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.

该项目的重点是基于一种识别新基因靶标的新方法，发现和临床前验证用于治疗三阴性乳腺癌（TNBC）的一流候选药物。TNBC 是一种高风险恶性肿瘤，具有很高的风险。未满足的需求。 TNBC 比其他浸润性乳腺癌亚型生长和扩散更快，治疗选择更少，预后更差。 TNBC 传统上被认为是最难治疗的乳腺癌类型，相对于其他亚型，患者的治疗效果极差。TNBC 约占所有乳腺癌的 15%。到 2023 年，英国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 已扩散到身体较远部位（例如肺、肝脏或骨骼）的患者的 5 年相对生存率为 12%。由于耐药性和复发而导致的不良结果在 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新型生物信息学平台的结果，该平台用于解决高度遗传介导的癌症问题。该平台利用人工智能、机器学习和合成生物学来促进新疾病的理解、识别新靶标并发现新生物药物。