Systematic prediction of drug combinations based on clinical side-effects

被引:45
作者
Huang, Hui [1 ,2 ]
Zhang, Ping [3 ]
Qu, Xiaoyan A. [4 ]
Sanseau, Philippe [5 ]
Yang, Lun [1 ]
机构
[1] GlaxoSmithKline, Computat Biol, Philadelphia, PA 19102 USA
[2] Indiana Univ, Sch Informat & Comp, Indianapolis, IN 46204 USA
[3] IBM TJ Watson Res Ctr, Healthcare Analyt Res, Yorktown Hts, NY USA
[4] GlaxoSmithKline, Computat Biol, Res Triangle Pk, NC USA
[5] GlaxoSmithKline, Computat Biol, Stevenage, Herts, England
来源
SCIENTIFIC REPORTS | 2014年 / 4卷
关键词
DISCOVERY; NETWORKS;
D O I
10.1038/srep07160
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.
引用
收藏
页数:7
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