Preclinical validation of synthetic biomolecules to treat Breast Cancer from AI-enabled discovery of novel lncRNA therapeutic targets.

通过人工智能发现新型 lncRNA 治疗靶点，对合成生物分子治疗乳腺癌进行临床前验证。

• 批准号: 10110204
• 金额: $ 12.74万
• 依托单位: AMPLY DISCOVERY LIMITED
• 依托单位国家: 英国
• 项目类别: Launchpad
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10110204
• 关键词: Preclinical; validation; synthetic; biomolecules; treat

This project is focused on the discovery and preclinical validation of first-in-class drug candidates for the treatment of Triple Negative Breast Cancer (TNBC) based on a novel approach to identifying novel genetic targets.TNBC is a high-risk malignancy with a high unmet need. TNBC grows and spreads faster than other invasive breast cancer subtypes, has fewer treatment options and a poorer prognosis. TNBC is traditionally considered the most difficult type of breast cancer to treat and patient outcomes are extremely poor relative to other subtypes.TNBC makes up around 15% of all breast cancers. In the UK around thousands of patients will be diagnosed with TNBC in 2023\. Incidence rates are high in women under 40\. The 5-year relative survival rate for patients where TNBC has spread to distant parts of the body (such as the lungs, liver, or bones) is 12%. Poor outcomes due to drug resistance and relapse are also common in patients with TNBC.This project will validate the results of a unique new AI-based bioinformatics platform being used to tackle the problem of highly genetically mediated cancers. The platform uses AI, machine learning and synthetic biology to advance new disease understanding, identify novel targets, and discover novel biologic drugs.

该项目致力于基于一种识别新基因靶点的新方法，发现和临床前验证治疗三阴性乳腺癌(TNBC)的一流候选药物。TNBC是一种高风险恶性肿瘤，具有高度未得到满足的需求。与其他浸润性乳腺癌亚型相比，TNBC生长和扩散更快，治疗选择更少，预后更差。传统上，TNBC被认为是最难治疗的乳腺癌类型，与其他亚型相比，患者的预后非常差。TNBC约占所有乳腺癌的15%。在英国，2023年将有大约数千名患者被诊断为TNBC。40岁以下的女性发病率较高。TNBC已扩散到身体远处(如肺、肝脏或骨骼)的患者的5年相对存活率为12%。耐药和复发导致的不良结果在TNBC患者中也很常见。该项目将验证一个独特的基于人工智能的生物信息学平台用于解决高度基因介导的癌症问题的结果。该平台使用人工智能、机器学习和合成生物学来促进对新疾病的理解，识别新的靶点，并发现新的生物药物。