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      "title_narrative":["A mathematical modeling framework for tuberculosis burden estimation and economic evaluation of pharmaceutical interventions"],
      "description_narrative":["Tuberculosis (TB) is a major cause of disease and death globally. In 2015, WHO estimated there were 9.6 million TB cases and 1.5 TB deaths. Nearly 500,000 of these cases were resistant to two or more of the main drugs used to treat TB. New drugs, and combinations of drugs, are being developed to treat tuberculosis, as are new vaccines that may protect against disease in adults.  Quantifying the burden of TB is fundamental to understanding its global epidemiology and for making appropriate resource allocation decisions. Most estimates of new TB case numbers each year rely strongly on the number of cases reported by countries in that year to WHO. Unfortunately, one in three TB cases are thought to go either undetected or unreported, so the number of cases reported underestimates the number of new cases. While one can correct for this, it is hard to know exactly how much to adjust the reported numbers. Some countries have good systems for recording causes of deaths, which can be used to estimate the number of deaths caused by TB. Increasingly, large and expensive prevalence surveys are being used to estimate the number of people with active disease in a population. These estimates are less subject to bias, but measure a different quantity. Little work has explored the best way of combining these three data sources.  A major goal of this work is to use mathematical transmission models for burden estimation and provide a unified framework for all data. These models yield the number of new cases, deaths, and also the prevalence of disease. They explicitly represent disease transmission and so introduce a dependence between the number of new cases in different years. These models involve parameters evidenced from previous epidemiological work, but must be calibrated to learn from data on TB reports, deaths and prevalence. Calibration means adjusting imperfectly known model parameters in order to match observed model outputs to the data. This process provides a model that may be used to make predictions about burden, but may also teach us something about the underlying processes. Many of the parameters concerning the epidemiology and disease course of TB are quite uncertain, and this uncertainty is rarely represented fully in models needing calibration, but will be done in this project using statistical techniques that also allow comparison of different models' performance.   TB burden estimation and calibration of transmission models are almost always carried out on a country-by-country basis. Many parameters describing disease progression are likely to be similar in different countries, even if their exact values differ for unknown reasons. Hierarchical modelling techniques allow such parameters to be correlated between countries. This can improve precision, particularly for countries with little data, as estimates can be informed by data from neighbouring countries. I will explore these techniques for the transmission model, and also in statistical modelling aiming to account for the observed patterns of drug-resistance. The transmission model will ultimately be extended to include different types of drug resistance.  As new treatments and vaccines emerge, those with responsibility for public health will want to understand the potential impact these new technologies can have in terms of gains in health, and changes in spending. Producing cost-effectiveness and budget impact evidence requires a model that includes transmission, in order to account for indirect benefits accrued by avoiding secondary cases. We will use our model to provide guidance to decision-makers seeking to maximise health gain with limited resources. We will also analyse sources of uncertainty in the model to identify future research that would have most value in increasing the precision of burden estimates and in reducing decision uncertainty around the introduction of new interventions.","The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world."],
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      "title_narrative":["Evaluating multiple loci modulating susceptibility of African malaria parasites to artemisinin"],
      "description_narrative":["Combination drugs for treatment of malaria deployed globally since the start of the 21st century have provided a substantial public health benefit. Malaria deaths across Africa in particular have plummeted since a peak in the 1990s, when failure of the drugs chloroquine and sulphadoxine-pyrimethamine precipitated a rise in infant and child deaths. A key component in all the currently effective drug combinations is the plant-derived chemical called artemisinin. The parasite-killing ability of artemisinin has recently been threatened by the emergence of reduced susceptibility in the Mekong region: this is manifest as a slowing of artemisinin clearance of P. falciparum from the blood of treated malaria patients. Although under combination treatments these patients should eventually experience a full cure in most cases, in this region the combination partner drugs are also failing in some patients. Careful monitoring and vigilance of malaria drug resistance is therefore needed. A parasite gene has been identified in Cambodia and the surrounding region which contributes to the loss of effectiveness of the artemisinin drug - this gene, which encodes a kelch-domain protein and is called K13, has accumulated a variety of mutations. These are strongly associated with the loss of effectiveness of artemisinin against these parasites. However, this same phenomenon has not yet been observed in African malaria parasites - in the small number of cases where drug treatment does not work, K13 mutations are not implicated as the cause.     In the past 2 years, gene-editing studies by ourselves and others have successfully demonstrated a direct effect of 3 genes in reducing susceptibility to artemisinin : pfap2mu, pfubp1 and pfcoronin. Variants of these genes elicit increased parasite survival in vitro in the ring-stage artemisinin survival assay (RSA). In the case of trafficking adaptor potein subunit AP-2mu, we have shown that the role of this protein in P. falciparum is unlike in other organisms as it does not interact with clathrin, but is associated with an as yet unknown compartment in the cell that appears to also contain K13, although we found no evidence of direct interaction between AP-2mu  and the K13 protein. There is, however, good evidence of direct interaction of AP-2mu with a different kelch protein , K10. Polymorphisms in the gene encoding the K10 protein have been previously identified as a genetic component of parasites from the Mekong region in which the K13-dependent reduced artemisinin susceptibility originally arose. This work was carried out by PhD student Ryan Henrici, who completed his doctoral studies in 2018 and has moved on.   We now request support to extend this work, using gene editing to test the effect of new mutations. In preliminary studies of both field samples and UK isolates, we have already identified a number of such new mutations in our current five genes of interest - pfk13, pfap2mu, pfubp1, pfcoronin and pfk10 (see Case for Support). For each new isolate (either from the UK or from our collaborator Dr Bismarck Dinko in Ho, Ghana) that is adapted to culture, we will fully test drug susceptibility, and genetically characterise them using genome sequencing. In gene-editing experiments we will then directly measure the impact of each candidate gene variant in both established laboratory lines and in our new cultures from UK and Ghanaian patients; any variant proven to generate a drug susceptibility phenotype may therefore be contributing to changing patterns of drug susceptibility, and could be developed as a surveillance marker for use in malaria endemic areas. Our work will thus assist our understanding of resistance to our current combination drugs, and help us to devise strategies for deploying these drugs carefully in both the UK and Africa, to maximise their useful life in curing malaria patients.","The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world."],
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