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应用谐振质量测量的最佳实践方法对生物治疗产品进行表征。

Application of a Best Practice Approach Using Resonant Mass Measurement for Biotherapeutic Product Characterization.

机构信息

KBI Biopharma, Inc, 2500 Central Avenue, Boulder, Colorado 80301.

Malvern Panalytical, 117 Flanders Road, Westborough, Massachusetts 01581.

出版信息

J Pharm Sci. 2019 May;108(5):1675-1685. doi: 10.1016/j.xphs.2018.12.017. Epub 2018 Dec 30.

DOI:10.1016/j.xphs.2018.12.017
PMID:30599170
Abstract

Characterizing and quantifying subvisible particles in protein drug products is critical to ensuring product quality. A variety of analytical methods are used to detect and make meaningful measurements of subvisible particles. Resonant mass measurement (RMM) is a novel technology that characterizes the subvisible particle content of samples on a particle-by-particle basis. The technology presents great promise in the study of therapeutic protein products. As an emerging tool in the biopharmaceutical field, the best practices and limitations of RMM for protein products have not been well established. One key challenge of particle analysis is producing robust and reliable data, with high precision and accuracy, for particle characterization. In this study, we develop a set of possible best practices for RMM using a model protein system. We test the effects of these practices on the repeatability and reproducibility of particle measurements. Additionally, we present the data collected under a rigorously controlled set of operating conditions at 3 collaborating sites as well as a summary of the resulting optimal practices. In employing these practices, we successfully obtained improved relative standard deviation values and achieved high reproducibility and repeatability in both sizing and concentration measurement results over a broad range of sample volumes.

摘要

在蛋白质药物产品中,对亚可见颗粒进行特征描述和定量分析对于确保产品质量至关重要。目前已经开发出了多种分析方法来检测和对亚可见颗粒进行有意义的测量。谐振质量测量(RMM)是一种新颖的技术,可对样品中的亚可见颗粒含量进行逐个颗粒的特征描述。这项技术在治疗性蛋白产品的研究中具有广阔的应用前景。作为生物制药领域的一种新兴工具,RMM 在蛋白质产品中的最佳实践和局限性尚未得到很好的确定。颗粒分析的一个关键挑战是,如何为颗粒特征描述生成具有高精度和高准确度的稳健可靠数据。在本研究中,我们使用模型蛋白系统开发了一套可能的 RMM 最佳实践方案。我们测试了这些实践方案对颗粒测量重复性和再现性的影响。此外,我们还展示了在三个合作地点严格控制操作条件下收集的数据,以及由此产生的最佳实践方案的总结。通过采用这些实践方案,我们成功地获得了更好的相对标准偏差值,并在广泛的样品体积范围内实现了粒度和浓度测量结果的高重复性和再现性。

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