By 2025 targeted biological medicines, personalised and stratified, will transform the precision of healthcare prescription, improve patient care and quality of life. Novel manufacturing solutions have to be created if this is to happen. This is the unique challenge we shall tackle. The current "one-size-fits-all" approach to drug development is being challenged by the growing ability to target therapies to only those patients most likely to respond well (stratified medicines), and to even create therapies for each individual (personalised medicines). Over the last ten years our understanding of the nature of disease has been transformed by revolutionary advances in genetics and molecular biology. Increasingly, treatment with drugs that are targeted to specific biomarkers, will be given only to patient populations identified as having those biomarkers, using companion diagnostic or genetic screening tests; thus enabling stratified medicine. For some indications, engineered cell and gene therapies are offering the promise of truly personalised medicine, where the therapy itself is derived at least partly from the individual patient. In the future the need will be to supply many more drug products, each targeted to relatively small patient populations. Presently there is a lack of existing technology and infrastructure to do this, and current methods will be unsustainable. These and other emerging advanced therapies will have a critical role in a new era of precision targeted-medicines. All will have to be made economically for healthcare systems under extreme financial pressure. The implications for health and UK society well-being are profound There are already a small number of targeted therapies on the market including Herceptin for breast cancer patients with the HER2 receptor and engineered T-cell therapies for acute lymphoblastic leukaemia. A much greater number of targeted therapies will be developed in the next decade, with some addressing diseases for which there is not currently a cure. To cope, the industry will need to create smarter systems for production and supply to increasingly fragmented markets, and to learn from other sectors. Concepts will need to address specific challenges presented by complex products, of processes and facilities capable of manufacture at smaller scales, and supply chains with the agility to cope with fluctuating demands and high levels of uncertainty. Innovative bioprocessing modes, not currently feasible for large-scale manufacturing, could potentially replace traditional manufacturing routes for stratified medicines, while simultaneously reducing process development time. Pressure to reduce development costs and time, to improve manufacturing efficiency, and to control the costs of supply, will be significant and will likely become the differentiating factor for commercialisation. We will create the technologies, skill-sets and trained personnel needed to enable UK manufacturers to deliver the promise of advanced medical precision and patient screening. The Future Targeted Healthcare Manufacturing Hub and its research and translational spokes will network with industrial users to create and apply the necessary novel methods of process development and manufacture. Hub tools will transform supply chain economics for targeted healthcare, and novel manufacturing, formulation and control technologies for stratified and personalised medicines. The Hub will herald a shift in manufacturing practice, provide the engineering infrastructure needed for sustainable healthcare. The UK economy and Society Wellbeing will gain from enhanced international competitiveness.

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.

The CDT in Molecules to Product addresses an overarching concern articulated by industry operating in the area of complex chemical products. It centres on the lack of a pipeline of doctoral graduates who understand the cross-scale issues that need to be addressed within the chemicals continuum. Translating their concern into a vision, the focus of the CDT is to train a new generation of research leaders with the skills and expertise to navigate the journey from a selected molecule or molecular system through to the final product that delivers the desired structure and required performance. To address this vision, three inter-related Themes form the foundation of the CDT - Product Functionalisation and Performance, Product Characterisation, and Process Modelling between Scales. More specifically, industry has identified a real need to recruit PGR graduates with the interdisciplinary skills covered by the CDT research and training programme. As future leaders they will be instrumental in delivering enhanced process and product understanding, and hence the manufacture of a desired end effect such as taste, dissolution or stability. For example, if industry is better informed regarding the effect of the manufacturing process on existing products, can the process be made more efficient and cost effective through identifying what changes can be made to the current process? Alternatively, if there is an enhanced understanding of the effect of raw materials, could stages in the process be removed, i.e. are some stages simply historical and not needed. For radically new products that have been developed, is it possible through characterisation techniques to understand (i) the role/effect of each component/raw material on the final product; and (ii) how the product structure is impacted by the process conditions both chemical and mechanical? Finally, can predictive models be developed to realise effective scale up? Such a focus will assist industry to mitigate against wasted development time and costs allowing them to focus on products and processes where the risk of failure is reduced. Although the ethos of the CDT embraces a wide range of sectors, it will focus primarily on companies within speciality chemicals, home and personal care, fast moving consumer goods, food and beverage, and pharma/biopharma sectors. The focus of the CDT is not singular to technical challenges: a core element will be to incorporate the concept of 'Education for Innovation' as described in The Royal Academy of Engineering Report, 'Educating engineers to drive the innovation economy'. This will be facilitated through the inclusion of innovation and enterprise as key strands within the research training programme. Through the combination of technical, entrepreneurial and business skills, the PGR students will have a unique set of skills that will set them apart from their peers and ultimately become the next generation of leaders in industry/academia. The training and research agendas are dependent on strong engagement with multi-national companies, SMEs, start-ups and stakeholders. Core input includes the offering, and supervision of research projects; hosting of students on site for a minimum period of 3 months; the provision of mentoring to students; engagement with the training through the shaping and delivery of modules and the provision of in-house courses. Additional to this will be, where relevant, access to materials and products that form the basis of projects, the provision of software, access to on-site equipment and the loan of equipment. In summary, the vision underpinning the CDT is too big and complex to be tackled through individual PhD projects - it is only through bringing academia and industry together from across multiple disciplines that a solution will be achievable. The CDT structure is the only route to addressing the overarching vision in a structured manner to realise delivery of the new approach to product development.

Batch processes are gaining ever increasing importance in manufacturing industries. They are particularly prevalent in the polymer, pharmaceutical and specialty chemical industries where the focus is on the production of low-volume, high-value added products. Yet, while advanced control of continuous processes has progressed significantly over the last few decades, the characteristics associated with batch processes make them particularly challenging to control. These include presence of nonlinear and time-varying dynamics, lack of on-line sensors for product quality variables, frequent operation close to process constraints and an abundance of unmeasured disturbances.In batch processing the objective for the control system can be divided into Batch End /Point Control and Trajectory Tracking Control problems. The fundamental difference between these two types of control problems is that an end-point controller is concerned with ensuring that the quality of the product at the end of a batch meets target specifications, whilst trajectory tracking involves the regulation of product quality to a, typically, time-varying set-point as a batch progresses. Another highly relevant control problem that has not yet been effectively addressed by the academic community is the reduction of batch run length. In fact, the ability to reduce batch run length, while also ensuring that the final product conforms to stringent quality specifications, is arguably the most critical business driver in batch processing industries. The aim of the proposed project is to develop a novel Model Predictive Controller that is capable of addressing a critical operational objective in industrial batch processing, which is real-time reduction of the batch run length. The MPC controller will employ a multivariate statistical data-driven prediction model and will also be applicable to both trajectory tracking and batch end-point control problems for processes that exhibit variable batch run lengths and contain irregular measurements of the controlled variables.The novelty of the proposed project stems from the fact that none of the existing advanced control techniques provide solutions to both the trajectory tracking and batch end-point control while dealing with variable batch run lengths and irregular measurements of the controlled variables. Also, none of the existing controllers address the critical control problem of batch run length minimisation. In contrast, the controllers developed in the proposed project will address all three control problems (trajectory tracking, batch end-point control and batch run length control) while also tolerating the presence of variable batch run lengths and irregular measurements of the controlled variables.

Although continuous crystallisation provides significant benefits to innovative manufacture, the key challenge of real time, robust monitoring of quantitative attributes (form, shape, size) of particulate products still remains a massive challenge. While particle attributes are crucial for downstream processing of products, no current solution allows the processing of data from in-line sensors to reliably extract these attributes in real time across multiple manufacturing steps and the subsequent use of this knowledge for IDS and control of processes. The development of solutions for the sector requires expertise across many technologies driven by end user requirements. Industrial co-creators will provide the requirements, the range of expertise within the applicants ensuring that the goals of the programme are met. The grant will enable the establishment of a process test-bed which as the project matures, will be made available to a range of national and international user and application communities. This activity will support the creation of a requirement and technology roadmap, in so doing informing both the research and commercial communities on future opportunities. The project will also yield the following added value to the community: - the cross-disciplinary nature of the project and participating teams will stimulate new solutions and promote creativity through sharing best practice in executing research from different perspectives - the PDRAs will be applying their know-how to joint development tasks, allowing them to gain comprehensive knowledge and expertise across a range of field and in so doing provide trained, talented engineers that will fuel the deployment of these innovative solutions - the project addresses the integration of a number of distinct architectural layers to transform a physical infrastructure into a flexible platform which supports a range of applications whilst accessible to users