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首页> 外文期刊>Biotechnology Progress >Ultra Scale-Down Approach to Correct Dispersive and Retentive Effects in Small-Scale Columns When Predicting Larger Scale Elution Profiles
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Ultra Scale-Down Approach to Correct Dispersive and Retentive Effects in Small-Scale Columns When Predicting Larger Scale Elution Profiles

机译：当预测较大规模的洗脱曲线时，采用超比例缩小方法校正小规模色谱柱中的分散和保留效应

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摘要

Ultra scale-down approaches represent valuable methods for chromatography development work in the biopharmaceutical sector,but for them to be of value,scale-down mimics must predict large-scale process performance accurately. For example,one application of a scale-down model involves using it to predict large-scale elution profiles correctly with respect to the size of a product peak and its position in a chromatogram relative to contaminants. Predicting large-scale profiles from data generated by small laboratory columns is complicated,however,by differences in dispersion and retention volumes between the two scales of operation. Correcting for these effects would improve the accuracy of the scale-down models when predicting outputs such as eluate volumes at larger scale and thus enable the efficient design and operation of subsequent steps. This paper describes a novel ultra scale-down approach which uses empirical correlations derived from conductivity changes during operation of laboratory and pilot columns to correct chromatographic profiles for the differences in dispersion and retention. The methodology was tested by using 1 mL column data to predict elution profiles of a chimeric monoclonal antibody obtained from Protein A chromatography columns at 3 mL laboratory- and 18.3 L pilot-scale. The predictions were then verified experimentally. Results showed that the empirical corrections enabled accurate estimations of the characteristics of larger-scale elution profiles. These data then provide the justification to adjust small-scale conditions to achieve an eluate volume and product concentration which is consistent with that obtained at large-scale and which can then be used for subsequent ultra scale-down operations.
机译：超小型化方法代表了生物制药领域色谱开发工作的宝贵方法，但要使它们具有价值，小型化模拟物必须准确地预测大规模工艺性能。例如，按比例缩小模型的一种应用涉及使用它来相对于产物峰的大小及其在色谱图中相对于污染物的位置正确地预测大规模洗脱曲线。然而，由于两个操作规模之间的分散度和保留体积的差异，从小型实验室色谱柱生成的数据预测大规模分布图非常复杂。校正这些影响将在预测输出（例如更大比例的洗脱液体积）时提高按比例缩小模型的准确性，从而实现后续步骤的高效设计和操作。本文介绍了一种新颖的超小型化方法，该方法使用了在实验室和中试柱运行过程中从电导率变化得出的经验相关性来校正色谱图，以解决分散度和保留度的差异。通过使用1 mL色谱柱数据预测从Protein A色谱柱获得的嵌合单克隆抗体在3 mL实验室规模和18.3 L中试规模下的洗脱曲线，对方法学进行了测试。然后通过实验验证了这些预测。结果表明，通过经验校正，可以准确估计大规模洗脱曲线的特征。然后，这些数据提供了调整小规模条件以实现洗脱液体积和产物浓度的理由，该洗脱液体积和产物浓度与大规模获得的洗脱液和产物浓度一致，然后可用于后续的超大规模缩减操作。

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