The UK pharmaceutical industry produces 16% of the world's well-known medicines, employs more than 66,000 people (200,000 more indirectly) and contributes over £8.8 billion to the UK GVA. The current covid-19 crisis has highlighted the need for the UK and the USA to have a strong, smart pharmaceutical manufacturing base. The FDA in the USA has identified continuous pharmaceutical manufacturing as a highly promising solution to these challenges by enabling lower capital cost, smaller footprint and highly efficient facilities, which can be distributed geographically, improve national security by reducing dependency on foreign suppliers and can produce multiple products on demand with minimum risk to quality. However, the UK Government Made Smarter Review highlights that we still have a way to go to achieve a Right First Time smart manufacturing system as an enabler for the digitalization of continuous manufacturing in pharmaceutical industry. Addressing these challenges are the domain of process systems engineers. By developing right-first-time (RFT) smart manufacturing systems incorporating Industry 4.0 concepts, we intend to address these key challenges in pharmaceutical manufacturing. Our hypothesis is that the development of a systematic framework for smart continuous pharmaceutical manufacturing can deliver key benefits to the industry including: - Reduced time to market of new products; - Reduced waste and increased resilience; and - Reduced cost of manufacture. To develop this framework, we have brought together a world leading team of process systems and pharmaceutical engineers from four universities in the UK and USA. An important and unique element of this proposal is the ability to validate state of the art models, control and optimization procedures on three cutting edge continuous manufacturing experimental platforms: (1) Consigma 25 wet granulation line at University of Sheffield (UK); (2) Dry granulation line at Purdue University (USA); and (3) Continuous direct compression line, also at Purdue. The outcome of this project will be a framework and computational tools for optimal design of pharmaceutical processes with a real-time process management system and a flexible real-time release testing framework, all verified at pilot scale.

Systems Biologists, by combining cell biology with mathematical approaches, have shown that feedback loops in molecular regulatory networks tightly control cellular homeostasis and responses. The interplay between endogenous feedbacks and the extracellular environment results in complex and non-linear cellular dynamics. Mathematical models can help in tackling this complexity, aiding in characterising the links between cellular dynamics and cell-decision making. However, the validity of models relies on modelling assumptions and the quality of data used for parameter fitting: stochasticity and noise limit the power of model predictions across Systems Biology and Systems Pharmacology applications. Conversely, the forward engineering of exogenous gene expression dynamics that recapitulate native cellular behaviours, often used by Synthetic Biologists, is limited by poor robustness to physical parameter variations, diverse modular parts and choice of chassis. To tackle these challenges, this Fellowship proposes to directly and automatically program complex dynamics in mammalian cells, by combining external feedback control to ensure robustness and a microfluidics/microscopy platform to observe and perturb cells in real-time. Exploitation of this technology will allow to: i) Unravel causation in coupled processes and dissect the role that temporal patterns across scales (i.e. gene expression dynamics and cell-cycle) play in stem cell fate, ultimately exploiting such dynamics for the design of superior stem cell culture protocols. ii) Directly track from experiments non-linear biochemical dynamics, without the need of mathematical models, to quantitatively determine causes/robustness of complex native/engineered behaviours, respectively, using experimental and Control-Based Continuation. Direct industrial applications will be explored, including the characterisation of stem cell culture protocols across culture scales, and the use of feedback control to design optimal drug dosing schedules for target cancer cell responses. Our aims are underpinned by two highly synergetic research tracks at the interface of interdisciplinary disciplines. The combination of methodologies from control theory, Synthetic, Systems and Stem cell biology will provide a quantitative framework and highly novel tools to understand, steer and design mammalian cell dynamic phenotypes, with great potential for future therapeutic purposes.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.