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Updated: Jun 12, 2026

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Expression of Recombinant Proteins in the Methylotrophic Yeast Pichia pastoris
Published on: February 25, 2010
Antibody expression kinetics in glycoengineered Pichia pastoris
Thomas I Potgieter1, Sean D Kersey, Muralidhar R Mallem
1Bioprocess Research and Development, Merck & Co, 126 E. Lincoln Avenue, Rahway, New Jersey 07065, USA. thomas_potgieter@merck.com
Biotechnology and Bioengineering
|May 28, 2010
Summary
This study introduces a simplified method to predict antibody production in glycoengineered yeast, optimizing growth and oxygen needs for large-scale bioprocessing. The findings help improve efficiency in producing therapeutic proteins.
Area of Science:
- Biotechnology
- Bioprocess Engineering
- Molecular Biology
Background:
- The growing antibody market necessitates efficient expression systems.
- Glycoengineered yeast, like Pichia pastoris, are explored for antibody production.
- Current titers in yeast are lower than for antibody fragments in fungal systems.
Purpose of the Study:
- To develop a simplified approach for estimating antibody secretion kinetics.
- To predict oxygen uptake rate requirements based on growth rate.
- To model cultivation performance for large-scale bioprocess development.
Main Methods:
- Utilized a growth-rate-controlled, methanol-limited fed-batch fermentation in glycoengineered Pichia pastoris.
- Estimated antibody secretion kinetics and oxygen uptake rates.
- Developed a mass balance model incorporating specific growth rate and productivity correlations.
Main Results:
- Biomass yield from methanol and specific oxygen requirements align with wild-type P. pastoris.
- Specific productivity showed a non-linear relationship with specific growth rate.
- The model successfully predicted cultivation performance under oxygen-limited conditions.
Conclusions:
- The intrinsic yields of Pichia pastoris are independent of glycoengineering or protein expression.
- The empirical correlation between specific growth rate and productivity is crucial for modeling.
- This simplified modeling approach aids in predicting large-scale performance and optimizing bioprocess development for antibody production.

