ARTICULAR: ARtificial inTelligence for Integrated ICT-enabled pharmaceUticaL mAnufactuRing
Lead Research Organisation:
University of Strathclyde
Department Name: Inst of Pharmacy and Biomedical Sci
Abstract
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.
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.
Planned Impact
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.
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.
Organisations
- University of Strathclyde (Lead Research Organisation)
- AstraZeneca (Collaboration)
- Perceptive Engineering Ltd (Collaboration)
- Pfizer Ltd (Collaboration)
- GlaxoSmithKline (GSK) (Collaboration)
- DAQRI (Project Partner)
- Booth Welsh (Project Partner)
- Arc Trinova Ltd (Arcinova) (Project Partner)
- Perceptive Engineering (United Kingdom) (Project Partner)
- Cambridge Crystallographic Data Centre (Project Partner)
- Siemens plc (UK) (Project Partner)
Publications
Anandan P
(2022)
32nd European Symposium on Computer Aided Process Engineering
Benyahia B
(2021)
31st European Symposium on Computer Aided Process Engineering
Connor L
(2019)
Structural investigation and compression of a co-crystal of indomethacin and saccharin
in CrystEngComm
Maldonado D
(2022)
Data mining crystallization kinetics
in Digital Discovery
Ottoboni S
(2018)
Impact of Paracetamol Impurities on Face Properties: Investigating the Surface of Single Crystals Using TOF-SIMS
in Crystal Growth & Design
Ottoboni S
(2021)
A Novel Integrated Workflow for Isolation Solvent Selection Using Prediction and Modeling.
in Organic process research & development
Paul Chapman
(2018)
Immersive Environments: Real Problems, Virtual Solutions
in Zhuangshi
Title | Process twins |
Description | 3D models of medicine manufacturing process equipment. Assets include: Laboratory environment COBR crystalliser Hot-melt extruder Lab-scale reactor Automated mixer Filtration equipment Tablet press |
Type Of Art | Artefact (including digital) |
Year Produced | 2019 |
Impact | Work here has attracted the attention of companies within the UK and has led to a KTP application to further developments (awaiting outcome). |
Title | Process twins |
Description | 3D models of medicine manufacturing process equipment. Assets include: Laboratory environment MSMPR reactor AWL filtration unit Dissolution tester |
Type Of Art | Artefact (including digital) |
Year Produced | 2021 |
Impact | ARTICULAR models of equipment and lab environment have now become the interface for EPSRC CMAC Digital Twins. With this work, we are creating impactful, world leading digital twins with the potential to change the way medicines manufacturing is carried out in the future. |
Description | The use of Artificial Intelligence to provide insight from data is showing particular promise in certain areas of medicines manufacturing we are currently developing. In particular: - Imaging of drug particles to better describe their size and shape and to convey this complex information to humans is possible through machine learning (ML) approaches. This rich information can also be used to control and optimise the medicines being manufactured to ensure better products for patients - When developing new medicines it is helpful to be able to predict the outcome from processes, such as crystallisation, in which drug materials are made. We have generated AI/ML tools which are able to tell researchers about the likely form a drug crystal will take (its constituent molecules) and the likely shape or morphology. Using these tool, it is possible to inform design choices to ensure that materials are simpler to handle and will perform well in patients - We have developed new virtual environments of labs and processing equipment which we intend to connect to data generated by sensors on the manufacturing processes themselves. This will provide novel ways for workers to engage with their data and surroundings and potentially allow for real-time cross site operations and collaborations - We are currently beginning studies of the effectiveness of VR/AR as data visualisation tools in the development of digital twins of medicine manufacturing equipment and processes. The outcomes of these studies will potentially demystify some of the hype around VR/AR technologies and support further developments in the field. |
Exploitation Route | Broadly, many of our digital twin demonstrators to date rely on commodity VR hardware which will currently be available in some homes. Over the next couple of years, we will likely see wider penetration of these technologies which would then allow us to make developments available to the public. In particular, undergraduate and postgraduate cohorts are likely to benefit from additional insight into the manufacture of medicines being able to immerse themselves within a lab or manufacturing plant from home and to interact with processes and equipment to learn more about what they do and how they operate. In a world where patient compliance with regard to taking medicines is a major issue, education on the complexities and costs of producing these treatments could potentially be informative and reduce costly wastage. We are currently exploring the exploitation of ARTICULAR demonstrators being developing with the EPSRC Future Manufacturing Research Hub for Continuous Manufacturing and Advanced Crystallisation (CMAC) and partners. These include seven of the largest pharmaceutical companies some of which are actively demoing our VR environments on their sites to raise the profile of Industry 4.0 technologies internally and with stakeholders. Learning from the ARTICULAR programme has led to new research collaborations and funding (see Further Funding) all of which provide opportunities to further develop models and to ensure they perform well on real-world problems. Datasets from the programme have been published (or are in the process of being published) for the benefit or pharmaceutical researchers and data scientists. Some of the programmes most mature digital assets are now being further developed into more robust software and data service products that will be expanded in the future with project partners and our wide network of EPSRC CMAC partners. |
Sectors | Chemicals Digital/Communication/Information Technologies (including Software) Healthcare Pharmaceuticals and Medical Biotechnology |
Description | The further funding of projects has been reported in the common outcomes for this award. Of particular note are: EPSRC - EP/V034723/1 - CMMI-EPSRC - Right First Time Manufacture of Pharmaceuticals (RiFTMaP); EPSRC - EP/S035990/1 - Accelerated Discovery and Development of New Medicines: Prosperity Partnership for a Healthier Nation; EPSRC - EP/W003295/1 - Digital Design and Manufacture of Amorphous Pharmaceuticals (DDMAP);EPSRC - EP/V062077/1 - Made Smarter Innovation - Digital Medicines Manufacturing Research Centre; and Innovate UK: 41299 - Digital Design Accelerator Platform to Connect Active Material Design to Product Performance. This award was part of the CMAC Portfolio of projects when the EPSRC Future Manufacturing Research Hub (EP/P006965/1) started in 2018. During 2021 CMAC reached its 10-year anniversary as a result of the CIM and Future Continuous Manufacturing and Advanced Crystallisation Research Hub (EP/P006965/1) funding, as well as the aligned portfolio of activity. At the celebration online event in November 2022, a forward strategy for the portfolio of projects was published. The CMAC Hub has a role to act as a National Centre and as such has aligned with key policy. This includes alignment with MMIP mission to support the UK to become a leading force in manufacturing innovation, ABPI's Manufacturing vison for the UK, The Made Smarter Report, FDA vision for Global Pharma. CMAC strategy includes aiming to achieve greater speed, quality, agility, security and sustainability, in pharmaceutical manufacturing and communicating and engaging around the need for advanced pharmaceutical manufacturing, analytics and industrial digital technology development. The need for skilled people to address the gap in Digital Transformation and Data-Driven Research has been highlighted. The UK has supported the portfolio of aligned projects underpinned by the Hub investment. The CMAC Skills pillar starts to address the requirements of the talent pipeline, and our industry partners have noted how the doctoral training at CMAC produces uniquely skilled people. Queens Anniversary Prize was awarded to University of Strathclyde for Excellence and Innovation in Advanced Manufacturing in early 2022. CMAC and MMIC were highlighted in the case supporting the award as Examples of Excellence and Innovation and the ARTICULAR digital research outputs have been an ongoing element of our research activities throughout. A further ISCMP event was held in London in 2018 and online version went ahead in 2021. |
First Year Of Impact | 2021 |
Sector | Chemicals,Digital/Communication/Information Technologies (including Software),Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
Impact Types | Cultural |
Description | Accelerated Discovery and Development of New Medicines: Prosperity Partnership for a Healthier Nation |
Amount | £5,495,023 (GBP) |
Funding ID | EP/S035990/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2019 |
End | 12/2024 |
Description | CMMI-EPSRC - Right First Time Manufacture of Pharmaceuticals (RiFTMaP) |
Amount | £1,543,632 (GBP) |
Funding ID | EP/V034723/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2021 |
End | 08/2024 |
Description | Digital Design Accelerator Platform to Connect Active Material Design to Product Performance |
Amount | £1,800,000 (GBP) |
Funding ID | 41299 |
Organisation | Innovate UK |
Sector | Public |
Country | United Kingdom |
Start | 03/2020 |
End | 05/2021 |
Description | Digital Design and Manufacture of Amorphous Pharmaceuticals (DDMAP) |
Amount | £1,251,700 (GBP) |
Funding ID | EP/W003295/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 02/2022 |
End | 03/2025 |
Description | KTP |
Amount | £129,452 (GBP) |
Funding ID | 12322 |
Organisation | Innovate UK |
Sector | Public |
Country | United Kingdom |
Start | 03/2021 |
End | 03/2023 |
Description | Made Smarter Innovation - Digital Medicines Manufacturing Research Centre |
Amount | £5,086,406 (GBP) |
Funding ID | EP/V062077/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2021 |
End | 03/2025 |
Title | CCDC 1902709: Experimental Crystal Structure Determination |
Description | Related Article: Lauren E. Connor, Antony D. Vassileiou, Gavin W. Halbert, Blair F. Johnston, Iain D. H. Oswald|2019|CrystEngComm|21|4465|doi:10.1039/C9CE00838A |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc21vxqk&sid=DataCite |
Title | CCDC 1902710: Experimental Crystal Structure Determination |
Description | Related Article: Lauren E. Connor, Antony D. Vassileiou, Gavin W. Halbert, Blair F. Johnston, Iain D. H. Oswald|2019|CrystEngComm|21|4465|doi:10.1039/C9CE00838A |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc21vxrl&sid=DataCite |
Title | CCDC 1902711: Experimental Crystal Structure Determination |
Description | Related Article: Lauren E. Connor, Antony D. Vassileiou, Gavin W. Halbert, Blair F. Johnston, Iain D. H. Oswald|2019|CrystEngComm|21|4465|doi:10.1039/C9CE00838A |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc21vxsm&sid=DataCite |
Title | CCDC 1902712: Experimental Crystal Structure Determination |
Description | Related Article: Lauren E. Connor, Antony D. Vassileiou, Gavin W. Halbert, Blair F. Johnston, Iain D. H. Oswald|2019|CrystEngComm|21|4465|doi:10.1039/C9CE00838A |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc21vxtn&sid=DataCite |
Title | CCDC 1902713: Experimental Crystal Structure Determination |
Description | Related Article: Lauren E. Connor, Antony D. Vassileiou, Gavin W. Halbert, Blair F. Johnston, Iain D. H. Oswald|2019|CrystEngComm|21|4465|doi:10.1039/C9CE00838A |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc21vxvp&sid=DataCite |
Title | CCDC 1902714: Experimental Crystal Structure Determination |
Description | Related Article: Lauren E. Connor, Antony D. Vassileiou, Gavin W. Halbert, Blair F. Johnston, Iain D. H. Oswald|2019|CrystEngComm|21|4465|doi:10.1039/C9CE00838A |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc21vxwq&sid=DataCite |
Title | CCDC 1902715: Experimental Crystal Structure Determination |
Description | Related Article: Lauren E. Connor, Antony D. Vassileiou, Gavin W. Halbert, Blair F. Johnston, Iain D. H. Oswald|2019|CrystEngComm|21|4465|doi:10.1039/C9CE00838A |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc21vxxr&sid=DataCite |
Title | CrystalEyes |
Description | An image analysis and Machine Learning (ML) model which is able to accurately model and classify particles by their shape from segmented images. Morphometric analysis is used to describe the shape in great detail and then these data can be used to assess large numbers of images to describe crystal shape populations. These shape descriptors can can also be correlated to processing steps to provide more control over particle shape during manufacture. |
Type Of Material | Computer model/algorithm |
Year Produced | 2019 |
Provided To Others? | No |
Impact | In-house tool at present but with intention to make available to wider research community. |
Title | Tablet press - data management and analysis |
Description | This code is useful for extracting detailed information derived from the sensors in a tablet press providing operators with visualization tools for tablet-to-tablet, batch-to-batch variation and that, with further AI developments, should provide opportunities for process fault detection which would include events such as material sticking. |
Type Of Material | Data analysis technique |
Year Produced | 2019 |
Provided To Others? | No |
Impact | We are currently developing these codes and will investigate their utility within the EPSRC CMAC programme and potentially in the Medicines Manufacturing Innovation Centre Grand Challenge 1. |
Description | Collaboration with the ISCF Digital Design Accelerator Platform (DDAP) |
Organisation | AstraZeneca |
Country | United Kingdom |
Sector | Private |
PI Contribution | Project name: Digital Design Accelerator Project to Connect Active Material Design to Product Performance Provided models and data for the development of new tools on the project. Provided software development capacity via ARTICULAR RAs. |
Collaborator Contribution | Data were provided to validate our AI models (large pharma: AZ, GSK, Pfizer). Partners provided end users to test our tools and interfaces. |
Impact | New data analysis and handling capabilities that can work with data across pharmaceutical organisations |
Start Year | 2019 |
Description | Collaboration with the ISCF Digital Design Accelerator Platform (DDAP) |
Organisation | GlaxoSmithKline (GSK) |
Department | Research and Development GSK |
Country | United Kingdom |
Sector | Private |
PI Contribution | Project name: Digital Design Accelerator Project to Connect Active Material Design to Product Performance Provided models and data for the development of new tools on the project. Provided software development capacity via ARTICULAR RAs. |
Collaborator Contribution | Data were provided to validate our AI models (large pharma: AZ, GSK, Pfizer). Partners provided end users to test our tools and interfaces. |
Impact | New data analysis and handling capabilities that can work with data across pharmaceutical organisations |
Start Year | 2019 |
Description | Collaboration with the ISCF Digital Design Accelerator Platform (DDAP) |
Organisation | Pfizer Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Project name: Digital Design Accelerator Project to Connect Active Material Design to Product Performance Provided models and data for the development of new tools on the project. Provided software development capacity via ARTICULAR RAs. |
Collaborator Contribution | Data were provided to validate our AI models (large pharma: AZ, GSK, Pfizer). Partners provided end users to test our tools and interfaces. |
Impact | New data analysis and handling capabilities that can work with data across pharmaceutical organisations |
Start Year | 2019 |
Description | Digital Twin development with Perceptive Engineering Ltd |
Organisation | Perceptive Engineering Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | We are currently working with Perceptive Engineering Ltd to deploy some of our process monitoring and control approaches on one of their demonstrator platforms. As a part of this work we have developed a virtual reality digital twin of their mixer platform and integration code to pass data from their PharmaMV software to the VR interface. |
Collaborator Contribution | They have provided access to their databases and software that enables us to pass data to our VR front-end and have provided their mixing demonstrator hardware for us to model. By working together, we have created a simple demonstrator of AI controlled processes with VR digital twin. |
Impact | This new demo is small, mobile and being demonstrated at events to give a broader audience exposure to Industry 4.0, the use of AI in process monitoring and control and the ARTICULAR project. It has created a new interface to the PharmaMV software which will be exploited across other CMAC platforms in the coming year. We are also currently preparing a 2-year KTP application to provide additional resource for working directly with Perceptive Engineering. |
Start Year | 2019 |
Description | KTP with Perceptive Engineering Ltd (now Applied Materials): AI for process monitoring and control in pharmaceuticals manufacturing |
Organisation | Perceptive Engineering Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Some of the ARTICULAR researchers and RAs are providing input to this project and will contribute expertise of ML/DL methodologies to this new application area. |
Collaborator Contribution | PEL are bringing in-house data, their proprietary hardware and software platform for process monitoring and control alongside their own expertise. |
Impact | This is a multi-disciplinary project with data scientists, chemists and chemical engineers. The project has just started and no outcomes have been generated yet. |
Start Year | 2021 |
Description | KTP: New approaches for routine sample preparation, laboratory and synchrotron data collection, and PDF analysis of pharmaceuticals |
Organisation | AstraZeneca |
Country | United Kingdom |
Sector | Private |
PI Contribution | We are the academic leads on this KTP. We are providing domain expertise and access to equipment and laboratories. |
Collaborator Contribution | Astrazenecca are the lead organisation and hosts for the KTP Associate. They too are providing access to experts, instrumentation and material/process data. |
Impact | Funded but commences Oct 22. |
Start Year | 2022 |
Description | CMAC Open Day Presentation discussing "Mixing the Arts and Sciences" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation to industrial experts about the importance of creativity in the sciences and how Glasgow School of Art had positively contributed to CMAC and their pharmaceutical research. SimVis: Mixing the Arts and Sciences Chapman, Paul (2018) SimVis: Mixing the Arts and Sciences. In: CMAC Open Day 2018, 25-26 October 2018, TIC Building, Strathclyde University. |
Year(s) Of Engagement Activity | 2018 |
URL | https://spider.science.strath.ac.uk/cmac_new/files/media/Open_Day_2018_web_prog_final_LG.pdf |
Description | Invited Keynote Speaker for the 5th Art and Science International Exhibition Symposium, China. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Policymakers/politicians |
Results and Impact | Chapman, Paul (2019) Invited Keynote for the 5th Art and Science International Exhibition Symposium, China. In: 5th International Art and Science Exhibition and Symposium, 1-3 November 2019, Beijing, China. Driven by the development of artificial intelligence, a new round of technological and industrial revolution has taken place, which accelerates further integration of art and science. In the context of AI, mankind's knowledge is renewing constantly while our mode of production and way of life are going through essential changes. At the meantime, the art paradigm is evolving as well. AI technology has provided an even wider stage for deep integration of art and science, and new possibilities for artistic aesthetics and technological innovation. However, it has stirred concerns among us like never before. My presentation focussed on a number of areas including xr technology potential in the medical and pharmaceutical sectors. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.enad.tsinghua.edu.cn/info/1091/1452.htm |
Description | Keynote |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Chapman, Paul (2019) Invited Keynote for the 5th Art and Science International Exhibition Symposium, China. In: 5th International Art and Science Exhibition and Symposium, 1-3 November 2019, Beijing, China |
Year(s) Of Engagement Activity | 2019 |
Description | Keynote |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | digital | visual | cultural 3 - models, volume, and digital 3D visualisation Chapman, Paul digital | visual | cultural 3 - models, volume, and digital 3D visualisation. In: digital | visual | cultural 3 - models, volume, and digital 3D visualisation, 17/18 June.2019, Oxford University. |
Year(s) Of Engagement Activity | 2019 |
Description | Keynote presentation: 16th annual International Technology, Education and Development Conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Mixed Realities: Real Problems, Virtual Solutions In this keynote speech I will review the evolution of computer graphics and the long anticipated 'coming of age' of immersive technologies. I will give some real-world examples of how SimVis are using these technologies to facilitate our understanding of complex real-world environments ranging from pharmaceutical engineering, heritage visualisation, medical training and dangerous sports. The talk concludes with an assessment of the future. |
Year(s) Of Engagement Activity | 2022 |
URL | https://iated.org/archive/inted2022 |
Description | National Sarcoma Conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Third sector organisations |
Results and Impact | Presentation of ARTICULAR research - The British Sarcoma Group, National Sarcoma Conference, Wednesday 26th and Thursday 27th February 2020, Hilton Hotel, Glasgow |
Year(s) Of Engagement Activity | 2020 |
Description | New Challenge - New Strategy Conference Speaker - Tsinghua Arts and Design Institute |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | I gave a 20m discussion about virtual reality and its application to medical visualisation and pharmaceutical training. Immersive Environments for Medical and Pharmaceutical Training Chapman, Paul (2020) Immersive Environments for Medical and Pharmaceutical Training. In: New Challenge - New Strategy - Tsinghua Arts and Design Institute in Milan, 22-30 June 2020, Online. |
Year(s) Of Engagement Activity | 2020 |
URL | https://radar.gsa.ac.uk/7358/ |
Description | Online speaker at Symposium 1 New Species? In A Cross Disciplinary Paradigm. In: Tsinghua International Conference on Art and Design, 29-31 October 2021, Beijing (Online). |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | The remit for the conference was: "When the art and design issues are becoming more and more complex, the importance of interdisciplinary innovation has become a consensus. In this context, many "new species" have emerged in our education. How shall we define these new disciplines? How to define the new boundary between disciplines? Or should there be the boundaries?" I spoke about VR and education and described work in XR and pharmaceuticals. |
Year(s) Of Engagement Activity | 2021 |
URL | http://radar.gsa.ac.uk/7799/ |
Description | Presentation at AWE International Conference., May 31 - June 2 2023, Santa Cara, California.. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | In this presentation, we demonstrate our novel XR patient leaflets for cleft lip allowing parents to better understand and visualise their child's surgery and demonstrate XR technology for working with complex pharmaceutical equipment by creating a digital twin of a typical laboratory. The conference is one of the largest XR conferences in the world and is held annually in California. |
Year(s) Of Engagement Activity | 2023 |
URL | https://www.awexr.com/ |
Description | The Future of Medical Visualisation |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | The Future of Medical Visualisation - Paul Chapman, School of Simulation and Visualisation, Glasgow School of Art |
Year(s) Of Engagement Activity | 2020 |