Mechanistic modelling of multitargeted therapeutics

多靶点治疗的机制建模

• 批准号: 2744704
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2744704
• 关键词: Mechanistic; modelling; multitargeted; therapeutics

Pancreatic cancers remain intractable, resulting in high mortality. Thus, there is an urgent need for innovative ways to tackle this disease. Aberrant epigenetics is a key "Hallmark of cancer" that cancer cells use to drive de-differentiation of normal cells into an embryonic squamous-like state which favours uncontrolled proliferation. We recently identified the epigenetic mechanism defining these phenotypic changes and found that a combination of biologically-targeted agents (Metformin and Vitamin C) could restore epigenetic control and revert squamous pancreatic cancer cells to a more regulated differentiated state [1]. Although we observe a shift towards normal cell epigenetics, this drug combination is not fully optimised to drive the phenotypic shift. Therefore, we aim to use an innovative data-driven artificial intelligence (AI) approach developed by Epicombi.ai, based on existing algorithms[2], to delineate novel molecules that can demonstrate improved and robust re-normalisation of epigenetic status and pancreatic differentiation, in order to offer new treatments for this hitherto intractable cancer. Our aim is to develop mechanistic mathematical models for AI-derived molecules targeting pancreatic cancer.The proposed project will explore the mechanism of action of molecules predicted by a AI/Machine Learning platform (Epicombi.ai) to modify cellular phenotype. This project will centre on the development of mechanistic models [3,4] to better understand how these novel agents achieve phenotypic changes through the perturbation of relevant cellular signalling networks. These molecules are likely to have multiple cellular targets, perturbing a number of signalling pathways, which may all combine to achieving phenotypic normalisation of the cell. Thus, mechanistic modelling is required to understand how such a 'multi-intervention' molecule functions to alter cell signalling networks. The models will then be employed to further refine the platform discovery and optimisation engine, and provide a mechanistic underpinning of Epicombi.ai data-driven drug-discovery platform to identify optimised drugs for this therapeutic indication.

胰腺癌仍然棘手，导致高死亡率。因此，迫切需要创新的方法来解决这种疾病。异常表观遗传学是癌细胞用来将正常细胞去分化为胚胎鳞状状态的关键“癌症标志”，该状态有利于不受控制的增殖。我们最近确定了定义这些表型变化的表观遗传机制，发现生物靶向剂（二甲双胍和维生素C）的结合可以恢复表观遗传控制并将鳞状胰腺癌细胞恢复到更受调节的差异化状态[1]。尽管我们观察到向正常细胞表观遗传学的转变，但这种药物组合并未完全优化以驱动表型转移。因此，我们旨在使用基于现有算法[2]开发的创新数据驱动的人工智能（AI）方法来描述可以改善和稳健的表观遗传状态和胰腺差异化的新型分子，以便为这种可用于这种疾病的癌症提供新的治疗方法。我们的目的是开发针对胰腺癌的AI衍生分子的机械数学模型。拟议的项目将探讨AI/机器学习平台（Epicombi.ai）预测的分子的作用机理，以修饰细胞表型。该项目将集中在机械模型[3,4]的开发上，以更好地了解这些新型药物如何通过扰动相关的细胞信号网络来实现表型变化。这些分子可能具有多个细胞靶标，会扰动许多信号通路，这些途径都可能结合在一起，以实现细胞的表型归一化。因此，需要机械建模来了解这种“多干预”分子如何改变细胞信号网络。然后，这些模型将用于进一步完善平台发现和优化引擎，并为Epicombi.AI数据驱动的药物发现平台提供机械基础，以确定该治疗指示的优化药物。