correlations between categorical variables (FC parameters) and response variables (yield parameters). The results indicate that the percentage of G1 cells and ER was significantly correlated with VCD and MAb in the case of batch culture, whereas for fed-batch culture, the percentage of G2 cells and ER was correlated significantly. There was a significant difference between cells in the batch and fed-batch cultures in their ER content, suggesting that the increase in protein synthesis as reflected by the ER content and consequent increase in growth rate and MAb productivity both can be monitored at the cellular level by FC analysis of ER content.

Development of optimal medium for production of commercially important monoclonal antibody 520C9 by hybridoma cell

Hybridoma HB-8696 produces monoclonal antibody (mAb) 520C9 (mouse IgG(1)), which recognizes breast cancer oncoprotein c-erbB2. The objective of this study was to optimize the medium recipe of HB 8696 cell for production of mAb 520C9. The optimization consisted of two steps: (1) screening of significant nutrients to make subsequent experiments more efficient with less runs and (2) locating their optimal concentrations. 29 variables including essential and non-essential amino acids, glucose, serum and 6 salts, namely NaCl, KCl, CaCl(2), NaH(2)PO(4), MgSO(4) and Na-pyruvate were chosen in screening phase. The Plackett-Burman method was used to screen the variables influencing mAb production. Seven factors namely glucose, serum, asparagine, threonine, serine, NaCl and NaH(2)PO(4) were identified to have a positive influencing role on mAb production with a confidence level >90 % (p < 0.1). Finally, Response surface methodology revealed the optimal level of the variables. The mAb production and average specific mAb production rate were enhanced by 111.05 and 105 %, respectively, compared to control medium.

Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination

Background

Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional redundancy of biological systems. However, most of these EFM are either thermodynamically unfeasible or inactive at pre-set environmental conditions.

Results

Here we present a new algorithm that discriminates the "active" set of EFM on the basis of dynamic envirome data. The algorithm merges together two well-known methods: projection to latent structures (PLS) and EFM analysis, and is therefore termed projection to latent pathways (PLP). PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome.

Conclusions

Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power. But more importantly, PLP is able to discriminate the subset of EFM with highest correlation with the envirome, thus providing in-depth knowledge of how the environment controls core cellular functions. This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.

Unraveling the metabolism of HEK-293 cells using lactate isotopomer analysis

HEK-293 is the most extensively used human cell line for the production of viral vectors and is gaining increasing attention for the production of recombinant proteins by transient transfection. To further improve the metabolic characterization of this cell line, we have performed cultures using ¹³C-labeled substrates and measured the resulting mass isotopomer distributions in lactate by LC/MS. Simultaneous metabolite and isotopomer balancing allowed improvement and validation of the metabolic model and quantification of key intracellular pathways. We have determined the amounts of glucose carbon channeled through the PPP, incorporated into the TCA cycle for energy production and lipids biosynthesis, as well as the cytosolic and mitochondrial malic enzyme fluxes. Our analysis also revealed that glutamine did not significantly contribute to lactate formation. An improved and quantitative understanding of the central carbon metabolism is greatly needed to pursue the rational development of engineering approaches at both the cellular and process levels.

Genome-scale modeling and in silico analysis of mouse cell metabolic network

Genome-scale metabolic modeling has been successfully applied to a multitude of microbial systems, thus improving our understanding of their cellular metabolisms. Nevertheless, only a handful of works have been done for describing mammalian cells, particularly mouse, which is one of the important model organisms, providing various opportunities for both biomedical research and biotechnological applications. Presented herein is a genome-scale mouse metabolic model that was systematically reconstructed by improving and expanding the previous generic model based on integrated biochemical and genomic data of Mus musculus. The key features of the updated model include additional information on gene-protein-reaction association, and improved network connectivity through lipid, amino acid, carbohydrate and nucleotide biosynthetic pathways. After examining the model predictability both quantitatively and qualitatively using constraints-based flux analysis, the structural and functional characteristics of the mouse metabolism were investigated by evaluating network statistics/centrality, gene/metabolite essentiality and their correlation. The results revealed that overall mouse metabolic network is topologically dominated by highly connected and bridging metabolites, and functionally by lipid metabolism that most of essential genes and metabolites are from. The current in silico mouse model can be exploited for understanding and characterizing the cellular physiology, identifying potential cell engineering targets for the enhanced production of recombinant proteins and developing diseased state models for drug targeting.

Enhanced antibody production following intermediate addition based on flux analysis in mammalian cell continuous culture

Generally, mammalian cells utilize glucose and glutamine as primary energy sources. To investigate the effect of energy sources on metabolic fluxes and antibody production, glucose- or glutamine-limited serum-free continuous culture of hybridoma 3A21 cells, which produce anti-ribonuclease A antibody, was carried out. The cell volume and dry cell weight were evaluated under various steady-state conditions. The specific consumption and production rates were evaluated on the basis of dry cell weight. On the basis of these results, the fluxes of the metabolic pathway were calculated. It was found that increasing the specific growth rate causes the specific ATP and antibody production rates to decrease. The fluxes between malate and pyruvate also decreased with the increase in specific growth rate. To increase the ATP production rate under steady-state conditions by the enhancement of fluxes between malate and pyruvate, the reduced metabolic fluxes were increased by an intermediate (pyruvate, malate, and citrate) addition. As a result, higher specific ATP and antibody production rates were achieved following the intermediate addition at a constant dilution rate.