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Acetaminophen, known as paracetamol in the UK and Tylenol in the United States, is a widespread and commonly used painkiller all over the world. Taken in large enough doses, however, it can cause fatal liver damage. In the U.S., 56000 people are admitted to hospital each year due to acetaminophen overdose and its related effects, at great cost to healthcare services. In this thesis we present a number of different models of acetaminophen metabolism and toxicity. Previously, models of acetaminophen toxicity have been complex and due to this complexity, do not lend themselves well to more advanced mathematical analysis such as the perturbation analysis presented later in this thesis. We begin with a simple model of acetaminophen metabolism, studying a single liver cell and performing numerical and sensitivity analysis to further understand the most important mechanisms and pathways of the model. Through this we identify key parameters that affect the total toxicity in our model. We then proceed to perform singular perturbation analysis, studying the behaviour of the model over different timescales, finding a number of key timescales for the depletion and subsequent recovery of various cofactors as well as critical dose above which we see toxicity occurring. Later in the thesis, this model is used to model metabolism in a spheroid cell culture, examining the difference spatial effects have on metabolism across a 3D cell culture. We then present a more complex model, examining the difference the addition of an adaptive response to acetaminophen overdose from the Nrf2 signalling pathway, has on our results. We aim to reproduce an unexplained result in the experimental data of our colleagues, and so analyse the steady states of our model when subjected to an infused dose, rather than a bolus one. We identify another critical dose which leads to GSH depletion in the infused dose case and find that Nrf2 adaptation decreases toxicity and model sensitivity. This model is then used as part of a whole-body PBPK model, exploring the effects that the distribution of the drug across the bloodstream and different organs has. We explore the affects of that a delay in up-regulation from the Nrf2 pathway has on the model, and find that with rescaled parameters we can qualitatively reproduce the results of our collaborators. Finally, we present the results of in vitro work that we have undertaken, the aim of which was to find new parameters for the model in human hepatocytes, rather than from rodent models, and find a new value for a parameter in our model from human cell lines.

This essay seeks to tie together thoughts on the political economy of academia, the inequities in access to the academic means of production and decolonial practice in data empowerment. To demonstrate this I will provide a brief analysis of the neo-colonial, extractive practices of the Western Academy, introduce concepts around decolonial AI practice and then use these to form an investigative framework. Using this framework, I present a brief case study of the AirQo project in Kampala, Uganda. The project aims to deploy a low-cost air pollution sensor network across the city, using machine learning methods to calibrate these sensors against reference instruments, providing high-quality air pollution data at a far lower cost.

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of \"following the science\" are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that allows easy annotation of data as they are consumed by analyses, while tracing the provenance of scientific outputs back through the analytical source code to data sources. Such a tool provides a mechanism for the public, and fellow scientists, to better assess the trust that should be placed in scientific evidence, while allowing scientists to support policy-makers in openly justifying their decisions. We believe that tools such as this should be promoted for use across all areas of policy-facing research.

To minimise unexpected toxicities in early phase clinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical test species and humans. We have used physiologically-based pharmacokinetic modelling to identify doses of the model hepatotoxin acetaminophen yielding similar hepatic burdens of the reactive metabolite N-acetyl-p-benzoquinoneimine in mice and rats, to enable comparison of tissue adaptive responses under conditions of equivalent chemical insult. Mice exhibited a greater degree of liver injury than rats, despite the equivalent hepatic NAPQI burden. Transcriptomic and proteomic analyses highlighted the stronger activation of stress response pathways (including the Nrf2 oxidative stress response and autophagy) in the livers of rats. Components of these pathways were also found to be expressed at a higher basal level in the livers of rats compared with both mice and humans. Our findings exemplify a systems approach to understanding differential species sensitivity to hepatotoxicity, and have important implications for species selection and human translation in the safety testing of new drug candidates.

In order to minimise the occurrence of unexpected toxicities as a new medicine transitions from the preclinical to clinical phases of development, it is vital to understand fundamental similarities and differences between preclinical species and humans. The well-known difference in sensitivity of mice and rats to acetaminophen (APAP) liver injury has been related to differences in the fraction that is bioactivated to the reactive metabolite N-acetyl-p-benzoquinoneimine (NAPQI). We have used physiologically-based pharmacokinetic modelling to identify doses of APAP (300 and 1000 mg/kg in mice and rats, respectively) yielding similar hepatic burdens of NAPQI to enable the comparison of temporal liver tissue responses under conditions of equivalent chemical insult. Despite pharmacokinetic and biochemical verification of the equivalent NAPQI insult, serum biomarker and tissue histopathology analyses revealed that mice still exhibited a greater degree of liver injury than rats. Transcriptomic and proteomic analyses highlighted the stronger activation of stress response pathways (including the Nrf2 oxidative stress response and autophagy) in the livers of rats, indicative of a more robust transcriptional adaptation to the equivalent insult. Using RNA-Seq data, we also found that genes associated with these stress responses are expressed at a higher basal level in the livers of rats compared with both mice and humans. Taken together, these data indicate that rats possess a greater basal and adaptive capacity for hepatic stress responses than mice and humans. This has important implications for species selection in preclinical safety testing of drugs associated with reactive metabolite formation and other forms of chemical stress. Competing Interest Statement The authors have declared no competing interest.