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DSF 方法优化及其在预测蛋白质热聚集动力学中的应用。

DSF method optimization and its application in predicting protein thermal aggregation kinetics.

机构信息

Sterile Product Development Group, Bioprocess Development, Merck Sharp & Dohme Corp., Summit, New Jersey 07901, USA.

出版信息

J Pharm Sci. 2013 Aug;102(8):2471-83. doi: 10.1002/jps.23633. Epub 2013 Jun 10.

Abstract

Differential scanning fluorimetry (DSF) has gained wide acceptance in the therapeutic protein development. However, the effects of dyes and surfactants that may affect structural transitions have not been studied thoroughly to date. We therefore first optimized the DSF method by studying surfactant-containing formulations and found that the presence of surfactants generally required medium-to-high protein concentrations and that high SYPRO® Orange concentration in a DSF experiment may lower protein thermal transitions. We also benchmarked DSF against differential scanning calorimetry (DSC) and evaluated the capability of thermal parameters (from DSF/DSC) to predict real-time thermal aggregation kinetics monitored by size exclusion chromatography (SEC) and analytical ultracentrifugation (AUC) in different scenarios. For monoclonal antibody (MAb) fragment, both DSF and DSC were predictive of thermal aggregation rate. For MAb3, a good correlation was observed between DSF and DSC, none of which was, however, indicative of protein aggregation kinetics. In a surfactant ranging study, DSF did not agree with DSC and was not predictive of the aggregation kinetics of the MAb fragment. The concentration-dependent thermal behavior was also studied by DSF. Although higher concentration, in general, tends to lower protein transition temperature, case where it was independent of protein concentration was also presented.

摘要

差示扫描荧光法(DSF)在治疗性蛋白开发中已得到广泛认可。然而,到目前为止,尚未对可能影响结构转变的染料和表面活性剂的影响进行彻底研究。因此,我们首先通过研究含表面活性剂的制剂优化了 DSF 方法,发现表面活性剂的存在通常需要中等至高浓度的蛋白质,并且 DSF 实验中高浓度的 SYPRO®Orange 可能会降低蛋白质的热转变。我们还将 DSF 与差示扫描量热法(DSC)进行了基准测试,并评估了热参数(来自 DSF/DSC)在不同情况下预测实时热聚集动力学的能力,这些动力学通过尺寸排阻色谱(SEC)和分析超速离心(AUC)进行监测。对于单克隆抗体(MAb)片段,DSF 和 DSC 都可以预测热聚集速率。对于 MAb3,DSF 和 DSC 之间观察到良好的相关性,但两者均不能指示蛋白质聚集动力学。在表面活性剂范围研究中,DSF 与 DSC 不一致,也不能预测 MAb 片段的聚集动力学。还通过 DSF 研究了浓度依赖性的热行为。尽管较高的浓度通常会降低蛋白质的转变温度,但也存在与蛋白质浓度无关的情况。

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