利用多尺度建模从蛋白质结构到优化色谱捕获步骤。

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Daphne Keulen, Tim Neijenhuis, Adamantia Lazopoulou, Roxana Disela, Geoffroy Geldhof, Olivier Le Bussy, Marieke E Klijn, Marcel Ottens
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引用次数: 0

摘要

优化生物制药色谱纯化工艺是目前工艺开发过程中面临的最大挑战。由于缺乏对工艺的了解,因此需要进行大量实验,以追求最佳工艺。机理建模或数据驱动建模等硅学技术可加深对工艺的理解,从而实现更具成本效益和时间效率的工艺优化。本研究提出了一种建模策略,将定量结构属性关系模型(QSPR)和色谱机理模型(MM)整合在一起,以优化阳离子交换(CEX)捕集步骤,限制实验次数。在 QSPR 中,从蛋白质结构中获得的结构特征被用来描述物理化学行为。这种 QSPR 信息可用于 MM 预测色谱图并优化整个过程。为了验证这种方法,实验测定了不同 pH 值(3.5、4.3、5.0 和 7.0)下混合物中六种蛋白质的保留曲线。不同 pH 值下的四种蛋白质被用来训练 QSPR 模型,预测保留体积和特征电荷,随后确定平衡常数。对于只知道蛋白质结构的未知蛋白质,相对于梯度长度(60 柱体积),模型峰和实验峰之间的保留峰差异为 0.2%。接下来,对 CEX 捕捉步骤进行了优化,结果显示实验法和基于 QSPR 的方法结果一致。模型参数置信度对最终优化结果的影响揭示了两种可行的工艺条件,其中一种与使用实验参数实现的优化结果类似。多尺度建模方法通过确定可优化的初始工艺条件,减少了所需的实验工作量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From protein structure to an optimized chromatographic capture step using multiscale modeling.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

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来源期刊
Biotechnology Progress
Biotechnology Progress 工程技术-生物工程与应用微生物
CiteScore
6.50
自引率
3.40%
发文量
83
审稿时长
4 months
期刊介绍: Biotechnology Progress , an official, bimonthly publication of the American Institute of Chemical Engineers and its technological community, the Society for Biological Engineering, features peer-reviewed research articles, reviews, and descriptions of emerging techniques for the development and design of new processes, products, and devices for the biotechnology, biopharmaceutical and bioprocess industries. Widespread interest includes application of biological and engineering principles in fields such as applied cellular physiology and metabolic engineering, biocatalysis and bioreactor design, bioseparations and downstream processing, cell culture and tissue engineering, biosensors and process control, bioinformatics and systems biology, biomaterials and artificial organs, stem cell biology and genetics, and plant biology and food science. Manuscripts concerning the design of related processes, products, or devices are also encouraged. Four types of manuscripts are printed in the Journal: Research Papers, Topical or Review Papers, Letters to the Editor, and R & D Notes.
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