ARTICULAR: ARtificial inTelligence for Integrated ICT-enabled pharmaceUticaL 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.

The ARTICULAR team engage and collaborate with a wide range of research beneficiaries: leading pharma companies (e.g. AZ, GSK, Bayer, Pfizer, Lilly); companies in the process industries (e.g. Mars, AB Sugar, Syngenta); technology and supply chain companies (e.g. Siemens). ARTICULAR team members are actively involved in the innovation landscape with collaborators including; HVM Catapult (CPI); the National Formulation Centre; medical charities (e.g. CRUK), Medicines Manufacturing Industry Partnership (MMIP) of the UK pharma manufacturing industry; International academic partners (Rutgers, Graz, Singapore) through the newly formed International Institute of Advanced Pharmaceutical Manufacturing. This strong, active and influential network of collaborators, stakeholders and business leaders maximises the potential to achieve the project Aims with associated impact and benefits including:

Economic: The project will contribute to the UK economy by increasing the competitiveness of pharmaceutical and technology partner companies with whom we will co-develop our commercialisation and exploitation plans. Companies will have access to the modular AI-driven ICT tools and applications that emerge from the project. This will increase competitiveness by accelerating process development using minimal amounts of API, improve quality control and minimize waste. The programme will also deliver cost effectiveness via novel adaptive control using machine learning to interpret data. In addition to implementation across CMACs £34m physical hub, establishing a world leading digital lab capability, our connectivity with the HVM (CPI) and Digital Catapults, Diamond and NPL will form critical links with the wider innovation system helping drive industrial translation where opportunities for existing and startup company benefits to be realised are identified

Societal: A key societal impact is to improve the accessibility and affordability of new and existing drug products and the speed at which they can be brought to market. ARTICULAR will do this by lowering development and production costs through intelligent systems, improving equipment utilisation, reducing energy consumption, waste and reject reduction, improved efficiency of material consumption and reduced environmental impacts. With machine learning, automated, intelligent control has the potential to enable intelligent decision making. These technology developments will have a major international impact, with significant potential for distributed manufacturing: small modular, reconfigurable, manufacturing capability with automated, intelligent control has the potential to enable manufacture close to the point of need. Furthermore, opportunities for anti-malarial or anti-retroviral products being made at low cost in developing economies for local distribution to patients will improve access to safe and effective healthcare in these poorer regions of the world.

Academic: The world-leading academic partner combination incorporating data analytics, control, visualisation, pharmaceutical sciences and chemical engineering will contribute within and across disciplines producing significant advances in theory and understanding directed towards innovative methodologies, equipment and techniques. A key output will be delivering a highly skilled talent pipeline of trained researchers capable of transferring the generated research and knowledge into UK based multinationals and SMEs to enhance the knowledge economy and revolutionise capability in AI for medicines manufacturing. As a further route to impact, we will provide new opportunities for them to obtain academic and industrial experience via researcher exchanges. CMAC is a recognised academic leader and its unique role was outlined in the Medicines Manufacturing Industry Partnership 'Manufacturing Vision for UK Pharma'. Through our active dissemination and outreach programmes we will maximise the lasting legacy of this research.