资 源 简 介
The possibility to selectively inhibit specific panels of multiple protein targets provides an unprecedented potential for improved therapeutic anticancer efficacy. We introduce a computational systems pharmacology strategy, which uses the concept of target inhibition networks to predict effective multi-target combinations for treating specific cancer types. The strategy is based on integration of two complementary information sources, drug treatment efficacies and drug-target binding affinities, which are readily available in drug screening labs. Compared to the cancer sequencing efforts, which often result in a huge number of non-targetable genetic alterations, the target combinations from our strategy are druggable, by definition, hence enabling more straightforward translation toward clinically actionable treatment strategies. The model predictions were experimentally validated using siRNA-mediated target silencing screens in three case studies involving MDA-MB-231 and MCF-7 bre
