Compared with monotherapy, combination therapy is the first choice and the most promising method for the treatment of many complex diseases. Due to the wide variety of drugs, it is often difficult to choose desirable combination drugs with synergy and low risk. Additional research should always be done before combining drugs because the combinatorial effects can be synergistic, additive, or even antagonistic. Synergistic drugs work together to cause an effect greater than the sum of its parts. Some studies propose different approaches to detect synergism between two or more drugs. Based on the framework of bifurcation-based method, we propose an approach to screen another potential synergistic drug for a given drug. Different from other methods, the approach can help us screen and detect drugs which have a synergistic effect with a known drug, thus playing critical roles in combination therapy. In order to demonstrate the effectiveness of the approach, we apply it to three models, i.e. the zeroth-order reaction model, the galactose model, and the epithelial-to-mesenchymal transition network. The approach provides a theoretical basis for rational design of combination drugs and new use of old drugs.

与单一疗法相比，联合疗法是治疗许多复杂疾病的首选和最有希望的方法。由于药物种类繁多，往往难以选择具有协同作用和低风险的理想组合药物。在组合药物之前应始终进行额外的研究，因为组合效应可以是协同的、累加的，甚至是拮抗的。协同药物共同作用产生的效果大于其各部分的总和。一些研究提出了不同的方法来检测两种或多种药物之间的协同作用。基于基于分叉的方法的框架，我们提出了一种为给定药物筛选另一种潜在协同药物的方法。与其他方法不同，该方法可以帮助我们筛选和检测与已知药物具有协同作用的药物，从而在联合治疗中发挥关键作用。为了证明该方法的有效性，我们将其应用于三个模型，即零级反应模型、半乳糖模型和上皮-间质转化网络。该方法为组合药物的合理设计和老药新用提供了理论依据。