在多式染色学中基于抗体序列的pH梯度化预测多式染色学
Rudger Hess1, Jan Faessler2, Doil Yun2
1Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
Journal of chromatography. A
|October 21, 2023
概括
现在可以使用氨基酸序列来预测多式染色体中的抗体化. 这种基于结构的预测方法加速了抗体净化过程的开发,并增强了对生物制药制造的理解.
科学领域:
- 生物制药制造业 生物制药制造业
- 蛋白质化学 蛋白质化学
- 染色体学 染色体学 是一种染色学.
背景情况:
- 多模式染色学对于抗体净化至关重要,但由于复杂的蛋白质-连接体相互作用,优化参数具有挑战性.
- 需要预测工具来简化这些染色学过程的开发和优化.
研究的目的:
- 开发基于氨基酸序列的多式染色体中抗体化行为的预测方法.
- 为了减少过程开发中的试验和错误,并使在制造中可行性评估.
主要方法:
- 分析了64个全长抗体 (IgG1,IgG4,多种特异格式),在Capto粘合树脂上用pH梯度 (9.0-4.0) 化.
- 构建了同质模型,并计算了1312个抗体特异性物理化学描述符.
- 确定了六个关键的结构特征,特别是在变量区域,与化行为相关.
主要成果:
- 开发了一个模型,预测不同抗体和格式的pH梯度化,测试组R2为0.898.8.
- 该模型有效地将抗体结构与化行为相关联,突出显示了变量区域的重要性.
- 证明了 in silico可制造性评估和工艺优化的潜力.
结论:
- 基于结构的预测是可行的增强抗体净化在生物制药行业.
- 这种方法可以带来更高效,更具成本效益的流程开发和提高流程理解.
- 开发的方法论为多式联的初始条件提供了信息,简化了优化.
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