Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, United States.
IBG-1: Biotechnology Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
J Chromatogr A. 2021 Dec 20;1660:462669. doi: 10.1016/j.chroma.2021.462669. Epub 2021 Nov 2.
Mechanistic models for ion-exchange chromatography of proteins are well-established and a broad consensus exists on most aspects of the detailed mathematical and physical description. A variety of specializations of these models can typically capture the general locations of elution peaks, but discrepancies are often observed in peak position and shape, especially if the column load level is in the non-linear range. These discrepancies may prevent the use of models for high-fidelity predictive applications such as process characterization and development of high-purity and -productivity process steps. Our objective is to develop a sufficiently robust mechanistic framework to make both conventional and anomalous phenomena more readily predictable using model parameters that can be evaluated based on independent measurements or well-accepted correlations. This work demonstrates the implementation of this approach for industry-relevant case studies using both a model protein, lysozyme, and biopharmaceutical product monoclonal antibodies, using cation-exchange resins with a variety of architectures (SP Sepharose FF, Fractogel EMD SO, Capto S and Toyopearl SP650M). The modeling employs the general rate model with the extension of the surface diffusivity to be variable, as a function of ionic strength or binding affinity. A colloidal isotherm that accounts for protein-surface and protein-protein interactions independently was used, with each characterized by a parameter determined as a function of ionic strength and pH. Both of these isotherm parameters, along with the variable surface diffusivity, were successfully estimated using breakthrough data at different ionic strengths and pH. The model developed was used to predict overloads and elution curves with high accuracy for a wide variety of gradients and different flow rates and protein loads. The in-silico methodology used in this work for parameter estimation, along with a minimal amount of experimental data, can help the industry adopt model-based optimization and control of preparative ion-exchange chromatography with high accuracy.
蛋白质离子交换色谱的机理模型已经成熟，对于详细的数学和物理描述的大多数方面都存在广泛共识。这些模型的各种专门化通常可以捕捉洗脱峰的大致位置，但在峰位置和形状上经常存在差异，尤其是在柱负荷水平处于非线性范围时。这些差异可能会阻止模型用于高保真预测应用，例如工艺表征和开发高纯度和高生产率的工艺步骤。我们的目标是开发一个足够强大的机理框架，以便使用可根据独立测量或公认的相关性进行评估的模型参数，更轻松地预测常规和异常现象。这项工作通过使用模型蛋白溶菌酶和生物制药产品单克隆抗体，以及具有各种结构（SP Sepharose FF、Fractogel EMD SO、Capto S 和 Toyopearl SP650M）的阳离子交换树脂，展示了这种方法在行业相关案例研究中的实施。该模型采用一般速率模型，并扩展了表面扩散系数，使其成为离子强度或结合亲和力的函数。使用了一种独立考虑蛋白质-表面和蛋白质-蛋白质相互作用的胶体等温线，每个相互作用都由一个参数来描述，该参数是离子强度和 pH 的函数。这些等温线参数以及可变表面扩散系数，都可以使用在不同离子强度和 pH 下的穿透数据成功地进行估计。所开发的模型用于预测各种梯度、不同流速和蛋白质负荷下的过载和洗脱曲线，具有很高的准确性。这项工作中用于参数估计的计算方法以及少量的实验数据，可以帮助行业采用基于模型的优化和控制，实现制备性离子交换色谱的高精度。
