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Details of Grant 

EPSRC Reference: EP/R032858/1
Title: ARTICULAR: ARtificial inTelligence for Integrated ICT-enabled pharmaceUticaL mAnufactuRing
Principal Investigator: Johnston, Dr BF
Other Investigators:
Love, Professor S Chapman, Professor P Benyahia, Dr B
Florence, Professor AJ Rielly, Professor C Brown, Dr C
Researcher Co-Investigators:
Project Partners:
Booth Welsh Cambridge Crystallographic Data Centre DAQRI
Perceptive Engineering Ltd Siemens
Department: Inst of Pharmacy and Biomedical Sci
Organisation: University of Strathclyde
Scheme: Standard Research
Starts: 01 July 2018 Ends: 31 December 2022 Value (£): 1,965,119
EPSRC Research Topic Classifications:
Artificial Intelligence Manufact. Enterprise Ops& Mgmt
EPSRC Industrial Sector Classifications:
Manufacturing Pharmaceuticals and Biotechnology
Related Grants:
Panel History:
Panel DatePanel NameOutcome
22 Feb 2018 Manufacturing Prioritisation Panel - Feb 2018 Announced
Summary on Grant Application Form
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.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.strath.ac.uk