Vazquez Alexei
The Simons Center for Systems Biology, Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540, USA.
BMC Syst Biol. 2009 Aug 6;3:81. doi: 10.1186/1752-0509-3-81.
Identifying effective drug combinations that significantly improve over single agents is a challenging problem. Pairwise combinations already represent a huge screening effort. Beyond two drug combinations the task seems unfeasible.
In this work we introduce a method to uncover drug combinations with a putative effective response when presented to a heterogeneous population of malignant agents (strains), such as cancer cell lines or viruses. Using data quantifying the effect of single drugs over several individual strains, we search for minimal drug combinations that successfully target all strains. We show that the latter problem can be mapped to a minimal hitting set problem in mathematics. We illustrate this approach using data for the NCI60 panel of tumor derived cell lines, uncovering 14 anticancer drug combinations.
The drug-response graph and the associated minimal hitting set method can be used to uncover effective drug combinations in anticancer drug screens and drug development programs targeting heterogeneous populations of infectious agents such as HIV.
识别出比单一药物显著更有效的药物组合是一个具有挑战性的问题。成对组合已经代表了巨大的筛选工作量。超过两种药物的组合，这项任务似乎不可行。
在这项工作中，我们介绍了一种方法，当将其应用于异质性的恶性病原体群体（菌株），如癌细胞系或病毒时，可发现具有假定有效反应的药物组合。利用量化单一药物对多个个体菌株作用的数据，我们寻找能成功靶向所有菌株的最小药物组合。我们表明，后一个问题可以映射到数学中的最小命中集问题。我们使用来自肿瘤衍生细胞系的NCI60面板的数据来说明这种方法，发现了14种抗癌药物组合。
药物反应图和相关的最小命中集方法可用于在抗癌药物筛选以及针对诸如HIV等异质性感染原群体的药物开发项目中发现有效的药物组合。
