Model-based monitoring of industrial reversed phase chromatography to predict insulin variants
Roch, Patricia ; Sellberg, Anton LU ; Andersson, Niklas LU
; Gunne, Matthias
; Hauptmann, Peter
; Nilsson, Bernt
LU
and Mandenius, Carl Fredrik
(2019)
In Biotechnology Progress
35(4).
- Abstract
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... (More)
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.
(Less)
- author
- Roch, Patricia
; Sellberg, Anton
LU
; Andersson, Niklas
LU
; Gunne, Matthias
; Hauptmann, Peter
; Nilsson, Bernt
LU
and Mandenius, Carl Fredrik
- organization
- publishing date
- 2019-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- biopharmaceuticals, HPLC, mechanistic modeling, pooling decision, preparative chromatography
- in
- Biotechnology Progress
- volume
- 35
- issue
- 4
- article number
- e2813
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- scopus:85064526870
- pmid:30938075
- ISSN
- 8756-7938
- DOI
- 10.1002/btpr.2813
- language
- English
- LU publication?
- yes
- id
- f8982231-6011-43eb-893e-ba70f9cbec60
- date added to LUP
- 2019-05-07 12:09:41
- date last changed
- 2024-03-19 06:34:23
@article{f8982231-6011-43eb-893e-ba70f9cbec60,
abstract = {{<p>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.</p>}},
author = {{Roch, Patricia and Sellberg, Anton and Andersson, Niklas and Gunne, Matthias and Hauptmann, Peter and Nilsson, Bernt and Mandenius, Carl Fredrik}},
issn = {{8756-7938}},
keywords = {{biopharmaceuticals; HPLC; mechanistic modeling; pooling decision; preparative chromatography}},
language = {{eng}},
number = {{4}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Biotechnology Progress}},
title = {{Model-based monitoring of industrial reversed phase chromatography to predict insulin variants}},
url = {{http://dx.doi.org/10.1002/btpr.2813}},
doi = {{10.1002/btpr.2813}},
volume = {{35}},
year = {{2019}},
}