Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications.
antibody purification
cation exchange chromatography
mechanistic chromatography modeling
model calibration
parameter estimation
Journal
Biotechnology progress
ISSN: 1520-6033
Titre abrégé: Biotechnol Prog
Pays: United States
ID NLM: 8506292
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
08
11
2019
revised:
03
02
2020
accepted:
19
02
2020
pubmed:
23
2
2020
medline:
5
8
2021
entrez:
23
2
2020
Statut:
ppublish
Résumé
Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.
Substances chimiques
Antibodies, Monoclonal
0
Cation Exchange Resins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2984Informations de copyright
© 2020 American Institute of Chemical Engineers.
Références
Shukla AA, Hubbard B, Tressel T, Guhan S, Low D. Downstream processing of monoclonal antibodies-application of platform approaches. J Chromatogr B Analyt Technol Biomed Life Sci. 2007;848(1):28-39.
Liu HF, Ma J, Winter C, Bayer R. Recovery and purification process development for monoclonal antibody production. mAbs. 2010;2(5):480-499.
Glynn J, Hagerty T, Pabst T, et al. The development and application of a monoclonal antibody purification platform: a purification scheme to maximize the efficiency of the purification process and product purity while minimizing the development time for early-phase therapeutic antibodies. Biopharm Int. 2009;16-20.
International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, ICH-Endorsed Guide for ICHQ8/Q9/Q10 Implementation. http://www.ich.org/products/guidelines/quality/article/quality-guidelines.html.
Mollerup JM, Hansen TB, Kidal S, Staby A. Quality by design-thermodynamic modelling of chromatographic separation of proteins. J Chromatogr A. 2008;1177(2):200-206.
Hahn T, Huuk T, Osberghaus A, et al. Calibration-free inverse modeling of ion-exchange chromatography in industrial antibody purification. Eng Life Sci. 2016;16(2):107-113.
Baumann P, Hubbuch J. Downstream process development strategies for effective bioprocesses: trends, progress, and combinatorial approaches. Eng Life Sci. 2016;17(11):1142-1158.
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.
Pirrung SM, van der Wielen LA, Van Beckhoven RF, van de Sandt EJ, Eppink MH, Ottens M. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks. Biotechnol Prog. 2017;33:696-707.
Close EJ, Salm JR, Bracewell DG, Sorensen E. A model based approach for identifying robust operating conditions for industrial chromatography with process variability. Chem Eng Sci. 2014;116:284-295.
Jakobsson N, Degerman M, Stenborg E, Nilsson B. Model based robustness analysis of an ion-exchange chromatography step. J Chromatogr A. 2007;1138(1):109-119.
Jakobsson N, Karlsson D, Axelsson JP, Zacchi G, Nilsson B. Using computer simulation to assist in the robustness analysis of an ion-exchange chromatography step. J Chromatogr A. 2005;1063(1):99-109.
Wang G, Briskot T, Hahn T, Baumann P, Hubbuch J. Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks. J Chromatogr A. 2017;1515:146-153.
Steinebach F, Angarita M, Karst DJ, Müller-Späth T, Morbidelli M. Model based adaptive control of a continuous capture process for monoclonal antibodies production. J Chromatogr A. 2016;1444:50-56.
Susanto A, Knieps-Grünhagen E, von Lieres E, Hubbuch J. High throughput screening for the design and optimization of chromatographic processes: assessment of model parameter determination from high throughput compatible data. Chem Eng Technol. 2008;31(12):1846-1855.
Osberghaus A, Drechsel K, Hansen S, et al. Model-integrated process development demonstrated on the optimization of a robotic cation exchange step. Chem Eng Sci. 2012;76:129-139.
Pirrung SM, Parruca da Cruz D, Hanke AT, et al. Chromatographic parameter determination for complex biological feedstocks. Biotechnol Prog. 2018;34(4):1006-1018.
Ishihara T, Kadoya T, Yoshida H, Tamada T, Yamamoto S. Rational methods for predicting human monoclonal antibodies retention in protein a affinity chromatography and cation exchange chromatography. J Chromatogr A. 2005;1093(1):126-138.
Yamamoto S, Nakanishi K, Matsuno R, Kamikubo T. Ion exchange chromatography of proteins-prediction of elution curves and operating conditions. I. Theoretical considerations. Biotechnol Bioeng. 1983;25(6):1465-1483.
Brooks CA, Cramer SM. Steric mass-action ion exchange: displacement profiles and induced salt gradients. AIChE J. 1992;38(12):1969-1978.
Yamamoto S, Nomura M, Sano Y. Resolution of proteins in linear gradient elution ion-exchange and hydrophobic interaction chromatography. J Chromatogr A. 1987;409:101-110.
Yoshimoto N. Simplified methods based on mechanistic models for understanding and designing chromatography processes for proteins and other biological products. In: Staby A, Ahuja S, eds. Preparative chromatography for separation of proteins. Somerset: John Wiley; 2017.
Rüdt M, Gillet F, Heege S, Hitzler J, Kalbfuss B, Guélat B. Combined Yamamoto approach for simultaneous estimation of adsorption isotherm and kinetic parameters in ion-exchange chromatography. J Chromatogr A. 2015;1413:68-76.
Wang G, Briskot T, Hahn T, Baumann P, Hubbuch J. Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks. J Chromatogr A. 2017;1487:211-217.
Ladiwala A, Rege K, Breneman CM, Cramer SM. A priori prediction of adsorption isotherm parameters and chromatographic behavior in ion-exchange systems. Proc Natl Acad Sci. 2005;102(33):11710-11715.
Osberghaus A, Hepbildikler S, Nath S, Haindl M, von Lieres E, Hubbuch J. Determination of parameters for the steric mass action model-a comparison between two approaches. J Chromatogr A. 2012;1233:54-65.
Hahn T, Baumann P, Huuk T, Heuveline V, Hubbuch J. UV absorption-based inverse modeling of protein chromatography. Eng Life Sci. 2016;16(2):99-106.
López CD, Barz T, Körkel S, Wozny G. Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design. Comput Cheml Eng. 2015;77:24-42. https://doi.org/10.1016/j.compchemeng.2015.03.002.
Bertero M, Poggio TA, Torre V. Ill-posed problems in early vision. Proc IEEE. 1988;76(8):869-889.
Briskot T, Stückler F, Wittkopp F, et al. Prediction uncertainty assessment of chromatography models using Bayesian inference. J Chromatogr A. 2019;1587:101-110.
Hahn T, Huuk T, Heuveline V, Hubbuch J. Simulating and optimizing preparative protein chromatography with ChromX. J Chem Educ. 2015;92(9):1497-1502.
Borg N, Brodsky Y, Moscariello J, et al. Modeling and robust pooling design of a preparative cation-exchange chromatography step for purification of monoclonal antibody monomer from aggregates. J Chromatogr A. 2014;1359:170-181.
Hunt S, Larsen T, Todd RJ. Modeling preparative cation exchange chromatography of monoclonal antibodies. In: Staby A, Ahuja S, eds. Preparative chromatography for separation of proteins. Somerset: John Wiley; 2017.
Schmidt M, Hafner M, Frech C. Modeling of salt and pH gradient elution in ion-exchange chromatography. J Sep Sci. 2014;37(1-2):5-13.
Kluters S, Wittkopp F, Johnck M, Frech C. Application of linear pH gradients for the modeling of ion exchange chromatography: separation of monoclonal antibody monomer from aggregates. J Sep Sci. 2016;39(4):663-675.
Ribeiro JM, Sillero A. An algorithm for the computer calculation of the coefficients of a polynomial that allows determination of isoelectric points of proteins and other macromolecules. Comput Biol Med. 1990;20(4):235-242.
Ribeiro JM, Sillero A. A program to calculate the isoelectric point of macromolecules. Comput Biol Med. 1991;21(3):131-141.
Huuk TC, Hahn T, Doninger K, Griesbach J, Hepbildikler S, Hubbuch J. Modeling of complex antibody elution behavior under high protein load densities in ion exchange chromatography using an asymmetric activity coefficient. Biotechnol J. 2017;12(3):1600336.
Yamamoto S, Nakanishi K, Matsuno R. Ion-exchange chromatography of proteins. Boca Raton: CRC Press; 1988.
Huuk TC, Briskot T, Hahn T, Hubbuch J. A versatile noninvasive method for adsorber quantification in batch and column chromatography based on the ionic capacity. Biotechnol Prog. 2016;32(3):666-677.
Guiochon G, Felinger A, Shirazi DG. Fundamentals of preparative and nonlinear chromatography. Boston: Academic Press; 2006.
Bird RB, Stewart WE, Lightfoot EN. Transport phenomena. New York, USA: John Wiley & Sons; 2007.
Potty A, Xenopoulos A. Stress-induced antibody aggregates. BioProcess Int. 2013;11(3):44-52.