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Model-based monitoring of industrial reversed phase chromatography to predict insulin variants
Linköping University, Department of Physics, Chemistry and Biology, Biotechnology. Linköping University, Faculty of Science & Engineering.
Lund Univ, Sweden.
Lund Univ, Sweden.
Sanofi Aventis Deutschland GmbH, Germany.
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2019 (English)In: Biotechnology progress (Print), ISSN 8756-7938, E-ISSN 1520-6033, Vol. 35, no 4, article id UNSP e2813Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
WILEY , 2019. Vol. 35, no 4, article id UNSP e2813
Keywords [en]
biopharmaceuticals; HPLC; mechanistic modeling; pooling decision; preparative chromatography
National Category
Bioprocess Technology
Identifiers
URN: urn:nbn:se:liu:diva-160061DOI: 10.1002/btpr.2813ISI: 000481421900007PubMedID: 30938075OAI: oai:DiVA.org:liu-160061DiVA, id: diva2:1349021
Note

Funding Agencies|Horizon 2020 Framework Program [643056]; European Unions Horizon 2020 [643056]

Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2019-09-25
In thesis
1. Monitoring of product variants in biopharmaceutical downstream processing: Mechanistic and data-driven modeling approaches
Open this publication in new window or tab >>Monitoring of product variants in biopharmaceutical downstream processing: Mechanistic and data-driven modeling approaches
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

During the manufacturing of biopharmaceuticals, a multistep purification strategy is employed to remove process-related impurities and product variants, to achieve high product quality, assuring patients’ safety. To guarantee that biopharmaceuticals are safe and to accomplish quality, strict policies were established by regulatory agencies as well as guiding principles, such as Quality by Design and process analytical technology. To make the manufacturing process economical, relatively high product yield and productivity are also desirable.

The removal of product variants often poses a challenge in downstream processing due to their structural similarity to the product resulting in similar behavior. One way of overcoming this issue is to employ additional monitoring tools capable to distinguish between the product and product variants.

This thesis demonstrates the development of novel monitoring tools, based on existing monitoring and modeling approaches, to facilitate downstream processing.

Existing techniques are evaluated and critically compared toward meeting the requirements on monitoring quality attributes in downstream processing.

A mechanistic model-based monitoring tool was established for a reversed phase chromatography polishing step of insulin to predict the elution profile of insulin and two insulin variants. By relying on model-based monitoring a significant increase in product yield was achieved.

Further, multi-wavelength fluorescence spectroscopy coupled with the multi-way algorithm parallel factor analysis was utilized to monitor product variants of biopharmaceuticals in downstream processing. This monitoring tool capitalizes on a shift in fluorescence emission between the product and its variant. Developed for monitoring aggregates during antibody purification, the transferability of the approach to other relevant biopharmaceuticals, such as factor VIII and erythropoietin, has been confirmed.

The monitoring tools developed in this thesis, extend existing monitoring tools for downstream processing of biopharmaceuticals. When implementing these monitoring tools into the different phases of biopharmaceuticals’ lifespan, their potential could range from optimizing downstream processes during purification strategy development to supporting manufacturing by facilitating process decisions.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 69
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2010
National Category
Bioprocess Technology
Identifiers
urn:nbn:se:liu:diva-160524 (URN)10.3384/diss.diva-160524 (DOI)9789176850022 (ISBN)
Public defence
2019-10-25, Planck, F Building, Campus Valla, Linköping, 13:00 (English)
Opponent
Supervisors
Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-09-30Bibliographically approved

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