Process intensification in biopharmaceutical process development and production – an industrial perspective

References

1. Mullard, A. FDA approves 100th monoclonal antibody product. Nat Rev Drug Discov 2021;20:491–5. https://doi.org/10.1038/d41573-021-00079-7.Search in Google Scholar PubMed

2. Kaplon, H, Chenoweth, A, Crescioli, S, Reichert, JM. Antibodies to watch in 2022. mAbs 2022;14:2014296. https://doi.org/10.1080/19420862.2021.2014296.Search in Google Scholar PubMed PubMed Central

3. Jin, S, Sun, Y, Liang, X, Gu, X, Ning, J, Xu, Y, et al.. Emerging new therapeutic antibody derivatives for cancer treatment. Signal Transduct Targeted Ther 2022;7:39. https://doi.org/10.1038/s41392-021-00868-x.Search in Google Scholar PubMed PubMed Central

4. Dunleavy, K. The top 20 drugs by worldwide sales in 2021. In: Fierce pharma; 2022. [Online]. Available: https://www.fiercepharma.com/special-reports/top-20-drugs-worldwide-sales-2021 [Accessed 31 May 2022].Search in Google Scholar

5. O’Flaherty, R, Bergin, A, Flampouri, E, Mota, LM, Obaidi, I, Quigley, A, et al.. Mammalian cell culture for production of recombinant proteins: a review of the critical steps in their biomanufacturing. Biotechnol Adv 2020;43:107552. https://doi.org/10.1016/j.biotechadv.2020.107552.Search in Google Scholar PubMed

6. Jagschies, G. Biopharmaceutical processing: development, design, and implementation of manufacturing processes. Amsterdam, Netherlands: Elsevier; 2018.Search in Google Scholar

7. FDA. FDA/biosimilar product information; 2022. https://www.fda.gov/drugs/biosimilars/biosimilar-product-information [Accessed 28 Sep 2022].Search in Google Scholar

8. Kelley, B, Kiss, R, Laird, M. A different perspective: how much innovation is really needed for monoclonal antibody production using mammalian cell technology? Adv Biochem Eng Biotechnol 2018;165:443–62. https://doi.org/10.1007/10_2018_59.Search in Google Scholar PubMed

9. Le, H, Chen, C, Goudar, CT. Biopharmaceuticals—continuous processing in upstream operations. In: Chemical engineering progress; 2015. [Online]. Available: https://www.aiche.org/resources/publications/cep/2015/december/sbe-special-section-biopharmaceuticals-continuous-processing-upstream-operations.Search in Google Scholar

10. Vogg, S, Müller-Späth, T, Morbidelli, M. Current status and future challenges in continuous biochromatography. Curr Opin Chem Eng 2018;22:138–44. https://doi.org/10.1016/j.coche.2018.09.001.Search in Google Scholar

11. Sawyer, D, Malmberg, L, Ramasubramanyan, N, Sanderson, K, Lu, R, Sur, E, et al. Biomanufacturing technology roadmap-overview. In: Biomanufacturing Technol. Roadmap; 2017. S.1–48 pp. [Online]. Available: https://www.biophorum.com/wp-content/uploads/bp_downloads/Overview-1.pdf.Search in Google Scholar

12. Yongky, A, Xu, J, Tian, J, Oliveira, C, Zhao, J, McFarland, K, et al.. Process intensification in fed-batch production bioreactors using non-perfusion seed cultures. mAbs 2019;11:1502–14. https://doi.org/10.1080/19420862.2019.1652075.Search in Google Scholar PubMed PubMed Central

13. Johnson, AS, Casey, ME, Guillen, N, Lawrence, SM. Medium development strategies and scale down models for a high density high productivity cell line. In: Presented at the cell culture engineering XVI. Tampa, Florida, USA: Cell culture engineering XVI; 2018. https://dc.engconfintl.org/ccexvi/223.Search in Google Scholar

14. Zhang, L, Shawley, R, Gawlitzek, M, Wong, B. Development towards a high-titer fed-batch CHO platform process yielding product titers > 10 g/L. In: Presented at the cell culture engineering XVI. Tampa, Florida, USA: Cell culture engineering XVI; 2018. https://dc.engconfintl.org/ccexvi/234.Search in Google Scholar

15. Schmieder, V, Fieder, J, Drerup, R, Gutierrez, EA, Guelch, C, Stolzenberger, J, et al.. Towards maximum acceleration of monoclonal antibody development: leveraging transposase-mediated cell line generation to enable GMP manufacturing within 3 months using a stable pool. J Biotechnol 2022;349:53–64. https://doi.org/10.1016/j.jbiotec.2022.03.010.Search in Google Scholar PubMed

16. Ritacco, FV, Wu, Y, Khetan, A. Cell culture media for recombinant protein expression in Chinese hamster ovary (CHO) cells: history, key components, and optimization strategies. Biotechnol Prog 2018;34:1407–26. https://doi.org/10.1002/btpr.2706.Search in Google Scholar PubMed

17. Brunner, M, Brosig, P, Losing, M, Kunzelmann, M, Calvet, A, Stiefel, F, et al.. Towards robust cell culture processes—Unraveling the impact of media preparation by spectroscopic online monitoring. Eng Life Sci 2019;19:666–80. https://doi.org/10.1002/elsc.201900050.Search in Google Scholar PubMed PubMed Central

18. Lin, H, Leighty, RW, Godfrey, S, Wang, SB. Principles and approach to developing mammalian cell culture media for high cell density perfusion process leveraging established fed‐batch media. Biotechnol Prog 2017;33:891–901. https://doi.org/10.1002/btpr.2472.Search in Google Scholar PubMed

19. Konstantinov, KB, Cooney, CL. White paper on continuous bioprocessing. May 20–21, 2014 continuous manufacturing Symposium. J Pharmaceut Sci 2015;104:813–20. https://doi.org/10.1002/jps.24268.Search in Google Scholar PubMed

20. Barrett, S, Franklin, J, Stangl, M, Cvetkovic, A, He, W. Intensification of a multi-product perfusion platform – managing growth characteristics at high cell density for maximized volumetric productivity. In: Presented at the cell culture engineering XVI. Tampa, Florida, USA: Cell culture engineering XVI; 2018. https://dc.engconfintl.org/ccexvi/236.Search in Google Scholar

21. Hiller, GW, Ovalle, AM, Gagnon, MP, Curran, ML, Wang, W. Cell‐controlled hybrid perfusion fed‐batch CHO cell process provides significant productivity improvement over conventional fed‐batch cultures. Biotechnol Bioeng 2017;114:1438–47. https://doi.org/10.1002/bit.26259.Search in Google Scholar PubMed

22. Bielser, J-M, Wolf, M, Souquet, J, Broly, H, Morbidelli, M. Perfusion mammalian cell culture for recombinant protein manufacturing – a critical review. Biotechnol Adv 2018;36:1328–40. https://doi.org/10.1016/j.biotechadv.2018.04.011.Search in Google Scholar PubMed

23. Wong, HE, Chen, C, Le, H, Goudar, CT. From chemostats to high‐density perfusion: the progression of continuous mammalian cell cultivation. J Chem Technol Biotechnol 2021. https://doi.org/10.1002/jctb.6841.Search in Google Scholar

24. Müller, D, Klein, L, Lemke, J, Schulze, M, Kruse, T, Saballus, M, et al.. Process intensification in the biopharma industry: improving efficiency of protein manufacturing processes from development to production scale using synergistic approaches. Chem Eng Process – Process Intensif 2022;171:108727. https://doi.org/10.1016/j.cep.2021.108727.Search in Google Scholar

25. Schulze, M, Lemke, J, Pollard, D, Wijffels, RH, Matuszczyk, J, Martens, DE. Automation of high CHO cell density seed intensification via online control of the cell specific perfusion rate and its impact on the N-stage inoculum quality. J Biotechnol 2021;335:65–75. https://doi.org/10.1016/j.jbiotec.2021.06.011.Search in Google Scholar PubMed

26. Xu, J, Xu, X, Huang, C, Angelo, J, Oliveira, CL, Xu, M, et al.. Biomanufacturing evolution from conventional to intensified processes for productivity improvement: a case study. mAbs 2020;12:1770669. https://doi.org/10.1080/19420862.2020.1770669.Search in Google Scholar PubMed PubMed Central

27. Xu, J, Rehmann, MS, Xu, M, Zheng, S, Hill, C, He, Q, et al.. Development of an intensified fed-batch production platform with doubled titers using N-1 perfusion seed for cell culture manufacturing. Bioresour Bioprocess 2020;7:17. https://doi.org/10.1186/s40643-020-00304-y.Search in Google Scholar

28. Stepper, L, Filser, FA, Fischer, S, Schaub, J, Gorr, I, Voges, R. Pre-stage perfusion and ultra-high seeding cell density in CHO fed-batch culture: a case study for process intensification guided by systems biotechnology. Bioproc Biosyst Eng 2020;43:1431–43. https://doi.org/10.1007/s00449-020-02337-1.Search in Google Scholar PubMed PubMed Central

29. Yang, WC, Lu, J, Kwiatkowski, C, Yuan, H, Kshirsagar, R, Ryll, T, et al.. Perfusion seed cultures improve biopharmaceutical fed‐batch production capacity and product quality. Biotechnol Prog 2014;30:616–25. https://doi.org/10.1002/btpr.1884.Search in Google Scholar PubMed

30. Padawer, I, Ling, WLW, Bai, Y. Case Study: an accelerated 8‐day monoclonal antibody production process based on high seeding densities. Biotechnol Prog 2013;29:829–32. https://doi.org/10.1002/btpr.1719.Search in Google Scholar PubMed

31. Pohlscheidt, M, Jacobs, M, Wolf, S, Thiele, J, Jockwer, A, Gabelsberger, J, et al.. Optimizing capacity utilization by large scale 3000 L perfusion in seed train bioreactors. Biotechnol Prog 2013;29:222–9. https://doi.org/10.1002/btpr.1672.Search in Google Scholar PubMed

32. Brunner, M, Kolb, K, Keitel, A, Stiefel, F, Wucherpfennig, T, Bechmann, J, et al.. Application of metabolic modeling for targeted optimization of high seeding density processes. Biotechnol Bioeng 2021;118:1793–804. https://doi.org/10.1002/bit.27693.Search in Google Scholar PubMed PubMed Central

33. Karst, DJ, Serra, E, Villiger, TK, Soos, M, Morbidelli, M. Characterization and comparison of ATF and TFF in stirred bioreactors for continuous mammalian cell culture processes. Biochem Eng J 2016;110:17–26. https://doi.org/10.1016/j.bej.2016.02.003.Search in Google Scholar

34. Bielser, J-M, Aeby, M, Caso, S, Roulet, A, Broly, H, Souquet, J. Continuous bleed recycling significantly increases recombinant protein production yield in perfusion cell cultures. Biochem Eng J 2021;169:107966. https://doi.org/10.1016/j.bej.2021.107966.Search in Google Scholar

35. Xu, S, Chen, H. High-density mammalian cell cultures in stirred-tank bioreactor without external pH control. J Biotechnol 2016;231:149–59. https://doi.org/10.1016/j.jbiotec.2016.06.019.Search in Google Scholar PubMed

36. Wolf, MKF, Closet, A, Bzowska, M, Bielser, JM, Souquet, J, Broly, H, et al.. Improved performance in mammalian cell perfusion cultures by growth inhibition. Biotechnol J 2019;14:1700722. https://doi.org/10.1002/biot.201700722.Search in Google Scholar PubMed

37. Warikoo, V, Godawat, R, Brower, K, Jain, S, Cummings, D, Simons, E, et al.. Integrated continuous production of recombinant therapeutic proteins. Biotechnol Bioeng 2012;109:3018–29. https://doi.org/10.1002/bit.24584.Search in Google Scholar PubMed

38. Schwarz, H, Mäkinen, ME, Castan, A, Chotteau, V. Monitoring of amino acids and antibody N-glycosylation in high cell density perfusion culture based on Raman spectroscopy. Biochem Eng J 2022;182:108426. https://doi.org/10.1016/j.bej.2022.108426.Search in Google Scholar

39. Dowd, JE, Jubb, A, Kwok, KE, Piret, JM. Optimization and control of perfusion cultures using a viable cell probe and cell specific perfusion rates. Cytotechnology 2003;42:35–45. https://doi.org/10.1023/a:1026192228471.10.1023/A:1026192228471Search in Google Scholar PubMed PubMed Central

40. Walther, J, Lu, J, Hollenbach, M, Yu, M, Hwang, C, McLarty, J, et al.. Perfusion cell culture decreases process and product heterogeneity in a head‐to‐head comparison with fed‐batch. Biotechnol J 2019;14:1700733. https://doi.org/10.1002/biot.201700733.Search in Google Scholar PubMed

41. Clincke, M-F, Mölleryd, C, Zhang, Y, Lindskog, E, Walsh, K, Chotteau, V. Study of a recombinant CHO cell line producing a monoclonal antibody by ATF or TFF external filter perfusion in a WAVE BioreactorTM. BMC Proc 2011;5(8 Suppl):105. https://doi.org/10.1186/1753-6561-5-s8-p105.Search in Google Scholar PubMed PubMed Central

42. Zhou, H, Fang, M, Zheng, X, Zhou, W. Improving an intensified and integrated continuous bioprocess platform for biologics manufacturing. Biotechnol Bioeng 2021;118:3618–23. https://doi.org/10.1002/bit.27768.Search in Google Scholar PubMed

43. Sartorius. Dynamic perfusion. https://www.sartorius.com/en/applications/biopharmaceutical-manufacturing/process-intensification/dynamic-perfusion [Accessed 11 Nov 2022].Search in Google Scholar

44. Kundu, AM, Hiller, GW. Hydrocyclones as cell retention devices for an N‐1 perfusion bioreactor linked to a continuous‐flow stirred tank production bioreactor. Biotechnol Bioeng 2021;118:1973–86. https://doi.org/10.1002/bit.27711.Search in Google Scholar PubMed

45. Yang, WC, Minkler, DF, Kshirsagar, R, Ryll, T, Huang, Y-M. Concentrated fed-batch cell culture increases manufacturing capacity without additional volumetric capacity. J Biotechnol 2016;217:1–11. https://doi.org/10.1016/j.jbiotec.2015.10.009.Search in Google Scholar PubMed

46. Rathore, AS, Zydney, AL, Anupa, A, Nikita, S, Gangwar, N. Enablers of continuous processing of biotherapeutic products. Trends Biotechnol 2022;40:804–15. https://doi.org/10.1016/j.tibtech.2021.12.003.Search in Google Scholar PubMed

47. Yang, O, Prabhu, S, Ierapetritou, M. Comparison between batch and continuous monoclonal antibody production and economic analysis. Ind Eng Chem Res 2019;58:5851–63. https://doi.org/10.1021/acs.iecr.8b04717.Search in Google Scholar

48. Chun, C, Edward, HW, Chetan, TG. Upstream process intensification and continuous manufacturing. Curr Opin Chem Eng 2018;22:191–8. https://doi.org/10.1016/j.coche.2018.10.006.Search in Google Scholar

49. Karst, DJ, Steinebach, F, Morbidelli, M. Continuous integrated manufacturing of therapeutic proteins. Curr Opin Biotechnol 2018;53:76–84. https://doi.org/10.1016/j.copbio.2017.12.015.Search in Google Scholar PubMed

50. Stube, J, Ditz, R, Kornecki, M, Huter, M, Schmidt, A, Thiess, H, et al.. Process intensification in biologics manufacturing. Chem Eng Process – Process Intensif 2018;133:278–93. https://doi.org/10.1016/j.cep.2018.09.022.Search in Google Scholar

51. Croughan, MS, Konstantinov, KB, Cooney, C. The future of industrial bioprocessing: batch or continuous? Biotechnol Bioeng 2015;112:648–51. https://doi.org/10.1002/bit.25529.Search in Google Scholar PubMed

52. Jordan, M, Kinnon, NM, Monchois, V, Stettler, M, Broly, H. Intensification of large-scale cell culture processes. Curr Opin Chem Eng 2018;22:253–7. https://doi.org/10.1016/j.coche.2018.11.008.Search in Google Scholar

53. Karst, DJ, Ramer, K, Hughes, EH, Jiang, C, Jacobs, PJ, Mitchelson, FG. Modulation of transmembrane pressure in manufacturing scale tangential flow filtration N‐1 perfusion seed culture. Biotechnol Prog 2020:e3040. https://doi.org/10.1002/btpr.3040.Search in Google Scholar PubMed

54. Bettinardi, IW, Castan, A, Medronho, RA, Castilho, LR. Hydrocyclones as cell retention device for CHO perfusion processes in single‐use bioreactors. Biotechnol Bioeng 2020;117:1915–28. https://doi.org/10.1002/bit.27335.Search in Google Scholar PubMed

55. Voisard, D, Meuwly, F, Ruffieux, P ‐A, Baer, G, Kadouri, A. Potential of cell retention techniques for large‐scale high‐density perfusion culture of suspended mammalian cells. Biotechnol Bioeng 2003;82:751–65. https://doi.org/10.1002/bit.10629.Search in Google Scholar PubMed

56. MacDonald, MA, Noebel, M, Recinos, DR, Martínez, VS, Schulz, BJ, Howard, CB, et al.. Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications. Crit Rev Biotechnol 2021;1–17. https://doi.org/10.1080/07388551.2021.1998821.Search in Google Scholar PubMed

57. Johnstone, P, Mast, E, Hughes, E, Peng, H. Development of a small‐scale rotary lobe‐pump cell culture model for examining cell damage in large‐scale N‐1 seed perfusion process. Biotechnol Prog 2020:e3044. https://doi.org/10.1002/btpr.3044.Search in Google Scholar PubMed

58. Amer, M, Vaca, A, Bowden, M. Evaluating shear in perfusion rotary lobe pump using nanoparticle aggregates and computational fluid dynamics. Bioproc Biosyst Eng 2022;45:1–12. https://doi.org/10.1007/s00449-022-02757-1.Search in Google Scholar PubMed

59. Kamaraju, H, Wetzel, K, Kelly, WJ. Modeling shear‐induced CHO cell damage in a rotary positive displacement pump. Biotechnol Prog 2010;26:1606–15. https://doi.org/10.1002/btpr.479.Search in Google Scholar PubMed

60. Blaschczok, K, Kaiser, SC, Loeffelholz, C, Imseng, C, Burkart, J, Boesch, P, et al.. Investigations on mechanical stress caused to CHO suspension cells by standard and single‐use pumps. Chem-Ing-Tech 2013;85:144–52. https://doi.org/10.1002/cite.201200135.Search in Google Scholar

61. Dittler, I, Kaiser, SC, Blaschczok, K, Loeffelholz, C, Boesch, P, Dornfeld, W, et al.. A cost‐effective and reliable method to predict mechanical stress in single‐use and standard pumps. Eng Life Sci 2014;14:311–7. https://doi.org/10.1002/elsc.201300068.Search in Google Scholar

62. Wang, S, Godfrey, S, Ravikrishnan, J, Lin, H, Vogel, J, Coffman, J. Shear contributions to cell culture performance and product recovery in ATF and TFF perfusion systems. J Biotechnol 2017;246:52–60. https://doi.org/10.1016/j.jbiotec.2017.01.020.Search in Google Scholar PubMed

63. Weinberger, ME, Kulozik, U. On the effect of flow reversal during crossflow microfiltration of a cell and protein mixture. Food Bioprod Process 2021;129:24–33. https://doi.org/10.1016/j.fbp.2021.07.001.Search in Google Scholar

64. Weinberger, ME, Kulozik, U. Understanding the fouling mitigation mechanisms of alternating crossflow during cell-protein fractionation by microfiltration. Food Bioprod Process 2022;131:136–43. https://doi.org/10.1016/j.fbp.2021.11.003.Search in Google Scholar

65. Madabhushi, SR, Huang, CJ, Wang, X, Bui, A, Atieh, TB, Rayfield, WJ, et al.. An innovative strategy to recycle permeate in biologics continuous manufacturing process to improve material efficiency and sustainability. Biotechnol Prog 2022;38:e3262. https://doi.org/10.1002/btpr.3262.Search in Google Scholar PubMed

66. Martínez-Monge, I, Roman, R, Comas, P, Fontova, A, Lecina, M, Casablancas, A, et al.. New developments in online OUR monitoring and its application to animal cell cultures. Appl Microbiol Biotechnol 2019;103:6903–17. https://doi.org/10.1007/s00253-019-09989-4.Search in Google Scholar PubMed

67. Seidel, S, Maschke, RW, Werner, S, Jossen, V, Eibl, D. Oxygen mass transfer in biopharmaceutical processes: numerical and experimental approaches. Chem-Ing-Tech 2021;93:42–61. https://doi.org/10.1002/cite.202000179.Search in Google Scholar

68. Konstantinov, K, Goudar, C, Ng, M, Meneses, R, Thrift, J, Chuppa, S, et al.. The “Push-to-Low” approach for optimization of high-density perfusion cultures of animal cells. Adv Biochem Eng Biotechnol 2006;101:75–98. https://doi.org/10.1007/10_016.Search in Google Scholar PubMed

69. Kornecki, M, Mestmäcker, F, Zobel-Roos, S, de Figueiredo, LH, Schlüter, H, Strube, J. Host cell proteins in biologics manufacturing: the good, the bad, and the ugly. Antibodies 2017;6. https://doi.org/10.3390/antib6030013.Search in Google Scholar PubMed PubMed Central

70. Levy, NE, Valente, KN, Choe, LH, Lee, KH, Lenhoff, AM. Identification and characterization of host cell protein product-associated impurities in monoclonal antibody bioprocessing. Biotechnol Bioeng 2014;111:904–12. https://doi.org/10.1002/bit.25158.Search in Google Scholar PubMed PubMed Central

71. Kelley, B. Industrialization of mAb production technology: the bioprocessing industry at a crossroads. mAbs 2009;1:443–52. https://doi.org/10.4161/mabs.1.5.9448.Search in Google Scholar PubMed PubMed Central

72. Kelley, B. Very large scale monoclonal antibody purification: the case for conventional unit operations. Biotechnol Prog 2007;23:995–1008. https://doi.org/10.1021/bp070117s.Search in Google Scholar PubMed

73. Saxena, V, Weil, A. Radial flow columns: a new approach to scaling-up biopurifications. Biochromatography 1987;2:90–7.Search in Google Scholar

74. Besselink, T, van der Padt, A, Janssen, AEM, Boom, RM. Are axial and radial flow chromatography different? J Chromatogr A 2013;1271:105–14. https://doi.org/10.1016/j.chroma.2012.11.027.Search in Google Scholar PubMed

75. Müller, E. Properties and characterization of high capacity resins for biochromatography. Chem Eng Technol 2005;28:1295–305. https://doi.org/10.1002/ceat.200500161.Search in Google Scholar

76. Marina Graalfs, H, Joehnck, M, Jacob, LR, Frech, C. Cation-exchange chromatography of monoclonal antibodies: characterisation of a novel stationary phase designed for production-scale purification. mAbs 2010;4:395–404. https://doi.org/10.4161/mabs.12303.Search in Google Scholar PubMed PubMed Central

77. Chen, J, Tetrault, J, Ley, A. Comparison of standard and new generation hydrophobic interaction chromatography resins in the monoclonal antibody purification process. J Chromatogr A 2008;1177:272–81. https://doi.org/10.1016/j.chroma.2007.07.083.Search in Google Scholar PubMed

78. Müller, E, Vajda, J. Routes to improve binding capacities of affinity resins demonstrated for protein A chromatography. J Chromatogr B 2016;1021:159–68. https://doi.org/10.1016/j.jchromb.2016.01.036.Search in Google Scholar PubMed

79. Ramos-de-la-Peña, AM, González-Valdez, J, Aguilar, O. Protein A chromatography: challenges and progress in the purification of monoclonal antibodies. J Separ Sci 2019;42:1816–27. https://doi.org/10.1002/jssc.201800963.Search in Google Scholar PubMed

80. Lu, W, Zhang, Z, Zhang, S, Zhang, T, Wan, Y, Li, Y. Screening of six cation exchange resins for high binding capacity, monomer purity and step yield: a case study. Protein Expr Purif 2022;199:106155. https://doi.org/10.1016/j.pep.2022.106155.Search in Google Scholar PubMed

81. Ghose, S, Tao, Y, Conley, L, Cecchini, D. Purification of monoclonal antibodies by hydrophobic interaction chromatography under no-salt conditions. mAbs 2013;5:795–800. https://doi.org/10.4161/mabs.25552.Search in Google Scholar PubMed PubMed Central

82. Ramakrishna, A, Maranholkar, V, Hadpe, S, Iyer, J, Rathore, A. Optimization of multi flow rate loading strategy for process intensification of protein A chromatography. J Chromatogr Open 2022;2:100049. https://doi.org/10.1016/j.jcoa.2022.100049.Search in Google Scholar

83. Brough, H, Antoniou, C, Carter, J, Jakubik, J, Xu, Y, Lutz, H. Performance of a novel viresolve NFR virus filter. Biotechnol Progr 2002;18:782–95. https://doi.org/10.1021/bp010193.Search in Google Scholar

84. Bohonak, DM, Mehta, U, Weiss, ER, Voyta, G. Adapting virus filtration to enable intensified and continuous monoclonal antibody processing. Biotechnol Prog 2020;37:e3088. https://doi.org/10.1002/btpr.3088.Search in Google Scholar PubMed

85. Goodrich, EM, Bohonak, DM, Genest, PW, Peterson, E. Recent advances in ultrafiltration and virus filtration for production of antibodies and related biotherapeutics. In: Matte, A, editor. Approaches to the purification, analysis and characterization of antibody-based therapeutics. Amsterdam: Elsevier; 2020. 137–66 pp.10.1016/B978-0-08-103019-6.00007-2Search in Google Scholar

86. Briskot, T, Hillebrandt, N, Kluters, S, Wang, G, Studts, J, Hahn, T, et al.. Modeling the Gibbs–Donnan effect during ultrafiltration and diafiltration processes using the Poisson–Boltzmann theory in combination with a basic Stern model. J Membrane Sci 2022;648:120333. https://doi.org/10.1016/j.memsci.2022.120333.Search in Google Scholar

87. Madsen, E, Kaiser, J, Krühne, U, Pinelo, M. Single pass tangential flow filtration: critical operational variables, fouling, and main current applications. Separ Purif Technol 2022;291:120949. https://doi.org/10.1016/j.seppur.2022.120949.Search in Google Scholar

88. Kruse, T, Kampmann, M, Rüddel, I, Greller, G. An alternative downstream process based on aqueous two-phase extraction for the purification of monoclonal antibodies. Biochem Eng J 2020;161:107703. https://doi.org/10.1016/j.bej.2020.107703.Search in Google Scholar

89. Prouzeau, T, Pezzini, J, Mothes, B. EASY: a disruptive mAb purification process to reduce cost of goods. Biopharm Int 2023;34:26–30.Search in Google Scholar

90. Nadar, S, Shooter, G, Somasundaram, B, Shave, E, Baker, K, Lua, LHL. Intensified downstream processing of monoclonal antibodies using membrane technology. Biotechnol J 2021;16:e2000309. https://doi.org/10.1002/biot.202000309.Search in Google Scholar PubMed

91. Gerstweiler, L, Bi, J, Middelberg, APJ. Continuous downstream bioprocessing for intensified manufacture of biopharmaceuticals and antibodies. Chem Eng Sci 2021;231:116272. https://doi.org/10.1016/j.ces.2020.116272.Search in Google Scholar

92. Gillespie, C, Holstein, M, Mullin, L, Cotoni, K, Tuccelli, R, Caulmare, J, et al.. Continuous in-line virus inactivation for next generation bioprocessing. Biotechnol J 2019;14:1700718. https://doi.org/10.1002/biot.201700718.Search in Google Scholar PubMed

93. Kateja, N, Nitika, N, Fadnis, RS, Rathore, AS. A novel reactor configuration for continuous virus inactivation. Biochem Eng J 2021;167:107885. https://doi.org/10.1016/j.bej.2020.107885.Search in Google Scholar

94. Chopda, V, Gyorgypal, A, Yang, O, Singh, R, Ramachandran, R, Zhang, H, et al.. Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies. J Chem Technol Biotechnol 2022;97:2317–35. https://doi.org/10.1002/jctb.6765.Search in Google Scholar

95. Kuiper, M, Spencer, C, Faeldt, R, Vuillemez, A, Holmes, W, Samuelsson, T, et al.. Repurposing fed‐batch media and feeds for highly productive CHO perfusion processes. Biotechnol Progr 2019;35:e2821. https://doi.org/10.1002/btpr.2821.Search in Google Scholar PubMed

96. Eagle, H. Nutrition needs of mammalian cells in tissue culture. Science 1955;122:501–4. https://doi.org/10.1126/science.122.3168.501.Search in Google Scholar PubMed

97. Freshney, RI. Culture of specific cell types. Culture of Animal Cells 2005. https://doi.org/10.1002/0471747599.cac023.Search in Google Scholar

98. BioPhorum. Raw materials: Best practice guide for preparation of cell culture media solution. London: BioPhorum; 2021. https://www.biophorum.com/download/raw-materials-best-practice-guide-for-preparation-of-cell-culture-media-solution/.Search in Google Scholar

99. Pérez‐Fernández, BA, Fernández‐de‐Cossio‐Díaz, J, Boggiano, T, León, K, Mulet, R. In‐silico media optimization for continuous cultures using genome scale metabolic networks: the case of CHO‐K1. Biotechnol Bioeng 2021;118:1884–97. https://doi.org/10.1002/bit.27704.Search in Google Scholar PubMed

100. Mulukutla, BC, Kale, J, Kalomeris, T, Jacobs, M, Hiller, GW. Identification and control of novel growth inhibitors in fed‐batch cultures of Chinese hamster ovary cells. Biotechnol Bioeng 2017;114:1779–90. https://doi.org/10.1002/bit.26313.Search in Google Scholar PubMed

101. Chevallier, V, Andersen, MR, Malphettes, L. Oxidative stress‐alleviating strategies to improve recombinant protein production in CHO cells. Biotechnol Bioeng 2020;117:1172–86. https://doi.org/10.1002/bit.27247.Search in Google Scholar PubMed PubMed Central

102. Henry, MN, MacDonald, MA, Orellana, CA, Gray, CA, Gillard, M, Baker, K, et al.. Attenuating apoptosis in Chinese hamster ovary cells for improved biopharmaceutical production. Biotechnol Bioeng 2020;117:1187–203. https://doi.org/10.1002/bit.27269.Search in Google Scholar PubMed

103. Pereira, S, Kildegaard, HF, Andersen, MR. Impact of CHO metabolism on cell growth and protein production: an overview of toxic and inhibiting metabolites and nutrients. Biotechnol J 2018;13:1700499. https://doi.org/10.1002/biot.201700499.Search in Google Scholar PubMed

104. Kshirsagar, R, Raju, R, Ali, A, Kwiatkowski, C, McElearney, K, Gilbert, A. Application of -omics knowledge yields enhanced bioprocess performance. In: Presented at the cell culture engineering XVI. Tampa, Florida, USA: Cell culture engineering XVI; 2018. https://dc.engconfintl.org/ccexvi/217.Search in Google Scholar

105. Salim, T, Chauhan, G, Templeton, N, Ling, WLW. Using MVDA with stoichiometric balances to optimize amino acid concentrations in chemically defined CHO cell culture medium for improved culture performance. Biotechnol Bioeng 2022;119:452–69. https://doi.org/10.1002/bit.27998.Search in Google Scholar PubMed

106. Koenitzer, JD, Mueller, MM, Leparc, G, Pauers, M, Bechmann, J, Schulz, P, et al.. A global RNA‐seq‐driven analysis of CHO host and production cell lines reveals distinct differential expression patterns of genes contributing to recombinant antibody glycosylation. Biotechnol J 2015;10:1412–23. https://doi.org/10.1002/biot.201400652.Search in Google Scholar PubMed

107. Birzele, F, Schaub, J, Rust, W, Clemens, C, Baum, P, Kaufmann, H, et al.. Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing. Nucleic Acids Res 2010;38:3999–4010. https://doi.org/10.1093/nar/gkq116.Search in Google Scholar PubMed PubMed Central

108. Wippermann, A, Rupp, O, Brinkrolf, K, Hoffrogge, R, Noll, T. The DNA methylation landscape of Chinese hamster ovary (CHO) DP-12 cells. J Biotechnol 2015;199:38–46. https://doi.org/10.1016/j.jbiotec.2015.02.014.Search in Google Scholar PubMed

109. Schaub, J, Clemens, C, Schorn, P, Hildebrandt, T, Rust, W, Mennerich, D, et al.. CHO gene expression profiling in biopharmaceutical process analysis and design. Biotechnol Bioeng 2010;105:431–8. https://doi.org/10.1002/bit.22549.Search in Google Scholar PubMed

110. Stolfa, G, Smonskey, MW, Boniface, R, Hachmann, AB, Gulde, P, Joshi, AD, et al.. CHO‐Omics review: the impact of current and emerging technologies on Chinese hamster ovary based bioproduction. Biotechnol J 2018;13:1700227. https://doi.org/10.1002/biot.201700227.Search in Google Scholar PubMed

111. Borth, N, Hu, W. Enhancing CHO by systems biotechnology. Biotechnol J 2018;13:1800488. https://doi.org/10.1002/biot.201800488.Search in Google Scholar PubMed

112. Matte, A. Recent advances and future directions in downstream processing of therapeutic antibodies. Int J Mol Sci 2022;23. https://doi.org/10.3390/ijms23158663.Search in Google Scholar PubMed PubMed Central

113. Gronemeyer, P, Ditz, R, Strube, J. DoE based integration approach of upstream and downstream processing regarding HCP and ATPE as harvest operation. Biochem Eng J 2016;113:158–66. https://doi.org/10.1016/j.bej.2016.06.016.Search in Google Scholar

114. Rischawy, F, Saleh, D, Hahn, T, Oelmeier, S, Spitz, J, Kluters, S. Good modeling practice for industrial chromatography: mechanistic modeling of ion exchange chromatography of a bispecific antibody. Comput Chem Eng 2019;130:106532. https://doi.org/10.1016/j.compchemeng.2019.106532.Search in Google Scholar

115. Saleh, D, Wang, G, Mueller, B, Rischawy, F, Kluters, S, Studts, J, et al.. Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications. Biotechnol Progr 2020;36:e2984. https://doi.org/10.1002/btpr.2984.Search in Google Scholar PubMed

116. Saleh, D, Hess, R, Ahlers-Hesse, M, Rischawy, F, Wang, G, Grosch, JH, et al.. A multiscale modeling method for therapeutic antibodies in ion exchange chromatography. Biotechnol Bioeng 2022;120:125–38. https://doi.org/10.1002/bit.28258.Search in Google Scholar PubMed

117. Briskot, T, Hahn, T, Huuk, T, Wang, G, Kluters, S, Studts, J, et al.. Analysis of complex protein elution behavior in preparative ion exchange processes using a colloidal particle adsorption model. J Chromatogr A 2021;1654:462439. https://doi.org/10.1016/j.chroma.2021.462439.Search in Google Scholar PubMed

118. Rischawy, F, Briskot, T, Schimek, A, Wang, G, Saleh, D, Kluters, S, et al.. Integrated process model for the prediction of biopharmaceutical manufacturing chromatography and adjustment steps. J Chromatogr A 2022;1681:463421. https://doi.org/10.1016/j.chroma.2022.463421.Search in Google Scholar PubMed

119. Taylor, C, Marschall, L, Kunzelmann, M, Richter, M, Rudolph, F, Vajda, J, et al.. Integrated process model applications linking bioprocess development to quality by design milestones. Bioengineering 2021;8. https://doi.org/10.3390/bioengineering8110156.Search in Google Scholar PubMed PubMed Central

120. Saleh, D, Wang, G, Rischawy, F, Kluters, S, Studts, J, Hubbuch, J. In silico process characterization for biopharmaceutical development following the quality by design concept. Biotechnol Progr 2021;37:e3196. https://doi.org/10.1002/btpr.3196.Search in Google Scholar PubMed

121. Saleh, D, Wang, G, Mueller, B, Rischawy, F, Kluters, S, Studts, J, et al.. Cross‐scale quality assessment of a mechanistic cation exchange chromatography model. Biotechnol Progr 2021;37:e3081. https://doi.org/10.1002/btpr.3081.Search in Google Scholar PubMed

122. Smiatek, J, Jung, A, Bluhmki, E. Towards a digital bioprocess replica: computational approaches in biopharmaceutical development and manufacturing. Trends Biotechnol 2020;38:1141–53. https://doi.org/10.1016/j.tibtech.2020.05.008.Search in Google Scholar PubMed

123. Smiatek, J, Clemens, C, Herrera, LM, Arnold, S, Knapp, B, Presser, B, et al.. Generic and specific recurrent neural network models: applications for large and small scale biopharmaceutical upstream processes. Biotechnology Reports 2021;31:e00640. https://doi.org/10.1016/j.btre.2021.e00640.Search in Google Scholar PubMed PubMed Central

124. Herrera, LM. Holistic process models: a Bayesian predictive ensemble method for single and coupled unit operation models. Process 2022;10:662. https://doi.org/10.3390/pr10040662.Search in Google Scholar

125. Wutz, J, Waterkotte, B, Heitmann, K, Wucherpfennig, T. Computational fluid dynamics (CFD) as a tool for industrial UF/DF tank optimization. Biochem Eng J 2020;160:107617. https://doi.org/10.1016/j.bej.2020.107617.Search in Google Scholar

126. Wutz, J, Steiner, R, Assfalg, K, Wucherpfennig, T. Establishment of a CFD‐based kLa model in microtiter plates to support CHO cell culture scale‐up during clone selection. Biotechnol Progr 2018;34:1120–8. https://doi.org/10.1002/btpr.2707.Search in Google Scholar PubMed

127. Wutz, J, Lapin, A, Siebler, F, Schaefer, JE, Wucherpfennig, T, Berger, M, et al.. Predictability of kLa in stirred tank reactors under multiple operating conditions using an Euler–Lagrange approach. Eng Life Sci 2016;16:633–42. https://doi.org/10.1002/elsc.201500135.Search in Google Scholar

128. Kuschel, M, Fitschen, J, Hoffmann, M, von Kameke, A, Schlüter, M, Wucherpfennig, T. Validation of Novel Lattice Boltzmann Large Eddy Simulations (LB LES) for equipment characterization in biopharma. Process 2021;9:950. https://doi.org/10.3390/pr9060950.Search in Google Scholar

129. Kuschel, M, Wutz, J, Salli, M, Monteil, D, Wucherpfennig, T. CFD supported scale up of perfusion bioreactors in biopharma. Front Chem Eng 2023;18:1076509.10.3389/fceng.2023.1076509Search in Google Scholar

130. FDA. PAT—a framework for innovative pharmaceutical development, manufacturing, and quality assurance. In: Pharmaceutical CGMPs; 2004. [Online]. Available: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pat-framework-innovative-pharmaceutical-development-manufacturing-and-quality-assurance.Search in Google Scholar

131. Wasalathanthri, DP, Rehmann, MS, Song, Y, Gu, Y, Mi, L, Shao, C, et al.. Technology outlook for real‐time quality attribute and process parameter monitoring in biopharmaceutical development—a review. Biotechnol Bioeng 2020;117:3182–98. https://doi.org/10.1002/bit.27461.Search in Google Scholar PubMed

132. Buckley, K, Ryder, AG. Applications of Raman spectroscopy in biopharmaceutical manufacturing: a short review. Appl Spectrosc 2017;71:1085–116. https://doi.org/10.1177/0003702817703270.Search in Google Scholar PubMed

133. 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 Progr 2015;31:566–77. https://doi.org/10.1002/btpr.2035.Search in Google Scholar PubMed

134. Domján, J, Pantea, E, Gyuerkés, M, Madarász, L, Kozák, D, Farkas, A, et al.. Real‐time amino acid and glucose monitoring system for the automatic control of nutrient feeding in CHO cell culture using Raman spectroscopy. Biotechnol J 2022;17:2100395. https://doi.org/10.1002/biot.202100395.Search in Google Scholar PubMed

135. Romann, P, Kolar, J, Tobler, D, Herwig, C, Bielser, J, Villiger, TK. Advancing Raman model calibration for perfusion bioprocesses using spiked harvest libraries. Biotechnol J 2022;17:2200184. https://doi.org/10.1002/biot.202200184.Search in Google Scholar PubMed

136. Santos, RM, Kessler, J, Salou, P, Menezes, JC, Peinado, A. Monitoring mAb cultivations with in‐situ Raman spectroscopy: the influence of spectral selectivity on calibration models and industrial use as reliable PAT tool. Biotechnol Progr 2018;34:659–70. https://doi.org/10.1002/btpr.2635.Search in Google Scholar PubMed

137. Abu‐Absi, NR, Kenty, BM, Cuellar, ME, Borys, MC, Sakhamuri, S, Strachan, DJ, et al.. Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in‐line Raman spectroscopy probe. Biotechnol Bioeng 2011;108:1215–21. https://doi.org/10.1002/bit.23023.Search in Google Scholar PubMed

138. Gibbons, L, Rafferty, C, Robinson, K, Abad, M, Maslanka, F, Le, N, et al.. Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process. Biotechnol Progr 2021;e3223. https://doi.org/10.1002/btpr.3223.Search in Google Scholar PubMed

139. Liu, Z, Zhang, Z, Qin, Y, Chen, G, Hun, J, Wang, Q, et al.. The application of Raman spectroscopy for monitoring product quality attributes in perfusion cell culture. Biochem Eng J 2021;173:108064. https://doi.org/10.1016/j.bej.2021.108064.Search in Google Scholar

140. Park, S-Y, Park, C-H, Choi, D-H, Hong, JK, Lee, D-Y. Bioprocess digital twins of mammalian cell culture for advanced biomanufacturing. Curr Opin Chem Eng 2021;33:100702. https://doi.org/10.1016/j.coche.2021.100702.Search in Google Scholar

141. Eyster, T, Talwar, S, Fernandez, J, Foster, S, Hayes, J, Allen, R, et al.. Tuning monoclonal antibody galactosylation using Raman spectroscopy‐controlled lactic acid feeding. Biotechnol Progr 2021;37. https://doi.org/10.1002/btpr.3085.Search in Google Scholar PubMed

142. Gillespie, C, Wasalathanthri, DP, Ritz, DB, Zhou, G, Davis, KA, Wucherpfennig, T, et al.. Systematic assessment of process analytical technologies for biologics. Biotechnol Bioeng 2021. https://doi.org/10.1002/bit.27990.Search in Google Scholar PubMed

143. Metze, S, Blioch, S, Matuszczyk, J, Greller, G, Grimm, C, Scholz, J, et al.. Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors. Anal Bioanal Chem 2020;412:2089–102. https://doi.org/10.1007/s00216-019-02096-3.Search in Google Scholar PubMed PubMed Central

144. Rittershaus, ESC, Rehmann, MS, Xu, J, He, Q, Hill, C, Swanberg, J, et al.. N-1 perfusion platform development using a capacitance probe for biomanufacturing. Bioengineering 2022;9:128. https://doi.org/10.3390/bioengineering9040128.Search in Google Scholar PubMed PubMed Central

145. Moore, B, Sanford, R, Zhang, A. Case study: the characterization and implementation of dielectric spectroscopy (biocapacitance) for process control in a commercial GMP CHO manufacturing process. Biotechnol Progr 2019;35:e2782. https://doi.org/10.1002/btpr.2782.Search in Google Scholar PubMed

146. Zhang, A, Liu Tsang, V, Moore, B, Shen, V, Huang, YM, Kshrisagar, R, et al.. Advanced process monitoring and feedback control to enhance cell culture process production and robustness. Biotechnol Bioeng 2015;112. https://doi.org/10.1002/bit.25684.Search in Google Scholar PubMed

147. Opel, CF, Li, J, Amanullah, A. Quantitative modeling of viable cell density, cell size, intracellular conductivity, and membrane capacitance in batch and fed‐batch CHO processes using dielectric spectroscopy. Biotechnol Progr 2010;26:1187–99. https://doi.org/10.1002/btpr.425.Search in Google Scholar PubMed

148. Ma, F, Zhang, A, Chang, D, Velev, OD, Wiltberger, K, Kshirsagar, R. Real-time monitoring and control of CHO cell apoptosis by in situ multifrequency scanning dielectric spectroscopy. Process Biochem 2019;80:138–45. https://doi.org/10.1016/j.procbio.2019.02.017.Search in Google Scholar

149. Cannizzaro, C, Gügerli, R, Marison, I, von Stockar, U. On‐line biomass monitoring of CHO perfusion culture with scanning dielectric spectroscopy. Biotechnol Bioeng 2003;84:597–610. https://doi.org/10.1002/bit.10809.Search in Google Scholar PubMed

150. Wu, S, Ketcham, SA, Corredor, CC, Both, D, Drennen, JK, Anderson, CA. Rapid at‐line early cell death quantification using capacitance spectroscopy. Biotechnol Bioeng 2022;119:857–67. https://doi.org/10.1002/bit.28011.Search in Google Scholar PubMed

151. Woodgate, JM. Perfusion N-1 culture—opportunities for process intensification. In: Biopharmaceutical Processing - Development, Design, and Implementation of Manufacturing Processes. Amsterdam: Elsevier; 2018:755–768 pp.10.1016/B978-0-08-100623-8.00037-2Search in Google Scholar

152. Claßen, J, Graf, A, Aupert, F, Solle, D, Höhse, M, Scheper, T. A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations – Part II. Eng Life Sci 2019;19:341–51. https://doi.org/10.1002/elsc.201800146.Search in Google Scholar PubMed PubMed Central

153. Graf, A, Claßen, J, Solle, D, Hitzmann, B, Rebner, K, Hoehse, M. A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line monitoring of Chinese hamster ovary cell cultivations – Part I. Eng Life Sci 2019;19:352–62. https://doi.org/10.1002/elsc.201800149.Search in Google Scholar PubMed PubMed Central

154. Li, B, Shanahan, M, Calvet, A, Leister, KJ, Ryder, AG. Comprehensive, quantitative bioprocess productivity monitoring using fluorescence EEM spectroscopy and chemometrics. Analyst 2014;139:1661–71. https://doi.org/10.1039/c4an00007b.Search in Google Scholar PubMed

155. Ryder, AG. Cell culture media analysis using rapid spectroscopic methods. Curr Opin Chem Eng 2018;22:11–7. https://doi.org/10.1016/j.coche.2018.08.008.Search in Google Scholar

156. Neutsch, L, Kroll, P, Brunner, M, Pansy, A, Kovar, M, Herwig, C, et al.. Media photo‐degradation in pharmaceutical biotechnology – impact of ambient light on media quality, cell physiology, and IgG production in CHO cultures. J Chem Technol Biotechnol 2018;93:2141–51. https://doi.org/10.1002/jctb.5643.Search in Google Scholar PubMed PubMed Central

157. Calvet, A, Li, B, Ryder, AG. A rapid fluorescence based method for the quantitative analysis of cell culture media photo-degradation. Anal Chim Acta 2014;807:111–9. https://doi.org/10.1016/j.aca.2013.11.028.Search in Google Scholar PubMed

158. Ryan, PW, Li, B, Shanahan, M, Leister, KJ, Ryder, AG. Prediction of cell culture media performance using fluorescence spectroscopy. Anal Chem 2010;82:1311–7. https://doi.org/10.1021/ac902337c.Search in Google Scholar PubMed

159. Bhatia, H, Mehdizadeh, H, Drapeau, D, Yoon, S. In‐line monitoring of amino acids in mammalian cell cultures using Raman spectroscopy and multivariate chemometrics models. Eng Life Sci 2018;18:55–61. https://doi.org/10.1002/elsc.201700084.Search in Google Scholar PubMed PubMed Central

160. Li, M, Ebel, B, Paris, C, Chauchard, F, Guedon, E, Marc, A. Real‐time monitoring of antibody glycosylation site occupancy by in situ Raman spectroscopy during bioreactor CHO cell cultures. Biotechnol Progr 2018;34:486–93. https://doi.org/10.1002/btpr.2604.Search in Google Scholar PubMed

161. Webster, TA, Hadley, BC, Dickson, M, Busa, JK, Jaques, C, Mason, C. Feedback control of two supplemental feeds during fed-batch culture on a platform process using inline Raman models for glucose and phenylalanine concentration. Bioproc Biosyst Eng 2021;44:127–40. https://doi.org/10.1007/s00449-020-02429-y.Search in Google Scholar PubMed

162. Matthews, TE, Smelko, JP, Berry, B, Romero-Torres, S, Hill, D, Kshirsagar, R, et al.. Glucose monitoring and adaptive feeding of mammalian cell culture in the presence of strong autofluorescence by near infrared Raman spectroscopy. Biotechnol Progr 2018;34:1574–80. https://doi.org/10.1002/btpr.2711.Search in Google Scholar PubMed

163. Milewska, A, Baekelandt, G, Boutaieb, S, Mozin, V, Falconbridge, A. In-line monitoring of protein concentration with MIR spectroscopy during UFDF. Eng Life Sci 2023;23:e2200050. https://doi.org/10.1002/elsc.202200050.Search in Google Scholar PubMed PubMed Central

164. Thakur, G, Hebbi, V, Rathore, AS. Near Infrared Spectroscopy as a PAT tool for monitoring and control of protein and excipient concentration in ultrafiltration of highly concentrated antibody formulations. Int J Pharm 2021;600:120456. https://doi.org/10.1016/j.ijpharm.2021.120456.Search in Google Scholar PubMed

165. Rolinger, L, Hubbuch, J, Rüdt, M. Monitoring of ultra- and diafiltration processes by Kalman-filtered Raman measurements. Anal Bioanal Chem 2023;415:841–54. https://doi.org/10.1007/s00216-022-04477-7.Search in Google Scholar PubMed PubMed Central

166. Rüdt, M, Brestrich, N, Rolinger, L, Hubbuch, J. Real-time monitoring and control of the load phase of a protein A capture step. Biotechnol Bioeng 2017;114:368–73. https://doi.org/10.1002/bit.26078.Search in Google Scholar PubMed PubMed Central

167. Thakur, G, Hebbi, V, Rathore, AS. An NIR-based PAT approach for real-time control of loading in protein A chromatography in continuous manufacturing of monoclonal antibodies. Biotechnol Bioeng 2020;117:673–86. https://doi.org/10.1002/bit.27236.Search in Google Scholar PubMed

168. Thakur, G, Bansode, V, Rathore, AS. Continuous manufacturing of monoclonal antibodies: automated downstream control strategy for dynamic handling of titer variations. J Chromatogr A 2022;1682:463496. https://doi.org/10.1016/j.chroma.2022.463496.Search in Google Scholar PubMed

169. Rolinger, L, Rüdt, M, Hubbuch, J. A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing. Anal Bioanal Chem 2020;412:2047–2064. https://doi.org/10.1007/s00216-020-02407-z.Search in Google Scholar PubMed PubMed Central

170. Schulze, M, Niemann, J, Wijffels, RH, Matuszczyk, J, Martens, DE. Rapid intensification of an established CHO cell fed‐batch process. Biotechnol Progr 2022;38:e3213. https://doi.org/10.1002/btpr.3213.Search in Google Scholar PubMed PubMed Central

171. Walsh, G. Biopharmaceutical benchmarks 2018. Nat Biotechnol 2018;36:1136–45. https://doi.org/10.1038/nbt.4305.Search in Google Scholar PubMed

172. Hunter, M, Yuan, P, Vavilala, D, Fox, M. Optimization of protein expression in mammalian cells. Curr Protoc Protein Sci 2019;95:e77. https://doi.org/10.1002/cpps.77.Search in Google Scholar PubMed

173. Wurm, FM. Production of recombinant protein therapeutics in cultivated mammalian cells. Nat Biotechnol 2004;22:1393–8. https://doi.org/10.1038/nbt1026.Search in Google Scholar PubMed

174. Bertrand, V, Karst, DJ, Bachmann, A, Cantalupo, K, Soos, M, Morbidelli, M. Transcriptome and proteome analysis of steady‐state in a perfusion CHO cell culture process. Biotechnol Bioeng 2019;116:1959–72. https://doi.org/10.1002/bit.26996.Search in Google Scholar PubMed

175. Wolf, MKF, Pechlaner, A, Lorenz, V, Karst, DJ, Souquet, J, Broly, H, et al.. A two-step procedure for the design of perfusion bioreactors. Biochem Eng J 2019;151:107295. https://doi.org/10.1016/j.bej.2019.107295.Search in Google Scholar

176. Karst, DJ, Steinebach, F, Soos, M, Morbidelli, M. Process performance and product quality in an integrated continuous antibody production process. Biotechnol Bioeng 2017;114:298–307. https://doi.org/10.1002/bit.26069.Search in Google Scholar PubMed

177. Kim, M, O’Callaghan, PM, Droms, KA, James, DC. A mechanistic understanding of production instability in CHO cell lines expressing recombinant monoclonal antibodies. Biotechnol Bioeng 2011;108:2434–46. https://doi.org/10.1002/bit.23189.Search in Google Scholar PubMed

178. Veith, N, Ziehr, H, MacLeod, RAF, Reamon-Buettner, SM. Mechanisms underlying epigenetic and transcriptional heterogeneity in Chinese hamster ovary (CHO) cell lines. BMC Biotechnol 2016;16:6. https://doi.org/10.1186/s12896-016-0238-0.Search in Google Scholar PubMed PubMed Central

179. Jamnikar, U, Nikolic, P, Belic, A, Blas, M, Gaser, D, Francky, A, et al.. Transcriptome study and identification of potential marker genes related to the stable expression of recombinant proteins in CHO clones. BMC Biotechnol 2015;15:98. https://doi.org/10.1186/s12896-015-0218-9.Search in Google Scholar PubMed PubMed Central

180. Yusufi, FNK, Lakshmanan, M, Ho, YS, Loo, BLW, Ariyaratne, P, Yang, Y, et al.. Mammalian systems biotechnology reveals global cellular adaptations in a recombinant CHO cell line. Cell Syst 2017;4:530–42.e6. https://doi.org/10.1016/j.cels.2017.04.009.Search in Google Scholar PubMed

181. Lee, JS, Park, JH, Ha, TK, Samoudi, M, Lewis, NE, Palsson, BO, et al.. Revealing key determinants of clonal variation in transgene expression in recombinant CHO cells using targeted genome editing. ACS Synth Biol 2018;7:2867–78. https://doi.org/10.1021/acssynbio.8b00290.Search in Google Scholar PubMed PubMed Central

182. Kostyrko, K, Neuenschwander, S, Junier, T, Regamey, A, Iseli, C, Schmid-Siegert, E, et al.. MAR‐Mediated transgene integration into permissive chromatin and increased expression by recombination pathway engineering. Biotechnol Bioeng 2017;114:384–96. https://doi.org/10.1002/bit.26086.Search in Google Scholar PubMed PubMed Central

183. Dahodwala, H, Lee, KH. The fickle CHO: a review of the causes, implications, and potential alleviation of the CHO cell line instability problem. Curr Opin Biotech 2019;60:128–37. https://doi.org/10.1016/j.copbio.2019.01.011.Search in Google Scholar PubMed

184. Balasubramanian, S, Rajendra, Y, Baldi, L, Hacker, DL, Wurm, FM. Comparison of three transposons for the generation of highly productive recombinant CHO cell pools and cell lines. Biotechnol Bioeng 2016;113:1234–43. https://doi.org/10.1002/bit.25888.Search in Google Scholar PubMed

185. Muñoz-López, M, García-Pérez, JL. DNA transposons: nature and applications in genomics. Curr Genomics 2010;11:115–28. https://doi.org/10.2174/138920210790886871.Search in Google Scholar PubMed PubMed Central

186. Villiger-Oberbek, A, Yang, Y, Zhou, W, Yang, J. Development and application of a high-throughput platform for perfusion-based cell culture processes. J Biotechnol 2015;212:21–9. https://doi.org/10.1016/j.jbiotec.2015.06.428.Search in Google Scholar PubMed

187. Gomez, N, Ambhaikar, M, Zhang, L, Huang, C, Barkhordarian, H, Lull, J, et al.. Analysis of Tubespins as a suitable scale‐down model of bioreactors for high cell density CHO cell culture. Biotechnol Progr 2017;33:490–9. https://doi.org/10.1002/btpr.2418.Search in Google Scholar PubMed

188. Wolf, MKF, Lorenz, V, Karst, DJ, Souquet, J, Broly, H, Morbidelli, M. Development of a shake tube‐based scale‐down model for perfusion cultures. Biotechnol Bioeng 2018;115:2703–13. https://doi.org/10.1002/bit.26804.Search in Google Scholar PubMed

189. Kreye, S, Stahn, R, Nawrath, K, Goralczyk, V, Zoro, B, Goletz, S. A novel scale‐down mimic of perfusion cell culture using sedimentation in an automated microbioreactor (SAM). Biotechnol Progr 2019;35:e2832. https://doi.org/10.1002/btpr.2832.Search in Google Scholar PubMed

190. Yin, L, Au, WY, Yu, CC, Kwon, T, Lai, Z, Shang, M, et al.. Miniature auto‐perfusion bioreactor system with spiral microfluidic cell retention device. Biotechnol Bioeng 2021;118:1951–61. https://doi.org/10.1002/bit.27709.Search in Google Scholar PubMed

191. Jin, L, Wang, Z-S, Cao, Y, Sun, R-Q, Zhou, H, Cao, R-Y. Establishment and optimization of a high-throughput mimic perfusion model in ambr® 15. Biotechnol Lett 2021;43:423–33. https://doi.org/10.1007/s10529-020-03026-5.Search in Google Scholar PubMed

192. Mozdzierz, NJ, Love, KR, Lee, KS, Lee, HLT, Shah, KA, Ram RJ, et al.. A perfusion-capable microfluidic bioreactor for assessing microbial heterologous protein production. Lab Chip 2015;15:2918–22. https://doi.org/10.1039/c5lc00443h.Search in Google Scholar PubMed PubMed Central

193. Hong, JK, Lakshmanan, M, Goudar, C, Lee, D-Y. Towards next generation CHO cell line development and engineering by systems approaches. Curr Opin Chem Eng 2018;22:1–10. https://doi.org/10.1016/j.coche.2018.08.002.Search in Google Scholar

194. Cui, Y, Cui, P, Chen, B, Li, S, Guan, H. Monoclonal antibodies: formulations of marketed products and recent advances in novel delivery system. Drug Dev Ind Pharm 2017;43:1–39. https://doi.org/10.1080/03639045.2017.1278768.Search in Google Scholar PubMed

195. Fischer, S, Handrick, R, Otte, K. The art of CHO cell engineering: a comprehensive retrospect and future perspectives. Biotechnol Adv 2015;33:1878–96. https://doi.org/10.1016/j.biotechadv.2015.10.015.Search in Google Scholar PubMed

196. Xu, X, Nagarajan, H, Lewis, NE, Pan, S, Cai, Z, Liu, X, et al.. The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line. Nat Biotechnol 2011;29:735–41. https://doi.org/10.1038/nbt.1932.Search in Google Scholar PubMed PubMed Central

197. DiMasi, JA, Grabowski, HG, Hansen, RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 2016;47:20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012.Search in Google Scholar PubMed

198. Paul, SM, Mytelka, DS, Dunwiddie, CT, Persinger, CC, Munos, BH, Lindborg, SR, et al.. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 2010;9:203–14. https://doi.org/10.1038/nrd3078.Search in Google Scholar PubMed

199. Jagschies, G. Management of Process Economy—Case Studies. In: Biopharmaceutical processing. Amsterdam: Elsevier; 2018:1191–1223 pp.10.1016/B978-0-08-100623-8.00055-4Search in Google Scholar

200. Roush, D, Asthagiri, D, Babi, AK, Benner, S, Bilodeau, C, Carta, G, et al.. Toward in silico CMC: an industrial collaborative approach to model‐based process development. Biotechnol Bioeng 2020;117:3986–4000. https://doi.org/10.1002/bit.27520.Search in Google Scholar PubMed

201. Maracelias, CT. Chemical production scheduling mixed integer programming models and methods | Chemical engineering | Cambridge University Press. In: Cambridge series in chemical engineering. Cambridge: Cambridge University Press; 2021.Search in Google Scholar

202. Babi, DK, Griesbach, J, Hunt, S, Insaidoo, F, Roush, D, Todd, R, et al.. Opportunities and challenges for model utilization in the biopharmaceutical industry: current versus future state. Curr Opin Chem Eng 2022;36:100813. https://doi.org/10.1016/j.coche.2022.100813.Search in Google Scholar

203. Pistikopoulos, EN, Tian, Y, Bindlish, R. Operability and control in process intensification and modular design: challenges and opportunities. AIChE J 2021;67. https://doi.org/10.1002/aic.17204.Search in Google Scholar

204. Petrides, D, Carmichael, D, Siletti, C, Koulouris, A. Biopharmaceutical process optimization with simulation and scheduling tools. Bioengineering 2014;1:154–87. https://doi.org/10.3390/bioengineering1040154.Search in Google Scholar PubMed

205. Farid, SS, Baron, M, Stamatis, C, Nie, W, Coffman, J. Benchmarking biopharmaceutical process development and manufacturing cost contributions to R&D. mAbs 2020;12:1754999. https://doi.org/10.1080/19420862.2020.1754999.Search in Google Scholar PubMed PubMed Central

206. Xu, S, Gavin, J, Jiang, R, Chen, H. Bioreactor productivity and media cost comparison for different intensified cell culture processes. Biotechnol Progr 2017;33:867–78. https://doi.org/10.1002/btpr.2415.Search in Google Scholar PubMed

207. Souza, JD, Scott, K, Genest, P. Virus-filtration process development optimization: the key to a more efficient and cost-effective step. BioProcess Int 2016;41:62–74.Search in Google Scholar

208. Franzreb, M, Müller, E, Vajda, J. Cost estimation for protein a chromatography: an in silico approach to MAb purification strategy. BioProcess Int 2014;12:44–52.Search in Google Scholar

209. Broly, H, Costioli, M, Guillemot-Potelle, C, Mitchell-Logean, C. Cost of goods modeling and quality by design for developing cost-effective processes. Biopharm Int 2010;23.Search in Google Scholar

210. Hummel, J, Pagkaliwangan, M, Gjoka, X, Davidovits, T, Stock, R, Ransohoff, T, et al.. Modeling the downstream processing of monoclonal antibodies reveals cost advantages for continuous methods for a broad range of manufacturing scales. Biotechnol J 2019;14:1700665. https://doi.org/10.1002/biot.201700665.Search in Google Scholar PubMed

211. Mahal, H, Branton, H, Farid, SS. End-to-end continuous bioprocessing: impact on facility design, cost of goods, and cost of development for monoclonal antibodies. Biotechnol Bioeng 2021;118:3468–85. https://doi.org/10.1002/bit.27774.Search in Google Scholar PubMed

212. UNFCC the Paris agreement. UNFCCC; 2016. [Online]. Available: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement. [Accessed 28 Sep 2022].Search in Google Scholar

213. Carbon dioxide now more than 50 % higher than pre-industrial levels. [Online]. Available: https://www.noaa.gov/news-release/carbon-dioxide-now-more-than-50-higher-than-pre-industrial-levels?fbclid=IwAR3_PAk4AmI4czOO5ikK_CAGca94LMwQwIEfG9lo3ZWi72BeR6KaX05hHSw. [Accessed 28 Sep 2022].Search in Google Scholar

214. Belkhir, L, Elmeligi, A. Carbon footprint of the global pharmaceutical industry and relative impact of its major players. J Clean Prod 2019;214:185–94. https://doi.org/10.1016/j.jclepro.2018.11.204.Search in Google Scholar

215. Budzinski, K, Constable, D, D’Aquila, D, Smith, P, Madabhushi, SR, Whiting, A, et al.. Streamlined life cycle assessment of single use technologies in biopharmaceutical manufacture. New Biotechnol 2022;68:28–36. https://doi.org/10.1016/j.nbt.2022.01.002.Search in Google Scholar PubMed

216. Budzinski, K, Blewis, M, Dahlin, P, D’Aquila, D, Esparza, J, Gavin, J, et al.. Introduction of a process mass intensity metric for biologics. New Biotechnol 2019;49:37–42. https://doi.org/10.1016/j.nbt.2018.07.005.Search in Google Scholar PubMed