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Expert Opin Drug Discov. 2015 Dec;10(12):1315-31. doi: 10.1517/17460441.2015.1082543. Epub 2015 Aug 25.
Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.
This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.
The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.
在过去的三十年中，药物发现的主要模式是设计针对特定靶点的选择性配体，以避免不必要的副作用。然而，在过去的 5 年中，目标已经转变为考虑它们相互作用的生物网络。定量和系统药理学（QSP）是一种新的模式，旨在了解药物如何在时间和空间上调节细胞网络，以便预测药物靶点及其在人类病理生理学中的作用。
本文讨论了现有的计算和实验 QSP 方法，如结合系统生物学信息的多药理学技术，并考虑在更广泛的“系统级”背景下使用新工具和思路，以便设计具有更好疗效和更少非靶标副作用的新药。
网络生物学的应用产生了有价值的信息，如新的药物适应证、药物相互作用、蛋白质与药物副作用以及途径与基因的关联。然而，我们离 QSP 的目标还很远，这既是因为精确建模生物网络模型需要巨大的努力，也是因为我们能够达到的准确性有限。因此，从“一个分子针对一个靶点产生一种治疗效果”向“基于大系统的图景”转变似乎是显而易见的，但我们目前的工具是否足以实现这一步骤仍存在争议。
