Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

被引:24
作者
Andre, Silvere [1 ]
Lagresle, Sylvain [2 ]
Hannas, Zahia [2 ]
Calvosa, Eric [3 ]
Duponchel, Ludovic [1 ]
机构
[1] Univ Lille Sci & Technol, LASIR CNRS UMR 8516, F-59655 Villeneuve Dascq, France
[2] Merial, 29 Ave Tony Garnier, F-69007 Lyon, France
[3] Sanofi Pasteur, 1541 Ave Marcel Merieux, F-69280 Marcy Letoile, France
关键词
Raman spectroscopy; bioprocess monitoring; process analytical technology; chemometrics; mammalian cell culture; NEAR-INFRARED SPECTROSCOPY; VARIABLE SELECTION METHODS; LEAST-SQUARES REGRESSION; RAMAN-SPECTROSCOPY; SPECTRA; CHEMOMETRICS; BIOREACTORS; MODELS;
D O I
10.1002/btpr.2430
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
In recent years, as a result of the process analytical technology initiative of the US Food and Drug Administration, many different works have been carried out on direct and in situ monitoring of critical parameters for mammalian cell cultures by Raman spectroscopy and multivariate regression techniques. However, despite interesting results, it cannot be said that the proposed monitoring strategies, which will reduce errors of the regression models and thus confidence limits of the predictions, are really optimized. Hence, the aim of this article is to optimize some critical steps of spectroscopic acquisition and data treatment in order to reach a higher level of accuracy and robustness of bioprocess monitoring. In this way, we propose first an original strategy to assess the most suited Raman acquisition time for the processes involved. In a second part, we demonstrate the importance of the interbatch variability on the accuracy of the predictive models with a particular focus on the optical probes adjustment. Finally, we propose a methodology for the optimization of the spectral variables selection in order to decrease prediction errors of multivariate regressions. (c) 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:308-316, 2017
引用
收藏
页码:308 / 316
页数:9
相关论文
共 29 条
[1]   Real Time Monitoring of Multiple Parameters in Mammalian Cell Culture Bioreactors Using an In-Line Raman Spectroscopy Probe [J].
Abu-Absi, Nicholas R. ;
Kenty, Brian M. ;
Cuellar, Maryann Ehly ;
Borys, Michael C. ;
Sakhamuri, Sivakesava ;
Strachan, David J. ;
Hausladen, Michael C. ;
Li, Zheng Jian .
BIOTECHNOLOGY AND BIOENGINEERING, 2011, 108 (05) :1215-1221
[2]   Variable selection in regression-a tutorial [J].
Andersen, C. M. ;
Bro, R. .
JOURNAL OF CHEMOMETRICS, 2010, 24 (11-12) :728-737
[3]   In-line and real-time prediction of recombinant antibody titer by in situ Raman spectroscopy [J].
Andre, Silvere ;
Saint Cristau, Lydia ;
Gaillard, Sabine ;
Devos, Olivier ;
Calvosa, Eric ;
Duponchel, Ludovic .
ANALYTICA CHIMICA ACTA, 2015, 892 :148-152
[4]  
Andrew JJ, 2006, ENCY ANAL CHEM, P13078
[5]  
[Anonymous], 1987, INTRO ORGANIC SPECTR
[6]   A review of recent variable selection methods in industrial and chemometrics applications [J].
Anzanello, Michel Jose ;
Fogliatto, Flavio Sanson .
EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING, 2014, 8 (05) :619-645
[7]   Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data [J].
Balabin, Roman M. ;
Smirnov, Sergey V. .
ANALYTICA CHIMICA ACTA, 2011, 692 (1-2) :63-72
[8]   STANDARD NORMAL VARIATE TRANSFORMATION AND DE-TRENDING OF NEAR-INFRARED DIFFUSE REFLECTANCE SPECTRA [J].
BARNES, RJ ;
DHANOA, MS ;
LISTER, SJ .
APPLIED SPECTROSCOPY, 1989, 43 (05) :772-777
[9]   Cross-Scale Predictive Modeling of CHO Cell Culture Growth and Metabolites Using Raman Spectroscopy and Multivariate Analysis [J].
Berry, Brandon ;
Moretto, Justin ;
Matthews, Thomas ;
Smelko, John ;
Wiltberger, Kelly .
BIOTECHNOLOGY PROGRESS, 2015, 31 (02) :566-577
[10]  
CARRABBA MM, 1992, Patent No. 5112127