Van Heron Labs litepaper

An introduction to precision feeding for cell-based applications and bio-processes.

By Dr. Rebecca C. Vaught, with edits and commentary from Dr. Alec M. Santiago and mentors.

Cell culture processes still lack thorough understanding in reaching higher titers and robustness using technological means (Taylor et el., 2021). In this pursuit, there are often four possibilities for improving outcomes 1) strain and cell line development, 2) infrastructure changes, 3) physiological adjustments, 4) media/feed optimization, and combinations of these.

Culture media optimization is critical but overlooked, mainly due to its complexity compared to adjusting other aspects of bio-based processes/applications and the painstaking nature of creating and implementing new formulations coupled with the reality that improvements driven via better feeding could remain modest even after considerable effort and expense (Weschselberger et al., 2012). These challenges position other aspects of the application or process as more desirable candidates for optimization in lieu of optimization of media formulations/feeding.

https://images.unsplash.com/photo-1578496481449-cf2e845cc00c?ixlib=rb-4.0.3&q=80&fm=jpg&crop=entropy&cs=tinysrgb

https://images.unsplash.com/photo-1562411052-105105232432?ixlib=rb-4.0.3&q=80&fm=jpg&crop=entropy&cs=tinysrgb

Thus it’s probably not surprising that most media formulations (traditional and modern specialized media) for both cell lines and microbes contain undefined components and are missing key vitamins, minerals, and trace elements, and include some ingredients like saccharides, salts, and certain amino acids in ratios that are completely out of sync with target cell physiology (Granat et al., 2019, Vande Voorde et al., 2019, Gardner et al., 2022).

Media component chemistry and the solubility of various components also present considerable challenges, and unstable or caustic components are often included to overcome this. These shortcomings directly impact cell metabolism and physiology, negatively affecting viability and performance.

 

Bio-processes and in-vitro conditions induce cell stress in the forms of nutrient stress (starvation and excess), oxidative stress, shear stress, and metabolic stress and cause the build-up of toxic wastes and widespread changes to gene-expression profiles (Nicolau et al., 2010, Wellen et al., 2010, Liu and Qian, 2014, Polizzi et al., 2015), beginning the first part of a vicious cycle (O’Brien and Hu, 2020) and often resulting in widespread apoptosis. Genetically modified cells and primary cell lines may be particularly prone to such stresses. Genetic modifications often introduce their own genomic stresses that exert detrimental effects on physiology.

Further, stressful conditions can enable genetic erosion/reversion and/or rampant selective sweeps for non-focal genotypes for cells/population (Bjedov et al., 2003, Maharjan and Ferenci, 2017, O’Brien and Hu, 2020, Horwitz, 2021), adding further to declines/variance. For instance, ROS produced from oxidative stress can induce or exacerbate other physiological stress (mentioned above) and create a runaway cycle (Forrester et al., 2018). ROS produced from oxidative stress and oxygen metabolism also cause mutations (Ragu et al., 2007), and stressful physiological conditions can cause mutant clones to rise to high frequency within the batch/population for cell lines and microbes (O’Brien and Hu, 2020, Rugberg and Olsson, 2020). Thus, overall physiological stresses introduce process and product issues.

While many researchers and organizations are expending considerable resources optimizing their cell lines and strains with genetic and metabolic engineering, some experts warn against this strategy as the risks of reversion/loss at scale, under increased stress, outweigh the benefits of continued cell line development (Horwitz, 2021).

Another consequence of note regarding the stresses of laboratory and industrial environments is the production of non-optimal glycosylation patterns for heterologous proteins. Post-translational features of proteins, such as folding and their glycobiology, are critical for appropriate pharmacokinetics and pharmacodynamics of biologic therapeutics, particularly monoclonal antibodies and fusion proteins (Liu et al., 2015). Better media design can achieve optimal glycosylation patterns for biologic therapeutics (Xu et al., 2021).

Optimal cell feeding can reduce these common physiological stresses and consequences for the system/product. Furthermore, biological fidelity and lower cellular stress from optimal feeding can reduce process variance (Farrell et al., 2014) and improve the parameter space for further optimization potential.

For example, most working in biology can appreciate the improved growth or protein production that occurs with enhanced oxygen availability. However, this also often increases oxidative stress, and induces complications negatively affecting the cells and/or product. Similarly, physiological effects caused by nutrient excess are well documented (Eguchi et al., 1996, Wellen et al., 2010), and excess catabolic expenditures result in ROS production (Wellen et al., 2010). Nutrient starvation, however, is also detrimental and well-documented. If cells can circumvent nutrient deprivation/maintain nutrient availability, they are better positioned to respond to stressful or changing environments (Eguchi et al., 1996, Lee et al., 2009, McLeod et al., 2010, Wood et al., 2020). This balance necessitates the urgency for precision nutrient landscapes to achieve greater metabolic output with fewer negative by-products, I.e., improve process robustness with fewer negative side reactions. Thus better feeding strategies are needed for ultimate cellular process and application quality.

Better feeding allows for further increases in productivity/titer driven by infrastructure and physiological modifications. For instance, an optimized media with no other changes to the process could improve productivity/titer by a relative maximum of 3X, for example (Table 1). Oppositely, modifications to infrastructure (increased mixing or surface-to-volume ratio) with no media optimization could improve productivity/titer by a relative maximum of 3X, for example, but combining improvements to both combinatorially multiples the effects to 9X (Table 1), experimental evidence of exactly this prediction has been observed multiple times for Van Heron Labs’ studies.

Optimizing the macro and micronutrients within the culture media also reduces the number of undefined components often needed within the media regime and enables the transition to chemically defined conditions. Because basal media formulations are often deficient or designed for other cell types rather than the ones the media is being deployed for, undefined blood products or extracts are often used to solve deficits and solubility issues. This is common for both microbes and cell lines. Using fetal calf serum as an example, fetal calf serum will alter cell physiology, including glucose metabolism (Tildon and Stevenson, 1984), cellular senescence/senescence phenotypes (Duggal and Brinchmann, 2011), and inflammatory phenotypes (Asai et al., 2020).

For more discussion on why chemically-defined conditions are important, please see: https://www.notion.so/vanheronlabs/Ser-X 5153d8d39ebb4c609ced3a173cc6e6ff

Innovation in media and feed design is desperately needed across microbes and cell lines.

Van Heron Labs uses omics data, primarily gene-expression data, and computing (bioinformatics and AI) to create better media and feed formulations for cells to increase the productivity/titer and robustness of bio-based processes and applications. The VHL platform uses genetic information as a tool to gain insights into cellular function and determines the most efficient macro and micronutrient inputs for any biological system. The bioinformatics and AI approach VHL utilizes is proprietary, but in essence, the bioinformatics pipeline determines optimal macronutrients, and the AI platform determines micronutrients. This precision nutrition approach optimizes cellular metabolism, which, in turn, optimizes the bio-process or cell-based application of interest.

Traditional approaches to designing cell culture media include Design of Experiments (DoE), where formulations with one variable are tested repeatedly until the highest-performing formulation is discovered. This approach is not only inefficient, given that culture media formulations have dozens of components (Cosenza et al., 2021), but also costly. In silico approaches, such as flux balance analysis and, recently, genome-scale metabolic modeling (GEMs), have been used to assess the metabolites the cells utilize in the highest concentrations. Metabolomics of in-vivo states is often utilized to create more optimal media formulations (Ex., gut metabolomics to create E. coli media, or serum metabolomics to create de-novo media for obscure species/cell types). In silico strategies save considerable experimental effort and accelerate development (Huang et al., 2020).https://images.unsplash.com/photo-1581091007718-0c50d599bfd0?ixlib=rb-4.0.3&q=80&fm=jpg&crop=entropy&cs=tinysrgb

Media and process optimization using metabolomics, spent media analysis, and FBA/GEMs are limited because the resolution of components over diverse molecular weight ranges is restricted by the techniques and instrumentation being harnessed, with metals/trace elements not assessed at all, despite being imperative for cell viability. Further, traditionally after utilizing other metabolic-based techniques, only a few key ingredients are typically added or optimized within the existing formulation or added as a feed, and thus, increases in titer are typically modest (10 – 55%) (Huang et al., 2021).

Van Heron Labs uses the only platform/method in the world that can determine optimal macro and micronutrients from cells just by using their genetic information. The platform provides an even better alternative to these that reduces development time and cost by creating an appropriate, comprehensive formulation from the start, speeding up the experimental media design process and the overall development timeline for bio-based products and applications. This reduces production costs via less time invested in media development, improving cell health, performance, and product quality, and can also aid the transition to chemically defined culture for better reproducibility, control, and physiological robustness.

Biologic drugs, petroleum replacements, cell-based food products, and next-generation bio-materials are often too expensive and difficult to produce. With innovation in media design, such as a precision approach that pinpoints optimal ratios of 20-70 components –  [often obscure components not typically found within media formulations as many media formulations were created before many nutrients were discovered as critical and physiological means of analysis were scarce], high levels of titer/productivity can be reached better, faster, and cheaper. With better approaches to media design, more innovation can leave benchtops and transition to the clinic or end-customer, plus scale and enjoy favorable economics.

Optimal feeding results in higher titers, faster development timelines, less experimental effort, and cost savings/scalability, which is particularly key for biologics production and cell therapies, fermentation, and biomaterials production. Greater manufacturability and process optimization through optimal feeding de-risk the bio-asset itself.

To date, our technology has produced the following for in-house tests and working with research groups and commercial partners. Our approach typically achieves higher cell viability, higher cell density, and higher productivity/titer than common methods, including DoE, spent media analysis, flux balance analysis, and genome-scale metabolic models (GEMs) (Huang et al., 2020) (raw data available at [email protected]):

Table 1. VHL Performance
Project/phenotype Improvement over controls Source genetic data Raw materials grade
Bacterial-specific production (client project) 360% Public data Pharma-grade ingredients
Bacterial growth liquid culture 710% Public data Food-grade ingredients
Bacterial growth solid culture 600% Public data Food-grade ingredients
Bacterial chemically defined transition (client project) 95% Public data Food-grade ingredients
Mammalian cell density 310%, 900% with adjuvant and additional oxygen Custom assemblies Pharma-grade ingredients
Mammalian serum reduction 90% (though 70% is more typical) Public data Food-grade ingredients
Mammalian media cost reduction 90% Public data Food-grade ingredients

Cells utilizing optimal nutrition expend far less catabolic effort (ATP) and can allocate ATP to focal processes instead. Further, mitochondrial ATP production carries costs. Mitochondrial function over time produces ROS, AGEs, and eventually leads to mitochondrial dysfunction (Loske et al., 1998), creating a detrimental loop. Thus, the economy of cellular energetic resources is critical to the success of bio-systems/bio-factories. Using cars as an example, premium fuel results in more MPG and less wear and tear over time. This principle also applies to cellular metabolism.

The Van Heron Labs precision approach streamlines metabolism by eliminating unnecessary catabolic and anabolic steps (breaking nutrients down and building up new focal metabolites) by supplying cells with exactly the nutrition they need as evidenced by their expression profiles so that cells don’t waste energy or create toxic by-products converting what’s present into what they need.

The best way to conserve cellular energetic resources is only to produce the minimum of enzymes necessary to complete core biological functions – 95% of a cell’s energy budget is spent on protein production, mainly for the enzymes used within cellular metabolism. By providing cells with the key intermediates and end-stage metabolites they need (eliminating much of catabolism), plus critical micronutrients based on their genetic fingerprint, the cellular energy budget can be reallocated to other physiologically expensive processes, like growth and heterologous production.

Evidence for this prediction has been repeatedly observed in VHL’s laboratory studies, where simultaneous increases in specific growth and specific production are consistently observed for bacteria. These two pathways directly trade off, and thus these effects would only be possible if metabolism was streamlined/reprogrammed. When applied to mammalian primary mesenchymal cells, VHL nutrients greatly enhance viability and prevent cell death, and these observations have been upheld across multiple VHL studies and cell types, including mammalian epithelial cells.

Imagine – many thousands of promising biologic assets are currently sitting on shelves due to manufacturability issues. However, by optimizing cellular metabolism and lowering physiological stress from optimal feeding, these constraints can be eroded, accelerating time to market for a greater diversity of biological assets (either cell products or cells themselves) across industries, including bio-pharma, and bio-materials, among others.

To revisit an earlier example, heterologous antibody production is one of the most biologically expensive and stressful types of expression/cellular bioproduction due to the size of antibodies and the amount of post-translational modifications required. Optimizing cell physiology and metabolism with optimal feeding, titer, post-translational modifications, and glycosylation patterns, critical to functionality and efficacy, can be optimized (Torkashvand et al., 2015), all of which suffer during cellular nutrient stress. Non-optimal post-translational modifications can even trigger detrimental immune responses for biologic therapeutics (Kaur et al., 2021).

Similarly, in the synthetic biology space, the introduction of certain new pathways and capabilities to host organisms may have large industrial and commercial value, however often, larger or more ambitious modifications have low manufacturing feasibility due to the strains or cell lines having lower fitness (Horwitz, 2021), excluding them from candidacy for scale-up. Better feeding could help rescue fitness losses and reposition heavily engineered/modified lines as candidates for commercial enterprise.

In sum, media and feed compositions should be a priority for anyone using biological systems for critical applications or biomanufacturing. Precision feeding may represent the best horizon for improving processes/applications, the quality of biologics, their time to market, and commercial bottom lines.

                                                                                   [vanheronlabs.com](<http://vanheronlabs.com>) 
  1. Ackermann, T. and Tardito, S., 2019. Cell culture medium formulation and its implications in cancer metabolism. Trends in Cancer, 5(6), pp.329-332.
  2. Asai, D., Kawano, T., Murata, M., Nakashima, H., Toita, R. and Kang, J.H., 2020. Effect of Fetal Bovine Serum Concentration on Lysophosphatidylcholine-mediated Proliferation and Apoptosis of Human Aortic Smooth Muscle Cells. Journal of oleo science, p.ess19268.
  3. Bjedov, I., Tenaillon, O., Gerard, B., Souza, V., Denamur, E., Radman, M., Taddei, F. and Matic, I., 2003. Stress-induced mutagenesis in bacteria. Science, 300(5624), pp.1404-1409.
  4. Cosenza, Z., Block, D.E., and Baar, K. (2021). Optimization of muscle cell culture media using nonlinear design of experiments. Biotechnology Journal 16(11). doi: 10.1002/biot.202100228.
  5. Duggal, S. and Brinchmann, J.E., 2011. Importance of serum source for the in vitro replicative senescence of human bone marrow derived mesenchymal stem cells. Journal of cellular physiology, 226(11), pp.2908-2915.
  6. Eguchi, M., Nishikawa, T., MacDonald, K., Cavicchioli, R., Gottschal, J.C. and Kjelleberg, S., 1996. Responses to stress and nutrient availability by the marine ultramicrobacterium Sphingomonas sp. strain RB2256. Applied and Environmental Microbiology, 62(4), pp.1287-1294.
  7. Farrell, A., McLoughlin, N., Milne, J.J., Marison, I.W. and Bones, J., 2014. Application of multi-omics techniques for bioprocess design and optimization in Chinese hamster ovary cells. Journal of proteome research, 13(7), pp.3144-3159.
  8. Forrester, S.J., Kikuchi, D.S., Hernandes, M.S., Xu, Q. and Griendling, K.K., 2018. Reactive oxygen species in metabolic and inflammatory signaling. Circulation research, 122(6), pp.877-902.
  9. Gardner, G.L., Moradi, F., Moffatt, C., Cliche, M., Garlisi, B., Gratton, J., Mehmood, F. and Stuart, J.A., 2022. Rapid nutrient depletion to below the physiological range by cancer cells cultured in Plasmax. American Journal of Physiology-Cell Physiology, 323(3), pp.C823-C834.
  10. Granat, L.M., Kambhampati, O., Klosek, S., Niedzwecki, B., Parsa, K. and Zhang, D., 2019. The promises and challenges of patient‐derived tumor organoids in drug development and precision oncology. Animal models and experimental medicine, 2(3), pp.150-161.
  11. Horwitz, A. 2021.Synbio: how minimal strain design de-risks scale-up. Sestina Bio, https://medium.com/sestina-bio-blog/synbio-how-minimal-strain-design-de-risks-scale-up-97910e94f0d2
  12. Huang, Z., Xu, J., Yongky, A., Morris, C.S., Polanco, A.L., Reily, M., et al., (2020). CHO cell productivity improvement by genome-scale modeling and pathway analysis: Application to feed supplements. Biochemical engineering journal. 160, 107638. doi: 10.1016/j.bej.2020.107638.
  13. Kaur, H., 2021. Characterization of glycosylation in monoclonal antibodies and its importance in therapeutic antibody development. Critical Reviews in Biotechnology, 41(2), pp.300-315.
  14. Lee, S.J., Trostel, A., Le, P., Harinarayanan, R., FitzGerald, P.C. and Adhya, S., 2009. Cellular stress created by intermediary metabolite imbalances. Proceedings of the National Academy of Sciences, 106(46), pp.19515-19520.
  15. Liu, L., 2015. Antibody glycosylation and its impact on the pharmacokinetics and pharmacodynamics of monoclonal antibodies and Fc-fusion proteins. Journal of pharmaceutical sciences, 104(6), pp.1866-1884.
  16. Liu, B. and Qian, S.B., 2014. Translational reprogramming in cellular stress response. Wiley Interdisciplinary Reviews: RNA, 5(3), pp.301-305.
  17. Loske, C., Neumann, A., Cunningham, A.M., Nichol, K., Schinzel, R., Riederer, P. and Münch, G., 1998. Cytotoxicity of advanced glycation endproducts is mediated by oxidative stress. Journal of neural transmission, 105(8), pp.1005-1015.
  18. Maharjan, R.P. and Ferenci, T., 2017. A shifting mutational landscape in 6 nutritional states: Stress-induced mutagenesis as a series of distinct stress input–mutation output relationships. PLoS biology, 15(6), p.e2001477.
  19. McLeod, C.J., Wang, L., Wong, C. and Jones, D.L., 2010. Stem cell dynamics in response to nutrient availability. Current Biology, 20(23), pp.2100-2105.
  20. Nicolaou, S.A., Gaida, S.M. and Papoutsakis, E.T., 2010. A comparative view of metabolite and substrate stress and tolerance in microbial bioprocessing: from biofuels and chemicals, to biocatalysis and bioremediation. Metabolic engineering, 12(4), pp.307-331.
  21. O’Brien, S.A. and Hu, W.S., 2020. Cell culture bioprocessing—the road taken and the path forward. Current Opinion in Chemical Engineering, 30, pp.128-134.
  22. O’Brien, S.A. and Hu, W.S., 2020. Cell culture bioprocessing—the road taken and the path forward. Current Opinion in Chemical Engineering, 30, pp.128-134.
  23. Polizzi, K.M. and Kontoravdi, C., 2015. Genetically-encoded biosensors for monitoring cellular stress in bioprocessing. Current opinion in biotechnology, 31, pp.50-56.
  24. Ragu, S., Faye, G., Iraqui, I., Masurel-Heneman, A., Kolodner, R.D. and Huang, M.E., 2007. Oxygen metabolism and reactive oxygen species cause chromosomal rearrangements and cell death. Proceedings of the National Academy of Sciences, 104(23), pp.9747-9752.
  25. Rugbjerg, P. and Olsson, L., 2020. The future of self-selecting and stable fermentations. Journal of Industrial Microbiology & Biotechnology: Official Journal of the Society for Industrial Microbiology and Biotechnology, 47(11), pp.993-1004.
  26. Taylor, C., Marschall, L., Kunzelmann, M., Richter, M., Rudolph, F., Vajda, J., Presser, B., Zahel, T., Studts, J. and Herwig, C., 2021. Integrated Process model applications linking bioprocess development to quality by design milestones. Bioengineering, 8(11), p.156.
  27. Tildon, J.T. and Stevenson, J.H., 1984. Decreased oxidation of labeled glucose by dissociated brain cells in the presence of fetal bovine serum. Science, 224(4651), pp.903-904.
  28. Torkashvand, F., Vaziri, B., Maleknia, S., Heydari, A., Vossoughi, M., Davami, F. and Mahboudi, F., 2015. Designed amino acid feed in improvement of production and quality targets of a therapeutic monoclonal antibody. PloS one, 10(10), p.e0140597.
  29. Vande Voorde, J., Ackermann, T., Pfetzer, N., Sumpton, D., Mackay, G., Kalna, G., Nixon, C., Blyth, K., Gottlieb, E. and Tardito, S., 2019. Improving the metabolic fidelity of cancer models with a physiological cell culture medium. Science advances, 5(1), p.eaau7314.
  30. Wechselberger, P., Sagmeister, P., Engelking, H., Schmidt, T., Wenger, J. and Herwig, C., 2012. Efficient feeding profile optimization for recombinant protein production using physiological information. Bioprocess and biosystems engineering, 35(9), pp.1637-1649.
  31. Wellen, K.E. and Thompson, C.B., 2010. Cellular metabolic stress: considering how cells respond to nutrient excess. Molecular cell, 40(2), pp.323-332.
  32. Wood, N.E., Kositangool, P., Hariri, H., Marchand, A.J. and Henne, W.M., 2020. Nutrient signaling, stress response, and inter-organelle communication are non-canonical determinants of cell fate. Cell reports, 33(9), p.108446.
  33. Xu, J., Shao, Z., Wang, Z., Huang, Y., Zou, X. and Shen, Y., 2021. Developing a medium combination to attain similar glycosylation profile to originator by DoE and cluster analysis method. Scientific Reports, 11(1), pp.1-9.

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