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Optimizing Bioprocessing with Metabolomics 

Bioprocessing, which utilizes living cells to produce desired compounds, underpins the large-scale production of a diverse range of products, including food ingredients, agricultural products, cosmetics, and, most crucially, biotherapeutics. This technology leverages a cell’s metabolic capabilities and enzymatic machinery, and optimizing these processes is essential for consistent and high-quality production. Central to this optimization is the manipulation of the metabolism of cells in culture. By precisely controlling how these cells function, researchers can ensure efficient production of target molecules with consistent quality, while minimizing waste products and maximizing final yields.  

Optimization must also be performed with consideration of meticulous control across multiple processes that are required to achieve consistent quality and regulatory compliance. This includes monitoring the culture environment itself, such as nutrient availability, pH, temperature, and dissolved oxygen levels. Additionally, cell growth and viability must be closely tracked, along with the production of the target molecule and any unwanted byproducts. This level of understanding and control over the bioprocess lifecycle ensures efficient, cost-effective processes that reduce time-to-market and overall program expense. 

Why metabolism matters in bioprocessing 

One of the biggest challenges bioprocess engineers face is knowing how far their cultures can be pushed toward maximum metabolic performance and protein production without generating critical stress responses that limit productivity. This is oftentimes the most relevant area where metabolomics analyses can help, but there are several additional application areas for metabolomics that can address a variety of challenges and comprehensively improve process monitoring and production.  

By interrogating amino acid, lipid, nucleotide, carbohydrate, and vitamin/co-factor metabolic pathways, and their interconnectivity, insights into redox balance, mitochondrial efficiency, antioxidant capacity, energetics, endoplasmic reticulum stress, lipid metabolism, and glycosylation can be gleaned. This information is useful for not only optimizing metabolic output while minimizing stress, but it can also provide a wealth of information around media/culture conditions, potential contamination, process efficiency, and much more. 

Areas along the bioprocessing workflow that can benefit from the integration of metabolomics include1-6: 

  • Process development: culture method selection (i.e., batch, fed-batch, perfusion), scale-up, tech transfer 
  • Process optimization: media optimization, root-cause analysis, increase production, reduce variability, resolve process gaps 
  • Process characterization: clone/cell-line selection, strain engineering, raw material profiling, batch/process fingerprinting 
  • Process monitoring: interventional strategy development, performance/quality prediction  

More metabolites = more insights 

Several studies have elegantly demonstrated how metabolomics can support biologic manufacturing (e.g., monoclonal antibodies)1,7–8, beverage fermentation (e.g., beer, wine)9,10, biochemical production (e.g., biofuels)5,11, gene therapy vectors (e.g., CAR-T vectors), vaccine development12, and therapeutic stem cell expansion13. Applying metabolomics analyses to bioprocessing optimization and process control provides a clearer and more comprehensive picture beyond what traditional in-house methodologies contribute, which is typically only a cursory overview of a handful of metabolites useful for media characterization and optimization and is no longer sufficient to meet bioprocessing demands. 

Global, untargeted metabolomic profiling can help achieve what current routine practices cannot (i.e., one-factor-at-a-time trial and error, design of experiments, etc.): a complete understanding of the underlying causes of metabolic bottlenecks and the intrinsic connections between the physiological requirements of cells and achieving peak performance.  

For example, simply adding more of a depleted amino acid to the media may not lead to the expected improvements in culture performance if that amino acid is being catabolized through alternative pathways rather than being utilized efficiently for proliferation, protein production, or energy assimilation. Likewise, a buildup of amino acid byproducts, which can cause media acidification or altered redox status, can occur when amino acid supply exceeds the demand to support cellular processes like growth and protein synthesis. Thus, a more comprehensive metabolite interrogation of not only the parental amino acids but also their downstream metabolites paints a clearer picture of what is actually happening inside the bioreactor, facilitating more informed decision making. 

The next level: multiomics 

Leveraging a multiomics approach has the added benefit of enabling researchers to identify solutions beyond simple observational data, evolving their insight to include confident system awareness and actionable strategies14,15. Systems biology computational models that integrate data streams from multiple omics can help to decipher the interconnectivity between cellular genetics, process parameters, and environmental factors (i.e., media/feed components).  Such efforts can also involve metabolic network model development using machine learning approaches16,17.  

For example, a recent multiomics effort performed by Biogen, Inc. elucidated the importance of cysteine feed concentration in maintaining cellular viability, preserving redox balance, mitigating ER stress, and supporting mitochondrial homeostasis7,18. By employing metabolomics, transcriptomics, and proteomics, the researchers were able to expand their understanding of the cellular system, identify bioprocess monitoring biomarkers, and reveal new targets for genetic engineering approaches.  These efforts allowed for improvements in cell growth, viability, titer, specific productivity, and mAb glycosylation. 

In another example, metabolomics, proteomics, and fermentation kinetics data were employed to help guide metabolic engineering strategies in a Clostridium cellulovorans culture for the production of butanol as an alternative fuel5.   Mathematical modeling based on the omics findings enabled researchers to pinpoint the limiting steps in butanol biosynthesis. Using this information, they engineered C. cellulovorans mutants with high-performance phenotypes capable of significantly increasing butanol production.   

Employing metabolomics solutions with Metabolon 

Metabolon’s untargeted biochemical profiling capabilities, a product of over 20 years of innovation in the areas of analytical chemistry, bioinformatics, and expert biochemical interpretation, supports accurate, thorough assessments of the influence of process variables on bioprocess management. Metabolon’s Global Discovery Panel employs UHPLC-MS/MS technology capable of identifying hundreds of distinct, fully annotated compounds from both the spent media and individual cells, providing a broad, extensive, accurate, and informative assessment of the complete biochemical space of your bioprocess cultures. This enables an expansion of optimization strategies beyond conventional technologies, driving predictability, increasing control, and improving performance.  

Conclusions 

Metabolomics has emerged as a powerful tool for bioprocess monitoring and optimization. By providing a real-time snapshot of cellular metabolism, bioprocess engineers can achieve more robust and reproducible processes. However, the true power lies in its synergy with other omics approaches. When combined with genomics, transcriptomics, and proteomics, metabolomics paints a comprehensive picture of cellular activity. This multiomics approach provides an unprecedented level of understanding, enabling researchers to fine-tune bioprocesses with unmatched precision. With tighter control over cellular activity, manufacturers can ensure consistent product quality, reduce production time, and ultimately, bring essential products to market faster. As metabolomics and multiomics approaches continue to evolve, their role in bioprocessing will undoubtedly become even more crucial, paving the way for a future of efficient, sustainable, and high-quality production.  

If you want to get ahead of the game and gain a market advantage, consider adding metabolomics analyses to your bioprocess optimization and monitoring workflows. With our decades of experience and unmatched metabolite libraries with level 1 metabolite identification, Metabolon can help you gain the insight you need to level up your bioprocessing workflows.  

References 

  1. Kuwae S, Miyawaka I, and Doi T. Development of a chemically defined platform fed-batch culture media for monoclonal antibody-producing CHO cell lines with optimized choline content. Cytotechnology. 2018;70(3):939-948. doi: 10.1007/s10616-017-0185-1   
  2. Gao Y, Ray S, Dai S, et al. Combined metabolomics and proteomics reveals hypoxia as a cause of lower productivity on scale-up to a 5000-liter CHO bioprocess. Biotechnol J. 2016;11(9):1190-200. doi: 10.1002/biot.201600030 
  3. Vodopivec M, Lah L, Narat M, et al. Metabolomic profiling of CHO fed-batch growth phases at 10, 100, and 1,000 L. Biotechnol Bioeng. 2019 Oct;116(10):2720-2729. doi: 10.1002/bit.27087 
  4. Ma N, Ellet J, Okediadi C, et al. A single nutrient feed supports both chemically defined NS0 and CHO fed-batch processes: Improved productivity and lactate metabolism. Biotechnol Prog. 2009;25(5):1353-63. doi: 10.1002/btpr.238 
  5. Ou J, Bao T, Ernst P, et al. Intracellular metabolism analysis of Clostridium cellulovorans via modeling integrating proteomics, metabolomics and fermentation. Process Biochem. 2020;89:9-19. doi: 10.1016/j.procbio.2019.10.032 
  6. Mullan B, Dravis B, Lim A, et al. Disulphide bond reduction of a therapeutic monoclonal antibody during cell culture manufacturing operations. BMC Proc. 2011;5 Suppl 8(Suppl 8):P110. doi: 10.1186/1753-6561-5-S8-P110 
  7. Ali AS, Raju R, Kshirsagar R, et al. Multi-Omics Study on the Impact of Cysteine Feed Level on Cell Viability and mAb Production in a CHO Bioprocess. Biotechnol J. 2019;14(4):e1800352. doi: 10.1002/biot.201800352 
  8. Kamachi Y and Omasa T. Development of hyper osmotic resistant CHO host cells for enhanced antibody production. J Biosci Bioeng. 2018;125(4):470-478. doi: 10.1016/j.jbiosc.2017.11.002 
  9. Lopez-Malo M, Querol A, and Guillamon JM. Metabolomic comparison of Saccharomyces cerevisiae and the cryotolerant species S. bayanus var. uvarum and S. kudriavzevii during wine fermentation at low temperature. PLoS One. 2013;8(3):e60135. doi: 10.1371/journal.pone.0060135.  
  10. Richter CL, Kennedy AD, Guo L, et al. Metabolomic Measurements at Three Time Points of a Chardonnay Wine Fermentation with Saccharomyces cerevisiae. Am J Enol Vitic. 2015;66:294-301. doi:  10.5344/ajev.2015.14062 
  11. Chen H, Zheng Y, Zhan J, et al. Comparative metabolic profiling of the lipid-producing green microalga Chlorella reveals that nitrogen and carbon metabolic pathways contribute to lipid metabolism. Biotechnol Biofuels. 2017;15:10.153. doi: 10.1186/s13068-017-0839-4. 
  12. Lin J, Yi X, and Zhuang Y. Coupling metabolomics analysis and DOE optimization strategy towards enhanced IBDV production by chicken embryo fibroblast DF-1 cell. J Biotechnol. 2020;307:114-124. doi: 10.1016/j.jbiotec.2019.10.018. 
  13. Fujisawa K, Takami T, Okada S, et al. Analysis of Metabolomic Changes in Mesenchymal Stem Cells on Treatment with Desferrioxamine as a Hypoxia Mimetic Compared with Hypoxic Conditions. Stem Cells. 2018;36(8):1226-1236. doi: 10.1002/ste,.2826 
  14. Masson HO, la Cour Karottki KJ, Tat J, et al. From observational to actionable: rethinking omics in biologics production. Trends Biotechnol. 2023;41(9):1127-1138. doi: 10.1016/j.tibtech.2023.03.009. 
  15. Wan S, Liu X, Sun W, et al. Current advances for omics-guided process optimization of microbial manufacturing. Bioresour Bioprocess. 2023;10(1):30. doi: 10.1186/s40643-023-00647-2. 
  16. Schinn SM, Morrison C, Wei W, et al. A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures. Biotechnol Bioeng. 2021;118(5):2118-2123. doi: 10.1002/bit.27714 
  17. Schinn SM, Morrison C, Wei W, et al. Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells. Metab Eng. 2021;66:21-30. doi: 10.1016/j.ymben.2021.03.013 
  18. Ali AS, Chen R, Raju R, et al. Multi-Omics Reveals Impact of Cysteine Feed Concentration and Resulting Redox Imbalance on Cellular Energy Metabolism and Specific Productivity in CHO Cell Bioprocessing. Biotechnol J. 2020;15(8):e1900565. doi: 10.1002/biot.201900565 
Brian Keppler, Ph.D.
Dr. Brian Keppler is the Director of Metabolon’s Commercial Discovery and Translational Sciences team and serves as the lead scientific liaison for global business opportunities with pharmaceutical, biotechnology, and applied markets companies.

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References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.