Wang Gang, Briskot Till, Hahn Tobias, Baumann Pascal, Hubbuch Jürgen
Karlsruhe Institute of Technology (KIT), Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe, Germany.
GoSilico GmbH, Karlsruhe, Germany.
J Chromatogr A. 2017 Mar 3;1487:211-217. doi: 10.1016/j.chroma.2017.01.068. Epub 2017 Jan 27.
Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture.
机理建模已多次成功应用于蛋白质色谱的工艺开发和控制。对于吸附质和吸附剂的每种组合，都必须对机理模型进行校准。一些模型参数，如系统特性，可以通过应用成熟的实验方法可靠地确定，而其他参数则无法直接测量。在蛋白质色谱建模的常见实践中，这些参数通过应用耗时的方法来识别，如前沿分析结合梯度实验、曲线拟合或山本组合方法。对于色谱系统中的新组分，这些传统的校准方法需要反复进行。在本研究中，应用了一种基于人工神经网络（ANN）建模的机理模型校准新方法。对可能的模型参数组合进行了计算机模拟筛选，以生成ANN模型的学习材料。一旦ANN模型经过训练能够识别色谱图并以相应的模型参数集做出响应，就可以用它从测量的色谱图中校准机理模型。通过预测梯度洗脱色谱图测试了ANN模型的参数估计能力。耗时的模型参数估计过程本身可以缩短至毫秒级。该方法的功能在一项针对蛋白质混合物的传输-扩散模型（TDM）和化学计量置换模型（SDM）校准的研究中得到了成功验证。
