Kisspeptin is the central controller of reproductive hormone secretion from the hypothalamus. Our group's research has identified a number of reproductive disorders that would benefit from treatment using kisspeptin based therapies. However, there remain many unanswered questions as to how kisspeptin interacts with its receptor. For example, it is known that giving too much kisspeptin can reduce the ability of kisspeptin to induce hormonal stimulation. Additionally, levels of oestrogen at the time of administration can affect the hormonal response to kisspeptin. Moreover, a number of new drugs have been generated that act through the kisspeptin receptor. However, the precise way by which these new kisspeptin based drugs or how oestrogen interacts with the kisspeptin receptor has not been fully described. Therefore, we will conduct research to understand how natural kisspeptins and newly developed drugs that act on the kisspeptin receptor exert their effects inside cells, how this is altered in times of excessive stimulation, and the effect of changes in oestrogen levels on these processes. In this proposal, we have assembled a team of experts from across the globe to conduct cutting edge experiments to better understand signalling at the kisspeptin receptor. We will work with an industry partner to test an oral kisspeptin based drug that is still in development, as well as other recently developed kisspeptin receptor stimulators. We will use an animal model of hypothalamic amenorrhoea (one of the commonest causes of loss of periods and subfertility) to develop a protocol using a kisspeptin-based therapy that can be used to restore reproductive health. In summary, it is essential to better understand the action of kisspeptin and kisspeptin-based drugs at the kisspeptin receptor. This work will enable us to develop treatment protocols using kisspeptin or kisspeptin-like drugs that will improve the care of patients suffering from reproductive disorders.

The vast majority of pharmaceutical processes operate in both batch or fed-batch mode, as it allows them a high degree of flexibility and a large number of manufacturing routes. Production processes involving recombinant cell lines are no exception and are operated in fed-batch mode as well. Fed-batch operation allows nutrients to be fed throughout the cycle, thus preventing the culture from nutrient depletion before production goals are met. Nevertheless, the pharmaceutical industry has dedicated great effort into transitioning their operations from batch to continuous. The reason for this is that operating in continuous mode offers several advantages such as high-volumetric productivity, reduced equipment size and low-cycle times. Additionally, some of the problems associated with operating in batch, such as product variability between batches, can be minimized as well. Chinese hamster ovary (CHO) cell systems are one of the most used host cell lines for therapeutic protein production. These systems enable the application of certain post-translational modifications in order to obtain specific structures of protein products that are compatible with humans. Thus, CHO cell systems are of great importance, being used for 70% of all recombinant biotherapeutics on the market. Though CHO cell systems are typically operated in fed-batch mode, they would greatly benefit from continuous operation, since it would ensure the production of a stable and consistent product, which is particularly relevant within the highly regulated pharmaceutical industry. It is important to understand how fed-batch CHO cell systems can be adapted to a continuous mode: what are the critical quality attributes (CQA) and if they remain the same, what are the new ranges of operability that guarantee product quality; what are the critical process parameters and how will they impact CQAs. In this project, we propose a framework, both computational and experimental, that allows the transition from fed-batch CHO cell culture to continuous. We will initially focus on adapting a genome scale model (GeM) of a CHO cell system to a continuous setting. Having a model compatible with continuous operation is of great importance as it it enables the prediction of CQAs given a set of critical process parameters, thus reducing experimental expense.

This project will focus on the development of a framework for developing hybrid models which contain empirical and hybrid components. This framework will combine the best of completely predictive first principles models with data driven approaches to deal with industrial applications and situations in which the underlying mechanisms are poorly understood. The framework would find applications within P&G in a variety of applications incuding perfume stability in packaging in Beauty, pneumatic conveying in Baby Care, agglomeration scale up in our Dry Laundry business, and surfactant paste blending and dissolution in our Soluble Unit Dose, Liquid Laundry, Hand Dish and Beauty businesses. The success of the framework will be demonstrated in two applications, one selected by and being of direct business relevance to P&G.

1. Our main aim is to elucidate transcriptional control in African trypanosomes, including how VSG expression sites (ESs) are regulated. What is the role of chromatin remodeling in T. brucei? How are ESs activated in a strictly monoallelic fashion? Which proteins mediate life-cycle specific differences in ES transcriptional control in bloodstream and insect form T. brucei? We will answer these questions through a functional analysis of T. brucei proteins which we have recently disc overed are involved in transcriptional control, including TbISWI, NLP, FACT, NAP1 and histone H1 in the first instance. We will also investigate the functional architecture of ESs, including potential regulatory roles for flanking regions of simple sequence repeats. 2. Our second aim is to understand how VSG protects the trypanosome from the mammalian immune system, and determine how VSG synthesis is monitored during the T. brucei cell cycle. We have found that VSG is essential, an d blocking its synthesis triggers an abrupt cell cycle arrest immediately before cytokinesis. We will dissect the mechanism behind this cell cycle checkpoint. What aspect of VSG is sensed during trypanosome cell cycle progression? We will investigate how VSG protects the trypanosome from the mammalian complement system.

The failure rate for new drugs entering clinics is in excess of 90%, with more than a quarter of drugs failing due to lack of efficacy. Earlier treatment decisions for complex diseases like lung cancer considered a small number of patient factors and prescribed a fixed treatment regimen for all patients, resulting in severe drug side effects for some and highly-varying outcomes. Recently, personalised treatments have become popular through the discovery and use of genetic markers that can explain a patient's response to a drug. If the goal of personalised medicine is to give the right drug to the right patient, we may be able to combine pharmacogenomics with machine learning to help make better treatment decisions. Due to the potential waste of testing ineffective drugs on patient cells and animal models in the laboratory, we are motivated to leverage the power of machine learning to predict drug response from a limited number of experiments. We and many others in drug development have used computational methods to learn from drug responses measured in vitro and provide evidence for clinical trials, however, existing machine learning methods do poorly at predicting drug response in disease types where we have a limited number of samples. This situation unfortunately happens quite often for rare cancers and other diseases like motor neurone disease (also known as ALS), because there are few patients or their samples are difficult to collect. Overcoming this limitation by extending machine learning to learn from different disease contexts would mean that we can reduce the time-consuming step of gathering biological resources and then accelerate drug development. In this project, we will develop machine learning algorithms that will take into account all of the dose-response data we have for each drug tested in only a few samples. To overcome the issue of few training cases in a disease, we will develop a transfer learning framework that will use knowledge from other diseases with more drug response data to address the problem in the disease with less data. The algorithms will be developed and tested in five stages: 1) develop a learning model that maps genomic information to drug response in both the disease with more data and the disease with limited data; 2) develop an inference model for predicting drug response in the disease with limited data; 3) apply the learning and inference models to use genomic relationships to drug sensitivity in lung cancer to predict drug response in bladder cancer; 4) learn from drug responses in cell lines and predict response in mice tumour models; 5) learn and predict biomarkers that describe a particular drug's sensitivity in both lung cancer and motor neurone disease. Genomic information will be used as inputs for the prediction algorithms because they can be reliably measured in the laboratory and in the clinic. We use prediction test cases of increasing difficulty, but successes in transferring pharmacogenomics information between diseases will highlight opportunities for scientists to leverage existing data sets to solve challenges of testing a drug in a new disease. We are conducting this interdisciplinary study as a team of computer scientists, clinicians and cell biologists with expertise in machine learning, cancer and neuroscience. The end goal is to eventually develop a suite of software tools that can be readily used flexibly by the drug development community to apply transfer learning to many different problems.