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.

中文翻译：

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用于工业应用的机械阳离子交换色谱模型校准的简单方法。

色谱过程的机械建模是生物制药过程开发数字化最有前途的技术之一。色谱模型的可能应用范围从早期开发中的计算机过程优化到制造过程中的计算机根本原因调查。尽管如此，繁琐复杂的模型校准仍然减缓了机械建模在工业中的实施。因此，行业需要模型校准策略，以确保在有限的时间内具有足够的模型确定性。本研究介绍了一种直接且直接的方法，用于校准工业应用的 pH 相关、多组分空间质量作用 (SMA) 等温线模型。在调查的案件中，该方法应用于包括四种蛋白质种类的单克隆抗体 (mAb) 精制步骤。所开发的策略结合了制备色谱的成熟理论（例如 Yamamoto 方法），并允许将未知模型参数从最初的 32 个系统地减少到 7 个。通过设计两个具有代表性的校准实验来对剩余模型参数进行逆估计，从而降低了模型不确定性。使用富含聚集体的负载材料进行的专门实验显着降低了模型对这种低浓度产品相关杂质的估计不确定性。该模型在最终单元操作的操作范围之外得到验证，使其能够应用于后期下游工艺开发。使用建议的模型校准策略，