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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611028
• 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（例如新的单克隆抗体和新疫苗）的推出，显然需要新的稳健且高效的生物工艺来制造这些产品。 在哺乳动物细胞培养物中生产的 mAb（单克隆抗体）目前是一种主要的生物产品，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，大约有 28 种单克隆抗体已获得批准，例如西妥昔单抗（转移性癌症）、曲妥珠单抗（乳腺癌），还有 350 种单克隆抗体正处于不同阶段的临床试验。疫苗制造是另一个主要战略领域，随着新疫苗的推出而不断重新焕发活力。 该提案研究了生物过程稳健设计的不同方面，并建立在之前资助期间在两个领域进行的研究的基础上：i）基于代谢通量平衡的模型和ii）使用不完美模型同时优化控制和设计化学过程。 美国食品和药物管理局 (FDA) 最近采用了质量源于设计 (QBD) 方法，整个生物过程经过先验设计和认证，可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，可以在设计空间内调整流程，以适应原材料的变化和干扰，而传统方法则采用大多数固定的操作配方来操作流程。为了应用 QBD 方法，有必要寻找最佳设计和最佳操作条件，同时考虑输入的先验预期变化，例如原材料以及用于优化和控制的模型的不准确性。这项研究的长期目标是开发一种基于模型的新生物过程优化设计或现有生物过程优化升级的方法，该方法对模型错误和过程变化具有鲁棒性。通过明确考虑模型误差和过程可变性的稳健性，这项工作非常适合 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强大的建模、优化和控制工具： I. 开发用于优化的哺乳动物细胞动态代谢通量平衡模型（DMFB）。 二.容忍模型误差和过程变异的生物过程的优化设计。 三． DMFB 和多元统计模型的集成，用于生物工艺的优化设计或升级。 四．基于动态代谢通量模型的同步优化设计和非线性模型预测控制（NMPC）。 我建议在生物技术过程建模、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另外 2 名）和 2 名硕士。这项工作包括动态代谢流分析、运行优化和非线性预测控制领域的理论发展，以及校准所提出的模型和验证优化程序的实验。将考虑应用于生产单克隆抗体的哺乳动物细胞培养物和百日咳疫苗生产过程。 总体而言，所提出的基于模型的稳健设计工具将以两种方式使行业受益：i-提出一种生物工艺设计方法，根据 QBD 理念确保预定的生产力和质量水平；ii-通过培训 HQP 在将工艺系统工程工具（建模、控制、统计和优化）应用于生物技术工艺方面具有多种专业知识。