Robust design of chemical processes with application to bio-manufacturing

化学工艺的稳健设计及其在生物制造中的应用

• 批准号: RGPIN-2014-04425
• 负责人: Budman, Hector
• 金额: $ 2.55万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566513
• 关键词: Robust; design; chemical; processes; application

The introduction of an increasing number of bio-products, such as new monoclonal antibodies and new vaccines, has created a clear need for new robust and efficient bioprocesses to manufacture these products. mAbs (monoclonal antibodies) produced in mammalian cell cultures are currently a predominant bioproduct with world-wide sales of $45 billion in 2012 and with a predicted annual growth in demand of 9.5%. For instance, approximately 28 mAbs have been approved, e.g cetuximab (metastatic cancers), trastuzumab (breast cancer), while 350 more are in various stages of clinical trials. Vaccine manufacturing is another major strategic area that is being continuously re-energized by the introduction of new vaccines. This proposal investigates different aspects of robust design of bioprocesses and builds upon research conducted in the previous funding period in two areas: i) metabolic flux balance-based models and ii) simultaneous optimal control and design of chemical processes using imperfect models. The US Food and Drug Administration (FDA) has recently adopted the quality-by-design (QBD) approach whereby the entire bioprocess is a priori designed and certified to provide quality and quantity of the final product within a range of bounds referred to as the design space. In the QBD approach a process can be adjusted within the design space to accommodate variability in raw materials and disturbances in contrast with the traditional approach where the process is operated with mostly fixed operating recipes. To apply the QBD approach it is necessary to search for an optimal design and optimal operating conditions while taking into account a priori expected variability in inputs, e.g. raw materials, and inaccuracies of the models to be used for optimization and control. The long term objective of this research is to develop a method for model-based optimal designs of new bioprocesses or optimal upgrades of existing ones that are robust to model errors and process variations. By explicitly considering robustness to model error and process variability, the work closely fit the QBD approach. To achieve our long term goal, robust modeling, optimization and control tools will be developed via the following four projects: I. development of dynamic metabolic flux balance models (DMFB) for mammalian cells to be used for optimization. II. optimal design of bioprocesses tolerant to model errors and process variabilities. III. integration of DMFBs and multivariate statistical models for optimal design or upgrade of bioprocesses. IV. simultaneous optimal design and nonlinear model predictive control (NMPC) based on dynamic metabolic flux models. I am proposing to train 2 PhD’s (and additional 2 towards the end of the granting period) and 2 MASc’s in different areas of modeling, control and optimization of biotechnological processes. The work includes both theoretical developments in the areas of dynamic metabolic flux analysis, run-to-run optimization and nonlinear predictive control as well as experimentation to calibrate the proposed models and to validate the optimization procedures. Applications to a mammalian cell culture producing mAb and a whooping cough vaccine manufacturing process will be considered. Overall, the proposed model-based robust design tools will benefit the industry in two ways: i-by proposing an approach to bioprocess design that ensures predefined levels of productivity and quality as per the QBD idea and ii- by training HQP with versatile expertise in the application of process systems engineering tools (modeling, control, statistics and optimization) to biotechnological processes.

越来越多的生物产品，如新的单克隆抗体和新的疫苗的引入，已经产生了对新的稳健和有效的生物工艺来制造这些产品的明确需求。在哺乳动物细胞培养物中产生的单克隆抗体（mAb）目前是主要的生物产品，2012年全球销售额为450亿美元，预计需求年增长率为9.5%。例如，已经批准了大约28种单克隆抗体，例如西妥昔单抗（转移性癌症），曲妥珠单抗（乳腺癌），而350多种处于临床试验的各个阶段。疫苗制造是另一个主要战略领域，由于新疫苗的推出而不断恢复活力。该提案调查了生物过程稳健设计的不同方面，并建立在上一个资助期在两个领域进行的研究基础上：i）基于代谢通量平衡的模型和ii）使用不完美模型的化学过程的同步优化控制和设计。美国食品和药物管理局（FDA）最近采用了设计质量（QBD）方法，其中整个生物过程是先验设计和认证的，以在称为设计空间的范围内提供最终产品的质量和数量。在QBD方法中，工艺可以在设计空间内进行调整，以适应原材料和干扰的变化，这与传统方法相反，传统方法中的工艺大多采用固定的操作配方。为了应用QBD方法，有必要搜索最佳设计和最佳操作条件，同时考虑输入（例如原材料）的先验预期变化以及用于优化和控制的模型的不准确性。本研究的长期目标是开发一种基于模型的新的生物过程的优化设计或现有的优化升级，是强大的模型误差和过程变化的方法。通过明确考虑模型误差和过程变异性的鲁棒性，工作密切配合QBD方法。为了实现我们的长期目标，将通过以下四个项目开发鲁棒建模，优化和控制工具：I。开发用于优化的哺乳动物细胞的动态代谢通量平衡模型（DMFB）。二.对模型误差和过程可变性具有耐受性的生物过程的优化设计。三. DMFB和多变量统计模型的集成，用于优化设计或升级生物过程。四.同时优化设计和非线性模型预测控制（NMPC）基于动态代谢通量模型。我建议在生物技术过程的建模、控制和优化的不同领域培养2名博士（以及在授予期结束时再培养2名）和2名硕士。这项工作包括理论发展领域的动态代谢通量分析，运行到运行的优化和非线性预测控制，以及实验来校准所提出的模型，并验证优化程序。将考虑应用于生产mAb的哺乳动物细胞培养物和百日咳疫苗生产工艺。总的来说，拟议的基于模型的稳健设计工具将有利于行业在两个方面：i-通过提出一种方法，以生物工艺设计，确保预定义的生产力和质量水平，按照QBD的想法和ii-通过培训HQP与过程系统工程工具（建模，控制，统计和优化）的生物技术过程中的应用的多方面的专业知识。