Public Funding for GSK PLC

Oxidation is a common synthetic transformation in the chemical industry, but it often involves the use of toxic, environmentally unfriendly and expensive heavy metal oxidants. An alternative that is often used on small scale is to use ozone, although the risks inherent with scaling up such a process (thermal instability of the reaction intermediates leading to highly exothermic decomposition) have rendered this approach unscalable. This project, a collaboration between leading UK universities and companies, will deliver a safe and scalable method of performing kilo-scale ozonolysis in a continuous manner and demonstrate its utility via application to an existing drug compound. Additionally, by using a fully sustainable starting material (ozone is generated from oxygen in the air), we will demonstrate sustainable manufacturing approach with significant reductions in carbon dioxide emissions, which will be broadly applicable to the UK chemical manufacturing sector.

To employ recent statistical innovations and introduce 'trial emulation' into the drug development process within pharmaceutical industry leader GSK to help design future research and accelerate access to drugs. Emulated trials use available data enabling faster exploration of hypotheses for new treatments without the need for a new clinical trial.

The project will deliver significant improvements in speed and quality of pharmaceutical product manufacturing processes. This will be achieved through an embedded understanding of the impact of pressure on polymorphs in active pharmaceutical ingredients (APIs), which impacts product stability and biopharmaceutics, ultimately resulting in increased profitability, through savings.

The project herein involves evaluating whether AI and other digital tools can improve understanding of how to progress tuberculosis science to mitigate its global burden. Tuberculosis is the leading global infectious threat and the poor are disproportionately affected. With the increasing burden of HIV-tuberculosis co-infections, tuberculosis has become a high priority for research and action in global health. The goal of the treatment is to cure the patient, prevent death, prevent a recurrence, hinder the transmission of the infection and prevent antimicrobial resistance. The key to successful treatment of tuberculosis is adherence to medication which proves to be a significant challenge due to the long period of therapy, the use of polypharmacy and the presence of adverse drug reactions. GSK, a key partner in global efforts to combat TB, identified engagement with the University of Cambridge through this secondment as an efficient practice in response to the WHO's urgent call to save lives in poor settings and co-create impact. Endeavours to develop integrated and sustainable advances in tuberculosis treatment is a complex clinical, technological, social and economic challenge that could be resolved with artificial intelligence (AI) for big data analytics. The first objective of this project is answering a fundamental question in tuberculosis treatment: How to reduce the risk of nonadherence at a patient level? I will evaluate the use of AI platforms as diagnostic tools to derive in-depth insights from tuberculosis databases, identify adherence patterns and the benefit-to-cost ratio of interventions. This will potentially lead to recommendations for improving treatment adherence which, in turn, will reduce the tuberculosis burden, associated mortality, and financial consequences. The second objective is to accelerate drug development in tuberculosis through earlier identification of regimens most likely to succeed, thereby enabling prioritisation of the most promising regimens. In order to achieve this, I will capitalise on the predictive capacity of AI for sophisticated analysis of big data to evaluate the feasibility of designing and validating innovative clinical trials. I will assess the accuracy of AI-based designed clinical trials and simulations with respect to data quality and stability across populations and requirements for continuous optimisation. This disruptive innovation in clinical trials has the potential to increase the likelihood of success and sustainability of resources to identify novel treatment regimen(s), which leads to rein in growing research and development costs.

Currently, there are no disease-modifying treatments that can prevent or slow down PD. PD, like other neurodegenerative conditions, is a disorder involving both genetic (e.g. _PINK1_ and _Parkin_ genes) and environmental factors (e.g. age, pesticides). Recently, multiple regions of PD patient's DNA were found to be different to those not having the disease, suggesting these differences may be involved in increasing the chances of developing PD. However, these DNA regions contain multiple genes, therefore we must specifically determine which gene(s) is responsible for increased disease risk so that we can ascertain why and how PD develops, thus allowing us to develop new therapies. From previous studies we know that a significant number of PD genes play a role in the maintenance of mitochondria health, thus various genetic changes can lead to mitochondrial dysfunction, causing cell death leading to PD. Mitochondria are important to the cell, especially in brain cells (called neurons) as they are the "power stations" of the cell, producing energy that is required for them to function and survive. Cells have developed a sophisticated mechanism to remove these dysfunctional mitochondria from the cell so that they do not cause cell death. This process is termed mitophagy. This is orchestrated by _PINK1_ and _Parkin_, however, this only happens in some PD patients, therefore there is likely to be several other genes that contribute to PD risk. The HPF lab, in collaboration with the AR-UK UCL DDI, has developed techniques to identify which genes play a role in mitophagy. In collaboration with GSK, we will generate iPSC dopaminergic neurons (cells that die in PD) and utilise advanced genetic screening techniques to determine which of these PD risk genes regulate mitophagy. The unique convergence of expertise will allow a powerful multidisciplinary approach to accelerate a deep understanding of the role of mitophagy in PD driving forward the design of novel treatments for PD.

New therapeutic products in the pharma industries are invariably large, complex chemical molecules -- e.g., synthetic active ingredients, amino acids, peptides etc. In the consumer goods and food industries, complex emulsions form the backbone of products such as detergents, beauty products, milk and other liquid-based foods. An essential pre-requisite for model-based Digital Design and Production of these materials is the accurate prediction of their physical and other behavioural properties. Quick and reliable property calculations will allow a transformational way of working which will benefit customers, e.g. by accelerating access to novel oncology therapies with improved efficacy.Traditional approaches for material property prediction for complex systems rely primarily on empirical methods that require extensive experimentation and offer limited predictive accuracy beyond the range of experimental data. This severely limits their applicability within Digital Design, where the ability to investigate a wide range of alternatives _in silico_ without the need for extensive experimentation is key.Recent advances in statistical mechanics of fluids are beginning to offer the promise of a more systematic and rigorous approach to addressing at least some key challenges, e.g., the prediction of solid/liquid equilibria (solubility) for pharmaceutical systems of industrial importance, speciation of complex reactive mixtures, and transport properties for a wide range of systems. These academic developments have been paralleled by the emergence of a new commercially-available software code called gPROMS Properties, which incorporates recent academic advances in this area and is already fully coupled within process modelling tools used to underpin Digital Design applications in the pharmaceutics, food and chemicals sectors.Today, gPROMS Properties is being successfully applied to the Oil & Gas and chemical/petrochemical industries, where the systems of interest are primarily gases and simple fluids. This fast-track project aims to develop and extend gPROMS Properties to become an enabling technology able to meet the more complex needs of the formulated products industries, using a two-pronged approach. The technological basis will be developed by expanding the _types_ of systems that can be handled (e.g. emulsions) and the _types_ of properties that can be predicted (e.g. solid/liquid equilibria, micelle formation conditions, and rheological properties). Simultaneously, the _ranges_ of molecules that can be modelled will be expanded by further exploration of publicly available data. These developments will be applied by industrial partners to solve business problems, demonstrating that virtual product and process design in the formulated products industries is now coming within reach.

Public description Industrial Digital Technologies (IDT's) are disrupting industries across the globe. The breadth and depth of these changes herald the transformation of entire systems of production, management, and governance. There is overwhelming evidence that IDTs can provide a step change in industrial productivity(2). The Smart Connected Factory is central to this goal; it will alter the way production is performed based on smart connected machines/ devices but also smart products. However, the application of these technologies at scale presents many challenges, and there are few examples of Smart factories which provide full real-time data integration from the 'shop floor to the top floor '. These factories tend to operate with single vendor propriety technologies or have undertaken significant investment in creating bespoke integration of multi-vendor solutions. Such approaches are costly to develop and maintain and prevent continuous open innovation. The application of these technologies at scale still presents many challenges such as lack of standards creating technical interoperability issues, cyber threats, high number of legacy assets and constrained devices limiting data exploitation opportunities This proposal delivers solutions for shop floor connectivity including legacy devices, analytics, and integration with the Manufacturing Execution layer of the ISA-95 architecture. It demonstrates how through rapidly maturing and fusing IDT through proof of value projects solutions can be scaled quickly to realise real productivity improvement. It addresses 3 broad innovation challenges :- 1. How to address the technical barriers to IDT introduction? Addressing the challenge of merging IT and OT. In industry 4.0 these entities must act as one to exploit technologies such as AI/ ML, big data etc. 2. How to rapidly mature and deploy IDT to match the exponential pace at which it is being developed? Demonstrate a rapid open innovation methodology to co create IDT solutions tailored for complex Industrial environments based on the 3 key tenets of Invent:- Focus on Open Innovation / design thinking and shared development of common solutions Incubate:- Focuses on the rapid prototyping of solutions to demonstrate Proof of Value Industrialise:- Understanding the route to scale utilizing 4 key methods of Design thinking, Design Sprints, Lean experiments and agile delivery. 3. How to leverage standards to innovate at pace by removing vendor 'lock out' due to the use of proprietary standards? The goal is to develop an Interoperability & standards road map in recognition of the role standards play in accelerating innovation and deployment. (2)made_smarter_review

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To transform both the depth and quality of consumer driven insights into brand perception, captured by the Shopper Science Laboratory, using a suite of novel interactive Augmented Reality technologies, leading to new product creation and a wider market share.

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"The London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare will improve NHS patient care and health outcomes, reduce healthcare costs and support the growth of companies, supporting the economy. It will do this by applying artificial intelligence technologies to medical imaging (for example MRI scans, CT scans PET scans and ultrasound). Artificial intelligence will enable faster and earlier diagnosis, automation of reporting, improved patient screening for disease, and identification of the best treatment for each person. We will create a powerful, dynamic Centre bringing together industry, small and medium sized enterprises (SMEs), world-leading researchers, healthcare professionals covering many areas of practice, and experts in data science/governance. The Centre is a collaboration between three excellent universities (King's, Imperial and QMUL), four leading NHS Trusts (Guy's & St Thomas', King's College Hospital, South London & the Maudsley and Barts Health), multinational industry (Siemens, NVIDIA, IBM, GSK), 10 UK-based SMEs and the Health Innovation Network. The Centre will have a physical hub embedded in St Thomas' Hospital, in the heart of one of the UK's top-performing NHS Trusts with specialist services and one of the UK's largest critical care units. It will be underpinned by the existing Wellcome/EPSRC Centre for Medical Engineering, a flagship investment in medical imaging. The Centre will deliver well-governed, controlled access to high-quality NHS imaging and patient data for academic researchers, SMEs and industry partners. This will be done while preserving patient privacy as the first requirement. We will add dedicated expertise in health economics & statistics, care pathway design and clinical implementation to create an environment where products can be created and tested. Recognising this is a new development in healthcare, patients, the wider public and policy makers will all have opportunities to input and shape priorities for the Centre. The Centre will drive progress through a selection of 12 exemplar projects, specifically chosen, with public input, to illustrate the breadth of opportunity-- covering early life (fetal diagnosis) to old age (dementia), various organ systems including heart, brain and lungs, and diseases such as heart failure, headache, congenital conditions and cancer. In addition to the direct benefits of the Centre, this activity will act as a beacon to attract multinational companies, venture capital investment and AI talent from across the world, creating jobs, broader economic benefit and contributing to the UK's prosperity."

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The project will develop a new, rapid and non-destructive test instrument for predicting tablet efficacy and performance, based on direct measurements of tablet porosity using terahertz light. In so doing, we expect to contribute to manufacturing efficiency improvements in production of advanced solid dose medicines. Our technology will initially be marketed as a test which can work alongside existing methods (dissolution/disintegration and hardness testing) to improve tablet quality during the design process. The ultimate goal is to act as a real-time, in-line test and feedback mechanism as the industry moves towards Continuous Manufacturing.

Mupirocin is an example of an antibiotic that is the product of a complex synthetic factory found naturally in bacteria. It is currently made industrially using bacteria that have been through successive rounds of selection to find strains that produce more antibiotic during controlled fermentation. It is particularly used against the well known superbug MRSA. The current problems with antibiotic resistant hospital superbugs means that we need more antiobitcs and new antibiotics. Current studies on the bacterial factories that make antibiotics and other products suggest that by changing gene order and improving the signals that switch these genes on it may be possible to increase production. In addition, by combining genes from different pathways one can create new antibiotics or other pharmaceutically active compounds. This project will explore how synthetic biology can be used to build better pathways using our knowledge of how these pathways are programmed.

This project involves a consortium of a UK based healthcare company (GlaxoSmithKline), a UK based university (King's College London), and a European based fruit extract producer (BerryPharma). Strategic partners of GSK are also being consulted as part of this project. These include a UK based crop research institute (James Hutton Institute) and a European beverage development company (Doehler). The aim of this project is to develop novel fruit beverages with proven health benefits, by sharing plant breeding, food science and nutrition expertise held within the consortia. A number of animal studies have shown metabolic health benefits of polyphenols. This project will help deliver evidence of this effect in humans. Recent technological developments have enabled the extraction and solubilisation of fruit polyphenols, however technical challenges still exist with high acidity, bitterness and the need for integration in a high sugar base, when incorporating the current extracts into food products. A critical aim of this project will be to prove the health benefits of these fruit polyphenols in stable products that are acceptable from a sensorial and nutritional (low sugar) basis.

Background The use of high quality human biological samples with associated data and their effective life cycle management (biobanking) are increasingly critical to our understanding of the underlying mechanisms of disease and the development of diagnostics. However, despite much progress, the inadequate supply of quality samples and the excessive complexity in the organisation of biobanks is hampering biomedical research. This is partly due to a lack of unified methods and approaches being applied across samples’ life cycle: acquisition, sample processing, storage, provision and use. Organisation Strategic Tissue Repository Alliances Through Unified Methods (STRATUM) is a public-private, 18-month project to provide building blocks for a national biobanking solution that facilitates biomedical research. The project is funded by the private sector and the government, via the Technology Strategy Board. There are six partners: two UK pharmaceutical companies (AstraZeneca, GlaxoSmithKline), a clinical diagnostic SME (Lab21) and the universities of Manchester, Nottingham and Leicester. A Project Steering Committee, with overall oversight of STRATUM has representatives from each partner, the MRC, the Royal College of Pathologists, British In Vitro Diagnostics Association and Patients. Operationally a Project Lead is responsible for the co-ordination and overall delivery of the project with Work Package leads focusing on specific areas. As biobanking is so broad in its application, one of the key factors for the success of STRATUM is to engage with the wide variety of disciplines and groups across both the public and private sectors, which have an interest in or could be impacted by the deliverables. This engagement is essential for the exploitation of STRATUM, from the delivery of the building blocks for biobanking, and thence enable benefits to be delivered across R&D and healthcare in general. As such, all deliverables from STRATUM will be the result of extensive consultation with experts and the public in the relevant areas, which will be enabled by a communication specialist. Aim and Objectives The aim of STRATUM is to facilitate the co-ordination of biobanking to support innovative research, in the UK by addressing the problems defined above. It will increase the effectiveness of sample provision to ensure that UK research institutions, pharmaceutical, biotechnology and diagnostic companies have the ability to access the large numbers of well-characterised samples with data, currently being held in individual collections. It will result in operating to agreed standards and a cost model for sample life cycle management. Ultimately, this approach should enable diagnostic companies, either singly or in partnership with pharmaceutical firms and research institutes, to develop companion diagnostics that are critical to delivering the personalised healthcare agenda. Scope and Deliverables To achieve this, STRATUM aims to define a framework of policy in biobanking, which will focus on governance, access, infrastructure and ethical principles for biobanking. This framework will be based on existing best practice and consensus across the diverse range of stakeholders involved in biobanking. Overall, the project focuses on: assessing public opinion ( biobanking and the use of samples); developing standards (for tissue sample characterisation and life cycle management); creating network requirements (biobank catalogue/directory); exploring financial arrangements (cost model); defining consent templates and enabling engagement (through two-way communication). The scope for individual work packages’ is outlined below: Public Engagement • To investigate the acceptability to donate samples and towards different models of consent for the use of samples, with the general public • To explore the attitudes, including barriers and drivers, within the NHS and biobanking community in collecting tissues samples The outcome of these findings will enable and help define the policy. Policy • To deliver a biobanking policy that provides the framework for governance, infrastructure and standards for human biological sample life cycle management across the UK; thereby enabling processes to be established that amongst other things enable increased visibility of sample collections (and unidentifiable data) with rules of access to facilitate equitable sharing of samples across the value chain and to provide assurance to donors that their samples’ use will be maximised. Sample Life Cycle Management Standards • To deliver standards for the life cycle management of respiratory and generic samples, including derivatives (acquisition, processing, storage, disposal) • To publish the standards with a process for their adoption and maintenance, with metrics for measuring compliance with the standards, to be able to improve sample quality and provide feedback for continuous improvement in HBS life cycle management Register for Samples/Minimum Datasets • To deliver a user requirements document for a system that will enable collections of samples with associated metadata to be registered and viewed via a single, secure, web-based user interface • To evaluate existing and proposed platforms and their operating environments against the user requirements and provide recommendations on how the user requirements can be implemented, defined in the STRATUM Exploitation Plan • To define the minimum datasets i.e. meta data/attributes that describe and characterise collections, donors and samples (respiratory and generic samples) to support the creation of a sample collection inventory (for use as a data in the register) and a histo-library of characterised samples, enabling researchers to identify samples and select them for research, in accordance with the consent • To pilot the recommended datasets for respiratory collections to inform the development of a register Cost Model • To increase understanding of the overall costs associated with biobanking, identify hidden costs, highlight organisational models that work, and explore relationships between key variables (e.g. funding mechanisms and optimal access arrangements). • The analysis aims to make the costs and benefits of coordinating biobanking nationally more explicit, in order to develop an ‘ideal’ cost-model for a national research infrastructure that will be defined in a report that will be taken forward to the exploitation strategy and plan for STRATUM Consent • To define consent templates that are consistent with the outputs of the public engagement and policy If you would like further information about STRATUM, please do not hesitate to contact: Julie Corfield (Areteva) at juliecorfield@areteva.com

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The project consists of three elements and seeks to determine the ability of fibrinogen to identify patients with COPD who have muscle and/or cardiovascular disease manifestations. The first part is focused on supporting Regulatory acceptance of clinical biomarkers representing the non-pulmonary disease subtype of COPD with the aim to ensure that assets in our Portfolio are not discarded due to an inappropriate clinical development plan. The second part includes a clinical trial with a novel Phase II anti-inflammatory in a selected subset of COPD patients with elevated blood fibrinogen to assess its effect on our chosen clinical biomarkers. The third part is an experimental medicine study with a Phase I novel drug candidate with an unprecedented mechanism of action which, together with the 2 other parts, may provide a path for a novel therapy for COPD patients at risk of cardiovascular-related events.

This project will develop a collaborative business model for the evaluation, and application of molecular biomarkers as companion diagnostics (CDx) in support of drug development projects. The business model will align commercial and clinical objectives of the pharmaceutical and diagnostic partners as well as set in place a mutually beneficial value sharing model. The project is comprised of 5 work streams that will cover: 1. Integration of CDx into the drug development process, 2. Financial modelling of the impact of CDx development, 3. Value sharing model development, 4. Pilot planning for model implementation and 5. Dissemination. The project will identify how we would practically apply CDx and set out how to apply this model systematically by embedding criteria for CDx development within the drug development process on an organisational basis.

This project aims to understand and utilise plant physical mechanisms for resistance to pest and diseases in soft fruit/bush crops, to overcome changes in EU Directive 91/414/EEC and WFD and satisfy consumer demand for residue free, high quality fruit grown in the UK. Fresh fruit accounts for a market of £4 billion in the UK, and soft fruit/berries account for 17% of this. UK raspberries have a value of £94 million, strawberries £196 million, blackcurrants £12 million and blueberry, currently a minor player has a value of £95 million. Demand for UK grown fruit is increasing dramatically, however few high quality soft fruit varieties are available with adequate pest and disease resistance due to the focus on fruit quality by the major commercial fruit breeding companies. For production to be sustainable, a greater understanding of plant-derived resistances to pests and diseases is required that can be deployed in IPDM programmes to reduce reliance on chemicals but still produce high quality fruit. Physical resistance traits are particularly promising for crop protection because they tend to be more robust against pest and disease adaptation, and unlike chemically-based resistance traits, are less likely to adversely affect fruit quality. This work aims to look at root architecture and morphology, leaf trichomes, cane/stem architecture and plant habit to determine how variation in these physical traits contributes to resistance against major soft fruit pest and diseases. Using the raspberry model, key genes in chromosomal regions controlling variation in these traits can be selected across different fruits and used to greatly reduce the time varieties are in development.

In order to underpin the competitiveness of the UK pharmaceutical industry, a step change improvement in manufacturing efficiency is needed. This project aims to deliver this for production of the largest proportion of dosage forms (tablets). The efficiency gains are targeted at improved manufacturing precision, improved productivity and improved mass yield, and will be deliverable at approximately 70% of the capital cost of conventional technology. In developing this capability, significant technical knowledge to be delivered which will enhance the offering of the equipment manufacturers who are part of our consortium. The project will use advanced manufacturing equipment, process measurement, control and information management to deliver the claimed benefits. Previous work has been fragmented; this project will deliver an integrated advanced manufacturing / product release data acquisition platform. It is proposed to set up and use test facilities to prove the feasibility of the required manufacturing platform, enabling the optimal design of a commercial plant capable of making saleable product. The key deliverables are (i) an optimised integration strategy, (ii) performance benchmarks, (iii) a de-risked design for a full scale integrated facility, and (iv) data to demonstrate design effectiveness to key stakeholders including regulatory agencies. The process uses continuous twin screw granulation, fluid bed drying, inline blending and tablet compression, whilst PAT instruments along with standard process parameters such as temperature, pressure and work input are used to monitor the stability and control the process Product release is focused around real time continuous process monitoring and control. It is based on an understanding of the process acquired during the experimental trials following DfM/QbD principles. A concept factory of the future will be designed for the layout and operation of continuous OSD pharmaceutical manufacture. Peripheral work to the above this project will also is deliver a POC continuous tablet coater that coats at a rate to compliment the continuous trial equipment and a continuous powder blender.

The aim of the project is to drive substantial cost efficiency in both drug development and drug manufacturing processes by developing commercial counter current separation technology which will allow selective purifications to be achieved at a loading of approximately 1kg/day. In contrast to high-performance liquid chromatography, counter current separations do not require expensive packing materials; are more tolerant of particulate matter and have the benefit of excellent reproducibility. The project will demonstrate the broad applicability of the technology to pharmaceutical separation problems and specific examples will be more closely examined demonstrating separation capability at scale.

The objective of the Advanced Process and Production Light Enable Sensors (APPLES) project was to develop new multi-parameter sensing technologies for monitoring production status in biochemical processes. Through demonstration of functionality and benefit for end-user applications, the technology would possess the potential to be exploited into process control. Anticipated potential gains through improved knowledge and characterisation of the production process include a reduction in raw material consumption, failed product and production times. The target markets include pharmaceutical, biofuels and chemical processing industries. A main milestone has been the successful development of multiple sensing technologies in a proof-of-concept unit that was deployed to multiple industrial partners for evaluation. The project has successfully addressed areas of potential for the technologies to serve industry needs and has identified the next steps in development required to realise a commercial product.

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