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.

包括连续药品制造 (C-PM) 在内的先进药品制造方法涉及复杂的操作，需要高度的过程控制、理解和经验。我们正在创新开发智能、人工智能驱动和机器人支持的开发平台或“数据工厂”，以加速产品和流程开发，并实现数字孪生的开发和应用。 CMAC 的愿景是实现化学、制造和控制 (CMC) 运营的数字化转型，作为新药审批的一部分，其关键目标是开发结晶分类系统 (CCS)，该系统将允许快速预测结晶有针对性的、节省材料的实验方法的结果。 CMAC 进行了大量投资，建立了新的数据工厂，用于加速结晶开发，使用模型驱动的实验设计和协作机器人来驱动初步实验，以估计热力学（溶解度）和动力学（成核、生长）参数以及关键颗粒属性（固体形式、尺寸和形状）。形状）将指导工艺设计。该平台由多个传感器组成，通过离线测量和数据分析来实现这一目标。目前，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。对 CCS DataFactory 中所有测量点的这些评估将提高过程理解、确定改进路线并建立对数字工具的信心。目前，仪器（元）数据的存储或此类大型异构数据的处理没有通用标准。虽然控制软件工具试图捕获临时调节过程所需的信息，但它们不利于从过程相关仪器捕获分析结果。因此，需要改进数据存储、治理和对复杂多阶段流程的大量信息的访问，以提高药品开发和制造的质量和效率，并充分发挥数字化转型的潜力。该博士将专注于使用 DataFactory 获取综合数据，并开发通过新型结晶参数数据库传播信息和不确定性的新方法，该数据库是 CMAC 愿景的一部分，旨在成为药品和工艺的数据中心并交付CCS。