Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA.
Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
Nat Commun. 2019 Mar 13;10(1):1197. doi: 10.1038/s41467-019-09186-x.
Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein-protein interactome, we show the existence of six distinct classes of drug-drug-disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.
药物组合通过提高治疗效果和降低毒性，在治疗多种复杂疾病方面发挥着重要作用。然而，由于药物组合数量庞大以及剂量组合的影响，我们识别和验证有效组合的能力受到了限制。在这里，我们提出了一种基于网络的方法，用于识别针对特定疾病的临床有效的药物组合。通过量化人类蛋白质-蛋白质互作网络中药物靶点和疾病蛋白之间的基于网络的关系，我们展示了六种不同类型的药物-药物-疾病组合的存在。基于高血压和癌症的已批准药物组合，我们发现只有六种类型之一与治疗效果相关：如果药物的靶点都涉及疾病模块，但靶点位于不同的区域。这一发现使我们能够识别和验证抗高血压药物组合，为药物开发中识别有效的联合治疗方法提供了一种通用的、强大的网络方法。
