Made Smarter Innovation - Digital Medicines Manufacturing Research Centre

更智能的创新 - 数字药品制造研究中心

• 批准号: EP/V062077/1
• 负责人: Alastair Florence
• 金额: $ 648.11万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV062077%2F1
• 关键词: Made; Smarter; Innovation; Digital; Medicines

Powered by data, Industrial Digital Technologies (IDTs) such as artificial intelligence and autonomous robots, can be used to improve all aspects of manufacturing and supply of products along supply chains to the customer. Many companies are embracing these technologies but uptake within the pharmaceutical sector has not been as rapid. The Medicines Made Smarter Data Centre (MMSDC) looks to address the key challenges which are slowing digitalisation, and adoption of IDTs that can transform processes to deliver medicines tailored to patient needs.Work will be carried out across five integrated platforms designed by academic and industrial researcher teams. These are: 1) The Data Platform, 2) Autonomous MicroScale Manufacturing Platform, 3) Digital Quality Control Platform, 4) Adaptive Digital Supply Platform, and 5) The MMSDC Network & Skills Platform.Platform 1 addresses one of the sector's core digitalisation challenges - a lack of large data sets and ways to access such data. The MMSDC data platform will store and analyse data from across the MMSDC project, making it accessible, searchable and reusable for the medicines manufacturing community. New approaches for ensuring consistently high-quality data, such as good practice guides and standards, will be developed alongside data science activities which will identify what the most important data are and how best to use them with IDTs in practice.Platform 2 will accelerate development of medicine products and manufacturing processes by creating agile, small-scale production facilities that rapidly generate large data sets and drive research. Robotic technologies will be assembled to create a unique small-scale medicine manufacturing and testing system to select drug formulations and processes to produce stable products with the desired in-vitro performance. Integrating several IDTs will accelerate drug product manufacture, significantly reducing experiments and dramatically reducing development time, raw materials and associated costs.Platform 3 focusses on the digitalisation of Quality Control (QC) aspects of medicines development which is important for ensuring a medicine's compliance with regulatory standards and patient safety requirements. Currently, QC checks are carried out after a process has been completed possibly spotting problems after they have occurred. This approach is inefficient, fragmented, costly (>20% of total production costs) and time consuming. The digital QC platform will research how to transform QC by utilising rich data from IDTs to confirm in real time product and process compliance. Platform 4 will generate new understanding on future supply chain needs of medicines to support adoption of adaptive digital supply chains for patient-centric supply. IDTs make smaller scale, autonomous factory concepts viable that support more flexible and distributed manufacture and supply. Supply flexibility and agility extends to scale, product variety, and shorter lead-times (from months to days) offering a responsive patient-centric or rapid replenishment operating model. Finally, technology developments closer to the patient, such as diagnostics provide visibility on patient specific needs.Platform 5 will establish the MMSDC Network & Skills Platform. This Network will lead engagement and collaboration across key stakeholder groups involved in medicines manufacturing and investments. The Network brings together the IDT-using community and other relevant academic and industrial groups to share developments across pharmaceuticals and broader digital manufacturing sectors ensuring cross-sector diffusion of MMSDC research. Existing strategic networks will support MMSDC and act as gateways for IDT dissemination and uptake. The lack of appropriate skills in the workforce has been highlighted as a key barrier to IDT adoption. An MMSDC priority is to identify skills needs and with partners develop and deliver training to over 100 users

在数据提供的支持下，工业数字技术（IDT），例如人工智能和自动机器人，可用于改善沿供应链的制造和产品的各个方面。许多公司正在接受这些技术，但是药品领域内的吸收并不那么迅速。制造的智能数据中心（MMSDC）旨在应对减缓数字化的主要挑战，并采用IDT，可以改变过程以提供针对患者需求量身定制的药物。将在由学术和工业研究人员团队设计的五个集成平台上进行工作。这些是：1）数据平台，2）自动微观制造平台，3）数字质量控制平台，4）自适应数字供应平台，以及5）MMSDC网络和技能平台。平台1解决了该行业的核心数字化挑战之一 - 缺乏大型数据集和访问此类数据的方法。 MMSDC数据平台将在MMSDC项目中存储和分析数据，使其可用于药品制造社区，可访问，可搜索和重复使用。将与数据科学活动一起开发用于确保始终如一的高质量数据（例如良好实践指南和标准）的新方法，这些方法将确定最重要的数据是什么以及如何在实践中与IDT一起使用它们。平台2将加速医学产品的开发和制造过程，通过创建迅速生成大型数据集和开发大型数据集合的敏捷的小型生产设施。机器人技术将组装，以创建独特的小规模药物制造和测试系统，以选择药物配方和过程，以生产所需的体外性能。整合多个IDT将加速药品生产，大大减少实验，并大大减少开发时间，原材料和相关成本。Platform3的重点是药物开发的数字化（QC）方面，这对于确保药物遵守监管标准和患者的安全要求非常重要。目前，QC检查是在过程完成后可能发现问题后进行的。这种方法效率低下，分散，昂贵（占总生产成本的20％）和耗时。数字QC平台将通过利用来自IDT的丰富数据来研究如何在实时产品和流程合规性中确认QC。平台4将对药物的未来供应链需求产生新的了解，以支持采用以患者为中心的自适应数字供应链。 IDT使较小的规模，自主工厂概念可行，以支持更灵活和分布式的制造和供应。供应灵活性和敏捷性扩展到尺度，产品多样性以及较短的铅时间（从几个月到几天），提供响应式患者以患者为中心或快速补充的操作模型。最后，更接近患者的技术发展，例如诊断，可在患者特定的需求上提供可见性。平台5将建立MMSDC网络和技能平台。该网络将领导参与药品制造和投资的主要利益相关者群体之间的参与和协作。该网络汇集了使用IDT社区和其他相关的学术和工业团体，以分享跨药物和更广泛的数字制造业的发展，以确保MMSDC研究的跨部门扩散。现有的战略网络将支持MMSDC并充当IDT传播和吸收的网关。在劳动力中缺乏适当的技能已被强调为IDT采用的关键障碍。 MMSDC的优先事项是确定技能需求，并随着合作伙伴而开发和提供向100多名用户的培训

项目成果

A Novel Complete-Surface-Finding Algorithm for Online Surface Scanning with Limited View Sensors.

• DOI: 10.3390/s21227692
• 发表时间: 2021-11-19
• 期刊: Sensors (Basel, Switzerland)
• 作者: Poole A;Sutcliffe M;Pierce G;Gachagan A
• 通讯作者: Gachagan A

Configuration of digital and physical infrastructure platforms: Private and public perspectives

数字和物理基础设施平台的配置：私人和公共视角

• DOI: 10.1111/poms.13865
• 发表时间: 2022
• 期刊: Production and Operations Management
• 影响因子: 5
• 作者: Joglekar N

32nd European Symposium on Computer Aided Process Engineering

第32届欧洲计算机辅助过程工程研讨会

• DOI: 10.1016/b978-0-323-95879-0.50161-2
• 发表时间: 2022
• 作者: Li D

Autonomous, Digital-Twin Free Path Planning and Deployment for Robotic NDT: Introducing LPAS: Locate, Plan, Approach, Scan Using Low Cost Vision Sensors

机器人 NDT 的自主、数字孪生自由路径规划和部署：LPAS 简介：使用低成本视觉传感器进行定位、规划、接近和扫描

• DOI: 10.3390/app12105288
• 发表时间: 2022
• 期刊: Applied Sciences
• 作者: Poole A

33rd European Symposium on Computer Aided Process Engineering

第33届欧洲计算机辅助过程工程研讨会

• DOI: 10.1016/b978-0-443-15274-0.50255-9
• 发表时间: 2023
• 作者: Marousi A