Recombinant Protein L: Production, Purification and Characterization of a Universal Binding Ligand

Kittler, Stefan; Ebner, Julian; Besleaga, Mihail; Larsbrink, Johan; Darnhofer, Barbara; Birner‐Gruenberger, Ruth; Schobesberger, Silvia; Akhgar, Christopher Karim; Schwaighofer, Andreas; Lendl, Bernhard; Spadiut, Oliver

doi:10.1016/j.jbiotec.2022.10.002

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…The 12 initial cultivations were executed in a DASGIP © Parallel Bioreactor System (max. working volume 2 L; Eppendorf, Hamburg, Germany) as described in [26]. In contrast to the original paper, where 11 out of 12 performed processes were used, according to the DoE, we use all 12 processes as training data.…”

Section: Methodsmentioning

confidence: 99%

“…The UltiMate 3000 HPLC system was equipped with a BioResolve reversed-phase Polyphenyl column (Waters Corporation, MA, USA). Further information about the analytical procedures can be found in [26].…”

Section: Methodsmentioning

confidence: 99%

“…Training Data Process data (biomass concentration, protein L concentration, and substrate concentration over time) from 12 fed-batch fermentations of E. coli strain BL21 DE3, with varying specific substrate feed and temperature [26], are used to train the general process model. To prevent overfitting, non-significant model terms are discarded (see below).…”

Section: Stage Ii: Fitmentioning

confidence: 99%

“…We illustrate our modeling framework by optimizing protein L production in a fedbatch fermentation of E. coli, and evaluate its effectiveness in comparison to RSM [29] in central composite design [25]. Data from twelve fermentations with varying specific substrate feed and temperature [26] are used as training input for both methods to predict an optimal bioprocess that maximizes the specific process yield YP X . The dataset represents nine conditions (see Figure 2a): • one center point (four runs),…”

Section: Case Studymentioning

confidence: 99%

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Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Schloegel,

Lueck,

Kittler

et al. 2024

Preprint

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Biotechnological production of a recombinant molecules relies heavily on fed-batch processes. However, as the cells' growth, substrate uptake, and production kinetics are often unclear, the fed-batches are frequently operated under sub-optimal conditions. For example, process designs are based on simple feed profiles (e.g., constant or exponential), operator experience, and basic statistical tools like response surface methodology (RSM), which are unable to harvest the full potential of the production processes. To address this challenge, we propose a general modeling framework, OptFed, which utilizes experimental data from non-optimal fed-batch processes to predict an optimal process. In detail, we assume the cell-specific production rate depends on all state variables and their changes over time. Using measurements of bioreactor volume, biomass, and product, we train an ordinary differential equation model. To avoid overfitting, we use a regression model to reduce the number of kinetic parameters. Then, we predict the optimal process conditions (temperature and feed rate) by solving an optimal control problem using orthogonal collocation and nonlinear programming. We apply OptFed to a recombinant protein L fed-batch production process. We determine optimal controls for feed rate and reactor temperature to maximize the product-to-biomass yield and successfully validate our predictions experimentally. Notably, our framework outperforms RSM in both simulation and experiments, capturing an optimum previously missed. We improve the experimental product-to-biomass ratio by 19 % and showcase OptFed's potential for enhancing process optimization in biotechnology.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Stage Ii: Fitmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Schloegel,

Lueck,

Kittler

et al. 2024

Preprint

Self Cite

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“…In contrast, it selectively binds to the VL domain of the kappa chain, enabling protein L to function as a ligand for entire Igs or as a kappa ligand for antibody fragments containing kappa light chains. 39 Immobilization of protein L on agarose resin has been successfully achieved. However, diffusion limitations and poor mechanical strength oen lead to instability.…”

Section: Introductionmentioning

confidence: 99%

Integration of protein L-immobilized epoxy magnetic bead capture with LC-MS/MS for therapeutic monoclonal antibody quantification in serum

Cao,

Xu,

et al. 2024

Anal. Methods

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Progress of ligand-modified agarose microspheres for protein isolation and purification

Qi,

Chen

2024

Microchim Acta

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Recombinant Protein L: Production, Purification and Characterization of a Universal Binding Ligand

Cited by 4 publications

References 28 publications

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield

Integration of protein L-immobilized epoxy magnetic bead capture with LC-MS/MS for therapeutic monoclonal antibody quantification in serum

Progress of ligand-modified agarose microspheres for protein isolation and purification

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