Literature DB >> 21618724

Predicting Mab product yields from cultivation media components, using near-infrared and 2D-fluorescence spectroscopies.

Gledson E Jose1, Francisca Folque, Jose C Menezes, Silke Werz, Ulrike Strauss, Christian Hakemeyer.   

Abstract

The yield of monoclonal antibody (Mab) production processes depends on media formulation, inocula quality, and process conditions. As in industrial processes tight cultivation conditions are used, and inocula quality and viable cell densities are controlled to reasonable levels, media formulation and raw materials lot-to-lot variability in quality will have, in those circumstances, the highest impact on process performance. In the particular Mab process studied, two different raw materials were used: a complex carbon and nitrogen source made of specific peptones and defined chemical media containing multiple components. Using different spectroscopy techniques for each of the raw material types, it was concluded that for the complex peptone-based ingredient, near-infrared (NIR) spectroscopy was more capable of capturing lot-to-lot variability. For the chemically defined media containing fluorophores, two-dimensional (2D)-fluorescence spectroscopy was more capable of capturing lot-to-lot variability. Because in Mab cultivation processes both types of raw materials are used, combining the NIR and 2D-fluorescence spectra for each of the media components enabled predictive models for yield to be developed that out-performed any other model involving either one raw material alone, or only one type of spectroscopic tool for both raw materials. For each particular raw material, the capability of each spectroscopy to detect lot-to-lot differences was demonstrated after spectra preprocessing and specific wavelength regions selection. The work described and the findings reported here open up several possibilities that could be used to feed-forward control the process. These include, for example, enabling specific actions to be taken regarding media formulation with particular lots, and all types of predictive control actions aimed at increasing batch-to-batch yield and product quality consistency at harvest.
Copyright © 2011 American Institute of Chemical Engineers (AIChE).

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Year:  2011        PMID: 21618724     DOI: 10.1002/btpr.638

Source DB:  PubMed          Journal:  Biotechnol Prog        ISSN: 1520-6033


  7 in total

1.  Switching industrial production processes from complex to defined media: method development and case study using the example of Penicillium chrysogenum.

Authors:  Andreas E Posch; Oliver Spadiut; Christoph Herwig
Journal:  Microb Cell Fact       Date:  2012-06-22       Impact factor: 5.328

2.  Peptone Supplementation of Culture Medium Has Variable Effects on the Productivity of CHO Cells.

Authors:  Fatemeh Davami; Lucia Baldi; Yashas Rajendra; Florian M Wurm
Journal:  Int J Mol Cell Med       Date:  2014

3.  Characterization of mammalian cell culture raw materials by combining spectroscopy and chemometrics.

Authors:  Nicholas Trunfio; Haewoo Lee; Jason Starkey; Cyrus Agarabi; Jay Liu; Seongkyu Yoon
Journal:  Biotechnol Prog       Date:  2017-05-16

4.  Prediction of Escherichia coli expression performance in microtiter plates by analyzing only the temporal development of scattered light during culture.

Authors:  Tobias Ladner; Martina Mühlmann; Andreas Schulte; Georg Wandrey; Jochen Büchs
Journal:  J Biol Eng       Date:  2017-07-03       Impact factor: 4.355

5.  Comparison of spectroscopy technologies for improved monitoring of cell culture processes in miniature bioreactors.

Authors:  Ruth C Rowland-Jones; Frans van den Berg; Andrew J Racher; Elaine B Martin; Colin Jaques
Journal:  Biotechnol Prog       Date:  2017-03-29

6.  Elucidation of auxotrophic deficiencies of Bacillus pumilus DSM 18097 to develop a defined minimal medium.

Authors:  Janina Müller; Mario Beckers; Nina Mußmann; Johannes Bongaerts; Jochen Büchs
Journal:  Microb Cell Fact       Date:  2018-07-09       Impact factor: 5.328

7.  Model-Based Methods in the Biopharmaceutical Process Lifecycle.

Authors:  Paul Kroll; Alexandra Hofer; Sophia Ulonska; Julian Kager; Christoph Herwig
Journal:  Pharm Res       Date:  2017-11-22       Impact factor: 4.200

  7 in total

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