Healthcare relies on medical devices, yet often these have significant risk of infection and failure. The medical device market is estimated to be just under US$500 billion, while US$25 billion is spent annually on treatment of chronic wounds. As our populations becomes older, our healthcare systems are also becoming stressed by multi-antibiotic resistance and viral outbreaks. For example, 50% of initial COVID-19 fatalities were due to secondary bacterial infections [Zhou et al. The Lancet, 2020]. Medical device failure rates of up to 20% burden our health service disproportionately through device centred infection, immune rejection, or both. The biomaterials that devices and external wound care products are made from significantly influence immune and healing responses and affect the outcome of infection. In the EPSRC Programme Grant "Next Generation Biomaterials Discovery", physical surface patterns (topographies) combined with novel polymers were found which both reduce bacterial biofilm formation and increase the immune acceptance of materials in vitro and in vivo in preclinical infection models. This provides a new paradigm for biomaterials used as implants and wound care products, where novel polymers can be topographically patterned to improved healing and acceptance using bio-instruction. To exploit these findings requires targeting to specific medical device environments and elucidation of the mechanism of action for translation by industry. This project will utilise 3D printing to manufacture ChemoTopoChips containing over a thousand polymer chemistry-topography combinations that allow the possible design space to be efficiently explored and mapped using semi-automated in-vitro measurements of host immune cell and infecting pathogen interactions individually and in co-culture. These ChemoTopoChips will allow a very high content of molecular information to be extracted from biomolecules secreted into the culture media (the secretome), those adsorbed to the surface (the biointerface) and their impact on both host cells and bacteria. The same fabrication approaches will be used to make devices for preclinical testing; in vivo information will be maximised using minimally invasive monitoring of infection and healing over time and detailed analysis of explants. These information streams will be merged using artificial intelligence (specifically machine learning) to build effective models of performance and provide mechanistic insight, allowing design of materials ready for translation as medical devices outside this project. After consultation with a wide range of clinicians we have chosen to target the following two devices: -Wound care products for chronic/non-healing wounds: dressings to reduce infection, induce immune-homeostasis and promote healing in chronic wounds that result in 7000 diabetes related amputations in the UK per year and cost the NHS £1bn a year to manage. -Implants requiring tissue integration but prone to fibrosis/adhesion and biofilm-associated infection: surgical meshes used for repair of hernias or pelvic organ prolapse commonly afflicting women after childbirth. The NHS undertakes 100k such operation each year with infection rates of up to 10%, plus foreign body response complications. The team assembled to exploit this opportunity has unique experience in the areas of biomaterials, artificial intelligence, additive manufacturing and in vitro and in vivo measurements of immune and bacterial responses to biomaterials. Facilities including the recently opened £100m Nottingham Biodiscovery Institute, the recently funded EPSRC £1m suite of high resolution/high throughput 3D printers and the unique £2.5m 3DOrbiSIMS Cat2 cryo-facility. These investments in Nottingham make this the only location in the world that is capable of undertaking this project. An Advisory Board of clinicians, industrial partners and leading academics will meet annually to provide input to the project.

Our Hub brings together a team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new quantitative methods for applications to diagnosing and managing long-term health conditions such as diabetes and psychosis and combating antimicrobial infections such as sepsis and bronchiectasis. This approach is underpinned by the world-leading expertise in diabetes, microbial communities, medical mycology and mental health concentrated at the University of Exeter. It uses the breadth of theoretical and methodological expertise of the Hub's team to give innovative approaches to both research and translational aspects. Although quantitative modelling is a well-established tool used in the fields of economics and finance, cutting-edge quantitative analysis has only recently become possible in health care. However, up to now it has been restricted to health economics in the context of healthcare services and systems management. Applications to develop future therapies, optimising treatments and improving community health and care are in its infancy. This is due to a number of challenges from both mathematical (methodological) as well as clinical and patients' perspectives. Our Hub approach will allow us to develop novel statistical and mathematical methodologies of relevance to our clinical and industrial partners, informed by relevant patient groups. Building this new generation of quantitative models requires that we advance our mathematical understanding of the effective network interaction and emergent patterns of health and disease. Clinical translation of mathematical and statistical advances necessitates that we further develop robust uncertainty quantification methodology for novel therapy, treatment or intervention prediction and evaluation. NHS long-term planning aspires to deliver healthcare that is more personalised and patient centred, more focused on prevention, and more likely to be delivered in the community, out of hospital. Our Hub will contribute to this through developing mathematical and statistical tools needed to inform clinical decision making on a patient-by-patient basis. The basis of this approach is quantitative patient-specific mathematical models, the parameters of which are determined directly from individual patient's data. As an example of this, our recent research in the field of mental health has revealed that movement signatures could be used to distinguish between healthy subjects and patients with schizophrenia. This hypothesis was tested in a cohort of people with schizophrenia and we developed a quantitative analysis pipe line allowing for classification of individuals as healthy or patients. The features used for classification involving data-driven models of individual movement properties as well as measures of coordination with a virtual partner were proposed as a novel biomarker of social phobias. To validate this in an NHS setting, we have recently carried out a feasibility study in collaboration with the early intervention for psychosis teams in Devon Partnership Mental Health Trust. The success of this study could significantly advance the early detection of psychosis by enabling diagnosis using novel markers that are easily measured and analysed and improve accuracy of diagnosis. Indeed, personalised quantitative models hold the promise for transforming prognosis, diagnosis and treatment of a wide range of clinical conditions. For example, in diabetes where a range of treatment options exist, identifying the optimal medication, and the pattern of its delivery, based upon the profile of the individual will enable us to maximise efficacy, whilst minimising unwanted side effects.

Drug resistance is often observed when we treat infected patients with drugs that were discovered, or designed, usually at great cost, with the express purpose of curing people of their infectious disease. This happens, for example, to HIV patients, malaria sufferers or when a pathogenic microbe, like E. coli, finds its way into someone's bloodstream. Cancers can soon become resistant to the chemotherapeutic agents we throw at them too, & all because of evolution. The evolutionary march towards drug resistance can take time. It can be years, or decades, after the introduction of a new drug before we see confirmation of clinical resistance to it and a ten-year timescale is thought typical of many antibiotics. Unfortunately, this stops pharmaceutical companies from seeking new antibiotic molecules. After all, why should they spend 10 years, at great cost, seeking to cure a disease with a pill that is profitable in the marketplace for only 10 more years? Intriguingly, drug resistance in tumours is seen in patients on a much shorter timescale, sometimes within months of the start of chemotherapy, depending on the drug used, the tumour type, and on the individual patient. So why should we not observe a similar phenomenon for antibiotics? In fact, we do, & we are now seeing the emergence of datasets showing that bacterial pathogens can evolve resistance within individual patients because of changes to the DNA of that bacterium in a matter of mere weeks, even days; & it can be lethal. This proposal cites a 2015 study (Blair et al, PNAS) whereby resistance to antibiotic treatment in a blood-borne Salmonella infection was traced, week-by-week, over a 20-week period, whereupon the patient died. That whole-genome sequencing study, using a range of computer and physical modelling techniques designed to track evolution in real time, showed very precisely how the resistance profile of the infection quickly changed by altering expression levels and structures of a variety of proteins within the Salmonella population. Within a week the population had doubled the amount of efflux protein it was making, moreover, it was now making even better efflux proteins than the original, infecting Salmonella. The efflux proteins are used to pump the antibiotics from inside Salmonella cells to prevent the antibiotic from hitting its target, so they stop working, but this was just one of a variety of mechanisms identified that were shown to correlate with the changes in drug resistance that took place during treatment. It is important to mention 'plasmids', loops of DNA that are disseminated across the planet by different microbial species that provide resistance to a range of antibiotics, given these, it seems our future ability to deal with microbial infection sits in a terribly parlous state if something is not done to mitigate such rapid evolution. But what can be done? Importantly, the 2015 study hints at possibilities. It shows that bacteria become susceptible to some antibiotics as they increase resistance to others; in other words there are cross- or collateral-sensitivities that emerge during treatment. So, sometimes, one could use one, and then another antibiotic. This is not outlandish, it is an idea that has been trialled in the clinic for Helicobacter pylori infections, but little else, so we now need to find novel cross sensitivities. We also need new ways of combining antibiotics into novel cocktails, & some of those are proposed here too. I claim that by bringing to bear modern tools of mathematical modelling and data analysis on microbes that are subjected to antibiotics in the laboratory, by observing how they respond, we can find weak spots in their defences that will help clinicians design new therapies & give pharma companies new methodologies to use within their analysis pipelines. Indeed, this is happening now & I am seeking funding to continue the efforts of my group in this task.

The offer, interpretation and consequences of genetic testing raise complex issues for counsellors, patients and families. These have received much attention but one important area that is little understood is how patients come to a decision about taking a genetic test (or not). Much is known about how people retrospectively describe their decision-making process and the effects of genetic knowledge on themselves and their families but less is known about how counsellors discuss the implications of taking genetic tests with patients and much less is known about how people make their decisions. By following people during this process, we aim to improve our understanding of how their thinking develops and the other people and factors that influence this. This is particularly important at a time when ever more information about genetics is communicated online, in newspapers and in popular culture, and as families gain more experience of dealing with genetics services. Our proposal is to focus on cases where the decision to take a genetic test is for the patient to make, supported by genetic counselling but without a clinical recommendation, as the genetic test result is of limited clinical utility. Using multiple methods, we propose to examine the communicative context in which patients make their decisions and how their thinking unfolds in this context. We will focus on the experiences of three groups of patients: patients seeking predictive genetic testing for a neuro-degenerative condition (e.g. Huntington's Disease, HD); patients seeking predictive genetic testing for a condition where testing has little utility or it is deferred; and prospective parents seeking pre-natal genetic testing, either for a known familial risk or following an antenatal foetal anomaly ultrasound scan. These cases will illuminate different experiences that patients may have in deciding on a genetic test. The case of HD will show how a patient settles on a decision to take a test knowing a 'bad' outcome foretells a future of impairment. The predictive test of little or deferred utility will mostly involve young adults and will illuminate the experience of wrestling with a decision in a formative period in life with no immediate clinical implications. Prospective parents working with the genetics service in light of a familial risk of a genetic condition will illuminate the importance of personal and family experience in the decision process, while those referred after an ultrasound anomaly scan will shed light on the experience of adjusting to unexpected information in a short period of time. In each case, patients and their families are faced with complex information about tests, testing pathways and potential outcomes. By following people as they make their decision we will observe the clinical encounters and the patients will gather information on their own thoughts, on what people are saying to them, and what other information they are seeking or interacting with. While fully aware of the need for great ethical sensitivity in this enquiry, we will document how genetic information from outside the clinic (as framed by scientists, marketers, journalists, charities and special interest groups) is brought into the clinic discussion and the patients' reports of their own thinking. The conversations between patients and counsellors in clinic are important to this process, but this conversation is increasingly relativized by rapidly evolving scientific insights and supplemented by outside perspectives. Combining insights from all involved will enable us to develop our understanding of how patients come to their decision, and the effect of outside ideas and framings on this process. Simultaneously, by comparing the thinking of the different groups of patients, we will gain insight into the effect of different experiences of time on this thinking, and explore whether and how these reflections might be facilitated by decision support tools.

The UK is projected to become a hyper-aged society in 2030 with 36% of its population over 55. The early diagnosis and treatment for tissue degeneration are one of the most pressing challenges in healthcare. Osteoarthritis is a form of cartilage degeneration and the most common musculoskeletal disorder. It is affecting nearly one third of adults over 45 years old and causing more than £850 million direct cost in NHS, plus £3.2 billion indirect cost for downtime and community care. By targeting the cartilage, this project will establish a fundamental link between highly sensitive structural biomarkers in tissue degeneration and biomechanical functionality, therefore providing the possibility of identifying new targets for early diagnosis and novel therapies. This will be achieved by combining 1) advanced imaging technique for the subtle structural changes in the cartilage, 2) micromechanical loading to visualise the structural responses under different cartilage conditions, and 3) numerical simulation for analysing the integrity of tissues and the mechanobiological communication of cells at different ages. The outcomes of this project will provide experimental and simulational evidence to inform the clinical translation of the imaging technique for early diagnosis of osteoarthritis, allow quantitative evaluation of the treatment effectiveness of anti-osteoarthritis drugs, and facilitate the development of novel cellular and regenerative therapies. The approach established in this project will lead to a new toolkit of studying biomechanics-centred dysfunctions in a wide range of tissues.