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Jan 15, 2012 (Vol. 32, No. 2)

Accelerating Biopharma Process Design

Using Data Obtained from the Study of Metabolic Network Models

  • Novel recombinant protein therapeutics and the increasing demand for existing molecules continuously raise the need for higher biopharmaceutical manufacturing capacities.

    This bolsters the need for efficient and robust bioprocesses. These are required for each individual molecule to ensure consistent product quality and to avoid interruptions in market supply due to production failures.

    Consequently, biopharma companies and regulatory authorities like the FDA have increased efforts toward improving the characterization and the mechanistic understanding of manufacturing processes in recent years.

    Widespread use of scale-down models and initiatives like PAT or QbD have resulted in growing collections of physicochemical product information and high-quality fermentation data. This often includes on-line signals as well as transcript and metabolite measurements.

    Such data potentially represents a rich resource for increasing the speed and efficiency of process development. However, evaluation and interpretation of the acquired data frequently lag behind.

  • Metabolic Network Models

    Cellular network models are well suited to guide process development for several reasons:

    • They provide a mechanistic description of cell physiology,
    • Genome-based model versions provide a direct link between cellular genotype and observed phenotypic behavior,
    • They permit integration of data types such as transcript and metabolite measurements with process variables like feed rates and media composition,
    • Most importantly, their mechanistic nature enables manufacturers to predict the impact of process changes on fermentation outcomes or to track down reasons why certain fermentation runs succeed while others fail.

    Metabolic network models add value to metabolomics data from industrial fermentations by enabling such predictions. Model simulations support rapid hypothesis testing to assess, e.g., the impact of media changes on growth, product formation, or on intracellular metabolism.

    In this article, we illustrate the application of network model-driven process design for predicting optimized media compositions for improved product titer in a CHO cell culture process.

  • CHO Metabolic Network Reconstruction

    Click Image To Enlarge +
    Figure 1. Time courses of extracellular metabolites and biomass during the process: Specific rates and process phases were determined from the total set of concentration time series and feed specifications using Monte Carlo simulation to calculate error propagation. Vertical lines demarcate boundaries between distinct process phases.

    The metabolic network of the CHO cell was reconstructed using information from public databases and primary literature. Stoichiometry, reversibility, and the elemental composition of metabolites was modeled for every reaction. Enzymatic reactions were mapped to corresponding genes based on a genome annotation for mouse.

    The model comprises different cellular compartments (cytosol, mitochondria, ER, Golgi) and the fermenter. It accounts for the glycoform structure and amino acid composition of the product molecule as well as for known cell line specific genetic modifications of metabolic enzymes.

    First, we applied the model to characterize the reference process based on time series of extracellular metabolite concentrations from a fed-batch fermentation. For each physiologically distinct process phase, cell-specific rates of nutrient uptake, growth, and product formation were calculated from mass balances (Figure 1), employing Monte Carlo simulation to calculate error propagation.

    From these extracellular rates, we computed intracellular flux distributions using metabolic flux analysis and the CHO network model. By comparing the flux distributions in different process phases, it is possible to identify (i) pathways with significant flux changes during the fermentation and (ii) process phases with favorable flux distribution.

    Such information cannot be inferred from measured concentration time series alone, but requires the use of an appropriate model. Moreover, the results obtained provide a first assessment of cellular energy status and of nutrient fractions supplied by cellular uptake and/or synthesis, respectively.



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