Systematic prediction of drug combinations based on clinical side-effects

By Hui Huang, Ping Zhang, Xiaoyan A. Qu, Philippe Sanseau, Lun Yang. 2014

62 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Pharmacology, Toxicology

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Citations in the corpus

Modeling polypharmacy side effects with graph convolutional networks (2018, methodology): “The ﬁnal network after linking entity vocabularies used by different databases has 645 drug and 19,085 protein nodes connected by 715,612 protein-protein, 4,651,131 drug-drug, and 18,596 drug-protein edges.”
Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features (2017, methodology): “This kind of approaches first represents each drug pair as a feature vector, which characterizes various attributes of the drug pair [5, 7].”
A survey of current trends in computational drug repositioning (2016, background): “[54] used side effects as features for drug–drug combinations (DDCs), and further classify the safe DDCs from the unsafe ones.”
Synergy Maps: exploring compound combinations using network-based visualization (2015, methodology): “Clinical side effect annotations have been used to predict effective combinations [13], and information from multiple domains have been integrated into a Probability Ensemble Approach to predict both efficacy and adverse effects of combinations with high predictive power [14].”