The pharmaceutical industry is undergoing a period of unprecedented change in terms of product development, with increased digitization, greater emphasis on continuous manufacture and the rapid advent of novel therapeutic paradigms, such as personalized medicines, becoming more and more business critical. This change is amplified by Quality by Design considerations and the now routine use of the Target Product Profile approach to the design of patient-centred dosage forms. The recent advances in the range of available therapeutic strategies, alongside the breadth of diseases that can now be successfully treated, has resulted in the need for both new dosage forms and manufacturing approaches. Crucially, there has been a shift from high volume, low cost manufacture towards a more specialized, higher value product development. Consequently, ever more sophisticated approaches, not merely to producing medicinal products, but also to controlling their quality at every stage of the manufacturing process, have become paramount. These would be greatly facilitated by the emerging technologies, based on artificial intelligence and machine learning techniques, for enhancing online process analysis as well as real-time responsive process control. These technologies are particularly important for products where the financial and practical margins for manufacturing error are low, as is the case for an increasing proportion of new therapies. In this proposal, we focus on a new way of screening, manufacturing and quality controlling drugs in the form of nanocrystals, that is, drugs prepared as nanosized crystalline particles stabilized by surface-active agents. In particular, we will combine continuous-flow processing, online advanced process analytical technology, real-time process control and quality assurance, design of experiments, advanced data analysis and artificial intelligence to deliver fully automated, self-optimizing platforms for screening and manufacturing drugs as nanocrystals via antisolvent precipitation. These dosage forms have attracted substantial interest as a means of delivering poorly water-soluble (and thus poorly bioavailable) drugs, a persistent and increasing problem for the pharmaceutical industry. While nanocrystals offer a suitable test system for our approach, our methodology and the manufacturing platform we intend to deliver can be applied to other drug delivery systems. We focus on nanocrystals because they are of considerable therapeutic and commercial significance both nationally and internationally. We intend to use continuous-flow small-scale (i.e. millifluidic) systems. These offer excellent process controllability, can generate crystals of nearly uniform size, and as the process is continuous, the product characteristics are more stable than in batch systems. Millifluidic systems are flexible (one platform can produce a larger variety of products) and agile - reacting rapidly to changes in market demands; they reduce the manufacturing time, speed up the supply chain and, being smaller, can be portable. These systems also expedite screening, curtailing the quantities of material required, benefits that design of experiments will amplify. This data-driven technique allows identifying the most informative experiments, maximizing learning while minimizing time and costs, advantages not fully exploited by the pharmaceutical industry. These technologies, coupled with online advanced process analytical methods, real-time process control, cutting-edge data analysis and machine learning methods, have the potential to disrupt the status quo, accelerate process development and deliver transformative platforms for the cost-effective and sustainable manufacturing of active pharmaceutical ingredients in solid dosage form, reducing the timeline from drug discovery to patient, and contributing to placing the UK at the forefront of innovation in the pharmaceutical sector.

HIV/AIDS remains a major public health threat, with approximately 36.7 million people worldwide infected. In 2016, HIV-related diseases claimed almost 1 million lives globally, with 1.8 million people being newly infected that same year. Around 20.9 million people were accessing antiretroviral therapy in 2017, constituting ~54% of adults and ~43% of children infected with the virus. . In 2016, 42% of diagnoses happened at a late stage of infection and awareness and knowledge around HIV is dropping in the UK, emphasising the value of preventative treatments. Current methods of delivering medicines for treatment and prevention of HIV are far from optimal, necessitating multiple daily tablets or painful monthly injections. In this project we will design and test a novel type of transdermal patch that has hundreds of tiny projections on its surface. Upon painless skin application, these "microneedles" will dissolve and leave behind microscopic particles of medicine for treatment or prevention of HIV. These particles will dissolve over weeks or months to deliver therapeutic doses of the medicine. We will use state-of-the art expertise, including high power computational models to design and predict the behaviour of the medicine particles, speeding up product design and informing laboratory experiments. The technology developed here is unique and could potentially revolutionise prevention and treatment of HIV infection. It offers the opportunity for dramatically improved treatment, with potential benefits for both patients and the NHS. Ultimately, commercialisation of the technology will be the primary route by which UK industry, the NHS and patients will derive benefits. In order to attract potential industrial or venture-funding partners, it is vitally important to demonstrate proof of concept for this technology, which is the over-arching aim of the present proposal.

Depression is common and disabling. There are effective treatments for depression but around 30% of patients are not helped by existing treatments. Recent evidence suggests that these patients respond well to a new class of medication acting on the brain's glutamate system. For example, a low dose of the anaesthetic agent ketamine, which acts on the glutamate system, led to improvements in depression very quickly which lasted for up to a week. Ketamine itself is not an ideal treatment for depression since it is also a drug of abuse and has many side effects. There is therefore a huge need to develop similar drugs to ketamine, which work well in this subgroup of patients but without these unwanted effects. Potential new drugs exist but it is very time consuming and expensive to test all these drugs, at different doses, in full clinical trials. Experimental medicine models can help as they allow these drugs to be tested in smaller groups in the lab, exploring effects on key mechanisms of illness rather than less sensitive clinical ratings of mood. This is thought to provide more accurate information and help select the most promising treatments to take forward to full clinical trial testing. Data from rodent models suggests that drugs like ketamine may have distinct effects on the brain and behaviour. These involve effects on how rewarding information is handled and how emotional memories are retrieved and experienced. However, these mechanisms have not been tested in humans. We therefore plan to test the effects of ketamine on reward learning and emotional memory retrieval in humans. We will first characterise these effects in healthy people as this will allow us to identify core processes affected by ketamine and ascertain whether these actions are dose specific. We will then validate these mechanisms in depressed patients who have not responded to conventional treatments. We will assess learning about reward and punishment and the recall of established memories as well as the brain networks which underpin these effects. We think that ketamine will have effects on a network in the brain involving the lateral habenula, medial prefrontal cortex and hippocampus, which have been implicated in the ketamine studies using animals and which play a key role in learning, memory and emotion. These two studies will help develop a set of measures which can be used in future studies to select the most promising drug treatments for treatment resistant depression.