Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation.
cell cultivation
development
monitoring
multivariate data analysis
optimization
scale-down
scale-up
Journal
Biotechnology journal
ISSN: 1860-7314
Titre abrégé: Biotechnol J
Pays: Germany
ID NLM: 101265833
Informations de publication
Date de publication:
Oct 2020
Oct 2020
Historique:
received:
15
03
2020
revised:
30
05
2020
pubmed:
20
7
2020
medline:
16
3
2021
entrez:
20
7
2020
Statut:
ppublish
Résumé
In recent years, multivariate data analysis (MVDA) and modeling approaches have found increasing applications for upstream bioprocess studies (e.g., monitoring, development, optimization, scale-up, etc.). Many of these studies look at variations in the concentrations of metabolites and cell-based measurements. However, these measures are subject to system inherent variations (e.g., changes in metabolic activity) but also intentional operational changes. It is proposed to perform MVDA and modeling on data representative of the underlying biological system operation, that is, the specific rates, which are per se independent of the scale, operational strategy (e.g., batch, fed-batch), and biomass content. Two industrial case studies are highlighted to showcase the approach: one is HEK medium performance comparison study and the other is CHO scale-up/-down study. It is shown that analyzing processes in this way reveals insights into behavior of the underlying biological system, which cannot to the same degree be deducted from the analysis of concentrations.
Identifiants
pubmed: 32683769
doi: 10.1002/biot.202000113
doi:
Substances chimiques
Culture Media
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2000113Informations de copyright
© 2020 Wiley-VCH GmbH.
Références
J. F. MacGregor, M. J. Bruwer, I. Miletic, M. Cardin, Z. Liu, IFAC-PapersOnLine 2015, 48, 520.
C. Duchesne, J. J. Liu, J. F. MacGregor, Chemom. Intell. Lab. Syst. 2012, 117, 116.
A. S. Rathore, S. Mittal, M. Pathak, A. Arora, Biotechnol. Prog. 2014, 30, 967.
S. M. Mercier, B. Diepenbroek, M. C. F. Dalm, R. H. Wijffels, M. Streefland, J. Biotechnol. 2013, 167, 262.
C. Ündey, S. Ertunç, T. Mistretta, B. Looze, J. Process Control 2010, 20, 1009.
A. Tulsyan, C. Garvin, C. Ündey, Biotechnol. Bioeng. 2018, 115, 1915.
A. Tulsyan, C. Garvin, C. Undey, J. Process Control 2019, 77, 114.
M. J. T. Carrondo, P. M. Alves, N. Carinhas, J. Glassey, F. Hesse, O.-W. Merten, M. Micheletti, T. Noll, R. Oliveira, U. Reichl, A. Staby, A. P. Teixeira, H. Weichert, C.-F. Mandenius, Biotechnol. J. 2012, 7, 1522.
M. Sokolov, J. Ritscher, N. MacKinnon, J. Souquet, H. Broly, M. Morbidelli, A. Butté, Biotechnol. Prog. 2017, 33, 1368.
D. Brühlmann, M. Sokolov, A. Butté, M. Sauer, J. Hemberger, J. Souquet, H. Broly, M. Jordan, Biotechnol. Bioeng. 2017, 114, 1448.
M. Sokolov, J. Ritscher, N. MacKinnon, J.-M. Bielser, D. Brühlmann, D. Rothenhäusler, G. Thanei, M. Soos, M. Stettler, J. Souquet, H. Broly, M. Morbidelli, A. Butté, Biotechnol. Prog. 2017, 33, 181.
M. Jenzsch, C. Bell, S. Buziol, F. Kepert, H. Wegele, C. Hakemeyer, in New Bioprocessing Strategies: Development and Manufacturing of Recombinant Antibodies and Proteins (Eds: B. Kiss, U. Gottschalk, M. Pohlscheidt), Springer, Cham 2018, pp. 211-252
J. Glassey, K. V. Gernaey, C. Clemens, T. W. Schulz, R. Oliveira, G. Striedner, C.-F. Mandenius, Biotechnol. J. 2011, 6, 369.
E. A. Blackstone, P. F. Joseph, Am. Health Drug Benefits 2013, 6, 469.
C. H. Song, J.-W. Han, SpringerPlus 2016, 5, 692.
I. O. Medicine, The Changing Economics of Medical Technology, The National Academies Press, Washington, DC 1991
S. Goldrick, V. Sandner, M. Cheeks, R. Turner, S. S. Farid, G. McCreath, J. Glassey, Biotechnol. J. 2019, 15, 1800684.
L. Mears, R. Nørregård, S. M. Stocks, M. O. Albaek, G. Sin, K. V. Gernaey, K. Villez, in Computer Aided Chemical Engineering (Eds: K. V. Gernaey, J. K. Huusom, R. Gani), Elsevier, Amsterdam 2015, pp. 1667-1672.
R. Luttmann, D. G. Bracewell, G. Cornelissen, K. V. Gernaey, J. Glassey, V. C. Hass, C. Kaiser, C. Preusse, G. Striedner, C.-F. Mandenius, Biotechnol. J. 2012, 7, 1040.
D. A. Suarez-Zuluaga, D. Borchert, N. N. Driessen, W. A. M. Bakker, Y. E. Thomassen, Vaccine 2019, 37, 7081.
S. Gnoth, M. Jenzsch, R. Simutis, A. Lübbert, J. Biotechnol. 2007, 132, 180.
L. Tescione, J. Lambropoulos, M. R. Paranandi, H. Makagiansar, T. Ryll, Biotechnol. Bioeng. 2015, 112, 84.
Whether a sample is drawn from a normal or uniform distribution depends on the availability of experimental or analytical replicates. In case of a predefined error related to the example of the analytical precision, sampling from a uniform distribution seems more appropriate as experimental values within the interval of experimental error have the same probability to be drawn. On the contrary, if replicates exist the “real” experimental error can be estimated, therefore normal distribution is a better fit.
M. Manahan, M. Nelson, J. J. Cacciatore, J. Weng, S. Xu, J. Pollard, Biotechnol. Prog. 2019, 35, e2870.
S. N. Wood, Generalized Additive Models: An Introduction with R, 2nd ed., CRC Press, Boca Raton, FL 2017.
Z. Xing, N. Bishop, K. Leister, Z. J. Li, Biotechnol. Prog. 2010, 26, 208.
S. Craven, N. Shirsat, J. Whelan, B. Glennon, Biotechnol. Prog. 2013, 29, 186.
M. J. Willis, M. von Stosch, Comput. Chem. Eng. 2017, 104, 366.
M. von Stosch, J.-M. Hamelink, R. Oliveira, Bioprocess Biosyst. Eng. 2016, 39, 773.
B. Bayer, M. von Stosch, G. Striedner, M. Duerkop, Biotechnol. J. 2020, 15, 1900551.
A. P. Teixeira, C. Alves, P. M. Alves, M. J. T. Carrondo, R. Oliveira, BMC Bioinformatics 2007, 8, 30.
H. Narayanan, M. Sokolov, M. Morbidelli, A. Butté, Biotechnol. Bioeng. 2019, 116, 2540.
A. Guerra, M. von Stosch, J. Glassey, Crit. Rev. Biotechnol. 2019, 39, 289.
Z. I. T. A. Soons, G. van Straten, L. A. van der Pol, A. J. B. van Boxtel, Bioprocess Biosyst. Eng. 2008, 31, 453.
Z. I. T. A. Soons, J. A. Voogt, G. van Straten, A. J. B. van Boxtel, J. Biotechnol. 2006, 125, 252.
R. Oliveira, Ferreira, E. C., Feyo de Azevedo, S., Stability, J. Process Control 2002, 12, 311.
D. Levisauskas, R. Simutis, D. Borvitz, A. Lübbert, Bioprocess Eng. 1996, 15, 145.
G. Bastin, D. Dochain, Automatica 1986, 22, 705.
S. T. F. C. Mortier, P.-J. Van Bockstal, J. Corver, I. Nopens, K. V. Gernaey, T. De Beer, Eur. J. Pharm. Biopharm. 2016, 103, 71.
B. Igne, Z. Shi, S. Talwar, J. K. Drennen, C. A. Anderson, J. Pharm. Innovation 2012, 7, 119.
C. N. Satterfield, AIChE J. 1973, 19, 206.
S. S. Ozturk, B. Ø. Palsson, J. Biotechnol. 1990, 16, 259.
R. M. C. Portela, A. Richelle, P. Dumas, M. von Stosch, Microorganisms 2019, 7, 620.
B. Bayer, B. Sissolak, M. Duerkop, M. von Stosch, G. Striedner, Bioprocess Biosyst. Eng. 2019, 43, 169.
M. Brendel, D. Bonvin, W. Marquardt, Chem. Eng. Sci. 2006, 61, 5404.
P. S. Swain, K. Stevenson, A. Leary, L. F. Montano-Gutierrez, I. B. N. Clark, J. Vogel, T. Pilizota, Nat. Commun. 2016, 7, 13766.
G. L. Tritsch, G. E. Moore, Exp. Cell Res. 1962, 28, 360.
Y. Sidorenko, A. Wahl, M. Dauner, Y. Genzel, U. Reichl, Biotechnol. Prog. 2008, 24, 311.
J. Möller, K. Bhat, K. Riecken, R. Pörtner, A.-P. Zeng, U. Jandt, Biotechnol. Bioeng. 2019, 116, 2931.