Digital Design for Crystallisation in Advanced Pharmaceutical Manufacturing: Uncertainty of information and how to find data

先进药品制造中结晶的数字化设计：信息的不确定性以及如何查找数据

• 批准号: 2890547
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890547
• 关键词: Digital; Design; Crystallisation; Advanced; Pharmaceutical

Advanced pharmaceutical manufacturing approaches including continuous pharmaceutical manufacturing (C-PM) involve complex operations requiring high degrees of process control, understanding and experience. We are innovating the development of smart, AI-driven and robotically enabled development platforms, or 'DataFactories' to accelerate product and process development and enable the development and application of digital twins. CMAC's vision is to deliver the digital transformation of Chemistry, Manufacturing and Control (CMC) operations as part of the approval of new medicines and a key aim of this is the development of a Crystallisation Classification System (CCS) that will allow rapid prediction of crystallisation outcomes from targeted, material sparing experimental approaches. Significant investment by CMAC has established new DataFactories for accelerated crystallisation development using model driven experimental design and collaborative robotics to drive initial experiments to estimate thermodynamic (solubility) and kinetic (nucleation, growth) parameters as well as key particle attributes (solid form, size and shape) that will direct process design. The platform's comprise multiple sensors with offline measurements and data analysis to achieve this. Currently, the measurement of uncertainties and their propagation throughout the digital design process are not well understood. Evaluation of these at all measurement points in the CCS DataFactory will improve process understanding, identify routes for improvement and build confidence in digital tools. Presently, there are no common standards for the storage of (meta)data of instruments or the handling of such large heterogenous data. While control software tools attempt to capture the information required to regulate the processes ad-hoc, they do not facilitate capturing analytical results from process-related instruments. Thus, there is a need for improved data storage, governance and access to the myriad of information about complex mulit-phase processes to enhance the quality and efficiency of medicines development and manufacturing and realise the full potential of digital transformation. The PhD will focus on the acquisition of comprehensive data using the DataFactory and develop novel methods for the propagation of information and uncertainty delivered through the novel Crystallisation Parameter database being established as part of CMAC's vision to become the data centre for pharmaceutical products and processes and deliver the CCS.

包括连续药物制造（C-PM）在内的先进制药制造方法涉及需要高度的过程控制，理解和经验的复杂操作。我们正在创新智能，AI驱动和机器人启用的开发平台的开发，或“ dataFactories”，以加速产品和过程开发，并使数字双胞胎的开发和应用。 CMAC的愿景是作为批准新药物的一部分进行化学，制造和控制（CMC）操作的数字化转型，其关键目的是开发结晶分类系统（CCS），该系统将允许快速预测目标的靶向，重大保证实验方法的结晶成果。 CMAC的大量投资已经建立了使用模型驱动的实验设计和协作机器人技术来加速结晶开发的新数据，以驱动初始实验，以估算热力学（溶解度）和动力学（成核，生长）参数以及关键粒子属性（固体形式，尺寸和形状），这些参数将引导工艺设计。该平台包括多个传感器，具有离线测量和数据分析以实现这一目标的多个传感器。当前，对不确定性的测量及其在整个数字设计过程中的传播尚不清楚。在CCS DataFactory中所有测量点上对这些的评估将提高过程的理解，确定改进路线并建立对数字工具的信心。目前，尚无通用标准来存储仪器的（元）数据或处理如此大的异源数据。当控制软件工具试图捕获调节流程临时所需的信息时，它们并不能促进从过程相关仪器中捕获分析结果。因此，需要改善数据存储，治理并访问有关复杂的混合过程过程的无数信息，以提高药品开发和制造的质量和效率，并实现数字化转型的全部潜力。博士将专注于使用数据策略的获取综合数据，并开发出新的方法，以通过新颖的结晶参数数据库作为CMAC成为CMAC愿景的一部分，以成为药物和过程数据中心的一部分，以传播信息和不确定性。