Mechanistic modeling of ion-exchange process chromatography of charge variants of monoclonal antibody products

Kumar, Vijesh; Leweke, Samuel; Lieres, Eric von; Rathore, Anurag S.

doi:10.1016/j.chroma.2015.11.062

Cited by 71 publications

(43 citation statements)

References 39 publications

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“…However, predicting the optimal resin and operating conditions is still not straightforward, and large DOE studies are often still needed to determine the binding characteristics of the protein to the resin. Researchers are addressing this need by developing structure based models for protein adsorption and transport and binding specificity (Hanke & Ottens, ; Kumar, Leweke, von Lieres, & Rathore, ; Lenhoff, ; Tyteca, Veuthey, Desmet, Guillarme, & Fekete, ).…”

Section: Separation and Purification Of Charge Variants In Downstreammentioning

confidence: 99%

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Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Chung

Tian

Tan

et al. 2018

Biotech & Bioengineering

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Controlling the charge profile of therapeutic protein is a critical challenge in the current quality-by-design (QbD) paradigm, throughout all phases of biologics process development (PD): cell line development, upstream cell culture, recovery process, downstream purification, and analytical characterization. Charge variant profiles may influence efficacy and/or lead to unintended side-effects. Thus, maintaining a consistent charge profile is of tremendous importance, and increasingly, researchers have focused efforts toward developing strategies to mitigate variability during cell culture and to improve separation and detection of charge variants. Current understanding of factors affecting charge variant formation during manufacturing remains inadequate, and sometimes, even substantial commitment of resources may still not fully achieve the desired or consistent profiles. As such, this review attempts to provide a comprehensive resource for the biologics community by summarizing the impact of charge variants and CQA management, analytical methods for charge variant detection, as well as strategies in downstream and upstream PD for controlling charge variant profiles.

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Section: Separation and Purification Of Charge Variants In Downstreammentioning

confidence: 99%

“…Researchers are addressing this need by developing structure based models for protein adsorption and transport and binding specificity (Hanke & Ottens, 2014;Kumar, Leweke, von Lieres, & Rathore, 2015;Lenhoff, 2016;Tyteca, Veuthey, Desmet, Guillarme, & Fekete, 2016).…”

Section: Case Studies For Process Scale Iex Chromatography For Charmentioning

confidence: 99%

Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Chung

Tian

Tan

et al. 2018

Biotech & Bioengineering

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“…CEX is often used as a polishing step for purification of biotherapeutics. In this article, five factors (a) resin chemistry, (b) elution pH, (c) RC, (d) GL, and (e) RT were examined for optimization so as to yield maximum selectivity with respect to charge variants and HMW aggregates . After performing resin screening, once again the split DOE approach was used with the first DOE examining resin and elution pH and the second DOE examining GL, RC, and RT.…”

Section: Resultsmentioning

confidence: 99%

Design of experiments applications in bioprocessing: Chromatography process development using split design of experiments

Shekhawat

Godara

Kumar

et al. 2018

Biotechnology Progress

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Development of a chromatographic step in a time and resource efficient manner remains a serious bottleneck in protein purification. Chromatographic performance typically depends on raw material attributes, feed material attributes, process factors, and their interactions. Design of experiments (DOE) based process development is often chosen for this purpose. A challenge is, however, in performing a DOE with such a large number of process factors. A split DOE approach based on process knowledge in order to reduce the number of experiments is proposed. The first DOE targets optimizing factors that are likely to significantly impact the process and their effect on process performance is unknown. The second DOE aims to fine‐tune another set of interacting process factors, impact of whom on process performance is known from process understanding. Furthermore, modeling of a large set of output response variables has been achieved by fitting the output responses to an empirical equation and then using the parametric constants of the equation as output response variables for regression modeling. Two case studies involving hydrophobic interaction chromatography for removal of aggregates and cation exchange chromatography for separation of charge variants and aggregates have been utilized to illustrate the proposed approach. Proposed methodology reduced total number of experiments by 25% and 72% compared to a single DOE based on central composite design and full factorial design, respectively. The proposed approach is likely to result in a significant reduction in resources required as well as time taken during process development. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2730, 2019

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“…In addition to the capability of mechanistic models in downstream process development, they have also been used for monitoring of biopharmaceuticals and their impurities. For example, monoclonal antibodies and their variants have been modeled in size exclusion chromatography, cation exchange chromatography, and hydrophobic interaction chromatography . The agreement of these mechanistic models with experimental data demonstrates their potential as monitoring tools.…”

Section: Introductionmentioning

confidence: 99%

Model‐based monitoring of industrial reversed phase chromatography to predict insulin variants

Roch

Sellberg

Andersson

et al. 2019

Biotechnology Progress

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Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process‐ and product‐related impurities. However, removing product‐related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model‐based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model‐based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model‐based monitoring in downstream polishing at industrial scale to take pooling decisions.

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Mechanistic modeling of ion-exchange process chromatography of charge variants of monoclonal antibody products

Cited by 71 publications

References 39 publications

Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Design of experiments applications in bioprocessing: Chromatography process development using split design of experiments

Model‐based monitoring of industrial reversed phase chromatography to predict insulin variants

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