Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway.
Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway.
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通过药物靶向 EGFR-ERK 信号通路的数学模型预测药物组合指数并模拟网络调节动态。
DOI：
10.1038/srep40752
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发表时间：
2017-01-19
影响因子：
4.6
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Huang L;Jiang Y;Chen Y
协同药物组合能够增强治疗。他们的发现通常涉及药物组合指数（CI）的测量和评估，这可以通过计算机CI预测工具的开发和应用来促进。在这项工作中，我们开发并测试了药物靶向EGFR-ERK通路的数学模型在预测CI和分析多个协同药物组合对观察结果的能力。我们的数学模型对文献报道的信号，药物反应动力学，和EGFR-MEK药物组合效应进行了验证。EGFR-BRaf、BRaf-MEK、FTI-MEK和FTI-BRaf抑制剂组合的预测CI和组合治疗效果显示出一致的协同作用。我们的研究结果表明，现有的途径模型可能会潜在地扩展开发药物靶向途径模型，以预测药物组合CI值，等效线图和药物响应面，以及分析单个和组合药物的动力学。通过我们的模型，可以预测潜在药物组合的疗效。我们的方法通过使用实验或验证的分子动力学常数从途径动力学的角度预测药物组合效应，补充了已开发的计算机模拟方法（例如化学基因组学特征和免疫推断网络模型），从而促进了药物组合效应在不同疾病系统范围内的集体预测。
Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.