病毒样颗粒(VLP)是一类有前途的用于疫苗和靶向递送的生物药物。从澄清的裂解物开始,VLP通常通过选择性沉淀来捕获。虽然通过逐步或连续添加沉淀剂诱导VLP沉淀,当前的监测方法不支持直接的产品量化,和分析方法通常需要各种,耗时的处理和样品制备步骤。这里,拉曼光谱与化学计量学方法相结合的应用可以允许同时定量沉淀的VLP和沉淀剂,因为它在分析原油方面具有明显的优势,复杂的混合物。在这项研究中,我们提出了一种基于拉曼光谱的过程分析技术(PAT)工具,用于乙型肝炎核心抗原VLP的分批和补料分批沉淀实验。我们进行了小规模的沉淀实验,提供了多样化的数据集,这些数据集具有由澄清的大肠杆菌来源的裂解物的初始稀释或加标引起的不同的沉淀动力学和背景。对于拉曼光谱数据,各种预处理操作被系统地组合在一起,允许识别预处理管道,这证明有效地消除了初始裂解物组成变化以及归因于沉淀物和溶液中存在的沉淀剂的大多数干扰。校准的偏最小二乘模型无缝预测了在分批和补料分批实验中R2为0.98和0.97的沉淀剂浓度,分别,并捕获了观测到的降水趋势,R2分别为0.74和0.64。尽管由于观察到的光谱数据和VLP浓度之间的非线性关系,实验之间的细微差异的分辨率受到限制,这项研究为使用拉曼光谱作为监测VLP沉淀过程的PAT传感器提供了基础,有可能将其适用性扩展到其他相位行为相关的过程或分子。
Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.