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Deep Learning for CAR-T cell therapy optimisation

Deep Learning for CAR-T cell therapy optimisation
用于 CAR-T 细胞治疗优化的深度学习
批准号:
MR/W003309/2
负责人:
Jonathan Lees
金额:
$11.21万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
翻译
过继细胞疗法是一种使用患者自身改良的免疫细胞的治疗方法。它包括从患者血液中收集T细胞——一种通常能识别和消除病原体的免疫细胞,对它们进行重新编程以识别癌细胞,并将新修饰的细胞重新引入患者体内以摧毁肿瘤。用来武装这些T细胞抵抗癌症的修饰因子被称为嵌合抗原受体,简称CAR。使用锁和钥匙的类比,正如每把锁只能用合适的钥匙打开一样,每种类型的癌症都有其独特的抗原,只能被一种特定的工程化CAR识别。目前,NHS正在为患有复发或难以治疗的白血病和淋巴瘤的儿童和年轻人提供针对表达CD19抗原的恶性B细胞的CAR-T细胞疗法。这种疗法对化疗无效的患者尤其有效,给那些没有其他选择的患者带来了真正的希望。然而,这种CAR疗法只能治疗一种非常特定的癌症,还有很大的改进空间。针对其他癌症的特异性CAR - t疗法的开发受到了冗长的靶点发现和受体优化过程的阻碍。编码。Bio简化了构建更好的受体的过程,使其更快、更便宜、更多样化。该项目将开发一种深度学习方法,从这些受体的大数据集中选择主要候选药物,使候选药物更有可能在临床中取得成功。我们的目标是帮助将这种变革性疗法带给更广泛的患者群体。
英文摘要
Adoptive cell therapy is a treatment of patients using their own, modified immune cells. It involves harvesting T cells - the immune cells that normally recognise and eliminate pathogens - from the patients blood, reprogramming them to recognise cancer cells and re-introducing the newly modified cells back into the patient to destroy tumours. The modifying factor used to arm these T cells against cancer is called Chimeric Antigen Receptor or CAR, in short. Using the lock and key analogy, just as every lock can only be opened with the appropriate key, each type of cancer, with its unique antigens, can only be recognised by a specific engineered CAR.Currently, the NHS is providing CAR-T cell therapy against malignant B cells that express antigen called CD19 to children and young adults with relapsed or difficult- to-treat leukaemia and lymphoma. The treatment is particularly effective in patients who did not respond to chemotherapy, offering real hope to those who otherwise would have no other options. However, this CAR therapy can only treat a very specific type of cancer, leaving much room for improvement.The concerted efforts into development of specific CAR therapies against other cancers are hindered by the tedious process of target discovery and receptor optimisation. Coding.bio streamlined the process of building better receptors, making it faster, cheaper, and more diverse. This project will develop a deep learning method to select lead candidates from big datasets of these receptors, making candidates more likely to succeed in the clinicOur objective is to help bring this transformative therapy to a wider patient population.
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Deep Learning for CAR-T cell therapy optimisation
  • 批准号:
    MR/W003309/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $18.89万
  • 财政年份:
    2021
  • 负责人:
    Jonathan Lees
  • 依托单位:
CEDAR: Lower-Upper Atmosphere Coupling via Acoustic Wave Energy
Collaborative Research: CDI-Type II: VolcanoSRI: 4D Volcano Tomography in a Large-Scale Sensor Network
Collaborative Research: Integrated Volcano Geodesy and Seismology: Earthquakes at Silicic Domes
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Understanding structural evolution of galaxies with machine learning
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位:
煤矿安全人机混合群智感知任务的约束动态多目标Q-learning进化分配
  • 批准号:
    --
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2022
  • 负责人:
    吉建娇
  • 依托单位:
基于领弹失效考量的智能弹药编队短时在线Q-learning协同控制机理
  • 批准号:
    62003314
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2020
  • 负责人:
    沈剑
  • 依托单位: