Mechanistic modelling of multitargeted therapeutics

胰腺癌仍然难以治愈，导致高死亡率。因此，迫切需要创新的方法来对付这一疾病。异常的表观遗传学是一个关键的“癌症标志”，癌细胞利用它来驱动正常细胞的去分化进入胚胎鳞状状态，这种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，并发现生物靶向药物（二甲双胍和维生素C）的组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分化状态[1]。虽然我们观察到向正常细胞表观遗传学的转变，但这种药物组合并没有完全优化以驱动表型转变。因此，我们的目标是使用Epicombi开发的创新数据驱动的人工智能（AI）方法。Ai，基于现有的算法[2]，描绘出新的分子，可以证明改善和稳健的表观遗传状态和胰腺分化的再正常化，以便为这种迄今为止难以治愈的癌症提供新的治疗方法。我们的目标是开发针对胰腺癌的人工智能衍生分子的机械数学模型。该项目将探索人工智能/机器学习平台（Epicombi.ai）预测的分子的作用机制，以改变细胞表型。该项目将集中于机制模型的发展[3,4]，以更好地理解这些新型药物如何通过相关细胞信号网络的扰动实现表型变化。这些分子可能有多个细胞靶标，干扰了许多信号通路，这些信号通路可能结合起来实现细胞的表型正常化。因此，需要建立机制模型来理解这种“多干预”分子如何改变细胞信号网络。这些模型将被用于进一步完善平台发现和优化引擎，并为Epicombi提供一个机制基础。Ai数据驱动的药物发现平台，以确定针对该治疗指征的优化药物。

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.