EPSRC-SFI: Cutting Edge Analytical Solutions for Smart, Integrated, Efficient Biopharmaceutical Production

曾经有一段时间，小分子主导着治疗癌症等疾病以及细菌、真菌和病毒感染等疾病的药物市场。然而，在过去的十年里，生物制药彻底改变了慢性病以及一些孤儿疾病的治疗方法。生物制药研究的进步有助于降低重大疾病的死亡率，也有助于延长预期寿命。目前正在研发的数千种药物中，约有40%是生物药物。这些蛋白质分子有可能为数百万患者提供改变生活的有针对性的个性化治疗，并解决与人口老龄化相关的挑战。虽然用于生物制药生产的哺乳动物细胞培养已经建立得很好，但产品必须在蛋白质的结构和糖基化方式(这些蛋白质上附着的糖的模式)方面具有正确的保真度。因此，需要在发酵过程中对产品质量进行监测，以确认最终产品的安全性和有效性。然而，这些电池工厂的发展和优化受到当前可用分析技术性能的限制。目前，生物制药过程的开发是以相当经验性的方式进行的，基于使用ELISA(抗体-抗原检测系统)测量诸如细胞生长、细胞活力和产品表达等有限特征的能力。此外，由于目前使用的分析平台的吞吐量低，以及缺乏将这些平台耦合到发酵系统中的能力，直到产品开发周期的后期才采用能够确定产品质量和安全特性的技术。为了解决这一未得到满足的需求，我们组建了一个独特的团队，他们在生物制品和蛋白质组学方面拥有互补的技能，利用拉曼光谱和基于质谱仪的代谢物和分泌蛋白质分析对发酵进行在线分析，以及使用多元化学计量学和机器学习进行数据处理。这个小组使我们处于一个难得的位置，可以利用生物制药、分析化学和信息学的尖端三分式工作。最后，也是最重要的是，我们可以使用NIBRT(国家生物处理研究和培训研究所)最先进的生物制剂生产自动化系统，我们将专注于中国仓鼠卵巢(CHO)细胞的抗体生产，因为这个生产系统主导着制药市场。我们将开发一个综合技术平台，其基础是：(I)MS，用于测量分泌到培养基中的蛋白质；(Ii)在线LC-MS，用于测量培养基中分泌或被细胞摄取的小分子；以及(Iii)拉曼光谱，用于在线监测蛋白质结构和糖基化模式。此外，我们将使用计算机方法根据分析的反馈控制发酵，并使用计算机软件设计发酵过程中的最佳生长介质和补料制度，以提高蛋白质产量和维持细胞健康。

There was a time when small molecules dominated the pharmaceutical market for the treatment of diseases like cancer as well as bacterial, fungal and viral infections. However, over the last decade biopharmaceuticals have revolutionised the treatment of chronic diseases, as well as a number of orphan diseases. Advances in biopharmaceutical research have contributed to reduction in mortality from major diseases and also contributed towards increased life expectancy. Around 40% of the thousands of pharmaceuticals in the current Research and Development pipeline are biopharmaceuticals. These protein molecules have the potential to offer life-changing targeted personalised treatments to millions of patients and address the challenges associated with ageing populations.While mammalian cell culture for biopharmaceutical production is well established, the product must have the correct fidelity in terms of the structure of the protein and the way in which it is glycosylated (the patterns of sugars attached to these proteins). Therefore, product quality needs to be monitored during the fermentation process to confirm the safety and efficacy of the final product. However, the development and optimisation of these cell factories is limited by the performance of currently available analytical technology. Currently biopharmaceutical process development is performed in a rather empirical manner, based on the ability to measure limited features such as cell growth, cell viability and product expression using ELISA (an antibody-antigen detection system). In addition, technologies that enable product quality and safety characteristics to be determined are not employed until late in the product development cycle due to the low throughput of currently employed analytical platforms, as well as the lack of ability to couple these within the fermentation system. In order to address this unmet need we have assembled a unique team with complementary skills in biologics and proteomics, on-line analysis of fermentations with Raman spectroscopy and mass spectrometry-based metabolite and secreted protein analyses, as well as data processing using multivariate chemometrics and machine learning. This grouping puts us in a rare position to exploit work at the cutting-edge trisection of biopharmaceuticals, analytical chemistry and informatics.Finally, and most importantly, we have access to a state-of-the-art automation system for the production of biologics at NIBRT (National Institute for Bioprocessing Research and Training) and we shall focus on antibody production by Chinese hamster ovary (CHO) cells as this production system dominates the pharmaceutical market. We shall develop an integrated technology platform based on: (i) MS for measuring proteins secreted into the culture media; (ii) at-line LC-MS for measuring small molecules in culture media that are either secreted or taken up by the cells; and (iii) Raman spectroscopy for on-line monitoring of the protein structure and glycosylation pattern. In addition, we shall use computational approaches to control the fermentation based on feed-back from the analytics as well as using computer software for designing optimal growth media and feeding regimes during fermentation to enhance protein production and maintaining cellular health.