Systems pharmacology - Towards the modeling of network interactions
Systems pharmacology - Towards the modeling of network interactions
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DOI：
10.1016/j.ejps.2016.04.027
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发表时间：
2016-10-30
影响因子：
4.6
通讯作者：
Danhof, Meindert
中科院分区：
文献类型：
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作者：
Danhof, Meindert
基于机理的药代动力学和药效学（PKPD）和疾病系统（DS）模型已被引入药物发现和开发研究中，以定量的方式预测药物治疗在健康和疾病中的体内效果。这需要考虑生物系统行为的几个基本特性，包括：滞后性，非线性，可变性，相互依赖性，收敛性，弹性和多稳态。经典的基于生理学的PKPD模型认为线性转导途径，连接药物给药和效应之间因果路径上的过程，作为药物作用的基础。根据药物及其生物学靶标，此类模型可包含表征i）所研究药物的处置和靶位点分布动力学、ii）靶标结合和活化的动力学和iii）转导动力学的表达式。当连接到基于生理学的DS模型时，PKPD模型可以以机械方式表征对疾病进展的影响。近年来，系统药理学作为一种新的预测体内药物效应的方法被引入，它将生物网络而不是单一的转导途径作为药物作用和疾病进展的基础。这些模型包含表征生物网络内的功能相互作用的表达式。当药物作用于网络中的多个靶点或当稳态反馈机制起作用时，这种相互作用是相关的。因此，系统药理学模型是特别有用的，以描述复杂的模式的药物作用（即协同作用，振荡行为）和疾病的进展（即发作性疾病）。在这方面的贡献，它是如何基于生理学的PKPD和疾病模型可以扩展到内部系统的相互作用。它演示了如何SP模型可以用来预测多靶点相互作用和稳态反馈的药理学反应的影响。此外，还显示了DS模型如何用于区分症状性疾病改善效应，并从短期生物标志物响应预测对疾病进展的长期效应。它的结论是，纳入表达式来描述生物网络分析中的相互作用开辟了新的途径，了解药物治疗对生物系统行为的基本方面的影响。(C)2016作者由Elsevier B. V.出版，这是CC BY-NC-ND许可下的开放获取文章。
Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior.Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders).In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior. (C) 2016 The Author. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.