Our Hub research is driven by the societal need to produce medicines and materials for modern living through novel manufacturing processes. The enormous value of the industries manufacturing these high value products is estimated to generate £50 billion p.a. in the UK economy. To ensure international competitiveness for this huge UK industry we must urgently create new approaches for the rapid design of these systems, controlling how molecules self-assemble into small crystals, in order to best formulate and deliver these for patient and customer. We must also develop the engineering tools, process operations and control methods to manufacture these products in a resource-efficient way, while delivering the highest quality materials. Changing the way in which these materials are made, from what is called "batch" crystallisation (using large volume tanks) to "continuous" crystallisation (a more dynamic, "flowing" process), gives many advantages, including smaller facilities, more efficient use of expensive ingredients such as solvents, reducing energy requirements, capital investment, working capital, minimising risk and variation and, crucially, improving control over the quality and performance of the particles making them more suitable for formulation into final products. The vision is to quickly and reliably design a process to manufacture a given material into the ideal particle using an efficient continuous process, and ensure its effective delivery to the consumer. This will bring precision medicines and other highly customisable projects to market more quickly. An exemplar is the hubs exciting innovation partnership with Cancer Research UK. Our research will develop robust design procedures for rapid development of new particulate products and innovative processes, integrate crystallisation and formulation to eliminate processing steps and develop reconfiguration strategies for flexible production. This will accelerate innovation towards redistributed manufacturing, more personalisation of products, and manufacturing closer to the patient/customer. We will develop a modular MicroFactory for integrated particle engineering, coupled with a fully integrated, computer-modelling approach to guide the design of processes and materials at molecule, particle and formulation levels. This will help optimise what we call the patient-centric supply chain and provide customisable products. We will make greater use of targeted experimental design, prediction and advanced computer simulation of new formulated materials, to control and optimise the processes to manufacture them. Our talented team of scientists will use the outstanding capabilities in the award winning £34m CMAC National Facility at Strathclyde and across our 6 leading university spokes (Bath, Cambridge, Imperial, Leeds, Loughborough, Sheffield). This builds on existing foundations independently recognised by global industry as 'exemplary collaboration between industry, academia and government which represents the future of pharmaceutical manufacturing and supply chain R&D framework'. Our vision will be translated from research into industry through partnership and co-investment of £31m. This includes 10 of world's largest pharmaceutical companies (eg AstraZeneca, GSK), chemicals and food companies (Syngenta, Croda, Mars) and 19 key technology companies (Siemens, 15 SMEs) Together, with innovation spokes eg Catapult (CPI) we aim to provide the UK with the most advanced, integrated capabilities to deliver continuous manufacture, leading to better materials, better value, more sustainable and flexible processes and better health and well-being for the people of the UK and worldwide. CMAC will create future competitive advantage for the UK in medicines manufacturing and chemicals sector and is strongly supported by industry / government bodies, positioning the UK as the investment location choice for future investments in research and manufacturing.

There are considerable challenges around digitalisation in science, engineering and manufacturing in part due to the inherent complexity in the data generated and the challenges in creating useful data sets with the scale required to allow big data approaches to identify patterns, trends and useful knowledge. Whilst other sectors are now realising the power of predictive data analytics; social media platforms, online retailers and advertisers, for example; much of the pharmaceutical manufacturing R&D community struggle with modest, poorly interconnected datasets, which ultimately tend to have short useful lifespans. A result of poor, under-utilised datasets, is that it is largely impossible to avoid "starting at the beginning" for every new drug that needs to be manufactured, which is very costly with new medicines currently doubling in cost every nine years; $1 billion US Dollars currently "buys" only half a new drug so addressing this issue is key for sustainability of the industry and future medicines supply. This project, ARTICULAR, will seek to develop novel machine learning approaches, a branch of artificial intelligence research, to learn from past and present manufacturing data and create new knowledge that aids in crucial manufacturing decisions. Machine learning approaches have been successfully applied to inform aspects of drug discovery, upstream of pharmaceutical manufacturing, where large genomic and molecule screening datasets provide rich information sources for analysis and training artificial intelligences (AI). They have also shown promise in classifying and predicting outcomes from individual unit operations used in medicines manufacturing, such as crystallisation. For the first time, there is an opportunity to use AI approaches to learn from the data and models from across multiple previous development and manufacturing efforts and then address the most commonly encountered problems when manufacturing new pharmaceutical products, which are knowing: (1) the processes and operations to employ; (2) the sensors and measurements to deploy to optimally deliver the product; and (3) the potential process upsets and their future impact on the quality of the medicine manufactured. All of these data and the AI "learning" will be made available via bespoke, personalisable AR and VR interfaces incorporating gesture and voice inputs alongside more traditional approaches such as dashboards. These immersive interfaces will facilitate pharmaceutical manufacturing process design, and visualisation of the complex data being captured and analysed in real-time. Detailed, interactive 3D visualisations of drug forms, products, equipment and manufacturing processes and their associated data will be created which provide intuitive access across the length scales of transformations involved from the drug molecule to final drug product. This will be unique tool, allowing the user to see their work and engage with their data in the context of upstream and downstream processes and performance data. Virtual and Augmented Reality technologies will be used in the lab/plant environment to visualise live data streams for process equipment as the next step in digitalisation. These advanced visualisation tools will add data rich, interactive visualisation to aid researchers in their work, allowing them to focus on the meaning of results and freeing them from menial manual data curation steps.