Modelling drug efficacy: capturing the target engagement of heterogeneous cancer cells

药物功效建模：捕获异质癌细胞的靶标参与

• 批准号: 2451541
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2451541
• 关键词: Modelling; drug; efficacy; capturing; target

Tumour heterogeneity is well known, leading to therapeutic limitations and failure as cancer cells that exhibit some degree of resistance prevail in the face of treatment, as highlighted by the ultimate failure of non-small cell lung carcinoma therapies with multiple tyrosine kinase inhibitors. These inhibitors antagonise numerous aberrant signalling pathways that have induced comprehensive changes in cell behaviour, including tumour formation. At the level of signalling pathways, the capabilities of a cell to buffer changes induced by external perturbations, such as the (partial) inhibition of tyrosine kinases, is generally complex as highlighted by a study of homeostasis in biochemical cellular networks, in the context of metabolism [1,2]. Thus, using diverse techniques in the mathematical modelling and simulation of dynamical systems together with parameter reduction [3], parameter estimation and model selection studies, our aim will be to generalise theoretical investigations of how cells maintain robust homeostasis for cancer relevant signalling pathways. In particular our objectives will include exploring how biochemical motifs within the signalling pathway of an individual cell may buffer antagonistic perturbations of aberrant pathways and investigating the impact of such mechanisms at the population level, including the parameterisation and predictions of population level Pharmacokinetic-Pharmacodynamic models. For example, to date we have refined a mathematical model capturing the EGFR signaling pathway [4]. A systematic sensitivity analysis has been implemented to explore how model outputs depend on parameters that are of interest. In addition, MATCONT (a graphical MATLAB software package) has been utilized to perform bifurcation and stability analysis of the steady states of the system and there is extensive further interest in signalling via the G protein coupled receptor [5]. More generally, the potential impact of such studies lies in understanding how an insensitivity to treatment, that is resistance, may arise together with the resulting predictions for single and multiple targets aimed at normalising signalling pathway responses. In addition, the novelty of this approach concerns the application of concepts from homeostasis to cancer relevant pathways and their perturbation. The industrial partner of this project is Dr James Yates of GlaxoSmithKline (GSK) and its theoretical study of relatively large signalling pathways falls within the remit of the EPSRC Mathematical Biology and Non-linear Systems research areas. [1] Reed, M., Best, J., Golubitsky, M., Stewart, I., & Nijhout, H. F. (2017). Analysis of Homeostatic Mechanisms in Biochemical Networks. Bulletin of Mathematical Biology, 79(11), 2534-2557. https://doi.org/10.1007/s11538-017-0340-z [2] Watson, E., Chappell, M., Ducrozet, F., Poucher, S., & Yates, J. (2009). A New General Glucose Homeostatic Model using a Proportional-Integral-Derivative Controller. IFAC Proceedings Volumes, 42(12), 79-84. https://doi.org/10.3182/20090812-3-dk-2006.0027 [3] Cheung, S. A., Majid, O., Yates, J. W., & Aarons, L. (2012). Structural identifiability analysis and reparameterisation (parameter reduction) of a cardiovascular feedback model. European Journal of Pharmaceutical Sciences, 46(4), 259-271. https://doi.org/10.1016/j.ejps.2011.12.017 [4] Shvartsman, S. Y., Hagan, M. P., Yacoub, A., Dent, P., Wiley, H. S., & Lauffenburger, D. A. (2002). Autocrine loops with positive feedback enable context-dependent cell signaling. American Journal of Physiology-Cell Physiology, 282(3), C545-C559. https://doi.org/10.1152/ajpcell.00260.2001 [5] Bridge, L., Mead, J., Frattini, E., Winfield, I., & Ladds, G. (2018). Modelling and simulation of biased agonism dynamics at a G protein-coupled receptor. Journal of Theoretical Biology, 442, 44-65. https://doi.org/10.1016/j.jtbi.2018.01.01

肿瘤异质性是众所周知的，导致治疗限制和失败，因为表现出一定程度的耐药性的癌细胞在面对治疗时占优势，如使用多种酪氨酸激酶抑制剂的非小细胞肺癌治疗的最终失败所强调的。这些抑制剂拮抗许多异常的信号传导途径，这些信号传导途径诱导细胞行为的全面变化，包括肿瘤形成。在信号传导途径水平，细胞缓冲外部扰动诱导的变化（如酪氨酸激酶的（部分）抑制）的能力通常很复杂，如代谢背景下生化细胞网络中的稳态研究所强调的那样[1，2]。因此，在动力系统的数学建模和模拟中使用不同的技术，以及参数减少[3]，参数估计和模型选择研究，我们的目标将是概括细胞如何保持癌症相关信号通路的稳定稳态的理论研究。特别是，我们的目标将包括探索单个细胞信号通路内的生化基序如何缓冲异常通路的拮抗性扰动，并研究这些机制在群体水平上的影响，包括群体水平药代动力学-药效学模型的参数化和预测。例如，迄今为止，我们已经完善了捕获EGFR信号通路的数学模型[4]。本集团已进行系统性敏感度分析，以探讨模型输出如何取决于相关参数。此外，MATCONT（图形MATLAB软件包）已被用于进行系统稳态的分叉和稳定性分析，并且对通过G蛋白偶联受体的信号传导有广泛的进一步兴趣[5]。更一般地说，这些研究的潜在影响在于了解对治疗的不敏感性，即耐药性，如何与旨在使信号通路反应正常化的单个和多个靶点的预测一起出现。此外，这种方法的新奇涉及从稳态到癌症相关途径及其扰动的概念的应用。该项目的工业合作伙伴是葛兰素史克（GSK）的James Yates博士，其相对较大的信号通路的理论研究福尔斯EPSRC数学生物学和非线性系统研究领域的职权范围。[1]里德，M.，贝斯特，杰Golubitsky，M.，斯图尔特岛，& Nijhout，H. F.（2017年）。生化网络中的稳态机制分析。Bulletin of Mathematical Biology，79（11），2534-2557. https://doi.org/10.1007/s11538-017-0340-z沃森，E.，查佩尔，M.，Ducrozet，F.，Poucher，S.，& Yates，J.（2009）.采用比例-积分-微分控制器的新的通用葡萄糖稳态模型。IFAC Proceedings，42（12），79-84. https://doi.org/10.3182/20090812-3-dk-2006.0027 Cheung，S.一、Majid，O.，耶茨，J.W.，& Aarons，L.（2012年）。心血管反馈模型的结构可识别性分析和重新参数化（参数减少）。European Journal of Pharmaceutical Sciences，46（4），259-271. https://doi.org/10.1016/j.ejps.2011.12.017 Shvartsman，S.是的，哈根，M。P.，Yacoub，A.，Dent，P.，Wiley，H.美国，& Lauffenburger，D. A.（2002年）的报告。具有正反馈的自分泌环使上下文依赖的细胞信号传导成为可能。American Journal of Physiology-Cell Physiology，282（3），C545-C559. https://doi.org/10.1152/ajpcell.00260.2001布里奇，L.，Mead，J.，Frattini，E.，温菲尔德岛，& Ladds，G.（2018年）。G蛋白偶联受体的偏向激动动力学的建模和模拟。理论生物学杂志，442，44-65。https://doi.org/10.1016/j.jtbi.2018.01.01