Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Monitoring of product variants in biopharmaceutical downstream processing: Mechanistic and data-driven modeling approaches
Linköping University, Department of Physics, Chemistry and Biology, Biotechnology. Linköping University, Faculty of Science & Engineering.
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: urn:nbn:se:liu:diva-160524DOI: 10.3384/diss.diva-160524ISBN: 9789176850022 (print)OAI: oai:DiVA.org:liu-160524DiVA, id: diva2:1354560
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
List of papers
1. On-line monitoring of downstream bioprocesses
Open this publication in new window or tab >>On-line monitoring of downstream bioprocesses
2016 (English)In: CURRENT OPINION IN CHEMICAL ENGINEERING, ISSN 2211-3398, Vol. 14, p. 112-120Article in journal (Refereed) Published
Abstract [en]

Downstream bioprocessing can benefit significantly from using on-line monitoring methods for surveillance, control and optimisation. Timely information on critical operational and product quality parameters provided by on-line monitoring may contribute to high product quality, more efficient process operation and better production economy. Here, recent advances in analytical techniques and tools are critically reviewed and assessed based on their capability to meet typical needs and requirements in the biotechnology industry. Soft sensors, which merge the signals generated from on-line monitoring devices into mathematical models, are highlighted for accessing critical information in downstream processing.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2016
National Category
Chemical Process Engineering
Identifiers
urn:nbn:se:liu:diva-133391 (URN)10.1016/j.coche.2016.09.007 (DOI)000388517100016 ()
Note

Funding Agencies|EU-Horizon 2020 Marie Curie ITN project BIORAPID [643056]; Linkoping University

Available from: 2016-12-27 Created: 2016-12-22 Last updated: 2019-09-25
2. Model-based monitoring of industrial reversed phase chromatography to predict insulin variants
Open this publication in new window or tab >>Model-based monitoring of industrial reversed phase chromatography to predict insulin variants
Show others...
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
Keywords
biopharmaceuticals; HPLC; mechanistic modeling; pooling decision; preparative chromatography
National Category
Bioprocess Technology
Identifiers
urn:nbn:se:liu:diva-160061 (URN)10.1002/btpr.2813 (DOI)000481421900007 ()30938075 (PubMedID)
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

Open Access in DiVA

fulltext(25016 kB)17 downloads
File information
File name FULLTEXT01.pdfFile size 25016 kBChecksum SHA-512
9f20e69737fe52577bb46a6fb02f59fbfce9ab10b4812f9dceef1b3dec4e4f6103b0bf2c0c0296f2c4e9d9f7f46570dab2a62466397da918488f3ec6e01322bf
Type fulltextMimetype application/pdf
Order online >>

Other links

Publisher's full text

Search in DiVA

By author/editor
Roch, Patricia
By organisation
BiotechnologyFaculty of Science & Engineering
Bioprocess Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 17 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 77 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf