1932

Abstract

Chromatography has long been, and remains, the workhorse of downstream processing in the production of biopharmaceuticals. As bioprocessing has matured, there has been a growing trend toward seeking a detailed fundamental understanding of the relevant unit operations, which for some operations include the use of mechanistic modeling in a way similar to its use in the conventional chemical process industries. Mechanistic models of chromatography have been developed for almost a century, but although the essential features are generally understood, the specialization of such models to biopharmaceutical processing includes several areas that require further elucidation. This review outlines the overall approaches used in such modeling and emphasizes current needs, specifically in the context of typical uses of such models; these include selection and improvement of isotherm models and methods to estimate isotherm and transport parameters independently. Further insights are likely to be aided by molecular-level modeling, as well as by the copious amounts of empirical data available for existing processes.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-chembioeng-102419-125430
2020-06-07
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/chembioeng/11/1/annurev-chembioeng-102419-125430.html?itemId=/content/journals/10.1146/annurev-chembioeng-102419-125430&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Li Y, Stern D, Lock LL, Mills J, Ou S-H et al. 2019. Emerging biomaterials for downstream manufacturing of therapeutic proteins. Acta Biomater 95:73–90
    [Google Scholar]
  2. 2. 
    Rathore AS, Kumar D, Kateja N 2018. Recent developments in chromatographic purification of biopharmaceuticals. Biotechnol. Lett. 40:6895–905
    [Google Scholar]
  3. 3. 
    Shukla AA, Wolfe LS, Mostafa SS, Norman C 2017. Evolving trends in mAb production processes. Bioeng. Transl. Med. 2:158–69
    [Google Scholar]
  4. 4. 
    Hummel J, Pagkaliwangan M, Gjoka X, Davidovits T, Stock R et al. 2019. Modeling the downstream processing of monoclonal antibodies reveals cost advantages for continuous methods for a broad range of manufacturing scales. Biotechnol. J. 14:21700665
    [Google Scholar]
  5. 5. 
    Zydney AL. 2016. Continuous downstream processing for high value biological products: a review. Biotechnol. Bioeng. 113:3465–75
    [Google Scholar]
  6. 6. 
    Xenopoulos A. 2015. A new, integrated, continuous purification process template for monoclonal antibodies: process modeling and cost of goods studies. J. Biotechnol. 213:42–53
    [Google Scholar]
  7. 7. 
    Somasundaram B, Pleitt K, Shave E, Baker K, Lua LHL 2018. Progression of continuous downstream processing of monoclonal antibodies: current trends and challenges. Biotechnol. Bioeng. 115:122893–907
    [Google Scholar]
  8. 8. 
    Przybycien TM, Pujar NS, Steele LM 2004. Alternative bioseparation operations: life beyond packed-bed chromatography. Curr. Opin. Biotechnol. 15:5469–78
    [Google Scholar]
  9. 9. 
    Willoughby N. 2009. Too big to bind? Will the purification of large and complex therapeutic targets spell the beginning of the end for column chromatography. ? J. Chem. Technol. Biotechnol. 84:2145–50
    [Google Scholar]
  10. 10. 
    Tran R, Lacki K, Davidson A, Sharma B, Titchener-Hooker N 2014. Changing manufacturing paradigms in downstream processing and the role of alternative bioseparation technologies. J. Chem. Technol. Biotechnol. 89:101534–44
    [Google Scholar]
  11. 11. 
    Shukla AA, Hubbard B, Tressel T, Guhan S, Low D 2007. Downstream processing of monoclonal antibodies—application of platform approaches. J. Chromatogr. B 848:128–39
    [Google Scholar]
  12. 12. 
    Hanke AT, Ottens M. 2014. Purifying biopharmaceuticals: knowledge-based chromatographic process development. Trends Biotechnol 32:4210–20
    [Google Scholar]
  13. 13. 
    Food Drug Admin. 2004. PAT: a framework for innovative pharmaceutical development, manufacturing, and quality assurance Guid. Ind., Food Drug Admin. Washington, DC:
  14. 14. 
    Kumar V, Bhalla A, Rathore AS 2014. Design of experiments applications in bioprocessing: concepts and approach. Biotechnol. Prog. 30:186–99
    [Google Scholar]
  15. 15. 
    Shekhawat LK, Godara A, Kumar V, Rathore AS 2019. Design of experiments applications in bioprocessing: chromatography process development using split design of experiments. Biotechnol. Prog. 35:1e2730
    [Google Scholar]
  16. 16. 
    Fischer V, Kucia‐Tran R, Lewis WJ, Velayudhan A 2019. Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture. Biotechnol. Prog. 35:5e2849
    [Google Scholar]
  17. 17. 
    Konstantinidis S, Welsh JP, Titchener‐Hooker NJ, Roush DJ, Velayudhan A 2018. Data‐driven multi‐objective optimization via grid compatible simplex technique and desirability approach for challenging high throughput chromatography applications. Biotechnol. Prog. 34:61393–406
    [Google Scholar]
  18. 18. 
    Sejergaard L, Ahmadian H, Hansen TB, Staby A, Hansen EB 2017. Model-based process development in the biopharmaceutical industry. Preparative Chromatography for Separation of Proteins A Staby, AS Rathore, S Ahuja 429–55 Hoboken, NJ: John Wiley & Sons
    [Google Scholar]
  19. 19. 
    Rischawy F, Saleh D, Hahn T, Oelmeier S, Spitz J, Kluters S 2019. Good modeling practice for industrial chromatography: mechanistic modeling of ion exchange chromatography of a bispecific antibody. Comput. Chem. Eng. 130:106532
    [Google Scholar]
  20. 20. 
    Bohart GS, Adams EQ. 1920. Some aspects of the behaviour of charcoal with respect to chlorine. J. Am. Chem. Soc. 42:3523–44
    [Google Scholar]
  21. 21. 
    Lenhoff AM. 2011. Protein adsorption and transport in polymer-functionalized ion-exchangers. J. Chromatogr. A 1218:498748–59
    [Google Scholar]
  22. 22. 
    DeVault D. 1943. The theory of chromatography. J. Am. Chem. Soc. 65:4532–40
    [Google Scholar]
  23. 23. 
    Thomas HC. 1944. Heterogeneous ion exchange in a flowing system. J. Am. Chem. Soc. 66:101664–66
    [Google Scholar]
  24. 24. 
    Michaels AS. 1952. Simplified method of interpreting kinetic data in fixed-bed ion exchange. Ind. Eng. Chem. 44:81922–30
    [Google Scholar]
  25. 25. 
    Glueckauf E, Coates JI. 1947. Theory of chromatography. Part IV. The influence of incomplete equilibrium on the front boundary of chromatograms and on the effectiveness of separation. J. Chem. Soc. 1947:1315–21
    [Google Scholar]
  26. 26. 
    Hall KR, Eagleton LC, Acrivos A, Vermeulen T 1966. Pore- and solid-diffusion kinetics in fixed-bed adsorption under constant-pattern conditions. Ind. Eng. Chem. Fundam. 5:212–23
    [Google Scholar]
  27. 27. 
    Vermeulen T. 1953. Theory for irreversible and constant-pattern solid diffusion. Ind. Eng. Chem. 45:81664–70
    [Google Scholar]
  28. 28. 
    Cooper RS, Liberman DA. 1970. Fixed-bed adsorption kinetics with pore diffusion control. Ind. Eng. Chem. Fundam. 9:4620–23
    [Google Scholar]
  29. 29. 
    Guiochon G, Felinger A, Shirazi DG 2006. Fundamentals of Preparative and Nonlinear Chromatography Cambridge, MA: Academic990 pp, 2nd ed..
  30. 30. 
    Schmidt-Traub H, Schulte M, Seidel-Morgenstern Aeds 2012. Preparative Chromatography Weinheim, Ger.: Wiley-VCH806 pp.
  31. 31. 
    Carta G, Jungbauer A. 2010. Protein Chromatography: Process Development and Scale-Up Weinheim, Ger.: Wiley-VCH
  32. 32. 
    Close EJ, Salm JR, Bracewell DG, Sorensen E 2014. A model based approach for identifying robust operating conditions for industrial chromatography with process variability. Chem. Eng. Sci. 116:284–95
    [Google Scholar]
  33. 33. 
    Brestrich N, Hahn T, Hubbuch J 2016. Application of spectral deconvolution and inverse mechanistic modelling as a tool for root cause investigation in protein chromatography. J. Chromatogr. A 1437:158–67
    [Google Scholar]
  34. 34. 
    Winderl J, Hahn T, Hubbuch J 2016. A mechanistic model of ion-exchange chromatography on polymer fiber stationary phases. J. Chromatogr. A 1475:18–30
    [Google Scholar]
  35. 35. 
    Hahn T, Huuk T, Osberghaus A, Doninger K, Nath S et al. 2016. Calibration-free inverse modeling of ion-exchange chromatography in industrial antibody purification. Eng. Life Sci. 16:2107–13
    [Google Scholar]
  36. 36. 
    Kluters S, Wittkopp F, Jöhnck M, Frech C 2016. Application of linear pH gradients for the modeling of ion exchange chromatography: separation of monoclonal antibody monomer from aggregates. J. Sep. Sci. 39:4663–75
    [Google Scholar]
  37. 37. 
    Guélat B, Khalaf R, Lattuada M, Costioli M, Morbidelli M 2016. Protein adsorption on ion exchange resins and monoclonal antibody charge variant modulation. J. Chromatogr. A 1447:82–91
    [Google Scholar]
  38. 38. 
    Rüdt M, Gillet F, Heege S, Hitzler J, Kalbfuss B, Guélat B 2015. Combined Yamamoto approach for simultaneous estimation of adsorption isotherm and kinetic parameters in ion-exchange chromatography. J. Chromatogr. A 1413:68–76
    [Google Scholar]
  39. 39. 
    Sellberg A, Holmqvist A, Magnusson F, Andersson C, Nilsson B 2017. Discretized multi-level elution trajectory: a proof-of-concept demonstration. J. Chromatogr. A 1481:73–81
    [Google Scholar]
  40. 40. 
    Traylor SJ, Xu X, Lenhoff AM 2011. Shrinking-core modeling of binary chromatographic breakthrough. J. Chromatogr. A 1218:162222–31
    [Google Scholar]
  41. 41. 
    Miyabe K. 2016. Moment theory for kinetic study of chromatography. TrAC Trends Anal. Chem. 81:79–86
    [Google Scholar]
  42. 42. 
    Leweke S, von Lieres E 2016. Fast arbitrary order moments and arbitrary precision solution of the general rate model of column liquid chromatography with linear isotherm. Comput. Chem. Eng. 84:350–62
    [Google Scholar]
  43. 43. 
    Brhane KW, Qamar S, Seidel-Morgenstern A 2019. Two-dimensional general rate model of liquid chromatography incorporating finite rates of adsorption-desorption kinetics and core-shell particles. Ind. Eng. Chem. Res. 58:8296–308
    [Google Scholar]
  44. 44. 
    Ishihara T, Yamamoto S. 2005. Optimization of monoclonal antibody purification by ion-exchange chromatography: application of simple methods with linear gradient elution experimental data. J. Chromatogr. A 1069:199–106
    [Google Scholar]
  45. 45. 
    David UU, Qamar S, Seidel-Morgenstern A 2018. Analytical and numerical solutions of two-dimensional general rate models for liquid chromatographic columns packed with core-shell particles. Chem. Eng. Res. Des. 130:295–320
    [Google Scholar]
  46. 46. 
    Tao Y, Chen N, Carta G, Ferreira G, Robbins D 2012. Modeling multicomponent adsorption of monoclonal antibody charge variants in cation exchange columns. AIChE J 58:82503–11
    [Google Scholar]
  47. 47. 
    Carta G, Bauer JS. 1990. Analytic solution for chromatography with nonuniform sorbent particles. AIChE J 36:1147–50
    [Google Scholar]
  48. 48. 
    von Lieres E, Andersson J 2010. A fast and accurate solver for the general rate model of column liquid chromatography. Comput. Chem. Eng. 34:81180–91
    [Google Scholar]
  49. 49. 
    Hahn T, Huuk T, Heuveline V, Hubbuch J 2015. Simulating and optimizing preparative protein chromatography with ChromX. J. Chem. Educ. 92:91497–502
    [Google Scholar]
  50. 50. 
    Hørsholt A, Christiansen LH, Meyer K, Huusom JK, Jørgensen JB 2019. Spatial discretization and Kalman filtering for ideal packed-bed chromatography. Proceedings of the 18th European Control Conference, June 25–28, Naples, Italy2356–61 Laxenburg, Austria: Int. Fed. Autom. Control
    [Google Scholar]
  51. 51. 
    Reck JM, Pabst TM, Hunter AK, Carta G 2017. Separation of antibody monomer-dimer mixtures by frontal analysis. J. Chromatogr. A 1500:96–104
    [Google Scholar]
  52. 52. 
    Lee YF, Jöhnck M, Frech C 2018. Evaluation of differences between dual salt-pH gradient elution and mono gradient elution using a thermodynamic model: simultaneous separation of six monoclonal antibody charge and size variants on preparative-scale ion exchange chromatographic resin. Biotechnol. Prog. 34:4973–86
    [Google Scholar]
  53. 53. 
    Stone MC, Borman J, Ferreira G, Robbins PD 2018. Effects of pH, conductivity, host cell protein, and DNA size distribution on DNA clearance in anion exchange chromatography media. Biotechnol. Prog. 34:1141–49
    [Google Scholar]
  54. 54. 
    Latour RA. 2015. The Langmuir isotherm: a commonly applied but misleading approach for the analysis of protein adsorption behavior. J. Biomed. Mater. Res. A 103:3949–58
    [Google Scholar]
  55. 55. 
    Azizian S, Eris S, Wilson LD 2018. Re-evaluation of the century-old Langmuir isotherm for modeling adsorption phenomena in solution. Chem. Phys. 513:99–104
    [Google Scholar]
  56. 56. 
    Xu A, Lenhoff AM. 2008. A predictive approach to correlating protein adsorption isotherms on ion-exchange media. J. Phys. Chem. B 112:31028–40
    [Google Scholar]
  57. 57. 
    Karlsson D, Jakobsson N, Axelsson A, Nilsson B 2004. Model-based optimization of a preparative ion-exchange step for antibody purification. J. Chromatogr. A 1055:1–229–39
    [Google Scholar]
  58. 58. 
    Kumar V, Leweke S, von Lieres E, Rathore AS 2015. Mechanistic modeling of ion-exchange process chromatography of charge variants of monoclonal antibody products. J. Chromatogr. A 1426:140–53
    [Google Scholar]
  59. 59. 
    Boardman NK, Partridge SM. 1953. Separation of neutral proteins on ion-exchange resins. Nature 171:4344208–10
    [Google Scholar]
  60. 60. 
    Kopaciewicz W, Rounds MA, Fausnaugh J, Regnier FE 1983. Retention model for high-performance ion-exchange chromatography. J. Chromatogr. A 266:C3–21
    [Google Scholar]
  61. 61. 
    Brooks CA, Cramer SM. 1992. Steric mass-action ion exchange: displacement profiles and induced salt gradients. AIChE J 38:121969–78
    [Google Scholar]
  62. 62. 
    Creasy A, Barker G, Yao Y, Carta G 2015. Systematic interpolation method predicts protein chromatographic elution from batch isotherm data without a detailed mechanistic isotherm model. Biotechnol. J. 10:91400–11
    [Google Scholar]
  63. 63. 
    Mollerup JM. 2008. A review of the thermodynamics of protein association to ligands, protein adsorption, and adsorption isotherms. Chem. Eng. Technol. 31:6864–74
    [Google Scholar]
  64. 64. 
    Huuk TC, Hahn T, Doninger K, Griesbach J, Hepbildikler S, Hubbuch J 2017. Modeling of complex antibody elution behavior under high protein load densities in ion exchange chromatography using an asymmetric activity coefficient. Biotechnol. J. 12:31600336
    [Google Scholar]
  65. 65. 
    Diedrich J, Heymann W, Leweke S, Hunt S, Todd R et al. 2017. Multi-state steric mass action model and case study on complex high loading behavior of mAb on ion exchange tentacle resin. J. Chromatogr. A 1525:60–70
    [Google Scholar]
  66. 66. 
    Creasy A, Barker G, Carta G 2017. Systematic interpolation method predicts protein chromatographic elution with salt gradients, pH gradients and combined salt/pH gradients. Biotechnol. J. 12:31600636
    [Google Scholar]
  67. 67. 
    Stahlberg J, Jonsson B, Horváth C 1992. Combined effect of Coulombic and van der Waals interactions in the chromatography of proteins. Anal. Chem. 64:243118–24
    [Google Scholar]
  68. 68. 
    Roth CM, Lenhoff AM. 1993. Electrostatic and van der Waals contributions to protein adsorption: computation of equilibrium constants. Langmuir 9:4962–72
    [Google Scholar]
  69. 69. 
    Roush DJ, Gill DS, Willson RC 1994. Electrostatic potentials and electrostatic interaction energies of rat cytochrome b5 and a simulated anion-exchange adsorbent surface. Biophys. J. 66:51290–300
    [Google Scholar]
  70. 70. 
    Oberholzer MR, Lenhoff AM. 1999. Protein adsorption isotherms through colloidal energetics. Langmuir 15:113905–14
    [Google Scholar]
  71. 71. 
    Xu X, Lenhoff AM. 2009. Binary adsorption of globular proteins on ion-exchange media. J. Chromatogr. A 1216:346177–95
    [Google Scholar]
  72. 72. 
    Guélat B, Ströhlein G, Lattuada M, Morbidelli M 2010. Electrostatic model for protein adsorption in ion-exchange chromatography and application to monoclonal antibodies, lysozyme and chymotrypsinogen A. J. Chromatogr. A 1217:355610–21
    [Google Scholar]
  73. 73. 
    Guélat B, Ströhlein G, Lattuada M, Delegrange L, Valax P, Morbidelli M 2012. Simulation model for overloaded monoclonal antibody variants separations in ion-exchange chromatography. J. Chromatogr. A 1253:32–43
    [Google Scholar]
  74. 74. 
    Hober S, Nord K, Linhult M 2007. Protein A chromatography for antibody purification. J. Chromatogr. B 848:140–47
    [Google Scholar]
  75. 75. 
    Ramos‐de‐la‐Peña AM, González‐Valdez J, Aguilar O 2019. Protein A chromatography: challenges and progress in the purification of monoclonal antibodies. J. Sep. Sci. 42:91816–27
    [Google Scholar]
  76. 76. 
    Ghose S, Zhang J, Conley L, Caple R, Williams KP, Cecchini D 2014. Maximizing binding capacity for protein A chromatography. Biotechnol. Prog. 30:61335–40
    [Google Scholar]
  77. 77. 
    Ng CKS, Rousset F, Valery E, Bracewell DG, Sorensen E 2014. Design of high productivity sequential multi-column chromatography for antibody capture. Food Bioprod. Process. 92:2233–41
    [Google Scholar]
  78. 78. 
    Pabst TM, Thai J, Hunter AK 2018. Evaluation of recent protein A stationary phase innovations for capture of biotherapeutics. J. Chromatogr. A 1554:45–60
    [Google Scholar]
  79. 79. 
    Pfister D, David L, Holzer M, Nicoud R-M 2017. Designing affinity chromatographic processes for the capture of antibodies. Part I: a simplified approach. J. Chromatogr. A 1494:27–39
    [Google Scholar]
  80. 80. 
    Benner SW, Welsh JP, Rauscher MA, Pollard JM 2019. Prediction of lab and manufacturing scale chromatography performance using mini-columns and mechanistic modeling. J. Chromatogr. A 1593:54–62
    [Google Scholar]
  81. 81. 
    Kaltenbrunner O, Diaz L, Hu X, Shearer M 2016. Continuous bind-and-elute protein A capture chromatography: optimization under process scale column constraints and comparison to batch operation. Biotechnol. Prog. 32:4938–48
    [Google Scholar]
  82. 82. 
    McCue JT, Engel P, Ng A, Macniven R, Thömmes J 2008. Modeling of protein monomer/aggregate purification and separation using hydrophobic interaction chromatography. Bioprocess Biosyst. Eng. 31:3261–75
    [Google Scholar]
  83. 83. 
    Arkell K, Breil MP, Frederiksen SS, Nilsson B 2017. Mechanistic modeling of reversed-phase chromatography of insulins with potassium chloride and ethanol as mobile-phase modulators. ACS Omega 2:1136–46
    [Google Scholar]
  84. 84. 
    Arkell K, Breil MP, Frederiksen SS, Nilsson B 2018. Mechanistic modeling of reversed-phase chromatography of insulins within the temperature range 10–40°C. ACS Omega 3:21946–54
    [Google Scholar]
  85. 85. 
    Queiroz JA, Tomaz CT, Cabral JMS 2001. Hydrophobic interaction chromatography of proteins. J. Biotechnol. 87:2143–59
    [Google Scholar]
  86. 86. 
    Xia F, Nagrath D, Cramer SM 2003. Modeling of adsorption in hydrophobic interaction chromatography systems using a preferential interaction quadratic isotherm. J. Chromatogr. A 989:147–54
    [Google Scholar]
  87. 87. 
    Melander WR, Corradini D, Horváth C 1984. Salt-mediated retention of proteins in hydrophobic-interaction chromatography. J. Chromatogr. A 317:67–85
    [Google Scholar]
  88. 88. 
    Shepard CC, Tiselius A. 1949. The chromatography of proteins. The effect of salt concentration and pH on the adsorption of proteins to silica gel. Discuss. Faraday Soc. 7:275
    [Google Scholar]
  89. 89. 
    Chen J, Sun Y. 2003. Modeling of the salt effects on hydrophobic adsorption equilibrium of protein. J. Chromatogr. A 992:1–229–40
    [Google Scholar]
  90. 90. 
    Wang G, Hahn T, Hubbuch J 2016. Water on hydrophobic surfaces: mechanistic modeling of hydrophobic interaction chromatography. J. Chromatogr. A 1465:71–78
    [Google Scholar]
  91. 91. 
    Perkins TW, Mak DS, Root TW, Lightfoot EN 1997. Protein retention in hydrophobic interaction chromatography: modeling variation with buffer ionic strength and column hydrophobicity. J. Chromatogr. A 766:1–21–14
    [Google Scholar]
  92. 92. 
    Creasy A, Lomino J, Carta G 2018. Gradient elution behavior of proteins in hydrophobic interaction chromatography with a U-shaped retention factor curve under overloaded conditions. J. Chromatogr. A 1578:28–34
    [Google Scholar]
  93. 93. 
    Nagrath D, Xia F, Cramer SM 2011. Characterization and modeling of nonlinear hydrophobic interaction chromatographic systems. J. Chromatogr. A 1218:91219–26
    [Google Scholar]
  94. 94. 
    Jakobsson N, Degerman M, Nilsson B 2005. Optimisation and robustness analysis of a hydrophobic interaction chromatography step. J. Chromatogr. A 1099:1–2157–66
    [Google Scholar]
  95. 95. 
    Halan V, Maity S, Bhambure R, Rathore AS 2018. Multimodal chromatography for purification of biotherapeutics—a review. Curr. Protein Pept. Sci. 20:14–13
    [Google Scholar]
  96. 96. 
    Karkov HS, Sejergaard L, Cramer SM 2013. Methods development in multimodal chromatography with mobile phase modifiers using the steric mass action model. J. Chromatogr. A 1318:149–55
    [Google Scholar]
  97. 97. 
    Zhang L, Parasnavis S, Li Z, Chen J, Cramer S 2019. Mechanistic modeling based process development for monoclonal antibody monomer-aggregate separations in multimodal cation exchange chromatography. J. Chromatogr. A 1602:317–25
    [Google Scholar]
  98. 98. 
    Zhu M, Carta G. 2016. Protein adsorption equilibrium and kinetics in multimodal cation exchange resins. Adsorption 22:2165–79
    [Google Scholar]
  99. 99. 
    Nfor BK, Noverraz M, Chilamkurthi S, Verhaert PDEM, van der Wielen LAM, Ottens M 2010. High-throughput isotherm determination and thermodynamic modeling of protein adsorption on mixed mode adsorbents. J. Chromatogr. A 1217:446829–50
    [Google Scholar]
  100. 100. 
    Lee YF, Graalfs H, Frech C 2016. Thermodynamic modeling of protein retention in mixed-mode chromatography: an extended model for isocratic and dual gradient elution chromatography. J. Chromatogr. A 1464:87–101
    [Google Scholar]
  101. 101. 
    Lee YF, Kluters S, Hillmann M, von Hirschheydt T, Frech C 2017. Modeling of bispecific antibody elution in mixed-mode cation-exchange chromatography. J. Sep. Sci. 40:183632–45
    [Google Scholar]
  102. 102. 
    Schultze-Jena A, Boon MA, Bussmann PJT, Janssen AEM, van der Padt A 2017. The counterintuitive role of extra-column volume in the determination of column efficiency and scaling of chromatographic processes. J. Chromatogr. A 1493:49–56
    [Google Scholar]
  103. 103. 
    Shankar A, Lenhoff AM. 1991. Dispersion in round tubes and its implications for extracolumn dispersion. J. Chromatogr. A 556:1–2235–48
    [Google Scholar]
  104. 104. 
    Carta G, Jungbauer A. 2010. Effects of dispersion and adsorption kinetics on column performance. See Reference 31:237–76
    [Google Scholar]
  105. 105. 
    Borg N, Brodsky Y, Moscariello J, Vunnum S, Vedantham G et al. 2014. Modeling and robust pooling design of a preparative cation-exchange chromatography step for purification of monoclonal antibody monomer from aggregates. J. Chromatogr. A 1359:170–81
    [Google Scholar]
  106. 106. 
    Frey DD, Schweinheim E, Horvath C 1993. Effect of intraparticle convection on the chromatography of biomacromolecules. Biotechnol. Prog. 9:3273–84
    [Google Scholar]
  107. 107. 
    Mackie JS, Meares P. 1955. The diffusion of electrolytes in a cation-exchange resin membrane I. Theoretical. Proc. R. Soc. Lond. A 232:1191498–509
    [Google Scholar]
  108. 108. 
    Deen WM. 1987. Hindered transport of large molecules in liquid-filled pores. AIChE J 33:91409–25
    [Google Scholar]
  109. 109. 
    Weaver LE, Carta G. 1996. Protein adsorption on cation exchangers: comparison of macroporous and gel-composite media. Biotechnol. Prog. 12:3342–55
    [Google Scholar]
  110. 110. 
    Yang K, Bai S, Sun Y 2008. Protein adsorption dynamics in cation-exchange chromatography quantitatively studied by confocal laser scanning microscopy. Chem. Eng. Sci. 63:164045–54
    [Google Scholar]
  111. 111. 
    Dziennik SR, Belcher EB, Barker GA, Lenhoff AM 2005. Effects of ionic strength on lysozyme uptake rates in cation exchangers. I: Uptake in SP Sepharose FF. Biotechnol. Bioeng. 91:2139–53
    [Google Scholar]
  112. 112. 
    Lenhoff AM. 2008. Multiscale modeling of protein uptake patterns in chromatographic particles. Langmuir 24:125991–95
    [Google Scholar]
  113. 113. 
    Baur D, Angelo JM, Chollangi S, Xu X, Müller-Späth T et al. 2018. Model assisted comparison of protein A resins and multi-column chromatography for capture processes. J. Biotechnol. 285:64–73
    [Google Scholar]
  114. 114. 
    Baur D, Angarita M, Müller-Späth T, Steinebach F, Morbidelli M 2016. Comparison of batch and continuous multi-column protein A capture processes by optimal design. Biotechnol. J. 11:7920–31
    [Google Scholar]
  115. 115. 
    Kumar V, Rathore AS. 2017. Mechanistic modeling based PAT implementation for ion-exchange process chromatography of charge variants of monoclonal antibody products. Biotechnol. J. 12:91700286
    [Google Scholar]
  116. 116. 
    Traylor SJ, Xu X, Li Y, Jin M, Li ZJ 2014. Adaptation of the pore diffusion model to describe multi-addition batch uptake high-throughput screening experiments. J. Chromatogr. A 1368:100–6
    [Google Scholar]
  117. 117. 
    Osberghaus A, Hepbildikler S, Nath S, Haindl M, von Lieres E, Hubbuch J 2012. Optimizing a chromatographic three component separation: a comparison of mechanistic and empiric modeling approaches. J. Chromatogr. A 1237:86–95
    [Google Scholar]
  118. 118. 
    Ghose S, Nagrath D, Hubbard B, Brooks C, Cramer SM 2004. Use and optimization of a dual-flowrate loading strategy to maximize throughput in protein-A affinity chromatography. Biotechnol. Prog. 20:3830–40
    [Google Scholar]
  119. 119. 
    Pagkaliwangan M, Hummel J, Gjoka X, Bisschops M, Schofield M 2019. Optimized continuous multicolumn chromatography enables increased productivities and cost savings by employing more columns. Biotechnol. J. 14:21800179
    [Google Scholar]
  120. 120. 
    Khanal O, Kumar V, Westerberg K, Schlegel F, Lenhoff AM 2019. Multi-column displacement chromatography for separation of charge variants of monoclonal antibodies. J. Chromatogr. A 1586:40–51
    [Google Scholar]
  121. 121. 
    Behere K, Cha B, Yoon S 2018. Protein a resin lifetime study: evaluation of protein A resin performance with a model-based approach in continuous capture. Prep. Biochem. Biotechnol. 48:3242–56
    [Google Scholar]
  122. 122. 
    Shekhawat LK, Pathak M, Sakar J, Rathore AS 2018. Process development in the Quality by Design paradigm: modeling of protein A chromatography resin fouling. J. Chromatogr. A 1570:56–66
    [Google Scholar]
  123. 123. 
    Lee YF, Schmidt M, Graalfs H, Hafner M, Frech C 2015. Modeling of dual gradient elution in ion exchange and mixed-mode chromatography. J. Chromatogr. A 1417:64–72
    [Google Scholar]
  124. 124. 
    Nfor BK, Ahamed T, Pinkse MWH, van der Wielen LAM, Verhaert PDEM et al. 2012. Multi-dimensional fractionation and characterization of crude protein mixtures: toward establishment of a database of protein purification process development parameters. Biotechnol. Bioeng. 109:123070–83
    [Google Scholar]
  125. 125. 
    Püttmann A, Schnittert S, Naumann U, von Lieres E 2013. Fast and accurate parameter sensitivities for the general rate model of column liquid chromatography. Comput. Chem. Eng. 56:46–57
    [Google Scholar]
  126. 126. 
    Püttmann A, Schnittert S, Leweke S, von Lieres E 2016. Utilizing algorithmic differentiation to efficiently compute chromatograms and parameter sensitivities. Chem. Eng. Sci. 139:152–62
    [Google Scholar]
  127. 127. 
    Briskot T, Stückler F, Wittkopp F, Williams C, Yang J et al. 2019. Prediction uncertainty assessment of chromatography models using Bayesian inference. J. Chromatogr. A 1587:101–10
    [Google Scholar]
  128. 128. 
    Steinebach F, Angarita M, Karst DJ, Müller-Späth T, Morbidelli M 2016. Model based adaptive control of a continuous capture process for monoclonal antibodies production. J. Chromatogr. A 1444:50–56
    [Google Scholar]
  129. 129. 
    Luo H, Cao M, Newell K, Afdahl C, Wang J et al. 2015. Double-peak elution profile of a monoclonal antibody in cation exchange chromatography is caused by histidine-protonation-based charge variants. J. Chromatogr. A 1424:92–101
    [Google Scholar]
  130. 130. 
    Guo J, Carta G. 2014. Unfolding and aggregation of a glycosylated monoclonal antibody on a cation exchange column. Part II. Protein structure effects by hydrogen deuterium exchange mass spectrometry. J. Chromatogr. A 1356:129–37
    [Google Scholar]
  131. 131. 
    Farys M, Gibson D, Lewis AP, Lewis W, Kucia-Tran R 2018. Isotype dependent on-column non-reversible aggregation of monoclonal antibodies. Biotechnol. Bioeng. 115:51279–87
    [Google Scholar]
  132. 132. 
    Guo J, Creasy AD, Barker G, Carta G 2016. Surface induced three-peak elution behavior of a monoclonal antibody during cation exchange chromatography. J. Chromatogr. A 1474:85–94
    [Google Scholar]
  133. 133. 
    Kimerer LK, Pabst TM, Hunter AK, Carta G 2019. Chromatographic behavior of bivalent bispecific antibodies on cation exchange columns. I. Experimental observations and phenomenological model. J. Chromatogr. A 1601:121–32
    [Google Scholar]
  134. 134. 
    Paloni M, Cavallotti C. 2015. Molecular modeling of the affinity chromatography of monoclonal antibodies. Methods Mol. Biol. 1286:321–35
    [Google Scholar]
  135. 135. 
    Kittelmann J, Lang KMH, Ottens M, Hubbuch J 2017. Orientation of monoclonal antibodies in ion-exchange chromatography: a predictive quantitative structure-activity relationship modeling approach. J. Chromatogr. A 1510:33–39
    [Google Scholar]
  136. 136. 
    Srinivasan K, Banerjee S, Parimal S, Sejergaard L, Berkovich R et al. 2017. Single molecule force spectroscopy and molecular dynamics simulations as a combined platform for probing protein face-specific binding. Langmuir 33:4110851–60
    [Google Scholar]
  137. 137. 
    Robinson J, Roush D, Cramer S 2018. Domain contributions to antibody retention in multimodal chromatography systems. J. Chromatogr. A 1563:89–98
    [Google Scholar]
  138. 138. 
    Tong H-F, Cavallotti C, Yao S-J, Lin D-Q 2017. Molecular insight into protein binding orientations and interaction modes on hydrophobic charge-induction resin. J. Chromatogr. A 1512:34–42
    [Google Scholar]
  139. 139. 
    Angelo JM, Lenhoff AM. 2016. Determinants of protein elution rates from preparative ion-exchange adsorbents. J. Chromatogr. A 1440:94–104
    [Google Scholar]
  140. 140. 
    Tallarek U, Hlushkou D, Rybka J, Höltzel A 2019. Multiscale simulation of diffusion in porous media: from interfacial dynamics to hierarchical porosity. J. Phys. Chem. C 123:2415099–112
    [Google Scholar]
  141. 141. 
    Pirrung SM, van der Wielen LAM, van Beckhoven RFWC, van de Sandt EJAX, Eppink MHM, Ottens M 2017. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks. Biotechnol. Prog. 33:3696–707
    [Google Scholar]
  142. 142. 
    Du X, Yuan Q, Zhao J, Li Y 2007. Comparison of general rate model with a new model-artificial neural network model in describing chromatographic kinetics of solanesol adsorption in packed column by macroporous resins. J. Chromatogr. A 1145:1–2165–74
    [Google Scholar]
  143. 143. 
    Wang G, Briskot T, Hahn T, Baumann P, Hubbuch J 2017. Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks. J. Chromatogr. A 1515:146–53
    [Google Scholar]
/content/journals/10.1146/annurev-chembioeng-102419-125430
Loading
/content/journals/10.1146/annurev-chembioeng-102419-125430
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error