Within-Host Individual-Based Model for Diabetic Tuberculosis Patients

几十年来，结核病是可以预防和治愈的。然而，全世界每15-20秒就有一人死于结核病，其中50%是儿童。有趣的是，世卫组织的数据证实，对于免疫功能低下的宿主，结核病感染的结果通常是不同的，因此，本项目将研究与糖尿病的共病。这将使我们深入了解免疫系统如何应对多种疾病，以及这如何影响结核病的治疗和结果。探索结核病和糖尿病是重要和及时的，因为数据显示，在未来10年，糖尿病患者人数将翻一番。这相当于世界上超过6亿人，因此构成重大威胁，因为糖尿病患者在感染结核病时死亡率更高，结核病复发的可能性也更大。为此，在Bowness等人（2018）的数学框架的基础上，本博士项目旨在使用基于个体的模型（也称为基于主体的模型）来开发糖尿病患者结核病感染的宿主内模型。目的是了解2型糖尿病对肺结核严重程度的影响（即糖尿病患者对结核病免疫反应的差异）。虽然我们不打算建立新的药物，但这将涉及新的治疗策略的模拟，可以作为2型糖尿病肺结核患者的个性化治疗方法。因此，我们对结核病/糖尿病模型疾病动力学改变的理解将有助于我们决定纳入不同的药物剂量和组合，这将有助于获得有利的治疗结果。例如，如果发现糖尿病患者通常具有更多富含脂质（生长较慢）的结核细菌，那么将模拟更有效的抗生素管理，特别是针对生长较慢的细菌。此外，如果有免疫疗法治疗的可用数据，也将进行模拟。我们将与实验人员合作，使用实验室数据来校准模型，并与能够访问临床数据的临床医生一起验证模型的发现。具体而言，该项目将受益于与Muge Cevik博士（圣安德鲁斯大学/NHS洛锡安）的合作，他是治疗糖尿病结核病患者的专家，目前正在参与该研究领域的临床试验。鲍恩斯，R.，牧师，m.a.，波瓦蒂尔，G.G.和吉莱斯皮，s.h., 2018。模拟细菌细胞状态和空间位置对结核病治疗的影响：来自混合多尺度细胞自动机模型的见解。生物工程学报，30(4)，页87-100。

For several decades, Tuberculosis (TB) has been preventable and curable. However, someone still dies from tuberculosis across the world every 15-20 seconds, and 50 percent of those deaths are children. Interestingly, data from WHO confirms the outcome of TB infection is usually different for an immunocompromised host, and thus, this project will look at comorbidity with diabetes. This would allow us to gain an in-depth understanding of how the immune system responds in tackling more than one disease and how this impacts the treatment and outcome of tuberculosis disease. Exploring TB and diabetes is important and timely, as data shows that in the next 10 years, the population of individuals with diabetes will double. This amounts to over 600 million people in the world, thus posing a major threat, as patients with diabetes have a higher mortality rate when infected with TB and have an increased likelihood of TB relapse. To this effect, building on the mathematical framework of Bowness et al. (2018), this Ph.D. project aims to use an individual-based model (also known as agent-based model) to develop a within-host model of TB infection in diabetic patients. The goal would be to understand the impact of type 2 diabetes on the severity of pulmonary tuberculosis (i.e., the differences in immune responses in diabetic patients to TB). Whilst we do not intend to establish novel drugs, this will involve the simulation of novel treatment strategies that could serve as personalised treatment therapy for tuberculosis patients with type 2 diabetes. Thus, our understanding of the altered disease dynamics of the TB/diabetes model will help inform our decision on the inclusion of different drug doses and combinations that will help with favourable treatment outcomes. For example, if it is found that diabetic patients typically have more lipid-rich (slower-growing) TB bacteria, more effective administration of antibiotics that are particularly known for targeting slow-growing bacteria will be simulated. In addition, provided there is available data on immunotherapy treatment, this will also be simulated. We will work with experimentalists, using laboratory data to calibrate the model, and clinicians who have access to clinical data to validate model findings. Specifically, this project will benefit from a collaboration with Dr. Muge Cevik (University of St Andrews/NHS Lothian), who is an expert on treating diabetic TB patients and is currently involved in a clinical trial in this area of research. Bowness, R., Chaplain, M.A., Powathil, G.G. and Gillespie, S.H., 2018. Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model. Journal of theoretical biology, 446, pp.87-100.