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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587800
• 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的推出，如新的单抗和新的疫苗，显然需要新的强大和有效的生物工艺来生产这些产品。 在哺乳动物细胞培养中生产的单抗目前是一种主要的生物制品，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种单抗，例如西妥昔单抗(转移性癌症)、曲妥珠单抗(乳腺癌)，还有350多种处于不同的临床试验阶段。疫苗制造是另一个重要的战略领域，新疫苗的推出正在不断重振该领域的活力。 这项建议调查了生物过程稳健设计的不同方面，并建立在前一个供资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)使用不完美的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)最近采用了按设计质量(QBD)的方法，即整个生物过程是先验的设计和认证，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，流程可以在设计空间内进行调整，以适应原材料的变化和干扰，这与传统方法不同，在传统方法中，流程的操作大多是固定的操作配方。要应用QBD方法，必须寻找最佳设计和最佳操作条件，同时考虑到输入的先验预期变异性，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的优化设计或现有生物过程的优化升级的方法，该方法对模型错误和过程变化具有健壮性。通过明确考虑对模型误差和过程变异性的稳健性，这项工作非常符合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.用于优化的哺乳动物细胞的动态代谢流量平衡模型(DMFB)的发展。 2.对模型误差和过程变异性容忍的生物过程的优化设计。 DMFBs和多变量统计模型的整合，用于生物过程的优化设计或升级。 基于动态代谢流量模型的同步优化设计和非线性模型预测控制。 我提议在生物技术过程的建模、控制和优化的不同领域培训2名博士(并在授权期结束时再培训2名)和2名硕士研究生。这项工作包括在动态代谢流量分析、逐次优化和非线性预测控制领域的理论发展，以及为校准所提出的模型和验证优化程序而进行的实验。将考虑应用于生产单抗的哺乳动物细胞培养和百日咳疫苗制造方法。 总体而言，拟议的基于模型的稳健设计工具将在两个方面使该行业受益：i--通过提出一种生物过程设计的方法，确保符合QBD思想的预定义的生产力和质量水平；ii-通过培训具有在生物技术过程中应用过程系统工程工具(建模、控制、统计和优化)的全面专业知识的HQP。