Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture.
Raman spectroscopy
bioprocess
cell culture
chemometric analysis
monitoring
process analytical technology
Journal
Biotechnology progress
ISSN: 1520-6033
Titre abrégé: Biotechnol Prog
Pays: United States
ID NLM: 8506292
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
27
08
2018
revised:
02
03
2019
accepted:
22
01
2020
pubmed:
6
2
2020
medline:
5
8
2021
entrez:
4
2
2020
Statut:
ppublish
Résumé
The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF), and Cubist to the commonly used linear PLS model for predicting cell culture components-glucose, lactate, and ammonia. This research is performed to assess whether the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from five small-scale bioreactors (2 × 1 L and 3 × 5 L) using two Chinese hamster ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2,000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr, and RF for glucose, lactate, and ammonia. The root mean square error of prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM), and ammonia (0.819 mM). Interpretation of variable importance (VI) results theorizes the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm
Substances chimiques
Culture Media
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2977Subventions
Organisme : Irish Research Council
Pays : International
Informations de copyright
© 2020 American Institute of Chemical Engineers.
Références
Matasci M, Hacker DL, Baldi L, Wurm FM. Recombinant therapeutic protein production in cultivated mammalian cells: current status and future prospects. Drug Discov Today Technol. 2008;5(2-3):37-42. https://doi.org/10.1016/j.ddtec.2008.12.003.
Walsh G. Biopharmaceutical benchmarks 2014. Nat Biotechnol. 2014;32(10):992-1000. https://doi.org/10.1038/nbt.3040.
Ahn WS, Antoniewicz MR. Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry. Metab Eng. 2011;13(5):598-609. https://doi.org/10.1016/j.ymben.2011.07.002.
Glacken MW, Fleischaker RJ, Sinskey AJ. Reduction of waste product excretion via nutrient control: possible strategies for maximizing product and cell yields on serum in cultures of mammalian cells. Biotechnol Bioeng. 1986;28(9):1376-1389. https://doi.org/10.1002/bit.260280912.
Chen K, Liu Q, Xie L, Sharp PA, Wang DIC. Engineering of a mammalian cell line for reduction of lactate formation and high monoclonal antibody production. Biotechnol Bioeng. 2000;72(1):55-61. https://doi.org/10.1002/1097-0290(20010105)72:1<55::aid-bit8>3.0.co;2-4.
Beutel S, Henkel S. In situ sensor techniques in modern bioprocess monitoring. Appl Microbiol Biotechnol. 2011;91(6):1493-1505. https://doi.org/10.1007/s00253-011-3470-5.
FDA. Guidance for industry Q8(R2) pharmaceutical development. FDA. https://www.fda.gov/downloads/drugs/guidances/ucm073507.pdf Accessed July 19, 2017.
Rathore AS, Winkle H. Quality by design for biopharmaceuticals. Nat Biotechnol. 2009;27(1):26-34. https://doi.org/10.1038/nbt0109-26.
Rathore AS, Bhambure R, Ghare V. Process analytical technology (PAT) for biopharmaceutical products. Anal Bioanal Chem. 2010;398(1):137-154. https://doi.org/10.1007/s00216-010-3781-x.
FDA; Guidance for industry PAT-a framework for innovative pharmaceutical development, manufacturing, and quality assurance. FDA. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070305.pdf. Published 2004. Accessed July 19, 2017.
Raman CV, Krishnan KS. A new type of secondary radiation. Nature. 1928;121(3048):501-502. https://doi.org/10.1038/121501c0.
Smith E, Dent G. Modern Raman Spectroscopy: A Practical Approach. Chichester, England: Wiley; 2008.
Li B, Ryan PW, Ray BH, Leister KJ, Sirimuthu NM, Ryder AG. Rapid characterization and quality control of complex cell culture media solutions using raman spectroscopy and chemometrics. Biotechnol Bioeng. 2010;107(2):290-301. https://doi.org/10.1002/bit.22813.
André SCA, Cristau LS, Gaillard S, Devos O, Calvosa É, Duponchel L. In-line and real-time prediction of recombinant antibody titer by in situ Raman spectroscopy. Anal Chim Acta. 2015;892:148-152. https://doi.org/10.1016/j.aca.2015.08.050.
Vankeirsbilck T, Vercauteren A, Baeyens W, Van der Weken G. Applications of Raman spectroscopy in pharmaceutical analysis. TrAC Trends Anal Chem. 2012;21(12):869-877. https://doi.org/10.1016/S0165-9936(02)01208-6.
Abu-Absi NR, Kenty BM, Cuellar ME, et al. Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe. Biotechnol Bioeng. 2010;108(5):1215-1221. https://doi.org/10.1002/bit.23023.
Moretto J, Smelko JP, Cuellar M, et al. Process Raman spectroscopy for in-line CHO cell culture monitoring. Am Pharmaceut Rev. http://www.americanpharmaceuticalreview.com/Featured-Articles/37040-Process-Raman-Spectroscopy-for-In-Line-CHO-Cell-Culture-Monitoring/. Published April 1, 2011. Accessed July 20, 2017.
Whelan J, Craven S, Glennon B. In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors. Biotechnol Prog. 2012;28(5):1355-1362. https://doi.org/10.1002/btpr.1590.
Lourenço ND, Lopes JA, Almeida CF, Sarraguça MC, Pinheiro HM. Bioreactor monitoring with spectroscopy and chemometrics: a review. Anal Bioanal Chemistry. 2012;404(4):1211-1237. https://doi.org/10.1007/s00216-012-6073-9.
Esmonde-White KA, Cuellar M, Uerpmann C, Lenain B, Lewis IR. Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing. Anal Bioanal Chem. 2016;409(3):637-649. https://doi.org/10.1007/s00216-016-9824-1.
Singh GP, Goh S, Canzoneri M, Ram RJ. Raman spectroscopy of complex defined media: biopharmaceutical applications. J Raman Spectrosc. 2015;46(6):545-550. https://doi.org/10.1002/jrs.4686.
Craven S, Whelan J, Glennon B. Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller. J Process Contr. 2014;24(4):344-357. https://doi.org/10.1016/j.jprocont.2014.02.007.
Matthews TE, Berry BN, Smelko J, Moretto J, Moore B, Wiltberger K. Closed loop control of lactate concentration in mammalian cell culture by Raman spectroscopy leads to improved cell density, viability, and biopharmaceutical protein production. Biotechnol Bioeng. 2016;113(11):2416-2424. https://doi.org/10.1002/bit.26018.
Mehdizadeh H, Lauri D, Karry KM, Moshgbar M, Procopio-Melino R, Drapeau D. Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors. Biotechnol Prog. 2015;31(4):1004-1013. https://doi.org/10.1002/btpr.2079.
André SCA, Lagresle S, Sliva AD, et al. Developing global regression models for metabolite concentration prediction regardless of cell line. Biotechnol Bioeng. 2017;114:2550-2559. https://doi.org/10.1002/bit.26368.
Berry B, Moretto J, Matthews T, Smelko J, Wiltberger K. Cross-scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis. Biotechnol Prog. 2014;31(2):566-577. https://doi.org/10.1002/btpr.2035.
Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intel Lab Syst. 2001;58(2):109-130. https://doi.org/10.1016/s0169-7439(01)00155-1.
Berry BN, Dobrowsky TM, Timson RC, Kshirsagar R, Ryll T, Wiltberger K. Quick generation of Raman spectroscopy based in-process glucose control to influence biopharmaceutical protein product quality during mammalian cell culture. Biotechnol Prog. 2015;32(1):224-234. https://doi.org/10.1002/btpr.2205.
Wolpert DH. The lack of a priori distinctions between learning algorithms. Neural Comput. 1996;8(7):1341-1390. https://doi.org/10.1162/neco.1996.8.7.1341.
Lee T-Y, Lin H-H, Chen C-L, Hwang S-M, Tseng C-P. Inhibitory effect of excessive glucose on its biochemical pathway and the growth of Chinese hamster ovary (CHO) cells. J Carbohydr Chem. 2015;34(1):1-11. https://doi.org/10.1080/07328303.2014.977908.
Lu S, Sun X, Zhang Y. Insight into metabolism of CHO cells at low glucose concentration on the basis of the determination of intracellular metabolites. Process Biochem. 2005;40(5):1917-1921. https://doi.org/10.1016/j.procbio.2004.07.004.
López-Meza JCA, Araíz-Hernández D, Carrillo-Cocom LM, López-Pacheco F, Rocha-Pizaña MDR, Alvarez MM. Using simple models to describe the kinetics of growth, glucose consumption, and monoclonal antibody formation in naive and infliximab producer CHO cells. Cytotechnology. 2015;68(4):1287-1300. https://doi.org/10.1007/s10616-015-9889-2.
Zagari F, Jordan M, Stettler M, Broly H, Wurm FM. Lactate metabolism shift in CHO cell culture: the role of mitochondrial oxidative activity. N Biotechnol. 2013;30(2):238-245. https://doi.org/10.1016/j.nbt.2012.05.021.
Wilkens CA, Altamirano C, Gerdtzen ZP. Comparative metabolic analysis of lactate for CHO cells in glucose and galactose. Biotechnol Bioproc Eng. 2011;16(4):714-724. https://doi.org/10.1007/s12257-010-0409-0.
Lao M-S, Toth D. Effects of ammonium and lactate on growth and metabolism of a recombinant Chinese hamster ovary cell culture. Biotechnol Prog. 1997;13(5):688-691. https://doi.org/10.1021/bp9602360.
Schneider M. The importance of ammonia in mammalian cell culture. J Biotechnol. 1996;46(3):161-185. https://doi.org/10.1016/0168-1656(95)00196-4.
Savitzky A, Golay MJE. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 1964;36(8):1627-1639. https://doi.org/10.1021/ac60214a047.
Difoggio R. Guidelines for applying Chemometrics to spectra: feasibility and error propagation. Appl Spectrosc. 2000;54(3):94A-113A. https://doi.org/10.1366/0003702001949546.
Barnes RJ, Dhanoa MS, Lister SJ. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc. 1989;43(5):772-777. https://doi.org/10.1366/0003702894202201.
Breiman L. Random forests. Mach Learn. 2001;45(1):5-32. https://doi.org/10.1023/A:1010933404324.
Quinlan JR. Simplifying decision trees. Int J Man Mach Stud. 1987;27(3):221-234. https://doi.org/10.1016/S0020-7373(87)80053-6.
Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V. Support vector regression machines. Advances in Neural Information Processing Systems. 9. Cambridge, MA: MIT Press; 1997:155-161.
Kuhn M, Johnson K. Applied predictive modeling. New York, NY: Springer; 2013.
Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statis Surv. 2010;4:40-79. https://doi.org/10.1214/09-ss054.
Abu-Absi NR, Martel RP, Lanza AM, Clements SJ, Borys MC, Li ZJ. Application of spectroscopic methods for monitoring of bioprocesses and the implications for the manufacture of biologics. Pharm Bioprocess. 2014;2(3):267-284. https://doi.org/10.4155/pbp.14.24.
Angel S, Carrabba M, Cooney T. The utilization of diode lasers for Raman spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc. 1995;51(11):1779-1799. https://doi.org/10.1016/0584-8539(95)01443-x.
Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Vikström C, Wold S. Multi- and Megavariate Data Analysis: Basic Principles and Applications. 2nd ed. Umeå, Sweden: MKS Umetrics; 2006.
Socrates G. Infrared and Raman Characteristic Group Frequencies: Tables and Charts. Chichester, England: Wiley; 2015.