Modeling and Model Parameters

Seidel‐Morgenstern, Andreas; Schmidt–Traub, H.; Michel, M.; Epping, Achim; Jupke, Andreas

doi:10.1002/9783527649280.ch6

Cited by 14 publications

(8 citation statements)

References 99 publications

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“…The axial dispersion coefficients in Table 1 show different values for each column scale. Theoretically,

{D}_{ax}

depends only on the packing properties and the flow conditions in lumped rate models [43]. Since different packing characteristics were determined for each column scale, the axial dispersion coefficient, determined with the same linear flow rates for each column scale, varies as expected in a small range [58].…”

Section: Resultsmentioning

confidence: 99%

“…The mobile phase pH was treated like a common mobile phase modulator with the same assumption for the salt in the solution. A linear driving force approximation, which considers the mass transfer in the solid phase to be dominant, was used to describe mass transfer limitations [42,43]:…”

Section: Column Simulation Modelmentioning

confidence: 99%

“…where 𝑘 𝑒𝑓𝑓,𝑖,𝑗 describes the effective mass transfer coefficient, 𝑑 𝑝 denotes the particle diameter of the resin and 𝑞 * 𝑒𝑞,𝑖 denotes the hypothetical adsorbed protein concentration at equilibrium with the mobile phase. Note that 𝑘 𝑒𝑓𝑓,𝑖,𝑗 is a lumped rate parameter describing the transfer between 𝑞 * 𝑖 and 𝑞 * 𝑒𝑞,𝑖 [43]. The column simulation model is discretized by a first-and/or second-order central finitedifference method, as shown by Ingham et al [44].…”

Section: Column Simulation Modelmentioning

confidence: 99%

“…A linear driving force approximation, which considers the mass transfer in the solid phase to be dominant, was used to describe mass transfer limitations [42, 43]:

\begin{equation}\frac{{\partial q_i^*}}{{\partial t}} = {k}_{eff,i,j}\frac{6}{{{d}_p}}\left( {q_{eq,i}^* - q_i^*} \right) \end{equation}

where

{k}_{eff,i,j}

describes the effective mass transfer coefficient,

{d}_p

denotes the particle diameter of the resin and

q_{eq,i}^*

denotes the hypothetical adsorbed protein concentration at equilibrium with the mobile phase. Note that

{k}_{eff,i,j}

is a lumped rate parameter describing the transfer between

q_i^*

and

q_{eq,i}^*

[43]. The column simulation model is discretized by a first‐ and/or second‐order central finite‐difference method, as shown by Ingham et al.…”

Section: Theoretical Considerationsmentioning

confidence: 99%

See 3 more Smart Citations

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Koch

Scheps

Gunne

et al. 2023

J of Separation Science

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In process development and characterization, the scale-up of chromatographic steps is a crucial part and brings a number of challenges. Usually, scale-down models are used to represent the process step, and constant column properties are assumed. The scaling is then typically based on the concept of linear scale-up. In this work, a mechanistic model describing an anti-Langmuirian to Langmuirian elution behavior of a polypeptide, calibrated with a pre-packed 1 ml column, is applied to demonstrate the scalability to larger column volumes up to 28.2 ml. Using individual column parameters for each column size, scaling to similar eluting salt concentrations, peak heights, and shapes is experimentally demonstrated by considering the model's relationship between the normalized gradient slope and the eluting salt concentration. Further scale-up simulations show improved model predictions when radial inhomogeneities in packing quality are considered.

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“…The axial dispersion coefficients in Table 1 show different values for each column scale. Theoretically,

{D}_{ax}

Section: Resultsmentioning

confidence: 99%

Section: Column Simulation Modelmentioning

confidence: 99%

Section: Column Simulation Modelmentioning

confidence: 99%

“…A linear driving force approximation, which considers the mass transfer in the solid phase to be dominant, was used to describe mass transfer limitations [42, 43]:

\begin{equation}\frac{{\partial q_i^*}}{{\partial t}} = {k}_{eff,i,j}\frac{6}{{{d}_p}}\left( {q_{eq,i}^* - q_i^*} \right) \end{equation}

where

{k}_{eff,i,j}

describes the effective mass transfer coefficient,

{d}_p

denotes the particle diameter of the resin and

q_{eq,i}^*

denotes the hypothetical adsorbed protein concentration at equilibrium with the mobile phase. Note that

{k}_{eff,i,j}

is a lumped rate parameter describing the transfer between

q_i^*

and

q_{eq,i}^*

[43]. The column simulation model is discretized by a first‐ and/or second‐order central finite‐difference method, as shown by Ingham et al.…”

Section: Theoretical Considerationsmentioning

confidence: 99%

See 2 more Smart Citations

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Koch

Scheps

Gunne

et al. 2023

J of Separation Science

View full text Add to dashboard Cite

show abstract

“…We perform the column chromatography zonal elution experiment to determine the Henry coefficient, which is a descriptor for the propensity of binding events between solid and liquid phase in chromatography. [46] The interaction of all the 20 AAs with the silica solid phase were investigated in presence of different buffers. We also formulated a multiscale modelling framework combining molecular dynamics (MD) simulation and different flavors of Langmuir model to understand the amino-acid adsorption in presence of different buffers.…”

Section: Introductionmentioning

confidence: 99%

Buffer Influence on the Amino Acid Silica Interaction

et al. 2020

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Protein-surface interactions are exploited in various processes in life sciences and biotechnology. Many of such processes are performed in presence of a buffer system, which is generally believed to have an influence on the protein-surface interaction but is rarely investigated systematically. Combining experimental and theoretical methodologies, we herein demonstrate the strong influence of the buffer type on protein-surface interactions. Using state of the art chromatographic experiments, we measure the interaction between individual amino acids and silica, as a reference to understand protein-surface interactions. Among all the 20 proteinogenic amino acids studied, we found that arginine (R) and lysine (K) bind most strongly to silica, a finding validated by free energy calculations. We further measured the binding of R and K at different pH in presence of two different buffers, MOPS (3-(N-morpholino)propanesulfonic acid) and TRIS (tris(hydroxymethyl)aminomethane), and find dramatically different behavior. In presence of TRIS, the binding affinity of R/K increases with pH, whereas we observe an opposite trend for MOPS. These results can be understood using a multiscale modelling framework combining molecular dynamics simulation and Langmuir adsorption model. The modelling approach helps to optimize buffer conditions in various fields like biosensors, drug delivery or bio separation engineering prior to the experiment.

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Mechanistic modeling of empty‐full separation in recombinant adeno‐associated virus production using anion‐exchange membrane chromatography

Gomis‐Fons,

Zee,

Hurwit

et al. 2023

Biotech & Bioengineering

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Recombinant adeno‐associated viral vectors (rAAVs) have become an industry‐standard technology in the field of gene therapy, but there are still challenges to be addressed in their biomanufacturing. One of the biggest challenges is the removal of capsid species other than that which contains the gene of interest. In this work, we develop a mechanistic model for the removal of empty capsids—those that contain no genetic material—and enrichment of full rAAV using anion‐exchange membrane chromatography. The mechanistic model was calibrated using linear gradient experiments, resulting in good agreement with the experimental data. The model was then applied to optimize the purification process through maximization of yield studying the impact of mobile phase salt concentration and pH, isocratic wash and elution length, flow rate, percent full (purity) requirement, loading density (challenge), and the use of single‐step or two‐step elution modes. A solution from the optimization with purity of 90% and recovery yield of 84% was selected and successfully validated, as the model could predict the recovery yield with remarkable fidelity and was able to find process conditions that led to significant enrichment. This is, to the best of our knowledge, the first case study of the application of de novo mechanistic modeling for the enrichment of full capsids in rAAV manufacturing, and it serves as demonstration of the potential of mechanistic modeling in rAAV process development.

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Modeling and Model Parameters

Cited by 14 publications

References 99 publications

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Mechanistic modeling of cation exchange chromatography scale‐up considering packing inhomogeneities

Buffer Influence on the Amino Acid Silica Interaction

Mechanistic modeling of empty‐full separation in recombinant adeno‐associated virus production using anion‐exchange membrane chromatography

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