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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

  • Optimizing Feed Media Compositions

    Click Image To Enlarge +
    Figure 2. Simulated response of intracellular metabolite dynamics in response to feeding simulations. The applied kinetic model is based on a clone-specific flux distribution observed in vivo.

    In practice, candidate production clones for scale-up often differ in metabolic phenotype resulting in different nutrient requirements during the process. Standardized feed media compositions cannot optimally support the individual nutrient demand, which can limit achievable product titers. Comprehensive metabolic characterization of the respective process not only furthers mechanistic understanding but also represents a good starting point for rational media optimization.

    We computed optimized media compositions for distinct process phases based on the observed clone specific flux distributions by combining stationary and dynamic model simulations. For dynamic simulations, uniform kinetic rate laws were assigned to individual reaction steps. Additionally, feeds and sampling were accounted for. In this way, the impact of modifications in medium composition or feed rates on product synthesis, byproduct formation, and intracellular metabolism could be predicted (Figure 2).

    Model parameters were determined using evolutionary strategies on high-performance computing clusters paying attention to reaction directionality where appropriate. The resulting model served to predict feed media compositions with optimized concentrations of glucose and amino acids for two sequential feeding streams.

    Application of the predicted optimized feed in the cell culture process increased final product titer by more than 50% and increased the integral of viable cells already in the first iteration (Figure 3).

    Moreover, a significant reduction in ammonium formation was observed. If necessary, the procedure can be repeated to further refine media compositions. Data from replicate fermentations can be included to increase the robustness of feed media proposals but is not essential.

    Due its mechanistic nature, the stoichiometric network model captures forced couplings between substrate uptake and formation of byproducts. By comparison, DoE approaches entail a much larger number of experiments to extract such information, thus requiring more time and effort.

  • Refined Predictions & Future Prospects

    Click Image To Enlarge +
    Figure 3. Product titer (A) and viable cell density (B) for CHO cells grown in the original medium or in the optimized medium that was designed by performing in silico simulations using the metabolic network model.

    The influence of different environmental conditions on the outcome of a cell culture process can already be predicted quite well using the presented approach. Our model-based approach currently focuses on predictions for amino acids and carbon sources like glucose and pyruvate.

    Extension to further compounds is straightforward as long as one can reliably quantify their uptake or production. Fluxes through alternative metabolic pathways and metabolic cycles can, however, not be estimated by mass balancing alone. These parts of the metabolic network model can be quantified using 13C tracer experiments in combination with transient 13C metabolic flux analysis. This method is also applicable to industrial fed-batch processes.

    Including this additional information in the metabolic network model can significantly improve the predictions. Further refinement of the model’s predictive capabilities is possible by incorporating intracellular metabolite measurements and by accurate knowledge of the cellular composition, e.g., of total protein content.

  • Conclusions

    Combining metabolomics data with validated metabolic network models has great potential for adding value to data collections acquired in PAT and QbD programs. It converts metabolomic data into a key asset for (i) quantitative predictions of fermentations outcomes, (ii) accelerated rational process optimization, and (iii) improved process understanding. Possible applications range from cell-line engineering to characterization and design of production-scale processes:

    • Engineering of cell lines for debottlenecking of growth and product formation through incorporation of intracellular metabolite measurements and other omics data (protein, transcript),
    • Selection of robust clones with high specific productivity for scale-up based on a comprehensive metabolic characterization at small volumes,
    • Derivation of media compositions tailored to clone-specific nutrient demand at reduced experimental effort compared to DoE approaches,
    • Prediction of future nutrient requirements from current process status for controlling feed additions in production fermenters.

    The present approach is readily transferable to other mammalian cell lines and is applicable to microbial fermentation processes as well.



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