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Abstract：
BACKGROUND: The current network pharmacology model focuses mainly on static and qualitative characterisation between drugs and targets or molecular pathway networks, but it does not reflect the multi-scale, dynamic and quantitative process of drug action.
OBJECTIVE: In this study, we developed a new model known as quantitative and network pharmacology (QNP) to characterise the dynamic and quantitative interventions of drugs within a multi-scale biological network.
METHODS: Firstly, we used a systems biology method to construct a molecule-cell dynamic network model to simulate the pathological processes of diseases. Secondly, according to the principles of enzymatic kinetics, we generated a multi-scale drug intervention model to simulate the intervention of drugs in multi-scale networks at different concentrations and pathological stages. Finally, we took rhein treatment of renal interstitial fibrosis (RIF) as an example to illustrate the QNP model.
RESULTS: We successfully constructed the a QNP model that includes both a multi-scale dynamic network disease model and drug intervention model. The QNP model accurately simulated the pathological process of RIF, and the simulation results were validated by a series of cell and animal experiments. Meanwhile, the QNP model demonstrated that rhein can delay the pathological process at the studied concentrations of 5 nM, 10 nM, and 20 nM, and can also exert a better therapeutic effect on fibrosis before the proliferation stage of RIF. Furthermore, through uncertainty and sensitivity analysis, we identified that FAK and Smad3 may be potential targets for RIF.
CONCLUSIONS: Our QNP model provides a molecular-cellular understanding of the pathological mechanisms of RIF, serving as a new approach and strategy for the construction of dynamic multi-scale network model of diseases and drug intervention.
OBJECTIVE: In this study, we developed a new model known as quantitative and network pharmacology (QNP) to characterise the dynamic and quantitative interventions of drugs within a multi-scale biological network.
METHODS: Firstly, we used a systems biology method to construct a molecule-cell dynamic network model to simulate the pathological processes of diseases. Secondly, according to the principles of enzymatic kinetics, we generated a multi-scale drug intervention model to simulate the intervention of drugs in multi-scale networks at different concentrations and pathological stages. Finally, we took rhein treatment of renal interstitial fibrosis (RIF) as an example to illustrate the QNP model.
RESULTS: We successfully constructed the a QNP model that includes both a multi-scale dynamic network disease model and drug intervention model. The QNP model accurately simulated the pathological process of RIF, and the simulation results were validated by a series of cell and animal experiments. Meanwhile, the QNP model demonstrated that rhein can delay the pathological process at the studied concentrations of 5 nM, 10 nM, and 20 nM, and can also exert a better therapeutic effect on fibrosis before the proliferation stage of RIF. Furthermore, through uncertainty and sensitivity analysis, we identified that FAK and Smad3 may be potential targets for RIF.
CONCLUSIONS: Our QNP model provides a molecular-cellular understanding of the pathological mechanisms of RIF, serving as a new approach and strategy for the construction of dynamic multi-scale network model of diseases and drug intervention.
摘要：
背景：当前的网络药理学模型主要关注药物与靶标或分子途径网络之间的静态和定性表征，但它并不反映多尺度，药物作用的动态和定量过程。
目的：在本研究中，我们开发了一种称为定量和网络药理学（QNP）的新模型，以描述多尺度生物网络中药物的动态和定量干预。
方法：首先，我们使用系统生物学方法构建了分子-细胞动态网络模型来模拟疾病的病理过程。其次,根据酶动力学原理，我们建立了多尺度药物干预模型,以模拟多尺度网络中不同浓度和病理阶段的药物干预.最后,我们以大黄酸治疗肾间质纤维化（RIF）为例说明QNP模型。
结果：我们成功构建了包括多尺度动态网络疾病模型和药物干预模型的QNP模型。QNP模型准确地模拟了RIF的病理过程,并通过一系列细胞和动物实验对模拟结果进行了验证。同时,QNP模型表明，在研究浓度为5nM时，大黄酸可以延缓病理过程，10nM,和20nM，在RIF增殖期之前对纤维化也能发挥较好的治疗作用。此外，通过不确定性和敏感性分析，我们确定FAK和Smad3可能是RIF的潜在靶标。
结论：我们的QNP模型提供了对RIF病理机制的分子-细胞理解，为构建疾病和药物干预的动态多尺度网络模型提供了新的途径和策略。
目的：在本研究中，我们开发了一种称为定量和网络药理学（QNP）的新模型，以描述多尺度生物网络中药物的动态和定量干预。
方法：首先，我们使用系统生物学方法构建了分子-细胞动态网络模型来模拟疾病的病理过程。其次,根据酶动力学原理，我们建立了多尺度药物干预模型,以模拟多尺度网络中不同浓度和病理阶段的药物干预.最后,我们以大黄酸治疗肾间质纤维化（RIF）为例说明QNP模型。
结果：我们成功构建了包括多尺度动态网络疾病模型和药物干预模型的QNP模型。QNP模型准确地模拟了RIF的病理过程,并通过一系列细胞和动物实验对模拟结果进行了验证。同时,QNP模型表明，在研究浓度为5nM时，大黄酸可以延缓病理过程，10nM,和20nM，在RIF增殖期之前对纤维化也能发挥较好的治疗作用。此外，通过不确定性和敏感性分析，我们确定FAK和Smad3可能是RIF的潜在靶标。
结论：我们的QNP模型提供了对RIF病理机制的分子-细胞理解，为构建疾病和药物干预的动态多尺度网络模型提供了新的途径和策略。
