Model-based high-throughput design of ion exchange protein chromatography

Khalaf, Rushd; Julia, Heymann; LeSaout, Xavier; Monard, Florence; Costioli, Matteo; Morbidelli, Massimo

doi:10.1016/j.chroma.2016.06.076

Cited by 17 publications

(9 citation statements)

References 30 publications

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“…Two participants tied for the position of the third‐most‐successful approach. One adapted the methodology presented in Khalaf et al (2016), which begins with the mass balance on a component i on a chromatography column using a lumped kinetic model described in Equations (11) and (12):

Mobile normal- phase MB \frac{\partial c_{i}}{\partial t} = \frac{u_{s f} d_{a x, i}}{ε_{i}} \frac{\partial^{2} c_{i}}{\partial x^{2}} - \frac{u_{s f}}{ε_{i}} \frac{\partial c_{i}}{\partial x} - true(\frac{1 - ε_{i}}{ε_{i}} true) \frac{\partial q_{i}}{\partial t} .

Solid normal- phase MB \frac{\partial q_{i}}{\partial t} = k_{m, i} (q_{i}^{e q} - q_{i}) .

where c i and q i are the mobile and solid‐phase concentrations as a function of time t and distance along the column x; d ax,i , k m,i , ε i , and q i eq are the axial dispersion coefficient, the lumped mass transfer coefficient, the accessible porosity and the equilibrium solid phase concentration (each for the component i ); and u sf is the superficial velocity of the mobile phase. With differential Equations (11) and (12), the concentration of any component (buffer, salt, protein) can be simulated at any position in the column and at any time.…”

Section: Prediction Methodsmentioning

confidence: 99%

“…The third‐most‐successful approach was as follows: with the pH and ionic strength known, the charge of the protein is calculated (at infinite dilution) based on the number of surface‐exposed amino acids. This is done using modified forms of equations reported elsewhere (Guélat et al, 2012; Guélat, Khalaf, Lattuada, Costioli, & Morbidelli, 2016; Guélat, Ströhlein, Lattuada, & Morbidelli, 2010; Khalaf et al, 2016).…”

Section: Prediction Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences

Coffman

Marques

Orozco

et al. 2020

Biotech & Bioengineering

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Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first “Highland Games” competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development‐related properties of six antibodies from their amino acid sequences alone. Predictions included purification‐influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all‐prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in‐house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools.

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Mobile normal- phase MB \frac{\partial c_{i}}{\partial t} = \frac{u_{s f} d_{a x, i}}{ε_{i}} \frac{\partial^{2} c_{i}}{\partial x^{2}} - \frac{u_{s f}}{ε_{i}} \frac{\partial c_{i}}{\partial x} - true(\frac{1 - ε_{i}}{ε_{i}} true) \frac{\partial q_{i}}{\partial t} .

Solid normal- phase MB \frac{\partial q_{i}}{\partial t} = k_{m, i} (q_{i}^{e q} - q_{i}) .

Section: Prediction Methodsmentioning

confidence: 99%

Section: Prediction Methodsmentioning

confidence: 99%

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences

Coffman

Marques

Orozco

et al. 2020

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mechanistic models of the various chromatography systems, such as ion exchange, hydrophobic interaction, and a nity, have been extensively published for process development [105][106][107][108][109][110][111][112][113][114]. Also, reversed phase chromatography and the separation of insulin on RPC have been modeled to evaluate column material, bu ers and temperature e ects [115][116][117].…”

Section: Mechanistic Model-based Monitoringmentioning

confidence: 99%

Monitoring of product variants in biopharmaceutical downstream processing : Mechanistic and data-driven modeling approaches

Roch

2019

Linköping Studies in Science and Technology. Dissertations

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“…Separations with three or more similar compounds in the feed are often complex and therefore typically require considerably more optimization than the initial mAb capture step. Examples of such process development efforts to achieve sufficient clearance from antibody aggregates are reported in the literature; examples of ternary separations involving aggregates as well as other product‐related impurities are less frequent . Other examples of ternary mixtures comprising non‐mAb proteins, namely BSA, ovalbumin, cytochrome C, and ribonuclease A, are commonly employed to study model‐based optimization problems …”

Section: Introductionmentioning

confidence: 99%

“…Examples of such process development efforts to achieve sufficient clearance from antibody aggregates are reported in the literature [1][2][3][4][5] ; examples of ternary separations involving aggregates as well as other product-related impurities are less frequent. [6][7][8][9] Other examples of ternary mixtures comprising non-mAb proteins, namely BSA, ovalbumin, cytochrome C, and ribonuclease A, are commonly employed to study model-based optimization problems. [10][11][12] Finding optimal operating conditions for chromatographic separations is a complex optimization problem which needs to consider multiple inputs and multiple objectives to ultimately deliver a robust and economic process that produces drugs of the highest quality and safety.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture

Fischer

Kucia-Tran

Lewis

et al. 2019

Biotechnology Progress

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In the purification of monoclonal antibodies, ion‐exchange chromatography is typically used among the polishing steps to reduce the amount of product‐related impurities such as aggregates and fragments, whilst simultaneously reducing HCP, residual Protein A and potential toxins and viruses. When the product‐related impurities are difficult to separate from the products, the optimization of these chromatographic steps can be complex and laborious. In this paper, we optimize the polishing chromatography of a monoclonal antibody from a challenging ternary feed mixture by introducing a hybrid approach of the simplex method and a form of local optimization. To maximize the productivity of this preparative bind‐and‐elute cation‐exchange chromatography, wide ranges of the three critical operational parameters—column loading, the initial salt concentration, and gradient slope—had to be considered. The hybrid optimization approach is shown to be extremely effective in dealing with this complex separation that was subject to multiple constraints based on yield, purity, and product breakthrough. Furthermore, it enabled the generation of a large knowledge space that was subsequently used to study the sensitivity of the objective function. Increased design space understanding was gained through the application of Monte Carlo simulations. Hence, this work proposes a powerful hybrid optimization method, applied to an industrially relevant process development challenge. The properties of this approach and the results and insights gained, make it perfectly suited for the rapid development of biotechnological unit operations during early‐stage bioprocess development.

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Model-based high-throughput design of ion exchange protein chromatography

Cited by 17 publications

References 30 publications

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences

Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences

Monitoring of product variants in biopharmaceutical downstream processing : Mechanistic and data-driven modeling approaches

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture

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