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.

Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory-to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

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