Application of novel Raman spectroscopy device and advanced data analytics to optimise viral vector production and design

应用新型拉曼光谱设备和先进的数据分析来优化病毒载体的生产和设计

• 批准号: 2827616
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2827616
• 关键词: Application; novel; Raman; spectroscopy; device

There are two methods to optimise viral vector production, the first is to improve the micro-environment within the bioreactor and the second is to improve the design of the viral vector constructs. Both of these approaches have the potential to improve viral vector titres, product quality and enable easier scale-up. The first part of this project will focus on optimising the control of the bioreactor micro-environment as this is of paramount importance to ensure product heterogeneity remains within a predefined specification defined by commercial good manufacturing practice (GMP) operations. However, continuous online monitoring is available only for a small number of process parameters (i.e pH, temperature and DO2) in viral vector manufacturing.Currently, the control of these bioreactor processes implements rudimentary and suboptimal control strategies that rely heavily on off-line data analytics and infrequent operator decisions. Machine learning techniques combined with advanced Process Analytical Technologies (PATs) such as Raman spectroscopy can enable real time predictions of key CPPs and CQAs. These real-time predictions can be combined with advanced feedback controllers that can be used to optimise process performance such as maximising viral titres and minimising manufacturing impurities such as empty capsids. Once the control algorithms have been developed, the second part of the project will be to modify the gene sequence of the cap or rep gens and evaluate how the design of these will improve clinical manufacturing processes within a well-defined and controlled bioreactor system.

有两种方法可以优化病毒载体的生产，一是改善生物反应器内的微环境，二是改进病毒载体结构的设计。这两种方法都有可能提高病毒载体的滴度、产品质量，并更容易扩大规模。该项目的第一部分将侧重于优化生物反应器微环境的控制，因为这对于确保产品异质性保持在商业良好制造规范(GMP)操作定义的预定义规格内至关重要。然而，在病毒载体生产中，只有少数过程参数(如pH、温度和DO2)可以进行连续在线监测。目前，对这些生物反应器过程的控制实施的是基本的和次最优的控制策略，严重依赖于离线数据分析和罕见的操作员决策。机器学习技术与拉曼光谱等先进的过程分析技术(PATS)相结合，可以实时预测关键的CPP和CQA。这些实时预测可与先进的反馈控制器相结合，可用于优化工艺性能，如最大化病毒滴度和最小化制造杂质，如空衣壳。一旦控制算法被开发出来，项目的第二部分将是修改帽或代表的基因序列，并评估这些设计将如何在定义良好和受控的生物反应器系统中改进临床制造过程。