Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Abstract

Mixed‐mode chromatography combines features of ion‐exchange chromatography and hydrophobic interaction chromatography and is increasingly used in antibody purification. As a replacement for flow‐through operations on traditional unmixed resins or as a pH‐controlled bind‐and‐elute step, the use of both interaction modes promises a better removal of product‐specific impurities. However, the combination of the functionalities makes industrial process development significantly more complex, in particular the identification of the often small elution window that delivers the desired selectivity. Mechanistic modeling has proven that even difficult separation problems can be solved in a computer‐optimized manner once the process dynamics have been modeled. The adsorption models described in the literature are also very complex, which makes model calibration difficult. In this work, we approach this problem with a newly constructed model that describes the adsorber saturation with the help of the surface coverage function of the colloidal particle adsorption model for ion‐exchange chromatography. In a case study, a model for a pH‐controlled antibody polishing step was created from six experiments. The behavior of fragments, aggregates, and host cell proteins was described with the help of offline analysis. After in silico optimization, a validation experiment confirmed an improved process performance in comparison to the historical process set point. In addition to these good results, the work also shows that the high dynamics of mixed‐mode chromatography can produce unexpected results if process parameters deviate too far from tried and tested conditions.

Predictive scaling of fiber‐based protein A capture chromatography using mechanistic modeling

“…For comparison, the log k eq value reported in Lane (2018) for a different mAb and adsorber combination at pH 7 was 3–4 which is similar to the magnitude reported here. The inverse value of k kin , which is approximately 3.65, is within in the range of 0.75–10 reported for IgG's in Sandoval et al (2012); and the slightly negative K s value is in the same order of magnitude as observed for the pH‐controlled mixed‐mode process in Hahn et al (2022).…”

“…Applying the law of mass action, the equilibrium formulation was derived to be $$$\frac{q}{c}={k}_{\mathrm{eq}}\bar{q},$$$where c and q are the molar protein concentrations in solution and bound to the stationary phase, respectively. $$\bar{q}$$ is the normalized concentration of available ligands which is later replaced by the CPA surface coverage function, as also applied in Hahn et al (2022).…”

“…The authors thank Till Briskot, who developed the type of adsorption models used in Hahn et al (2022), as well as this study, and thus created an essential basis for this work.…”