<br>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。,Scientific Reports

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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。
Scientific Reports ( IF 3.8 ) Pub Date : 2017-01-19 , DOI: 10.1038/srep40752
Lu Huang _{1,

2,

3} , Yuyang Jiang ₁ , Yuzong Chen _{3,

4}

Affiliation

协同药物组合可以增强治疗效果。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 通路数学模型在预测 CI 和根据观察结果分析多种协同药物组合的能力。我们的数学模型根据文献报道的信号传导、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 和联合治疗效果显示出一致的协同作用。我们的结果表明，现有的通路模型可能会扩展用于开发药物靶向通路模型，以预测药物组合 CI 值、等效线图和药物反应面，以及分析单个药物和药物组合的动态。通过我们的模型，可以预测潜在药物组合的功效。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方法（例如化学基因组图谱和统计推断的网络模型），从而促进药物组合效应的集体预测在不同范围的疾病系统中。

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Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway.

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.

更新日期：2017-01-20

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