Medicines are complex products. In addition to the drug (a molecule which causes a pharmacological effect in the body), they also contain a number of other ingredients (excipients). These are added for a variety of reasons (e.g. to ensure stability or to target the drug to a particular part of the body). A very careful assessment is required to prepare a potent and safe medicine. New types of drug molecule are being devised rapidly and have the potential to transform patients' lives. However, there is a long time-lag (10 - 15 years) between the discovery of a new drug and its translation into a medicine. Most of this time is taken up by developing a suitable "formulation" (drug + excipients) and then testing this. There are very significant benefits that would be realised from accelerating the process: this was made clear by the COVID-19 pandemic, in which the rapid development of vaccines led to millions of lives being saved, and is particularly important as society ages and patients live for prolonged periods of time with multiple conditions. The UK traditionally has been a powerhouse for medicines discovery, and the medical technology and pharmaceutical sector is still a vital part of the economy. However, productivity has recently declined, and compared to peer countries the UK has a lack of high-innovation firms. If medicines development can be accelerated in the UK, there will be huge economic and societal benefits, in addition to profound improvements to the lives of individual patients. To realise this ambition, the UK pharmaceutical sector needs highly-trained, doctoral-level, scientists with the skills required to accelerate research programmes in medicines development. The Centre for Doctoral Training (CDT) in Accelerated Medicines Design & Development seeks to meet this user need, by building a cohort of innovators and future leaders. We will do this between two universities and in collaboration with a network of industrial and clinical partners from across the UK pharmaceutical, healthcare and medical technologies sector. Comprehensive science training will enable our students to develop the high-level laboratory and computational skills needed to overcome the major challenges in medicines development. Our alumni will be expert practitioners at integrating lab and digital research, recognised by industry as crucial to accelerate medicines development. Our students will receive extensive transferable skills training, ensuring that they graduate with high-level teamworking, communication, leadership and entrepreneurial skills. We will foster an open and supportive environment in which students can challenge ideas, experiment, and learn from mistakes. Equality, diversity and inclusiveness, sustainability, and responsible innovation will be at the heart of the CDT, and embedded throughout our training. By liaising closely with industry and clinical partners, we will ensure that the research undertaken in the CDT is directly relevant to the most significant current challenges in medicines development. We will further embed interactions with patients to ensure that the products are acceptable to both patients and clinicians. This will allow us to directly contribute to the acceleration of medicines development, and ultimately will deliver major benefits to patients as new products come on to the market. Our graduates will join companies across the pharmaceutical, medical technology and healthcare fields, where they will innovate and drive forward research programmes to accelerate medicines development for a broad range of diseases. They will ensure that new therapies come to market and the health and well-being of individuals across the world is improved. Others will enter academia, training the next generation. Our alumni will seed a future landscape in which medicines are designed and manufactured in a manner which protects our environment, and in which there is equality of opportunity for all.

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.