Innovations in Downstream Processing

aicandy - stock.adobe.com

Downstream processing in the biomanufacturing industry has been under relentless pressure for several decades. The source of this pressure is the much greater scalability of upstream production, which can be scaled up by increasing the productivity of cells without increasing the size of equipment or the volume of media. In contrast, the scale of downstream processing depends on the mass of product, and there is a strictly linear relationship between this mass and the size of equipment, the volume of buffers, the area of filters, and the quantity of chromatography resins required. In short, there is no economy of scale in downstream processing. Accordingly, it costs more to accommodate a larger-scale upstream process because the only solution is the addition of further downstream process trains to cope with the extra demand, which can be termed “numbering up” (1). Previously, the authors explored the need for innovation in downstream processing as a means to overcome this bottleneck and increase process efficiency and sustainability (2,3). Such innovations include the streamlining of existing processes (4), the integration of simpler and less expensive technologies that are used in other industries (5), the replacement of fixed equipment with disposable modules (6,7), and the development of high-tech solutions that are game-changers in terms of process redesign. This article explores recent progress in downstream processing, emphasizing innovative purification approaches and the growing interest in mechanistic modeling and machine learning methods for the development of precise predictive models. It also considers the incorporation of process analytical technology (PAT) and green chemistry concepts to reduce water and energy use, limit the amount of waste, and establish more environmentally conscious purification processes.

Novel affinity ligands

Many downstream process trains begin with a capture chromatography step because this is often the most efficient way to separate the target product from most impurities. Protein A, a cell wall protein from Staphylococcus aureus, is widely used for antibody purification due to its strong and selective binding to the Fc region of immunoglobulins. However, protein A chromatography has limitations, including high costs in early clinical manufacturing, ligand leaching, and low affinity for certain antibody variants. The demand for novel affinity ligands has increased along with the prevalence of diverse therapeutic modalities, such as antibody–drug conjugates (ADCs) and bispecific antibodies. Protein A mimetics have been developed to address these challenges because they are more stable than protein A under harsh cleaning conditions, thus reducing the impact of leaching while also saving costs (8).

As an alternative to peptides, aptamers are single-stranded DNA or RNA oligonucleotides that bind to target molecules with high specificity and affinity. Aptamers are easily synthesized and modified to enhance stability, specificity and binding capacity (9). They have been developed for the purification of coagulation factors, interferons and ADCs, showcasing their versatility and potential for diverse therapeutic modalities.

Molecularly imprinted polymers (MIPs) are synthetic polymers containing molecular recognition sites that complement target molecules in terms of shape, size and functional groups. MIPs are created by polymerizing functional monomers around a template molecule, then removing the template to leave behind specific binding sites (10). MIPs offer advantages over natural affinity ligands, including robustness, stability and lower costs. Researchers have investigated the use of MIPs to purify therapeutic proteins such as lysozyme (11) and interferons (12), although further development and optimization are needed.

Ultra-scale-down bioprocessing

Scale-down modeling has traditionally been used during process development to road-test new process variants at a smaller volume than the normal process, reducing space requirements and costs (13). A more recent innovation is ultra-scale-down (USD) bioprocessing, a powerful technique for rapid process development and optimization in biopharmaceutical manufacturing that enables experiments at much smaller volumes and with accelerated timescales, minimizing the use of time and resources and allowing multiple options to be tested in parallel (14). Miniaturized high-throughput systems, such as microscale bioreactors and microfluidic systems, are used to facilitate the efficient screening of numerous process parameters (15,16).

Microscale bioreactors closely mimic the performance of larger-scale systems while requiring less sample material, whereas microfluidic systems can precisely control the flow of small volumes of liquid (17). High-throughput experimentation can be integrated with microscale bioreactors and microfluidic systems to streamline process development even further, thus reducing the time to clinic for new biopharmaceuticals (18).

Clarification/purification innovations

New filtration technologies have been developed to increase the efficiency and selectivity of clarification following the harvesting of cell cultures (19). Examples include single-use, high-capacity depth filters that improve clarification while reducing fouling and preventing large pressure drops (20), and advanced membrane chromatography modules, such as multimodal membranes, that facilitate the removal of aggregates and other impurities with higher selectivity and capacity (21). Fiber-based purification technologies have emerged as another alternative to traditional packed-bed chromatography, offering advantages such as shorter processing times and greater scalability (22). These technologies use hollow-fiber membranes or monolithic fibers as stationary phases.

Hollow-fiber membrane chromatography has been used to capture and polish monoclonal antibodies (mAbs) and virus-like particles, whereas monolithic fibers have been used to purify therapeutic proteins and nucleic acids with high resolution and throughput (23). Modular bed-supported cassette chromatography allows the flexible and scalable design of purification systems using interchangeable cassettes containing different chromatography media. This approach improves the performance of affinity, ion exchange and hydrophobic interaction chromatography by enhancing mass transfer, flow distribution, and media utilization. The modularity of this technology allows for the rapid screening and optimization of chromatography conditions, as well as the seamless scale-up of purification processes.

Continuous chromatography uses less resin than traditional batch chromatography but is more productive. It enables steady-state operations with a higher throughput, increasing both the purity and yield of the product (24). Examples of continuous chromatography include simulated moving bed chromatography (25) and periodic counter-current chromatography (26). These technologies have been used to purify mAbs and other therapeutic proteins (27).

Mechanistic modeling and simulation

As the biopharmaceutical industry moves toward the manufacturing of more complex molecules and personalized medicines, there is a growing need for the fast and reliable development of robust downstream processes. This can be challenging and expensive if there are many different parameters that need to be tested and varied simultaneously. One promising solution is the development of digital twins, which are models of real-world processes. Digital twins are constructed using mechanistic models, which are based on natural physiochemical laws that describe a process mathematically. Advanced software is now available to develop such models, which incorporate relevant effects such as adsorption and fluid dynamics. A digital twin of a chromatography process based on mechanistic models allows the industry to perform thousands of experiments in silico—even outside of the calibration range—while avoiding the need for similar numbers of laboratory experiments (28).

As more data become available, machine learning has been integrated with mechanistic modeling to create more accurate predictive models (29,30). Machine learning algorithms have therefore been used to optimize chromatography process parameters (28). Dynamic end-to-end bioprocess digital twins use mechanistic models of each unit operation and integrate them into a holistic model encompassing the entire bioprocess (31). Digital twins can be used to study the impact of process changes, identify bottlenecks, and optimize overall process performance. For example, digital twins have been used to simulate the performance of continuous and integrated bioprocessing systems, providing valuable insights into process dynamics, equipment sizing, and scheduling issues (32,33).

Process analytical technology

PAT is a set of methods that are used to analyze and control pharmaceutical manufacturing processes by measuring critical process parameters (CPPs) that affect the critical quality attributes (CQAs) of a product (34). The application of PAT in biopharmaceutical manufacturing has been shown to enhance process understanding, reduce variability, and increase overall efficiency. PAT tools detect deviations as they occur and allow them to be controlled, thus ensuring product quality and consistency (35). Examples of PAT tools include Raman spectroscopy, near-infrared spectroscopy, and at-line high-performance liquid chromatography (HPLC) (36–38). Advanced analytical techniques such as mass spectrometry and capillary electrophoresis can also be used to achieve the rapid and accurate characterization of complex biomolecules (39). These methods can also detect impurities and contaminants, improving product quality and safety. Integrating these advanced analytical techniques with automation and digitalization can enable real-time monitoring and control, further enhancing overall process efficiency (40).

Sustainability

The biopharmaceutical industry is increasingly focused on reducing its environmental impact (41). This includes the development of more sustainable manufacturing approaches by reducing water and energy use and implementing green chemistry principles during purification (42). Water and energy consumption are significant concerns due to the extensive use of water for cleaning and cooling and the energy-intensive nature of many purification methods. To minimize water and energy use, companies are investing in more efficient equipment and processes, including water reuse strategies and the optimization of chromatography to reduce buffer consumption. Green chemistry principles are also being implemented to develop more environmentally friendly purification processes, such as the use of safer solvents and auxiliaries, minimizing waste generation, and the design of energy-efficient processes (43). For example, aqueous two-phase systems can be used as alternatives to traditional solvent-based extraction methods (44).

Conclusion

The rapid evolution of downstream processing in the biopharmaceutical industry is marked by continuous innovation and technological development (45). The authors have described novel affinity ligands, purification technologies, mechanistic modeling, processing modes, automation, digitalization, and sustainability as key innovation areas to address the challenges of purifying increasingly complex therapeutic modalities. As the industry continues to expand and adapt, these advances will be integrated to improve the efficiency, productivity, and quality of biopharmaceutical manufacturing processes, leading to safer and more efficacious biotherapeutics for patients, including personalized medicines. Historically, the regulators saw innovation as a threat to well-established, conservative processes but gradually came to see it in a positive light, especially following the launch of the quality by design initiative (46). By embracing the latest trends and technologies, the biopharmaceutical industry can resist the pressure on the scalability of downstream processing and can continue to revolutionize the manufacturing of life-saving therapeutics, as it has for several decades (47).

References

1. Aldridge, S. Downstream Processing Needs a Boost. Genet. Eng. News 2006, 26 (1).
2. Gottschalk, U.; Brorson, K.; Shukla, A. A. The Need for Innovation in Biomanufacturing. Nat. Biotechnol. 2012, 30 (6), 489–492. DOI: 10.1038/nbt.2263
3. Gottschalk, U. The Future of Downstream Processing. Pharmaceutical Technology Bioprocessing Supplement May 2011, s36–s41.
4. Gottschalk, U. New and Unknown Challenges Facing Biomanufacturing. BioPharm Intl. 2005, 18 (3), 24–28.
5. Thömmes, J.; Twyman, R. M.; Gottschalk, U. Alternatives to Packed-bed Chromatography for Antibody Extraction and Purification. In Process Scale Purification of Antibodies, 2nd ed.; Gottschalk, U., Ed.; John Wiley & Sons, 2017; pp. 215–232.
6. Shukla, A. A., Gottschalk, U. Single-use Disposable Technologies for Biopharmaceutical Manufacturing. Trends Biotechnol. 2013, 31 (3), 147–154. DOI: 10.1016/j.tibtech.2012.10.004
7. Gottschalk, U. Disposables in Downstream Processing. Adv. Biochem. Eng. Biotechnol. 2009, 115, 171–183. DOI: 10.1007/10_2008_22
8. Hober, S.; Nord, K.; Linhult, M. Protein A Chromatography for Antibody Purification. J. Chromatogr. B 2007, 848 (1), 40–47. DOI: 10.1016/j.jchromb.2006.09.030
9. Keefe, A. D.; Pai, S.; Ellington, A. Aptamers as Therapeutics. Nat. Rev. Drug Discov. 2010, 9 (7), 537–550. DOI: 10.1038/nrd3141
10. Haupt, K.; Mosbach, K. Molecularly Imprinted Polymers and Their Use in Biomimetic Sensors. Chem. Rev. 2000, 100 (7), 2495–2504. DOI: 10.1021/cr990099w
11. Li, J.; Ma, M.; Zhang, C.; et al. Synthesis of a Molecularly Imprinted Polymer using MOF-74(Ni) as Matrix for Selective Recognition of Lysozyme. Analyt. Bioanalyt. Chem. 2020, 412 (26), 7227–7236. DOI: 10.1007/s00216-020-02855-7
12. Bossi, A.; Bonini, F.; Turner, A. P. F.; Piletsky, S. A. Molecularly Imprinted Polymers for the Recognition of Proteins: the State of the Art. Biosens. Bioelectron. 2007, 22 (6), 1131–1137. DOI: 10.1016/j.bios.2006.06.023
13. Zhou, J. X.; Tressel, T.; Gottschalk, U.; et al.New Q Membrane Scale-down Model for Process-scale Antibody Purification. J. Chromatogr. A 2006, 1134 (1–2), 66–73. DOI: 10.1016/j.chroma.2006.08.064
14. Rathore, A. S.; Pathak, M.; Godara, A. Process Development in the QbD Paradigm: Role of Process Integration in Process Optimization for Production of Biotherapeutics. Biotechnol. Prog. 2016, 32 (2), 355–362. DOI: 10.1002/btpr.2209
15. Bareither, R.; Pollard, D. A Review of Advanced Small-scale Parallel Bioreactor Technology for Accelerated Process Development: Current State and Future Need. Biotechnol. Prog. 2011, 27 (1), 2–14. DOI: 10.1002/btpr.522
16. Micheletti, M.; Lye, G. J. Microscale Bioprocess Optimisation. Curr. Opin. Biotechnol. 2006, 17 (6), 611–618. DOI: 10.1016/j.copbio.2006.10.006
17. Sackmann, E. K.; Fulton, A. L.; Beebe, D. J. The Present and Future Role of Microfluidics in Biomedical Research. Nature 2014, 507 (7491), 181–189. DOI: 10.1038/nature13118
18. Rameez, S.; Mostafa, S. S.; Miller, C.; Shukla, A. A. High-Throughput Miniaturized Bioreactors for Cell Culture Process Development: Reproducibility, Scalability, and Control. Biotechnol. Prog. 2014, 30 (3), 718–727. DOI: 10.1002/btpr.1874
19. Singh, N.; Arunkumar, A.; Chollangi, S.; et al. Clarification Technologies for Monoclonal Antibody Manufacturing Processes: Current State and Future Perspectives. Biotechnol. Bioeng. 2016, 113 (4), 698–716. DOI: 10.1002/bit.25810
20. Zydney, A. L. Continuous Downstream Processing for High Value Biological Products: A Review. Biotechnol. Bioeng. 2016, 113 (3), 465–475. DOI: 10.1002/bit.25695
21. Ghose, S.; Allen, M.; Hubbard, B.; Brooks, C.; Cramer, S. M. Antibody Variable Region Interactions with Protein A: Implications for the Development of Generic Purification Processes. Biotechnol. Bioeng. 2005, 92 (6), 665–673. DOI: 10.1002/bit.20729
22. Jungbauer, A.; Hahn, R. Engineering Protein A Affinity Chromatography. Curr. Opin. Drug Discov. Dev. 2004, 7 (2), 248–256.
23. Podgornik, A.; Yamamoto, S.; Peterka, M.; Lendero Krajnc, N. Fast Separation of Large Biomolecules Using Short Monolithic Columns. J. Chromatogr. B 2013, 927, 80–89. DOI: 10.1016/j.jchromb.2013.02.004
24. Khanal, O.; Lenhoff, A. M. Developments and Opportunities in Continuous Biopharmaceutical Manufacturing. mAbs 2021, 13 (1), 1903664. DOI: 10.1080/19420862.2021.1903664
25. Diehm, J.; Ballweg, T.; Franzreb, M. Development of a 3D Printed Micro-Simulated Moving Bed Chromatography System. J. Chromatogr. A 2023, 1695, 463928. DOI: 10.1016/j.chroma.2023.463928
26. Godawat, R.; Brower, K.; Jain, S.; et al.. Periodic Counter-Current Chromatography—Design and Operational Considerations for Integrated and Continuous Purification of Proteins. Biotechnol. J. 2012, 7 (12), 1496–1508. DOI: 10.1002/biot.201200068
27. Angarita, M.; Müller-Späth, T.; Baur, D.; et al. Twin-Column CaptureSMB: a Novel Cyclic Process for Protein A Affinity Chromatography. J. Chromatogr. A 2015, 1389, 85–95. DOI: 10.1016/j.chroma.2015.02.046
28. Bernau, C. R.; Knödler, M.; Emonts, J.; Jäpel, R. C.; Buyel, J. F. The Use of Predictive Models to Develop Chromatography-Based Purification Processes. Front. Bioeng. Biotechnol. 2022, 10, 1009102. DOI: 10.3389/fbioe.2022.1009102
29. Zhang, J.; Petersen, S. D.; Radivojevic, T.; et al. Combining Mechanistic and Machine Learning Models for Predictive Engineering and Optimization of Tryptophan Metabolism. Nat. Commun. 2020, 11 (1), 4880. DOI: 10.1038/s41467-020-17910-1
30. Pirrung, S. M.; van der Wielen, L. A. M.; van Beckhoven, R. F. W. C.; et al. Optimization of Biopharmaceutical Downstream Processes Supported by Mechanistic Models and Artificial Neural Networks. Biotechnol. Prog. 2017, 33 (3), 696–707. DOI: 10.1002/btpr.2435
31. Lakerveld, R.; Benyahia, B.; Heider, P. L.; et al. The Application of an Automated Control Strategy for an Integrated Continuous Pharmaceutical Pilot Plant. Org. Proc. Res. Dev. 2015, 19 (9), 1088–1100. DOI: 10.1021/op500104d
32. Walther, J.; Godawat, R.; Hwang, C.; et al. The Business Impact of an Integrated Continuous Biomanufacturing Platform for Recombinant Protein Production. J. Biotechnol. 2015, 213, 3–12. DOI: 10.1016/j.jbiotec.2015.05.010
33. Pollock, J.; Coffman, J.; Ho, S. V.; Farid, S. S. Integrated Continuous Bioprocessing: Economic, Operational, and Environmental Feasibility for Clinical and Commercial Antibody Manufacture. Biotechnol. Prog. 2017, 33 (4), 854–866. DOI: 10.1002/btpr.2492
34. FDA, Guidance for Industry, PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance (CDER, October 2004).
35. Saxena, N.; Mishra, S.; Gupta, K.; et al. Advances in Bioreactor Control for Production of Biotherapeutic Products. Biotechnol. Bioeng. 2023, 120 (5), 1189–1214. DOI: 10.1002/bit.28346
36. Esmonde-White, K. A.; Cuellar, M.; Uerpmann, C.; Lenain, B.; Lewis, I. R. Raman Spectroscopy as a Process Analytical Technology for Pharmaceutical Manufacturing and Bioprocessing. Analyt. Bioanalyt. Chem. 2017, 409 (3), 637–649. DOI: 10.1007/s00216-016-9824-1
37. Jamrógiewicz, M. Application of the Near-infrared Spectroscopy in the Pharmaceutical Technology. J. Pharma. Biomed. Analysis 2012, 66, 1–10. DOI: 10.1016/j.jpba.2012.03.009
38. Tiwari, A.; Kateja, N.; Chanana, S.; Rathore, A. S. Use of HPLC as an Enabler of Process Analytical Technology in Process Chromatography. Anal. Chem. 2018, 90 (13), 7824–7829. DOI: 10.1021/acs.analchem.8b00897
39. Rathore, D.; Faustino, A.; Schiel, J.; et al. The Role of Mass Spectrometry in the Characterization of Biologic Protein Products. Expert Rev. Proteomics 2018, 15 (5), 431–449. DOI: 10.1080/14789450.2018.1469982
40. Gargalo, C. L.; Udugama, I.; Pontius, K.; et al. Towards Smart Biomanufacturing: a Perspective on Recent Developments in Industrial Measurement and Monitoring Technologies for Bio-based Production Processes. J. Indust. Microbiol. Biotechnol. 2020, 47 (11), 947–964. DOI: 10.1007/s10295-020-02308-1
41. Lalor, F.; Fitzpatrick, J.; Sage, C.; Byrne, E. Sustainability in the Biopharmaceutical Industry: Seeking a Holistic Perspective. Biotechnol. Adv. 2019, 37 (5), 698–707. DOI: 10.1016/j.biotechadv.2019.03.015
42. Budzinski, K.; Blewis, M.; Dahlin, P.; et al.Introduction of a Process Mass Intensity Metric for Biologics. New Biotechnol. 2018, 49, 37–42. DOI: 10.1016/j.nbt.2018.07.005.
43. Stanton, D. Bioprocesses at Risk from Proposed ‘Forever Chemicals’ Regulations. BioProcess Int. online, April 4, 2023.
44. Rito-Palomares, M. Practical Application of Aqueous Two-Phase Partition to Process Development for the Recovery of Biological Products. J. Chromatogr. B 2004, 807 (1), 3–11. DOI: 10.1016/j.jchromb.2004.01.008
45. Gottschalk, U. Bioseparation in Antibody Manufacturing: the Good, the Bad and the Ugly. Biotechnol. Prog. 2008, 24 (3), 496–503. DOI: 10.1021/bp070452g
46. Rathore, A. S.; Winkle, H. Quality by Design for Biopharmaceuticals. Nature Biotechnol. 2009, 27 (1), 26–34. DOI: 10.1038/nbt0109-26
47. Gottschalk, U. The Renaissance in Protein Purification. BioPharm International Supplement June 2006, s8–s9.

About the authors

Hemanth Kaligotla is a bioprocessing and commercial business development leader, and Uwe Gottschalk is operating partner at Keensight Capital.

Article Details

BioPharm International
Volume 36, No. 9
September 2023
Pages: 30–34

Citation

When referring to this article, please cite it as Kaligotla, H.; Gottschalk, U. Innovations in Downstream Processing. BioPharm International 2023, 36 (9), 30–34.