BRIC DOCTORATE PROGRAMME on Linking High Throughput Cell Culture Multivariate Analysis and Economics for More Effective Process Integration

金砖四国博士项目：将高通量细胞培养多元分析与经济学联系起来，实现更有效的流程整合

• 批准号: BB/J003816/1
• 负责人: Suzanne Farid
• 金额: $ 13.24万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FJ003816%2F1
• 关键词: BRIC; DOCTORATE; PROGRAMME; Linking; Throughput

As the antibody sector has matured, it has seen SIGNIFICANT INCREASES IN UPSTREAM (USP) PRODUCTIVITES that have opened up the possibility for radical changes to the design and operation of cell culture suites. However, due to the inherently complex set of interactions that can affect cell culture performance, IT IS HARD TO PREDICT THE CONSEQUENCES ON THE IMPURITY PROFILES AND HENCE ROBUSTNESS OF DOWNSTREAM (DSP) OPERATIONS as titres increase. Quality by Design initiatives are driving the need for greater understanding of the impact of cell culture strategies on the downstream processing equipment duties so as to enable EFFECTIVE PROCESS INTEGRATION and hence CONTINUOUS IMPROVEMENTS. This project will explore STATE-OF-THE-ART HIGH THROUGHPUT CELL CULTURE and MULTIVARIATE DATA ANALYSIS techniques to characterise cell culture operations, not only in terms of growth and productivity BUT ALSO IMPURITY PROFILES AND QUALITY. The resulting cell culture statistical cause-and-effect correlations will be integrated into PROCESS ECONOMICS MODELS developed in the UCL Decisional Tools team so as to TO IDENTIFY THE MOST COST-EFFECTIVE INTEGRATED USP AND DSP MANUFACTURING STRATEGIES FOR THE FUTURE. The proposed programme will link MedImmune's leadership in antibody production and UCL's leadership in bioprocess decisional tools and scale-down techniques to tackle these intricate process-business decisions. Project stages: 1. MICROSCALE DATA GENERATION (Yr 1, 2). Increases in titres can be a result of different combinations of increases in cell densities or specific cell productivities, each with a different impact on impurity burdens on DSP. This depends on factors such as the cell line characteristics, bioreactor operating parameters, medium or feed type and the feeding strategy. These have a significant impact on cell growth, productivity, viability, impurity profiles and may also have an impact on product quality. Initially large datasets characterising cell culture strategies will be generated by a) leveraging historical data from MedImmune and b) high throughput experimentation using both the 'ambr microscale bioreactor system' (developed in a collaboration between The Automation Partnership and MedImmune) and microwell systems from current UCL EPSRC IMRC activities. 2. MULTIVARIATE ANALYSIS (Yr 2, 3). The next challenge will be exploring effective techniques to analyse such large datasets and reduce to predictive correlations. Advanced multivariate analysis techniques will be investigated to help predict the product and impurity profiles resulting from different cell culture strategies. Statistical (rather than mechanistic) cause-and-effect correlations will then be derived to link the impurity profile to key factors such as the cell density, specific cell productivity, culture duration, and titre. Key quality attributes that will be focused on are the levels of HCP (host cell proteins), HMW (aggregates), cell viability and product potency. 3. LINKAGE TO PROCESS ECONOMICS MODELS (Yr 3). The resulting cell culture predictive correlations will be linked to a whole bioprocess cost model developed at UCL in a TSB/EPSRC collaboration with MedImmune so as to predict the equipment sizes, COG and risks associated with different cell culture strategies. 5. SCENARIO ANALYSIS (Yr 3, 4). Several cell culture strategies will be plugged into the integrated cell culture and process economics model to enable rapid identification of the most promising and robust combinations of USP and DSP activities for more streamlined development in both existing and new facilities. The overall outputs of this research will be a systematic framework combining microscale experimentation with statistical correlations and cost modelling so as to enable selection of innovative cell culture strategies early in the development cycle that BALANCE THE NEEDS OF UPSTREAM AND DOWNSTREAM MANUFACTURABILITY, ROBUSTNESS TO PROCESS VARIABILITIES AND COST-EFFECTIVENESS.

随着抗体领域的成熟，上游（USP）生产能力显著增加，这为细胞培养套件的设计和操作带来了根本性变化的可能性。然而，由于可能影响细胞培养性能的一组固有复杂的相互作用，很难预测随着滴度增加对杂质谱的影响以及下游（DSP）操作的耐用性。质量源于设计的倡议推动了对细胞培养策略对下游加工设备职责的影响的更深入理解，以实现连续的工艺集成，从而实现持续改进。该项目将探索最先进的高通量细胞培养和多变量数据分析技术，以优化细胞培养操作，不仅在生长和生产力方面，而且在杂质谱和质量方面。将得到的细胞培养统计因果关系整合到UCL决策工具团队开发的工艺经济学模型中，以确定未来最具成本效益的USP和DSP集成策略。拟议的计划将联系MedImmune在抗体生产方面的领导地位和UCL在生物过程决策工具和缩小规模技术方面的领导地位，以解决这些复杂的过程业务决策。项目阶段：1.微型数据生成（第1、2年）。滴度增加可能是细胞密度或比细胞生产率增加的不同组合的结果，每种组合对DSP上的杂质负荷有不同的影响。这取决于细胞系特征、生物反应器操作参数、培养基或补料类型以及补料策略等因素。这些对细胞生长、生产率、活力、杂质谱有显著影响，也可能对产品质量有影响。最初，将通过a）利用MedImmune的历史数据和B）使用“ambr微尺度生物反应器系统”（The Automation Partnership和MedImmune合作开发）和来自当前UCL EPSRC IMRC活动的微孔系统的高通量实验来生成表征细胞培养策略的大型数据集。2.多变量分析（第2、3年）。下一个挑战将是探索有效的技术来分析如此大的数据集并减少预测相关性。将研究先进的多变量分析技术，以帮助预测不同细胞培养策略产生的产物和杂质谱。然后将推导出统计学（而非机械）因果相关性，以将杂质谱与关键因素（如细胞密度、比细胞生产率、培养持续时间和滴度）联系起来。重点关注的关键质量属性是HCP（宿主细胞蛋白）、HMW（聚集体）水平、细胞活力和产品效价。3.与过程经济学模型的联系（3年级）。由此产生的细胞培养预测相关性将与TSB/EPSRC与MedImmune合作在UCL开发的整个生物工艺成本模型相关联，以便预测与不同细胞培养策略相关的设备尺寸，COG和风险。5.情景分析（3、4年级）。几种细胞培养策略将被插入到集成的细胞培养和工艺经济学模型中，以快速识别USP和DSP活动的最有前途和最稳健的组合，从而在现有和新设施中进行更精简的开发。这项研究的总体成果将是一个系统的框架，将微规模实验与统计相关性和成本建模相结合，以便在开发周期的早期选择创新的细胞培养策略，平衡上游和下游的可重复性，工艺可变性和成本可重复性的需求。