A bayesian approach for quantifying trace amounts of antibody aggregates by sedimentation velocity analytical ultracentrifugation.
A bayesian approach for quantifying trace amounts of antibody aggregates by sedimentation velocity analytical ultracentrifugation.
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DOI：
10.1208/s12248-008-9058-z
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发表时间：
2008-09
期刊：
影响因子：
--
通讯作者：
Schuck P
中科院分区：
文献类型：
--
作者：
Brown PH;Balbo A;Schuck P
沉降速度分析超离心（SV-AUC）已成为鉴定蛋白质治疗药物纯度的重要工具。这里提出的工作解决了需要正交的方法，以尺寸排除色谱法，以确保可靠的定量免疫原性低聚物，例如，在抗体制剂。目前最常用的SV-AUC分析方法是扩散-反卷积沉降系数分布c(s)方法，该方法之前是由我们开发的通用技术，并在SEDFIT软件中实现。在实践和理论研究中，不同的研究小组报告了远低于1%水平的痕量低聚物组分的灵敏度c(s)。在目前的工作中，我们提出了c(s)的一种变体，设计用于跟踪检测，具有定制的贝叶斯正则化。原始的c(s)方法依赖于最大熵正则化，提供与数据一致的最简洁的分布。在本文中，我们使用抗体系统的计算机模拟作为示例来证明，由于其设计，标准最大熵正则化导致低聚物痕量检测的理论下限和痕量种群的一致低估约0.1%（取决于正则化水平）。这可以通过最近开发的贝叶斯c(s)扩展来克服（生物大分子（2007），8,2011 -2024），利用感兴趣的单体和低聚物的已知沉积系数区域作为分布中峰值位置的先验期望。我们表明，这导致更清楚地识别和一致的峰值和较低的理论极限量化大约一个数量级的一些实验条件。讨论了SV-AUC实验设计和实际检测限的意义。
Sedimentation velocity analytical ultracentrifugation (SV-AUC) has become an important tool for the characterization of the purity of protein therapeutics. The work presented here addresses a need for methods orthogonal to size-exclusion chromatography for ensuring the reliable quantitation of immunogenic oligomers, for example, in antibody preparations. Currently the most commonly used approach for SV-AUC analysis is the diffusion-deconvoluted sedimentation coefficient distribution c(s) method, previously developed by us as a general purpose technique and implemented in the software SEDFIT. In both practical and theoretical studies, different groups have reported a sensitivity of c(s) for trace oligomeric fractions well below the 1% level. In the present work we present a variant of c(s) designed for the purpose of trace detection, with customized Bayesian regularization. The original c(s) method relies on maximum entropy regularization providing the most parsimonious distribution consistent with the data. In the present paper, we use computer simulations of an antibody system as example to demonstrate that the standard maximum entropy regularization, due to its design, leads to a theoretical lower limit for the detection of oligomeric traces and a consistent underestimate of the trace populations by ∼0.1% (dependent on the level of regularization). This can be overcome with a recently developed Bayesian extension of c(s) (Biomacromolecules (2007), 8, 2011-2024), utilizing the known regions of sedimentation coefficients for the monomer and oligomers of interest as prior expectation for the peak positions in the distribution. We show that this leads to more clearly identifiable and consistent peaks and lower theoretical limits of quantization by approximately an order of magnitude for some experimental conditions. Implications for the experimental design of SV-AUC and practical detection limits are discussed.
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影响因子：
2.9
作者：
LITZEN, A;WALTER, JK;WAHLUND, KG
通讯作者：
WAHLUND, KG
影响因子：
6.3
作者：
PROVENCHER, SW
通讯作者：
PROVENCHER, SW
影响因子：
3.8
作者：
Lu, Yanling;Harding, Stephen E.;Mulot, Sandrine
通讯作者：
Mulot, Sandrine
影响因子：
3.8
作者：
Schuck, P
通讯作者：
Schuck, P
影响因子：
6.2
作者：
Brown, Patrick H.;Balbo, Andrea;Schuck, Peter
通讯作者：
Schuck, Peter