Bigdata analytics identifies metabolic inhibitors and promoters for productivity improvement and optimization of monoclonal antibody (mAb) production process

Abstract

Recent advances in metabolite quantification and identification have enabled new research into the detection and control of titer inhibitors and promoters. This paper presents a bigdata analytics study to identify both inhibitors and promoters using multivariate data analysis of metabolomics data. By applying multi-way partial least squares (PLS) model to metabolite data from four fed-batch bioreactor conditions where feed formulation and selection agent concentrations varied, metabolites which exhibited the most significant impact on titer during cultivation were ranked from highest to lowest. The model outputs were then constrained to reduce the number of statistically relevant inhibitors or promoters to the top ten, which were used to conduct metabolic pathway analysis. Furthermore, a method is presented for identifying amino acids that prevent the accumulation of the inhibitors and/or enhance the formation of promoters during production. Finally, the metabolomics and pathway analysis results were integrated and validated with transcriptomics data to characterize metabolic changes occurring among different growth conditions. From these results, new feeding strategies were implemented which resulted in increased fed-batch production titer. Methodology from this work could be applied to future process optimization strategies for biotherapeutic production.

Keywords

Metabolomics / Multivariate data analysis / Titer improvement / Feed media optimization

References

[1] Bradley SA, Ouyang A, Purdie J, Smitka TA, Wang T, Kaerner A. Fermentanomics: monitoring mammalian cell cultures with NMR spectroscopy. J Am Chem Soc, 2010, 132: 9531-9533.

[2] Bylesjö M, . OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemom, 2006, 20(8–10): 341-351.

[3] Camire Joseph, Kim Dongjoo, Kwon Soonjo. Enhanced production of recombinant proteins by a small molecule protein synthesis enhancer in combination with an antioxidant in recombinant chinese hamster ovary cells. Bioprocess Biosyst Eng, 2017, 40(7): 1049-1056.

[4] Chong WP, Goh LT, Reddy SG, Yusufi FN, Lee DY, Wong NS, Heng CK, Yap MG, Ho YS. Metabolomics profiling of extracellular metabolites in recombinant Chinese Hamster Ovary fed-batch culture. Rapid Commun Mass Spectrom, 2009, 23: 3763-3771.

[5] Čuperlović-Culf M, Barnett DA, Culf AS, Chute I. Cell culture metabolomics: applications and future directions. Drug Discov Today, 2010, 15(15): 610-621.

[6] Dippel B (2011) http://www.raman.de/htmlEN/home/advantageEn.html. Accessed 25 Sept 2012

[7] Dunn Warwick B, Ellis David I. Metabolomics: current analytical platforms and methodologies. Trends Anal Chem, 2005, 24(4): 285-294.

[8] Fan Lianchun, Frye Christopher, Racher Andrew. The use of glutamine synthetase as a selection marker: recent advances in Chinese hamster ovary cell line generation processes. Pharm Bioprocess, 2013, 1: 487-502.

[9] Ferrara CT, Wang P, Neto EC, Stevens RD, Bain JR, Wenner BR, Attie AD. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling. PLoS Genet, 2008, 4(3): e1000034.

[10] Gagnon M, . High-end PH-controlled delivery of glucose effectively suppresses lactate accumulation in CHO fed-batch cultures. Biotechnol Bioeng, 2011, 108(6): 1328-1337.

[11] Griffin JL. Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Curr Opin Chem Biol, 2003, 7: 648-654.

[12] Hnatyshyn S, Shipkova P, Sanders M. Expedient data mining for nontargeted high-resolution LC-MS profiles of biological samples. Bioanalysis, 2013, 5(10): 1195-1210.

[13] Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res, 2000, 28: 27-30.

[14] Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res, 2016, 44: D457-D462.

[15] Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res, 2017, 45: D353-D361.

[16] Khoo SHG, Al-Rubeai M. Metabolomics as a complementary tool in cell culture. Biotechnol App Biochem, 2007, 47: 71-84.

[17] Kucukural A, Yukselen O, Ozata DM, Moore MJ, Garber M. DEBrowser: interactive differential expression analysis and visualization tool for count data. BMC Genom, 2019, 20: 6.

[18] Lei Z, Huhman DV, Sumner LW. Mass spectrometry strategies in metabolomics. J Biol Chem, 2011, 286: 25435-25442.

[19] Love MI, Huber W, Anders SJGB. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014, 15(12): 550.

[20] Mohmad-Saberi SE, Hashim YZ, Mel M, Amid A, Ahmad-Raus R, Packeer-Mohamed V. Metabolomics profiling of extracellular metabolites in CHO-K1 cells cultured in different types of growth media. Cytotechnology, 2010, 28(9): 476-484.

[21] Mulukutla BC, Khan S, Lange A, Hu WS. Glucose metabolism in mammalian cell culture: new insights for tweaking vintage pathways. Trends Biotechnol, 2010, 28(9): 476-484.

[22] Mulukutla BC, . Identification and control of novel growth inhibitors in fed-batch cultures of Chinese Hamster Ovary Cells. Biotechnol Bioeng, 2017, 114(8): 1779-1790.

[23] Powers DN, Wang Y, Fratz-Berilla EJ, . Real-time quantification and supplementation of bioreactor amino acids to prolong culture time and maintain antibody product quality. Biotechnol Progress, 2019

[24] Sha S, Bhatia H, Yoon S. An RNA-seq based transcriptomic investigation into the productivity and growth variants with Chinese hamster ovary cells. J Biotechnol, 2018, 271: 37-46.

[25] Svante W, Michael S. SIMCA: a method for analyzing chemical data in terms of similarity and analogy. ACS Symp Ser, 1977, 52: 243-282.

[26] Templeton N, Smith KD, McAtee-Pereira AG, Dorai H, Betenbaugh MJ, Lang SE, Young JD. Application of (13)C flux analysis to identify high-productivity CHO metabolic phenotypes. Metab Eng, 2017, 43(Pt B): 218-225.

[27] Wu Z, Li D, Meng J, Wang H. Esposito Vinzi V, Chin W, Henseler J, Wang H. Introduction to SIMCA-P and its application. Handbook of Partial Least Squares, 2010, Springer: Springer Handbooks of Computational Statistics.

[28] Xu J, Rehmann MS, Xu X, Huang C, Tian J, Qian NX, Li ZJ. Improving titer while maintaining quality of final formulated drug substance via optimization of CHO cell culture conditions in low-iron chemically defined media. mAbs, 2018, 10(3): 488-499.

[29] Zhang A, Sun H, Xu H, Qiu S, Wang X. Cell metabolomics. OMICS, 2013, 17(10): 495-501.

Funding

Bristol-Myers Squibb