Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Simplified estimation for steric shielding factor

Mechanistic models play a crucial role in the process development and optimization of ion-exchange chromatography (IEC). Recent researches in steric mass action (SMA) model have heightened the need for better estimation of nonlinear parameter, steric shielding factor a. In this work, a straightforward approach combination of simplified linear approximation (SLA) and inverse method (IM) was proposed to initialize and further determine a, respectively. An existed, unique, and positive a can be derived from SLA. Compared with linear approximation (LA) developed in our previous study, a of the multi -component system can be calculated easily without solving the complex system of linear equations, leading to a time complexity reduction from O(n3) to O(n). The proposed method was verified first in numerical experiments about the separation of three charge variants. The calculated a was more reason-able than that of LA, and the error of elution profiles with the parameters estimated by SLA + IM was only one-sixth of that by LA in numerical experiments. Moreover, the error accumulation effect could also be reduced. The proposed method was further confirmed in real-world experiments about the separation of monomer-dimer mixtures of monoclonal antibody. The results gave a lower error and better physical understanding compared to LA. In conclusion, SLA + IM developed in the present work provides a novel and straightforward way to determine a. This simplification would help to save the effort of calibration experiments and accelerate the process development for the multi-component IEC separation. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Mechanistic models are important in the development and optimization of ion-exchange chromatography (IEC). The steric mass action (SMA) model has led to a need for better estimation of the nonlinear parameter, steric shielding factor a. In this study, a combination of simplified linear approximation (SLA) and inverse method (IM) was proposed to initialize and determine a. The proposed method showed improved accuracy and reduced time complexity compared to previous methods.