Itkonen Jaakko, Ghemtio Leo, Pellegrino Daniela, Jokela Née Heinonen Pia J, Xhaard Henri, Casteleijn Marco G
Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00100 Helsinki, Finland.
Orion Pharma, 02101 Espoo, Finland.
Pharmaceutics. 2022 Aug 5;14(8):1639. doi: 10.3390/pharmaceutics14081639.
Pharmaceutical proteins, compared to small molecular weight drugs, are relatively fragile molecules, thus necessitating monitoring protein unfolding and aggregation during production and post-marketing. Currently, many analytical techniques take offline measurements, which cannot directly assess protein folding during production and unfolding during processing and storage. In addition, several orthogonal techniques are needed during production and market surveillance. In this study, we introduce the use of time-gated Raman spectroscopy to identify molecular descriptors of protein unfolding. Raman spectroscopy can measure the unfolding of proteins in-line and in real-time without labels. Using K-means clustering and PCA analysis, we could correlate local unfolding events with traditional analytical methods. This is the first step toward predictive modeling of unfolding events of proteins during production and storage.
与小分子药物相比，药用蛋白质是相对脆弱的分子，因此在生产过程中和上市后都需要监测蛋白质的解折叠和聚集情况。目前，许多分析技术采用离线测量，无法直接评估生产过程中的蛋白质折叠以及加工和储存过程中的解折叠情况。此外，在生产和市场监管过程中需要几种正交技术。在本研究中，我们引入了时间分辨拉曼光谱法来识别蛋白质解折叠的分子描述符。拉曼光谱法可以在线实时测量蛋白质的解折叠，无需标记。通过K均值聚类和主成分分析，我们可以将局部解折叠事件与传统分析方法相关联。这是朝着预测蛋白质在生产和储存过程中的解折叠事件建模迈出的第一步。
