Generalization of Dynamic Metabolic Flux Models and their Application for Economic Optimization of Bioprocesses

生物制药市场正以前所未有的速度增长。例如，全球销售额从2009年的990亿美元增加到2017年的2280亿美元。在中国仓鼠卵巢（CHO）细胞培养中产生的单克隆抗体（mAb）是该方案的重点应用之一，是最畅销的前五大产品。为了满足这一需求，制药行业正在与FDA合作，积极寻求新的稳健设计、控制和优化策略。数学模型是这些策略的核心。当前应用的重点是开发新的CHO细胞动态代谢通量模型（DMFM），并研究其在工艺优化中的应用。提出的建模方法和基于这些模型的稳健优化算法将为加拿大制药行业在优化培养基和细胞系的开发以及生物过程的稳健经济优化操作方面赋予独特的优势。这些技术的应用将是竞争力和遵守管制限制的关键。DMFM是基于一个受约束的约束优化的解。据推测，DMFM需要较少数量的参数来描述数据，因此所得模型不太容易过度拟合。虽然DMFM对细菌和藻类已有研究，但DMFM对CHO的研究还处于初级阶段。由于DMFM是基于基因组信息的，并且考虑到已知的哺乳动物细胞基因组的知识空白，模型误差是可以预料的。因此，为了确保行业采用所提出的技术，必须解决模型误差的鲁棒性问题。根据上述建议，该建议包括两个组成部分：1-开发哺乳动物细胞的DMFM模型；2-开发稳健策略，其中模型是逐步学习的，而过程是朝着最佳方向发展的。建模部分包括：i-寻找适合哺乳动物细胞DMFM的生物目标功能，ii-包含热力学和遗传调控约束，以及iii-培养细胞异质性的影响。这些模型将在我们的动物细胞实验室收集数据进行验证。优化部分将研究：i-用DMFM制定经济优化，ii-在更新模型的同时分批对分批方法优化培养基，iii-研究基于DMFM的经济模型预测控制（EMPC）在灌注和连续操作中的使用。该研究项目将培养和支持3名博士和1名硕士，他们将获得工业界非常需要的理论和实验技能的独特组合，如基于基因组的动态建模和鲁棒优化，细胞培养和过程分析技术。

The market for biopharmaceuticals is increasing at an unprecedented rate. For example, the world sales increased from US $99 billion in 2009 to US $228 billion in 2017. Monoclonal antibodies (mAb) produced in Chinese Hamster Ovary (CHO) cell cultures, which is one of the focal applications of this proposal, are the top five best-selling products. To respond to this demand, the pharma industry in collaboration with the FDA is actively seeking novel robust design, control and optimization strategies. Mathematical models are the heart of such strategies. The focus of the current application is to develop novel dynamic metabolic flux models (DMFM) for CHO cells and to study their use for process optimization. The proposed modelling approach and the robust optimization algorithms to be based on these models will confer a distinctive advantage to the Canadian pharma industry in the development of optimized culture media and cell lines and for robust economically optimal operation of bioprocesses. The application of these techniques will be the key for competitiveness and compliance with regulatory constraints. The DMFM is based on the solution of a constrained optimization subject to constraints. It has been postulated that DMFM requires a smaller number of parameters to describe data and thus the resulting models are much less prone to over-fitting. Although DMFM has been studied for bacteria and algae, the study for DMFM for CHO is very preliminary. Since DMFM are based on genomic information, and in view of the recognized knowledge-gaps of the mammalian cells' genome, model error is expected. Thus, to ensure adoption of the proposed techniques by the industry, robustness to model error must be addressed. Following the above, the proposal comprises two components: 1- development of DMFM models for mammalian cells and 2- development of robust strategies where the model is progressively learned while the process is driven towards the optimum. The modeling component includes: i-the search for suitable biological objective functions for DMFM of mammalian cells, ii-the inclusion of thermodynamic and genetic regulatory constraints and iii-the effect of heterogeneity of cells in culture . The models will be validated with data to be collected in our animal cell laboratory. The optimization component will investigate: i-the formulation of economic optimization with a DMFM, ii- Batch-to-Batch approach to optimize the media while updating the model, and iii- the investigation of the use of economic model predictive control (EMPC) based on a DMFM for perfusion and continuous operations. The research program will train and support 3 PhDs and 1 MSc who will acquire a unique combination of theoretical and experimental skills that are in very high demand in industry, such as genome based dynamic modeling and robust optimization, cell culturing and process analytical technologies.