An integrated computational-experimental approach to identify and optimize combination drug therapy for non-alcoholic fatty liver disease (NAFLD).

一种综合计算实验方法，用于识别和优化非酒精性脂肪肝病 (NAFLD) 的联合药物治疗。

• 批准号: 2446069
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2446069
• 关键词: integrated; computational; experimental; approach; identify

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease not produced by alcohol, affecting 1 in 4 people worldwide. 20% of the patients that suffer NAFLD, transition to non-alcoholic steatohepatitis (NASH). This disease characterises by having a biological complexity leading to symptoms that affect not only the liver but the whole organism, as well as increasing the risk of liver cirrhosis, hepatocellular carcinoma or even premature death. Single therapy for NASH has not succeed in multiple clinical trials, mostly due to the complexity of the disease. For this reason, it is impossible to reach personalised medicine, hence a synergic combination of different drugs could be the best approach. This approach would be achieved by a computational-experimental methodology. Several combinations of targets and drugs are identified and tested in silico, which would include a control engineering algorithm, microfluidics and fluorescence microscopy. This would allow an intensive study of multiple combinations of drugs to make sure the most suitable ones are chosen. This would be followed by biological assays that would prove the computational studies. Likewise, in order to achieve this, an optimisation of the liver cell line would be needed. The final step of this outstanding project would be, if possible, trying this combination of drugs in vivo too. In more detail this project goals are: To characterize the different stages of NAFLD progression through human hepatic transcriptional profiles. To shortlist in silico drug combinations using drug networks such as Connectivity Map. To optimise an in vitro human liver cellular model of NAFLD for testing the drug candidates. To define the treatment of the best drug combinations by combining algorithms with a microfluidic platform in which the response of the cell line treatment is measured though fluorescence microscopy. To establish a human precision-cut liver slice platform for the study of the drug combinations. Furthermore, as this is an iCASE studentship, there is also a collaboration with Galecto Biotech. This company is involved in the development of novel therapeutics that target multiple diseases that involve fibrosis, inflammation, or cancer. Their studies make this partnership ideal for the development of this project.

非酒精性脂肪性肝病（NAFLD）是最常见的非酒精性慢性肝病，影响全球四分之一的人。20%的NAFLD患者转变为非酒精性脂肪性肝炎（NASH）。这种疾病的特点是具有生物学复杂性，导致不仅影响肝脏而且影响整个生物体的症状，以及增加肝硬化，肝细胞癌甚至过早死亡的风险。NASH的单一疗法在多项临床试验中没有成功，主要是由于疾病的复杂性。因此，不可能实现个性化药物，因此不同药物的协同组合可能是最好的方法。这种方法将通过计算-实验方法来实现。靶点和药物的几种组合在计算机上进行了鉴定和测试，其中包括控制工程算法，微流体和荧光显微镜。这将允许对多种药物组合进行深入研究，以确保选择最合适的药物。随后将进行生物测定，以证明计算研究。同样，为了实现这一点，需要优化肝细胞系。如果可能的话，这个杰出项目的最后一步将是在体内尝试这种药物组合。更详细地说，该项目的目标是：通过人类肝脏转录谱来表征NAFLD进展的不同阶段。使用药物网络（如连接图）在计算机上筛选药物组合。优化NAFLD的体外人肝细胞模型，用于测试候选药物。通过将算法与微流体平台相结合来定义最佳药物组合的治疗，其中通过荧光显微镜测量细胞系治疗的反应。建立一个用于药物联合研究的人体精密肝切片平台。此外，由于这是一个iCASE学生奖学金，也有与Galecto Biotech的合作。该公司参与开发针对涉及纤维化，炎症或癌症的多种疾病的新型疗法。他们的研究使这种伙伴关系成为该项目发展的理想选择。