Robust mechanistic modeling of protein ion-exchange chromatography

Kumar, Vijesh; Leweke, Samuel; Heymann, William; Lieres, Eric von; Schlegel, Fabrice; Westerberg, Karin; Lenhoff, Abraham M.

doi:10.1016/j.chroma.2021.462669

Cited by 36 publications

(19 citation statements)

References 49 publications

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“…The estimated D s , K eq , and B pp were then fitted to exponential or power-law equations (eqs –) that include ionic strength dependence. In these equations, k 1 , k 2 , and k 3 are constants (Table S2) and c 0 is the ionic strength. , Constant D p ’s of 3 × 10 –11 and 2.6 × 10 –11 were found to be suitable to describe BSA diffusivity in Poros HS resin and lysozyme diffusivity in SP Sepharose Fast Flow resin, respectively. Given that external mass transfer is rapid, it was kept constant at 1 × 10 –4 m/s for all cases.…”

Section: Resultsmentioning

confidence: 99%

“…Initial estimate values for Poros HQ bead porosity was inferred from the total porosity and column interstitial porosity using eq . These values were further refined after fitting the model to the breakthrough curve as explained in a prior publication The mean radii of the resin beads were obtained from the respective companies and are listed in Table . The initial estimates for the column dispersion coefficients were determined using a correlation that accounts for both molecular diffusion and column void volume.…”

Section: Methodsmentioning

confidence: 99%

“…These parameters were then further fitted to an exponential or a power-law equation to specify them as functions of ionic strength. The parameter determination process is described in depth in a prior publication …”

Section: Methodsmentioning

confidence: 99%

“…26 Initial estimate values for Poros HQ bead porosity was inferred from the total porosity and column interstitial porosity using eq 8. These values were further refined after fitting the model to the breakthrough curve as explained in a prior publication 27 (1 )…”

Section: Model Parameter Estimationmentioning

confidence: 99%

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Modeling the Impact of Holdup Volume from Chromatographic Workstations on Ion-Exchange Chromatography

Kumar

Khanal

Jin

2022

Ind. Eng. Chem. Res.

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Chromatographic workstations can significantly impact column chromatography performance by altering the peak shape and retention time. Inclusion of system contributions can help prevent misinterpretation of process data during column qualification, process scale-up, tech transfer to a different facility, or the implementation of equipment changes over the product life cycle. Holdup volumes, not usually considered, can pose significant risks during process development. Using bench-scale and pilot-scale AKTA chromatography systems, we investigated the impact of individual units within the flow path on ion-exchange chromatography operation. Experimental data was used to build a comprehensive model comprising of dispersive plug flow and ideal continuous stir tank reactors to mimic the holdup volume. Our models, which include a column model and a system model, demonstrate the importance of accounting for system holdup volume in chromatography modeling. These comprehensive models can also predict the impact of foreseeable system variations.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Model Parameter Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the Impact of Holdup Volume from Chromatographic Workstations on Ion-Exchange Chromatography

Kumar

Khanal

Jin

2022

Ind. Eng. Chem. Res.

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“…Dynamic methods require fewer experiments than static methods, but rely on a continuous flux applied over the (packed-bed) column and the protein concentration at the column outlet must be measured over time, for example during elution. Expensive equipment is also necessary, typically an FPLC system (e.g., ÄKTA series devices) and corresponding analytics such as in-line UV and conductivity Frontiers in Bioengineering and Biotechnology frontiersin.org determination (Seidel-Morgenstern 2004;Luca et al, 2020;Kumar et al, 2021). One widely-used approach is the inverse method (Osberghaus et al, 2012a;Hahn et al, 2016a;Hahn et al, 2016b), which fits isotherm parameters to an experimental elution peak by minimizing the discrepancies between experimental and simulated peaks (Dose et al, 1991;Leweke and Lieres 2018).…”

Section: Protein-specific Isotherm Parametersmentioning

confidence: 99%

The use of predictive models to develop chromatography-based purification processes

Bernau

Knödler

Emonts

et al. 2022

Front. Bioeng. Biotechnol.

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Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

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Assessment of data‐driven modeling approaches for chromatographic separation processes

Michalopoulou,

Papathanasiou

2024

AIChE Journal

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Chromatographic separation processes are described by nonlinear partial differential and algebraic equations, which may result in high computational cost, hindering further online applications. To decrease the computational burden, different data‐driven modeling approaches can be implemented. In this work, we investigate different strategies of data‐driven modeling for chromatographic processes, using artificial neural networks to predict pseudo‐dynamic elution profiles, without the use of explicit temporal information. We assess the performance of the surrogates trained on different dataset sizes, achieving good predictions with a minimum of 3400 data points. Different activation functions are used and evaluated against the original high‐fidelity model, using accuracy, interpolation, and simulation time as performance metrics. Based on these metrics, the best performing data‐driven models are implemented in a process optimization framework. The results indicate that data‐driven models can capture the nonlinear profile of the process and that can be considered as reliable surrogates used to aid process development.

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Robust mechanistic modeling of protein ion-exchange chromatography

Cited by 36 publications

References 49 publications

Modeling the Impact of Holdup Volume from Chromatographic Workstations on Ion-Exchange Chromatography

Modeling the Impact of Holdup Volume from Chromatographic Workstations on Ion-Exchange Chromatography

The use of predictive models to develop chromatography-based purification processes

Assessment of data‐driven modeling approaches for chromatographic separation processes

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