Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications.

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 implantation 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 lead 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. This article is protected by copyright. All rights reserved.

[1]  Jürgen Hubbuch,et al.  Prediction uncertainty assessment of chromatography models using Bayesian inference. , 2019, Journal of chromatography. A.

[2]  Arne Staby,et al.  Quality by design--thermodynamic modelling of chromatographic separation of proteins. , 2008, Journal of chromatography. A.

[3]  Jürgen Hubbuch,et al.  Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks. , 2017, Journal of chromatography. A.

[4]  Paul Johnson,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 , 2009 .

[5]  Marcus Degerman,et al.  Model based robustness analysis of an ion-exchange chromatography step. , 2007, Journal of chromatography. A.

[6]  Jürgen Hubbuch,et al.  Modeling of complex antibody elution behavior under high protein load densities in ion exchange chromatography using an asymmetric activity coefficient. , 2017, Biotechnology journal.

[7]  Kaushal Rege,et al.  A priori prediction of adsorption isotherm parameters and chromatographic behavior in ion-exchange systems. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Jürgen Hubbuch,et al.  Simulating and Optimizing Preparative Protein Chromatography with ChromX , 2015 .

[9]  Steven M. Cramer,et al.  Steric mass‐action ion exchange: Displacement profiles and induced salt gradients , 1992 .

[10]  Jürgen Hubbuch,et al.  Calibration‐free inverse modeling of ion‐exchange chromatography in industrial antibody purification , 2016 .

[11]  R. Bayer,et al.  Recovery and purification process development for monoclonal antibody production , 2010, mAbs.

[12]  Karin Westerberg,et al.  Modeling and robust pooling design of a preparative cation-exchange chromatography step for purification of monoclonal antibody monomer from aggregates. , 2014, Journal of chromatography. A.

[13]  Federico Rischawy,et al.  Good modeling practice for industrial chromatography: Mechanistic modeling of ion exchange chromatography of a bispecific antibody , 2019, Comput. Chem. Eng..

[14]  Jürgen Hubbuch,et al.  UV absorption‐based inverse modeling of protein chromatography , 2016 .

[15]  J M Ribeiro,et al.  An algorithm for the computer calculation of the coefficients of a polynomial that allows determination of isoelectric points of proteins and other macromolecules. , 1990, Computers in biology and medicine.

[16]  Massimo Morbidelli,et al.  Model based adaptive control of a continuous capture process for monoclonal antibodies production. , 2016, Journal of chromatography. A.

[17]  Simon Kluters,et al.  Application of linear pH gradients for the modeling of ion exchange chromatography: Separation of monoclonal antibody monomer from aggregates. , 2016, Journal of separation science.

[18]  Marcel Ottens,et al.  Chromatographic parameter determination for complex biological feedstocks , 2018, Biotechnology progress.

[19]  Jürgen Hubbuch,et al.  Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks. , 2017, Journal of chromatography. A.

[20]  Noriko Yoshimoto,et al.  Simplified Methods Based on Mechanistic Models for Understanding and Designing Chromatography Processes for Proteins and Other Biological Products‐Yamamoto Models and Yamamoto Approach , 2017 .

[21]  Brian Hubbard,et al.  Downstream processing of monoclonal antibodies--application of platform approaches. , 2007, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[22]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[23]  Jürgen Hubbuch,et al.  Downstream process development strategies for effective bioprocesses: Trends, progress, and combinatorial approaches , 2017, Engineering in life sciences.

[24]  M. Hafner,et al.  Modeling of salt and pH gradient elution in ion-exchange chromatography. , 2014, Journal of separation science.

[25]  Eva Sorensen,et al.  A model based approach for identifying robust operating conditions for industrial chromatography with process variability , 2014 .

[26]  E. von Lieres,et al.  Determination of parameters for the steric mass action model--a comparison between two approaches. , 2012, Journal of chromatography. A.

[27]  Günter Wozny,et al.  Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design , 2015, Comput. Chem. Eng..

[28]  Jürgen Hubbuch,et al.  High Throughput Screening for the Design and Optimization of Chromatographic Processes: Assessment of Model Parameter Determination from High Throughput Compatible Data , 2008 .

[29]  Taro Tamada,et al.  Rational methods for predicting human monoclonal antibodies retention in protein A affinity chromatography and cation exchange chromatography. Structure-based chromatography design for monoclonal antibodies. , 2005, Journal of chromatography. A.

[30]  J M Ribeiro,et al.  A program to calculate the isoelectric point of macromolecules. , 1991, Computers in biology and medicine.

[31]  Y. Sano,et al.  Resolution of proteins in linear gradient elution ion-exchange and hydrophobic interaction chromatography. , 1987, Journal of chromatography.

[32]  Marcel Ottens,et al.  Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks , 2017, Biotechnology progress.

[33]  Matthias Rüdt,et al.  Combined Yamamoto approach for simultaneous estimation of adsorption isotherm and kinetic parameters in ion-exchange chromatography. , 2015, Journal of chromatography. A.

[34]  Jürgen Hubbuch,et al.  Model-integrated process development demonstrated on the optimization of a robotic cation exchange step , 2012 .

[35]  Jürgen Hubbuch,et al.  A versatile noninvasive method for adsorber quantification in batch and column chromatography based on the ionic capacity , 2016, Biotechnology progress.

[36]  K. Nakanishi,et al.  Ion exchange chromatography of proteins—prediction of elution curves and operating conditions. I. Theoretical considerations , 1983, Biotechnology and bioengineering.

[37]  Guido Zacchi,et al.  Using computer simulation to assist in the robustness analysis of an ion-exchange chromatography step. , 2005, Journal of chromatography. A.

[38]  S. Hunt,et al.  Modeling Preparative Cation Exchange Chromatography of Monoclonal Antibodies , 2017 .