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.

In the past decade, over 2.5 million people in the UK had a metal device implanted to replace a skeletal joint in their body. With our chances of living to 100 years old predicted to double in the next 50 years, these bone implants will need to last substantially longer. Alarmingly, current data demonstrates that failure rates rapidly increase each subsequent year after implantation. The metals we currently make bone implants from were not specifically developed for use within the body. Instead, these materials were originally designed for aerospace applications. In addition to being much stiffer than bone, these metal alloys may also contain toxic elements that cause adverse biological reactions. The aim of this fellowship is to design a new generation of bioinspired alloys that promote advantageous cellular responses while exhibiting mechanical properties that are aligned with the body. In order to design the ideal biomedical alloy, there are a number of properties that need to be balanced, for example biocompatibility (i.e. non-toxic), mechanical performance, and wear resistance. Optimising lots of parameters simultaneously via current trial-and-error approaches may take years or even decades. To significantly speed up this process, a computational modelling approach, called Alloys-By-Design (ABD), will be used to discover a range of titanium compositions that match the mechanical properties of bone. For the first time, by searching for alloys with specific microstructures, ABD will be employed to identify compositions with promising biological functionality, such as infection prevention. Since ABD is a theory-based approach, it will be important to validate the model predictions. This will be done by using a unique laser-based system to melt together all the alloying elements. To maintain rapid progress towards using these new metals clinically, a novel high throughput test will be developed as a screening tool to identify compositions that provoke promising mammalian and bacterial cell responses. From these results, non-toxic and antimicrobial compositions will be selected. High resolution microscopy will subsequently be used to understand the relationships between alloying elements, microstructure and biological behaviour. Before bone implants made of these new alloys may be implanted into patients, it will be critical to deepen our understanding of how the body may respond. Importantly, the behaviour of various cell types involved in bone regeneration will be considered, including bone forming osteoblasts and stem cells found in bone marrow. The rate at which these cells grow and their ability to form new bone on the surface of the novel alloys will be benchmarked against currently used metals. Since it is known that ions may leach from alloys within the body and cause damage to surrounding tissue, this will also be carefully studied. The patient and economic benefits gained from personalised devices that anatomically fit perfectly is rapidly growing in bone implants. As such, the possibility to 3D print bespoke implants made from the most promising bioinspired alloy will be explored. For the first time, the ability to locally tailor alloy composition in-situ using a metal laser-based 3D printer will be investigated. By systematically changing the laser processing parameters and characterising the resultant composition, a universal protocol to optimise in-situ alloy formation will be developed. This will open up an entirely new dimension of bone implant customisation, making it possible to tailor mechanical performance or biological functionality in selected areas of a single implant. Underpinning this fellowship is an experienced clinical and industrial advisory board that will support translation of these novel bioinspired alloys. This will ensure that the research may be transformed into approved medical devices that improve patient lives, reduce healthcare costs, and grow the UK economy.

Additive manufacturing (AM), otherwise known as 3D printing, is enabling the production of medical implants that are customised, in terms of size and shape, to a person's skeleton. Compared with devices of a standard size, these personalised designs fit the patient better and as such offer improved aesthetics and reduce surgery times. While customisation has many benefits, the challenge is to ensure each bespoke device is made to the same quality. This is difficult because the implant shape is completely unique and may be very complex. Currently in an effort to ensure quality, researchers make lots of plain cube test samples using various manufacturing settings and then compare properties before deciding what combination to use for the real implant. This trial and error approach takes a lot of time and may not even produce very predictable devices because the optimisation is not performed on shapes that are representative of real implants. In this project we will make various design features common to medical implants (e.g. curved surfaces, screw holes) and collect key performance data during and post manufacture. By using cutting edge mathematics, we will create a network that allows us to accurately predict which manufacturing settings will produce the best quality for any design shape. This tool will help businesses to standardise production of customised medical devices in a quick and accurate manner that is not dependent on the user's knowledge. Thereby we will open up the advantages of AM to more companies and help existing adopters to meet the standardisation requirements of the impending new Medical Device Regulations. Overall this project aims to better understand the relationships between additive manufacturing settings and implant properties, which will help us to improve the quality of these anatomically personalised devices. Beyond this we plan to create a tool to enable the creation of implants that are not only customised to the size and shape of the patient's skeleton but also two critical functionalities: mechanical strength and cell adhesion. It is known that if an implant is too strong compared with the surrounding native bone this can cause it to fail. As such, developing a way to select manufacturing or design parameters that enable mechanical matching to the patient's skeleton will help implants to last longer and reduce the number of failures. Besides mechanical mismatch, the other biggest threat to bone implants is infection. Our preliminary work has shown that surface roughness directly impacts the ability of cells, mammalian and bacterial, to stick onto AM devices. In this project we will exploit this knowledge to enable users to select manufacturing settings that result in a defined surface roughness that either enables or prevents cell attachment. This novel capability could be used, for example to create implants with a surface that stops bacterial cells from sticking and thus minimises infection risks. There is also potential that this tool could help to improve bonding between the implant and native tissue by recommending manufacturing settings that result in surface topographies that encourage growth of bone forming osteoblast cells. In summary, this project is focused on standardising the way we use 3D printing to ensure the properties of bespoke implants are predictable. This will be achieved by using mathematics to move the AM field away from trial and error. By understanding the relationships between manufacturing settings and key properties, we will create two tools that will enable us to make functionally personalised devices. The ability to predictively and selectively tailor mechanical properties and surface roughness will drive a new generation of implants that last longer and fail less often. Thereby, this project will ultimately improve the lives of millions of people who receive bone implants and help to reduce the associated healthcare costs.

Neuromuscular diseases (NMD) are an important group of disabling conditions affecting about 150,000 children and adults in the UK. They are caused by impairment of peripheral nerve and/or skeletal muscle function. Patients with these diseases develop muscle weakness and the severity can range from death in childhood or early adult life through to life long disability & dependence. Many patients also have heart and breathing muscle weakness which can add to disability and sometimes be fatal. These NMD conditions are commonly genetic and may run in families. They can also be acquired-for example through antibody attack as in "autoimmune" NMD or due to premature degeneration of muscle. Genetic examples include muscular dystrophy (~1 in 3500), Charcot Marie Tooth (CMT) neuropathy (~1 in 2500) and mitochondrial diseases (~1 in 5000). Acquired examples include chronic nerve inflammation (~1 in 1500) and a muscle degeneration/inflammation condition called inclusion body myositis (~1 in 10,000). It is clear that NMD represent an important unmet health burden for the nation. However, relative to other neurological diseases such as epilepsy and multiple sclerosis, NMD have received less attention by government and other UK funding bodies. This is despite the excellent clinical infrastructure provided by several large clinical neuromuscular centres and the nationally commissioned NHS funding for care and diagnosis of some NMD lead by MRC Centre PI's (eg congenital muscular dystrophy, channelopathies and mitochondrial diseases). Furthermore, there has been significant progress in NMD discovery science, frequently lead by internationally high profile UK clinicians and scientists, but translation of this scientific discovery into clear benefit for UK patients has been disappointing so far. We set up this MRC Centre to develop ways to bridge this "translational gap" between scientific discovery and patient benefit. We identified six main reasons (obstacles) why scientific discoveries were not clearly benefiting patients. We developed specific core activities to overcome each obstacle. Most notably we found there was a lack of UK trials culture for these conditions. That means that there were not many trials happening, doctors treating patients did not think there was much that could be done, and patients were not being given the opportunity to get involved in the research & trials that were happening. By setting up key core activites, in just four years, we have shifted the situation towards a trial and experimental medicine culture in the UK. Key activities we developed & which are now valuable UK available resources: 1. Stratified cohorts: collections of patients eligible for entry into trials and research 2. Experimental trials support: a system of coordination and support to enable testing of new therapies in patients 3. Neuromuscular human cell biobank: collecting muscle cells from patients to test new therapies 4. MRI biomarker studies: using MRI scans to accurately measure muscles and assess if experimental treatments are working 5. Training programmes to train more young scientists to undertake trials and develop new therapies 6. Getting clinicians & animal scientists working closely together to work out which are the best cell & animal models on which to test new therapies These core activities & our clinician scientist networks have resulted in a ten-fold increase in clinical trials & an even larger increase in patients entered into research cohorts. We now want to build on this success to embed a trials culture in UK practice. In the UK there is no other centre that focuses on systematically linking discovery research to experimental medicine for NMD. This MRC Centre has lead the UK efforts in the last four years. The mission of a renewed MRC Centre is to achieve impact by translating science into experimental medicine & find treatments for adults & children with disabling/fatal neuromuscular diseases.

Delivery of pathology tissue diagnoses, most of which are cancer, in the current format is unsustainable. Advances in genomic medicine and immune-oncology have shown that the classification of tumours into subtypes allows selection of patients for specific treatments but also spares patients unnecessary toxic and expensive therapies. Still, making such diagnoses has become more time-consuming, involving the selection and interpretation of ancillary tests which requires an ever-growing specialist knowledge for each cancer type. Whilst the need for diagnostic expertise is increasing, there is already a shortfall of 25% of pathologists who are able to report results: this is set to decline. We propose that the use of AI can ensure that the delivery of tissue diagnoses by pathologists is sustainable and supports delivery of personalised treatments. The benefits of AI in pathology are beginning to be seen, e.g. identification of high-grade areas of prostate cancer shows a reduction in errors and pathologists' time. The development of AI for diagnoses is timely as full adoption of digitised histological images, allowing them to be interrogated by both humans and artificial intelligence (AI), is expected in the UK by 2025. AI is a data-hungry process; it is unrealistic to provide 100,000s images that are required to train a model. Even the most common cancers (e.g. breast) have multiple subtypes; identification of these is required for selection of patients for personalised treatments. To address this challenge, we propose to develop a novel AI strategy using a relatively small sample size (~1000 images per class). Such a model could be adapted to any cancer type. A multiple-instance learning framework will be developed, using transformers for feature extraction and classification. A tool that flags samples that cannot be confidently classified will be applied thereby alerting the pathologist of potentially unseen diseases. The deep learning model will be strengthened by the injection of pathologists' domain knowledge. Soft tissue and bone tumours We will develop the AI model on tumours of soft tissue (muscle, fat, blood vessels, etc.) and bone, an area considered to be one of the most challenging diagnostically. These tumours comprise approximately 100 different subtypes, and represent some of the most common cancers in children and young adults. We will build on our existing deep learning model of 15 different subtypes trained on 2122 images, which predicts the correct diagnosis in 87% of cases. Selection of confirmatory ancillary tests is then prompted by the algorithm and streamlines the diagnostic pathway. 17,000 images that have already been scanned will be added to the library and allow the rapid development and extension of the classification model. The image library will be linked to clinical outcomes and expanded to 35,000 images during the project. Added to this is the commitment of the established Sarcoma Network of at least 20 pathologists from across all countries in the UK, to provide the additional 20,000 images mentioned above. Additional benefits The study and infrastructure will serve as the framework for the continued development of the model which can rapidly be expanded prospectively with the introduction of digital pathology in the NHS and globally. The model can be developed over time in response to new advances. The image library will be available for training future pathologists, research, validation of other AI algorithms, and contribute to the Sarcoma Genomics England Clinical Interpretation Partnership (GeCIP) offering a valuable resource for future multi-modal multi-omic research. Working closely with Sarcoma charities, and partners, we will involve and engage patients, their families, and the public, to build trust in the use of AI in health care. Development of AI models for digitised pathology images can avert the crisis facing this medical specialty.