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

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
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --

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中文摘要
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英文摘要
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.
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国内基金
海外基金
物体运动对流场扰动的数学模型研究
  • 批准号:
    51072241
  • 项目类别:
    专项基金项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2010
  • 负责人:
    李廷秋
  • 依托单位:
Computational Methods for Analyzing Toponome Data