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

在数据的支持下，人工智能和自主机器人等工业数字技术（IDT）可用于改善沿着供应链向客户提供产品的制造和供应的各个方面。许多公司正在接受这些技术，但制药行业的吸收速度并不快。Medicines Made Smarter Data Centre（MMSDC）旨在应对数字化进程放缓的关键挑战，并采用IDT来改变流程，以提供符合患者需求的药物。工作将在学术和工业研究团队设计的五个集成平台上进行。这些是：1）数据平台，2）自主微型制造平台，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数据平台将存储和分析整个MMSDC项目的数据，使其可供药品制造界访问、搜索和重复使用。在开展数据科学活动的同时，还将制定确保数据质量始终如一的新方法，如良好做法指南和标准，以确定哪些数据最重要，以及如何在实践中最好地将这些数据用于IDT。平台2将通过创建敏捷的小规模生产设施，快速生成大型数据集并推动研究，加快药品和制造工艺的开发。机器人技术将被组装起来，以创建一个独特的小规模药物制造和测试系统，以选择药物配方和工艺，生产具有所需体外性能的稳定产品。整合多个IDT将加速药品生产，显著减少实验，大幅降低开发时间、原材料和相关成本。平台3专注于药品开发质量控制（QC）方面的数字化，这对于确保药品符合监管标准和患者安全要求至关重要。目前，QC检查是在一个过程完成后进行的，可能会在问题发生后发现问题。这种方法效率低，分散，成本高（超过总生产成本的20%）且耗时。数字化QC平台将研究如何通过利用IDT的丰富数据来转换QC，以真实的时间确认产品和过程的合规性。平台4将对未来的药品供应链需求产生新的理解，以支持采用适应性数字供应链，实现以患者为中心的供应。IDT使更小规模的自主工厂概念变得可行，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规模、产品种类和更短的交货期（从数月到数天），提供以患者为中心的响应式或快速补货运营模式。最后，更贴近患者的技术发展，如诊断，提供了患者特定需求的可见性。平台5将建立MMSDC网络和技能平台。该网络将引导参与药品生产和投资的主要利益攸关方群体的参与和合作。该网络汇集了IDT使用社区和其他相关的学术和工业团体，以分享制药和更广泛的数字制造部门的发展，确保MMSDC研究的跨部门传播。现有的战略网络将支持MMSDC，并作为IDT传播和吸收的网关。劳动力缺乏适当的技能已被强调为采用IDT的关键障碍。MMSDC的优先事项是确定技能需求，并与合作伙伴一起为100多名用户开发和提供培训

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